From e597110d31c41642a01d4c22e485bd5204ac7c81 Mon Sep 17 00:00:00 2001
From: Ines Montani <ines@ines.io>
Date: Sun, 17 Feb 2019 19:31:19 +0100
Subject: [PATCH] =?UTF-8?q?=F0=9F=92=AB=20Update=20website=20(#3285)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

<!--- Provide a general summary of your changes in the title. -->

## Description

The new website is implemented using [Gatsby](https://www.gatsbyjs.org) with [Remark](https://github.com/remarkjs/remark) and [MDX](https://mdxjs.com/). This allows authoring content in **straightforward Markdown** without the usual limitations. Standard elements can be overwritten with powerful [React](http://reactjs.org/) components and wherever Markdown syntax isn't enough, JSX components can be used. Hopefully, this update will also make it much easier to contribute to the docs. Once this PR is merged, I'll implement auto-deployment via [Netlify](https://netlify.com) on a specific branch (to avoid building the website on every PR). There's a bunch of other cool stuff that the new setup will allow us to do – including writing front-end tests, service workers, offline support, implementing a search and so on.

This PR also includes various new docs pages and content.
Resolves #3270. Resolves #3222. Resolves #2947. Resolves #2837.


### Types of change
enhancement

## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
---
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diff --git a/.gitignore b/.gitignore
index 55f3de909..ef586ac8d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,9 +5,15 @@ corpora/
 keys/
 
 # Website
+website/.cache/
+website/public/
+website/node_modules
+website/.npm
+website/logs
+*.log
+npm-debug.log*
 website/www/
 website/_deploy.sh
-website/.gitignore
 
 # Cython / C extensions
 cythonize.json
diff --git a/website/.prettierrc b/website/.prettierrc
new file mode 100644
index 000000000..7555c734a
--- /dev/null
+++ b/website/.prettierrc
@@ -0,0 +1,38 @@
+{
+    "semi": false,
+    "singleQuote": true,
+    "trailingComma": "es5",
+    "tabWidth": 4,
+    "printWidth": 100,
+    "overrides": [
+        {
+            "files": "*.sass",
+            "options": {
+                "printWidth": 999
+            }
+        },
+        {
+            "files": "*.mdx",
+            "options": {
+                "tabWidth": 2,
+                "printWidth": 80,
+                "proseWrap": "always"
+            }
+        },
+        {
+            "files": "*.md",
+            "options": {
+                "tabWidth": 2,
+                "printWidth": 80,
+                "proseWrap": "always",
+                "htmlWhitespaceSensitivity": "strict"
+            }
+        },
+        {
+            "files": "*.html",
+            "options": {
+                "htmlWhitespaceSensitivity": "strict"
+            }
+        }
+    ]
+}
diff --git a/website/404.jade b/website/404.jade
deleted file mode 100644
index af4e7d0f2..000000000
--- a/website/404.jade
+++ /dev/null
@@ -1,12 +0,0 @@
-//- 💫 404 ERROR
-
-include _includes/_mixins
-
-+landing-header
-    h1.c-landing__title.u-heading-0
-        | Ooops, this page#[br]
-        | does not exist!
-
-    h2.c-landing__title.u-heading-3.u-padding-small
-        +button(false, true, "secondary-light")(href="javascript:history.go(-1)")
-            |  Click here to go back
diff --git a/website/README.md b/website/README.md
index 15e73c651..7c38d0371 100644
--- a/website/README.md
+++ b/website/README.md
@@ -1,143 +1,559 @@
-<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
+<Comment>
 
 # spacy.io website and docs
 
-The [spacy.io](https://spacy.io) website is implemented in [Jade (aka Pug)](https://www.jade-lang.org), and is built or served by [Harp](https://harpjs.com). Jade is an extensible templating language with a readable syntax, that compiles to HTML.
-The website source makes extensive use of Jade mixins, so that the design system is abstracted away from the content you're
-writing. You can read more about our approach in our blog post, ["Rebuilding a Website with Modular Markup"](https://explosion.ai/blog/modular-markup).
+_This page contains the documentation and styleguide for the spaCy website. Its
+rendered version is available at https://spacy.io/styleguide._
 
+---
 
-## Viewing the site locally
+</Comment>
+
+The [spacy.io](https://spacy.io) website is implemented using
+[Gatsby](https://www.gatsbyjs.org) with
+[Remark](https://github.com/remarkjs/remark) and [MDX](https://mdxjs.com/). This
+allows authoring content in **straightforward Markdown** without the usual
+limitations. Standard elements can be overwritten with powerful
+[React](http://reactjs.org/) components and wherever Markdown syntax isn't
+enough, JSX components can be used.
+
+> #### Contributing to the site
+>
+> The docs can always use another example or more detail, and they should always
+> be up to date and not misleading. We always appreciate a
+> [pull request](https://github.com/explosion/spaCy/pulls). To quickly find the
+> correct file to edit, simply click on the "Suggest edits" button at the bottom
+> of a page.
+>
+> For more details on editing the site locally, see the installation
+> instructions and markdown reference below.
+
+## Logo {#logo source="website/src/images/logo.svg"}
+
+import { Logos } from 'widgets/styleguide'
+
+If you would like to use the spaCy logo on your site, please get in touch and
+ask us first. However, if you want to show support and tell others that your
+project is using spaCy, you can grab one of our
+[spaCy badges](/usage/spacy-101#faq-project-with-spacy).
+
+<Logos />
+
+## Colors {#colors}
+
+import { Colors, Patterns } from 'widgets/styleguide'
+
+<Colors />
+
+### Patterns
+
+<Patterns />
+
+## Typography {#typography}
+
+import { H1, H2, H3, H4, H5, Label, InlineList, Comment } from
+'components/typography'
+
+> #### Markdown
+>
+> ```markdown_
+> ## Headline 2
+> ## Headline 2 {#some_id}
+> ## Headline 2 {#some_id tag="method"}
+> ```
+>
+> #### JSX
+>
+> ```jsx
+> <H2>Headline 2</H2>
+> <H2 id="some_id">Headline 2</H2>
+> <H2 id="some_id" tag="method">Headline 2</H2>
+> ```
+
+Headlines are set in
+[HK Grotesk](http://cargocollective.com/hanken/HK-Grotesk-Open-Source-Font) by
+Hanken Design. All other body text and code uses the best-matching default
+system font to provide a "native" reading experience.
+
+<Infobox title="Important note" variant="warning">
+
+Level 2 headings are automatically wrapped in `<section>` elements at compile
+time, using a custom
+[Markdown transformer](https://github.com/explosion/spaCy/tree/master/website/plugins/remark-wrap-section.js).
+This makes it easier to highlight the section that's currently in the viewpoint
+in the sidebar menu.
+
+</Infobox>
+
+<div>
+<H1>Headline 1</H1>
+<H2>Headline 2</H2>
+<H3>Headline 3</H3>
+<H4>Headline 4</H4>
+<H5>Headline 5</H5>
+<Label>Label</Label>
+</div>
+
+---
+
+The following optional attributes can be set on the headline to modify it. For
+example, to add a tag for the documented type or mark features that have been
+introduced in a specific version or require statistical models to be loaded.
+Tags are also available as standalone `<Tag />` components.
+
+| Argument | Example                    | Result                                    |
+| -------- | -------------------------- | ----------------------------------------- |
+| `tag`    | `{tag="method"}`           | <Tag>method</Tag>                         |
+| `new`    | `{new="2"}`                | <Tag variant="new">2</Tag>                |
+| `model`  | `{model="tagger, parser"}` | <Tag variant="model">tagger, parser</Tag> |
+| `hidden` | `{hidden="true"}`          |                                           |
+
+## Elements {#elements}
+
+### Links {#links}
+
+> #### Markdown
+>
+> ```markdown
+> [I am a link](https://spacy.io)
+> ```
+>
+> #### JSX
+>
+> ```jsx
+> <Link to="https://spacy.io">I am a link</Link>
+> ```
+
+Special link styles are used depending on the link URL.
+
+- [I am a regular external link](https://explosion.ai)
+- [I am a link to the documentation](/api/doc)
+- [I am a link to GitHub](https://github.com/explosion/spaCy)
+
+### Abbreviations {#abbr}
+
+import { Abbr } from 'components/typography'
+
+> #### JSX
+>
+> ```jsx
+> <Abbr title="Explanation">Abbreviation</Abbr>
+> ```
+
+Some text with <Abbr title="Explanation here">an abbreviation</Abbr>. On small
+screens, I collapse and the explanation text is displayed next to the
+abbreviation.
+
+### Tags {#tags}
+
+import Tag from 'components/tag'
+
+> ```jsx
+> <Tag>method</Tag>
+> <Tag variant="new">2.1</Tag>
+> <Tag variant="model">tagger, parser</Tag>
+> ```
+
+Tags can be used together with headlines, or next to properties across the
+documentation, and combined with tooltips to provide additional information. An
+optional `variant` argument can be used for special tags. `variant="new"` makes
+the tag take a version number to mark new features. Using the component,
+visibility of this tag can later be toggled once the feature isn't considered
+new anymore. Setting `variant="model"` takes a description of model capabilities
+and can be used to mark features that require a respective model to be
+installed.
+
+<InlineList>
+
+<Tag>method</Tag> <Tag variant="new">2</Tag> <Tag variant="model">tagger,
+parser</Tag>
+
+</InlineList>
+
+### Buttons {#buttons}
+
+import Button from 'components/button'
+
+> ```jsx
+> <Button to="#" variant="primary">Primary small</Button>
+> <Button to="#" variant="secondary">Secondary small</Button>
+> ```
+
+Link buttons come in two variants, `primary` and `secondary` and two sizes, with
+an optional `large` size modifier. Since they're mostly used as enhanced links,
+the buttons are implemented as styled links instead of native button elements.
+
+<InlineList><Button to="#" variant="primary">Primary small</Button>
+<Button to="#" variant="secondary">Secondary small</Button></InlineList>
+
+<InlineList><Button to="#" variant="primary" large>Primary large</Button>
+<Button to="#" variant="secondary" large>Secondary large</Button></InlineList>
+
+## Components
+
+### Table
+
+> #### Markdown
+>
+> ```markdown_
+> | Header 1 | Header 2 |
+> | --- | --- |
+> | Column 1 | Column 2 |
+> ```
+>
+> #### JSX
+>
+> ```markup
+> <Table>
+>     <Tr><Th>Header 1</Th><Th>Header 2</Th></Tr></thead>
+>     <Tr><Td>Column 1</Td><Td>Column 2</Td></Tr>
+> </Table>
+> ```
+
+Tables are used to present data and API documentation. Certain keywords can be
+used to mark a footer row with a distinct style, for example to visualise the
+return values of a documented function.
+
+| Header 1    | Header 2 | Header 3 | Header 4 |
+| ----------- | -------- | :------: | -------: |
+| Column 1    | Column 2 | Column 3 | Column 4 |
+| Column 1    | Column 2 | Column 3 | Column 4 |
+| Column 1    | Column 2 | Column 3 | Column 4 |
+| Column 1    | Column 2 | Column 3 | Column 4 |
+| **RETURNS** | Column 2 | Column 3 | Column 4 |
+
+### List
+
+> #### Markdown
+>
+> ```markdown_
+> 1. One
+> 2. Two
+> ```
+>
+> #### JSX
+>
+> ```markup
+> <Ol>
+>     <Li>One</Li>
+>     <Li>Two</Li>
+> </Ol>
+> ```
+
+Lists are available as bulleted and numbered. Markdown lists are transformed
+automatically.
+
+- I am a bulleted list
+- I have nice bullets
+- Lorem ipsum dolor
+- consectetur adipiscing elit
+
+1. I am an ordered list
+2. I have nice numbers
+3. Lorem ipsum dolor
+4. consectetur adipiscing elit
+
+### Aside
+
+> #### Markdown
+>
+> ```markdown_
+> > #### Aside title
+> > This is aside text.
+> ```
+>
+> #### JSX
+>
+> ```jsx
+> <Aside title="Aside title">This is aside text.</Aside>
+> ```
+
+Asides can be used to display additional notes and content in the right-hand
+column. Asides can contain text, code and other elements if needed. Visually,
+asides are moved to the side on the X-axis, and displayed at the same level they
+were inserted. On small screens, they collapse and are rendered in their
+original position, in between the text.
+
+To make them easier to use in Markdown, paragraphs formatted as blockquotes will
+turn into asides by default. Level 4 headlines (with a leading `####`) will
+become aside titles.
+
+### Code Block
+
+> #### Markdown
+>
+> ````markdown_
+> ```python
+> ### This is a title
+> import spacy
+> ```
+> ````
+>
+> #### JSX
+>
+> ```jsx
+> <CodeBlock title="This is a title" lang="python">
+>   import spacy
+> </CodeBlock>
+> ```
+
+Code blocks use the [Prism](http://prismjs.com/) syntax highlighter with a
+custom theme. The language can be set individually on each block, and defaults
+to raw text with no highlighting. An optional label can be added as the first
+line with the prefix `####` (Python-like) and `///` (JavaScript-like). the
+indented block as plain text and preserve whitespace.
+
+```python
+### Using spaCy
+import spacy
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"This is a sentence.")
+for token in doc:
+    print(token.text, token.pos_)
+```
+
+Code blocks and also specify an optional range of line numbers to highlight by
+adding `{highlight="..."}` to the headline. Acceptable ranges are spans like
+`5-7`, but also `5-7,10` or `5-7,10,13-14`.
+
+> #### Markdown
+>
+> ````markdown_
+> ```python
+> ### This is a title {highlight="1-2"}
+> import spacy
+> nlp = spacy.load("en_core_web_sm")
+> ```
+> ````
+
+```python
+### Using the matcher {highlight="5-7"}
+import spacy
+from spacy.matcher import Matcher
+
+nlp = spacy.load('en_core_web_sm')
+matcher = Matcher(nlp.vocab)
+pattern = [{'LOWER': 'hello'}, {'IS_PUNCT': True}, {'LOWER': 'world'}]
+matcher.add('HelloWorld', None, pattern)
+doc = nlp(u'Hello, world! Hello world!')
+matches = matcher(doc)
+```
+
+Adding `{executable="true"}` to the title turns the code into an executable
+block, powered by [Binder](https://mybinder.org) and
+[Juniper](https://github.com/ines/juniper). If JavaScript is disabled, the
+interactive widget defaults to a regular code block.
+
+> #### Markdown
+>
+> ````markdown_
+> ```python
+> ### {executable="true"}
+> import spacy
+> nlp = spacy.load("en_core_web_sm")
+> ```
+> ````
+
+```python
+### {executable="true"}
+import spacy
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"This is a sentence.")
+for token in doc:
+    print(token.text, token.pos_)
+```
+
+If a code block only contains a URL to a GitHub file, the raw file contents are
+embedded automatically and syntax highlighting is applied. The link to the
+original file is shown at the top of the widget.
+
+> #### Markdown
+>
+> ````markdown_
+> ```python
+> https://github.com/...
+> ```
+> ````
+>
+> #### JSX
+>
+> ```jsx
+> <GitHubCode url="https://github.com/..." lang="python" />
+> ```
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/pipeline/custom_component_countries_api.py
+```
+
+### Infobox
+
+import Infobox from 'components/infobox'
+
+> #### JSX
+>
+> ```jsx
+> <Infobox title="Information">Regular infobox</Infobox>
+> <Infobox title="Important note" variant="warning">This is a warning.</Infobox>
+> <Infobox title="Be careful!" variant="danger">This is dangerous.</Infobox>
+> ```
+
+Infoboxes can be used to add notes, updates, warnings or additional information
+to a page or section. Semantically, they're implemented and interpreted as an
+`aside` element. Infoboxes can take an optional `title` argument, as well as an
+optional `variant` (either `"warning"` or `"danger"`).
+
+<Infobox title="This is an infobox">
+
+If needed, an infobox can contain regular text, `inline code`, lists and other
+blocks.
+
+</Infobox>
+
+<Infobox title="This is a warning" variant="warning">
+
+If needed, an infobox can contain regular text, `inline code`, lists and other
+blocks.
+
+</Infobox>
+
+<Infobox title="This is dangerous" variant="danger">
+
+If needed, an infobox can contain regular text, `inline code`, lists and other
+blocks.
+
+</Infobox>
+
+### Accordion
+
+import Accordion from 'components/accordion'
+
+> #### JSX
+>
+> ```jsx
+> <Accordion title="This is an accordion">
+>   Accordion content goes here.
+> </Accordion>
+> ```
+
+Accordions are collapsible sections that are mostly used for lengthy tables,
+like the tag and label annotation schemes for different languages. They all need
+to be presented – but chances are the user doesn't actually care about _all_ of
+them, especially not at the same time. So it's fairly reasonable to hide them
+begin a click. This particular implementation was inspired by the amazing
+[Inclusive Components blog](https://inclusive-components.design/collapsible-sections/).
+
+<Accordion title="This is an accordion">
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque enim ante,
+pretium a orci eget, varius dignissim augue. Nam eu dictum mauris, id tincidunt
+nisi. Integer commodo pellentesque tincidunt. Nam at turpis finibus tortor
+gravida sodales tincidunt sit amet est. Nullam euismod arcu in tortor auctor,
+sit amet dignissim justo congue.
+
+</Accordion>
+
+## Setup and installation {#setup}
+
+Before running the setup, make sure your versions of
+[Node](https://nodejs.org/en/) and [npm](https://www.npmjs.com/) are up to date.
 
 ```bash
-sudo npm install --global harp
+# Clone the repository
 git clone https://github.com/explosion/spaCy
 cd spaCy/website
-harp server
+
+# Install Gatsby's command-line tool
+npm install --global gatsby-cli
+
+# Install the dependencies
+npm install
+
+# Start the development server
+npm run dev
 ```
 
-This will serve the site on [http://localhost:9000](http://localhost:9000).
+If you are planning on making edits to the site, you should also set up the
+[Prettier](https://prettier.io/) code formatter. It takes care of formatting
+Markdown and other files automatically.
+[See here](https://prettier.io/docs/en/editors.html) for the available
+extensions for your code editor. The
+[`.prettierrc`](https://github.com/explosion/spaCy/tree/master/website/.prettierrc)
+file in the root defines the settings used in this codebase.
 
+## Markdown reference {#markdown}
 
-## Making changes to the site
+All page content and page meta lives in the `.md` files in the `/docs`
+directory. The frontmatter block at the top of each file defines the page title
+and other settings like the sidebar menu.
 
-The docs can always use another example or more detail, and they should always be up to date and not misleading. If you see something, say something – we always appreciate a [pull request](https://github.com/explosion/spaCy/pulls). To quickly find the correct file to edit, simply click on the "Suggest edits" button at the bottom of a page.
+````markdown
+---
+title: Page title
+---
 
-### File structure
+## Headline starting a section {#some_id}
 
-While all page content lives in the `.jade` files, article meta (page titles, sidebars etc.) is stored as JSON. Each folder contains a `_data.json` with all required meta for its files.
+This is a regular paragraph with a [link](https://spacy.io) and **bold text**.
 
-### Markup language and conventions
+> #### This is an aside title
+>
+> This is aside text.
 
-Jade/Pug is a whitespace-sensitive markup language that compiles to HTML. Indentation is used to nest elements, and for template logic, like `if`/`else` or `for`, mainly used to iterate over objects and arrays in the meta data. It also allows inline JavaScript expressions.
+### Subheadline
 
-For an overview of Harp and Jade, see [this blog post](https://ines.io/blog/the-ultimate-guide-static-websites-harp-jade). For more info on the Jade/Pug syntax, check out their [documentation](https://pugjs.org).
+| Header 1 | Header 2 |
+| -------- | -------- |
+| Column 1 | Column 2 |
 
-In the [spacy.io](https://spacy.io) source, we use 4 spaces to indent and hard-wrap at 80 characters.
-
-```pug
-p This is a very short paragraph. It stays inline.
-
-p
-    |  This is a much longer paragraph. It's hard-wrapped at 80 characters to
-    |  make it easier to read on GitHub and in editors that do not have soft
-    |  wrapping enabled. To prevent Jade from interpreting each line as a new
-    |  element, it's prefixed with a pipe and two spaces. This ensures that no
-    |  spaces are dropped – for example, if your editor strips out trailing
-    |  whitespace by default. Inline links are added using the inline syntax,
-    |  like this: #[+a("https://google.com") Google].
+```python
+### Code block title {highlight="2-3"}
+import spacy
+nlp = spacy.load("en_core_web_sm")
+doc = nlp("Hello world")
 ```
 
-Note that for external links, `+a("...")` is used instead of `a(href="...")` – it's a mixin that takes care of adding all required attributes. If possible, always use a mixin instead of regular HTML elements. The only plain HTML elements we use are:
+<Infobox title="Important note" variant="warning">
 
-| Element | Description |
-| --- | --- |
-| `p` | paragraphs |
-| `code` | inline `code` |
-| `em` | *italicized* text |
-| `strong` | **bold** text |
+This is content in the infobox.
 
-### Mixins
+</Infobox>
+````
 
-Each file includes a collection of [custom mixins](_includes/_mixins.jade) that make it easier to add content components – no HTML or class names required.
+In addition to the native markdown elements, you can use the components
+[`<Infobox />`][infobox], [`<Accordion />`][accordion], [`<Abbr />`][abbr] and
+[`<Tag />`][tag] via their JSX syntax.
 
-For example:
-```pug
-//- Bulleted list
+[infobox]: https://spacy.io/styleguide#infobox
+[accordion]: https://spacy.io/styleguide#accordion
+[abbr]: https://spacy.io/styleguide#abbr
+[tag]: https://spacy.io/styleguide#tag
 
-+list
-    +item This is a list item.
-    +item This is another list item.
+## Project structure {#structure}
 
-//- Table with header
-
-+table([ "Header one", "Header two" ])
-    +row
-        +cell Table cell
-        +cell Another one
-
-    +row
-        +cell And one more.
-        +cell And the last one.
-
-//- Headlines with optional permalinks
-
-+h(2, "link-id") Headline 2 with link to #link-id
+```yaml
+### Directory structure
+├── docs                 # the actual markdown content
+├── meta                 # JSON-formatted site metadata
+|   ├── languages.json   # supported languages and statistical models
+|   ├── logos.json       # logos and links for landing page
+|   ├── sidebars.json    # sidebar navigations for different sections
+|   ├── site.json        # general site metadata
+|   └── universe.json    # data for the spaCy universe section
+├── public               # compiled site
+├── src                  # source
+|   ├── components       # React components
+|   ├── fonts            # webfonts
+|   ├── images           # images used in the layout
+|   ├── plugins          # custom plugins to transform Markdown
+|   ├── styles           # CSS modules and global styles
+|   ├── templates        # page layouts
+|   |   ├── docs.js      # layout template for documentation pages
+|   |   ├── index.js     # global layout template
+|   |   ├── models.js    # layout template for model pages
+|   |   └── universe.js  # layout templates for universe
+|   └── widgets          # non-reusable components with content, e.g. changelog
+├── gatsby-browser.js    # browser-specific hooks for Gatsby
+├── gatsby-config.js     # Gatsby configuration
+├── gatsby-node.js       # Node-specific hooks for Gatsby
+└── package.json         # package settings and dependencies
 ```
-
-Code blocks are implemented using `+code` or `+aside-code` (to display them in the right sidebar). A `.` is added after the mixin call to preserve whitespace:
-
-```pug
-+code("This is a label").
-    import spacy
-    en_nlp = spacy.load('en')
-    en_doc = en_nlp(u'Hello, world. Here are two sentences.')
-```
-
-You can find the documentation for the available mixins in [`_includes/_mixins.jade`](_includes/_mixins.jade).
-
-### Helpers for linking to content
-
-Aside from the `+a()` mixin, there are three other helpers to make linking to content more convenient.
-
-#### Linking to GitHub
-
-Since GitHub links can be long and tricky, you can use the `gh()` function to generate them automatically for spaCy and all repositories owned by [explosion](https://github.com/explosion):
-
-```javascript
-// Syntax: gh(repo, [file], [branch])
-
-gh("spaCy", "spacy/matcher.pyx")
-// https://github.com/explosion/spaCy/blob/master/spacy/matcher.pyx
-
-```
-
-#### Linking to source
-
-`+src()` generates a link with a little source icon to indicate it's linking to a code source. Ideally, it's used in combination with `gh()`:
-
-```pug
-+src(gh("spaCy", "spacy/matcher.pyx")) matcher.pxy
-```
-
-#### Linking to API reference
-
-`+api()` generates a link to a page in the API docs, with an added icon. It should only be used across the workflows in the usage section, and only on the first mention of the respective class.
-
-It takes the slug of an API page as the argument. You can also use anchors to link to specific sections – they're usually the method or property names.
-
-```pug
-+api("tokenizer") #[code Tokenizer]
-+api("doc#similarity") #[code Doc.similarity()]
-```
-
-### Most common causes of compile errors
-
-| Problem | Fix |
-| --- | --- |
-| JSON formatting errors | make sure last elements of objects don't end with commas and/or use a JSON linter |
-| unescaped characters like `<` or `>` and sometimes `'` in inline elements | replace with encoded version: `&lt;`, `&gt;` etc. |
-| "Cannot read property 'call' of undefined" / "foo is not a function" | make sure mixin names are spelled correctly and mixins file is included with the correct path |
-| "no closing bracket found" | make sure inline elements end with a `]`, like `#[code spacy.load('en')]` and for nested inline elements, make sure they're all on the same line and contain spaces between them (**bad:** `#[+api("doc")#[code Doc]]`) |
-
-If Harp fails and throws a Jade error, don't take the reported line number at face value – it's often wrong, as the page is compiled from templates and several files.
diff --git a/website/_data.json b/website/_data.json
deleted file mode 100644
index 573d18ba0..000000000
--- a/website/_data.json
+++ /dev/null
@@ -1,59 +0,0 @@
-{
-    "index": {
-        "landing": true,
-        "logos": [
-            {
-                "airbnb": [ "https://www.airbnb.com", 150, 45],
-                "quora": [ "https://www.quora.com", 120, 34 ],
-                "retriever": [ "https://www.retriever.no", 150, 33 ],
-                "stitchfix": [ "https://www.stitchfix.com", 150, 18 ]
-            },
-            {
-                "chartbeat": [ "https://chartbeat.com", 180, 25 ],
-                "allenai": [ "https://allenai.org", 220, 37 ]
-            }
-        ],
-        "features": [
-            {
-                "recode": ["https://www.recode.net/2017/6/22/15855492/ai-artificial-intelligence-nonprofit-good-human-chatbots-machine-learning", 100, 25],
-                "wapo": ["https://www.washingtonpost.com/news/wonk/wp/2016/05/18/googles-new-artificial-intelligence-cant-understand-these-sentences-can-you/", 100, 77],
-                "bbc": ["http://www.bbc.co.uk/rd/blog/2017-08-irfs-weeknotes-number-250", 90, 26],
-                "microsoft": ["https://www.microsoft.com/developerblog/2016/09/13/training-a-classifier-for-relation-extraction-from-medical-literature/", 130, 28]
-            },
-            {
-                "venturebeat": ["https://venturebeat.com/2017/01/27/4-ai-startups-that-analyze-customer-reviews/", 150, 19],
-                "thoughtworks": ["https://www.thoughtworks.com/radar/tools", 150, 28]
-            }
-        ]
-    },
-
-    "robots.txt": {
-        "layout": false
-    },
-
-    "404": {
-        "title": "404 Error",
-        "landing": true
-    },
-
-    "styleguide": {
-        "title": "Styleguide",
-        "sidebar": {
-            "Styleguide": { "": "styleguide" },
-            "Resources": {
-                "Website Source": "https://github.com/explosion/spacy/tree/master/website",
-                "Contributing Guide": "https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md"
-            }
-        },
-        "menu": {
-            "Introduction": "intro",
-            "Logo": "logo",
-            "Colors": "colors",
-            "Typography": "typography",
-            "Elements": "elements",
-            "Components": "components",
-            "Embeds": "embeds",
-            "Markup Reference": "markup"
-        }
-    }
-}
diff --git a/website/_harp.json b/website/_harp.json
deleted file mode 100644
index 9c4d155c6..000000000
--- a/website/_harp.json
+++ /dev/null
@@ -1,97 +0,0 @@
-{
-    "globals": {
-        "title": "spaCy",
-        "description": "spaCy is a free open-source library for Natural Language Processing in Python. It features NER, POS tagging, dependency parsing, word vectors and more.",
-
-        "SITENAME": "spaCy",
-        "SLOGAN": "Industrial-strength Natural Language Processing in Python",
-        "SITE_URL": "https://spacy.io",
-        "EMAIL": "contact@explosion.ai",
-
-        "COMPANY": "Explosion AI",
-        "COMPANY_URL": "https://explosion.ai",
-        "DEMOS_URL": "https://explosion.ai/demos",
-        "MODELS_REPO": "explosion/spacy-models",
-
-        "SPACY_VERSION": "2.1",
-        "BINDER_VERSION": "2.0.16",
-
-        "SOCIAL": {
-            "twitter": "spacy_io",
-            "github": "explosion",
-            "reddit": "spacynlp",
-            "codepen": "explosion",
-            "gitter": "explosion/spaCy"
-        },
-
-        "NAVIGATION": {
-            "Usage": "/usage",
-            "Models": "/models",
-            "API": "/api",
-            "Universe": "/universe"
-        },
-
-        "FOOTER": {
-            "spaCy": {
-                "Usage": "/usage",
-                "Models": "/models",
-                "API Reference": "/api",
-                "Universe": "/universe"
-            },
-            "Support": {
-                "Issue Tracker": "https://github.com/explosion/spaCy/issues",
-                "Stack Overflow": "http://stackoverflow.com/questions/tagged/spacy",
-                "Reddit Usergroup": "https://www.reddit.com/r/spacynlp/",
-                "Gitter Chat": "https://gitter.im/explosion/spaCy"
-            },
-            "Connect": {
-                "Twitter": "https://twitter.com/spacy_io",
-                "GitHub": "https://github.com/explosion/spaCy",
-                "Blog": "https://explosion.ai/blog",
-                "Contact": "mailto:contact@explosion.ai"
-            }
-        },
-
-        "QUICKSTART": [
-            { "id": "os", "title": "Operating system", "options": [
-                { "id": "mac", "title": "macOS / OSX", "checked": true },
-                { "id": "windows", "title": "Windows" },
-                { "id": "linux", "title": "Linux" }]
-            },
-            { "id": "package", "title": "Package manager", "options": [
-                { "id": "pip", "title": "pip", "checked": true },
-                { "id": "conda", "title": "conda" },
-                { "id": "source", "title": "from source" }]
-            },
-            { "id": "python", "title": "Python version", "options": [
-                { "id": 2, "title": "2.x" },
-                { "id": 3, "title": "3.x", "checked": true }]
-            },
-            { "id": "config", "title": "Configuration", "multiple": true, "options": [
-                {"id": "venv", "title": "virtualenv", "help": "Use a virtual environment and install spaCy into a user directory" }]
-            },
-            { "id": "model", "title": "Models", "multiple": true }
-        ],
-
-        "QUICKSTART_MODELS": [
-            { "id": "lang", "title": "Language"},
-            { "id": "load", "title": "Loading style", "options": [
-                { "id": "spacy", "title": "Use spacy.load()", "checked": true, "help": "Use spaCy's built-in loader to load the model by name." },
-                {  "id": "module", "title": "Import as module", "help": "Import the model explicitly as a Python module." }]
-            },
-            { "id": "config", "title": "Options", "multiple": true, "options": [
-                { "id": "example", "title": "Show usage example" }]
-            }
-        ],
-
-        "V_CSS": "2.2.1",
-        "V_JS": "2.2.4",
-        "DEFAULT_SYNTAX": "python",
-        "ANALYTICS": "UA-58931649-1",
-        "MAILCHIMP": {
-            "user": "spacy.us12",
-            "id": "83b0498b1e7fa3c91ce68c3f1",
-            "list": "89ad33e698"
-        }
-    }
-}
diff --git a/website/_includes/_footer.jade b/website/_includes/_footer.jade
deleted file mode 100644
index 4d0d34cb5..000000000
--- a/website/_includes/_footer.jade
+++ /dev/null
@@ -1,28 +0,0 @@
-//- 💫 INCLUDES > FOOTER
-
-footer.o-footer.u-text
-    +grid.o-content
-        each group, label in FOOTER
-            +grid-col("quarter")
-                ul
-                    li.u-text-label.u-color-subtle=label
-
-                    each url, item in group
-                        li
-                            +a(url)=item
-
-        if SECTION == "index"
-            +grid-col("quarter")
-                include _newsletter
-
-    if SECTION != "index"
-        .o-content.o-block.u-border-dotted
-            include _newsletter
-
-    .o-inline-list.u-text-center.u-text-tiny.u-color-subtle
-        span &copy; 2016-#{new Date().getFullYear()} #[+a(COMPANY_URL, true)=COMPANY]
-
-        +a(COMPANY_URL, true)(aria-label="Explosion AI")
-            +icon("explosion", 45).o-icon.u-color-theme.u-grayscale
-
-        +a(COMPANY_URL + "/legal", true) Legal / Imprint
diff --git a/website/_includes/_functions.jade b/website/_includes/_functions.jade
deleted file mode 100644
index a80fd716d..000000000
--- a/website/_includes/_functions.jade
+++ /dev/null
@@ -1,95 +0,0 @@
-//- 💫 INCLUDES > FUNCTIONS
-
-//- Descriptive variables, available in the global scope
-
-- CURRENT = current.source
-- SECTION = current.path[0]
-- LANGUAGES = public.models._data.LANGUAGES
-- MODELS = public.models._data.MODELS
-- CURRENT_MODELS = MODELS[current.source] || []
-
-- MODEL_COUNT = Object.keys(MODELS).map(m => Object.keys(MODELS[m]).length).reduce((a, b) => a + b)
-- MODEL_LANG_COUNT = Object.keys(MODELS).length
-- LANG_COUNT = Object.keys(LANGUAGES).length - 1
-
-- MODEL_META = public.models._data.MODEL_META
-- MODEL_LICENSES = public.models._data.MODEL_LICENSES
-- MODEL_BENCHMARKS = public.models._data.MODEL_BENCHMARKS
-- EXAMPLE_SENT_LANGS = public.models._data.EXAMPLE_SENT_LANGS
-- EXAMPLE_SENTENCES = public.models._data.EXAMPLE_SENTENCES
-
-- IS_PAGE = (SECTION != "index") && !landing
-- IS_MODELS = (SECTION == "models" && LANGUAGES[current.source])
-- HAS_MODELS = IS_MODELS && CURRENT_MODELS.length
-
-//- Get page URL
-
--    function getPageUrl() {
--       var path = current.path;
--       if(path[path.length - 1] == 'index') path = path.slice(0, path.length - 1);
--       return `${SITE_URL}/${path.join('/')}`;
--   }
-
-//- Get pretty page title depending on section
-
--   function getPageTitle() {
--       var sections = ['api', 'usage', 'models'];
--       if (sections.includes(SECTION)) {
--           var titleSection = (SECTION == "api") ? 'API' : SECTION.charAt(0).toUpperCase() + SECTION.slice(1);
--           return `${title} · ${SITENAME} ${titleSection} Documentation`;
--       }
--       else if (SECTION != 'index') return `${title} · ${SITENAME}`;
--       return `${SITENAME} · ${SLOGAN}`;
--   }
-
-//- Get social image based on section and settings
-
--   function getPageImage() {
--       var img = (SECTION == 'api') ? 'api' : 'default';
--       return `${SITE_URL}/assets/img/social/preview_${preview || img}.jpg`;
--   }
-
-//- Add prefixes to items of an array (for modifier CSS classes)
-    array   - [array] list of class names or options, e.g. ["foot"]
-    prefix  - [string] prefix to add to each class, e.g. "c-table__row"
-    RETURNS - [array] list of modified class names
-
--   function prefixArgs(array, prefix) {
--       return array.map(arg => prefix + '--' + arg).join(' ');
--   }
-
-
-//- Convert API paths (semi-temporary fix for renamed sections)
-    path    - [string] link path supplied to +api mixin
-    RETURNS - [string] new link path to correct location
-
--   function convertAPIPath(path) {
--       if (path.startsWith('spacy#') || path.startsWith('displacy#') || path.startsWith('util#')) {
--           var comps = path.split('#');
--           return "top-level#" + comps[0] + '.' + comps[1];
--       }
--       return path;
--   }
-
-
-//- Get model components from ID. Components can then be looked up in LANGUAGES
-    and MODEL_META respectively, to get their human-readable form.
-    id      - [string] model ID, e.g. "en_core_web_sm"
-    RETURNS - [object] object keyed by components lang, type, genre and size
-
--   function getModelComponents(id) {
--       var comps = id.split('_');
--       return {'lang': comps[0], 'type': comps[1], 'genre': comps[2], 'size': comps[3]}
--   }
-
-
-//- Generate GitHub links
-    repo     - [string] name of repo owned by explosion
-    filepath - [string] logical path to file relative to repository root
-    branch   - [string] optional branch, defaults to "master"
-    RETURNS  - [string] the correct link to the file on GitHub
-
--   function gh(repo, filepath, branch) {
--       var branch = ALPHA ? 'develop' : branch
--       return 'https://github.com/' + SOCIAL.github + '/' + (repo || '') + (filepath ? '/blob/' + (branch || 'master') + '/' + filepath : '' );
--   }
diff --git a/website/_includes/_mixins.jade b/website/_includes/_mixins.jade
deleted file mode 100644
index ab995ceee..000000000
--- a/website/_includes/_mixins.jade
+++ /dev/null
@@ -1,749 +0,0 @@
-//- 💫 INCLUDES > MIXINS
-
-include _functions
-
-
-//- Section
-    id - [string] anchor assigned to section (used for breadcrumb navigation)
-
-mixin section(id)
-    section.o-section(id=id ? "section-" + id : null data-section=id)&attributes(attributes)
-        block
-
-
-//- Accordion (collapsible sections)
-    title - [string] Section title.
-    id    - [string] Optional section ID for permalinks.
-    level - [integer] Headline level for section title.
-
-mixin accordion(title, id, level)
-    section.o-accordion.o-block
-        +h(level || 4).o-no-block(id=id)
-            button.o-accordion__button.o-grid.o-grid--vcenter.o-grid--space.js-accordion(aria-expanded="false")=title
-                svg.o-accordion__icon(width="20" height="20" viewBox="0 0 10 10" aria-hidden="true" focusable="false")
-                    rect.o-accordion__hide(height="8" width="2" y="1" x="4")
-                    rect(height="2" width="8" y="4" x="1")
-
-        .o-accordion__content(hidden="")
-            block
-
-
-//- Headlines Helper Mixin
-    level - [integer] 1, 2, 3, 4, or 5
-
-mixin headline(level)
-    if level == 1
-        h1.u-heading-1&attributes(attributes)
-            block
-
-    else if level == 2
-        h2.u-heading-2&attributes(attributes)
-            block
-
-    else if level == 3
-        h3.u-heading-3&attributes(attributes)
-            block
-
-    else if level == 4
-        h4.u-heading-4&attributes(attributes)
-            block
-
-    else if level == 5
-        h5.u-heading-5&attributes(attributes)
-            block
-
-
-//- Headlines
-    level - [integer] headline level, corresponds to h1, h2, h3 etc.
-    id    - [string] unique identifier, creates permalink (optional)
-
-mixin h(level, id, source)
-    +headline(level).u-heading(id=id)&attributes(attributes)
-        +permalink(id)
-            block
-
-        if source
-            +button(gh("spacy", source), false, "secondary", "small").u-nowrap.u-float-right
-                span Source #[+icon("code", 14).o-icon--inline]
-
-
-//- Permalink rendering
-    id - [string] permalink ID used for link anchor
-
-mixin permalink(id)
-    if id
-        a.u-permalink(href="##{id}")
-            block
-
-    else
-        block
-
-
-//- External links
-    url     - [string] link href
-    trusted - [boolean] if not set / false, rel="noopener nofollow" is added
-              info: https://mathiasbynens.github.io/rel-noopener/
-
-mixin a(url, trusted)
-    - external = url.includes("http")
-    a(href=url target=external ? "_blank" : null rel=external && !trusted ? "noopener nofollow" : null)&attributes(attributes)
-        block
-
-
-//- Source link (with added icon for "code")
-    url - [string] link href, can also be gh() function to generate GitHub link
-          see _functions.jade for more info
-
-mixin src(url)
-    span.u-inline-block.u-nowrap
-        +a(url)
-            block
-
-        |   #[+icon("code", 16).o-icon--inline.u-color-theme]
-
-
-//- API link (with added tag and automatically generated path)
-    path - [string] path to API docs page relative to /api/
-
-mixin api(path)
-    - path = convertAPIPath(path)
-    +a("/api/" + path, true)(target="_self").u-no-border.u-inline-block.u-nowrap
-        block
-
-        |  #[+icon("book", 16).o-icon--inline.u-color-theme]
-
-
-//- Help icon with tooltip
-    tooltip   - [string] Tooltip text
-    icon_size - [integer] Optional size of help icon in px.
-
-mixin help(tooltip, icon_size)
-    span(data-tooltip=tooltip)&attributes(attributes)
-        if tooltip
-            span.u-hidden(aria-role="tooltip")=tooltip
-        +icon("help_o", icon_size || 16).o-icon--inline
-
-
-//- Abbreviation
-
-mixin abbr(title)
-    abbr.o-abbr(data-tooltip=title data-tooltip-style="code" aria-label=title)&attributes(attributes)
-        block
-
-//- Aside wrapper
-    label - [string] aside label
-
-mixin aside-wrapper(label, emoji)
-    aside.c-aside
-        .c-aside__content(role="complementary")&attributes(attributes)
-            if label
-                h4.u-text-label.u-text-label--dark
-                    if emoji
-                        span.o-emoji=emoji
-                    |  #{label}
-            block
-
-
-//- Aside for text
-    label - [string] aside title (optional)
-
-mixin aside(label, emoji)
-    +aside-wrapper(label, emoji)
-        .c-aside__text.u-text-small&attributes(attributes)
-            block
-
-
-//- Aside for code
-    label    - [string] aside title (optional or false for no label)
-    language - [string] language for syntax highlighting (default: "python")
-               supports basic relevant languages available for PrismJS
-    prompt   - [string] prompt displayed before first line, e.g. "$"
-
-mixin aside-code(label, language, prompt)
-    +aside-wrapper(label)&attributes(attributes)
-        +code(false, language, prompt).o-no-block
-            block
-
-
-//- Infobox
-    label - [string] infobox title (optional or false for no title)
-    emoji - [string] optional emoji displayed before the title, necessary as
-            argument to be able to wrap it for spacing
-
-mixin infobox(label, emoji)
-    aside.o-box.o-block.u-text-small&attributes(attributes)
-        if label
-            h3.u-heading.u-text-label.u-color-theme
-                if emoji
-                    span.o-emoji=emoji
-                |  #{label}
-
-        block
-
-
-//- Logos displayed in the top corner of some infoboxes
-    logos - [array] List of icon ID, width, height and link.
-
-mixin infobox-logos(...logos)
-    .o-box__logos.u-text-right.u-float-right
-        for logo in logos
-            if logo[3]
-                |  #[+a(logo[3]).u-inline-block.u-hide-link.u-padding-small #[+icon(logo[0], logo[1], logo[2]).u-color-dark]]
-            else
-                |  #[+icon(logo[0], logo[1], logo[2]).u-color-dark]
-
-
-//- SVG from map (uses embedded SVG sprite)
-    name   - [string] SVG symbol id
-    width  - [integer] width in px
-    height - [integer] height in px (default: same as width)
-
-mixin svg(name, width, height)
-    svg(aria-hidden="true" viewBox="0 0 #{width} #{height || width}" width=width height=(height || width))&attributes(attributes)
-        use(xlink:href="#svg_#{name}")
-
-
-//- Icon
-    name   - [string] icon name (will be used as symbol id: #svg_{name})
-    width  - [integer] icon width (default: 20)
-    height - [integer] icon height (defaults to width)
-
-mixin icon(name, width, height)
-    - var width = width || 20
-    - var height = height || width
-    +svg(name, width, height).o-icon(style="min-width: #{width}px")&attributes(attributes)
-
-
-//- Pro/Con/Neutral icon
-    icon - [string] "pro", "con" or "neutral" (default: "neutral")
-    size - [integer] icon size (optional)
-
-mixin procon(icon, label, show_label, size)
-    - var colors = { yes: "green", no: "red", neutral: "subtle" }
-    span.u-nowrap
-        +icon(icon, size || 20)(class="u-color-#{colors[icon] || 'subtle'}").o-icon--inline&attributes(attributes)
-        span.u-text-small(class=show_label ? null : "u-hidden")=(label || icon)
-
-
-//- Link button
-    url      - [string] link href
-    trusted  - [boolean] if not set / false, rel="noopener nofollow" is added
-               info: https://mathiasbynens.github.io/rel-noopener/
-    ...style - all other arguments are added as class names c-button--argument
-               see assets/css/_components/_buttons.sass
-
-mixin button(url, trusted, ...style)
-    - external = url && url.includes("http")
-    a.c-button.u-text-label(href=url class=prefixArgs(style, "c-button") role="button" target=external ? "_blank" : null rel=external && !trusted ? "noopener nofollow" : null)&attributes(attributes)
-        block
-
-
-//- Code block
-    label    - [string] aside title (optional or false for no label)
-    language - [string] language for syntax highlighting (default: "python")
-               supports basic relevant languages available for PrismJS
-    prompt   - [string] prompt displayed before first line, e.g. "$"
-    height   - [integer] optional height to clip code block to
-    icon     - [string] icon displayed next to code block (e.g. "accept" for new code)
-    wrap     - [boolean] wrap text and disable horizontal scrolling
-
-mixin code(label, language, prompt, height, icon, wrap)
-    - var lang = (language != "none") ? (language || DEFAULT_SYNTAX) : null
-    - var lang_class = (language != "none") ? "lang-" + (language || DEFAULT_SYNTAX) : null
-    pre.c-code-block.o-block(data-language=lang class=lang_class class=icon ? "c-code-block--has-icon" : null style=height ? "height: #{height}px" : null)&attributes(attributes)
-        if label
-            h4.u-text-label.u-text-label--dark=label
-        if icon
-            - var classes = {'accept': 'u-color-green', 'reject': 'u-color-red'}
-            .c-code-block__icon(class=classes[icon] || null class=classes[icon] ? "c-code-block__icon--border" : null)
-                +icon(icon, 18)
-
-        code.c-code-block__content(class=wrap ? "u-wrap" : null data-prompt=prompt)
-            block
-
-//- Executable code
-
-mixin code-exec(label, large)
-    - label = (label || "Editable code example") + " (experimental)"
-    +terminal-wrapper(label, !large)
-        figure.juniper-wrapper
-            span.juniper-wrapper__text.u-text-tiny v#{BINDER_VERSION} &middot; Python 3 &middot; via #[+a("https://mybinder.org/").u-hide-link Binder]
-            +code(data-executable="true")&attributes(attributes)
-                block
-
-//- Wrapper for code blocks to display old/new versions
-
-mixin code-wrapper()
-    span.u-inline-block.u-padding-top.u-width-full
-        block
-
-//- Code blocks to display old/new versions
-    label - [string] ARIA label for block. Defaults to "correct"/"incorrect".
-
-mixin code-old(label, lang, prompt)
-    - var label = label || 'incorrect'
-    +code(false, lang, prompt, false, "reject").o-block-small(aria-label=label)
-        block
-
-mixin code-new(label, lang, prompt)
-    - var label = label || 'correct'
-    +code(false, lang, prompt, false, "accept").o-block-small(aria-label=label)
-        block
-
-
-//- CodePen embed
-    slug        - [string] ID of CodePen demo (taken from URL)
-    height      - [integer] height of demo embed iframe
-    default_tab - [string] code tab(s) visible on load (default: "result")
-
-mixin codepen(slug, height, default_tab)
-    figure.o-block(style="min-height: #{height}px")&attributes(attributes)
-        .codepen(data-height=height data-theme-id="31335" data-slug-hash=slug data-default-tab=(default_tab || "result") data-embed-version="2" data-user=SOCIAL.codepen)
-            +a("https://codepen.io/" + SOCIAL.codepen + "/" + slug) View on CodePen
-
-        script(async src="https://assets.codepen.io/assets/embed/ei.js")
-
-
-//- GitHub embed
-    repo     - [string] repository owned by explosion organization
-    file     - [string] logical path to file, relative to repository root
-    alt_file - [string] alternative file path used in footer and link button
-    height   - [integer] height of code preview in px
-
-mixin github(repo, file, height, alt_file, language)
-    - var branch = ALPHA ? "develop" : "master"
-    - var height = height || 250
-
-    figure.o-block
-        pre.c-code-block.o-block-small(class="lang-#{(language || DEFAULT_SYNTAX)}" style="height: #{height}px; min-height: #{height}px")
-            code.c-code-block__content(data-gh-embed="#{repo}/#{branch}/#{file}").
-                Can't fetch code example from GitHub :(
-
-                Please use the link below to view the example. If you've come across
-                a broken link, we always appreciate a pull request to the repository,
-                or a report on the issue tracker. Thanks!
-
-        footer.o-grid.u-text
-            .o-block-small.u-flex-full.u-padding-small #[+icon("github")] #[code.u-break.u-break--all=repo + '/' + (alt_file || file)]
-            div
-                +button(gh(repo, alt_file || file), false, "primary", "small") View on GitHub
-
-
-//- Youtube video embed
-    id    - [string] ID of YouTube video.
-    ratio - [string] Video ratio, "16x9" or "4x3".
-
-mixin youtube(id, ratio)
-    figure.o-video.o-block(class="o-video--" + (ratio || "16x9"))
-        iframe.o-video__iframe(src="https://www.youtube.com/embed/#{id}" frameborder="0" height="500" allowfullscreen)
-
-
-//- Images / figures
-    url     - [string] url or path to image
-    width   - [integer] image width in px, for better rendering (default: 500)
-    caption - [string] image caption
-    alt     - [string] alternative image text, defaults to caption
-
-mixin image(url, width, caption, alt)
-    figure.o-block&attributes(attributes)
-        if url
-            img(src=url alt=(alt || caption) width="#{width || 500}")
-
-        if caption
-            +image-caption=caption
-
-        block
-
-
-//- Image caption
-
-mixin image-caption()
-    figcaption.u-text-small.u-color-subtle.u-padding-small&attributes(attributes)
-        block
-
-
-//- Graphic or illustration with button
-    original - [string] Path to original image
-
-mixin graphic(original)
-    +image
-        block
-        if original
-            .u-text-right
-                +button(original, false, "secondary", "small") View large graphic
-
-
-//- Chart.js
-    id - [string] chart ID, will be assigned as #chart_{id}
-
-mixin chart(id, height)
-    figure.o-block&attributes(attributes)
-        canvas(id="chart_#{id}" width="800" height=(height || "400") style="max-width: 100%")
-
-
-//- Labels
-
-mixin label()
-    .u-text-label.u-color-dark&attributes(attributes)
-        block
-
-
-mixin label-inline()
-    strong.u-text-label.u-color-dark&attributes(attributes)
-        block
-
-
-//- Tag
-    tooltip   - [string] optional tooltip text.
-    hide_icon - [boolean] hide tooltip icon
-
-mixin tag(tooltip, hide_icon)
-    div.u-text-tag.u-text-tag--spaced(data-tooltip=tooltip)&attributes(attributes)
-        block
-        if tooltip
-            if !hide_icon
-                |  #[+icon("help", 12).o-icon--tag]
-            |  #[span.u-hidden(aria-role="tooltip")=tooltip]
-
-
-//- "Requires model" tag with tooltip and list of capabilities
-    ...capabs - [string] Required model capabilities, e.g. "vectors".
-
-mixin tag-model(...capabs)
-    - var intro = "To use this functionality, spaCy needs a model to be installed"
-    - var ext = capabs.length ? " that supports the following capabilities: " + capabs.join(', ') : ""
-    +tag(intro + ext + ".") Needs model
-
-
-//- "New" tag to label features new in a specific version
-    By using a separate mixin with a version ID, it becomes easy to quickly
-    enable/disable tags without having to modify the markup in the docs.
-    version - [string or integer] version number, without "v" prefix
-
-mixin tag-new(version)
-    - var version = (typeof version == 'number') ? version.toFixed(1) : version
-    - var tooltip = "This feature is new and was introduced in spaCy v" + version
-    +tag(tooltip, true) v#{version}
-
-
-//- List
-    type  - [string] "numbers", "letters", "roman" (bulleted list if none set)
-    start - [integer] start number
-
-mixin list(type, start)
-    if type
-        ol.c-list.o-block.u-text(class="c-list--#{type}" style=(start === 0 || start) ? "counter-reset: li #{(start - 1)}" : null)&attributes(attributes)
-            block
-
-    else
-        ul.c-list.c-list--bullets.o-block.u-text&attributes(attributes)
-            block
-
-
-//- List item (only used within +list)
-
-mixin item()
-    li.c-list__item&attributes(attributes)
-        block
-
-
-//- Table
-    head - [array] table headings (should match number of columns)
-
-mixin table(head)
-    table.c-table.o-block&attributes(attributes)
-
-        if head
-            +row("head")
-                each column in head
-                    +head-cell=column
-
-        block
-
-
-//- Table row (only used within +table)
-
-mixin row(...style)
-    tr.c-table__row(class=prefixArgs(style, "c-table__row"))&attributes(attributes)
-        block
-
-
-
-//- Header table cell (only used within +row)
-
-mixin head-cell()
-    th.c-table__head-cell.u-text-label&attributes(attributes)
-        block
-
-
-//- Table cell (only used within +row in +table)
-
-mixin cell(...style)
-    td.c-table__cell.u-text(class=prefixArgs(style, "c-table__cell"))&attributes(attributes)
-        block
-
-
-//- Grid Container
-    ...style - all arguments are added as class names o-grid--argument
-               see assets/css/_base/_grid.sass
-
-mixin grid(...style)
-    .o-grid.o-block(class=prefixArgs(style, "o-grid"))&attributes(attributes)
-        block
-
-
-//- Grid Column (only used within +grid)
-    width - [string] "quarter", "third", "half", "two-thirds", "three-quarters"
-    see $grid in assets/css/_variables.sass
-
-mixin grid-col(...style)
-    .o-grid__col(class=prefixArgs(style, "o-grid__col"))&attributes(attributes)
-        block
-
-
-//- Card (only used within +grid)
-    title  - [string] card title
-    url    - [string] link for card
-    author - [string] optional author, displayed as byline at the bottom
-    icon   - [string] optional ID of icon displayed with card
-    width  - [string] optional width of grid column, defaults to "half"
-
-mixin card(title, url, author, icon, width)
-    +grid-col(width || "half").o-box.o-grid.o-grid--space.u-text&attributes(attributes)
-        +a(url)
-            h4.u-heading.u-text-label
-                if icon
-                    +icon(icon, 25).u-float-right
-                if title
-                    span.u-color-dark=title
-            .o-block-small.u-text-small
-                block
-        if author
-            .u-color-subtle.u-text-tiny by #{author}
-
-
-//- Table of contents, to be used with +item mixins for links
-    col - [string] width of column (see +grid-col)
-
-mixin table-of-contents(col)
-    +grid-col(col || "half")
-        +infobox
-            +label.o-block-small Table of contents
-            +list("numbers").u-text-small.o-no-block
-                block
-
-
-//- Bibliography
-    id - [string] ID of bibliography component, for anchor links. Can be used if
-         there's more than one bibliography on one page.
-
-mixin bibliography(id)
-    section(id=id || "bibliography")
-        +infobox
-            +label.o-block-small Bibliography
-            +list("numbers").u-text-small.o-no-block
-                block
-
-
-//- Footnote
-    id      - [string / integer] ID of footnote.
-    bib_id  - [string] ID of bibliography component, defaults to "bibliography".
-    tooltip - [string] optional text displayed as tooltip
-
-mixin fn(id, bib_id, tooltip)
-    sup.u-padding-small(id="bib" + id data-tooltip=tooltip)
-        span.u-text-tag
-            +a("#" + (bib_id || "bibliography")).u-hide-link #{id}
-
-
-//- Table rows for annotation specs
-
-mixin pos-row(tag, pos, morph, desc)
-    +row
-        +cell #[code(class=(tag.length > 10) ? "u-break u-break--all" : null)=tag]
-        +cell #[code=pos]
-        +cell
-            - var morphs = morph.includes("|") ? morph.split("|") : morph.split(" ")
-            for m in morphs
-                if m
-                    |  #[code=m]
-        +cell.u-text-small=desc
-
-mixin ud-row(tag, desc, example)
-        +row
-            +cell #[code=tag]
-            +cell.u-text-small=desc
-            if example
-                +cell.u-text-small
-                    em=example
-
-mixin dep-row(label, desc)
-    +row
-        +cell #[code=label]
-        +cell=desc
-
-
-//- Table rows for linguistic annotations
-    annots [array] - array of cell content
-    style [array] array of 1 (display as code) or 0 (display as text)
-
-mixin annotation-row(annots, style)
-    +row
-        for cell, i in annots
-            if style && style[i]
-                - cell = (typeof(cell) != 'boolean') ? cell : cell ? 'True' : 'False'
-                +cell #[code=cell]
-            else
-                +cell=cell
-        block
-
-
-//- spaCy logo
-
-mixin logo()
-    +svg("spacy", 675, 215).o-logo&attributes(attributes)
-
-
-//- Gitter chat button and widget
-    button - [string] text shown on button
-    label  - [string] title of chat window (default: same as button)
-
-mixin gitter(button, label)
-    aside.js-gitter.c-chat.is-collapsed(data-title=(label || button))
-
-    button.js-gitter-button.c-chat__button.u-text-tag
-        +icon("chat", 16).o-icon--inline
-        !=button
-
-
-//- Badge
-    image - [string] path to badge image
-    url   - [string] badge link
-
-mixin badge(image, url)
-    +a(url).u-padding-small.u-hide-link&attributes(attributes)
-        img.o-badge(src=image alt=url height="20")
-
-
-//- Quickstart widget
-    quickstart.js with manual markup, inspired by PyTorch's "Getting started"
-    groups - [object] option groups, uses global variable QUICKSTART
-    headline - [string] optional text to be rendered as widget headline
-
-mixin quickstart(groups, headline, description, hide_results)
-    .c-quickstart.o-block-small#qs
-        .c-quickstart__content
-            if headline
-                +h(2)=headline
-            if description
-                p=description
-            for group in groups
-                .c-quickstart__group.u-text-small(data-qs-group=group.id)
-                    if group.title
-                        .c-quickstart__legend=group.title
-                            if group.help
-                                |  #[+help(group.help)]
-                    .c-quickstart__fields
-                        for option in group.options
-                            input.c-quickstart__input(class="c-quickstart__input--" + (group.input_style ? group.input_style : group.multiple ? "check" : "radio") type=group.multiple ? "checkbox" : "radio" name=group.id id="qs-#{option.id}" value=option.id checked=option.checked)
-                            label.c-quickstart__label.u-text-tiny(for="qs-#{option.id}")!=option.title
-                                if option.meta
-                                    |  #[span.c-quickstart__label__meta (#{option.meta})]
-                                if option.help
-                                    |  #[+help(option.help)]
-
-        if hide_results
-            block
-        else
-            pre.c-code-block
-                code.c-code-block__content.c-quickstart__code(data-qs-results="")
-                    block
-
-
-//- Quickstart code item
-    data  - [object] Rendering conditions (keyed by option group ID, value: option)
-    style - [string] modifier ID for line style
-
-mixin qs(data, style)
-    - args = {}
-    for value, setting in data
-        - args['data-qs-' + setting] = value
-    span.c-quickstart__line(class="c-quickstart__line--#{style || 'bash'}")&attributes(args)
-        block
-
-
-//- Terminal-style code window
-    label - [string] title displayed in top bar of terminal window
-
-mixin terminal-wrapper(label, small)
-    .x-terminal(class=small ? "x-terminal--small" : null)
-        .x-terminal__icons(class=small ? "x-terminal__icons--small" : null): span
-        .u-padding-small.u-text-center(class=small ? "u-text-tiny" : "u-text")
-            strong=label
-        block
-
-mixin terminal(label, button_text, button_url, exec)
-    +terminal-wrapper(label)
-        +code.x-terminal__code(data-executable=exec ? "" : null)
-            block
-
-        if button_text && button_url
-            +button(button_url, true, "primary", "small").x-terminal__button=button_text
-
-
-//- Landing
-
-mixin landing-header()
-    header.c-landing
-        .c-landing__wrapper
-            .c-landing__content
-                block
-
-mixin landing-banner(headline, label)
-    .c-landing__banner.u-padding.o-block.u-color-light
-        +grid.c-landing__banner__content.o-no-block
-            +grid-col("third")
-                h3.u-heading.u-heading-1
-                    if label
-                        div
-                            span.u-text-label.u-text-label--light=label
-                    !=headline
-
-            +grid-col("two-thirds").c-landing__banner__text
-                block
-
-
-mixin landing-logos(title, logos)
-    .o-content.u-text-center&attributes(attributes)
-        h3.u-heading.u-text-label.u-color-dark=title
-
-        each row, i in logos
-            - var is_last = i == logos.length - 1
-            +grid("center").o-inline-list.o-no-block(class=is_last ? "o-no-block" : null)
-                each details, name in row
-                    +a(details[0]).u-padding-medium
-                        +icon(name, details[1], details[2])
-
-                if is_last
-                    block
-
-
-//- Under construction (temporary)
-    Marks sections that still need to be completed for the v2.0 release.
-
-mixin under-construction()
-    +infobox("Under construction", "🚧")
-        |  This section is still being written and will be updated as soon as
-        |  possible. Is there anything that you think should definitely
-        |  mentioned or explained here? Any examples you'd like to see?
-        |  #[strong Let us know] on the #[+a(gh("spacy") + "/issues") issue tracker]!
-
-
-//- Legacy docs
-
-mixin legacy()
-    +aside("Looking for the old docs?", "📖")
-        |  To help you make the transition from v1.x to v2.0, we've uploaded the
-        |  old website to #[strong #[+a("https://legacy.spacy.io/docs") legacy.spacy.io]].
-        |  Wherever possible, the new docs also include notes on features that have
-        |  changed in v2.0, and features that were introduced in the new version.
diff --git a/website/_includes/_navigation.jade b/website/_includes/_navigation.jade
deleted file mode 100644
index 71868987a..000000000
--- a/website/_includes/_navigation.jade
+++ /dev/null
@@ -1,16 +0,0 @@
-//- 💫 INCLUDES > TOP NAVIGATION
-
-nav.c-nav.u-text.js-nav(class=landing ? "c-nav--theme" : null)
-    a(href="/" aria-label=SITENAME) #[+logo]
-
-    ul.c-nav__menu
-        - var current_url = '/' + current.path[0]
-        each url, item in NAVIGATION
-            - var is_active = (current_url == url)
-            li.c-nav__menu__item(class=is_active ? "is-active" : null)
-                +a(url)(tabindex=is_active ? "-1" : null)=item
-
-        li.c-nav__menu__item
-            +a(gh("spaCy"))(aria-label="GitHub") #[+icon("github", 20)]
-
-    progress.c-progress.js-progress(value="0" max="1")
diff --git a/website/_includes/_newsletter.jade b/website/_includes/_newsletter.jade
deleted file mode 100644
index 352cb7c36..000000000
--- a/website/_includes/_newsletter.jade
+++ /dev/null
@@ -1,16 +0,0 @@
-//- 💫 INCLUDES > NEWSLETTER
-
-ul.o-block-small
-    li.u-text-label.u-color-subtle Stay in the loop!
-    li Receive updates about new releases, tutorials and more.
-
-form.o-grid#mc-embedded-subscribe-form(action="//#{MAILCHIMP.user}.list-manage.com/subscribe/post?u=#{MAILCHIMP.id}&amp;id=#{MAILCHIMP.list}" method="post" name="mc-embedded-subscribe-form" target="_blank" novalidate)
-
-    //- MailChimp spam protection
-    div(style="position: absolute; left: -5000px;" aria-hidden="true")
-        input(type="text" name="b_#{MAILCHIMP.id}_#{MAILCHIMP.list}" tabindex="-1" value="")
-
-    .o-grid-col.o-grid.o-grid--nowrap.o-field.u-padding-small
-        div
-            input#mce-EMAIL.o-field__input.u-text(type="email" name="EMAIL" placeholder="Your email" aria-label="Your email")
-        button#mc-embedded-subscribe.o-field__button.u-text-label.u-color-theme.u-nowrap(type="submit" name="subscribe") Sign up
diff --git a/website/_includes/_page-docs.jade b/website/_includes/_page-docs.jade
deleted file mode 100644
index 6295491a6..000000000
--- a/website/_includes/_page-docs.jade
+++ /dev/null
@@ -1,54 +0,0 @@
-//- 💫 INCLUDES > DOCS PAGE TEMPLATE
-
-- sidebar_content = (public[SECTION] ? public[SECTION]._data.sidebar : public._data[SECTION] ? public._data[SECTION].sidebar : false) || FOOTER
-
-include _sidebar
-
-main.o-main.o-main--sidebar.o-main--aside
-    article.o-content
-        +grid.o-no-block
-            +h(1).u-heading--title=title.replace("'", "’")
-                if tag
-                    +tag=tag
-                if tag_new
-                    +tag-new(tag_new)
-
-                if teaser
-                    .u-heading__teaser.u-text-small.u-color-dark=teaser
-                else if IS_MODELS
-                    .u-heading__teaser.u-text-small.u-color-dark
-                        |  Available statistical models for
-                        |  #[code=current.source] (#{LANGUAGES[current.source]}).
-
-            if source
-                .o-block.u-text-right
-                    +button(gh("spacy", source), false, "secondary", "small").u-nowrap
-                        |  Source #[+icon("code", 14)]
-
-        if IS_MODELS
-            include _page_models
-        else
-            !=yield
-
-    +grid.o-content.u-text
-        +grid-col("half")
-            if !IS_MODELS
-                .o-inline-list
-                    +button(gh("spacy", "website/" + current.path.join('/') + ".jade"), false, "secondary", "small")
-                        |  #[span.o-icon Suggest edits] #[+icon("code", 14)]
-
-        +grid-col("half").u-text-right
-            if next && public[SECTION]._data[next]
-                - data = public[SECTION]._data[next]
-
-                +grid("vcenter")
-                    +a(next).u-text-small.u-flex-full
-                        h4.u-text-label.u-color-dark Read next
-                        |  #{data.title}
-
-                    +a(next).c-icon-button.c-icon-button--right(aria-hidden="true")
-                        +icon("arrow-right", 24)
-
-    +gitter("spaCy chat")
-
-    include _footer
diff --git a/website/_includes/_page_models.jade b/website/_includes/_page_models.jade
deleted file mode 100644
index fc49bdb85..000000000
--- a/website/_includes/_page_models.jade
+++ /dev/null
@@ -1,109 +0,0 @@
-//- 💫 INCLUDES > MODELS PAGE TEMPLATE
-
-for id in CURRENT_MODELS
-    - var comps = getModelComponents(id)
-    +section(id)
-        section(data-vue=id data-model=id)
-            +grid("vcenter").o-no-block(id=id)
-                +grid-col("two-thirds")
-                    +h(2)
-                        +a("#" + id).u-permalink=id
-
-                +grid-col("third").u-text-right
-                    .u-color-subtle.u-text-tiny
-                        +button(gh("spacy-models") + "/releases", true, "secondary", "small")(v-bind:href="releaseUrl")
-                            |  Release details
-                        .u-padding-small Latest: #[code(v-text="version") n/a]
-
-            +aside-code("Installation", "bash", "$").
-                python -m spacy download #{id}
-
-            p(v-if="description" v-text="description")
-
-            +infobox(v-if="error")
-                |  Unable to load model details from GitHub. To find out more
-                |  about this model, see the overview of the
-                |  #[+a(gh("spacy-models") + "/releases") latest model releases].
-
-            +table.o-block-small(v-bind:data-loading="loading")
-                +row
-                    +cell #[+label Language]
-                    +cell #[+tag=comps.lang] #{LANGUAGES[comps.lang]}
-                for comp, label in {"Type": comps.type, "Genre": comps.genre}
-                    +row
-                        +cell #[+label=label]
-                        +cell #[+tag=comp] #{MODEL_META[comp]}
-                +row
-                    +cell #[+label Size]
-                    +cell #[+tag=comps.size] #[span(v-text="sizeFull" v-if="sizeFull")] #[em(v-else="") n/a]
-
-                +row(v-if="pipeline && pipeline.length" v-cloak="")
-                    +cell
-                        +label Pipeline #[+help(MODEL_META.pipeline).u-color-subtle]
-                    +cell
-                        span(v-for="(pipe, index) in pipeline" v-if="pipeline")
-                            code(v-text="pipe")
-                            span(v-if="index != pipeline.length - 1") ,&nbsp;
-
-                +row(v-if="vectors" v-cloak="")
-                    +cell
-                        +label Vectors #[+help(MODEL_META.vectors).u-color-subtle]
-                    +cell(v-text="vectors")
-
-                +row(v-if="sources && sources.length" v-cloak="")
-                    +cell
-                        +label Sources #[+help(MODEL_META.sources).u-color-subtle]
-                    +cell
-                        span(v-for="(source, index) in sources") {{ source }}
-                            span(v-if="index != sources.length - 1") ,&nbsp;
-
-                +row(v-if="author" v-cloak="")
-                    +cell #[+label Author]
-                    +cell
-                        +a("")(v-bind:href="url" v-if="url" v-text="author")
-                        span(v-else="" v-text="author") {{ model.author }}
-
-                +row(v-if="license" v-cloak="")
-                    +cell #[+label License]
-                    +cell
-                        +a("")(v-bind:href="modelLicenses[license]" v-if="modelLicenses[license]") {{ license }}
-                        span(v-else="") {{ license }}
-
-                +row(v-cloak="")
-                    +cell #[+label Compat #[+help(MODEL_META.compat).u-color-subtle]]
-                    +cell
-                        .o-field.u-float-left
-                            select.o-field__select.u-text-small(v-model="spacyVersion")
-                                option(v-for="version in orderedCompat" v-bind:value="version") spaCy v{{ version }}
-                        code(v-if="compatVersion" v-text="compatVersion")
-                        em(v-else="") not compatible
-
-            +grid.o-block-small(v-cloak="" v-if="hasAccuracy")
-                for keys, label in MODEL_BENCHMARKS
-                    .u-flex-full.u-padding-small
-                        +table.o-block-small
-                            +row("head")
-                                +head-cell(colspan="2")=(MODEL_META["benchmark_" + label] || label)
-                            for label, field in keys
-                                +row
-                                    +cell.u-nowrap
-                                        +label=label
-                                            if MODEL_META[field]
-                                                |  #[+help(MODEL_META[field]).u-color-subtle]
-                                    +cell("num")
-                                        span(v-if="#{field}" v-text="#{field}")
-                                        em(v-if="!#{field}") n/a
-
-            p.u-text-small.u-color-dark(v-if="notes" v-text="notes" v-cloak="")
-
-        if comps.size == "sm" && EXAMPLE_SENT_LANGS.includes(comps.lang)
-            section
-                +code-exec("Test the model live").
-                    import spacy
-                    from spacy.lang.#{comps.lang}.examples import sentences
-
-                    nlp = spacy.load('#{id}')
-                    doc = nlp(sentences[0])
-                    print(doc.text)
-                    for token in doc:
-                        print(token.text, token.pos_, token.dep_)
diff --git a/website/_includes/_scripts.jade b/website/_includes/_scripts.jade
deleted file mode 100644
index c486cfa84..000000000
--- a/website/_includes/_scripts.jade
+++ /dev/null
@@ -1,28 +0,0 @@
-//- 💫 INCLUDES > SCRIPTS
-
-- scripts = ["vendor/prism.min", "vendor/vue.min"]
-- if (SECTION == "universe") scripts.push("vendor/vue-markdown.min")
-- if (quickstart) scripts.push("vendor/quickstart.min")
-- if (IS_PAGE) scripts.push("vendor/in-view.min")
-- if (IS_PAGE || SECTION == "index") scripts.push("vendor/juniper.min")
-
-for script in scripts
-    script(src="/assets/js/" + script + ".js")
-script(src="/assets/js/main.js?v#{V_JS}" type=(environment == "deploy") ? null : "module")
-
-if environment == "deploy"
-    script(src="https://www.google-analytics.com/analytics.js", async)
-    script
-        | window.ga=window.ga||function(){
-        | (ga.q=ga.q||[]).push(arguments)}; ga.l=+new Date;
-        | ga('create', '#{ANALYTICS}', 'auto'); ga('send', 'pageview');
-
-if IS_PAGE
-    script(src="https://sidecar.gitter.im/dist/sidecar.v1.js" async defer)
-    script
-        | ((window.gitter = {}).chat = {}).options = {
-        |     useStyles: false,
-        |     activationElement: '.js-gitter-button',
-        |     targetElement: '.js-gitter',
-        |     room: '!{SOCIAL.gitter}'
-        | };
diff --git a/website/_includes/_sidebar.jade b/website/_includes/_sidebar.jade
deleted file mode 100644
index 6bce6630c..000000000
--- a/website/_includes/_sidebar.jade
+++ /dev/null
@@ -1,23 +0,0 @@
-//- 💫 INCLUDES > SIDEBAR
-
-menu.c-sidebar.js-sidebar.u-text
-    if sidebar_content
-        each items, sectiontitle in sidebar_content
-            ul.c-sidebar__section.o-block-small
-                li.u-text-label.u-color-dark=sectiontitle
-
-                each url, item in items
-                    - var is_current = CURRENT == url || (CURRENT == "index" && url == "./")
-                    li.c-sidebar__item
-                        +a(url)(class=is_current ? "is-active" : null tabindex=is_current ? "-1" : null data-sidebar-active=is_current ? "" : null)=item
-
-                        if is_current
-                            if IS_MODELS && CURRENT_MODELS.length
-                                - menu = Object.assign({}, ...CURRENT_MODELS.map(id => ({ [id]: id })))
-                            if menu
-                                ul.c-sidebar__crumb.u-hidden-sm
-                                    - var counter = 0
-                                    for id, title in menu
-                                        - counter++
-                                        li.c-sidebar__crumb__item(data-nav=id)
-                                            +a("#section-" + id)=title
diff --git a/website/_includes/_svg.jade b/website/_includes/_svg.jade
deleted file mode 100644
index b7b988db9..000000000
--- a/website/_includes/_svg.jade
+++ /dev/null
@@ -1,180 +0,0 @@
-//- 💫 INCLUDES > SVG
-
-svg(style="position: absolute; visibility: hidden; width: 0; height: 0;" width="0" height="0" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink")
-    defs
-        //- UI icons
-
-        symbol#svg_github(viewBox="0 0 27 32")
-            path(d="M13.714 2.286q3.732 0 6.884 1.839t4.991 4.991 1.839 6.884q0 4.482-2.616 8.063t-6.759 4.955q-0.482 0.089-0.714-0.125t-0.232-0.536q0-0.054 0.009-1.366t0.009-2.402q0-1.732-0.929-2.536 1.018-0.107 1.83-0.321t1.679-0.696 1.446-1.188 0.946-1.875 0.366-2.688q0-2.125-1.411-3.679 0.661-1.625-0.143-3.643-0.5-0.161-1.446 0.196t-1.643 0.786l-0.679 0.429q-1.661-0.464-3.429-0.464t-3.429 0.464q-0.286-0.196-0.759-0.482t-1.491-0.688-1.518-0.241q-0.804 2.018-0.143 3.643-1.411 1.554-1.411 3.679 0 1.518 0.366 2.679t0.938 1.875 1.438 1.196 1.679 0.696 1.83 0.321q-0.696 0.643-0.875 1.839-0.375 0.179-0.804 0.268t-1.018 0.089-1.17-0.384-0.991-1.116q-0.339-0.571-0.866-0.929t-0.884-0.429l-0.357-0.054q-0.375 0-0.518 0.080t-0.089 0.205 0.161 0.25 0.232 0.214l0.125 0.089q0.393 0.179 0.777 0.679t0.563 0.911l0.179 0.411q0.232 0.679 0.786 1.098t1.196 0.536 1.241 0.125 0.991-0.063l0.411-0.071q0 0.679 0.009 1.58t0.009 0.973q0 0.321-0.232 0.536t-0.714 0.125q-4.143-1.375-6.759-4.955t-2.616-8.063q0-3.732 1.839-6.884t4.991-4.991 6.884-1.839zM5.196 21.982q0.054-0.125-0.125-0.214-0.179-0.054-0.232 0.036-0.054 0.125 0.125 0.214 0.161 0.107 0.232-0.036zM5.75 22.589q0.125-0.089-0.036-0.286-0.179-0.161-0.286-0.054-0.125 0.089 0.036 0.286 0.179 0.179 0.286 0.054zM6.286 23.393q0.161-0.125 0-0.339-0.143-0.232-0.304-0.107-0.161 0.089 0 0.321t0.304 0.125zM7.036 24.143q0.143-0.143-0.071-0.339-0.214-0.214-0.357-0.054-0.161 0.143 0.071 0.339 0.214 0.214 0.357 0.054zM8.054 24.589q0.054-0.196-0.232-0.286-0.268-0.071-0.339 0.125t0.232 0.268q0.268 0.107 0.339-0.107zM9.179 24.679q0-0.232-0.304-0.196-0.286 0-0.286 0.196 0 0.232 0.304 0.196 0.286 0 0.286-0.196zM10.214 24.5q-0.036-0.196-0.321-0.161-0.286 0.054-0.25 0.268t0.321 0.143 0.25-0.25z")
-
-        symbol#svg_twitter(viewBox="0 0 30 32")
-            path(d="M28.929 7.286q-1.196 1.75-2.893 2.982 0.018 0.25 0.018 0.75 0 2.321-0.679 4.634t-2.063 4.437-3.295 3.759-4.607 2.607-5.768 0.973q-4.839 0-8.857-2.589 0.625 0.071 1.393 0.071 4.018 0 7.161-2.464-1.875-0.036-3.357-1.152t-2.036-2.848q0.589 0.089 1.089 0.089 0.768 0 1.518-0.196-2-0.411-3.313-1.991t-1.313-3.67v-0.071q1.214 0.679 2.607 0.732-1.179-0.786-1.875-2.054t-0.696-2.75q0-1.571 0.786-2.911 2.161 2.661 5.259 4.259t6.634 1.777q-0.143-0.679-0.143-1.321 0-2.393 1.688-4.080t4.080-1.688q2.5 0 4.214 1.821 1.946-0.375 3.661-1.393-0.661 2.054-2.536 3.179 1.661-0.179 3.321-0.893z")
-
-        symbol#svg_website(viewBox="0 0 32 32")
-            path(d="M22.658 10.988h5.172c0.693 1.541 1.107 3.229 1.178 5.012h-5.934c-0.025-1.884-0.181-3.544-0.416-5.012zM20.398 3.896c2.967 1.153 5.402 3.335 6.928 6.090h-4.836c-0.549-2.805-1.383-4.799-2.092-6.090zM16.068 9.986v-6.996c1.066 0.047 2.102 0.216 3.092 0.493 0.75 1.263 1.719 3.372 2.33 6.503h-5.422zM9.489 22.014c-0.234-1.469-0.396-3.119-0.421-5.012h5.998v5.012h-5.577zM9.479 10.988h5.587v5.012h-6.004c0.025-1.886 0.183-3.543 0.417-5.012zM11.988 3.461c0.987-0.266 2.015-0.435 3.078-0.469v6.994h-5.422c0.615-3.148 1.591-5.265 2.344-6.525zM3.661 9.986c1.551-2.8 4.062-4.993 7.096-6.131-0.715 1.29-1.559 3.295-2.114 6.131h-4.982zM8.060 16h-6.060c0.066-1.781 0.467-3.474 1.158-5.012h5.316c-0.233 1.469-0.39 3.128-0.414 5.012zM8.487 22.014h-5.29c-0.694-1.543-1.139-3.224-1.204-5.012h6.071c0.024 1.893 0.188 3.541 0.423 5.012zM8.651 23.016c0.559 2.864 1.416 4.867 2.134 6.142-3.045-1.133-5.557-3.335-7.11-6.142h4.976zM15.066 23.016v6.994c-1.052-0.033-2.067-0.199-3.045-0.46-0.755-1.236-1.736-3.363-2.356-6.534h5.401zM21.471 23.016c-0.617 3.152-1.592 5.271-2.344 6.512-0.979 0.271-2.006 0.418-3.059 0.465v-6.977h5.403zM16.068 17.002h5.998c-0.023 1.893-0.188 3.542-0.422 5.012h-5.576v-5.012zM22.072 16h-6.004v-5.012h5.586c0.235 1.469 0.393 3.126 0.418 5.012zM23.070 17.002h5.926c-0.066 1.787-0.506 3.468-1.197 5.012h-5.152c0.234-1.471 0.398-3.119 0.423-5.012zM27.318 23.016c-1.521 2.766-3.967 4.949-6.947 6.1 0.715-1.276 1.561-3.266 2.113-6.1h4.834z")
-
-        symbol#svg_code(viewBox="0 0 20 20")
-            path(d="M5.719 14.75c-0.236 0-0.474-0.083-0.664-0.252l-5.060-4.498 5.341-4.748c0.412-0.365 1.044-0.33 1.411 0.083s0.33 1.045-0.083 1.412l-3.659 3.253 3.378 3.002c0.413 0.367 0.45 0.999 0.083 1.412-0.197 0.223-0.472 0.336-0.747 0.336zM14.664 14.748l5.341-4.748-5.060-4.498c-0.413-0.367-1.045-0.33-1.411 0.083s-0.33 1.045 0.083 1.412l3.378 3.003-3.659 3.252c-0.413 0.367-0.45 0.999-0.083 1.412 0.197 0.223 0.472 0.336 0.747 0.336 0.236 0 0.474-0.083 0.664-0.252zM9.986 16.165l2-12c0.091-0.545-0.277-1.060-0.822-1.151-0.547-0.092-1.061 0.277-1.15 0.822l-2 12c-0.091 0.545 0.277 1.060 0.822 1.151 0.056 0.009 0.11 0.013 0.165 0.013 0.48 0 0.904-0.347 0.985-0.835z")
-
-        symbol#svg_arrow-right(viewBox="0 0 24 24")
-            path(d="M20.744 12.669c0 0 0 0 0 0 0.006-0.006 0.006-0.006 0.006-0.006s0 0 0 0 0.006-0.006 0.006-0.006c0 0 0.006-0.006 0.006-0.006s0 0 0 0 0.006-0.006 0.006-0.006c0 0 0 0 0 0 0.063-0.075 0.112-0.156 0.15-0.244 0 0 0 0 0-0.006 0 0 0-0.006 0-0.006s0-0.006 0-0.006c0 0 0 0 0 0 0.038-0.094 0.063-0.194 0.069-0.3 0 0 0 0 0 0s0-0.006 0-0.006c0 0 0-0.006 0-0.006s0-0.006 0-0.006c0 0 0-0.006 0-0.006s0 0 0-0.006c0-0.025 0-0.050 0-0.075 0 0 0 0 0-0.006 0 0 0-0.006 0-0.006s0-0.006 0-0.006c0 0 0-0.006 0-0.006s0-0.006 0-0.006c0 0 0 0 0 0-0.006-0.106-0.031-0.206-0.069-0.3 0 0 0 0 0-0.006 0 0 0 0 0-0.006 0 0 0-0.006-0.006-0.006 0 0 0 0 0 0-0.038-0.094-0.094-0.175-0.156-0.256 0 0 0 0 0 0s-0.006-0.006-0.006-0.006c0 0 0 0 0 0s-0.006-0.006-0.006-0.006-0.006-0.006-0.006-0.006 0 0 0-0.006c-0.012-0.012-0.025-0.025-0.037-0.037l-6-6c-0.387-0.387-1.025-0.387-1.413 0s-0.387 1.025 0 1.413l4.294 4.294h-13.581c-0.55 0-1 0.45-1 1s0.45 1 1 1h13.587l-4.294 4.294c-0.387 0.387-0.387 1.025 0 1.413 0.194 0.194 0.45 0.294 0.706 0.294s0.513-0.1 0.706-0.294l5.994-5.994c0.019-0.025 0.031-0.044 0.044-0.056z")
-
-        symbol#svg_book(viewBox="0 0 20 20")
-            path(d="M15.5 11h-11c-0.275 0-0.5 0.225-0.5 0.5v1c0 0.276 0.225 0.5 0.5 0.5h11c0.276 0 0.5-0.224 0.5-0.5v-1c0-0.275-0.224-0.5-0.5-0.5zM15.5 7h-11c-0.275 0-0.5 0.225-0.5 0.5v1c0 0.276 0.225 0.5 0.5 0.5h11c0.276 0 0.5-0.224 0.5-0.5v-1c0-0.275-0.224-0.5-0.5-0.5zM10.5 15h-6c-0.275 0-0.5 0.225-0.5 0.5v1c0 0.276 0.225 0.5 0.5 0.5h6c0.276 0 0.5-0.224 0.5-0.5v-1c0-0.275-0.224-0.5-0.5-0.5zM15.5 3h-11c-0.275 0-0.5 0.225-0.5 0.5v1c0 0.276 0.225 0.5 0.5 0.5h11c0.276 0 0.5-0.224 0.5-0.5v-1c0-0.275-0.224-0.5-0.5-0.5z")
-
-        symbol#svg_yes(viewBox="0 0 24 24")
-            path(d="M9.984 17.016l9-9-1.406-1.453-7.594 7.594-3.563-3.563-1.406 1.406zM12 2.016c5.531 0 9.984 4.453 9.984 9.984s-4.453 9.984-9.984 9.984-9.984-4.453-9.984-9.984 4.453-9.984 9.984-9.984z")
-
-        symbol#svg_no(viewBox="0 0 24 24")
-            path(d="M17.016 15.609l-3.609-3.609 3.609-3.609-1.406-1.406-3.609 3.609-3.609-3.609-1.406 1.406 3.609 3.609-3.609 3.609 1.406 1.406 3.609-3.609 3.609 3.609zM12 2.016c5.531 0 9.984 4.453 9.984 9.984s-4.453 9.984-9.984 9.984-9.984-4.453-9.984-9.984 4.453-9.984 9.984-9.984z")
-
-        symbol#svg_neutral(viewBox="0 0 24 24")
-            path(d="M12 2.016c5.531 0 9.984 4.453 9.984 9.984s-4.453 9.984-9.984 9.984-9.984-4.453-9.984-9.984 4.453-9.984 9.984-9.984z")
-
-        symbol#svg_chat(viewBox="0 0 30 30")
-            path(d="M28.74 25.2c-1.73-.3-3.77-1.46-4.74-3.6 3.64-2.2 6-5.68 6-9.6 0-6.63-6.72-12-15-12S0 5.37 0 12s6.72 12 15 12c1.1 0 2.2-.1 3.23-.3 2.86 2 6.25 2.62 10.4 2.15.26-.02.37-.15.37-.32 0-.16-.1-.3-.26-.32zM23 14c0 .55-.45 1-1 1H8c-.55 0-1-.45-1-1s.45-1 1-1h14c.55 0 1 .45 1 1zm0-4c0 .55-.45 1-1 1H8c-.55 0-1-.45-1-1s.45-1 1-1h14c.55 0 1 .45 1 1z")
-
-        symbol#svg_star(viewBox="0 0 24 24")
-            path(d="M12 17.25l-6.188 3.75 1.641-7.031-5.438-4.734 7.172-0.609 2.813-6.609 2.813 6.609 7.172 0.609-5.438 4.734 1.641 7.031z")
-
-        symbol#svg_help(viewBox="0 0 24 28")
-            path(d="M14 21.5v-3c0-0.281-0.219-0.5-0.5-0.5h-3c-0.281 0-0.5 0.219-0.5 0.5v3c0 0.281 0.219 0.5 0.5 0.5h3c0.281 0 0.5-0.219 0.5-0.5zM18 11c0-2.859-3-5-5.688-5-2.547 0-4.453 1.094-5.797 3.328-0.141 0.219-0.078 0.5 0.125 0.656l2.063 1.563c0.078 0.063 0.187 0.094 0.297 0.094 0.141 0 0.297-0.063 0.391-0.187 0.734-0.938 1.047-1.219 1.344-1.437 0.266-0.187 0.781-0.375 1.344-0.375 1 0 1.922 0.641 1.922 1.328 0 0.812-0.422 1.219-1.375 1.656-1.109 0.5-2.625 1.797-2.625 3.313v0.562c0 0.281 0.219 0.5 0.5 0.5h3c0.281 0 0.5-0.219 0.5-0.5v0c0-0.359 0.453-1.125 1.188-1.547 1.188-0.672 2.812-1.578 2.812-3.953zM24 14c0 6.625-5.375 12-12 12s-12-5.375-12-12 5.375-12 12-12 12 5.375 12 12z")
-
-        symbol#svg_help_o(viewBox="0 0 24 28")
-            path(d="M13.75 18.75v2.5c0 0.281-0.219 0.5-0.5 0.5h-2.5c-0.281 0-0.5-0.219-0.5-0.5v-2.5c0-0.281 0.219-0.5 0.5-0.5h2.5c0.281 0 0.5 0.219 0.5 0.5zM17.75 11c0 2.219-1.547 3.094-2.688 3.734-0.812 0.469-1.313 0.766-1.313 1.266v0.5c0 0.281-0.219 0.5-0.5 0.5h-2.5c-0.281 0-0.5-0.219-0.5-0.5v-1.062c0-1.922 1.375-2.531 2.484-3.031 0.938-0.438 1.516-0.734 1.516-1.437 0-0.906-1.141-1.578-2.172-1.578-0.547 0-1.125 0.172-1.484 0.422-0.344 0.234-0.672 0.578-1.25 1.297-0.094 0.125-0.234 0.187-0.391 0.187-0.109 0-0.219-0.031-0.297-0.094l-1.687-1.281c-0.203-0.156-0.25-0.453-0.109-0.672 1.281-2.016 3.078-3 5.453-3v0c2.562 0 5.437 2.031 5.437 4.75zM12 4c-5.516 0-10 4.484-10 10s4.484 10 10 10 10-4.484 10-10-4.484-10-10-10zM24 14c0 6.625-5.375 12-12 12s-12-5.375-12-12 5.375-12 12-12v0c6.625 0 12 5.375 12 12z")
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-        symbol#svg_jupyter(viewBox="245 20 270 270")
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-            path(d="M299.5 286c-9.2.3-17-6.8-17.3-16-.4-9 6.7-16.8 15.8-17.2 9.2-.4 17 6.7 17.3 15.8.4 9.2-6.7 17-15.8 17.3zM286.8 70.4c-5.7.2-10.6-4.2-10.8-10-.3-5.6 4.2-10.5 10-10.7 5.6-.2 10.4 4.2 10.6 10 .3 5.6-4 10.4-9.8 10.7zM451 58c-9 .5-16.4-6.4-16.8-15.3-.3-8.8 6.5-16.3 15.4-16.7 9-.4 16.4 6.5 16.7 15.4.4 8.8-6.5 16.3-15.3 16.7z" )
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-
-        //- Logos
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-        symbol#svg_spacy(viewBox="0 0 675 215")
-            path(fill="currentColor" d="M83.6 83.3C68.3 81.5 67.2 61 47.5 62.8c-9.5 0-18.4 4-18.4 12.7 0 13.2 20.3 14.4 32.5 17.7 20.9 6.3 41 10.7 41 33.3 0 28.8-22.6 38.8-52.4 38.8-24.9 0-50.2-8.9-50.2-31.8 0-6.4 6.1-11.3 12-11.3 7.5 0 10.1 3.2 12.7 8.4 5.8 10.2 12.3 15.6 28.3 15.6 10.2 0 20.6-3.9 20.6-12.7 0-12.6-12.8-15.3-26.1-18.4-23.5-6.6-43.6-10-46-36.1C-1 34.5 91.7 32.9 97 71.9c.1 7.1-6.5 11.4-13.4 11.4zm110.2-39c32.5 0 51 27.2 51 60.8 0 33.7-17.9 60.8-51 60.8-18.4 0-29.8-7.8-38.1-19.8v44.5c0 13.4-4.3 19.8-14.1 19.8-11.9 0-14.1-7.6-14.1-19.8V61.3c0-10.6 4.4-17 14.1-17 9.1 0 14.1 7.2 14.1 17v3.6c9.2-11.6 19.7-20.6 38.1-20.6zm-7.7 98.4c19.1 0 27.6-17.6 27.6-38.1 0-20.1-8.6-38.1-27.6-38.1-19.8 0-29 16.3-29 38.1 0 21.2 9.2 38.1 29 38.1zM266.9 76c0-23.4 26.9-31.7 52.9-31.7 36.6 0 51.7 10.7 51.7 46v34c0 8.1 5 24.1 5 29 0 7.4-6.8 12-14.1 12-8.1 0-14.1-9.5-18.4-16.3-11.9 9.5-24.5 16.3-43.8 16.3-21.3 0-38.1-12.6-38.1-33.3 0-18.4 13.2-28.9 29-32.5 0 .1 51-12 51-12.1 0-15.7-5.5-22.6-22-22.6-14.5 0-21.9 4-27.5 12.7-4.5 6.6-4 10.6-12.7 10.6-6.9-.1-13-4.9-13-12.1zm43.6 70.2c22.3 0 31.8-11.8 31.8-35.3v-5c-6 2-30.3 8-36.8 9.1-7 1.4-14.1 6.6-14.1 14.9.1 9.1 9.4 16.3 19.1 16.3zM474.5 0c31.5 0 65.7 18.8 65.7 48.8 0 7.7-5.8 14.1-13.4 14.1-10.3 0-11.8-5.5-16.3-13.4-7.6-13.9-16.5-23.3-36.1-23.3-30.2-.2-43.7 25.6-43.7 57.8 0 32.4 11.2 55.8 42.4 55.8 20.7 0 32.2-12 38.1-27.6 2.4-7.1 6.7-14.1 15.6-14.1 7 0 14.1 7.2 14.1 14.8 0 31.8-32.4 53.8-65.8 53.8-36.5 0-57.2-15.4-68.5-41-5.5-12.2-9.1-24.9-9.1-42.4-.1-49.2 28.6-83.3 77-83.3zm180.3 44.3c8 0 12.7 5.2 12.7 13.4 0 3.3-2.6 9.9-3.6 13.4L625.1 173c-8.6 22.1-15.1 37.4-44.5 37.4-14 0-26.1-1.2-26.1-13.4 0-7 5.3-10.6 12.7-10.6 1.4 0 3.6.7 5 .7 2.1 0 3.6.7 5 .7 14.7 0 16.8-15.1 22-25.5l-37.4-92.6c-2.1-5-3.6-8.4-3.6-11.3 0-8.2 6.4-14.1 14.8-14.1 9.5 0 13.3 7.5 15.6 15.6l24.7 73.5L638 65.5c3.9-10.5 4.2-21.2 16.8-21.2z" )
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-        symbol#svg_explosion(viewBox="0 0 500 500")
-            path(fill="currentColor" d="M111.7 74.9L91.2 93.1l9.1 10.2 17.8-15.8 7.4 8.4-17.8 15.8 10.1 11.4 20.6-18.2 7.7 8.7-30.4 26.9-41.9-47.3 30.3-26.9 7.6 8.6zM190.8 59.6L219 84.3l-14.4 4.5-20.4-18.2-6.4 26.6-14.4 4.5 8.9-36.4-26.9-24.1 14.3-4.5L179 54.2l5.7-25.2 14.3-4.5-8.2 35.1zM250.1 21.2l27.1 3.4c6.1.8 10.8 3.1 14 7.2 3.2 4.1 4.5 9.2 3.7 15.5-.8 6.3-3.2 11-7.4 14.1-4.1 3.1-9.2 4.3-15.3 3.5L258 63.2l-2.8 22.3-13-1.6 7.9-62.7zm11.5 13l-2.2 17.5 12.6 1.6c5.1.6 9.1-2 9.8-7.6.7-5.6-2.5-9.2-7.6-9.9l-12.6-1.6zM329.1 95.4l23.8 13.8-5.8 10L312 98.8l31.8-54.6 11.3 6.6-26 44.6zM440.5 145c-1.3 8.4-5.9 15.4-13.9 21.1s-16.2 7.7-24.6 6.1c-8.4-1.6-15.3-6.3-20.8-14.1-5.5-7.9-7.6-16-6.4-24.4 1.3-8.5 6-15.5 14-21.1 8-5.6 16.2-7.7 24.5-6 8.4 1.6 15.4 6.3 20.9 14.2 5.5 7.6 7.6 15.7 6.3 24.2zM412 119c-5.1-.8-10.3.6-15.6 4.4-5.2 3.7-8.4 8.1-9.4 13.2-1 5.2.2 10.1 3.5 14.8 3.4 4.8 7.5 7.5 12.7 8.2 5.2.8 10.4-.7 15.6-4.4 5.3-3.7 8.4-8.1 9.4-13.2 1.1-5.1-.1-9.9-3.4-14.7-3.4-4.8-7.6-7.6-12.8-8.3zM471.5 237.9c-2.8 4.8-7.1 7.6-13 8.7l-2.6-13.1c5.3-.9 8.1-5 7.2-11-.9-5.8-4.3-8.8-8.9-8.2-2.3.3-3.7 1.4-4.5 3.3-.7 1.9-1.4 5.2-1.7 10.1-.8 7.5-2.2 13.1-4.3 16.9-2.1 3.9-5.7 6.2-10.9 7-6.3.9-11.3-.5-15.2-4.4-3.9-3.8-6.3-9-7.3-15.7-1.1-7.4-.2-13.7 2.6-18.8 2.8-5.1 7.4-8.2 13.7-9.2l2.6 13c-5.6 1.1-8.7 6.6-7.7 13.4 1 6.6 3.9 9.5 8.6 8.8 4.4-.7 5.7-4.5 6.7-14.1.3-3.5.7-6.2 1.1-8.4.4-2.2 1.2-4.4 2.2-6.8 2.1-4.7 6-7.2 11.8-8.1 5.4-.8 10.3.4 14.5 3.7 4.2 3.3 6.9 8.5 8 15.6.9 6.9-.1 12.6-2.9 17.3zM408.6 293.5l2.4-12.9 62 11.7-2.4 12.9-62-11.7zM419.6 396.9c-8.3 2-16.5.3-24.8-5-8.2-5.3-13.2-12.1-14.9-20.5-1.6-8.4.1-16.6 5.3-24.6 5.2-8.1 11.9-13.1 20.2-15.1 8.4-1.9 16.6-.3 24.9 5 8.2 5.3 13.2 12.1 14.8 20.5 1.7 8.4 0 16.6-5.2 24.7-5.2 8-12 13-20.3 15zm13.4-36.3c-1.2-5.1-4.5-9.3-9.9-12.8s-10.6-4.7-15.8-3.7-9.3 4-12.4 8.9-4.1 9.8-2.8 14.8c1.2 5.1 4.5 9.3 9.9 12.8 5.5 3.5 10.7 4.8 15.8 3.7 5.1-.9 9.2-3.8 12.3-8.7s4.1-9.9 2.9-15zM303.6 416.5l9.6-5.4 43.3 20.4-19.2-34 11.4-6.4 31 55-9.6 5.4-43.4-20.5 19.2 34.1-11.3 6.4-31-55zM238.2 468.8c-49 0-96.9-17.4-134.8-49-38.3-32-64-76.7-72.5-125.9-2-11.9-3.1-24-3.1-35.9 0-36.5 9.6-72.6 27.9-104.4 2.1-3.6 6.7-4.9 10.3-2.8 3.6 2.1 4.9 6.7 2.8 10.3-16.9 29.5-25.9 63.1-25.9 96.9 0 11.1 1 22.3 2.9 33.4 7.9 45.7 31.8 87.2 67.3 116.9 35.2 29.3 79.6 45.5 125.1 45.5 11.1 0 22.3-1 33.4-2.9 4.1-.7 8 2 8.7 6.1.7 4.1-2 8-6.1 8.7-11.9 2-24 3.1-36 3.1z")
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-        symbol#svg_prodigy(viewBox="0 0 538.5 157.6")
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-
-
-        //- Machine learning & NLP libraries
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-        symbol#svg_tensorflow(viewBox="0 0 31 33")
-            path(d="M17.3 5v5l8.7 5v-5zM0 10v5l4.3 2.5v-5zm13 2.5L8.7 15v15l4.3 2.5v-10l4.3 2.5v-5L13 17.5z" fill="#e55b2d")
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-        symbol#svg_keras(viewBox="0 0 512 512")
-            path(fill="#D01317" d="M64 64h384v384H64z")
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-        symbol#svg_pytorch(viewBox="0 0 200 41")
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-        symbol#svg_scikitlearn(viewBox="0 0 278 150")
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-            path(fill="#00A4EF" d="M0 67.7h61.3V129H0z")
-            path(fill="#FFB900" d="M67.7 67.7H129V129H67.7z")
-
-        symbol#svg_bbc(viewBox="0 0 1000 285")
-            path(fill="#111" d="M542.1 184.3c0 30.97-38.56 29.14-38.56 29.14H465v-56h38.54c39.7-.23 38.56 26.85 38.56 26.85M465 72.05h29.36c30.53 1.6 29.38 24.33 29.38 24.33 0 28.22-33.74 28.68-33.74 28.68h-25V72.06zm70 64.03s26.38-11.25 26.15-41.1c0 0 4-48.87-60.76-54.84h-71.9V245.1h82.4s68.84.24 68.84-57.83c0 0 1.6-39.47-44.75-51.18M348.95 0h302.6v285.17h-302.6V0zM193.14 184.3c0 30.97-38.56 29.14-38.56 29.14h-38.56v-56h38.56c39.7-.23 38.56 26.85 38.56 26.85M116.02 72.05h29.38c30.52 1.6 29.38 24.33 29.38 24.33 0 28.22-33.74 28.68-33.74 28.68h-25.02V72.06zm70 64.03s26.4-11.25 26.17-41.1c0 0 4-48.87-60.78-54.84h-71.9V245.1h82.4s68.86.24 68.86-57.83c0 0 1.6-39.47-44.76-51.18M0 0h302.6v285.17H0V0zM938.8 54.85v37.87S901.85 70 861 69.54c0 0-76.2-1.5-79.64 73.04 0 0-2.75 68.57 78.72 72.47 0 0 34.2 4.13 80.56-25.48v39.25s-62.2 36.95-134.26 8.5c0 0-60.6-22.15-62.9-94.74 0 0-2.52-74.65 78.27-99.43 0 0 21.58-8.26 60.36-4.6 0 0 23.2 2.3 56.7 16.3M697.93 285.17h302.6V0h-302.6v285.17z")
-
-    //- Filters etc.
-    defs
-        radialGradient#gradient_allenai1(cx="75.721" cy="20.894" r="11.05" gradientUnits="userSpaceOnUse")
-            stop(offset=".3" stop-color="#FDEA65")
-            stop(offset="1" stop-color="#FCB431")
-        radialGradient#gradient_allenai2(cx="75.4" cy="42.297" r="82.993" gradientUnits="userSpaceOnUse")
-            stop(offset="0" stop-color="#3FA9D0")
-            stop(offset="1" stop-color="#183A74")
diff --git a/website/_layout.jade b/website/_layout.jade
deleted file mode 100644
index b0a1342e5..000000000
--- a/website/_layout.jade
+++ /dev/null
@@ -1,57 +0,0 @@
-//- 💫 GLOBAL LAYOUT
-
-include _includes/_mixins
-
-- title = IS_MODELS ? LANGUAGES[current.source] || title : title
-
-- PAGE_URL = getPageUrl()
-- PAGE_TITLE = getPageTitle()
-- PAGE_IMAGE = getPageImage()
-
-doctype html
-html(lang="en")
-    head
-        title=PAGE_TITLE
-        meta(charset="utf-8")
-        meta(name="viewport" content="width=device-width, initial-scale=1.0")
-        meta(name="referrer" content="always")
-        meta(name="description" content=description)
-
-        meta(property="og:type" content="website")
-        meta(property="og:site_name" content=sitename)
-        meta(property="og:url" content=PAGE_URL)
-        meta(property="og:title" content=PAGE_TITLE)
-        meta(property="og:description" content=description)
-        meta(property="og:image" content=PAGE_IMAGE)
-
-        meta(name="twitter:card" content="summary_large_image")
-        meta(name="twitter:site" content="@" + SOCIAL.twitter)
-        meta(name="twitter:title" content=PAGE_TITLE)
-        meta(name="twitter:description" content=description)
-        meta(name="twitter:image" content=PAGE_IMAGE)
-
-        link(rel="shortcut icon" href="/assets/img/favicon.ico")
-        link(rel="icon" type="image/x-icon" href="/assets/img/favicon.ico")
-
-        if SECTION == "api"
-            link(href="/assets/css/style_green.css?v#{V_CSS}" rel="stylesheet")
-        else if SECTION == "universe"
-            link(href="/assets/css/style_purple.css?v#{V_CSS}" rel="stylesheet")
-        else
-            link(href="/assets/css/style.css?v#{V_CSS}" rel="stylesheet")
-
-    body
-        include _includes/_svg
-        include _includes/_navigation
-
-        if !landing
-            include _includes/_page-docs
-
-        else if SECTION == "universe"
-            !=yield
-
-        else
-            main!=yield
-                include _includes/_footer
-
-        include _includes/_scripts
diff --git a/website/api/_annotation/_biluo.jade b/website/api/_annotation/_biluo.jade
deleted file mode 100644
index 34d93f768..000000000
--- a/website/api/_annotation/_biluo.jade
+++ /dev/null
@@ -1,43 +0,0 @@
-//- 💫 DOCS > API > ANNOTATION > BILUO
-
-+table(["Tag", "Description"])
-    +row
-        +cell #[code #[span.u-color-theme B] EGIN]
-        +cell The first token of a multi-token entity.
-
-    +row
-        +cell #[code #[span.u-color-theme I] N]
-        +cell An inner token of a multi-token entity.
-
-    +row
-        +cell #[code #[span.u-color-theme L] AST]
-        +cell The final token of a multi-token entity.
-
-    +row
-        +cell #[code #[span.u-color-theme U] NIT]
-        +cell A single-token entity.
-
-    +row
-        +cell #[code #[span.u-color-theme O] UT]
-        +cell A non-entity token.
-
-+aside("Why BILUO, not IOB?")
-    |  There are several coding schemes for encoding entity annotations as
-    |  token tags.  These coding schemes are equally expressive, but not
-    |  necessarily equally learnable.
-    |  #[+a("http://www.aclweb.org/anthology/W09-1119") Ratinov and Roth]
-    |  showed that the minimal #[strong Begin], #[strong In], #[strong Out]
-    |  scheme was more difficult to learn than the #[strong BILUO] scheme that
-    |  we use, which explicitly marks boundary tokens.
-
-p
-    |  spaCy translates the character offsets into this scheme, in order to
-    |  decide the cost of each action given the current state of the entity
-    |  recogniser. The costs are then used to calculate the gradient of the
-    |  loss, to train the model. The exact algorithm is a pastiche of
-    |  well-known methods, and is not currently described in any single
-    |  publication. The model is a greedy transition-based parser guided by a
-    |  linear model whose weights are learned using the averaged perceptron
-    |  loss, via the #[+a("http://www.aclweb.org/anthology/C12-1059") dynamic oracle]
-    |  imitation learning strategy. The transition system is equivalent to the
-    |  BILOU tagging scheme.
diff --git a/website/api/_annotation/_dep-labels.jade b/website/api/_annotation/_dep-labels.jade
deleted file mode 100644
index 3c7d73eb6..000000000
--- a/website/api/_annotation/_dep-labels.jade
+++ /dev/null
@@ -1,158 +0,0 @@
-//- 💫 DOCS > API > ANNOTATION > DEPENDENCY LABELS
-
-p
-    |  This section lists the syntactic dependency labels assigned by
-    |  spaCy's #[+a("/models") models]. The individual labels are
-    |  language-specific and depend on the training corpus.
-
-+accordion("Universal Dependency Labels")
-    p
-        |  The #[+a("http://universaldependencies.org/u/dep/") Universal Dependencies scheme]
-        |  is used in all languages trained on Universal Dependency Corpora.
-
-    +table(["Dep", "Description"])
-        +ud-row("acl", "clausal modifier of noun (adjectival clause)")
-        +ud-row("advcl", "adverbial clause modifier")
-        +ud-row("advmod", "adverbial modifier")
-        +ud-row("amod", "adjectival modifier")
-        +ud-row("appos", "appositional modifier")
-        +ud-row("aux", "auxiliary")
-        +ud-row("case", "case marking")
-        +ud-row("cc", "coordinating conjunction")
-        +ud-row("ccomp", "clausal complement")
-        +ud-row("clf", "classifier")
-        +ud-row("compound", "compound")
-        +ud-row("conj", "conjunct")
-        +ud-row("cop", "copula")
-        +ud-row("csubj", "clausal subject")
-        +ud-row("dep", "unspecified dependency")
-        +ud-row("det", "determiner")
-        +ud-row("discourse", "discourse element")
-        +ud-row("dislocated", "dislocated elements")
-        +ud-row("expl", "expletive")
-        +ud-row("fixed", "fixed multiword expression")
-        +ud-row("flat", "flat multiword expression")
-        +ud-row("goeswith", "goes with")
-        +ud-row("iobj", "indirect object")
-        +ud-row("list", "list")
-        +ud-row("mark", "marker")
-        +ud-row("nmod", "nominal modifier")
-        +ud-row("nsubj", "nominal subject")
-        +ud-row("nummod", "numeric modifier")
-        +ud-row("obj", "object")
-        +ud-row("obl", "oblique nominal")
-        +ud-row("orphan", "orphan")
-        +ud-row("parataxis", "parataxis")
-        +ud-row("punct", "punctuation")
-        +ud-row("reparandum", "overridden disfluency")
-        +ud-row("root", "root")
-        +ud-row("vocative", "vocative")
-        +ud-row("xcomp", "open clausal complement")
-
-+accordion("English", "dependency-parsing-english")
-    p
-        |  The English dependency labels use the
-        |  #[+a("https://github.com/clir/clearnlp-guidelines/blob/master/md/specifications/dependency_labels.md") CLEAR Style]
-        |  by #[+a("http://www.clearnlp.com") ClearNLP].
-
-    +table(["Label", "Description"])
-        +dep-row("acl", "clausal modifier of noun (adjectival clause)")
-        +dep-row("acomp", "adjectival complement")
-        +dep-row("advcl", "adverbial clause modifier")
-        +dep-row("advmod", "adverbial modifier")
-        +dep-row("agent", "agent")
-        +dep-row("amod", "adjectival modifier")
-        +dep-row("appos", "appositional modifier")
-        +dep-row("attr", "attribute")
-        +dep-row("aux", "auxiliary")
-        +dep-row("auxpass", "auxiliary (passive)")
-        +dep-row("case", "case marking")
-        +dep-row("cc", "coordinating conjunction")
-        +dep-row("ccomp", "clausal complement")
-        +dep-row("compound", "compound")
-        +dep-row("conj", "conjunct")
-        +dep-row("cop", "copula")
-        +dep-row("csubj", "clausal subject")
-        +dep-row("csubjpass", "clausal subject (passive)")
-        +dep-row("dative", "dative")
-        +dep-row("dep", "unclassified dependent")
-        +dep-row("det", "determiner")
-        +dep-row("dobj", "direct object")
-        +dep-row("expl", "expletive")
-        +dep-row("intj", "interjection")
-        +dep-row("mark", "marker")
-        +dep-row("meta", "meta modifier")
-        +dep-row("neg", "negation modifier")
-        +dep-row("nn", "noun compound modifier")
-        +dep-row("nounmod", "modifier of nominal")
-        +dep-row("npmod", "noun phrase as adverbial modifier")
-        +dep-row("nsubj", "nominal subject")
-        +dep-row("nsubjpass", "nominal subject (passive)")
-        +dep-row("nummod", "numeric modifier")
-        +dep-row("oprd", "object predicate")
-        +dep-row("obj", "object")
-        +dep-row("obl", "oblique nominal")
-        +dep-row("parataxis", "parataxis")
-        +dep-row("pcomp", "complement of preposition")
-        +dep-row("pobj", "object of preposition")
-        +dep-row("poss", "possession modifier")
-        +dep-row("preconj", "pre-correlative conjunction")
-        +dep-row("prep", "prepositional modifier")
-        +dep-row("prt", "particle")
-        +dep-row("punct", "punctuation")
-        +dep-row("quantmod", "modifier of quantifier")
-        +dep-row("relcl", "relative clause modifier")
-        +dep-row("root", "root")
-        +dep-row("xcomp", "open clausal complement")
-
-+accordion("German", "dependency-parsing-german")
-    p
-        |  The German dependency labels use the
-        |  #[+a("http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/TIGERCorpus/annotation/index.html") TIGER Treebank]
-        |  annotation scheme.
-
-    +table(["Label", "Description"])
-        +dep-row("ac", "adpositional case marker")
-        +dep-row("adc", "adjective component")
-        +dep-row("ag", "genitive attribute")
-        +dep-row("ams", "measure argument of adjective")
-        +dep-row("app", "apposition")
-        +dep-row("avc", "adverbial phrase component")
-        +dep-row("cc", "comparative complement")
-        +dep-row("cd", "coordinating conjunction")
-        +dep-row("cj", "conjunct")
-        +dep-row("cm", "comparative conjunction")
-        +dep-row("cp", "complementizer")
-        +dep-row("cvc", "collocational verb construction")
-        +dep-row("da", "dative")
-        +dep-row("dh", "discourse-level head")
-        +dep-row("dm", "discourse marker")
-        +dep-row("ep", "expletive es")
-        +dep-row("hd", "head")
-        +dep-row("ju", "junctor")
-        +dep-row("mnr", "postnominal modifier")
-        +dep-row("mo", "modifier")
-        +dep-row("ng", "negation")
-        +dep-row("nk", "noun kernel element")
-        +dep-row("nmc", "numerical component")
-        +dep-row("oa", "accusative object")
-        +dep-row("oa", "second accusative object")
-        +dep-row("oc", "clausal object")
-        +dep-row("og", "genitive object")
-        +dep-row("op", "prepositional object")
-        +dep-row("par", "parenthetical element")
-        +dep-row("pd", "predicate")
-        +dep-row("pg", "phrasal genitive")
-        +dep-row("ph", "placeholder")
-        +dep-row("pm", "morphological particle")
-        +dep-row("pnc", "proper noun component")
-        +dep-row("rc", "relative clause")
-        +dep-row("re", "repeated element")
-        +dep-row("rs", "reported speech")
-        +dep-row("sb", "subject")
-        +dep-row("sbp", "passivised subject")
-        +dep-row("sp", "subject or predicate")
-        +dep-row("svp", "separable verb prefix")
-        +dep-row("uc", "unit component")
-        +dep-row("vo", "vocative")
-        +dep-row("ROOT", "root")
diff --git a/website/api/_annotation/_named-entities.jade b/website/api/_annotation/_named-entities.jade
deleted file mode 100644
index fa8bc842b..000000000
--- a/website/api/_annotation/_named-entities.jade
+++ /dev/null
@@ -1,109 +0,0 @@
-//- 💫 DOCS > API > ANNOTATION > NAMED ENTITIES
-
-p
-    |  Models trained on the
-    |  #[+a("https://catalog.ldc.upenn.edu/LDC2013T19") OntoNotes 5] corpus
-    |  support the following entity types:
-
-+table(["Type", "Description"])
-    +row
-        +cell #[code PERSON]
-        +cell People, including fictional.
-
-    +row
-        +cell #[code NORP]
-        +cell Nationalities or religious or political groups.
-
-    +row
-        +cell #[code FAC]
-        +cell Buildings, airports, highways, bridges, etc.
-
-    +row
-        +cell #[code ORG]
-        +cell Companies, agencies, institutions, etc.
-
-    +row
-        +cell #[code GPE]
-        +cell Countries, cities, states.
-
-    +row
-        +cell #[code LOC]
-        +cell Non-GPE locations, mountain ranges, bodies of water.
-
-    +row
-        +cell #[code PRODUCT]
-        +cell Objects, vehicles, foods, etc. (Not services.)
-
-    +row
-        +cell #[code EVENT]
-        +cell Named hurricanes, battles, wars, sports events, etc.
-
-    +row
-        +cell #[code WORK_OF_ART]
-        +cell Titles of books, songs, etc.
-
-    +row
-        +cell #[code LAW]
-        +cell Named documents made into laws.
-
-    +row
-        +cell #[code LANGUAGE]
-        +cell Any named language.
-
-    +row
-        +cell #[code DATE]
-        +cell Absolute or relative dates or periods.
-
-    +row
-        +cell #[code TIME]
-        +cell Times smaller than a day.
-
-    +row
-        +cell #[code PERCENT]
-        +cell Percentage, including "%".
-
-    +row
-        +cell #[code MONEY]
-        +cell Monetary values, including unit.
-
-    +row
-        +cell #[code QUANTITY]
-        +cell Measurements, as of weight or distance.
-
-    +row
-        +cell #[code ORDINAL]
-        +cell "first", "second", etc.
-
-    +row
-        +cell #[code CARDINAL]
-        +cell Numerals that do not fall under another type.
-
-+h(4, "ner-wikipedia-scheme") Wikipedia scheme
-
-p
-    |  Models trained on Wikipedia corpus
-    |  (#[+a("http://www.sciencedirect.com/science/article/pii/S0004370212000276") Nothman et al., 2013])
-    |  use a less fine-grained NER annotation scheme and recognise the
-    |  following entities:
-
-+table(["Type", "Description"])
-    +row
-        +cell #[code PER]
-        +cell Named person or family.
-
-    +row
-        +cell #[code LOC]
-        +cell
-            |  Name of politically or geographically defined location (cities,
-            |  provinces, countries, international regions, bodies of water,
-            |  mountains).
-
-    +row
-        +cell #[code ORG]
-        +cell Named corporate, governmental, or other organizational entity.
-
-    +row
-        +cell #[code MISC]
-        +cell
-            |  Miscellaneous entities, e.g. events, nationalities, products or
-            |  works of art.
diff --git a/website/api/_annotation/_pos-tags.jade b/website/api/_annotation/_pos-tags.jade
deleted file mode 100644
index 6aa8b4466..000000000
--- a/website/api/_annotation/_pos-tags.jade
+++ /dev/null
@@ -1,179 +0,0 @@
-//- 💫 DOCS > API > ANNOTATION > POS TAGS
-
-p
-    |  This section lists the fine-grained and coarse-grained part-of-speech
-    |  tags assigned by spaCy's #[+a("/models") models]. The individual mapping
-    |  is specific to the training corpus and can be defined in the respective
-    |  language data's #[+a("/usage/adding-languages#tag-map") #[code tag_map.py]].
-
-+accordion("Universal Part-of-speech Tags")
-    p
-        |  spaCy also maps all language-specific part-of-speech tags to a small,
-        |  fixed set of word type tags following the
-        |  #[+a("http://universaldependencies.org/u/pos/") Universal Dependencies scheme].
-        |  The universal tags don't code for any morphological features and only
-        |  cover the word type. They're available as the
-        |  #[+api("token#attributes") #[code Token.pos]] and
-        |  #[+api("token#attributes") #[code Token.pos_]] attributes.
-
-    +table(["POS", "Description", "Examples"])
-        +ud-row("ADJ", "adjective", "big, old, green, incomprehensible, first")
-        +ud-row("ADP", "adposition", "in, to, during")
-        +ud-row("ADV", "adverb", "very, tomorrow, down, where, there")
-        +ud-row("AUX", "auxiliary", "is, has (done), will (do), should (do)")
-        +ud-row("CONJ", "conjunction", "and, or, but")
-        +ud-row("CCONJ", "coordinating conjunction", "and, or, but")
-        +ud-row("DET", "determiner", "a, an, the")
-        +ud-row("INTJ", "interjection", "psst, ouch, bravo, hello")
-        +ud-row("NOUN", "noun", "girl, cat, tree, air, beauty")
-        +ud-row("NUM", "numeral", "1, 2017, one, seventy-seven, IV, MMXIV")
-        +ud-row("PART", "particle", "'s, not, ")
-        +ud-row("PRON", "pronoun", "I, you, he, she, myself, themselves, somebody")
-        +ud-row("PROPN", "proper noun", "Mary, John, London, NATO, HBO")
-        +ud-row("PUNCT", "punctuation", "., (, ), ?")
-        +ud-row("SCONJ", "subordinating conjunction", "if, while, that")
-        +ud-row("SYM", "symbol", "$, %, §, ©, +, −, ×, ÷, =, :), 😝")
-        +ud-row("VERB", "verb", "run, runs, running, eat, ate, eating")
-        +ud-row("X", "other", "sfpksdpsxmsa")
-        +ud-row("SPACE", "space", "")
-
-+accordion("English", "pos-en")
-    p
-        |  The English part-of-speech tagger uses the
-        |  #[+a("https://catalog.ldc.upenn.edu/LDC2013T19") OntoNotes 5] version of
-        |  the Penn Treebank tag set. We also map the tags to the simpler Google
-        |  Universal POS tag set.
-
-    +table(["Tag", "POS", "Morphology", "Description"])
-        +pos-row("-LRB-", "PUNCT", "PunctType=brck PunctSide=ini", "left round bracket")
-        +pos-row("-RRB-", "PUNCT", "PunctType=brck PunctSide=fin", "right round bracket")
-        +pos-row(",", "PUNCT", "PunctType=comm", "punctuation mark, comma")
-        +pos-row(":", "PUNCT", "", "punctuation mark, colon or ellipsis")
-        +pos-row(".", "PUNCT", "PunctType=peri", "punctuation mark, sentence closer")
-        +pos-row("''", "PUNCT", "PunctType=quot PunctSide=fin", "closing quotation mark")
-        +pos-row("\"\"", "PUNCT", "PunctType=quot PunctSide=fin", "closing quotation mark")
-        +pos-row("#", "SYM", "SymType=numbersign", "symbol, number sign")
-        +pos-row("``", "PUNCT", "PunctType=quot PunctSide=ini", "opening quotation mark")
-        +pos-row("$", "SYM", "SymType=currency", "symbol, currency")
-        +pos-row("ADD", "X", "", "email")
-        +pos-row("AFX", "ADJ", "Hyph=yes", "affix")
-        +pos-row("BES", "VERB", "", 'auxiliary "be"')
-        +pos-row("CC", "CONJ", "ConjType=coor", "conjunction, coordinating")
-        +pos-row("CD", "NUM", "NumType=card", "cardinal number")
-        +pos-row("DT", "DET", "determiner")
-        +pos-row("EX", "ADV", "AdvType=ex", "existential there")
-        +pos-row("FW", "X", "Foreign=yes", "foreign word")
-        +pos-row("GW", "X", "", "additional word in multi-word expression")
-        +pos-row("HVS", "VERB", "", 'forms of "have"')
-        +pos-row("HYPH", "PUNCT", "PunctType=dash", "punctuation mark, hyphen")
-        +pos-row("IN", "ADP", "", "conjunction, subordinating or preposition")
-        +pos-row("JJ", "ADJ", "Degree=pos", "adjective")
-        +pos-row("JJR", "ADJ", "Degree=comp", "adjective, comparative")
-        +pos-row("JJS", "ADJ", "Degree=sup", "adjective, superlative")
-        +pos-row("LS", "PUNCT", "NumType=ord", "list item marker")
-        +pos-row("MD", "VERB", "VerbType=mod", "verb, modal auxiliary")
-        +pos-row("NFP", "PUNCT", "", "superfluous punctuation")
-        +pos-row("NIL", "", "", "missing tag")
-        +pos-row("NN", "NOUN", "Number=sing", "noun, singular or mass")
-        +pos-row("NNP", "PROPN", "NounType=prop Number=sign", "noun, proper singular")
-        +pos-row("NNPS", "PROPN", "NounType=prop Number=plur", "noun, proper plural")
-        +pos-row("NNS", "NOUN", "Number=plur", "noun, plural")
-        +pos-row("PDT", "ADJ", "AdjType=pdt PronType=prn", "predeterminer")
-        +pos-row("POS", "PART", "Poss=yes", "possessive ending")
-        +pos-row("PRP", "PRON", "PronType=prs", "pronoun, personal")
-        +pos-row("PRP$", "ADJ", "PronType=prs Poss=yes", "pronoun, possessive")
-        +pos-row("RB", "ADV", "Degree=pos", "adverb")
-        +pos-row("RBR", "ADV", "Degree=comp", "adverb, comparative")
-        +pos-row("RBS", "ADV", "Degree=sup", "adverb, superlative")
-        +pos-row("RP", "PART", "", "adverb, particle")
-        +pos-row("_SP", "SPACE", "", "space")
-        +pos-row("SYM", "SYM", "", "symbol")
-        +pos-row("TO", "PART", "PartType=inf VerbForm=inf", "infinitival to")
-        +pos-row("UH", "INTJ", "", "interjection")
-        +pos-row("VB", "VERB", "VerbForm=inf", "verb, base form")
-        +pos-row("VBD", "VERB", "VerbForm=fin Tense=past", "verb, past tense")
-        +pos-row("VBG", "VERB", "VerbForm=part Tense=pres Aspect=prog", "verb, gerund or present participle")
-        +pos-row("VBN", "VERB", "VerbForm=part Tense=past Aspect=perf", "verb, past participle")
-        +pos-row("VBP", "VERB", "VerbForm=fin Tense=pres", "verb, non-3rd person singular present")
-        +pos-row("VBZ", "VERB", "VerbForm=fin Tense=pres Number=sing Person=3", "verb, 3rd person singular present")
-        +pos-row("WDT", "ADJ", "PronType=int|rel", "wh-determiner")
-        +pos-row("WP", "NOUN", "PronType=int|rel", "wh-pronoun, personal")
-        +pos-row("WP$", "ADJ", "Poss=yes PronType=int|rel", "wh-pronoun, possessive")
-        +pos-row("WRB", "ADV", "PronType=int|rel", "wh-adverb")
-        +pos-row("XX", "X", "", "unknown")
-
-+accordion("German", "pos-de")
-    p
-        |  The German part-of-speech tagger uses the
-        |  #[+a("http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/TIGERCorpus/annotation/index.html") TIGER Treebank]
-        |  annotation scheme. We also map the tags to the simpler Google
-        |  Universal POS tag set.
-
-    +table(["Tag", "POS", "Morphology", "Description"])
-        +pos-row("$(", "PUNCT", "PunctType=brck", "other sentence-internal punctuation mark")
-        +pos-row("$,", "PUNCT", "PunctType=comm", "comma")
-        +pos-row("$.", "PUNCT", "PunctType=peri", "sentence-final punctuation mark")
-        +pos-row("ADJA", "ADJ", "", "adjective, attributive")
-        +pos-row("ADJD", "ADJ", "Variant=short", "adjective, adverbial or predicative")
-        +pos-row("ADV", "ADV", "", "adverb")
-        +pos-row("APPO", "ADP", "AdpType=post", "postposition")
-        +pos-row("APPR", "ADP", "AdpType=prep", "preposition; circumposition left")
-        +pos-row("APPRART", "ADP", "AdpType=prep PronType=art", "preposition with article")
-        +pos-row("APZR", "ADP", "AdpType=circ", "circumposition right")
-        +pos-row("ART", "DET", "PronType=art", "definite or indefinite article")
-        +pos-row("CARD", "NUM", "NumType=card", "cardinal number")
-        +pos-row("FM", "X", "Foreign=yes", "foreign language material")
-        +pos-row("ITJ", "INTJ", "", "interjection")
-        +pos-row("KOKOM", "CONJ", "ConjType=comp", "comparative conjunction")
-        +pos-row("KON", "CONJ", "", "coordinate conjunction")
-        +pos-row("KOUI", "SCONJ", "", 'subordinate conjunction with "zu" and infinitive')
-        +pos-row("KOUS", "SCONJ", "", "subordinate conjunction with sentence")
-        +pos-row("NE", "PROPN", "", "proper noun")
-        +pos-row("NNE", "PROPN", "", "proper noun")
-        +pos-row("NN", "NOUN", "", "noun, singular or mass")
-        +pos-row("PAV", "ADV", "PronType=dem", "pronominal adverb")
-        +pos-row("PROAV", "ADV", "PronType=dem", "pronominal adverb")
-        +pos-row("PDAT", "DET", "PronType=dem", "attributive demonstrative pronoun")
-        +pos-row("PDS", "PRON", "PronType=dem", "substituting demonstrative pronoun")
-        +pos-row("PIAT", "DET", "PronType=ind|neg|tot", "attributive indefinite pronoun without determiner")
-        +pos-row("PIDAT", "DET", "AdjType=pdt PronType=ind|neg|tot", "attributive indefinite pronoun with determiner")
-        +pos-row("PIS", "PRON", "PronType=ind|neg|tot", "substituting indefinite pronoun")
-        +pos-row("PPER", "PRON", "PronType=prs", "non-reflexive personal pronoun")
-        +pos-row("PPOSAT", "DET", "Poss=yes PronType=prs", "attributive possessive pronoun")
-        +pos-row("PPOSS", "PRON", "PronType=rel", "substituting possessive pronoun")
-        +pos-row("PRELAT", "DET", "PronType=rel", "attributive relative pronoun")
-        +pos-row("PRELS", "PRON", "PronType=rel", "substituting relative pronoun")
-        +pos-row("PRF", "PRON", "PronType=prs Reflex=yes", "reflexive personal pronoun")
-        +pos-row("PTKA", "PART", "", "particle with adjective or adverb")
-        +pos-row("PTKANT", "PART", "PartType=res", "answer particle")
-        +pos-row("PTKNEG", "PART", "Negative=yes", "negative particle")
-        +pos-row("PTKVZ", "PART", "PartType=vbp", "separable verbal particle")
-        +pos-row("PTKZU", "PART", "PartType=inf", '"zu" before infinitive')
-        +pos-row("PWAT", "DET", "PronType=int", "attributive interrogative pronoun")
-        +pos-row("PWAV", "ADV", "PronType=int", "adverbial interrogative or relative pronoun")
-        +pos-row("PWS", "PRON", "PronType=int", "substituting interrogative pronoun")
-        +pos-row("TRUNC", "X", "Hyph=yes", "word remnant")
-        +pos-row("VAFIN", "AUX", "Mood=ind VerbForm=fin", "finite verb, auxiliary")
-        +pos-row("VAIMP", "AUX", "Mood=imp VerbForm=fin", "imperative, auxiliary")
-        +pos-row("VAINF", "AUX", "VerbForm=inf", "infinitive, auxiliary")
-        +pos-row("VAPP", "AUX", "Aspect=perf VerbForm=fin", "perfect participle, auxiliary")
-        +pos-row("VMFIN", "VERB", "Mood=ind VerbForm=fin VerbType=mod", "finite verb, modal")
-        +pos-row("VMINF", "VERB", "VerbForm=fin VerbType=mod", "infinitive, modal")
-        +pos-row("VMPP", "VERB", "Aspect=perf VerbForm=part VerbType=mod", "perfect participle, modal")
-        +pos-row("VVFIN", "VERB", "Mood=ind VerbForm=fin", "finite verb, full")
-        +pos-row("VVIMP", "VERB", "Mood=imp VerbForm=fin", "imperative, full")
-        +pos-row("VVINF", "VERB", "VerbForm=inf", "infinitive, full")
-        +pos-row("VVIZU", "VERB", "VerbForm=inf", 'infinitive with "zu", full')
-        +pos-row("VVPP", "VERB", "Aspect=perf VerbForm=part", "perfect participle, full")
-        +pos-row("XY", "X", "", "non-word containing non-letter")
-        +pos-row("SP", "SPACE", "", "space")
-
-for _, lang in MODELS
-    - var exclude = ["en", "de", "xx"]
-    if !exclude.includes(lang)
-        - var lang_name = LANGUAGES[lang]
-        - var file_path = "lang/" + lang +  "/tag_map.py"
-        +accordion(lang_name, "pos-" + lang)
-            p
-                |  For details on the #{lang_name} tag map, see
-                |  #[+src(gh("spacy", "spacy/" + file_path)) #[code=file_path]].
diff --git a/website/api/_annotation/_text-processing.jade b/website/api/_annotation/_text-processing.jade
deleted file mode 100644
index 564e76f08..000000000
--- a/website/api/_annotation/_text-processing.jade
+++ /dev/null
@@ -1,55 +0,0 @@
-//- 💫 DOCS > API > ANNOTATION > TEXT PROCESSING
-
-+aside-code("Example").
-    from spacy.lang.en import English
-    nlp = English()
-    tokens = nlp('Some\nspaces  and\ttab characters')
-    tokens_text = [t.text for t in tokens]
-    assert tokens_text == ['Some', '\n', 'spaces', ' ', 'and',
-                        '\t', 'tab', 'characters']
-
-p
-    |  Tokenization standards are based on the
-    |  #[+a("https://catalog.ldc.upenn.edu/LDC2013T19") OntoNotes 5] corpus.
-    |  The tokenizer differs from most by including
-    |  #[strong tokens for significant whitespace]. Any sequence of
-    |  whitespace characters beyond a single space (#[code ' ']) is included
-    |  as a token. The whitespace tokens are useful for much the same reason
-    |  punctuation is – it's often an important delimiter in the text. By
-    |  preserving it in the token output, we are able to maintain a simple
-    |  alignment between the tokens and the original string, and we ensure
-    |  that #[strong no information is lost] during processing.
-
-+h(3, "lemmatization") Lemmatization
-
-+aside("Examples")
-    |   In English, this means:#[br]
-    |  #[strong Adjectives]: happier, happiest &rarr; happy#[br]
-    |  #[strong Adverbs]: worse, worst &rarr; badly#[br]
-    |  #[strong Nouns]: dogs, children &rarr; dog, child#[br]
-    |  #[strong Verbs]: writes, wirting, wrote, written &rarr; write
-
-
-p
-    |  A lemma is the uninflected form of a word. The English lemmatization
-    |  data is taken from #[+a("https://wordnet.princeton.edu") WordNet].
-    |  Lookup tables are taken from
-    |  #[+a("http://www.lexiconista.com/datasets/lemmatization/") Lexiconista].
-    |  spaCy also adds a #[strong special case for pronouns]: all pronouns
-    |  are lemmatized to the special token #[code -PRON-].
-
-+infobox("About spaCy's custom pronoun lemma", "⚠️")
-    |  Unlike verbs and common nouns, there's no clear base form of a personal
-    |  pronoun. Should the lemma of "me" be "I", or should we normalize person
-    |  as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a
-    |  novel symbol, #[code -PRON-], which is used as the lemma for
-    |  all personal pronouns.
-
-+h(3, "sentence-boundary") Sentence boundary detection
-
-p
-    |  Sentence boundaries are calculated from the syntactic parse tree, so
-    |  features such as punctuation and capitalisation play an important but
-    |  non-decisive role in determining the sentence boundaries. Usually this
-    |  means that the sentence boundaries will at least coincide with clause
-    |  boundaries, even given poorly punctuated text.
diff --git a/website/api/_annotation/_training.jade b/website/api/_annotation/_training.jade
deleted file mode 100644
index 8658866aa..000000000
--- a/website/api/_annotation/_training.jade
+++ /dev/null
@@ -1,104 +0,0 @@
-//- 💫 DOCS > API > ANNOTATION > TRAINING
-
-+h(3, "json-input") JSON input format for training
-
-p
-    |  spaCy takes training data in JSON format. The built-in
-    |  #[+api("cli#convert") #[code convert]] command helps you convert the
-    |  #[code .conllu] format used by the
-    |  #[+a("https://github.com/UniversalDependencies") Universal Dependencies corpora]
-    |  to spaCy's training format.
-
-+aside("Annotating entities")
-    |  Named entities are provided in the #[+a("/api/annotation#biluo") BILUO]
-    |  notation. Tokens outside an entity are set to #[code "O"] and tokens
-    |  that are part of an entity are set to the entity label, prefixed by the
-    |  BILUO marker. For example #[code "B-ORG"] describes the first token of
-    |  a multi-token #[code ORG] entity and #[code "U-PERSON"] a single
-    |  token representing a #[code PERSON] entity. The
-    |  #[+api("goldparse#biluo_tags_from_offsets") #[code biluo_tags_from_offsets]]
-    |  function can help you convert entity offsets to the right format.
-
-+code("Example structure").
-    [{
-        "id": int,                      # ID of the document within the corpus
-        "paragraphs": [{                # list of paragraphs in the corpus
-            "raw": string,              # raw text of the paragraph
-            "sentences": [{             # list of sentences in the paragraph
-                "tokens": [{            # list of tokens in the sentence
-                    "id": int,          # index of the token in the document
-                    "dep": string,      # dependency label
-                    "head": int,        # offset of token head relative to token index
-                    "tag": string,      # part-of-speech tag
-                    "orth": string,     # verbatim text of the token
-                    "ner": string       # BILUO label, e.g. "O" or "B-ORG"
-                }],
-                "brackets": [{          # phrase structure (NOT USED by current models)
-                    "first": int,       # index of first token
-                    "last": int,        # index of last token
-                    "label": string     # phrase label
-                }]
-            }]
-        }]
-    }]
-
-p
-    |  Here's an example of dependencies, part-of-speech tags and names
-    |  entities, taken from the English Wall Street Journal portion of the Penn
-    |  Treebank:
-
-+github("spacy", "examples/training/training-data.json", false, false, "json")
-
-+h(3, "vocab-jsonl") Lexical data for vocabulary
-    +tag-new(2)
-
-p
-    |  To populate a model's vocabulary, you can use the
-    |  #[+api("cli#vocab") #[code spacy vocab]] command and load in a
-    |  #[+a("https://jsonlines.readthedocs.io/en/latest/") newline-delimited JSON]
-    |  (JSONL) file containing one lexical entry per line. The first line
-    |  defines the language and vocabulary settings. All other lines are
-    |  expected to be JSON objects describing an individual lexeme. The lexical
-    |  attributes will be then set as attributes on spaCy's
-    |  #[+api("lexeme#attributes") #[code Lexeme]] object. The #[code vocab]
-    |  command outputs a ready-to-use spaCy model with a #[code Vocab]
-    |  containing the lexical data.
-
-+code("First line").
-    {"lang": "en", "settings": {"oov_prob": -20.502029418945312}}
-
-+code("Entry structure").
-    {
-        "orth": string,
-        "id": int,
-        "lower": string,
-        "norm": string,
-        "shape": string
-        "prefix": string,
-        "suffix": string,
-        "length": int,
-        "cluster": string,
-        "prob": float,
-        "is_alpha": bool,
-        "is_ascii": bool,
-        "is_digit": bool,
-        "is_lower": bool,
-        "is_punct": bool,
-        "is_space": bool,
-        "is_title": bool,
-        "is_upper": bool,
-        "like_url": bool,
-        "like_num": bool,
-        "like_email": bool,
-        "is_stop": bool,
-        "is_oov": bool,
-        "is_quote": bool,
-        "is_left_punct": bool,
-        "is_right_punct": bool
-    }
-
-p
-    |  Here's an example of the 20 most frequent lexemes in the English
-    |  training data:
-
-+github("spacy", "examples/training/vocab-data.jsonl", false, false, "json")
diff --git a/website/api/_cython/_doc.jade b/website/api/_cython/_doc.jade
deleted file mode 100644
index e54f09b70..000000000
--- a/website/api/_cython/_doc.jade
+++ /dev/null
@@ -1,71 +0,0 @@
-//- 💫 DOCS > API > CYTHON > CLASSES > DOC
-
-p
-    |  The #[code Doc] object holds an array of
-    |  #[+api("cython-structs#tokenc") #[code TokenC]] structs.
-
-+infobox
-    |  This section documents the extra C-level attributes and methods that
-    |  can't be accessed from Python. For the Python documentation, see
-    |  #[+api("doc") #[code Doc]].
-
-+h(3, "doc_attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code mem]
-        +cell #[code cymem.Pool]
-        +cell
-            |  A memory pool. Allocated memory will be freed once the
-            |  #[code Doc] object is garbage collected.
-
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell A reference to the shared #[code Vocab] object.
-
-    +row
-        +cell #[code c]
-        +cell #[code TokenC*]
-        +cell
-            |  A pointer to a #[+api("cython-structs#tokenc") #[code TokenC]]
-            |  struct.
-
-    +row
-        +cell #[code length]
-        +cell #[code int]
-        +cell The number of tokens in the document.
-
-    +row
-        +cell #[code max_length]
-        +cell #[code int]
-        +cell The underlying size of the #[code Doc.c] array.
-
-+h(3, "doc_push_back") Doc.push_back
-    +tag method
-
-p
-    |  Append a token to the #[code Doc]. The token can be provided as a
-    |  #[+api("cython-structs#lexemec") #[code LexemeC]] or
-    |  #[+api("cython-structs#tokenc") #[code TokenC]] pointer, using Cython's
-    |  #[+a("http://cython.readthedocs.io/en/latest/src/userguide/fusedtypes.html") fused types].
-
-+aside-code("Example").
-    from spacy.tokens cimport Doc
-    from spacy.vocab cimport Vocab
-
-    doc = Doc(Vocab())
-    lexeme = doc.vocab.get(u'hello')
-    doc.push_back(lexeme, True)
-    assert doc.text == u'hello '
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code lex_or_tok]
-        +cell #[code LexemeOrToken]
-        +cell The word to append to the #[code Doc].
-
-    +row
-        +cell #[code has_space]
-        +cell #[code bint]
-        +cell Whether the word has trailing whitespace.
diff --git a/website/api/_cython/_lexeme.jade b/website/api/_cython/_lexeme.jade
deleted file mode 100644
index 219c7ec45..000000000
--- a/website/api/_cython/_lexeme.jade
+++ /dev/null
@@ -1,30 +0,0 @@
-//- 💫 DOCS > API > CYTHON > CLASSES > LEXEME
-
-p
-    |  A Cython class providing access and methods for an entry in the
-    |  vocabulary.
-
-+infobox
-    |  This section documents the extra C-level attributes and methods that
-    |  can't be accessed from Python. For the Python documentation, see
-    |  #[+api("lexeme") #[code Lexeme]].
-
-+h(3, "lexeme_attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code c]
-        +cell #[code LexemeC*]
-        +cell
-            |  A pointer to a #[+api("cython-structs#lexemec") #[code LexemeC]]
-            |  struct.
-
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell A reference to the shared #[code Vocab] object.
-
-    +row
-        +cell #[code orth]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell ID of the verbatim text content.
diff --git a/website/api/_cython/_lexemec.jade b/website/api/_cython/_lexemec.jade
deleted file mode 100644
index be574a235..000000000
--- a/website/api/_cython/_lexemec.jade
+++ /dev/null
@@ -1,200 +0,0 @@
-//- 💫 DOCS > API > CYTHON > STRUCTS > LEXEMEC
-
-p
-    |  Struct holding information about a lexical type. #[code LexemeC]
-    |  structs are usually owned by the #[code Vocab], and accessed through a
-    |  read-only pointer on the #[code TokenC] struct.
-
-+aside-code("Example").
-    lex = doc.c[3].lex
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code flags]
-        +cell #[+abbr("uint64_t") #[code flags_t]]
-        +cell Bit-field for binary lexical flag values.
-
-    +row
-        +cell #[code id]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell
-            |  Usually used to map lexemes to rows in a matrix, e.g. for word
-            |  vectors. Does not need to be unique, so currently misnamed.
-
-    +row
-        +cell #[code length]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell Number of unicode characters in the lexeme.
-
-    +row
-        +cell #[code orth]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell ID of the verbatim text content.
-
-    +row
-        +cell #[code lower]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell ID of the lowercase form of the lexeme.
-
-    +row
-        +cell #[code norm]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell ID of the lexeme's norm, i.e. a normalised form of the text.
-
-    +row
-        +cell #[code shape]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell Transform of the lexeme's string, to show orthographic features.
-
-    +row
-        +cell #[code prefix]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell
-            |  Length-N substring from the start of the lexeme. Defaults to
-            |  #[code N=1].
-
-    +row
-        +cell #[code suffix]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell
-            |  Length-N substring from the end of the lexeme. Defaults to
-            |  #[code N=3].
-
-    +row
-        +cell #[code cluster]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell Brown cluster ID.
-
-    +row
-        +cell #[code prob]
-        +cell #[code float]
-        +cell Smoothed log probability estimate of the lexeme's type.
-
-    +row
-        +cell #[code sentiment]
-        +cell #[code float]
-        +cell A scalar value indicating positivity or negativity.
-
-+h(3, "lexeme_get_struct_attr", "spacy/lexeme.pxd") Lexeme.get_struct_attr
-    +tag staticmethod
-    +tag nogil
-
-p Get the value of an attribute from the #[code LexemeC] struct by attribute ID.
-
-+aside-code("Example").
-    from spacy.attrs cimport IS_ALPHA
-    from spacy.lexeme cimport Lexeme
-
-    lexeme = doc.c[3].lex
-    is_alpha = Lexeme.get_struct_attr(lexeme, IS_ALPHA)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code lex]
-        +cell #[code const LexemeC*]
-        +cell A pointer to a #[code LexemeC] struct.
-
-    +row
-        +cell #[code feat_name]
-        +cell #[code attr_id_t]
-        +cell
-            |  The ID of the attribute to look up. The attributes are
-            |  enumerated in #[code spacy.typedefs].
-
-    +row("foot")
-        +cell returns
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell The value of the attribute.
-
-+h(3, "lexeme_set_struct_attr", "spacy/lexeme.pxd") Lexeme.set_struct_attr
-    +tag staticmethod
-    +tag nogil
-
-p Set the value of an attribute of the #[code LexemeC] struct by attribute ID.
-
-+aside-code("Example").
-    from spacy.attrs cimport NORM
-    from spacy.lexeme cimport Lexeme
-
-    lexeme = doc.c[3].lex
-    Lexeme.set_struct_attr(lexeme, NORM, lexeme.lower)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code lex]
-        +cell #[code const LexemeC*]
-        +cell A pointer to a #[code LexemeC] struct.
-
-    +row
-        +cell #[code feat_name]
-        +cell #[code attr_id_t]
-        +cell
-            |  The ID of the attribute to look up. The attributes are
-            |  enumerated in #[code spacy.typedefs].
-
-    +row
-        +cell #[code value]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell The value to set.
-
-+h(3, "lexeme_c_check_flag", "spacy/lexeme.pxd") Lexeme.c_check_flag
-    +tag staticmethod
-    +tag nogil
-
-p Check the value of a binary flag attribute.
-
-+aside-code("Example").
-    from spacy.attrs cimport IS_STOP
-    from spacy.lexeme cimport Lexeme
-
-    lexeme = doc.c[3].lex
-    is_stop = Lexeme.c_check_flag(lexeme, IS_STOP)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code lexeme]
-        +cell #[code const LexemeC*]
-        +cell A pointer to a #[code LexemeC] struct.
-
-    +row
-        +cell #[code flag_id]
-        +cell #[code attr_id_t]
-        +cell
-            |  The ID of the flag to look up. The flag IDs are enumerated in
-            |  #[code spacy.typedefs].
-
-    +row("foot")
-        +cell returns
-        +cell #[code bint]
-        +cell The boolean value of the flag.
-
-+h(3, "lexeme_c_set_flag", "spacy/lexeme.pxd") Lexeme.c_set_flag
-    +tag staticmethod
-    +tag nogil
-
-p Set the value of a binary flag attribute.
-
-+aside-code("Example").
-    from spacy.attrs cimport IS_STOP
-    from spacy.lexeme cimport Lexeme
-
-    lexeme = doc.c[3].lex
-    Lexeme.c_set_flag(lexeme, IS_STOP, 0)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code lexeme]
-        +cell #[code const LexemeC*]
-        +cell A pointer to a #[code LexemeC] struct.
-
-    +row
-        +cell #[code flag_id]
-        +cell #[code attr_id_t]
-        +cell
-            |  The ID of the flag to look up. The flag IDs are enumerated in
-            |  #[code spacy.typedefs].
-
-    +row
-        +cell #[code value]
-        +cell #[code bint]
-        +cell The value to set.
diff --git a/website/api/_cython/_span.jade b/website/api/_cython/_span.jade
deleted file mode 100644
index 0899081a7..000000000
--- a/website/api/_cython/_span.jade
+++ /dev/null
@@ -1,43 +0,0 @@
-//- 💫 DOCS > API > CYTHON > CLASSES > SPAN
-
-p
-    |  A Cython class providing access and methods for a slice of a #[code Doc]
-    |  object.
-
-+infobox
-    |  This section documents the extra C-level attributes and methods that
-    |  can't be accessed from Python. For the Python documentation, see
-    |  #[+api("span") #[code Span]].
-
-+h(3, "span_attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The parent document.
-
-    +row
-        +cell #[code start]
-        +cell #[code int]
-        +cell The index of the first token of the span.
-
-    +row
-        +cell #[code end]
-        +cell #[code int]
-        +cell The index of the first token after the span.
-
-    +row
-        +cell #[code start_char]
-        +cell #[code int]
-        +cell The index of the first character of the span.
-
-    +row
-        +cell #[code end_char]
-        +cell #[code int]
-        +cell The index of the last character of the span.
-
-    +row
-        +cell #[code label]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell A label to attach to the span, e.g. for named entities.
diff --git a/website/api/_cython/_stringstore.jade b/website/api/_cython/_stringstore.jade
deleted file mode 100644
index 0fed5fd59..000000000
--- a/website/api/_cython/_stringstore.jade
+++ /dev/null
@@ -1,23 +0,0 @@
-//- 💫 DOCS > API > CYTHON > CLASSES > STRINGSTORE
-
-p A lookup table to retrieve strings by 64-bit hashes.
-
-+infobox
-    |  This section documents the extra C-level attributes and methods that
-    |  can't be accessed from Python. For the Python documentation, see
-    |  #[+api("stringstore") #[code StringStore]].
-
-+h(3, "stringstore_attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code mem]
-        +cell #[code cymem.Pool]
-        +cell
-            |  A memory pool. Allocated memory will be freed once the
-            |  #[code StringStore] object is garbage collected.
-
-    +row
-        +cell #[code keys]
-        +cell #[+abbr("vector[uint64_t]") #[code vector[hash_t]]]
-        +cell A list of hash values in the #[code StringStore].
diff --git a/website/api/_cython/_token.jade b/website/api/_cython/_token.jade
deleted file mode 100644
index 1ed2bc552..000000000
--- a/website/api/_cython/_token.jade
+++ /dev/null
@@ -1,73 +0,0 @@
-//- 💫 DOCS > API > CYTHON > CLASSES > TOKEN
-
-p
-    |  A Cython class providing access and methods for a
-    |  #[+api("cython-structs#tokenc") #[code TokenC]] struct. Note that the
-    |  #[code Token] object does not own the struct. It only receives a pointer
-    |  to it.
-
-+infobox
-    |  This section documents the extra C-level attributes and methods that
-    |  can't be accessed from Python. For the Python documentation, see
-    |  #[+api("token") #[code Token]].
-
-+h(3, "token_attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell A reference to the shared #[code Vocab] object.
-
-    +row
-        +cell #[code c]
-        +cell #[code TokenC*]
-        +cell
-            |  A pointer to a #[+api("cython-structs#tokenc") #[code TokenC]]
-            |  struct.
-
-    +row
-        +cell #[code i]
-        +cell #[code int]
-        +cell The offset of the token within the document.
-
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The parent document.
-
-+h(3, "token_cinit") Token.cinit
-    +tag method
-
-p Create a #[code Token] object from a #[code TokenC*] pointer.
-
-+aside-code("Example").
-    token = Token.cinit(&doc.c[3], doc, 3)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell A reference to the shared #[code Vocab].
-
-    +row
-        +cell #[code c]
-        +cell #[code TokenC*]
-        +cell
-            |  A pointer to a #[+api("cython-structs#tokenc") #[code TokenC]]
-            |  struct.
-
-    +row
-        +cell #[code offset]
-        +cell #[code int]
-        +cell The offset of the token within the document.
-
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The parent document.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Token]
-        +cell The newly constructed object.
diff --git a/website/api/_cython/_tokenc.jade b/website/api/_cython/_tokenc.jade
deleted file mode 100644
index 967012843..000000000
--- a/website/api/_cython/_tokenc.jade
+++ /dev/null
@@ -1,270 +0,0 @@
-//- 💫 DOCS > API > CYTHON > STRUCTS > TOKENC
-
-p
-    |  Cython data container for the #[code Token] object.
-
-+aside-code("Example").
-    token = &doc.c[3]
-    token_ptr = &doc.c[3]
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code lex]
-        +cell #[code const LexemeC*]
-        +cell A pointer to the lexeme for the token.
-
-    +row
-        +cell #[code morph]
-        +cell #[code uint64_t]
-        +cell An ID allowing lookup of morphological attributes.
-
-    +row
-        +cell #[code pos]
-        +cell #[code univ_pos_t]
-        +cell Coarse-grained part-of-speech tag.
-
-    +row
-        +cell #[code spacy]
-        +cell #[code bint]
-        +cell A binary value indicating whether the token has trailing whitespace.
-
-    +row
-        +cell #[code tag]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell Fine-grained part-of-speech tag.
-
-    +row
-        +cell #[code idx]
-        +cell #[code int]
-        +cell The character offset of the token within the parent document.
-
-    +row
-        +cell #[code lemma]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell Base form of the token, with no inflectional suffixes.
-
-    +row
-        +cell #[code sense]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell Space for storing a word sense ID, currently unused.
-
-    +row
-        +cell #[code head]
-        +cell #[code int]
-        +cell Offset of the syntactic parent relative to the token.
-
-    +row
-        +cell #[code dep]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell Syntactic dependency relation.
-
-    +row
-        +cell #[code l_kids]
-        +cell #[code uint32_t]
-        +cell Number of left children.
-
-    +row
-        +cell #[code r_kids]
-        +cell #[code uint32_t]
-        +cell Number of right children.
-
-    +row
-        +cell #[code l_edge]
-        +cell #[code uint32_t]
-        +cell Offset of the leftmost token of this token's syntactic descendents.
-
-    +row
-        +cell #[code r_edge]
-        +cell #[code uint32_t]
-        +cell Offset of the rightmost token of this token's syntactic descendents.
-
-    +row
-        +cell #[code sent_start]
-        +cell #[code int]
-        +cell
-            |  Ternary value indicating whether the token is the first word of
-            |  a sentence. #[code 0] indicates a missing value, #[code -1]
-            |  indicates #[code False] and #[code 1] indicates #[code True]. The default value, 0,
-            |  is interpretted as no sentence break. Sentence boundary detectors will usually
-            |  set 0 for all tokens except tokens that follow a sentence boundary.
-
-    +row
-        +cell #[code ent_iob]
-        +cell #[code int]
-        +cell
-            |  IOB code of named entity tag. #[code 0] indicates a missing
-            |  value, #[code 1] indicates #[code I], #[code 2] indicates
-            |  #[code 0] and #[code 3] indicates #[code B].
-
-    +row
-        +cell #[code ent_type]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell Named entity type.
-
-    +row
-        +cell #[code ent_id]
-        +cell #[+abbr("uint64_t") #[code hash_t]]
-        +cell
-            |  ID of the entity the token is an instance of, if any. Currently
-            |  not used, but potentially for coreference resolution.
-
-+h(3, "token_get_struct_attr", "spacy/tokens/token.pxd") Token.get_struct_attr
-    +tag staticmethod
-    +tag nogil
-
-p Get the value of an attribute from the #[code TokenC] struct by attribute ID.
-
-+aside-code("Example").
-    from spacy.attrs cimport IS_ALPHA
-    from spacy.tokens cimport Token
-
-    is_alpha = Token.get_struct_attr(&doc.c[3], IS_ALPHA)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code token]
-        +cell #[code const TokenC*]
-        +cell A pointer to a #[code TokenC] struct.
-
-    +row
-        +cell #[code feat_name]
-        +cell #[code attr_id_t]
-        +cell
-            |  The ID of the attribute to look up. The attributes are
-            |  enumerated in #[code spacy.typedefs].
-
-    +row("foot")
-        +cell returns
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell The value of the attribute.
-
-+h(3, "token_set_struct_attr", "spacy/tokens/token.pxd") Token.set_struct_attr
-    +tag staticmethod
-    +tag nogil
-
-p Set the value of an attribute of the #[code TokenC] struct by attribute ID.
-
-+aside-code("Example").
-    from spacy.attrs cimport TAG
-    from spacy.tokens cimport Token
-
-    token = &doc.c[3]
-    Token.set_struct_attr(token, TAG, 0)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code token]
-        +cell #[code const TokenC*]
-        +cell A pointer to a #[code TokenC] struct.
-
-    +row
-        +cell #[code feat_name]
-        +cell #[code attr_id_t]
-        +cell
-            |  The ID of the attribute to look up. The attributes are
-            |  enumerated in #[code spacy.typedefs].
-
-    +row
-        +cell #[code value]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell The value to set.
-
-+h(3, "token_by_start", "spacy/tokens/doc.pxd") token_by_start
-    +tag function
-
-p Find a token in a #[code TokenC*] array by the offset of its first character.
-
-+aside-code("Example").
-    from spacy.tokens.doc cimport Doc, token_by_start
-    from spacy.vocab cimport Vocab
-
-    doc = Doc(Vocab(), words=[u'hello', u'world'])
-    assert token_by_start(doc.c, doc.length, 6) == 1
-    assert token_by_start(doc.c, doc.length, 4) == -1
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code tokens]
-        +cell #[code const TokenC*]
-        +cell A #[code TokenC*] array.
-
-    +row
-        +cell #[code length]
-        +cell #[code int]
-        +cell The number of tokens in the array.
-
-    +row
-        +cell #[code start_char]
-        +cell #[code int]
-        +cell The start index to search for.
-
-    +row("foot")
-        +cell returns
-        +cell #[code int]
-        +cell The index of the token in the array or #[code -1] if not found.
-
-+h(3, "token_by_end", "spacy/tokens/doc.pxd") token_by_end
-    +tag function
-
-p Find a token in a #[code TokenC*] array by the offset of its final character.
-
-+aside-code("Example").
-    from spacy.tokens.doc cimport Doc, token_by_end
-    from spacy.vocab cimport Vocab
-
-    doc = Doc(Vocab(), words=[u'hello', u'world'])
-    assert token_by_end(doc.c, doc.length, 5) == 0
-    assert token_by_end(doc.c, doc.length, 1) == -1
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code tokens]
-        +cell #[code const TokenC*]
-        +cell A #[code TokenC*] array.
-
-    +row
-        +cell #[code length]
-        +cell #[code int]
-        +cell The number of tokens in the array.
-
-    +row
-        +cell #[code end_char]
-        +cell #[code int]
-        +cell The end index to search for.
-
-    +row("foot")
-        +cell returns
-        +cell #[code int]
-        +cell The index of the token in the array or #[code -1] if not found.
-
-+h(3, "set_children_from_heads", "spacy/tokens/doc.pxd") set_children_from_heads
-    +tag function
-
-p
-    |  Set attributes that allow lookup of syntactic children on a
-    |  #[code TokenC*] array. This function must be called after making changes
-    |  to the #[code TokenC.head] attribute, in order to make the parse tree
-    |  navigation consistent.
-
-+aside-code("Example").
-    from spacy.tokens.doc cimport Doc, set_children_from_heads
-    from spacy.vocab cimport Vocab
-
-    doc = Doc(Vocab(), words=[u'Baileys', u'from', u'a', u'shoe'])
-    doc.c[0].head = 0
-    doc.c[1].head = 0
-    doc.c[2].head = 3
-    doc.c[3].head = 1
-    set_children_from_heads(doc.c, doc.length)
-    assert doc.c[3].l_kids == 1
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code tokens]
-        +cell #[code const TokenC*]
-        +cell A #[code TokenC*] array.
-
-    +row
-        +cell #[code length]
-        +cell #[code int]
-        +cell The number of tokens in the array.
diff --git a/website/api/_cython/_vocab.jade b/website/api/_cython/_vocab.jade
deleted file mode 100644
index b0b975577..000000000
--- a/website/api/_cython/_vocab.jade
+++ /dev/null
@@ -1,88 +0,0 @@
-//- 💫 DOCS > API > CYTHON > CLASSES > VOCAB
-
-p
-    |  A Cython class providing access and methods for a vocabulary and other
-    |  data shared across a language.
-
-+infobox
-    |  This section documents the extra C-level attributes and methods that
-    |  can't be accessed from Python. For the Python documentation, see
-    |  #[+api("vocab") #[code Vocab]].
-
-+h(3, "vocab_attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code mem]
-        +cell #[code cymem.Pool]
-        +cell
-            |  A memory pool. Allocated memory will be freed once the
-            |  #[code Vocab] object is garbage collected.
-
-    +row
-        +cell #[code strings]
-        +cell #[code StringStore]
-        +cell
-            |  A #[code StringStore] that maps string to hash values and vice
-            |  versa.
-
-    +row
-        +cell #[code length]
-        +cell #[code int]
-        +cell The number of entries in the vocabulary.
-
-+h(3, "vocab_get") Vocab.get
-    +tag method
-
-p
-    |  Retrieve a #[+api("cython-structs#lexemec") #[code LexemeC*]] pointer
-    |  from the vocabulary.
-
-+aside-code("Example").
-    lexeme = vocab.get(vocab.mem, u'hello')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code mem]
-        +cell #[code cymem.Pool]
-        +cell
-            |  A memory pool. Allocated memory will be freed once the
-            |  #[code Vocab] object is garbage collected.
-
-    +row
-        +cell #[code string]
-        +cell #[code unicode]
-        +cell The string of the word to look up.
-
-    +row("foot")
-        +cell returns
-        +cell #[code const LexemeC*]
-        +cell The lexeme in the vocabulary.
-
-+h(3, "vocab_get_by_orth") Vocab.get_by_orth
-    +tag method
-
-p
-    |  Retrieve a #[+api("cython-structs#lexemec") #[code LexemeC*]] pointer
-    |  from the vocabulary.
-
-+aside-code("Example").
-    lexeme = vocab.get_by_orth(doc[0].lex.norm)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code mem]
-        +cell #[code cymem.Pool]
-        +cell
-            |  A memory pool. Allocated memory will be freed once the
-            |  #[code Vocab] object is garbage collected.
-
-    +row
-        +cell #[code orth]
-        +cell #[+abbr("uint64_t") #[code attr_t]]
-        +cell ID of the verbatim text content.
-
-    +row("foot")
-        +cell returns
-        +cell #[code const LexemeC*]
-        +cell The lexeme in the vocabulary.
diff --git a/website/api/_data.json b/website/api/_data.json
deleted file mode 100644
index 6e269f074..000000000
--- a/website/api/_data.json
+++ /dev/null
@@ -1,251 +0,0 @@
-{
-    "sidebar": {
-        "Overview": {
-            "Architecture": "./",
-            "Annotation Specs": "annotation",
-            "Command Line": "cli",
-            "Functions": "top-level"
-        },
-
-        "Containers": {
-            "Doc": "doc",
-            "Token": "token",
-            "Span": "span",
-            "Lexeme": "lexeme"
-        },
-
-        "Pipeline": {
-            "Language": "language",
-            "Pipe": "pipe",
-            "Tagger": "tagger",
-            "DependencyParser": "dependencyparser",
-            "EntityRecognizer": "entityrecognizer",
-            "TextCategorizer": "textcategorizer",
-            "Tokenizer": "tokenizer",
-            "Lemmatizer": "lemmatizer",
-            "Matcher": "matcher",
-            "PhraseMatcher": "phrasematcher"
-        },
-
-        "Other": {
-            "Vocab": "vocab",
-            "StringStore": "stringstore",
-            "Vectors": "vectors",
-            "GoldParse": "goldparse",
-            "GoldCorpus": "goldcorpus"
-        },
-
-        "Cython": {
-            "Architecture": "cython",
-            "Structs": "cython-structs",
-            "Classes": "cython-classes"
-        }
-    },
-
-    "index": {
-        "title": "Architecture",
-        "next": "annotation",
-        "menu": {
-            "Basics": "basics",
-            "Neural Network Model": "nn-model"
-        }
-    },
-
-    "cli": {
-        "title": "Command Line Interface",
-        "teaser": "Download, train and package models, and debug spaCy.",
-        "source": "spacy/cli"
-    },
-
-    "top-level": {
-        "title": "Top-level Functions",
-        "menu": {
-            "spacy": "spacy",
-            "displacy": "displacy",
-            "Utility Functions": "util",
-            "Compatibility": "compat"
-        }
-    },
-
-    "language": {
-        "title": "Language",
-        "tag": "class",
-        "teaser": "A text-processing pipeline.",
-        "source": "spacy/language.py"
-    },
-
-    "doc": {
-        "title": "Doc",
-        "tag": "class",
-        "teaser": "A container for accessing linguistic annotations.",
-        "source": "spacy/tokens/doc.pyx"
-    },
-
-    "token": {
-        "title": "Token",
-        "tag": "class",
-        "source": "spacy/tokens/token.pyx"
-    },
-
-    "span": {
-        "title": "Span",
-        "tag": "class",
-        "source": "spacy/tokens/span.pyx"
-    },
-
-    "lexeme": {
-        "title": "Lexeme",
-        "tag": "class",
-        "source": "spacy/lexeme.pyx"
-    },
-
-    "vocab": {
-        "title": "Vocab",
-        "teaser": "A storage class for vocabulary and other data shared across a language.",
-        "tag": "class",
-        "source": "spacy/vocab.pyx"
-    },
-
-    "stringstore": {
-        "title": "StringStore",
-        "tag": "class",
-        "source": "spacy/strings.pyx"
-    },
-
-    "matcher": {
-        "title": "Matcher",
-        "teaser": "Match sequences of tokens, based on pattern rules.",
-        "tag": "class",
-        "source": "spacy/matcher.pyx"
-    },
-
-    "phrasematcher": {
-        "title": "PhraseMatcher",
-        "teaser": "Match sequences of tokens, based on documents.",
-        "tag": "class",
-        "tag_new": 2,
-        "source": "spacy/matcher.pyx"
-    },
-
-    "pipe": {
-        "title": "Pipe",
-        "teaser": "Abstract base class defining the API for pipeline components.",
-        "tag": "class",
-        "tag_new": 2,
-        "source": "spacy/pipeline.pyx"
-    },
-
-    "dependenyparser": {
-        "title": "DependencyParser",
-        "tag": "class",
-        "source": "spacy/pipeline.pyx"
-    },
-
-    "entityrecognizer": {
-        "title": "EntityRecognizer",
-        "teaser": "Annotate named entities on documents.",
-        "tag": "class",
-        "source": "spacy/pipeline.pyx"
-    },
-
-    "textcategorizer": {
-        "title": "TextCategorizer",
-        "teaser": "Add text categorization models to spaCy pipelines.",
-        "tag": "class",
-        "tag_new": 2,
-        "source": "spacy/pipeline.pyx"
-    },
-
-    "dependencyparser": {
-        "title": "DependencyParser",
-        "teaser": "Annotate syntactic dependencies on documents.",
-        "tag": "class",
-        "source": "spacy/pipeline.pyx"
-    },
-
-    "tokenizer": {
-        "title": "Tokenizer",
-        "teaser": "Segment text into words, punctuations marks etc.",
-        "tag": "class",
-        "source": "spacy/tokenizer.pyx"
-    },
-
-    "lemmatizer": {
-        "title": "Lemmatizer",
-        "teaser": "Assign the base forms of words.",
-        "tag": "class",
-        "source": "spacy/lemmatizer.py"
-    },
-
-    "tagger": {
-        "title": "Tagger",
-        "teaser": "Annotate part-of-speech tags on documents.",
-        "tag": "class",
-        "source": "spacy/pipeline.pyx"
-    },
-
-    "goldparse": {
-        "title": "GoldParse",
-        "tag": "class",
-        "source": "spacy/gold.pyx"
-    },
-
-    "goldcorpus": {
-        "title": "GoldCorpus",
-        "teaser": "An annotated corpus, using the JSON file format.",
-        "tag": "class",
-        "tag_new": 2,
-        "source": "spacy/gold.pyx"
-    },
-
-    "vectors": {
-        "title": "Vectors",
-        "teaser": "Store, save and load word vectors.",
-        "tag": "class",
-        "tag_new": 2,
-        "source": "spacy/vectors.pyx"
-    },
-
-    "annotation": {
-        "title": "Annotation Specifications",
-        "teaser": "Schemes used for labels, tags and training data.",
-        "menu": {
-            "Text Processing": "text-processing",
-            "POS Tagging": "pos-tagging",
-            "Dependencies": "dependency-parsing",
-            "Named Entities": "named-entities",
-            "Models & Training": "training"
-        }
-    },
-
-    "cython": {
-        "title": "Cython Architecture",
-        "next": "cython-structs",
-        "menu": {
-            "Overview": "overview",
-            "Conventions": "conventions"
-        }
-    },
-
-    "cython-structs": {
-        "title": "Cython Structs",
-        "teaser": "C-language objects that let you group variables together in a single contiguous block.",
-        "next": "cython-classes",
-        "menu": {
-            "TokenC": "tokenc",
-            "LexemeC": "lexemec"
-        }
-    },
-
-    "cython-classes": {
-        "title": "Cython Classes",
-        "menu": {
-            "Doc": "doc",
-            "Token": "token",
-            "Span": "span",
-            "Lexeme": "lexeme",
-            "Vocab": "vocab",
-            "StringStore": "stringstore"
-        }
-    }
-}
diff --git a/website/api/_top-level/_compat.jade b/website/api/_top-level/_compat.jade
deleted file mode 100644
index 7de2f4102..000000000
--- a/website/api/_top-level/_compat.jade
+++ /dev/null
@@ -1,84 +0,0 @@
-//- 💫 DOCS > API > TOP-LEVEL > COMPATIBILITY
-
-p
-    |  All Python code is written in an
-    |  #[strong intersection of Python 2 and Python 3]. This is easy in Cython,
-    |  but somewhat ugly in Python. Logic that deals with Python or platform
-    |  compatibility only lives in #[code spacy.compat]. To distinguish them from
-    |  the builtin functions, replacement functions are suffixed with an
-    |  underscore, e.e #[code unicode_].
-
-+aside-code("Example").
-    from spacy.compat import unicode_
-
-    compatible_unicode = unicode_('hello world')
-
-+table(["Name", "Python 2", "Python 3"])
-    +row
-        +cell #[code compat.bytes_]
-        +cell #[code str]
-        +cell #[code bytes]
-
-    +row
-        +cell #[code compat.unicode_]
-        +cell #[code unicode]
-        +cell #[code str]
-
-    +row
-        +cell #[code compat.basestring_]
-        +cell #[code basestring]
-        +cell #[code str]
-
-    +row
-        +cell #[code compat.input_]
-        +cell #[code raw_input]
-        +cell #[code input]
-
-    +row
-        +cell #[code compat.path2str]
-        +cell #[code str(path)] with #[code .decode('utf8')]
-        +cell #[code str(path)]
-
-+h(3, "is_config") compat.is_config
-    +tag function
-
-p
-    |  Check if a specific configuration of Python version and operating system
-    |  matches the user's setup. Mostly used to display targeted error messages.
-
-+aside-code("Example").
-    from spacy.compat import is_config
-
-    if is_config(python2=True, windows=True):
-        print("You are using Python 2 on Windows.")
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code python2]
-        +cell bool
-        +cell spaCy is executed with Python 2.x.
-
-    +row
-        +cell #[code python3]
-        +cell bool
-        +cell spaCy is executed with Python 3.x.
-
-    +row
-        +cell #[code windows]
-        +cell bool
-        +cell spaCy is executed on Windows.
-
-    +row
-        +cell #[code linux]
-        +cell bool
-        +cell spaCy is executed on Linux.
-
-    +row
-        +cell #[code osx]
-        +cell bool
-        +cell spaCy is executed on OS X or macOS.
-
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the specified configuration matches the user's platform.
diff --git a/website/api/_top-level/_displacy.jade b/website/api/_top-level/_displacy.jade
deleted file mode 100644
index f0e2dc93f..000000000
--- a/website/api/_top-level/_displacy.jade
+++ /dev/null
@@ -1,259 +0,0 @@
-//- 💫 DOCS > API > TOP-LEVEL > DISPLACY
-
-p
-    |  As of v2.0, spaCy comes with a built-in visualization suite. For more
-    |  info and examples, see the usage guide on
-    |  #[+a("/usage/visualizers") visualizing spaCy].
-
-
-+h(3, "displacy.serve") displacy.serve
-    +tag method
-    +tag-new(2)
-
-p
-    |  Serve a dependency parse tree or named entity visualization to view it
-    |  in your browser. Will run a simple web server.
-
-+aside-code("Example").
-    import spacy
-    from spacy import displacy
-    nlp = spacy.load('en')
-    doc1 = nlp(u'This is a sentence.')
-    doc2 = nlp(u'This is another sentence.')
-    displacy.serve([doc1, doc2], style='dep')
-
-+table(["Name", "Type", "Description", "Default"])
-    +row
-        +cell #[code docs]
-        +cell list, #[code Doc], #[code Span]
-        +cell Document(s) to visualize.
-        +cell
-
-    +row
-        +cell #[code style]
-        +cell unicode
-        +cell Visualization style, #[code 'dep'] or #[code 'ent'].
-        +cell #[code 'dep']
-
-    +row
-        +cell #[code page]
-        +cell bool
-        +cell Render markup as full HTML page.
-        +cell #[code True]
-
-    +row
-        +cell #[code minify]
-        +cell bool
-        +cell Minify HTML markup.
-        +cell #[code False]
-
-    +row
-        +cell #[code options]
-        +cell dict
-        +cell #[+a("#options") Visualizer-specific options], e.g. colors.
-        +cell #[code {}]
-
-    +row
-        +cell #[code manual]
-        +cell bool
-        +cell
-            |  Don't parse #[code Doc] and instead, expect a dict or list of
-            |  dicts. #[+a("/usage/visualizers#manual-usage") See here]
-            |  for formats and examples.
-        +cell #[code False]
-
-    +row
-        +cell #[code port]
-        +cell int
-        +cell Port to serve visualization.
-        +cell #[code 5000]
-
-    +row
-        +cell #[code host]
-        +cell unicode
-        +cell Host to serve visualization.
-        +cell #[code '0.0.0.0']
-
-+h(3, "displacy.render") displacy.render
-    +tag method
-    +tag-new(2)
-
-p Render a dependency parse tree or named entity visualization.
-
-+aside-code("Example").
-    import spacy
-    from spacy import displacy
-    nlp = spacy.load('en')
-    doc = nlp(u'This is a sentence.')
-    html = displacy.render(doc, style='dep')
-
-+table(["Name", "Type", "Description", "Default"])
-    +row
-        +cell #[code docs]
-        +cell list, #[code Doc], #[code Span]
-        +cell Document(s) to visualize.
-        +cell
-
-    +row
-        +cell #[code style]
-        +cell unicode
-        +cell Visualization style, #[code 'dep'] or #[code 'ent'].
-        +cell #[code 'dep']
-
-    +row
-        +cell #[code page]
-        +cell bool
-        +cell Render markup as full HTML page.
-        +cell #[code False]
-
-    +row
-        +cell #[code minify]
-        +cell bool
-        +cell Minify HTML markup.
-        +cell #[code False]
-
-    +row
-        +cell #[code jupyter]
-        +cell bool
-        +cell
-            |  Explicitly enable "#[+a("http://jupyter.org/") Jupyter] mode" to
-            |  return markup ready to be rendered in a notebook.
-        +cell detected automatically
-
-    +row
-        +cell #[code options]
-        +cell dict
-        +cell #[+a("#options") Visualizer-specific options], e.g. colors.
-        +cell #[code {}]
-
-    +row
-        +cell #[code manual]
-        +cell bool
-        +cell
-            |  Don't parse #[code Doc] and instead, expect a dict or list of
-            |  dicts. #[+a("/usage/visualizers#manual-usage") See here]
-            |  for formats and examples.
-        +cell #[code False]
-
-    +row("foot")
-        +cell returns
-        +cell unicode
-        +cell Rendered HTML markup.
-        +cell
-
-+h(3, "displacy_options") Visualizer options
-
-p
-    |  The #[code options] argument lets you specify additional settings for
-    |  each visualizer. If a setting is not present in the options, the default
-    |  value will be used.
-
-+h(4, "options-dep") Dependency Visualizer options
-
-+aside-code("Example").
-    options = {'compact': True, 'color': 'blue'}
-    displacy.serve(doc, style='dep', options=options)
-
-+table(["Name", "Type", "Description", "Default"])
-    +row
-        +cell #[code collapse_punct]
-        +cell bool
-        +cell
-            |  Attach punctuation to tokens. Can make the parse more readable,
-            |  as it prevents long arcs to attach punctuation.
-        +cell #[code True]
-
-    +row
-        +cell #[code collapse_phrases]
-        +cell bool
-        +cell Merge noun phrases into one token.
-        +cell #[code False]
-
-    +row
-        +cell #[code compact]
-        +cell bool
-        +cell "Compact mode" with square arrows that takes up less space.
-        +cell #[code False]
-
-    +row
-        +cell #[code color]
-        +cell unicode
-        +cell Text color (HEX, RGB or color names).
-        +cell #[code '#000000']
-
-    +row
-        +cell #[code bg]
-        +cell unicode
-        +cell Background color (HEX, RGB or color names).
-        +cell #[code '#ffffff']
-
-    +row
-        +cell #[code font]
-        +cell unicode
-        +cell Font name or font family for all text.
-        +cell #[code 'Arial']
-
-    +row
-        +cell #[code offset_x]
-        +cell int
-        +cell Spacing on left side of the SVG in px.
-        +cell #[code 50]
-
-    +row
-        +cell #[code arrow_stroke]
-        +cell int
-        +cell Width of arrow path in px.
-        +cell #[code 2]
-
-    +row
-        +cell #[code arrow_width]
-        +cell int
-        +cell Width of arrow head in px.
-        +cell #[code 10] / #[code 8] (compact)
-
-    +row
-        +cell #[code arrow_spacing]
-        +cell int
-        +cell Spacing between arrows in px to avoid overlaps.
-        +cell #[code 20] / #[code 12] (compact)
-
-    +row
-        +cell #[code word_spacing]
-        +cell int
-        +cell Vertical spacing between words and arcs in px.
-        +cell #[code 45]
-
-    +row
-        +cell #[code distance]
-        +cell int
-        +cell Distance between words in px.
-        +cell #[code 175] / #[code 85] (compact)
-
-+h(4, "displacy_options-ent") Named Entity Visualizer options
-
-+aside-code("Example").
-    options = {'ents': ['PERSON', 'ORG', 'PRODUCT'],
-               'colors': {'ORG': 'yellow'}}
-    displacy.serve(doc, style='ent', options=options)
-
-+table(["Name", "Type", "Description", "Default"])
-    +row
-        +cell #[code ents]
-        +cell list
-        +cell
-            |  Entity types to highlight (#[code None] for all types).
-        +cell #[code None]
-
-    +row
-        +cell #[code colors]
-        +cell dict
-        +cell
-            |  Color overrides. Entity types in uppercase should be mapped to
-            |  color names or values.
-        +cell #[code {}]
-
-p
-    |  By default, displaCy comes with colours for all
-    |  #[+a("/api/annotation#named-entities") entity types supported by spaCy].
-    |  If you're using custom entity types, you can use the #[code colors]
-    |  setting to add your own colours for them.
diff --git a/website/api/_top-level/_spacy.jade b/website/api/_top-level/_spacy.jade
deleted file mode 100644
index c838d51ba..000000000
--- a/website/api/_top-level/_spacy.jade
+++ /dev/null
@@ -1,201 +0,0 @@
-//- 💫 DOCS > API > TOP-LEVEL > SPACY
-
-+h(3, "spacy.load") spacy.load
-    +tag function
-    +tag-model
-
-p
-    |  Load a model via its #[+a("/usage/models#usage") shortcut link],
-    |  the name of an installed
-    |  #[+a("/usage/training#models-generating") model package], a unicode
-    |  path or a #[code Path]-like object. spaCy will try resolving the load
-    |  argument in this order. If a model is loaded from a shortcut link or
-    |  package name, spaCy will assume it's a Python package and import it and
-    |  call the model's own #[code load()] method. If a model is loaded from a
-    |  path, spaCy will assume it's a data directory, read the language and
-    |  pipeline settings off the meta.json and initialise the #[code Language]
-    |  class. The data will be loaded in via
-    |  #[+api("language#from_disk") #[code Language.from_disk()]].
-
-+aside-code("Example").
-    nlp = spacy.load('en') # shortcut link
-    nlp = spacy.load('en_core_web_sm') # package
-    nlp = spacy.load('/path/to/en') # unicode path
-    nlp = spacy.load(Path('/path/to/en')) # pathlib Path
-
-    nlp = spacy.load('en', disable=['parser', 'tagger'])
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode or #[code Path]
-        +cell Model to load, i.e. shortcut link, package name or path.
-
-    +row
-        +cell #[code disable]
-        +cell list
-        +cell
-            |  Names of pipeline components to
-            |  #[+a("/usage/processing-pipelines#disabling") disable].
-
-    +row("foot")
-        +cell returns
-        +cell #[code Language]
-        +cell A #[code Language] object with the loaded model.
-
-p
-    |  Essentially, #[code spacy.load()] is a convenience wrapper that reads
-    |  the language ID and pipeline components from a model's #[code meta.json],
-    |  initialises the #[code Language] class, loads in the model data and
-    |  returns it.
-
-+code("Abstract example").
-    cls = util.get_lang_class(lang)         #  get language for ID, e.g. 'en'
-    nlp = cls()                             #  initialise the language
-    for name in pipeline:
-        component = nlp.create_pipe(name)   #  create each pipeline component
-        nlp.add_pipe(component)             #  add component to pipeline
-    nlp.from_disk(model_data_path)          #  load in model data
-
-+infobox("Changed in v2.0", "⚠️")
-    |  As of spaCy 2.0, the #[code path] keyword argument is deprecated. spaCy
-    |  will also raise an error if no model could be loaded and never just
-    |  return an empty #[code Language] object. If you need a blank language,
-    |  you can use the new function #[+api("spacy#blank") #[code spacy.blank()]]
-    |  or import the class explicitly, e.g.
-    |  #[code from spacy.lang.en import English].
-
-    +code-wrapper
-        +code-new nlp = spacy.load('/model')
-        +code-old nlp = spacy.load('en', path='/model')
-
-+h(3, "spacy.blank") spacy.blank
-    +tag function
-    +tag-new(2)
-
-p
-    |  Create a blank model of a given language class. This function is the
-    |  twin of #[code spacy.load()].
-
-+aside-code("Example").
-    nlp_en = spacy.blank('en')
-    nlp_de = spacy.blank('de')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell
-            |  #[+a("https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes") ISO code]
-            |  of the language class to load.
-
-    +row
-        +cell #[code disable]
-        +cell list
-        +cell
-            |  Names of pipeline components to
-            |  #[+a("/usage/processing-pipelines#disabling") disable].
-
-    +row("foot")
-        +cell returns
-        +cell #[code Language]
-        +cell An empty #[code Language] object of the appropriate subclass.
-
-
-+h(4, "spacy.info") spacy.info
-    +tag function
-
-p
-    |  The same as the #[+api("cli#info") #[code info] command]. Pretty-print
-    |  information about your installation, models and local setup from within
-    |  spaCy. To get the model meta data as a dictionary instead, you can
-    |  use the #[code meta] attribute on your #[code nlp] object with a
-    |  loaded model, e.g. #[code nlp.meta].
-
-+aside-code("Example").
-    spacy.info()
-    spacy.info('en')
-    spacy.info('de', markdown=True)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code model]
-        +cell unicode
-        +cell A model, i.e. shortcut link, package name or path (optional).
-
-    +row
-        +cell #[code markdown]
-        +cell bool
-        +cell Print information as Markdown.
-
-
-+h(3, "spacy.explain") spacy.explain
-    +tag function
-
-p
-    |  Get a description for a given POS tag, dependency label or entity type.
-    |  For a list of available terms, see
-    |  #[+src(gh("spacy", "spacy/glossary.py")) #[code glossary.py]].
-
-+aside-code("Example").
-    spacy.explain(u'NORP')
-    # Nationalities or religious or political groups
-
-    doc = nlp(u'Hello world')
-    for word in doc:
-        print(word.text, word.tag_, spacy.explain(word.tag_))
-    # Hello UH interjection
-    # world NN noun, singular or mass
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code term]
-        +cell unicode
-        +cell Term to explain.
-
-    +row("foot")
-        +cell returns
-        +cell unicode
-        +cell The explanation, or #[code None] if not found in the glossary.
-
-+h(3, "spacy.prefer_gpu") spacy.prefer_gpu
-    +tag function
-    +tag-new("2.0.14")
-
-p
-    |  Allocate data and perform operations on #[+a("/usage/#gpu") GPU], if
-    |  available. If data has already been allocated on CPU, it will not be
-    |  moved. Ideally, this function should be called right after
-    |  importing spaCy and #[em before] loading any models.
-
-+aside-code("Example").
-    import spacy
-    activated = spacy.prefer_gpu()
-    nlp = spacy.load('en_core_web_sm')
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the GPU was activated.
-
-+h(3, "spacy.require_gpu") spacy.require_gpu
-    +tag function
-    +tag-new("2.0.14")
-
-p
-    |  Allocate data and perform operations on #[+a("/usage/#gpu") GPU]. Will
-    |  raise an error if no GPU is available. If data has already been allocated
-    |  on CPU, it will not be moved. Ideally, this function should be called
-    |  right after importing spaCy and #[em before] loading any models.
-
-+aside-code("Example").
-    import spacy
-    spacy.require_gpu()
-    nlp = spacy.load('en_core_web_sm')
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell #[code True]
diff --git a/website/api/_top-level/_util.jade b/website/api/_top-level/_util.jade
deleted file mode 100644
index e2204ae6f..000000000
--- a/website/api/_top-level/_util.jade
+++ /dev/null
@@ -1,454 +0,0 @@
-//- 💫 DOCS > API > TOP-LEVEL > UTIL
-
-p
-    |  spaCy comes with a small collection of utility functions located in
-    |  #[+src(gh("spaCy", "spacy/util.py")) #[code spacy/util.py]].
-    |  Because utility functions are mostly intended for
-    |  #[strong internal use within spaCy], their behaviour may change with
-    |  future releases. The functions documented on this page should be safe
-    |  to use and we'll try to ensure backwards compatibility. However, we
-    |  recommend having additional tests in place if your application depends on
-    |  any of spaCy's utilities.
-
-+h(3, "util.get_data_path") util.get_data_path
-    +tag function
-
-p
-    |  Get path to the data directory where spaCy looks for models. Defaults to
-    |  #[code spacy/data].
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code require_exists]
-        +cell bool
-        +cell Only return path if it exists, otherwise return #[code None].
-
-    +row("foot")
-        +cell returns
-        +cell #[code Path] / #[code None]
-        +cell Data path or #[code None].
-
-+h(3, "util.set_data_path") util.set_data_path
-    +tag function
-
-p
-    |  Set custom path to the data directory where spaCy looks for models.
-
-+aside-code("Example").
-    util.set_data_path('/custom/path')
-    util.get_data_path()
-    # PosixPath('/custom/path')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode or #[code Path]
-        +cell Path to new data directory.
-
-+h(3, "util.get_lang_class") util.get_lang_class
-    +tag function
-
-p
-    |  Import and load a #[code Language] class. Allows lazy-loading
-    |  #[+a("/usage/adding-languages") language data] and importing
-    |  languages using the two-letter language code. To add a language code
-    |  for a custom language class, you can use the
-    |  #[+api("top-level#util.set_lang_class") #[code set_lang_class]] helper.
-
-+aside-code("Example").
-    for lang_id in ['en', 'de']:
-        lang_class = util.get_lang_class(lang_id)
-        lang = lang_class()
-        tokenizer = lang.Defaults.create_tokenizer()
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code lang]
-        +cell unicode
-        +cell Two-letter language code, e.g. #[code 'en'].
-
-    +row("foot")
-        +cell returns
-        +cell #[code Language]
-        +cell Language class.
-
-+h(3, "util.set_lang_class") util.set_lang_class
-    +tag function
-
-p
-    |  Set a custom #[code Language] class name that can be loaded via
-    |  #[+api("top-level#util.get_lang_class") #[code get_lang_class]]. If
-    |  your model uses a custom language, this is required so that spaCy can
-    |  load the correct class from the two-letter language code.
-
-+aside-code("Example").
-    from spacy.lang.xy import CustomLanguage
-
-    util.set_lang_class('xy', CustomLanguage)
-    lang_class = util.get_lang_class('xy')
-    nlp = lang_class()
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Two-letter language code, e.g. #[code 'en'].
-
-    +row
-        +cell #[code cls]
-        +cell #[code Language]
-        +cell The language class, e.g. #[code English].
-
-+h(3, "util.load_model") util.load_model
-    +tag function
-    +tag-new(2)
-
-p
-    |  Load a model from a shortcut link, package or data path. If called with a
-    |  shortcut link or package name, spaCy will assume the model is a Python
-    |  package and import and call its #[code load()] method. If called with a
-    |  path, spaCy will assume it's a data directory, read the language and
-    |  pipeline settings from the meta.json and initialise a #[code Language]
-    |  class. The model data will then be loaded in via
-    |  #[+api("language#from_disk") #[code Language.from_disk()]].
-
-+aside-code("Example").
-    nlp = util.load_model('en')
-    nlp = util.load_model('en_core_web_sm', disable=['ner'])
-    nlp = util.load_model('/path/to/data')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Package name, shortcut link or model path.
-
-    +row
-        +cell #[code **overrides]
-        +cell -
-        +cell Specific overrides, like pipeline components to disable.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Language]
-        +cell #[code Language] class with the loaded model.
-
-+h(3, "util.load_model_from_path") util.load_model_from_path
-    +tag function
-    +tag-new(2)
-
-p
-    |  Load a model from a data directory path. Creates the
-    |  #[+api("language") #[code Language]] class and pipeline based on the
-    |  directory's meta.json and then calls
-    |  #[+api("language#from_disk") #[code from_disk()]] with the path. This
-    |  function also makes it easy to test a new model that you haven't packaged
-    |  yet.
-
-+aside-code("Example").
-    nlp = load_model_from_path('/path/to/data')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code model_path]
-        +cell unicode
-        +cell Path to model data directory.
-
-    +row
-        +cell #[code meta]
-        +cell dict
-        +cell
-            |  Model meta data. If #[code False], spaCy will try to load the
-            |  meta from a meta.json in the same directory.
-
-    +row
-        +cell #[code **overrides]
-        +cell -
-        +cell Specific overrides, like pipeline components to disable.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Language]
-        +cell #[code Language] class with the loaded model.
-
-+h(3, "util.load_model_from_init_py") util.load_model_from_init_py
-    +tag function
-    +tag-new(2)
-
-p
-    |  A helper function to use in the #[code load()] method of a model package's
-    |  #[+src(gh("spacy-models", "template/model/xx_model_name/__init__.py")) #[code __init__.py]].
-
-+aside-code("Example").
-    from spacy.util import load_model_from_init_py
-
-    def load(**overrides):
-        return load_model_from_init_py(__file__, **overrides)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code init_file]
-        +cell unicode
-        +cell Path to model's __init__.py, i.e. #[code __file__].
-
-    +row
-        +cell #[code **overrides]
-        +cell -
-        +cell Specific overrides, like pipeline components to disable.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Language]
-        +cell #[code Language] class with the loaded model.
-
-+h(3, "util.get_model_meta") util.get_model_meta
-    +tag function
-    +tag-new(2)
-
-p
-    |  Get a model's meta.json from a directory path and validate its contents.
-
-+aside-code("Example").
-    meta = util.get_model_meta('/path/to/model')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode or #[code Path]
-        +cell Path to model directory.
-
-    +row("foot")
-        +cell returns
-        +cell dict
-        +cell The model's meta data.
-
-+h(3, "util.is_package") util.is_package
-    +tag function
-
-p
-    |  Check if string maps to a package installed via pip. Mainly used to
-    |  validate #[+a("/usage/models") model packages].
-
-+aside-code("Example").
-    util.is_package('en_core_web_sm') # True
-    util.is_package('xyz') # False
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of package.
-
-    +row("foot")
-        +cell returns
-        +cell #[code bool]
-        +cell #[code True] if installed package, #[code False] if not.
-
-+h(3, "util.get_package_path") util.get_package_path
-    +tag function
-    +tag-new(2)
-
-p
-    |  Get path to an installed package. Mainly used to resolve the location of
-    |  #[+a("/usage/models") model packages]. Currently imports the package
-    |  to find its path.
-
-+aside-code("Example").
-    util.get_package_path('en_core_web_sm')
-    # /usr/lib/python3.6/site-packages/en_core_web_sm
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code package_name]
-        +cell unicode
-        +cell Name of installed package.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Path]
-        +cell Path to model package directory.
-
-+h(3, "util.is_in_jupyter") util.is_in_jupyter
-    +tag function
-    +tag-new(2)
-
-p
-    |  Check if user is running spaCy from a #[+a("https://jupyter.org") Jupyter]
-    |  notebook by detecting the IPython kernel. Mainly used for the
-    |  #[+api("top-level#displacy") #[code displacy]] visualizer.
-
-+aside-code("Example").
-    html = '&lt;h1&gt;Hello world!&lt;/h1&gt;'
-    if util.is_in_jupyter():
-        from IPython.core.display import display, HTML
-        display(HTML(html))
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell #[code True] if in Jupyter, #[code False] if not.
-
-+h(3, "util.update_exc") util.update_exc
-    +tag function
-
-p
-    |  Update, validate and overwrite
-    |  #[+a("/usage/adding-languages#tokenizer-exceptions") tokenizer exceptions].
-    |  Used to combine global  exceptions with custom, language-specific
-    |  exceptions. Will raise an error if key doesn't match #[code ORTH] values.
-
-+aside-code("Example").
-    BASE =  {"a.": [{ORTH: "a."}], ":)": [{ORTH: ":)"}]}
-    NEW = {"a.": [{ORTH: "a.", LEMMA: "all"}]}
-    exceptions = util.update_exc(BASE, NEW)
-    # {"a.": [{ORTH: "a.", LEMMA: "all"}], ":)": [{ORTH: ":)"}]}
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code base_exceptions]
-        +cell dict
-        +cell Base tokenizer exceptions.
-
-    +row
-        +cell #[code *addition_dicts]
-        +cell dicts
-        +cell Exception dictionaries to add to the base exceptions, in order.
-
-    +row("foot")
-        +cell returns
-        +cell dict
-        +cell Combined tokenizer exceptions.
-
-+h(3, "util.minibatch") util.minibatch
-    +tag function
-    +tag-new(2)
-
-p
-    |  Iterate over batches of items. #[code size] may be an iterator, so that
-    |  batch-size can vary on each step.
-
-+aside-code("Example").
-    batches = minibatch(train_data)
-    for batch in batches:
-        texts, annotations = zip(*batch)
-        nlp.update(texts, annotations)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code items]
-        +cell iterable
-        +cell The items to batch up.
-
-    +row
-        +cell #[code size]
-        +cell int / iterable
-        +cell
-            |  The batch size(s). Use
-            |  #[+api("top-level#util.compounding") #[code util.compounding]] or
-            |  #[+api("top-level#util.decaying") #[code util.decaying]] or
-            |  for an infinite series of compounding or decaying values.
-
-    +row("foot")
-        +cell yields
-        +cell list
-        +cell The batches.
-
-+h(3, "util.compounding") util.compounding
-    +tag function
-    +tag-new(2)
-
-p
-    |  Yield an infinite series of compounding values. Each time the generator
-    |  is called, a value is produced by multiplying the previous value by the
-    |  compound rate.
-
-+aside-code("Example").
-    sizes = compounding(1., 10., 1.5)
-    assert next(sizes) == 1.
-    assert next(sizes) == 1. * 1.5
-    assert next(sizes) == 1.5 * 1.5
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code start]
-        +cell int / float
-        +cell The first value.
-
-    +row
-        +cell #[code stop]
-        +cell int / float
-        +cell The maximum value.
-
-    +row
-        +cell #[code compound]
-        +cell int / float
-        +cell The compounding factor.
-
-    +row("foot")
-        +cell yields
-        +cell int
-        +cell Compounding values.
-
-+h(3, "util.decaying") util.decaying
-    +tag function
-    +tag-new(2)
-
-p
-    |  Yield an infinite series of linearly decaying values.
-
-+aside-code("Example").
-    sizes = decaying(1., 10., 0.001)
-    assert next(sizes) == 1.
-    assert next(sizes) == 1. - 0.001
-    assert next(sizes) == 0.999 - 0.001
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code start]
-        +cell int / float
-        +cell The first value.
-
-    +row
-        +cell #[code end]
-        +cell int / float
-        +cell The maximum value.
-
-    +row
-        +cell #[code decay]
-        +cell int / float
-        +cell The decaying factor.
-
-    +row("foot")
-        +cell yields
-        +cell int
-        +cell The decaying values.
-
-+h(3, "util.itershuffle") util.itershuffle
-    +tag function
-    +tag-new(2)
-
-p
-    |  Shuffle an iterator. This works by holding #[code bufsize] items back and
-    |  yielding them sometime later. Obviously, this is not unbiased – but
-    |  should be good enough for batching. Larger bufsize means less bias.
-
-+aside-code("Example").
-    values = range(1000)
-    shuffled = itershuffle(values)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code iterable]
-        +cell iterable
-        +cell Iterator to shuffle.
-
-    +row
-        +cell #[code buffsize]
-        +cell int
-        +cell Items to hold back.
-
-    +row("foot")
-        +cell yields
-        +cell iterable
-        +cell The shuffled iterator.
diff --git a/website/api/annotation.jade b/website/api/annotation.jade
deleted file mode 100644
index bff9a71cb..000000000
--- a/website/api/annotation.jade
+++ /dev/null
@@ -1,46 +0,0 @@
-//- 💫 DOCS > API > ANNOTATION SPECS
-
-include ../_includes/_mixins
-
-+section("text-processing")
-    +h(2, "text-processing") Text Processing
-    include _annotation/_text-processing
-
-+section("pos-tagging")
-    +h(2, "pos-tagging") Part-of-speech Tagging
-
-    +aside("Tip: Understanding tags")
-        |  You can also use #[code spacy.explain()] to get the description for the
-        |  string representation of a tag. For example,
-        |  #[code spacy.explain("RB")] will return "adverb".
-
-    include _annotation/_pos-tags
-
-+section("dependency-parsing")
-    +h(2, "dependency-parsing") Syntactic Dependency Parsing
-
-    +aside("Tip: Understanding labels")
-        |  You can also use #[code spacy.explain()] to get the description for the
-        |  string representation of a label. For example,
-        |  #[code spacy.explain("prt")] will return "particle".
-
-    include _annotation/_dep-labels
-
-+section("named-entities")
-    +h(2, "named-entities") Named Entity Recognition
-
-    +aside("Tip: Understanding entity types")
-        |  You can also use #[code spacy.explain()] to get the description for the
-        |  string representation of an entity label. For example,
-        |  #[code spacy.explain("LANGUAGE")] will return "any named language".
-
-    include _annotation/_named-entities
-
-    +h(3, "biluo") BILUO Scheme
-
-    include _annotation/_biluo
-
-+section("training")
-    +h(2, "training") Models and training data
-
-    include _annotation/_training
diff --git a/website/api/cli.jade b/website/api/cli.jade
deleted file mode 100644
index 3fbeb5ece..000000000
--- a/website/api/cli.jade
+++ /dev/null
@@ -1,738 +0,0 @@
-//- 💫 DOCS > API > COMMAND LINE INTERFACE
-
-include ../_includes/_mixins
-
-p
-    |  As of v1.7.0, spaCy comes with new command line helpers to download and
-    |  link models and show useful debugging information. For a list of available
-    |  commands, type #[code spacy --help].
-
-+h(3, "download") Download
-
-p
-    |  Download #[+a("/usage/models") models] for spaCy. The downloader finds the
-    |  best-matching compatible version, uses pip to download the model as a
-    |  package and automatically creates a
-    |  #[+a("/usage/models#usage") shortcut link] to load the model by name.
-    |  Direct downloads don't perform any compatibility checks and require the
-    |  model name to be specified with its version (e.g.
-    |  #[code en_core_web_sm-2.0.0]).
-
-+aside("Downloading best practices")
-    |  The #[code download] command is mostly intended as a convenient,
-    |  interactive wrapper – it performs compatibility checks and prints
-    |  detailed messages in case things go wrong. It's #[strong not recommended]
-    |  to use this command as part of an automated process. If you know which
-    |  model your project needs, you should consider a
-    |  #[+a("/usage/models#download-pip") direct download via pip], or
-    |  uploading the model to a local PyPi installation and fetching it straight
-    |  from there. This will also allow you to add it as a versioned package
-    |  dependency to your project.
-
-+code(false, "bash", "$").
-    python -m spacy download [model] [--direct]
-
-+table(["Argument", "Type", "Description"])
-    +row
-        +cell #[code model]
-        +cell positional
-        +cell
-            |  Model name or shortcut (#[code en], #[code de],
-            |  #[code en_core_web_sm]).
-
-    +row
-        +cell #[code --direct], #[code -d]
-        +cell flag
-        +cell Force direct download of exact model version.
-
-    +row
-        +cell other
-            +tag-new(2.1)
-        +cell -
-        +cell
-            |  Additional installation options to be passed to
-            |  #[code pip install] when installing the model package. For
-            |  example, #[code --user] to install to the user home directory.
-
-    +row
-        +cell #[code --help], #[code -h]
-        +cell flag
-        +cell Show help message and available arguments.
-
-    +row("foot")
-        +cell creates
-        +cell directory, symlink
-        +cell
-            |  The installed model package in your #[code site-packages]
-            |  directory and a shortcut link as a symlink in #[code spacy/data].
-
-+h(3, "link") Link
-
-p
-    |  Create a #[+a("/usage/models#usage") shortcut link] for a model,
-    |  either a Python package or a local directory. This will let you load
-    |  models from any location using a custom name via
-    |  #[+api("spacy#load") #[code spacy.load()]].
-
-+infobox("Important note")
-    |  In spaCy v1.x, you had to use the model data directory to set up a shortcut
-    |  link for a local path. As of v2.0, spaCy expects all shortcut links to
-    |  be #[strong loadable model packages]. If you want to load a data directory,
-    |  call #[+api("spacy#load") #[code spacy.load()]] or
-    |  #[+api("language#from_disk") #[code Language.from_disk()]] with the path,
-    |  or use the #[+api("cli#package") #[code package]] command to create a
-    |  model package.
-
-+code(false, "bash", "$").
-    python -m spacy link [origin] [link_name] [--force]
-
-+table(["Argument", "Type", "Description"])
-    +row
-        +cell #[code origin]
-        +cell positional
-        +cell Model name if package, or path to local directory.
-
-    +row
-        +cell #[code link_name]
-        +cell positional
-        +cell Name of the shortcut link to create.
-
-    +row
-        +cell #[code --force], #[code -f]
-        +cell flag
-        +cell Force overwriting of existing link.
-
-    +row
-        +cell #[code --help], #[code -h]
-        +cell flag
-        +cell Show help message and available arguments.
-
-    +row("foot")
-        +cell creates
-        +cell symlink
-        +cell
-            |  A shortcut link of the given name as a symlink in
-            |  #[code spacy/data].
-
-+h(3, "info") Info
-
-p
-    |  Print information about your spaCy installation, models and local setup,
-    |  and generate #[+a("https://en.wikipedia.org/wiki/Markdown") Markdown]-formatted
-    |  markup to copy-paste into #[+a(gh("spacy") + "/issues") GitHub issues].
-
-+code(false, "bash").
-    python -m spacy info [--markdown]
-    python -m spacy info [model] [--markdown]
-
-+table(["Argument", "Type", "Description"])
-    +row
-        +cell #[code model]
-        +cell positional
-        +cell A model, i.e. shortcut link, package name or path (optional).
-
-    +row
-        +cell #[code --markdown], #[code -md]
-        +cell flag
-        +cell Print information as Markdown.
-
-    +row
-        +cell #[code --silent], #[code -s]
-            +tag-new("2.0.12")
-        +cell flag
-        +cell Don't print anything, just return the values.
-
-    +row
-        +cell #[code --help], #[code -h]
-        +cell flag
-        +cell Show help message and available arguments.
-
-    +row("foot")
-        +cell prints
-        +cell #[code stdout]
-        +cell Information about your spaCy installation.
-
-+h(3, "validate") Validate
-    +tag-new(2)
-
-p
-    |  Find all models installed in the current environment (both packages and
-    |  shortcut links) and check whether they are compatible with the currently
-    |  installed version of spaCy. Should be run after upgrading spaCy via
-    |  #[code pip install -U spacy] to ensure that all installed models are
-    |  can be used with the new version. The command is also useful to detect
-    |  out-of-sync model links resulting from links created in different virtual
-    |  environments. It will a list of models, the installed versions, the
-    |  latest compatible version (if out of date) and the commands for updating.
-
-+aside("Automated validation")
-    |  You can also use the #[code validate] command as part of your build
-    |  process or test suite, to ensure all models are up to date before
-    |  proceeding. If incompatible models or shortcut links are found, it will
-    |  return #[code 1].
-
-+code(false, "bash", "$").
-    python -m spacy validate
-
-+table(["Argument", "Type", "Description"])
-    +row("foot")
-        +cell prints
-        +cell #[code stdout]
-        +cell Details about the compatibility of your installed models.
-
-+h(3, "convert") Convert
-
-p
-    |  Convert files into spaCy's #[+a("/api/annotation#json-input") JSON format]
-    |  for use with the #[code train] command and other experiment management
-    |  functions. The converter can be specified on the command line, or
-    |  chosen based on the file extension of the input file.
-
-+code(false, "bash", "$", false, false, true).
-    python -m spacy convert [input_file] [output_dir] [--converter] [--n-sents]
-    [--morphology]
-
-+table(["Argument", "Type", "Description"])
-    +row
-        +cell #[code input_file]
-        +cell positional
-        +cell Input file.
-
-    +row
-        +cell #[code output_dir]
-        +cell positional
-        +cell Output directory for converted JSON file.
-
-    +row
-        +cell #[code converter], #[code -c]
-        +cell option
-        +cell #[+tag-new(2)] Name of converter to use (see below).
-
-    +row
-        +cell #[code --n-sents], #[code -n]
-        +cell option
-        +cell Number of sentences per document.
-
-    +row
-        +cell #[code --morphology], #[code -m]
-        +cell option
-        +cell Enable appending morphology to tags.
-
-    +row
-        +cell #[code --help], #[code -h]
-        +cell flag
-        +cell Show help message and available arguments.
-
-    +row("foot")
-        +cell creates
-        +cell JSON
-        +cell Data in spaCy's #[+a("/api/annotation#json-input") JSON format].
-
-p The following file format converters are available:
-
-+table(["ID", "Description"])
-    +row
-        +cell #[code auto]
-        +cell Automatically pick converter based on file extension (default).
-
-    +row
-        +cell #[code conllu], #[code conll]
-        +cell Universal Dependencies #[code .conllu] or #[code .conll] format.
-
-    +row
-        +cell #[code ner]
-        +cell Tab-based named entity recognition format.
-
-    +row
-        +cell #[code iob]
-        +cell IOB or IOB2 named entity recognition format.
-
-+h(3, "train") Train
-
-p
-    |  Train a model. Expects data in spaCy's
-    |  #[+a("/api/annotation#json-input") JSON format]. On each epoch, a model
-    |  will be saved out to the directory. Accuracy scores and model details
-    |  will be added to a #[+a("/usage/training#models-generating") #[code meta.json]]
-    |  to allow packaging the model using the
-    |  #[+api("cli#package") #[code package]] command.
-
-+infobox("Changed in v2.1", "⚠️")
-    |  As of spaCy 2.1, the #[code --no-tagger], #[code --no-parser] and
-    |  #[code --no-parser] flags have been replaced by a #[code --pipeline]
-    |  option, which lets you define comma-separated names of pipeline
-    |  components to train. For example, #[code --pipeline tagger,parser] will
-    |  only train the tagger and parser.
-
-+code(false, "bash", "$", false, false, true).
-    python -m spacy train [lang] [output_path] [train_path] [dev_path]
-    [--base-model] [--pipeline] [--vectors] [--n-iter] [--n-examples] [--use-gpu]
-    [--version] [--meta-path] [--init-tok2vec] [--parser-multitasks]
-    [--entity-multitasks] [--gold-preproc] [--noise-level] [--learn-tokens]
-    [--verbose]
-
-+table(["Argument", "Type", "Description"])
-    +row
-        +cell #[code lang]
-        +cell positional
-        +cell Model language.
-
-    +row
-        +cell #[code output_path]
-        +cell positional
-        +cell Directory to store model in. Will be created if it doesn't exist.
-
-    +row
-        +cell #[code train_path]
-        +cell positional
-        +cell Location of JSON-formatted training data.
-
-    +row
-        +cell #[code dev_path]
-        +cell positional
-        +cell Location of JSON-formatted development data for evaluation.
-
-    +row
-        +cell #[code --base-model], #[code -b]
-        +cell option
-        +cell
-            |  Optional name of base model to update. Can be any loadable
-            |  spaCy model.
-
-    +row
-        +cell #[code --pipeline], #[code -p]
-            +tag-new("2.1.0")
-        +cell option
-        +cell
-            |  Comma-separated names of pipeline components to train. Defaults
-            |  to #[code 'tagger,parser,ner'].
-
-    +row
-        +cell #[code --vectors], #[code -v]
-        +cell option
-        +cell Model to load vectors from.
-
-    +row
-        +cell #[code --n-iter], #[code -n]
-        +cell option
-        +cell Number of iterations (default: #[code 30]).
-
-    +row
-        +cell #[code --n-examples], #[code -ns]
-        +cell option
-        +cell Number of examples to use (defaults to #[code 0] for all examples).
-
-    +row
-        +cell #[code --use-gpu], #[code -g]
-        +cell option
-        +cell
-            |  Whether to use GPU. Can be either #[code 0], #[code 1] or
-            |  #[code -1].
-
-    +row
-        +cell #[code --version], #[code -V]
-        +cell option
-        +cell
-            |  Model version. Will be written out to the model's
-            |  #[code meta.json] after training.
-
-    +row
-        +cell #[code --meta-path], #[code -m]
-            +tag-new(2)
-        +cell option
-        +cell
-            |  Optional path to model
-            |  #[+a("/usage/training#models-generating") #[code meta.json]].
-            |  All relevant properties like #[code lang], #[code pipeline] and
-            |  #[code spacy_version] will be overwritten.
-
-    +row
-        +cell #[code --init-tok2vec], #[code -t2v]
-            +tag-new("2.1.0")
-        +cell option
-        +cell
-            |  Path to pretrained weights for the token-to-vector parts of the
-            |  models. See #[code spacy pretrain]. Experimental.
-
-    +row
-        +cell #[code --parser-multitasks], #[code -pt]
-        +cell option
-        +cell
-            |  Side objectives for parser CNN, e.g. #[code 'dep'] or
-            |  #[code 'dep,tag']
-
-    +row
-        +cell #[code --entity-multitasks], #[code -et]
-        +cell option
-        +cell
-            |  Side objectives for NER CNN, e.g. #[code 'dep'] or
-            |  #[code 'dep,tag']
-
-    +row
-        +cell #[code --noise-level], #[code -nl]
-        +cell option
-        +cell Float indicating the amount of corruption for data agumentation.
-
-    +row
-        +cell #[code --gold-preproc], #[code -G]
-        +cell flag
-        +cell Use gold preprocessing.
-
-    +row
-        +cell #[code --learn-tokens], #[code -T]
-        +cell flag
-        +cell
-            |  Make parser learn gold-standard tokenization by merging
-            ]  subtokens. Typically used for languages like Chinese.
-
-    +row
-        +cell #[code --verbose], #[code -VV]
-            +tag-new("2.0.13")
-        +cell flag
-        +cell Show more detailed messages during training.
-
-    +row
-        +cell #[code --help], #[code -h]
-        +cell flag
-        +cell Show help message and available arguments.
-
-    +row("foot")
-        +cell creates
-        +cell model, pickle
-        +cell A spaCy model on each epoch.
-
-+h(4, "train-hyperparams") Environment variables for hyperparameters
-    +tag-new(2)
-
-p
-    |  spaCy lets you set hyperparameters for training via environment variables.
-    |  This is useful, because it keeps the command simple and allows you to
-    |  #[+a("https://askubuntu.com/questions/17536/how-do-i-create-a-permanent-bash-alias/17537#17537") create an alias]
-    |  for your custom #[code train] command while still being able to easily
-    |  tweak the hyperparameters. For example:
-
-+code(false, "bash", "$").
-    parser_hidden_depth=2 parser_maxout_pieces=1 spacy train [...]
-
-+code("Usage with alias", "bash", "$").
-    alias train-parser="spacy train en /output /data /train /dev -n 1000"
-    parser_maxout_pieces=1 train-parser
-
-+table(["Name", "Description", "Default"])
-    +row
-        +cell #[code dropout_from]
-        +cell Initial dropout rate.
-        +cell #[code 0.2]
-
-    +row
-        +cell #[code dropout_to]
-        +cell Final dropout rate.
-        +cell #[code 0.2]
-
-    +row
-        +cell #[code dropout_decay]
-        +cell Rate of dropout change.
-        +cell #[code 0.0]
-
-    +row
-        +cell #[code batch_from]
-        +cell Initial batch size.
-        +cell #[code 1]
-
-    +row
-        +cell #[code batch_to]
-        +cell Final batch size.
-        +cell #[code 64]
-
-    +row
-        +cell #[code batch_compound]
-        +cell Rate of batch size acceleration.
-        +cell #[code 1.001]
-
-    +row
-        +cell #[code token_vector_width]
-        +cell Width of embedding tables and convolutional layers.
-        +cell #[code 128]
-
-    +row
-        +cell #[code embed_size]
-        +cell Number of rows in embedding tables.
-        +cell #[code 7500]
-
-    //- +row
-    //-     +cell #[code parser_maxout_pieces]
-    //-     +cell Number of pieces in the parser's and NER's first maxout layer.
-    //-     +cell #[code 2]
-
-    //- +row
-    //-     +cell #[code parser_hidden_depth]
-    //-     +cell Number of hidden layers in the parser and NER.
-    //-     +cell #[code 1]
-
-    +row
-        +cell #[code hidden_width]
-        +cell Size of the parser's and NER's hidden layers.
-        +cell #[code 128]
-
-    //- +row
-    //-     +cell #[code history_feats]
-    //-     +cell Number of previous action ID features for parser and NER.
-    //-     +cell #[code 128]
-
-    //- +row
-    //-     +cell #[code history_width]
-    //-     +cell Number of embedding dimensions for each action ID.
-    //-     +cell #[code 128]
-
-    +row
-        +cell #[code learn_rate]
-        +cell Learning rate.
-        +cell #[code 0.001]
-
-    +row
-        +cell #[code optimizer_B1]
-        +cell Momentum for the Adam solver.
-        +cell #[code 0.9]
-
-    +row
-        +cell #[code optimizer_B2]
-        +cell Adagrad-momentum for the Adam solver.
-        +cell #[code 0.999]
-
-    +row
-        +cell #[code optimizer_eps]
-        +cell Epsilon value for the Adam solver.
-        +cell #[code 1e-08]
-
-    +row
-        +cell #[code L2_penalty]
-        +cell L2 regularisation penalty.
-        +cell #[code 1e-06]
-
-    +row
-        +cell #[code grad_norm_clip]
-        +cell Gradient L2 norm constraint.
-        +cell #[code 1.0]
-
-+h(3, "vocab") Vocab
-    +tag-new(2)
-
-p
-    |  Compile a vocabulary from a
-    |  #[+a("/api/annotation#vocab-jsonl") lexicon JSONL] file and optional
-    |  word vectors. Will save out a valid spaCy model that you can load via
-    |  #[+api("spacy#load") #[code spacy.load]] or package using the
-    |  #[+api("cli#package") #[code package]] command.
-
-+code(false, "bash", "$").
-    python -m spacy vocab [lang] [output_dir] [lexemes_loc] [vectors_loc]
-
-+table(["Argument", "Type", "Description"])
-    +row
-        +cell #[code lang]
-        +cell positional
-        +cell
-            |  Model language
-            |  #[+a("https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes") ISO code],
-            |  e.g. #[code en].
-
-    +row
-        +cell #[code output_dir]
-        +cell positional
-        +cell Model output directory. Will be created if it doesn't exist.
-
-    +row
-        +cell #[code lexemes_loc]
-        +cell positional
-        +cell
-            |  Location of lexical data in spaCy's
-            |  #[+a("/api/annotation#vocab-jsonl") JSONL format].
-
-    +row
-        +cell #[code vectors_loc]
-        +cell positional
-        +cell Optional location of vectors data as numpy #[code .npz] file.
-
-    +row("foot")
-        +cell creates
-        +cell model
-        +cell A spaCy model containing the vocab and vectors.
-
-+h(3, "init-model") Init Model
-    +tag-new(2)
-
-p
-    |  Create a new model directory from raw data, like word frequencies, Brown
-    |  clusters and word vectors. This command is similar to the
-    |  #[code spacy model] command in v1.x.
-
-+code(false, "bash", "$", false, false, true).
-    python -m spacy init-model [lang] [output_dir] [freqs_loc] [--clusters-loc] [--vectors-loc] [--prune-vectors]
-
-+table(["Argument", "Type", "Description"])
-    +row
-        +cell #[code lang]
-        +cell positional
-        +cell
-            |  Model language
-            |  #[+a("https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes") ISO code],
-            |  e.g. #[code en].
-
-    +row
-        +cell #[code output_dir]
-        +cell positional
-        +cell Model output directory. Will be created if it doesn't exist.
-
-    +row
-        +cell #[code freqs_loc]
-        +cell positional
-        +cell
-            |  Location of word frequencies file. Should be a tab-separated
-            |  file with three columns: frequency, document frequency and
-            |  frequency count.
-
-    +row
-        +cell #[code --clusters-loc], #[code -c]
-        +cell option
-        +cell
-            |  Optional location of clusters file. Should be a tab-separated
-            |  file with three columns: cluster, word and frequency.
-
-    +row
-        +cell #[code --vectors-loc], #[code -v]
-        +cell option
-        +cell
-            |  Optional location of vectors file. Should be a tab-separated
-            |  file in Word2Vec format where the first column contains the word
-            |  and the remaining columns the values. File can be provided in
-            |  #[code .txt] format or as a zipped text file in #[code .zip] or
-            |  #[code .tar.gz] format.
-
-    +row
-        +cell #[code --prune-vectors], #[code -V]
-        +cell flag
-        +cell
-            |  Number of vectors to prune the vocabulary to. Defaults to
-            |  #[code -1] for no pruning.
-
-    +row("foot")
-        +cell creates
-        +cell model
-        +cell A spaCy model containing the vocab and vectors.
-
-+h(3, "evaluate") Evaluate
-    +tag-new(2)
-
-p
-    |  Evaluate a model's accuracy and speed on JSON-formatted annotated data.
-    |  Will print the results and optionally export
-    |  #[+a("/usage/visualizers") displaCy visualizations] of a sample set of
-    |  parses to #[code .html] files. Visualizations for the dependency parse
-    |  and NER will be exported as separate files if the respective component
-    |  is present in the model's pipeline.
-
-+code(false, "bash", "$", false, false, true).
-    python -m spacy evaluate [model] [data_path] [--displacy-path] [--displacy-limit] [--gpu-id] [--gold-preproc]
-
-+table(["Argument", "Type", "Description"])
-    +row
-        +cell #[code model]
-        +cell positional
-        +cell
-            |  Model to evaluate. Can be a package or shortcut link name, or a
-            |  path to a model data directory.
-
-    +row
-        +cell #[code data_path]
-        +cell positional
-        +cell Location of JSON-formatted evaluation data.
-
-    +row
-        +cell #[code --displacy-path], #[code -dp]
-        +cell option
-        +cell
-            |  Directory to output rendered parses as HTML. If not set, no
-            |  visualizations will be generated.
-
-    +row
-        +cell #[code --displacy-limit], #[code -dl]
-        +cell option
-        +cell
-            |  Number of parses to generate per file. Defaults to #[code 25].
-            |  Keep in mind that a significantly higher number might cause the
-            |  #[code .html] files to render slowly.
-
-    +row
-        +cell #[code --gpu-id], #[code -g]
-        +cell option
-        +cell GPU to use, if any. Defaults to #[code -1] for CPU.
-
-    +row
-        +cell #[code --gold-preproc], #[code -G]
-        +cell flag
-        +cell Use gold preprocessing.
-
-    +row("foot")
-        +cell prints / creates
-        +cell #[code stdout], HTML
-        +cell Training results and optional displaCy visualizations.
-
-
-+h(3, "package") Package
-
-p
-    |  Generate a #[+a("/usage/training#models-generating") model Python package]
-    |  from an existing model data directory. All data files are copied over.
-    |  If the path to a #[code meta.json] is supplied, or a #[code meta.json] is
-    |  found in the input directory, this file is used. Otherwise, the data can
-    |  be entered directly from the command line. After packaging, you can run
-    |  #[code python setup.py sdist] from the newly created directory to turn
-    |  your model into an installable archive file.
-
-+code(false, "bash", "$", false, false, true).
-    python -m spacy package [input_dir] [output_dir] [--meta-path] [--create-meta] [--force]
-
-+aside-code("Example", "bash").
-    python -m spacy package /input /output
-    cd /output/en_model-0.0.0
-    python setup.py sdist
-    pip install dist/en_model-0.0.0.tar.gz
-
-+table(["Argument", "Type", "Description"])
-    +row
-        +cell #[code input_dir]
-        +cell positional
-        +cell Path to directory containing model data.
-
-    +row
-        +cell #[code output_dir]
-        +cell positional
-        +cell Directory to create package folder in.
-
-    +row
-        +cell #[code --meta-path], #[code -m]
-        +cell option
-        +cell #[+tag-new(2)] Path to #[code meta.json] file (optional).
-
-    +row
-        +cell #[code --create-meta], #[code -c]
-        +cell flag
-        +cell
-            |  #[+tag-new(2)] Create a #[code meta.json] file on the command
-            |  line, even if one already exists in the directory. If an
-            |  existing file is found, its entries will be shown as the defaults
-            |  in the command line prompt.
-    +row
-        +cell #[code --force], #[code -f]
-        +cell flag
-        +cell Force overwriting of existing folder in output directory.
-
-    +row
-        +cell #[code --help], #[code -h]
-        +cell flag
-        +cell Show help message and available arguments.
-
-    +row("foot")
-        +cell creates
-        +cell directory
-        +cell A Python package containing the spaCy model.
diff --git a/website/api/cython-classes.jade b/website/api/cython-classes.jade
deleted file mode 100644
index 9d7d8cc53..000000000
--- a/website/api/cython-classes.jade
+++ /dev/null
@@ -1,39 +0,0 @@
-//- 💫 DOCS > API > CYTHON > CLASSES
-
-include ../_includes/_mixins
-
-+section("doc")
-    +h(2, "doc", "spacy/tokens/doc.pxd") Doc
-        +tag cdef class
-
-    include _cython/_doc
-
-+section("token")
-    +h(2, "token", "spacy/tokens/token.pxd") Token
-        +tag cdef class
-
-    include _cython/_token
-
-+section("span")
-    +h(2, "span", "spacy/tokens/span.pxd") Span
-        +tag cdef class
-
-    include _cython/_span
-
-+section("lexeme")
-    +h(2, "lexeme", "spacy/lexeme.pxd") Lexeme
-        +tag cdef class
-
-    include _cython/_lexeme
-
-+section("vocab")
-    +h(2, "vocab", "spacy/vocab.pxd") Vocab
-        +tag cdef class
-
-    include _cython/_vocab
-
-+section("stringstore")
-    +h(2, "stringstore", "spacy/strings.pxd") StringStore
-        +tag cdef class
-
-    include _cython/_stringstore
diff --git a/website/api/cython-structs.jade b/website/api/cython-structs.jade
deleted file mode 100644
index bdaf01ac4..000000000
--- a/website/api/cython-structs.jade
+++ /dev/null
@@ -1,15 +0,0 @@
-//- 💫 DOCS > API > CYTHON > STRUCTS
-
-include ../_includes/_mixins
-
-+section("tokenc")
-    +h(2, "tokenc", "spacy/structs.pxd") TokenC
-        +tag C struct
-
-    include _cython/_tokenc
-
-+section("lexemec")
-    +h(2, "lexemec", "spacy/structs.pxd") LexemeC
-        +tag C struct
-
-    include _cython/_lexemec
diff --git a/website/api/cython.jade b/website/api/cython.jade
deleted file mode 100644
index 5a1853046..000000000
--- a/website/api/cython.jade
+++ /dev/null
@@ -1,176 +0,0 @@
-//- 💫 DOCS > API > CYTHON > ARCHITECTURE
-
-include ../_includes/_mixins
-
-+section("overview")
-    +aside("What's Cython?")
-        |  #[+a("http://cython.org/") Cython] is a language for writing
-        |  C extensions for Python. Most Python code is also valid Cython, but
-        |  you can add type declarations to get efficient memory-managed code
-        |  just like C or C++.
-
-    p
-        |  This section documents spaCy's C-level data structures and
-        |  interfaces, intended for use from Cython. Some of the attributes are
-        |  primarily for internal use, and all C-level functions and methods are
-        |  designed for speed over safety – if you make a mistake and access an
-        |  array out-of-bounds, the program may crash abruptly.
-
-    p
-        |  With Cython there are four ways of declaring complex data types.
-        |  Unfortunately we use all four in different places, as they all have
-        |  different utility:
-
-    +table(["Declaration", "Description", "Example"])
-        +row
-            +cell #[code class]
-            +cell A normal Python class.
-            +cell #[+api("language") #[code Language]]
-
-        +row
-            +cell #[code cdef class]
-            +cell
-                |  A Python extension type. Differs from a normal Python class
-                |  in that its attributes can be defined on the underlying
-                |  struct. Can have C-level objects as attributes (notably
-                |  structs and pointers), and can have methods which have
-                |  C-level objects as arguments or return types.
-            +cell #[+api("cython-classes#lexeme") #[code Lexeme]]
-
-        +row
-            +cell #[code cdef struct]
-            +cell
-                |  A struct is just a collection of variables, sort of like a
-                |  named tuple, except the memory is contiguous. Structs can't
-                |  have methods, only attributes.
-            +cell #[+api("cython-structs#lexemec") #[code LexemeC]]
-
-        +row
-            +cell #[code cdef cppclass]
-            +cell
-                |  A C++ class. Like a struct, this can be allocated on the
-                |  stack, but can have methods, a constructor and a destructor.
-                |  Differs from `cdef class` in that it can be created and
-                |  destroyed without acquiring the Python global interpreter
-                |  lock. This style is the most obscure.
-            +cell #[+src(gh("spacy", "spacy/syntax/_state.pxd")) #[code StateC]]
-
-    p
-        |  The most important classes in spaCy are defined as #[code cdef class]
-        |  objects. The underlying data for these objects is usually gathered
-        |  into a struct, which is usually named #[code c]. For instance, the
-        |  #[+api("cython-classses#lexeme") #[code Lexeme]] class holds a
-        |  #[+api("cython-structs#lexemec") #[code LexemeC]] struct, at
-        |  #[code Lexeme.c]. This lets you shed the Python container, and pass
-        |  a pointer to the underlying data into C-level functions.
-
-+section("conventions")
-    +h(2, "conventions") Conventions
-
-    p
-        |  spaCy's core data structures are implemented as
-        |  #[+a("http://cython.org/") Cython] #[code cdef] classes. Memory is
-        |  managed through the #[+a(gh("cymem")) #[code cymem]]
-        |  #[code cymem.Pool] class, which allows you
-        |  to allocate memory which will be freed when the #[code Pool] object
-        |  is garbage collected. This means you usually don't have to worry
-        |  about freeing memory. You just have to decide which Python object
-        |  owns the memory, and make it own the #[code Pool]. When that object
-        |  goes out of scope, the memory will be freed. You do have to take
-        |  care that no pointers outlive the object that owns them — but this
-        |  is generally quite easy.
-
-    p
-        |  All Cython modules should have the #[code # cython: infer_types=True]
-        |  compiler directive at the top of the file. This makes the code much
-        |  cleaner, as it avoids the need for many type declarations. If
-        |  possible, you should prefer to declare your functions #[code nogil],
-        |  even if you don't especially care about multi-threading. The reason
-        |  is that #[code nogil] functions help the Cython compiler reason about
-        |  your code quite a lot — you're telling the compiler that no Python
-        |  dynamics are possible. This lets many errors be raised, and ensures
-        |  your function will run at C speed.
-
-
-    p
-        |  Cython gives you many choices of sequences: you could have a Python
-        |  list, a numpy array, a memory view, a C++ vector, or a pointer.
-        |  Pointers are preferred, because they are fastest, have the most
-        |  explicit semantics, and let the compiler check your code more
-        |  strictly. C++ vectors are also great — but you should only use them
-        |  internally in functions. It's less friendly to accept a vector as an
-        |  argument, because that asks the user to do much more work. Here's
-        |  how to get a pointer from a numpy array, memory view or vector:
-
-    +code.
-        cdef void get_pointers(np.ndarray[int, mode='c'] numpy_array, vector[int] cpp_vector, int[::1] memory_view) nogil:
-        pointer1 = &lt;int*&gt;numpy_array.data
-        pointer2 = cpp_vector.data()
-        pointer3 = &memory_view[0]
-
-    p
-        |  Both C arrays and C++ vectors reassure the compiler that no Python
-        |  operations are possible on your variable. This is a big advantage:
-        |  it lets the Cython compiler raise many more errors for you.
-
-    p
-        |  When getting a pointer from a numpy array or memoryview, take care
-        |  that the data is actually stored in C-contiguous order — otherwise
-        |  you'll get a pointer to nonsense. The type-declarations in the code
-        |  above should generate runtime errors if buffers with incorrect
-        |  memory layouts are passed in. To iterate over the array, the
-        |  following style is preferred:
-
-    +code.
-        cdef int c_total(const int* int_array, int length) nogil:
-            total = 0
-            for item in int_array[:length]:
-                total += item
-            return total
-
-    p
-        |  If this is confusing, consider that the compiler couldn't deal with
-        |  #[code for item in int_array:] — there's no length attached to a raw
-        |  pointer, so how could we figure out where to stop? The length is
-        |  provided in the slice notation as a solution to this. Note that we
-        |  don't have to declare the type of #[code item] in the code above —
-        |  the compiler can easily infer it. This gives us tidy code that looks
-        |  quite like Python, but is exactly as fast as C — because we've made
-        |  sure the compilation to C is trivial.
-
-    p
-        |  Your functions cannot be declared #[code nogil] if they need to
-        |  create Python objects or call Python functions. This is perfectly
-        |  okay — you shouldn't torture your code just to get #[code nogil]
-        |  functions. However, if your function isn't #[code nogil], you should
-        |  compile your module with #[code cython -a --cplus my_module.pyx] and
-        |  open the resulting #[code my_module.html] file in a browser. This
-        |  will let you see how Cython is compiling your code. Calls into the
-        |  Python run-time will be in bright yellow. This lets you easily see
-        |  whether Cython is able to correctly type your code, or whether there
-        |  are unexpected problems.
-
-    p
-        |  Working in Cython is very rewarding once you're over the initial
-        |  learning curve. As with C and C++, the first way you write something
-        |  in Cython will often be the performance-optimal approach. In
-        |  contrast, Python optimisation generally requires a lot of
-        |  experimentation. Is it faster to have an #[code if item in my_dict]
-        |  check, or to use #[code .get()]? What about
-        |  #[code try]/#[code except]? Does this numpy operation create a copy?
-        |  There's no way to guess the answers to these questions, and you'll
-        |  usually be dissatisfied with your results — so there's no way to
-        |  know when to stop this process. In the worst case, you'll make a
-        |  mess that invites the next reader to try their luck too. This is
-        |  like one of those
-        |  #[+a("http://www.wemjournal.org/article/S1080-6032%2809%2970088-2/abstract") volcanic gas-traps],
-        |  where the rescuers keep passing out from low oxygen, causing
-        |  another rescuer to follow — only to succumb themselves. In short,
-        |  just say no to optimizing your Python. If it's not fast enough the
-        |  first time, just switch to Cython.
-
-    +infobox("Resources")
-        +list.o-no-block
-            +item #[+a("http://docs.cython.org/en/latest/") Official Cython documentation] (cython.org)
-            +item #[+a("https://explosion.ai/blog/writing-c-in-cython", true) Writing C in Cython] (explosion.ai)
-            +item #[+a("https://explosion.ai/blog/multithreading-with-cython") Multi-threading spaCy’s parser and named entity recogniser] (explosion.ai)
diff --git a/website/api/dependencyparser.jade b/website/api/dependencyparser.jade
deleted file mode 100644
index e557ef9da..000000000
--- a/website/api/dependencyparser.jade
+++ /dev/null
@@ -1,6 +0,0 @@
-//- 💫 DOCS > API > DEPENDENCYPARSER
-
-include ../_includes/_mixins
-
-//- This class inherits from Pipe, so this page uses the template in pipe.jade.
-!=partial("pipe", { subclass: "DependencyParser", short: "parser", pipeline_id: "parser" })
diff --git a/website/api/doc.jade b/website/api/doc.jade
deleted file mode 100644
index 7d304926c..000000000
--- a/website/api/doc.jade
+++ /dev/null
@@ -1,827 +0,0 @@
-//- 💫 DOCS > API > DOC
-
-include ../_includes/_mixins
-
-p
-    |  A #[code Doc] is a sequence of #[+api("token") #[code Token]] objects.
-    |  Access sentences and named entities, export annotations to numpy arrays,
-    |  losslessly serialize to compressed binary strings. The #[code Doc] object
-    |  holds an array of #[code TokenC] structs. The Python-level #[code Token]
-    |  and #[+api("span") #[code Span]] objects are views of this array, i.e.
-    |  they don't own the data themselves.
-
-+aside-code("Example").
-    # Construction 1
-    doc = nlp(u'Some text')
-
-    # Construction 2
-    from spacy.tokens import Doc
-    doc = Doc(nlp.vocab, words=[u'hello', u'world', u'!'],
-                               spaces=[True, False, False])
-
-+h(2, "init") Doc.__init__
-    +tag method
-
-p
-    |  Construct a #[code Doc] object. The most common way to get a #[code Doc]
-    |  object is via the #[code nlp] object.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell A storage container for lexical types.
-
-    +row
-        +cell #[code words]
-        +cell -
-        +cell A list of strings to add to the container.
-
-    +row
-        +cell #[code spaces]
-        +cell -
-        +cell
-            |  A list of boolean values indicating whether each word has a
-            |  subsequent space. Must have the same length as #[code words], if
-            |  specified. Defaults to a sequence of #[code True].
-
-    +row("foot")
-        +cell returns
-        +cell #[code Doc]
-        +cell The newly constructed object.
-
-+h(2, "getitem") Doc.__getitem__
-    +tag method
-
-p
-    |  Get a #[+api("token") #[code Token]] object at position #[code i], where
-    |  #[code i] is an integer. Negative indexing is supported, and follows the
-    |  usual Python semantics, i.e. #[code doc[-2]] is #[code doc[len(doc) - 2]].
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    assert doc[0].text == 'Give'
-    assert doc[-1].text == '.'
-    span = doc[1:3]
-    assert span.text == 'it back'
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code i]
-        +cell int
-        +cell The index of the token.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Token]
-        +cell The token at #[code doc[i]].
-
-p
-    |  Get a #[+api("span") #[code Span]] object, starting at position
-    |  #[code start] (token index) and ending at position #[code end] (token
-    |  index).
-
-p
-    |  For instance, #[code doc[2:5]] produces a span consisting of tokens 2, 3
-    |  and 4. Stepped slices (e.g. #[code doc[start : end : step]]) are not
-    |  supported, as #[code Span] objects must be contiguous (cannot have gaps).
-    |  You can use negative indices and open-ended ranges, which have their
-    |  normal Python semantics.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code start_end]
-        +cell tuple
-        +cell The slice of the document to get.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Span]
-        +cell The span at #[code doc[start : end]].
-
-+h(2, "iter") Doc.__iter__
-    +tag method
-
-p
-    |  Iterate over #[code Token] objects, from which the annotations can be
-    |  easily accessed.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back')
-    assert [t.text for t in doc] == [u'Give', u'it', u'back']
-
-p
-    |  This is the main way of accessing #[+api("token") #[code Token]] objects,
-    |  which are the main way annotations are accessed from Python. If
-    |  faster-than-Python speeds are required, you can instead access the
-    |  annotations as a numpy array, or access the underlying C data directly
-    |  from Cython.
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Token]
-        +cell A #[code Token] object.
-
-+h(2, "len") Doc.__len__
-    +tag method
-
-p Get the number of tokens in the document.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    assert len(doc) == 7
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of tokens in the document.
-
-+h(2, "set_extension") Doc.set_extension
-    +tag classmethod
-    +tag-new(2)
-
-p
-    |  Define a custom attribute on the #[code Doc] which becomes available via
-    |  #[code Doc._]. For details, see the documentation on
-    |  #[+a("/usage/processing-pipelines#custom-components-attributes") custom attributes].
-
-+aside-code("Example").
-    from spacy.tokens import Doc
-    city_getter = lambda doc: any(city in doc.text for city in ('New York', 'Paris', 'Berlin'))
-    Doc.set_extension('has_city', getter=city_getter)
-    doc = nlp(u'I like New York')
-    assert doc._.has_city
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell
-            |  Name of the attribute to set by the extension. For example,
-            |  #[code 'my_attr'] will be available as #[code doc._.my_attr].
-
-    +row
-        +cell #[code default]
-        +cell -
-        +cell
-            |  Optional default value of the attribute if no getter or method
-            |  is defined.
-
-    +row
-        +cell #[code method]
-        +cell callable
-        +cell
-            |  Set a custom method on the object, for example
-            |  #[code doc._.compare(other_doc)].
-
-    +row
-        +cell #[code getter]
-        +cell callable
-        +cell
-            |  Getter function that takes the object and returns an attribute
-            |  value. Is called when the user accesses the #[code ._] attribute.
-
-    +row
-        +cell #[code setter]
-        +cell callable
-        +cell
-            |  Setter function that takes the #[code Doc] and a value, and
-            |  modifies the object. Is called when the user writes to the
-            |  #[code Doc._] attribute.
-
-+h(2, "get_extension") Doc.get_extension
-    +tag classmethod
-    +tag-new(2)
-
-p
-    |  Look up a previously registered extension by name. Returns a 4-tuple
-    |  #[code.u-break (default, method, getter, setter)] if the extension is
-    |  registered. Raises a #[code KeyError] otherwise.
-
-+aside-code("Example").
-    from spacy.tokens import Doc
-    Doc.set_extension('has_city', default=False)
-    extension = Doc.get_extension('has_city')
-    assert extension == (False, None, None, None)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of the extension.
-
-    +row("foot")
-        +cell returns
-        +cell tuple
-        +cell
-            |  A #[code.u-break (default, method, getter, setter)] tuple of the
-            |  extension.
-
-+h(2, "has_extension") Doc.has_extension
-    +tag classmethod
-    +tag-new(2)
-
-p Check whether an extension has been registered on the #[code Doc] class.
-
-+aside-code("Example").
-    from spacy.tokens import Doc
-    Doc.set_extension('has_city', default=False)
-    assert Doc.has_extension('has_city')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of the extension to check.
-
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the extension has been registered.
-
-+h(2, "remove_extension") Doc.remove_extension
-    +tag classmethod
-    +tag-new("2.0.12")
-
-p Remove a previously registered extension.
-
-+aside-code("Example").
-    from spacy.tokens import Doc
-    Doc.set_extension('has_city', default=False)
-    removed = Doc.remove_extension('has_city')
-    assert not Doc.has_extension('has_city')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of the extension.
-
-    +row("foot")
-        +cell returns
-        +cell tuple
-        +cell
-            |  A #[code.u-break (default, method, getter, setter)] tuple of the
-            |  removed extension.
-
-+h(2, "char_span") Doc.char_span
-    +tag method
-    +tag-new(2)
-
-p
-    |  Create a #[code Span] object from the slice #[code doc.text[start : end]].
-    |  Returns #[code None] if the character indices don't map to a valid span.
-
-+aside-code("Example").
-    doc = nlp(u'I like New York')
-    span = doc.char_span(7, 15, label=u'GPE')
-    assert span.text == 'New York'
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code start]
-        +cell int
-        +cell The index of the first character of the span.
-
-    +row
-        +cell #[code end]
-        +cell int
-        +cell The index of the last character after the span.
-
-    +row
-        +cell #[code label]
-        +cell uint64 / unicode
-        +cell A label to attach to the Span, e.g. for named entities.
-
-    +row
-        +cell #[code vector]
-        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
-        +cell A meaning representation of the span.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Span]
-        +cell The newly constructed object or #[code None].
-
-+h(2, "similarity") Doc.similarity
-    +tag method
-    +tag-model("vectors")
-
-p
-    |  Make a semantic similarity estimate. The default estimate is cosine
-    |  similarity using an average of word vectors.
-
-+aside-code("Example").
-    apples = nlp(u'I like apples')
-    oranges = nlp(u'I like oranges')
-    apples_oranges = apples.similarity(oranges)
-    oranges_apples = oranges.similarity(apples)
-    assert apples_oranges == oranges_apples
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code other]
-        +cell -
-        +cell
-            |  The object to compare with. By default, accepts #[code Doc],
-            |  #[code Span], #[code Token] and #[code Lexeme] objects.
-
-    +row("foot")
-        +cell returns
-        +cell float
-        +cell A scalar similarity score. Higher is more similar.
-
-+h(2, "count_by") Doc.count_by
-    +tag method
-
-p
-    |  Count the frequencies of a given attribute. Produces a dict of
-    |  #[code {attr (int): count (ints)}] frequencies, keyed by the values
-    |  of the given attribute ID.
-
-+aside-code("Example").
-    from spacy.attrs import ORTH
-    doc = nlp(u'apple apple orange banana')
-    assert doc.count_by(ORTH) == {7024L: 1, 119552L: 1, 2087L: 2}
-    doc.to_array([attrs.ORTH])
-    # array([[11880], [11880], [7561], [12800]])
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code attr_id]
-        +cell int
-        +cell The attribute ID
-
-    +row("foot")
-        +cell returns
-        +cell dict
-        +cell A dictionary mapping attributes to integer counts.
-
-+h(2, "get_lca_matrix") Doc.get_lca_matrix
-    +tag method
-
-p
-    |  Calculates the lowest common ancestor matrix for a given #[code Doc].
-    |  Returns LCA matrix containing the integer index of the ancestor, or
-    |  #[code -1] if no common ancestor is found, e.g. if span excludes a
-    |  necessary ancestor.
-
-+aside-code("Example").
-    doc = nlp(u"This is a test")
-    matrix = doc.get_lca_matrix()
-    # array([[0, 1, 1, 1], [1, 1, 1, 1], [1, 1, 2, 3], [1, 1, 3, 3]], dtype=int32)
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell #[code.u-break numpy.ndarray[ndim=2, dtype='int32']]
-        +cell The lowest common ancestor matrix of the #[code Doc].
-
-+h(2, "to_array") Doc.to_array
-    +tag method
-
-p
-    |  Export given token attributes to a numpy #[code ndarray].
-    |  If #[code attr_ids] is a sequence of #[code M] attributes,
-    |  the output array will  be of shape #[code (N, M)], where #[code N]
-    |  is the length of the #[code Doc] (in tokens). If #[code attr_ids] is
-    |  a single attribute, the output shape will be #[code (N,)]. You can
-    |  specify attributes by integer ID (e.g. #[code spacy.attrs.LEMMA])
-    |  or string name (e.g. 'LEMMA' or 'lemma'). The values will be 64-bit
-    |  integers.
-
-+aside-code("Example").
-    from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
-    doc = nlp(text)
-    # All strings mapped to integers, for easy export to numpy
-    np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
-    np_array = doc.to_array("POS")
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code attr_ids]
-        +cell list or int or string
-        +cell
-            | A list of attributes (int IDs or string names) or
-            | a single attribute (int ID or string name)
-
-    +row("foot")
-        +cell returns
-        +cell
-            | #[code.u-break numpy.ndarray[ndim=2, dtype='uint64']] or
-            | #[code.u-break numpy.ndarray[ndim=1, dtype='uint64']] or
-        +cell
-            |  The exported attributes as a 2D numpy array, with one row per
-            |  token and one column per attribute (when #[code attr_ids] is a
-            |  list), or as a 1D numpy array, with one item per attribute (when
-            |  #[code attr_ids] is a single value).
-
-+h(2, "from_array") Doc.from_array
-    +tag method
-
-p
-    |  Load attributes from a numpy array. Write to a #[code Doc] object, from
-    |  an #[code (M, N)] array of attributes.
-
-+aside-code("Example").
-    from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
-    from spacy.tokens import Doc
-    doc = nlp("Hello world!")
-    np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
-    doc2 = Doc(doc.vocab, words=[t.text for t in doc])
-    doc2.from_array([LOWER, POS, ENT_TYPE, IS_ALPHA], np_array)
-    assert doc[0].pos_ == doc2[0].pos_
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code attrs]
-        +cell ints
-        +cell A list of attribute ID ints.
-
-    +row
-        +cell #[code array]
-        +cell #[code.u-break numpy.ndarray[ndim=2, dtype='int32']]
-        +cell The attribute values to load.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Doc]
-        +cell Itself.
-
-+h(2, "to_disk") Doc.to_disk
-    +tag method
-    +tag-new(2)
-
-p Save the current state to a directory.
-
-+aside-code("Example").
-    doc.to_disk('/path/to/doc')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode or #[code Path]
-        +cell
-            |  A path to a directory, which will be created if it doesn't exist.
-            |  Paths may be either strings or #[code Path]-like objects.
-
-+h(2, "from_disk") Doc.from_disk
-    +tag method
-    +tag-new(2)
-
-p Loads state from a directory. Modifies the object in place and returns it.
-
-+aside-code("Example").
-    from spacy.tokens import Doc
-    from spacy.vocab import Vocab
-    doc = Doc(Vocab()).from_disk('/path/to/doc')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode or #[code Path]
-        +cell
-            |  A path to a directory. Paths may be either strings or
-            |  #[code Path]-like objects.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Doc]
-        +cell The modified #[code Doc] object.
-
-+h(2, "to_bytes") Doc.to_bytes
-    +tag method
-
-p Serialize, i.e. export the document contents to a binary string.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    doc_bytes = doc.to_bytes()
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell bytes
-        +cell
-            |  A losslessly serialized copy of the #[code Doc], including all
-            |  annotations.
-
-+h(2, "from_bytes") Doc.from_bytes
-    +tag method
-
-p Deserialize, i.e. import the document contents from a binary string.
-
-+aside-code("Example").
-    from spacy.tokens import Doc
-    text = u'Give it back! He pleaded.'
-    doc = nlp(text)
-    bytes = doc.to_bytes()
-    doc2 = Doc(doc.vocab).from_bytes(bytes)
-    assert doc.text == doc2.text
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code data]
-        +cell bytes
-        +cell The string to load from.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Doc]
-        +cell The #[code Doc] object.
-
-+h(2, "merge") Doc.merge
-    +tag method
-
-p
-    |  Retokenize the document, such that the span at
-    |  #[code doc.text[start_idx : end_idx]] is merged into a single token. If
-    |  #[code start_idx] and #[code end_idx] do not mark start and end token
-    |  boundaries, the document remains unchanged.
-
-+aside-code("Example").
-    doc = nlp(u'Los Angeles start.')
-    doc.merge(0, len('Los Angeles'), 'NNP', 'Los Angeles', 'GPE')
-    assert [t.text for t in doc] == [u'Los Angeles', u'start', u'.']
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code start_idx]
-        +cell int
-        +cell The character index of the start of the slice to merge.
-
-    +row
-        +cell #[code end_idx]
-        +cell int
-        +cell The character index after the end of the slice to merge.
-
-    +row
-        +cell #[code **attributes]
-        +cell -
-        +cell
-            |  Attributes to assign to the merged token. By default,
-            |  attributes are inherited from the syntactic root token of
-            |  the span.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Token]
-        +cell
-            |  The newly merged token, or #[code None] if the start and end
-            |  indices did not fall at token boundaries
-
-+h(2, "print_tree") Doc.print_tree
-    +tag method
-    +tag-model("parse")
-
-p
-    |  Returns the parse trees in JSON (dict) format. Especially useful for
-    |  web applications.
-
-+aside-code("Example").
-    doc = nlp(u'Alice ate the pizza.')
-    trees = doc.print_tree()
-    # {'modifiers': [
-    #   {'modifiers': [], 'NE': 'PERSON', 'word': 'Alice', 'arc': 'nsubj', 'POS_coarse': 'PROPN', 'POS_fine': 'NNP', 'lemma': 'Alice'},
-    #   {'modifiers': [{'modifiers': [], 'NE': '', 'word': 'the', 'arc': 'det', 'POS_coarse': 'DET', 'POS_fine': 'DT', 'lemma': 'the'}], 'NE': '', 'word': 'pizza', 'arc': 'dobj', 'POS_coarse': 'NOUN', 'POS_fine': 'NN', 'lemma': 'pizza'},
-    #   {'modifiers': [], 'NE': '', 'word': '.', 'arc': 'punct', 'POS_coarse': 'PUNCT', 'POS_fine': '.', 'lemma': '.'}
-    # ], 'NE': '', 'word': 'ate', 'arc': 'ROOT', 'POS_coarse': 'VERB', 'POS_fine': 'VBD', 'lemma': 'eat'}
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code light]
-        +cell bool
-        +cell Don't include lemmas or entities.
-
-    +row
-        +cell #[code flat]
-        +cell bool
-        +cell Don't include arcs or modifiers.
-
-    +row("foot")
-        +cell returns
-        +cell dict
-        +cell Parse tree as dict.
-
-+h(2, "ents") Doc.ents
-    +tag property
-    +tag-model("NER")
-
-p
-    |  Iterate over the entities in the document. Yields named-entity
-    |  #[code Span] objects, if the entity recognizer has been applied to the
-    |  document.
-
-+aside-code("Example").
-    doc = nlp(u'Mr. Best flew to New York on Saturday morning.')
-    ents = list(doc.ents)
-    assert ents[0].label == 346
-    assert ents[0].label_ == 'PERSON'
-    assert ents[0].text == 'Mr. Best'
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Span]
-        +cell Entities in the document.
-
-+h(2, "noun_chunks") Doc.noun_chunks
-    +tag property
-    +tag-model("parse")
-
-p
-    |  Iterate over the base noun phrases in the document. Yields base
-    |  noun-phrase #[code Span] objects, if the document has been syntactically
-    |  parsed. A base noun phrase, or "NP chunk", is a noun phrase that does not
-    |  permit other NPs to be nested within it – so no NP-level coordination, no
-    |  prepositional phrases, and no relative clauses.
-
-+aside-code("Example").
-    doc = nlp(u'A phrase with another phrase occurs.')
-    chunks = list(doc.noun_chunks)
-    assert chunks[0].text == "A phrase"
-    assert chunks[1].text == "another phrase"
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Span]
-        +cell Noun chunks in the document.
-
-+h(2, "sents") Doc.sents
-    +tag property
-    +tag-model("parse")
-
-p
-    |  Iterate over the sentences in the document. Sentence spans have no label.
-    |  To improve accuracy on informal texts, spaCy calculates sentence boundaries
-    |  from the syntactic dependency parse. If the parser is disabled,
-    |  the #[code sents] iterator will be unavailable.
-
-+aside-code("Example").
-    doc = nlp(u"This is a sentence. Here's another...")
-    sents = list(doc.sents)
-    assert len(sents) == 2
-    assert [s.root.text for s in sents] == ["is", "'s"]
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Span]
-        +cell Sentences in the document.
-
-+h(2, "has_vector") Doc.has_vector
-    +tag property
-    +tag-model("vectors")
-
-p
-    |  A boolean value indicating whether a word vector is associated with the
-    |  object.
-
-+aside-code("Example").
-    doc = nlp(u'I like apples')
-    assert doc.has_vector
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the document has a vector data attached.
-
-+h(2, "vector") Doc.vector
-    +tag property
-    +tag-model("vectors")
-
-p
-    |  A real-valued meaning representation. Defaults to an average of the
-    |  token vectors.
-
-+aside-code("Example").
-    doc = nlp(u'I like apples')
-    assert doc.vector.dtype == 'float32'
-    assert doc.vector.shape == (300,)
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
-        +cell A 1D numpy array representing the document's semantics.
-
-+h(2, "vector_norm") Doc.vector_norm
-    +tag property
-    +tag-model("vectors")
-
-p
-    |  The L2 norm of the document's vector representation.
-
-+aside-code("Example").
-    doc1 = nlp(u'I like apples')
-    doc2 = nlp(u'I like oranges')
-    doc1.vector_norm # 4.54232424414368
-    doc2.vector_norm # 3.304373298575751
-    assert doc1.vector_norm != doc2.vector_norm
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell float
-        +cell The L2 norm of the vector representation.
-
-+h(2, "attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code text]
-        +cell unicode
-        +cell A unicode representation of the document text.
-
-    +row
-        +cell #[code text_with_ws]
-        +cell unicode
-        +cell
-            |  An alias of #[code Doc.text], provided for duck-type compatibility
-            |  with #[code Span] and #[code Token].
-
-    +row
-        +cell #[code mem]
-        +cell #[code Pool]
-        +cell The document's local memory heap, for all C data it owns.
-
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell The store of lexical types.
-
-    +row
-        +cell #[code tensor] #[+tag-new(2)]
-        +cell object
-        +cell Container for dense vector representations.
-
-    +row
-        +cell #[code cats] #[+tag-new(2)]
-        +cell dictionary
-        +cell
-            |  Maps either a label to a score for categories applied to whole
-            |  document, or #[code (start_char, end_char, label)] to score for
-            |  categories applied to spans. #[code start_char] and #[code end_char]
-            |  should be character offsets, label can be either a string or an
-            |  integer ID, and score should be a float.
-
-    +row
-        +cell #[code user_data]
-        +cell -
-        +cell A generic storage area, for user custom data.
-
-    +row
-        +cell #[code is_tagged]
-        +cell bool
-        +cell
-            |  A flag indicating that the document has been part-of-speech
-            |  tagged.
-
-    +row
-        +cell #[code is_parsed]
-        +cell bool
-        +cell A flag indicating that the document has been syntactically parsed.
-
-    +row
-        +cell #[code is_sentenced]
-        +cell bool
-        +cell
-            |  A flag indicating that sentence boundaries have been applied to
-            |  the document.
-
-    +row
-        +cell #[code sentiment]
-        +cell float
-        +cell The document's positivity/negativity score, if available.
-
-    +row
-        +cell #[code user_hooks]
-        +cell dict
-        +cell
-            |  A dictionary that allows customisation of the #[code Doc]'s
-            |  properties.
-
-    +row
-        +cell #[code user_token_hooks]
-        +cell dict
-        +cell
-            |  A dictionary that allows customisation of properties of
-            |  #[code Token] children.
-
-    +row
-        +cell #[code user_span_hooks]
-        +cell dict
-        +cell
-            |  A dictionary that allows customisation of properties of
-            |  #[code Span] children.
-
-    +row
-        +cell #[code _]
-        +cell #[code Underscore]
-        +cell
-            |  User space for adding custom
-            |  #[+a("/usage/processing-pipelines#custom-components-attributes") attribute extensions].
diff --git a/website/api/entityrecognizer.jade b/website/api/entityrecognizer.jade
deleted file mode 100644
index a8b68e453..000000000
--- a/website/api/entityrecognizer.jade
+++ /dev/null
@@ -1,6 +0,0 @@
-//- 💫 DOCS > API > ENTITYRECOGNIZER
-
-include ../_includes/_mixins
-
-//- This class inherits from Pipe, so this page uses the template in pipe.jade.
-!=partial("pipe", { subclass: "EntityRecognizer", short: "ner", pipeline_id: "ner" })
diff --git a/website/api/goldcorpus.jade b/website/api/goldcorpus.jade
deleted file mode 100644
index 5609c2530..000000000
--- a/website/api/goldcorpus.jade
+++ /dev/null
@@ -1,35 +0,0 @@
-//- 💫 DOCS > API > GOLDCORPUS
-
-include ../_includes/_mixins
-
-p
-    |  This class manages annotations for tagging, dependency parsing and NER.
-
-+h(2, "init") GoldCorpus.__init__
-    +tag method
-
-p Create a #[code GoldCorpus].
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code train]
-        +cell unicode or #[code Path] or iterable
-        +cell
-            |  Training data, as a path (file or directory) or iterable. If an
-            |  iterable, each item should be a #[code (text, paragraphs)]
-            |  tuple, where each paragraph is a tuple
-            |  #[code.u-break (sentences, brackets)],and each sentence is a
-            |  tuple #[code.u-break (ids, words, tags, heads, ner)]. See the
-            |  implementation of
-            |  #[+src(gh("spacy", "spacy/gold.pyx")) #[code gold.read_json_file]]
-            |  for further details.
-
-    +row
-        +cell #[code dev]
-        +cell unicode or #[code Path] or iterable
-        +cell Development data, as a path (file or directory) or iterable.
-
-    +row("foot")
-        +cell returns
-        +cell #[code GoldCorpus]
-        +cell The newly constructed object.
diff --git a/website/api/goldparse.jade b/website/api/goldparse.jade
deleted file mode 100644
index c96c0ccc0..000000000
--- a/website/api/goldparse.jade
+++ /dev/null
@@ -1,203 +0,0 @@
-//- 💫 DOCS > API > GOLDPARSE
-
-include ../_includes/_mixins
-
-p Collection for training annotations.
-
-+h(2, "init") GoldParse.__init__
-    +tag method
-
-p Create a #[code GoldParse].
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The document the annotations refer to.
-
-    +row
-        +cell #[code words]
-        +cell iterable
-        +cell A sequence of unicode word strings.
-
-    +row
-        +cell #[code tags]
-        +cell iterable
-        +cell A sequence of strings, representing tag annotations.
-
-    +row
-        +cell #[code heads]
-        +cell iterable
-        +cell A sequence of integers, representing syntactic head offsets.
-
-    +row
-        +cell #[code deps]
-        +cell iterable
-        +cell A sequence of strings, representing the syntactic relation types.
-
-    +row
-        +cell #[code entities]
-        +cell iterable
-        +cell A sequence of named entity annotations, either as BILUO tag strings, or as #[code (start_char, end_char, label)] tuples, representing the entity positions.
-
-    +row("foot")
-        +cell returns
-        +cell #[code GoldParse]
-        +cell The newly constructed object.
-
-+h(2, "len") GoldParse.__len__
-    +tag method
-
-p Get the number of gold-standard tokens.
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of gold-standard tokens.
-
-+h(2, "is_projective") GoldParse.is_projective
-    +tag property
-
-p
-    |  Whether the provided syntactic annotations form a projective dependency
-    |  tree.
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether annotations form projective tree.
-
-
-+h(2, "attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code tags]
-        +cell list
-        +cell The part-of-speech tag annotations.
-
-    +row
-        +cell #[code heads]
-        +cell list
-        +cell The syntactic head annotations.
-
-    +row
-        +cell #[code labels]
-        +cell list
-        +cell The syntactic relation-type annotations.
-
-    +row
-        +cell #[code ents]
-        +cell list
-        +cell The named entity annotations.
-
-    +row
-        +cell #[code cand_to_gold]
-        +cell list
-        +cell The alignment from candidate tokenization to gold tokenization.
-
-    +row
-        +cell #[code gold_to_cand]
-        +cell list
-        +cell The alignment from gold tokenization to candidate tokenization.
-
-    +row
-        +cell #[code cats] #[+tag-new(2)]
-        +cell list
-        +cell
-            |  Entries in the list should be either a label, or a
-            |  #[code (start, end, label)] triple. The tuple form is used for
-            |  categories applied to spans of the document.
-
-
-+h(2, "util") Utilities
-
-+h(3, "biluo_tags_from_offsets") gold.biluo_tags_from_offsets
-    +tag function
-
-p
-    |  Encode labelled spans into per-token tags, using the
-    |  #[+a("/api/annotation#biluo") BILUO scheme] (Begin/In/Last/Unit/Out).
-
-p
-    |  Returns a list of unicode strings, describing the tags. Each tag string
-    |  will be of the form of either #[code ""], #[code "O"] or
-    |  #[code "{action}-{label}"], where action is one of #[code "B"],
-    |  #[code "I"], #[code "L"], #[code "U"]. The string #[code &quot;-&quot;]
-    |  is used where the entity offsets don't align with the tokenization in the
-    |  #[code Doc] object. The training algorithm will view these as missing
-    |  values. #[code O] denotes a non-entity token. #[code B] denotes the
-    |  beginning of a multi-token entity, #[code I] the inside of an entity
-    |  of three or more tokens, and #[code L] the end of an entity of two or
-    |  more tokens. #[code U] denotes a single-token entity.
-
-+aside-code("Example").
-    from spacy.gold import biluo_tags_from_offsets
-
-    doc = nlp(u'I like London.')
-    entities = [(7, 13, 'LOC')]
-    tags = biluo_tags_from_offsets(doc, entities)
-    assert tags == ['O', 'O', 'U-LOC', 'O']
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell
-            |  The document that the entity offsets refer to. The output tags
-            |  will refer to the token boundaries within the document.
-
-    +row
-        +cell #[code entities]
-        +cell iterable
-        +cell
-            |  A sequence of #[code (start, end, label)] triples. #[code start]
-            |  and #[code end] should be character-offset integers denoting the
-            |  slice into the original string.
-
-    +row("foot")
-        +cell returns
-        +cell list
-        +cell
-            |  Unicode strings, describing the
-            |  #[+a("/api/annotation#biluo") BILUO] tags.
-
-+h(3, "offsets_from_biluo_tags") gold.offsets_from_biluo_tags
-
-p
-    |  Encode per-token tags following the
-    |  #[+a("/api/annotation#biluo") BILUO scheme] into entity offsets.
-
-+aside-code("Example").
-    from spacy.gold import offsets_from_biluo_tags
-
-    doc = nlp('I like London.')
-    tags = ['O', 'O', 'U-LOC', 'O']
-    entities = offsets_from_biluo_tags(doc, tags)
-    assert entities == [(7, 13, 'LOC')]
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The document that the BILUO tags refer to.
-
-    +row
-        +cell #[code entities]
-        +cell iterable
-        +cell
-            |  A sequence of #[+a("/api/annotation#biluo") BILUO] tags with
-            |  each tag describing one token. Each tag string will be of the
-            |  form of either #[code ""], #[code "O"] or
-            |  #[code "{action}-{label}"], where action is one of #[code "B"],
-            |  #[code "I"], #[code "L"], #[code "U"].
-
-    +row("foot")
-        +cell returns
-        +cell list
-        +cell
-            |  A sequence of #[code (start, end, label)] triples. #[code start]
-            |  and #[code end] will be character-offset integers denoting the
-            |  slice into the original string.
diff --git a/website/api/index.jade b/website/api/index.jade
deleted file mode 100644
index 441d185e5..000000000
--- a/website/api/index.jade
+++ /dev/null
@@ -1,157 +0,0 @@
-//- 💫 DOCS > API > ARCHITECTURE
-
-include ../_includes/_mixins
-
-+section("basics")
-    include ../usage/_spacy-101/_architecture
-
-+section("nn-model")
-    +h(2, "nn-model") Neural network model architecture
-
-    p
-        |  spaCy's statistical models have been custom-designed to give a
-        |  high-performance mix of speed and accuracy. The current architecture
-        |  hasn't been published yet, but in the meantime we prepared a video that
-        |  explains how the models work, with particular focus on NER.
-
-    +youtube("sqDHBH9IjRU")
-
-    p
-        |  The parsing model is a blend of recent results. The two recent
-        |  inspirations have been the work of Eli Klipperwasser and Yoav Goldberg at
-        |  Bar Ilan#[+fn(1)], and the SyntaxNet team from Google. The foundation of
-        |  the parser is still based on the work of Joakim Nivre#[+fn(2)], who
-        |  introduced the transition-based framework#[+fn(3)], the arc-eager
-        |  transition system, and the imitation learning objective. The model is
-        |  implemented using #[+a(gh("thinc")) Thinc], spaCy's machine learning
-        |  library. We first predict context-sensitive vectors for each word in the
-        |  input:
-
-    +code.
-        (embed_lower | embed_prefix | embed_suffix | embed_shape)
-            &gt;&gt; Maxout(token_width)
-            &gt;&gt; convolution ** 4
-
-    p
-        |  This convolutional layer is shared between the tagger, parser and NER,
-        |  and will also be shared by the future neural lemmatizer. Because the
-        |  parser shares these layers with the tagger, the parser does not require
-        |  tag features. I got this trick from David Weiss's "Stack Combination"
-        |  paper#[+fn(4)].
-
-    p
-        |  To boost the representation, the tagger actually predicts a "super tag"
-        |  with POS, morphology and dependency label#[+fn(5)]. The tagger predicts
-        |  these supertags by adding a softmax layer onto the convolutional layer –
-        |  so, we're teaching the convolutional layer to give us a representation
-        |  that's one affine transform from this informative lexical information.
-        |  This is obviously good for the parser (which backprops to the
-        |  convolutions too). The parser model makes a state vector by concatenating
-        |  the vector representations for its context tokens.  The current context
-        |  tokens:
-
-    +table
-        +row
-            +cell #[code S0], #[code S1], #[code S2]
-            +cell Top three words on the stack.
-
-        +row
-            +cell #[code B0], #[code B1]
-            +cell First two words of the buffer.
-
-        +row
-            +cell
-                |  #[code S0L1], #[code S1L1], #[code S2L1], #[code B0L1],
-                |  #[code B1L1]#[br]
-                |  #[code S0L2], #[code S1L2], #[code S2L2], #[code B0L2],
-                |  #[code B1L2]
-            +cell
-                |  Leftmost and second leftmost children of #[code S0], #[code S1],
-                |  #[code S2], #[code B0] and #[code B1].
-
-        +row
-            +cell
-                |  #[code S0R1], #[code S1R1], #[code S2R1], #[code B0R1],
-                |  #[code B1R1]#[br]
-                |  #[code S0R2], #[code S1R2], #[code S2R2], #[code B0R2],
-                |  #[code B1R2]
-            +cell
-                |  Rightmost and second rightmost children of #[code S0], #[code S1],
-                |  #[code S2], #[code B0] and #[code B1].
-
-    p
-        |  This makes the state vector quite long: #[code 13*T], where #[code T] is
-        |  the token vector width (128 is working well). Fortunately, there's a way
-        |  to structure the computation to save some expense (and make it more
-        |  GPU-friendly).
-
-    p
-        |  The parser typically visits #[code 2*N] states for a sentence of length
-        |  #[code N] (although it may visit more, if it back-tracks with a
-        |  non-monotonic transition#[+fn(4)]). A naive implementation would require
-        |  #[code 2*N (B, 13*T) @ (13*T, H)] matrix multiplications for a batch of
-        |  size #[code B]. We can instead perform one #[code (B*N, T) @ (T, 13*H)]
-        |  multiplication, to pre-compute the hidden weights for each positional
-        |  feature with respect to the words in the batch. (Note that our token
-        |  vectors come from the CNN — so we can't play this trick over the
-        |  vocabulary. That's how Stanford's NN parser#[+fn(3)] works — and why its
-        |  model is so big.)
-
-    p
-        |  This pre-computation strategy allows a nice compromise between
-        |  GPU-friendliness and implementation simplicity. The CNN and the wide
-        |  lower layer are computed on the GPU, and then the precomputed hidden
-        |  weights are moved to the CPU, before we start the transition-based
-        |  parsing process. This makes a lot of things much easier. We don't have to
-        |  worry about variable-length batch sizes, and we don't have to implement
-        |  the dynamic oracle in CUDA to train.
-
-    p
-        |  Currently the parser's loss function is multilabel log loss#[+fn(6)], as
-        |  the dynamic oracle allows multiple states to be 0 cost. This is defined
-        |  as follows, where #[code gZ] is the sum of the scores assigned to gold
-        |  classes:
-
-    +code.
-        (exp(score) / Z) - (exp(score) / gZ)
-
-    +bibliography
-        +item
-            |  #[+a("https://www.semanticscholar.org/paper/Simple-and-Accurate-Dependency-Parsing-Using-Bidir-Kiperwasser-Goldberg/3cf31ecb2724b5088783d7c96a5fc0d5604cbf41") Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations]
-            br
-            |  Eliyahu Kiperwasser, Yoav Goldberg. (2016)
-
-        +item
-            |  #[+a("https://www.semanticscholar.org/paper/A-Dynamic-Oracle-for-Arc-Eager-Dependency-Parsing-Goldberg-Nivre/22697256ec19ecc3e14fcfc63624a44cf9c22df4") A Dynamic Oracle for Arc-Eager Dependency Parsing]
-            br
-            |  Yoav Goldberg, Joakim Nivre (2012)
-
-        +item
-            |  #[+a("https://explosion.ai/blog/parsing-english-in-python") Parsing English in 500 Lines of Python]
-            br
-            |  Matthew Honnibal (2013)
-
-        +item
-            |  #[+a("https://www.semanticscholar.org/paper/Stack-propagation-Improved-Representation-Learning-Zhang-Weiss/0c133f79b23e8c680891d2e49a66f0e3d37f1466") Stack-propagation: Improved Representation Learning for Syntax]
-            br
-            |  Yuan Zhang, David Weiss (2016)
-
-        +item
-            |  #[+a("https://www.semanticscholar.org/paper/Deep-multi-task-learning-with-low-level-tasks-supe-S%C3%B8gaard-Goldberg/03ad06583c9721855ccd82c3d969a01360218d86") Deep multi-task learning with low level tasks supervised at lower layers]
-            br
-            |  Anders Søgaard, Yoav Goldberg (2016)
-
-        +item
-            |  #[+a("https://www.semanticscholar.org/paper/An-Improved-Non-monotonic-Transition-System-for-De-Honnibal-Johnson/4094cee47ade13b77b5ab4d2e6cb9dd2b8a2917c") An Improved Non-monotonic Transition System for Dependency Parsing]
-            br
-            |  Matthew Honnibal, Mark Johnson (2015)
-
-        +item
-            |  #[+a("http://cs.stanford.edu/people/danqi/papers/emnlp2014.pdf") A Fast and Accurate Dependency Parser using Neural Networks]
-            br
-            |  Danqi Cheng, Christopher D. Manning (2014)
-
-        +item
-            |  #[+a("https://www.semanticscholar.org/paper/Parsing-the-Wall-Street-Journal-using-a-Lexical-Fu-Riezler-King/0ad07862a91cd59b7eb5de38267e47725a62b8b2") Parsing the Wall Street Journal using a Lexical-Functional Grammar and Discriminative Estimation Techniques]
-            br
-            |  Stefan Riezler et al. (2002)
diff --git a/website/api/language.jade b/website/api/language.jade
deleted file mode 100644
index 417e3543c..000000000
--- a/website/api/language.jade
+++ /dev/null
@@ -1,702 +0,0 @@
-//- 💫 DOCS > API > LANGUAGE
-
-include ../_includes/_mixins
-
-p
-    |  Usually you'll load this once per process as #[code nlp] and pass the
-    |  instance around your application. The #[code Language] class is created
-    |  when you call #[+api("spacy#load") #[code spacy.load()]] and contains
-    |  the shared vocabulary and #[+a("/usage/adding-languages") language data],
-    |  optional model data loaded from a #[+a("/models") model package] or
-    |  a path, and a #[+a("/usage/processing-pipelines") processing pipeline]
-    |  containing components like the tagger or parser that are called on a
-    |  document in order. You can also add your own processing pipeline
-    |  components that take a #[code Doc] object, modify it and return it.
-
-+h(2, "init") Language.__init__
-    +tag method
-
-p Initialise a #[code Language] object.
-
-+aside-code("Example").
-    from spacy.vocab import Vocab
-    from spacy.language import Language
-    nlp = Language(Vocab())
-
-    from spacy.lang.en import English
-    nlp = English()
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell
-            |  A #[code Vocab] object. If #[code True], a vocab is created via
-            |  #[code Language.Defaults.create_vocab].
-
-    +row
-        +cell #[code make_doc]
-        +cell callable
-        +cell
-            |  A function that takes text and returns a #[code Doc] object.
-            |  Usually a #[code Tokenizer].
-
-    +row
-        +cell #[code meta]
-        +cell dict
-        +cell
-            |  Custom meta data for the #[code Language] class. Is written to by
-            |  models to add model meta data.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Language]
-        +cell The newly constructed object.
-
-+h(2, "call") Language.__call__
-    +tag method
-
-p
-    |  Apply the pipeline to some text. The text can span multiple sentences,
-    |  and can contain arbtrary whitespace. Alignment into the original string
-    |  is preserved.
-
-+aside-code("Example").
-    doc = nlp(u'An example sentence. Another sentence.')
-    assert (doc[0].text, doc[0].head.tag_) == ('An', 'NN')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code text]
-        +cell unicode
-        +cell The text to be processed.
-
-    +row
-        +cell #[code disable]
-        +cell list
-        +cell
-            |  Names of pipeline components to
-            |  #[+a("/usage/processing-pipelines#disabling") disable].
-
-    +row("foot")
-        +cell returns
-        +cell #[code Doc]
-        +cell A container for accessing the annotations.
-
-+infobox("Changed in v2.0", "⚠️")
-    |  Pipeline components to prevent from being loaded can now be added as
-    |  a list to #[code disable], instead of specifying one keyword argument
-    |  per component.
-
-    +code-wrapper
-        +code-new doc = nlp(u"I don't want parsed", disable=['parser'])
-        +code-old doc = nlp(u"I don't want parsed", parse=False)
-
-+h(2, "pipe") Language.pipe
-    +tag method
-
-p
-    |  Process texts as a stream, and yield #[code Doc] objects in order.
-    |  Supports GIL-free multi-threading.
-
-+infobox("Important note for spaCy v2.0.x", "⚠️")
-    |  By default, multiple threads will be launched for matrix multiplication,
-    |  which may be inefficient on multi-core machines. Setting
-    |  #[code OPENBLAS_NUM_THREADS=1] should fix this problem. spaCy v2.1.x
-    |  will be switching to single-thread by default.
-
-+aside-code("Example").
-    texts = [u'One document.', u'...', u'Lots of documents']
-    for doc in nlp.pipe(texts, batch_size=50, n_threads=4):
-        assert doc.is_parsed
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code texts]
-        +cell -
-        +cell A sequence of unicode objects.
-
-    +row
-        +cell #[code as_tuples]
-        +cell bool
-        +cell
-            |  If set to #[code True], inputs should be a sequence of
-            |  #[code (text, context)] tuples. Output will then be a sequence of
-            |  #[code (doc, context)] tuples. Defaults to #[code False].
-
-    +row
-        +cell #[code n_threads]
-        +cell int
-        +cell
-            |  The number of worker threads to use. If #[code -1], OpenMP will
-            |  decide how many to use at run time. Default is #[code 2].
-
-    +row
-        +cell #[code batch_size]
-        +cell int
-        +cell The number of texts to buffer.
-
-    +row
-        +cell #[code disable]
-        +cell list
-        +cell
-            |  Names of pipeline components to
-            |  #[+a("/usage/processing-pipelines#disabling") disable].
-
-    +row("foot")
-        +cell yields
-        +cell #[code Doc]
-        +cell Documents in the order of the original text.
-
-+h(2, "update") Language.update
-    +tag method
-
-p Update the models in the pipeline.
-
-+aside-code("Example").
-    for raw_text, entity_offsets in train_data:
-        doc = nlp.make_doc(raw_text)
-        gold = GoldParse(doc, entities=entity_offsets)
-        nlp.update([doc], [gold], drop=0.5, sgd=optimizer)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code docs]
-        +cell iterable
-        +cell
-            |  A batch of #[code Doc] objects or unicode. If unicode, a
-            |  #[code Doc] object will be created from the text.
-
-    +row
-        +cell #[code golds]
-        +cell iterable
-        +cell
-            |  A batch of #[code GoldParse] objects or dictionaries.
-            |  Dictionaries will be used to create
-            |  #[+api("goldparse") #[code GoldParse]] objects. For the available
-            |  keys and their usage, see
-            |  #[+api("goldparse#init") #[code GoldParse.__init__]].
-
-    +row
-        +cell #[code drop]
-        +cell float
-        +cell The dropout rate.
-
-    +row
-        +cell #[code sgd]
-        +cell callable
-        +cell An optimizer.
-
-    +row("foot")
-        +cell returns
-        +cell dict
-        +cell Results from the update.
-
-+h(2, "begin_training") Language.begin_training
-    +tag method
-
-p
-    |  Allocate models, pre-process training data and acquire an optimizer.
-
-+aside-code("Example").
-    optimizer = nlp.begin_training(gold_tuples)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code gold_tuples]
-        +cell iterable
-        +cell Gold-standard training data.
-
-    +row
-        +cell #[code **cfg]
-        +cell -
-        +cell Config parameters.
-
-    +row("foot")
-        +cell returns
-        +cell callable
-        +cell An optimizer.
-
-+h(2, "use_params") Language.use_params
-    +tag contextmanager
-    +tag method
-
-p
-    |  Replace weights of models in the pipeline with those provided in the
-    |  params dictionary. Can be used as a contextmanager, in which case, models
-    |  go back to their original weights after the block.
-
-+aside-code("Example").
-    with nlp.use_params(optimizer.averages):
-        nlp.to_disk('/tmp/checkpoint')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code params]
-        +cell dict
-        +cell A dictionary of parameters keyed by model ID.
-
-    +row
-        +cell #[code **cfg]
-        +cell -
-        +cell Config parameters.
-
-+h(2, "preprocess_gold") Language.preprocess_gold
-    +tag method
-
-p
-    |  Can be called before training to pre-process gold data. By default, it
-    |  handles nonprojectivity and adds missing tags to the tag map.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code docs_golds]
-        +cell iterable
-        +cell Tuples of #[code Doc] and #[code GoldParse] objects.
-
-    +row("foot")
-        +cell yields
-        +cell tuple
-        +cell Tuples of #[code Doc] and #[code GoldParse] objects.
-
-+h(2, "create_pipe") Language.create_pipe
-    +tag method
-    +tag-new(2)
-
-p Create a pipeline component from a factory.
-
-+aside-code("Example").
-    parser = nlp.create_pipe('parser')
-    nlp.add_pipe(parser)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell
-            |  Factory name to look up in
-            |  #[+api("language#class-attributes") #[code Language.factories]].
-
-    +row
-        +cell #[code config]
-        +cell dict
-        +cell Configuration parameters to initialise component.
-
-    +row("foot")
-        +cell returns
-        +cell callable
-        +cell The pipeline component.
-
-+h(2, "add_pipe") Language.add_pipe
-    +tag method
-    +tag-new(2)
-
-p
-    |  Add a component to the processing pipeline. Valid components are
-    |  callables that take a #[code Doc] object, modify it and return it. Only
-    |  one of #[code before], #[code after], #[code first] or #[code last] can
-    |  be set. Default behaviour is #[code last=True].
-
-+aside-code("Example").
-    def component(doc):
-        # modify Doc and return it
-        return doc
-
-    nlp.add_pipe(component, before='ner')
-    nlp.add_pipe(component, name='custom_name', last=True)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code component]
-        +cell callable
-        +cell The pipeline component.
-
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell
-            |  Name of pipeline component. Overwrites existing
-            |  #[code component.name] attribute if available. If no #[code name]
-            |  is set and the component exposes no name attribute,
-            |  #[code component.__name__] is used. An error is raised if the
-            |  name already exists in the pipeline.
-
-    +row
-        +cell #[code before]
-        +cell unicode
-        +cell Component name to insert component directly before.
-
-    +row
-        +cell #[code after]
-        +cell unicode
-        +cell Component name to insert component directly after:
-
-    +row
-        +cell #[code first]
-        +cell bool
-        +cell Insert component first / not first in the pipeline.
-
-    +row
-        +cell #[code last]
-        +cell bool
-        +cell Insert component last / not last in the pipeline.
-
-+h(2, "has_pipe") Language.has_pipe
-    +tag method
-    +tag-new(2)
-
-p
-    |  Check whether a component is present in the pipeline. Equivalent to
-    |  #[code name in nlp.pipe_names].
-
-+aside-code("Example").
-    nlp.add_pipe(lambda doc: doc, name='component')
-    assert 'component' in nlp.pipe_names
-    assert nlp.has_pipe('component')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of the pipeline component to check.
-
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether a component of that name exists in the pipeline.
-
-+h(2, "get_pipe") Language.get_pipe
-    +tag method
-    +tag-new(2)
-
-p Get a pipeline component for a given component name.
-
-+aside-code("Example").
-    parser = nlp.get_pipe('parser')
-    custom_component = nlp.get_pipe('custom_component')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of the pipeline component to get.
-
-    +row("foot")
-        +cell returns
-        +cell callable
-        +cell The pipeline component.
-
-+h(2, "replace_pipe") Language.replace_pipe
-    +tag method
-    +tag-new(2)
-
-p Replace a component in the pipeline.
-
-+aside-code("Example").
-    nlp.replace_pipe('parser', my_custom_parser)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of the component to replace.
-
-    +row
-        +cell #[code component]
-        +cell callable
-        +cell The pipeline component to inser.
-
-
-+h(2, "rename_pipe") Language.rename_pipe
-    +tag method
-    +tag-new(2)
-
-p
-    |  Rename a component in the pipeline. Useful to create custom names for
-    |  pre-defined and pre-loaded components. To change the default name of
-    |  a component added to the pipeline, you can also use the #[code name]
-    |  argument on #[+api("language#add_pipe") #[code add_pipe]].
-
-+aside-code("Example").
-    nlp.rename_pipe('parser', 'spacy_parser')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code old_name]
-        +cell unicode
-        +cell Name of the component to rename.
-
-    +row
-        +cell #[code new_name]
-        +cell unicode
-        +cell New name of the component.
-
-+h(2, "remove_pipe") Language.remove_pipe
-    +tag method
-    +tag-new(2)
-
-p
-    |  Remove a component from the pipeline. Returns the removed component name
-    |  and component function.
-
-+aside-code("Example").
-    name, component = nlp.remove_pipe('parser')
-    assert name == 'parser'
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of the component to remove.
-
-    +row("foot")
-        +cell returns
-        +cell tuple
-        +cell A #[code (name, component)] tuple of the removed component.
-
-+h(2, "disable_pipes") Language.disable_pipes
-    +tag contextmanager
-    +tag-new(2)
-
-p
-    |  Disable one or more pipeline components. If used as a context manager,
-    |  the pipeline will be restored to the initial state at the end of the
-    |  block. Otherwise, a #[code DisabledPipes] object is returned, that has a
-    |  #[code .restore()] method you can use to undo your changes.
-
-+aside-code("Example").
-    with nlp.disable_pipes('tagger', 'parser'):
-        optimizer = nlp.begin_training(gold_tuples)
-
-    disabled = nlp.disable_pipes('tagger', 'parser')
-    optimizer = nlp.begin_training(gold_tuples)
-    disabled.restore()
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code *disabled]
-        +cell unicode
-        +cell Names of pipeline components to disable.
-
-    +row("foot")
-        +cell returns
-        +cell #[code DisabledPipes]
-        +cell
-            |  The disabled pipes that can be restored by calling the object's
-            |  #[code .restore()] method.
-
-+h(2, "to_disk") Language.to_disk
-    +tag method
-    +tag-new(2)
-
-p
-    |  Save the current state to a directory. If a model is loaded, this will
-    |  #[strong include the model].
-
-+aside-code("Example").
-    nlp.to_disk('/path/to/models')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode or #[code Path]
-        +cell
-            |  A path to a directory, which will be created if it doesn't exist.
-            |  Paths may be either strings or #[code Path]-like objects.
-
-    +row
-        +cell #[code disable]
-        +cell list
-        +cell
-            |  Names of pipeline components to
-            |  #[+a("/usage/processing-pipelines#disabling") disable]
-            |  and prevent from being saved.
-
-+h(2, "from_disk") Language.from_disk
-    +tag method
-    +tag-new(2)
-
-p
-    |  Loads state from a directory. Modifies the object in place and returns
-    |  it. If the saved #[code Language] object contains a model, the
-    |  model will be loaded. Note that this method is commonly used via the
-    |  subclasses like #[code English] or #[code German] to make
-    |  language-specific functionality like the
-    |  #[+a("/usage/adding-languages#lex-attrs") lexical attribute getters]
-    |  available to the loaded object.
-
-+aside-code("Example").
-    from spacy.language import Language
-    nlp = Language().from_disk('/path/to/model')
-
-    # using language-specific subclass
-    from spacy.lang.en import English
-    nlp = English().from_disk('/path/to/en_model')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode or #[code Path]
-        +cell
-            |  A path to a directory. Paths may be either strings or
-            |  #[code Path]-like objects.
-
-    +row
-        +cell #[code disable]
-        +cell list
-        +cell
-            |  Names of pipeline components to
-            |  #[+a("/usage/processing-pipelines#disabling") disable].
-
-    +row("foot")
-        +cell returns
-        +cell #[code Language]
-        +cell The modified #[code Language] object.
-
-+infobox("Changed in v2.0", "⚠️")
-    |  As of spaCy v2.0, the #[code save_to_directory] method has been
-    |  renamed to #[code to_disk], to improve consistency across classes.
-    |  Pipeline components to prevent from being loaded can now be added as
-    |  a list to #[code disable], instead of specifying one keyword argument
-    |  per component.
-
-    +code-wrapper
-        +code-new nlp = English().from_disk(disable=['tagger', 'ner'])
-        +code-old nlp = spacy.load('en', tagger=False, entity=False)
-
-+h(2, "to_bytes") Language.to_bytes
-    +tag method
-
-p Serialize the current state to a binary string.
-
-+aside-code("Example").
-    nlp_bytes = nlp.to_bytes()
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code disable]
-        +cell list
-        +cell
-            |  Names of pipeline components to
-            |  #[+a("/usage/processing-pipelines#disabling") disable]
-            |  and prevent from being serialized.
-
-    +row("foot")
-        +cell returns
-        +cell bytes
-        +cell The serialized form of the #[code Language] object.
-
-+h(2, "from_bytes") Language.from_bytes
-    +tag method
-
-p
-    |  Load state from a binary string. Note that this method is commonly used
-    |  via the subclasses like #[code English] or #[code German] to make
-    |  language-specific functionality like the
-    |  #[+a("/usage/adding-languages#lex-attrs") lexical attribute getters]
-    |  available to the loaded object.
-
-+aside-code("Example").
-    from spacy.lang.en import English
-    nlp_bytes = nlp.to_bytes()
-    nlp2 = English()
-    nlp2.from_bytes(nlp_bytes)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code bytes_data]
-        +cell bytes
-        +cell The data to load from.
-
-    +row
-        +cell #[code disable]
-        +cell list
-        +cell
-            |  Names of pipeline components to
-            |  #[+a("/usage/processing-pipelines#disabling") disable].
-
-    +row("foot")
-        +cell returns
-        +cell #[code Language]
-        +cell The #[code Language] object.
-
-+infobox("Changed in v2.0", "⚠️")
-    |  Pipeline components to prevent from being loaded can now be added as
-    |  a list to #[code disable], instead of specifying one keyword argument
-    |  per component.
-
-    +code-wrapper
-        +code-new nlp = English().from_bytes(bytes, disable=['tagger', 'ner'])
-        +code-old nlp = English().from_bytes('en', tagger=False, entity=False)
-
-+h(2, "attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell A container for the lexical types.
-
-    +row
-        +cell #[code tokenizer]
-        +cell #[code Tokenizer]
-        +cell The tokenizer.
-
-    +row
-        +cell #[code make_doc]
-        +cell #[code lambda text: Doc]
-        +cell Create a #[code Doc] object from unicode text.
-
-    +row
-        +cell #[code pipeline]
-        +cell list
-        +cell
-            |  List of #[code (name, component)] tuples describing the current
-            |  processing pipeline, in order.
-
-    +row
-        +cell #[code pipe_names]
-            +tag-new(2)
-        +cell list
-        +cell List of pipeline component names, in order.
-
-    +row
-        +cell #[code meta]
-        +cell dict
-        +cell
-            |  Custom meta data for the Language class. If a model is loaded,
-            |  contains meta data of the model.
-
-    +row
-        +cell #[code path]
-            +tag-new(2)
-        +cell #[code Path]
-        +cell
-            |  Path to the model data directory, if a model is loaded. Otherwise
-            |  #[code None].
-
-+h(2, "class-attributes") Class attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code Defaults]
-        +cell class
-        +cell
-            |  Settings, data and factory methods for creating the
-            |  #[code nlp] object and processing pipeline.
-
-    +row
-        +cell #[code lang]
-        +cell unicode
-        +cell
-            |  Two-letter language ID, i.e.
-            |  #[+a("https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes") ISO code].
-
-    +row
-        +cell #[code factories]
-            +tag-new(2)
-        +cell dict
-        +cell
-            |  Factories that create pre-defined pipeline components, e.g. the
-            |  tagger, parser or entity recognizer, keyed by their component
-            |  name.
diff --git a/website/api/lemmatizer.jade b/website/api/lemmatizer.jade
deleted file mode 100644
index eb061f10a..000000000
--- a/website/api/lemmatizer.jade
+++ /dev/null
@@ -1,160 +0,0 @@
-//- 💫 DOCS > API > LEMMATIZER
-
-include ../_includes/_mixins
-
-p
-    |  The #[code Lemmatizer] supports simple part-of-speech-sensitive suffix
-    |  rules and lookup tables.
-
-+h(2, "init") Lemmatizer.__init__
-    +tag method
-
-p Create a #[code Lemmatizer].
-
-+aside-code("Example").
-    from spacy.lemmatizer import Lemmatizer
-    lemmatizer = Lemmatizer()
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code index]
-        +cell dict / #[code None]
-        +cell Inventory of lemmas in the language.
-
-    +row
-        +cell #[code exceptions]
-        +cell dict / #[code None]
-        +cell Mapping of string forms to lemmas that bypass the #[code rules].
-
-    +row
-        +cell #[code rules]
-        +cell dict / #[code None]
-        +cell List of suffix rewrite rules.
-
-    +row
-        +cell #[code lookup]
-        +cell dict / #[code None]
-        +cell Lookup table mapping string to their lemmas.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Lemmatizer]
-        +cell The newly created object.
-
-+h(2, "call") Lemmatizer.__call__
-    +tag method
-
-p Lemmatize a string.
-
-+aside-code("Example").
-    from spacy.lemmatizer import Lemmatizer
-    from spacy.lang.en import LEMMA_INDEX, LEMMA_EXC, LEMMA_RULES
-    lemmatizer = Lemmatizer(LEMMA_INDEX, LEMMA_EXC, LEMMA_RULES)
-    lemmas = lemmatizer(u'ducks', u'NOUN')
-    assert lemmas == [u'duck']
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code string]
-        +cell unicode
-        +cell The string to lemmatize, e.g. the token text.
-
-    +row
-        +cell #[code univ_pos]
-        +cell unicode / int
-        +cell The token's universal part-of-speech tag.
-
-    +row
-        +cell #[code morphology]
-        +cell dict / #[code None]
-        +cell
-            |  Morphological features following the
-            |  #[+a("http://universaldependencies.org/") Universal Dependencies]
-            |  scheme.
-
-    +row("foot")
-        +cell returns
-        +cell list
-        +cell The available lemmas for the string.
-
-+h(2, "lookup") Lemmatizer.lookup
-    +tag method
-    +tag-new(2)
-
-p
-    |  Look up a lemma in the lookup table, if available. If no lemma is found,
-    |  the original string is returned. Languages can provide a
-    |  #[+a("/usage/adding-languages#lemmatizer") lookup table] via the
-    |  #[code lemma_lookup] variable, set on the individual #[code Language]
-    |  class.
-
-+aside-code("Example").
-    lookup = {u'going': u'go'}
-    lemmatizer = Lemmatizer(lookup=lookup)
-    assert lemmatizer.lookup(u'going') == u'go'
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code string]
-        +cell unicode
-        +cell The string to look up.
-
-    +row("foot")
-        +cell returns
-        +cell unicode
-        +cell The lemma if the string was found, otherwise the original string.
-
-+h(2, "is_base_form") Lemmatizer.is_base_form
-    +tag method
-
-p
-    |  Check whether we're dealing with an uninflected paradigm, so we can
-    |  avoid lemmatization entirely.
-
-+aside-code("Example").
-    pos = 'verb'
-    morph = {'VerbForm': 'inf'}
-    is_base_form = lemmatizer.is_base_form(pos, morph)
-    assert is_base_form == True
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code univ_pos]
-        +cell unicode / int
-        +cell The token's universal part-of-speech tag.
-
-    +row
-        +cell #[code morphology]
-        +cell dict
-        +cell The token's morphological features.
-
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell
-            |  Whether the token's part-of-speech tag and morphological features
-            |  describe a base form.
-
-+h(2, "attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code index]
-        +cell dict / #[code None]
-        +cell Inventory of lemmas in the language.
-
-    +row
-        +cell #[code exc]
-        +cell dict / #[code None]
-        +cell Mapping of string forms to lemmas that bypass the #[code rules].
-
-    +row
-        +cell #[code rules]
-        +cell dict / #[code None]
-        +cell List of suffix rewrite rules.
-
-    +row
-        +cell #[code lookup_table]
-            +tag-new(2)
-        +cell dict / #[code None]
-        +cell The lemma lookup table, if available.
diff --git a/website/api/lexeme.jade b/website/api/lexeme.jade
deleted file mode 100644
index c4da5b43e..000000000
--- a/website/api/lexeme.jade
+++ /dev/null
@@ -1,384 +0,0 @@
-//- 💫 DOCS > API > LEXEME
-
-include ../_includes/_mixins
-
-p
-    |  An entry in the vocabulary. A #[code Lexeme] has no string context – it's
-    |  a word type, as opposed to a word token. It therefore has no
-    |  part-of-speech tag, dependency parse, or lemma (if lemmatization depends
-    |  on the part-of-speech tag).
-
-+h(2, "init") Lexeme.__init__
-    +tag method
-
-p Create a #[code Lexeme] object.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell The parent vocabulary.
-
-    +row
-        +cell #[code orth]
-        +cell int
-        +cell The orth id of the lexeme.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Lexeme]
-        +cell The newly constructed object.
-
-+h(2, "set_flag") Lexeme.set_flag
-    +tag method
-
-p Change the value of a boolean flag.
-
-+aside-code("Example").
-    COOL_FLAG = nlp.vocab.add_flag(lambda text: False)
-    nlp.vocab[u'spaCy'].set_flag(COOL_FLAG, True)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code flag_id]
-        +cell int
-        +cell The attribute ID of the flag to set.
-
-    +row
-        +cell #[code value]
-        +cell bool
-        +cell The new value of the flag.
-
-+h(2, "check_flag") Lexeme.check_flag
-    +tag method
-
-p Check the value of a boolean flag.
-
-+aside-code("Example").
-    is_my_library = lambda text: text in ['spaCy', 'Thinc']
-    MY_LIBRARY = nlp.vocab.add_flag(is_my_library)
-    assert nlp.vocab[u'spaCy'].check_flag(MY_LIBRARY) == True
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code flag_id]
-        +cell int
-        +cell The attribute ID of the flag to query.
-
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell The value of the flag.
-
-+h(2, "similarity") Lexeme.similarity
-    +tag method
-    +tag-model("vectors")
-
-p Compute a semantic similarity estimate. Defaults to cosine over vectors.
-
-+aside-code("Example").
-    apple = nlp.vocab[u'apple']
-    orange = nlp.vocab[u'orange']
-    apple_orange = apple.similarity(orange)
-    orange_apple = orange.similarity(apple)
-    assert apple_orange == orange_apple
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell other
-        +cell -
-        +cell
-            |  The object to compare with. By default, accepts #[code Doc],
-            |  #[code Span], #[code Token] and #[code Lexeme] objects.
-
-    +row("foot")
-        +cell returns
-        +cell float
-        +cell A scalar similarity score. Higher is more similar.
-
-
-+h(2, "has_vector") Lexeme.has_vector
-    +tag property
-    +tag-model("vectors")
-
-p
-    |  A boolean value indicating whether a word vector is associated with the
-    |  lexeme.
-
-+aside-code("Example").
-    apple = nlp.vocab[u'apple']
-    assert apple.has_vector
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the lexeme has a vector data attached.
-
-+h(2, "vector") Lexeme.vector
-    +tag property
-    +tag-model("vectors")
-
-p A real-valued meaning representation.
-
-+aside-code("Example").
-    apple = nlp.vocab[u'apple']
-    assert apple.vector.dtype == 'float32'
-    assert apple.vector.shape == (300,)
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
-        +cell A 1D numpy array representing the lexeme's semantics.
-
-+h(2, "vector_norm") Lexeme.vector_norm
-    +tag property
-    +tag-model("vectors")
-
-p The L2 norm of the lexeme's vector representation.
-
-+aside-code("Example").
-    apple = nlp.vocab[u'apple']
-    pasta = nlp.vocab[u'pasta']
-    apple.vector_norm # 7.1346845626831055
-    pasta.vector_norm # 7.759851932525635
-    assert apple.vector_norm != pasta.vector_norm
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell float
-        +cell The L2 norm of the vector representation.
-
-+h(2, "attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell The lexeme's vocabulary.
-
-    +row
-        +cell #[code text]
-        +cell unicode
-        +cell Verbatim text content.
-
-    +row
-        +cell #[code orth]
-        +cell int
-        +cell ID of the verbatim text content.
-
-    +row
-        +cell #[code orth_]
-        +cell unicode
-        +cell
-            |  Verbatim text content (identical to #[code Lexeme.text]). Exists
-            |  mostly for consistency with the other attributes.
-
-    +row
-        +cell #[code lex_id]
-        +cell int
-        +cell ID of the lexeme's lexical type.
-
-    +row
-        +cell #[code rank]
-        +cell int
-        +cell
-            |  Sequential ID of the lexemes's lexical type, used to index into
-            |  tables, e.g. for word vectors.
-
-    +row
-        +cell #[code flags]
-        +cell int
-        +cell Container of the lexeme's binary flags.
-
-    +row
-        +cell #[code norm]
-        +cell int
-        +cell The lexemes's norm, i.e. a normalised form of the lexeme text.
-
-    +row
-        +cell #[code norm_]
-        +cell unicode
-        +cell The lexemes's norm, i.e. a normalised form of the lexeme text.
-
-    +row
-        +cell #[code lower]
-        +cell int
-        +cell Lowercase form of the word.
-
-    +row
-        +cell #[code lower_]
-        +cell unicode
-        +cell Lowercase form of the word.
-
-    +row
-        +cell #[code shape]
-        +cell int
-        +cell Transform of the word's string, to show orthographic features.
-
-    +row
-        +cell #[code shape_]
-        +cell unicode
-        +cell Transform of the word's string, to show orthographic features.
-
-    +row
-        +cell #[code prefix]
-        +cell int
-        +cell
-            |  Length-N substring from the start of the word. Defaults to
-            |  #[code N=1].
-
-    +row
-        +cell #[code prefix_]
-        +cell unicode
-        +cell
-            |  Length-N substring from the start of the word. Defaults to
-            |  #[code N=1].
-
-    +row
-        +cell #[code suffix]
-        +cell int
-        +cell
-            |  Length-N substring from the end of the word. Defaults to
-            |  #[code N=3].
-
-    +row
-        +cell #[code suffix_]
-        +cell unicode
-        +cell
-            |  Length-N substring from the start of the word. Defaults to
-            |  #[code N=3].
-
-    +row
-        +cell #[code is_alpha]
-        +cell bool
-        +cell
-            |  Does the lexeme consist of alphabetic characters? Equivalent to
-            |  #[code lexeme.text.isalpha()].
-
-    +row
-        +cell #[code is_ascii]
-        +cell bool
-        +cell
-            |  Does the lexeme consist of ASCII characters? Equivalent to
-            |  #[code [any(ord(c) >= 128 for c in lexeme.text)]].
-
-    +row
-        +cell #[code is_digit]
-        +cell bool
-        +cell
-            |  Does the lexeme consist of digits? Equivalent to
-            |  #[code lexeme.text.isdigit()].
-
-    +row
-        +cell #[code is_lower]
-        +cell bool
-        +cell
-            |  Is the lexeme in lowercase? Equivalent to
-            |  #[code lexeme.text.islower()].
-
-    +row
-        +cell #[code is_upper]
-        +cell bool
-        +cell
-            |  Is the lexeme in uppercase? Equivalent to
-            |  #[code lexeme.text.isupper()].
-
-    +row
-        +cell #[code is_title]
-        +cell bool
-        +cell
-            |  Is the lexeme in titlecase? Equivalent to
-            |  #[code lexeme.text.istitle()].
-
-    +row
-        +cell #[code is_punct]
-        +cell bool
-        +cell Is the lexeme punctuation?
-
-    +row
-        +cell #[code is_left_punct]
-        +cell bool
-        +cell Is the lexeme a left punctuation mark, e.g. #[code (]?
-
-    +row
-        +cell #[code is_right_punct]
-        +cell bool
-        +cell Is the lexeme a right punctuation mark, e.g. #[code )]?
-
-    +row
-        +cell #[code is_space]
-        +cell bool
-        +cell
-            |  Does the lexeme consist of whitespace characters? Equivalent to
-            |  #[code lexeme.text.isspace()].
-
-    +row
-        +cell #[code is_bracket]
-        +cell bool
-        +cell Is the lexeme a bracket?
-
-    +row
-        +cell #[code is_quote]
-        +cell bool
-        +cell Is the lexeme a quotation mark?
-
-    +row
-        +cell #[code is_currency]
-            +tag-new("2.0.8")
-        +cell bool
-        +cell Is the lexeme a currency symbol?
-
-    +row
-        +cell #[code like_url]
-        +cell bool
-        +cell Does the lexeme resemble a URL?
-
-    +row
-        +cell #[code like_num]
-        +cell bool
-        +cell Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc.
-
-    +row
-        +cell #[code like_email]
-        +cell bool
-        +cell Does the lexeme resemble an email address?
-
-    +row
-        +cell #[code is_oov]
-        +cell bool
-        +cell Is the lexeme out-of-vocabulary?
-
-    +row
-        +cell #[code is_stop]
-        +cell bool
-        +cell Is the lexeme part of a "stop list"?
-
-    +row
-        +cell #[code lang]
-        +cell int
-        +cell Language of the parent vocabulary.
-
-    +row
-        +cell #[code lang_]
-        +cell unicode
-        +cell Language of the parent vocabulary.
-
-    +row
-        +cell #[code prob]
-        +cell float
-        +cell Smoothed log probability estimate of the lexeme's type.
-
-    +row
-        +cell #[code cluster]
-        +cell int
-        +cell Brown cluster ID.
-
-    +row
-        +cell #[code sentiment]
-        +cell float
-        +cell
-            |  A scalar value indicating the positivity or negativity of the
-            |  lexeme.
diff --git a/website/api/matcher.jade b/website/api/matcher.jade
deleted file mode 100644
index 00260a109..000000000
--- a/website/api/matcher.jade
+++ /dev/null
@@ -1,281 +0,0 @@
-//- 💫 DOCS > API > MATCHER
-
-include ../_includes/_mixins
-
-+infobox("Changed in v2.0", "⚠️")
-    |  As of spaCy 2.0, #[code Matcher.add_pattern] and #[code Matcher.add_entity]
-    |  are deprecated and have been replaced with a simpler
-    |  #[+api("matcher#add") #[code Matcher.add]] that lets you add a list of
-    |  patterns and a callback for a given match ID. #[code Matcher.get_entity]
-    |  is now called #[+api("matcher#get") #[code matcher.get]].
-    |  #[code Matcher.load] (not useful, as it didn't allow specifying callbacks),
-    |  and #[code Matcher.has_entity] (now redundant) have been removed. The
-    |  concept of "acceptor functions" has also been retired – this logic can
-    |  now be handled in the callback functions.
-
-+h(2, "init") Matcher.__init__
-    +tag method
-
-p Create the rule-based #[code Matcher].
-
-+aside-code("Example").
-    from spacy.matcher import Matcher
-
-    patterns = {'HelloWorld': [{'LOWER': 'hello'}, {'LOWER': 'world'}]}
-    matcher = Matcher(nlp.vocab)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell
-            |  The vocabulary object, which must be shared with the documents
-            |  the matcher will operate on.
-
-    +row
-        +cell #[code patterns]
-        +cell dict
-        +cell Patterns to add to the matcher, keyed by ID.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Matcher]
-        +cell The newly constructed object.
-
-+h(2, "call") Matcher.__call__
-    +tag method
-
-p Find all token sequences matching the supplied patterns on the #[code Doc].
-
-+aside-code("Example").
-    from spacy.matcher import Matcher
-
-    matcher = Matcher(nlp.vocab)
-    pattern = [{'LOWER': "hello"}, {'LOWER': "world"}]
-    matcher.add("HelloWorld", None, pattern)
-    doc = nlp(u'hello world!')
-    matches = matcher(doc)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The document to match over.
-
-    +row("foot")
-        +cell returns
-        +cell list
-        +cell
-            |  A list of #[code (match_id, start, end)] tuples, describing the
-            |  matches. A match tuple describes a span #[code doc[start:end]].
-            |  The #[code match_id] is the ID of the added match pattern.
-
-+infobox("Important note")
-    |  By default, the matcher #[strong does not perform any action] on matches,
-    |  like tagging matched phrases with entity types. Instead, actions need to
-    |  be specified when #[strong adding patterns or entities], by
-    |  passing in a callback function as the #[code on_match] argument on
-    |  #[+api("matcher#add") #[code add]]. This allows you to define custom
-    |  actions per pattern within the same matcher. For example, you might only
-    |  want to merge some entity types, and set custom flags for other matched
-    |  patterns. For more details and examples, see the usage guide on
-    |  #[+a("/usage/linguistic-features#rule-based-matching") rule-based matching].
-
-+h(2, "pipe") Matcher.pipe
-    +tag method
-
-p Match a stream of documents, yielding them in turn.
-
-+aside-code("Example").
-    from spacy.matcher import Matcher
-    matcher = Matcher(nlp.vocab)
-    for doc in matcher.pipe(docs, batch_size=50, n_threads=4):
-        pass
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code docs]
-        +cell iterable
-        +cell A stream of documents.
-
-    +row
-        +cell #[code batch_size]
-        +cell int
-        +cell The number of documents to accumulate into a working set.
-
-    +row
-        +cell #[code n_threads]
-        +cell int
-        +cell
-            |  The number of threads with which to work on the buffer in
-            |  parallel, if the #[code Matcher] implementation supports
-            |  multi-threading.
-
-    +row
-        +cell #[code return_matches]
-            +tag-new(2.1)
-        +cell bool
-        +cell
-            |  Yield the match lists along with the docs, making results
-            |  #[code (doc, matches)] tuples.
-
-    +row
-        +cell #[code as_tuples]
-            +tag-new(2.1)
-        +cell bool
-        +cell
-            |  Interpret the input stream as #[code (doc, context)] tuples, and
-            |  yield #[code (result, context)] tuples out. If both
-            |  #[code return_matches] and #[code as_tuples] are #[code True],
-            |  the output will be a sequence of
-            |  #[code ((doc, matches), context)] tuples.
-
-    +row("foot")
-        +cell yields
-        +cell #[code Doc]
-        +cell Documents, in order.
-
-+h(2, "len") Matcher.__len__
-    +tag method
-    +tag-new(2)
-
-p
-    |  Get the number of rules added to the matcher. Note that this only returns
-    |  the number of rules (identical with the number of IDs), not the number
-    |  of individual patterns.
-
-+aside-code("Example").
-    matcher = Matcher(nlp.vocab)
-    assert len(matcher) == 0
-    matcher.add('Rule', None, [{'ORTH': 'test'}])
-    assert len(matcher) == 1
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of rules.
-
-+h(2, "contains") Matcher.__contains__
-    +tag method
-    +tag-new(2)
-
-p Check whether the matcher contains rules for a match ID.
-
-+aside-code("Example").
-    matcher = Matcher(nlp.vocab)
-    assert 'Rule' not in matcher
-    matcher.add('Rule', None, [{'ORTH': 'test'}])
-    assert 'Rule' in matcher
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code key]
-        +cell unicode
-        +cell The match ID.
-
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell Whether the matcher contains rules for this match ID.
-
-+h(2, "add") Matcher.add
-    +tag method
-    +tag-new(2)
-
-p
-    |  Add a rule to the matcher, consisting of an ID key, one or more patterns, and
-    |  a callback function to act on the matches. The callback function will
-    |  receive the arguments #[code matcher], #[code doc], #[code i] and
-    |  #[code matches]. If a pattern already exists for the given ID, the
-    |  patterns will be extended. An #[code on_match] callback will be
-    |  overwritten.
-
-+aside-code("Example").
-    def on_match(matcher, doc, id, matches):
-        print('Matched!', matches)
-
-    matcher = Matcher(nlp.vocab)
-    matcher.add('HelloWorld', on_match, [{'LOWER': 'hello'}, {'LOWER': 'world'}])
-    matcher.add('GoogleMaps', on_match, [{'ORTH': 'Google'}, {'ORTH': 'Maps'}])
-    doc = nlp(u'HELLO WORLD on Google Maps.')
-    matches = matcher(doc)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code match_id]
-        +cell unicode
-        +cell An ID for the thing you're matching.
-
-    +row
-        +cell #[code on_match]
-        +cell callable or #[code None]
-        +cell
-            |  Callback function to act on matches. Takes the arguments
-            |  #[code matcher], #[code doc], #[code i] and #[code matches].
-
-    +row
-        +cell #[code *patterns]
-        +cell list
-        +cell
-            |  Match pattern. A pattern consists of a list of dicts, where each
-            |  dict describes a token.
-
-+infobox("Changed in v2.0", "⚠️")
-    |  As of spaCy 2.0, #[code Matcher.add_pattern] and #[code Matcher.add_entity]
-    |  are deprecated and have been replaced with a simpler
-    |  #[+api("matcher#add") #[code Matcher.add]] that lets you add a list of
-    |  patterns and a callback for a given match ID.
-
-    +code-wrapper
-        +code-new.
-            matcher.add('GoogleNow', merge_phrases, [{ORTH: 'Google'}, {ORTH: 'Now'}])
-
-        +code-old.
-            matcher.add_entity('GoogleNow', on_match=merge_phrases)
-            matcher.add_pattern('GoogleNow', [{ORTH: 'Google'}, {ORTH: 'Now'}])
-
-+h(2, "remove") Matcher.remove
-    +tag method
-    +tag-new(2)
-
-p
-    |  Remove a rule from the matcher. A #[code KeyError] is raised if the match
-    |  ID does not exist.
-
-+aside-code("Example").
-    matcher.add('Rule', None, [{'ORTH': 'test'}])
-    assert 'Rule' in matcher
-    matcher.remove('Rule')
-    assert 'Rule' not in matcher
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code key]
-        +cell unicode
-        +cell The ID of the match rule.
-
-+h(2, "get") Matcher.get
-    +tag method
-    +tag-new(2)
-
-p
-    |  Retrieve the pattern stored for a key. Returns the rule as an
-    |  #[code (on_match, patterns)] tuple containing the callback and available
-    |  patterns.
-
-+aside-code("Example").
-    pattern = [{'ORTH': 'test'}]
-    matcher.add('Rule', None, pattern)
-    on_match, patterns = matcher.get('Rule')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code key]
-        +cell unicode
-        +cell The ID of the match rule.
-
-    +row("foot")
-        +cell returns
-        +cell tuple
-        +cell The rule, as an #[code (on_match, patterns)] tuple.
diff --git a/website/api/phrasematcher.jade b/website/api/phrasematcher.jade
deleted file mode 100644
index 219b8c0bf..000000000
--- a/website/api/phrasematcher.jade
+++ /dev/null
@@ -1,181 +0,0 @@
-//- 💫 DOCS > API > PHRASEMATCHER
-
-include ../_includes/_mixins
-
-p
-    |  The #[code PhraseMatcher] lets you efficiently match large terminology
-    |  lists. While the #[+api("matcher") #[code Matcher]] lets you match
-    |  sequences based on lists of token descriptions, the #[code PhraseMatcher]
-    |  accepts match patterns in the form of #[code Doc] objects.
-
-+h(2, "init") PhraseMatcher.__init__
-    +tag method
-
-p Create the rule-based #[code PhraseMatcher].
-
-+aside-code("Example").
-    from spacy.matcher import PhraseMatcher
-    matcher = PhraseMatcher(nlp.vocab, max_length=6)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell
-            |  The vocabulary object, which must be shared with the documents
-            |  the matcher will operate on.
-
-    +row
-        +cell #[code max_length]
-        +cell int
-        +cell Maximum length of a phrase pattern to add.
-
-    +row("foot")
-        +cell returns
-        +cell #[code PhraseMatcher]
-        +cell The newly constructed object.
-
-+h(2, "call") PhraseMatcher.__call__
-    +tag method
-
-p Find all token sequences matching the supplied patterns on the #[code Doc].
-
-+aside-code("Example").
-    from spacy.matcher import PhraseMatcher
-
-    matcher = PhraseMatcher(nlp.vocab)
-    matcher.add('OBAMA', None, nlp(u"Barack Obama"))
-    doc = nlp(u"Barack Obama lifts America one last time in emotional farewell")
-    matches = matcher(doc)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The document to match over.
-
-    +row("foot")
-        +cell returns
-        +cell list
-        +cell
-            |  A list of #[code (match_id, start, end)] tuples, describing the
-            |  matches. A match tuple describes a span #[code doc[start:end]].
-            |  The #[code match_id] is the ID of the added match pattern.
-
-+h(2, "pipe") PhraseMatcher.pipe
-    +tag method
-
-p Match a stream of documents, yielding them in turn.
-
-+aside-code("Example").
-    from spacy.matcher import PhraseMatcher
-    matcher = PhraseMatcher(nlp.vocab)
-    for doc in matcher.pipe(texts, batch_size=50, n_threads=4):
-        pass
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code docs]
-        +cell iterable
-        +cell A stream of documents.
-
-    +row
-        +cell #[code batch_size]
-        +cell int
-        +cell The number of documents to accumulate into a working set.
-
-    +row
-        +cell #[code n_threads]
-        +cell int
-        +cell
-            |  The number of threads with which to work on the buffer in
-            |  parallel, if the #[code PhraseMatcher] implementation supports
-            |  multi-threading.
-
-    +row("foot")
-        +cell yields
-        +cell #[code Doc]
-        +cell Documents, in order.
-
-+h(2, "len") PhraseMatcher.__len__
-    +tag method
-
-p
-    |  Get the number of rules added to the matcher. Note that this only returns
-    |  the number of rules (identical with the number of IDs), not the number
-    |  of individual patterns.
-
-+aside-code("Example").
-    matcher = PhraseMatcher(nlp.vocab)
-    assert len(matcher) == 0
-    matcher.add('OBAMA', None, nlp(u"Barack Obama"))
-    assert len(matcher) == 1
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of rules.
-
-+h(2, "contains") PhraseMatcher.__contains__
-    +tag method
-
-p Check whether the matcher contains rules for a match ID.
-
-+aside-code("Example").
-    matcher = PhraseMatcher(nlp.vocab)
-    assert 'OBAMA' not in matcher
-    matcher.add('OBAMA', None, nlp(u"Barack Obama"))
-    assert 'OBAMA' in matcher
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code key]
-        +cell unicode
-        +cell The match ID.
-
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell Whether the matcher contains rules for this match ID.
-
-+h(2, "add") PhraseMatcher.add
-    +tag method
-
-p
-    |  Add a rule to the matcher, consisting of an ID key, one or more patterns, and
-    |  a callback function to act on the matches. The callback function will
-    |  receive the arguments #[code matcher], #[code doc], #[code i] and
-    |  #[code matches]. If a pattern already exists for the given ID, the
-    |  patterns will be extended. An #[code on_match] callback will be
-    |  overwritten.
-
-+aside-code("Example").
-    def on_match(matcher, doc, id, matches):
-        print('Matched!', matches)
-
-    matcher = PhraseMatcher(nlp.vocab)
-    matcher.add('OBAMA', on_match, nlp(u"Barack Obama"))
-    matcher.add('HEALTH', on_match, nlp(u"health care reform"),
-                                    nlp(u"healthcare reform"))
-    doc = nlp(u"Barack Obama urges Congress to find courage to defend his healthcare reforms")
-    matches = matcher(doc)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code match_id]
-        +cell unicode
-        +cell An ID for the thing you're matching.
-
-    +row
-        +cell #[code on_match]
-        +cell callable or #[code None]
-        +cell
-            |  Callback function to act on matches. Takes the arguments
-            |  #[code matcher], #[code doc], #[code i] and #[code matches].
-
-    +row
-        +cell #[code *docs]
-        +cell list
-        +cell
-            |  #[code Doc] objects of the phrases to match.
diff --git a/website/api/pipe.jade b/website/api/pipe.jade
deleted file mode 100644
index c0ec86972..000000000
--- a/website/api/pipe.jade
+++ /dev/null
@@ -1,449 +0,0 @@
-//- 💫 DOCS > API > PIPE
-
-include ../_includes/_mixins
-
-//- This page can be used as a template for all other classes that inherit
-//-  from `Pipe`.
-
-if subclass
-    +infobox
-        |  This class is a subclass of #[+api("pipe") #[code Pipe]] and
-        |  follows the same API. The pipeline component is available in the
-        |  #[+a("/usage/processing-pipelines") processing pipeline] via the ID
-        |  #[code "#{pipeline_id}"].
-
-else
-    p
-        |  This class is not instantiated directly. Components inherit from it,
-        |  and it defines the interface that components should follow to
-        |  function as components in a spaCy analysis pipeline.
-
-- CLASSNAME = subclass || 'Pipe'
-- VARNAME = short || CLASSNAME.toLowerCase()
-
-
-+h(2, "model") #{CLASSNAME}.Model
-    +tag classmethod
-
-p
-    |  Initialise a model for the pipe. The model should implement the
-    |  #[code thinc.neural.Model] API. Wrappers are under development for
-    |  most major machine learning libraries.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code **kwargs]
-        +cell -
-        +cell Parameters for initialising the model
-
-    +row("foot")
-        +cell returns
-        +cell object
-        +cell The initialised model.
-
-+h(2, "init") #{CLASSNAME}.__init__
-    +tag method
-
-p Create a new pipeline instance.
-
-+aside-code("Example").
-    from spacy.pipeline import #{CLASSNAME}
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    #{VARNAME}.from_disk('/path/to/model')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell The shared vocabulary.
-
-    +row
-        +cell #[code model]
-        +cell #[code thinc.neural.Model] or #[code True]
-        +cell
-            |  The model powering the pipeline component. If no model is
-            |  supplied, the model is created when you call
-            |  #[code begin_training], #[code from_disk] or #[code from_bytes].
-
-    +row
-        +cell #[code **cfg]
-        +cell -
-        +cell Configuration parameters.
-
-    +row("foot")
-        +cell returns
-        +cell #[code=CLASSNAME]
-        +cell The newly constructed object.
-
-+h(2, "call") #{CLASSNAME}.__call__
-    +tag method
-
-p
-    |  Apply the pipe to one document. The document is modified in place, and
-    |  returned. Both #[code #{CLASSNAME}.__call__] and
-    |  #[code #{CLASSNAME}.pipe] should delegate to the
-    |  #[code #{CLASSNAME}.predict] and #[code #{CLASSNAME}.set_annotations]
-    |  methods.
-
-+aside-code("Example").
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    doc = nlp(u"This is a sentence.")
-    processed = #{VARNAME}(doc)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The document to process.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Doc]
-        +cell The processed document.
-
-+h(2, "pipe") #{CLASSNAME}.pipe
-    +tag method
-
-p
-    |  Apply the pipe to a stream of documents. Both
-    |  #[code #{CLASSNAME}.__call__] and #[code #{CLASSNAME}.pipe] should
-    |  delegate to the #[code #{CLASSNAME}.predict] and
-    |  #[code #{CLASSNAME}.set_annotations] methods.
-
-+aside-code("Example").
-    texts = [u'One doc', u'...', u'Lots of docs']
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    for doc in #{VARNAME}.pipe(texts, batch_size=50):
-        pass
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code stream]
-        +cell iterable
-        +cell A stream of documents.
-
-    +row
-        +cell #[code batch_size]
-        +cell int
-        +cell The number of texts to buffer. Defaults to #[code 128].
-
-    +row
-        +cell #[code n_threads]
-        +cell int
-        +cell
-            |  The number of worker threads to use. If #[code -1], OpenMP will
-            |  decide how many to use at run time. Default is #[code -1].
-
-    +row("foot")
-        +cell yields
-        +cell #[code Doc]
-        +cell Processed documents in the order of the original text.
-
-+h(2, "predict") #{CLASSNAME}.predict
-    +tag method
-
-p
-    |  Apply the pipeline's model to a batch of docs, without modifying them.
-
-+aside-code("Example").
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    scores = #{VARNAME}.predict([doc1, doc2])
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code docs]
-        +cell iterable
-        +cell The documents to predict.
-
-    +row("foot")
-        +cell returns
-        +cell -
-        +cell Scores from the model.
-
-+h(2, "set_annotations") #{CLASSNAME}.set_annotations
-    +tag method
-
-p
-    |  Modify a batch of documents, using pre-computed scores.
-
-+aside-code("Example").
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    scores = #{VARNAME}.predict([doc1, doc2])
-    #{VARNAME}.set_annotations([doc1, doc2], scores)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code docs]
-        +cell iterable
-        +cell The documents to modify.
-
-    +row
-        +cell #[code scores]
-        +cell -
-        +cell The scores to set, produced by #[code #{CLASSNAME}.predict].
-
-+h(2, "update") #{CLASSNAME}.update
-    +tag method
-
-p
-    |  Learn from a batch of documents and gold-standard information, updating
-    |  the pipe's model. Delegates to #[code #{CLASSNAME}.predict] and
-    |  #[code #{CLASSNAME}.get_loss].
-
-+aside-code("Example").
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    losses = {}
-    optimizer = nlp.begin_training()
-    #{VARNAME}.update([doc1, doc2], [gold1, gold2], losses=losses, sgd=optimizer)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code docs]
-        +cell iterable
-        +cell A batch of documents to learn from.
-
-    +row
-        +cell #[code golds]
-        +cell iterable
-        +cell The gold-standard data. Must have the same length as #[code docs].
-
-    +row
-        +cell #[code drop]
-        +cell float
-        +cell The dropout rate.
-
-    +row
-        +cell #[code sgd]
-        +cell callable
-        +cell
-            |  The optimizer. Should take two arguments #[code weights] and
-            |  #[code gradient], and an optional ID.
-
-    +row
-        +cell #[code losses]
-        +cell dict
-        +cell
-            |  Optional record of the loss during training. The value keyed by
-            |  the model's name is updated.
-
-+h(2, "get_loss") #{CLASSNAME}.get_loss
-    +tag method
-
-p
-    |  Find the loss and gradient of loss for the batch of documents and their
-    |  predicted scores.
-
-+aside-code("Example").
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    scores = #{VARNAME}.predict([doc1, doc2])
-    loss, d_loss = #{VARNAME}.get_loss([doc1, doc2], [gold1, gold2], scores)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code docs]
-        +cell iterable
-        +cell The batch of documents.
-
-    +row
-        +cell #[code golds]
-        +cell iterable
-        +cell The gold-standard data. Must have the same length as #[code docs].
-
-    +row
-        +cell #[code scores]
-        +cell -
-        +cell Scores representing the model's predictions.
-
-    +row("foot")
-        +cell returns
-        +cell tuple
-        +cell The loss and the gradient, i.e. #[code (loss, gradient)].
-
-+h(2, "begin_training") #{CLASSNAME}.begin_training
-    +tag method
-
-p
-    |  Initialise the pipe for training, using data exampes if available. If no
-    |  model has been initialised yet, the model is added.
-
-+aside-code("Example").
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    nlp.pipeline.append(#{VARNAME})
-    optimizer = #{VARNAME}.begin_training(pipeline=nlp.pipeline)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code gold_tuples]
-        +cell iterable
-        +cell
-            |  Optional gold-standard annotations from which to construct
-            |  #[+api("goldparse") #[code GoldParse]] objects.
-
-    +row
-        +cell #[code pipeline]
-        +cell list
-        +cell
-            |  Optional list of #[+api("pipe") #[code Pipe]] components that
-            |  this component is part of.
-
-    +row
-        +cell #[code sgd]
-        +cell callable
-        +cell
-            |  An optional optimizer. Should take two arguments #[code weights]
-            |  and #[code gradient], and an optional ID. Will be created via
-            |  #[+api(CLASSNAME.toLowerCase() + "#create_optimizer") #[code create_optimizer]]
-            |  if not set.
-
-    +row("foot")
-        +cell returns
-        +cell callable
-        +cell An optimizer.
-
-+h(2, "create_optimizer") #{CLASSNAME}.create_optimizer
-    +tag method
-
-p
-    |  Create an optmizer for the pipeline component.
-
-+aside-code("Example").
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    optimizer = #{VARNAME}.create_optimizer()
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell callable
-        +cell The optimizer.
-
-+h(2, "use_params") #{CLASSNAME}.use_params
-    +tag method
-    +tag contextmanager
-
-p Modify the pipe's model, to use the given parameter values.
-
-+aside-code("Example").
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    with #{VARNAME}.use_params():
-        #{VARNAME}.to_disk('/best_model')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code params]
-        +cell -
-        +cell
-            |  The parameter values to use in the model. At the end of the
-            |  context, the original parameters are restored.
-
-+h(2, "add_label") #{CLASSNAME}.add_label
-    +tag method
-
-p Add a new label to the pipe.
-
-if CLASSNAME == "Tagger"
-    +aside-code("Example").
-        #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-        #{VARNAME}.add_label('MY_LABEL', {POS: 'NOUN'})
-else
-    +aside-code("Example").
-        #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-        #{VARNAME}.add_label('MY_LABEL')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code label]
-        +cell unicode
-        +cell The label to add.
-
-    if CLASSNAME == "Tagger"
-        +row
-            +cell #[code values]
-            +cell dict
-            +cell
-                |  Optional values to map to the label, e.g. a tag map
-                |  dictionary.
-
-+h(2, "to_disk") #{CLASSNAME}.to_disk
-    +tag method
-
-p Serialize the pipe to disk.
-
-+aside-code("Example").
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    #{VARNAME}.to_disk('/path/to/#{VARNAME}')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode or #[code Path]
-        +cell
-            |  A path to a directory, which will be created if it doesn't exist.
-            |  Paths may be either strings or #[code Path]-like objects.
-
-+h(2, "from_disk") #{CLASSNAME}.from_disk
-    +tag method
-
-p Load the pipe from disk. Modifies the object in place and returns it.
-
-+aside-code("Example").
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    #{VARNAME}.from_disk('/path/to/#{VARNAME}')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode or #[code Path]
-        +cell
-            |  A path to a directory. Paths may be either strings or
-            |  #[code Path]-like objects.
-
-    +row("foot")
-        +cell returns
-        +cell #[code=CLASSNAME]
-        +cell The modified #[code=CLASSNAME] object.
-
-+h(2, "to_bytes") #{CLASSNAME}.to_bytes
-    +tag method
-
-+aside-code("example").
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    #{VARNAME}_bytes = #{VARNAME}.to_bytes()
-
-p Serialize the pipe to a bytestring.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code **exclude]
-        +cell -
-        +cell Named attributes to prevent from being serialized.
-
-    +row("foot")
-        +cell returns
-        +cell bytes
-        +cell The serialized form of the #[code=CLASSNAME] object.
-
-+h(2, "from_bytes") #{CLASSNAME}.from_bytes
-    +tag method
-
-p Load the pipe from a bytestring. Modifies the object in place and returns it.
-
-+aside-code("Example").
-    #{VARNAME}_bytes = #{VARNAME}.to_bytes()
-    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
-    #{VARNAME}.from_bytes(#{VARNAME}_bytes)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code bytes_data]
-        +cell bytes
-        +cell The data to load from.
-
-    +row
-        +cell #[code **exclude]
-        +cell -
-        +cell Named attributes to prevent from being loaded.
-
-    +row("foot")
-        +cell returns
-        +cell #[code=CLASSNAME]
-        +cell The #[code=CLASSNAME] object.
diff --git a/website/api/span.jade b/website/api/span.jade
deleted file mode 100644
index 8f9837889..000000000
--- a/website/api/span.jade
+++ /dev/null
@@ -1,655 +0,0 @@
-//- 💫 DOCS > API > SPAN
-
-include ../_includes/_mixins
-
-p A slice from a #[+api("doc") #[code Doc]] object.
-
-+h(2, "init") Span.__init__
-    +tag method
-
-p Create a Span object from the #[code slice doc[start : end]].
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    span = doc[1:4]
-    assert [t.text for t in span] ==  [u'it', u'back', u'!']
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The parent document.
-
-    +row
-        +cell #[code start]
-        +cell int
-        +cell The index of the first token of the span.
-
-    +row
-        +cell #[code end]
-        +cell int
-        +cell The index of the first token after the span.
-
-    +row
-        +cell #[code label]
-        +cell int
-        +cell A label to attach to the span, e.g. for named entities.
-
-    +row
-        +cell #[code vector]
-        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
-        +cell A meaning representation of the span.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Span]
-        +cell The newly constructed object.
-
-+h(2, "getitem") Span.__getitem__
-    +tag method
-
-p Get a #[code Token] object.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    span = doc[1:4]
-    assert span[1].text == 'back'
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code i]
-        +cell int
-        +cell The index of the token within the span.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Token]
-        +cell The token at #[code span[i]].
-
-p Get a #[code Span] object.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    span = doc[1:4]
-    assert span[1:3].text == 'back!'
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code start_end]
-        +cell tuple
-        +cell The slice of the span to get.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Span]
-        +cell The span at #[code span[start : end]].
-
-+h(2, "iter") Span.__iter__
-    +tag method
-
-p Iterate over #[code Token] objects.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    span = doc[1:4]
-    assert [t.text for t in span] == ['it', 'back', '!']
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Token]
-        +cell A #[code Token] object.
-
-+h(2, "len") Span.__len__
-    +tag method
-
-p Get the number of tokens in the span.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    span = doc[1:4]
-    assert len(span) == 3
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of tokens in the span.
-
-+h(2, "set_extension") Span.set_extension
-    +tag classmethod
-    +tag-new(2)
-
-p
-    |  Define a custom attribute on the #[code Span] which becomes available via
-    |  #[code Span._]. For details, see the documentation on
-    |  #[+a("/usage/processing-pipelines#custom-components-attributes") custom attributes].
-
-+aside-code("Example").
-    from spacy.tokens import Span
-    city_getter = lambda span: any(city in span.text for city in ('New York', 'Paris', 'Berlin'))
-    Span.set_extension('has_city', getter=city_getter)
-    doc = nlp(u'I like New York in Autumn')
-    assert doc[1:4]._.has_city
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell
-            |  Name of the attribute to set by the extension. For example,
-            |  #[code 'my_attr'] will be available as #[code span._.my_attr].
-
-    +row
-        +cell #[code default]
-        +cell -
-        +cell
-            |  Optional default value of the attribute if no getter or method
-            |  is defined.
-
-    +row
-        +cell #[code method]
-        +cell callable
-        +cell
-            |  Set a custom method on the object, for example
-            |  #[code span._.compare(other_span)].
-
-    +row
-        +cell #[code getter]
-        +cell callable
-        +cell
-            |  Getter function that takes the object and returns an attribute
-            |  value. Is called when the user accesses the #[code ._] attribute.
-
-    +row
-        +cell #[code setter]
-        +cell callable
-        +cell
-            |  Setter function that takes the #[code Span] and a value, and
-            |  modifies the object. Is called when the user writes to the
-            |  #[code Span._] attribute.
-
-+h(2, "get_extension") Span.get_extension
-    +tag classmethod
-    +tag-new(2)
-
-p
-    |  Look up a previously registered extension by name. Returns a 4-tuple
-    |  #[code.u-break (default, method, getter, setter)] if the extension is
-    |  registered. Raises a #[code KeyError] otherwise.
-
-+aside-code("Example").
-    from spacy.tokens import Span
-    Span.set_extension('is_city', default=False)
-    extension = Span.get_extension('is_city')
-    assert extension == (False, None, None, None)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of the extension.
-
-    +row("foot")
-        +cell returns
-        +cell tuple
-        +cell
-            |  A #[code.u-break (default, method, getter, setter)] tuple of the
-            |  extension.
-
-+h(2, "has_extension") Span.has_extension
-    +tag classmethod
-    +tag-new(2)
-
-p Check whether an extension has been registered on the #[code Span] class.
-
-+aside-code("Example").
-    from spacy.tokens import Span
-    Span.set_extension('is_city', default=False)
-    assert Span.has_extension('is_city')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of the extension to check.
-
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the extension has been registered.
-
-+h(2, "remove_extension") Span.remove_extension
-    +tag classmethod
-    +tag-new("2.0.12")
-
-p Remove a previously registered extension.
-
-+aside-code("Example").
-    from spacy.tokens import Span
-    Span.set_extension('is_city', default=False)
-    removed = Span.remove_extension('is_city')
-    assert not Span.has_extension('is_city')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of the extension.
-
-    +row("foot")
-        +cell returns
-        +cell tuple
-        +cell
-            |  A #[code.u-break (default, method, getter, setter)] tuple of the
-            |  removed extension.
-
-+h(2, "similarity") Span.similarity
-    +tag method
-    +tag-model("vectors")
-
-p
-    |  Make a semantic similarity estimate. The default estimate is cosine
-    |  similarity using an average of word vectors.
-
-+aside-code("Example").
-    doc = nlp(u'green apples and red oranges')
-    green_apples = doc[:2]
-    red_oranges = doc[3:]
-    apples_oranges = green_apples.similarity(red_oranges)
-    oranges_apples = red_oranges.similarity(green_apples)
-    assert apples_oranges == oranges_apples
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code other]
-        +cell -
-        +cell
-            |  The object to compare with. By default, accepts #[code Doc],
-            |  #[code Span], #[code Token] and #[code Lexeme] objects.
-
-    +row("foot")
-        +cell returns
-        +cell float
-        +cell A scalar similarity score. Higher is more similar.
-
-+h(2, "get_lca_matrix") Span.get_lca_matrix
-    +tag method
-
-p
-    |  Calculates the lowest common ancestor matrix for a given #[code Span].
-    |  Returns LCA matrix containing the integer index of the ancestor, or
-    |  #[code -1] if no common ancestor is found, e.g. if span excludes a
-    |  necessary ancestor.
-
-+aside-code("Example").
-    doc = nlp(u'I like New York in Autumn')
-    span = doc[1:4]
-    matrix = span.get_lca_matrix()
-    # array([[0, 0, 0], [0, 1, 2], [0, 2, 2]], dtype=int32)
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell #[code.u-break numpy.ndarray[ndim=2, dtype='int32']]
-        +cell The lowest common ancestor matrix of the #[code Span].
-
-
-+h(2, "to_array") Span.to_array
-    +tag method
-    +tag-new(2)
-
-p
-    |  Given a list of #[code M] attribute IDs, export the tokens to a numpy
-    |  #[code ndarray] of shape #[code (N, M)], where #[code N] is the length of
-    |  the document. The values will be 32-bit integers.
-
-+aside-code("Example").
-    from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
-    doc = nlp(u'I like New York in Autumn.')
-    span = doc[2:3]
-    # All strings mapped to integers, for easy export to numpy
-    np_array = span.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code attr_ids]
-        +cell list
-        +cell A list of attribute ID ints.
-
-    +row("foot")
-        +cell returns
-        +cell #[code.u-break numpy.ndarray[long, ndim=2]]
-        +cell
-            |  A feature matrix, with one row per word, and one column per
-            |  attribute indicated in the input #[code attr_ids].
-
-+h(2, "merge") Span.merge
-    +tag method
-
-p Retokenize the document, such that the span is merged into a single token.
-
-+aside-code("Example").
-    doc = nlp(u'I like New York in Autumn.')
-    span = doc[2:4]
-    span.merge()
-    assert len(doc) == 6
-    assert doc[2].text == 'New York'
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code **attributes]
-        +cell -
-        +cell
-            |  Attributes to assign to the merged token. By default, attributes
-            |  are inherited from the syntactic root token of the span.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Token]
-        +cell The newly merged token.
-
-+h(2, "ents") Span.ents
-    +tag property
-    +tag-model("NER")
-    +tag-new("2.0.12")
-
-p
-    |  Iterate over the entities in the span. Yields named-entity
-    |  #[code Span] objects, if the entity recognizer has been applied to the
-    |  parent document.
-
-+aside-code("Example").
-    doc = nlp(u'Mr. Best flew to New York on Saturday morning.')
-    span = doc[0:6]
-    ents = list(span.ents)
-    assert ents[0].label == 346
-    assert ents[0].label_ == 'PERSON'
-    assert ents[0].text == 'Mr. Best'
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Span]
-        +cell Entities in the document.
-
-
-+h(2, "as_doc") Span.as_doc
-
-p
-    |  Create a new #[code Doc] object corresponding to the #[code Span], with
-    |  a copy of the data.
-
-+aside-code("Example").
-    doc = nlp(u'I like New York in Autumn.')
-    span = doc[2:4]
-    doc2 = span.as_doc()
-    assert doc2.text == 'New York'
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell #[code Doc]
-        +cell A #[code Doc] object of the #[code Span]'s content.
-
-
-+h(2, "root") Span.root
-    +tag property
-    +tag-model("parse")
-
-p
-    |  The token within the span that's highest in the parse tree. If there's a
-    |  tie, the earliest is preferred.
-
-+aside-code("Example").
-    doc = nlp(u'I like New York in Autumn.')
-    i, like, new, york, in_, autumn, dot = range(len(doc))
-    assert doc[new].head.text == 'York'
-    assert doc[york].head.text == 'like'
-    new_york = doc[new&#58;york+1]
-    assert new_york.root.text == 'York'
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell #[code Token]
-        +cell The root token.
-
-+h(2, "lefts") Span.lefts
-    +tag property
-    +tag-model("parse")
-
-p Tokens that are to the left of the span, whose heads are within the span.
-
-+aside-code("Example").
-    doc = nlp(u'I like New York in Autumn.')
-    lefts = [t.text for t in doc[3:7].lefts]
-    assert lefts == [u'New']
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Token]
-        +cell A left-child of a token of the span.
-
-+h(2, "rights") Span.rights
-    +tag property
-    +tag-model("parse")
-
-p Tokens that are to the right of the span, whose heads are within the span.
-
-+aside-code("Example").
-    doc = nlp(u'I like New York in Autumn.')
-    rights = [t.text for t in doc[2:4].rights]
-    assert rights == [u'in']
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Token]
-        +cell A right-child of a token of the span.
-
-+h(2, "n_lefts") Span.n_lefts
-    +tag property
-    +tag-model("parse")
-
-p
-    |  The number of tokens that are to the left of the span, whose heads are
-    |  within the span.
-
-+aside-code("Example").
-    doc = nlp(u'I like New York in Autumn.')
-    assert doc[3:7].n_lefts == 1
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of left-child tokens.
-
-+h(2, "n_rights") Span.n_rights
-    +tag property
-    +tag-model("parse")
-
-p
-    |  The number of tokens that are to the right of the span, whose heads are
-    |  within the span.
-
-+aside-code("Example").
-    doc = nlp(u'I like New York in Autumn.')
-    assert doc[2:4].n_rights == 1
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of right-child tokens.
-
-+h(2, "subtree") Span.subtree
-    +tag property
-    +tag-model("parse")
-
-p Tokens within the span and tokens which descend from them.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    subtree = [t.text for t in doc[:3].subtree]
-    assert subtree == [u'Give', u'it', u'back', u'!']
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Token]
-        +cell A token within the span, or a descendant from it.
-
-+h(2, "has_vector") Span.has_vector
-    +tag property
-    +tag-model("vectors")
-
-p
-    |  A boolean value indicating whether a word vector is associated with the
-    |  object.
-
-+aside-code("Example").
-    doc = nlp(u'I like apples')
-    assert doc[1:].has_vector
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the span has a vector data attached.
-
-+h(2, "vector") Span.vector
-    +tag property
-    +tag-model("vectors")
-
-p
-    |  A real-valued meaning representation. Defaults to an average of the
-    |  token vectors.
-
-+aside-code("Example").
-    doc = nlp(u'I like apples')
-    assert doc[1:].vector.dtype == 'float32'
-    assert doc[1:].vector.shape == (300,)
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
-        +cell A 1D numpy array representing the span's semantics.
-
-+h(2, "vector_norm") Span.vector_norm
-    +tag property
-    +tag-model("vectors")
-
-p
-    |  The L2 norm of the span's vector representation.
-
-+aside-code("Example").
-    doc = nlp(u'I like apples')
-    doc[1:].vector_norm # 4.800883928527915
-    doc[2:].vector_norm # 6.895897646384268
-    assert doc[1:].vector_norm != doc[2:].vector_norm
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell float
-        +cell The L2 norm of the vector representation.
-
-+h(2, "attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The parent document.
-
-    +row
-        +cell #[code sent]
-        +cell #[code Span]
-        +cell The sentence span that this span is a part of.
-
-    +row
-        +cell #[code start]
-        +cell int
-        +cell The token offset for the start of the span.
-
-    +row
-        +cell #[code end]
-        +cell int
-        +cell The token offset for the end of the span.
-
-    +row
-        +cell #[code start_char]
-        +cell int
-        +cell The character offset for the start of the span.
-
-    +row
-        +cell #[code end_char]
-        +cell int
-        +cell The character offset for the end of the span.
-
-    +row
-        +cell #[code text]
-        +cell unicode
-        +cell A unicode representation of the span text.
-
-    +row
-        +cell #[code text_with_ws]
-        +cell unicode
-        +cell
-            |  The text content of the span with a trailing whitespace character
-            |  if the last token has one.
-
-    +row
-        +cell #[code orth]
-        +cell int
-        +cell ID of the verbatim text content.
-
-    +row
-        +cell #[code orth_]
-        +cell unicode
-        +cell
-            |  Verbatim text content (identical to #[code Span.text]). Exists
-            |  mostly for consistency with the other attributes.
-
-    +row
-        +cell #[code label]
-        +cell int
-        +cell The span's label.
-
-    +row
-        +cell #[code label_]
-        +cell unicode
-        +cell The span's label.
-
-    +row
-        +cell #[code lemma_]
-        +cell unicode
-        +cell The span's lemma.
-
-    +row
-        +cell #[code ent_id]
-        +cell int
-        +cell The hash value of the named entity the token is an instance of.
-
-    +row
-        +cell #[code ent_id_]
-        +cell unicode
-        +cell The string ID of the named entity the token is an instance of.
-
-    +row
-        +cell #[code sentiment]
-        +cell float
-        +cell
-            |  A scalar value indicating the positivity or negativity of the
-            |  span.
-
-    +row
-        +cell #[code _]
-        +cell #[code Underscore]
-        +cell
-            |  User space for adding custom
-            |  #[+a("/usage/processing-pipelines#custom-components-attributes") attribute extensions].
diff --git a/website/api/stringstore.jade b/website/api/stringstore.jade
deleted file mode 100644
index 9d03404cc..000000000
--- a/website/api/stringstore.jade
+++ /dev/null
@@ -1,239 +0,0 @@
-//- 💫 DOCS > API > STRINGSTORE
-
-include ../_includes/_mixins
-
-p
-    |  Look up strings by 64-bit hashes. As of v2.0, spaCy uses hash values
-    |  instead of integer IDs. This ensures that strings always map to the
-    |  same ID, even from different #[code StringStores].
-
-+h(2, "init") StringStore.__init__
-    +tag method
-
-p
-    |  Create the #[code StringStore].
-
-+aside-code("Example").
-    from spacy.strings import StringStore
-    stringstore = StringStore([u'apple', u'orange'])
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code strings]
-        +cell iterable
-        +cell A sequence of unicode strings to add to the store.
-
-    +row("foot")
-        +cell returns
-        +cell #[code StringStore]
-        +cell The newly constructed object.
-
-+h(2, "len") StringStore.__len__
-    +tag method
-
-p Get the number of strings in the store.
-
-+aside-code("Example").
-    stringstore = StringStore([u'apple', u'orange'])
-    assert len(stringstore) == 2
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of strings in the store.
-
-+h(2, "getitem") StringStore.__getitem__
-    +tag method
-
-p Retrieve a string from a given hash, or vice versa.
-
-+aside-code("Example").
-    stringstore = StringStore([u'apple', u'orange'])
-    apple_hash = stringstore[u'apple']
-    assert apple_hash == 8566208034543834098
-    assert stringstore[apple_hash] == u'apple'
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code string_or_id]
-        +cell bytes, unicode or uint64
-        +cell The value to encode.
-
-    +row("foot")
-        +cell returns
-        +cell unicode or int
-        +cell The value to be retrieved.
-
-+h(2, "contains") StringStore.__contains__
-    +tag method
-
-p Check whether a string is in the store.
-
-+aside-code("Example").
-    stringstore = StringStore([u'apple', u'orange'])
-    assert u'apple' in stringstore
-    assert not u'cherry' in stringstore
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code string]
-        +cell unicode
-        +cell The string to check.
-
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the store contains the string.
-
-+h(2, "iter") StringStore.__iter__
-    +tag method
-
-p
-    |  Iterate over the strings in the store, in order. Note that a newly
-    |  initialised store will always include an empty string #[code ''] at
-    |  position #[code 0].
-
-+aside-code("Example").
-    stringstore = StringStore([u'apple', u'orange'])
-    all_strings = [s for s in stringstore]
-    assert all_strings == [u'apple', u'orange']
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell unicode
-        +cell A string in the store.
-
-+h(2, "add") StringStore.add
-    +tag method
-    +tag-new(2)
-
-p Add a string to the #[code StringStore].
-
-+aside-code("Example").
-    stringstore = StringStore([u'apple', u'orange'])
-    banana_hash = stringstore.add(u'banana')
-    assert len(stringstore) == 3
-    assert banana_hash == 2525716904149915114
-    assert stringstore[banana_hash] == u'banana'
-    assert stringstore[u'banana'] == banana_hash
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code string]
-        +cell unicode
-        +cell The string to add.
-
-    +row("foot")
-        +cell returns
-        +cell uint64
-        +cell The string's hash value.
-
-
-+h(2, "to_disk") StringStore.to_disk
-    +tag method
-    +tag-new(2)
-
-p Save the current state to a directory.
-
-+aside-code("Example").
-    stringstore.to_disk('/path/to/strings')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode or #[code Path]
-        +cell
-            |  A path to a directory, which will be created if it doesn't exist.
-            |  Paths may be either strings or #[code Path]-like objects.
-
-+h(2, "from_disk") StringStore.from_disk
-    +tag method
-    +tag-new(2)
-
-p Loads state from a directory. Modifies the object in place and returns it.
-
-+aside-code("Example").
-    from spacy.strings import StringStore
-    stringstore = StringStore().from_disk('/path/to/strings')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode or #[code Path]
-        +cell
-            |  A path to a directory. Paths may be either strings or
-            |  #[code Path]-like objects.
-
-    +row("foot")
-        +cell returns
-        +cell #[code StringStore]
-        +cell The modified #[code StringStore] object.
-
-+h(2, "to_bytes") StringStore.to_bytes
-    +tag method
-
-p Serialize the current state to a binary string.
-
-+aside-code("Example").
-    store_bytes = stringstore.to_bytes()
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code **exclude]
-        +cell -
-        +cell Named attributes to prevent from being serialized.
-
-    +row("foot")
-        +cell returns
-        +cell bytes
-        +cell The serialized form of the #[code StringStore] object.
-
-+h(2, "from_bytes") StringStore.from_bytes
-    +tag method
-
-p Load state from a binary string.
-
-+aside-code("Example").
-    fron spacy.strings import StringStore
-    store_bytes = stringstore.to_bytes()
-    new_store = StringStore().from_bytes(store_bytes)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code bytes_data]
-        +cell bytes
-        +cell The data to load from.
-
-    +row
-        +cell #[code **exclude]
-        +cell -
-        +cell Named attributes to prevent from being loaded.
-
-    +row("foot")
-        +cell returns
-        +cell #[code StringStore]
-        +cell The #[code StringStore] object.
-
-+h(2, "util") Utilities
-
-+h(3, "hash_string") strings.hash_string
-    +tag function
-
-p Get a 64-bit hash for a given string.
-
-+aside-code("Example").
-    from spacy.strings import hash_string
-    assert hash_string(u'apple') == 8566208034543834098
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code string]
-        +cell unicode
-        +cell The string to hash.
-
-    +row("foot")
-        +cell returns
-        +cell uint64
-        +cell The hash.
diff --git a/website/api/tagger.jade b/website/api/tagger.jade
deleted file mode 100644
index 7a7e9214f..000000000
--- a/website/api/tagger.jade
+++ /dev/null
@@ -1,6 +0,0 @@
-//- 💫 DOCS > API > TAGGER
-
-include ../_includes/_mixins
-
-//- This class inherits from Pipe, so this page uses the template in pipe.jade.
-!=partial("pipe", { subclass: "Tagger", pipeline_id: "tagger" })
diff --git a/website/api/textcategorizer.jade b/website/api/textcategorizer.jade
deleted file mode 100644
index 0f81b56eb..000000000
--- a/website/api/textcategorizer.jade
+++ /dev/null
@@ -1,19 +0,0 @@
-//- 💫 DOCS > API > TEXTCATEGORIZER
-
-include ../_includes/_mixins
-
-p
-    |  The model supports classification with multiple, non-mutually exclusive
-    |  labels. You can change the model architecture rather easily, but by
-    |  default, the #[code TextCategorizer] class uses a convolutional
-    |  neural network to assign position-sensitive vectors to each word in the
-    |  document. The #[code TextCategorizer] uses its own CNN model, to
-    |  avoid sharing weights with the other pipeline components. The document
-    |  tensor is then summarized by concatenating max and mean pooling, and a
-    |  multilayer perceptron is used to predict an output vector of length
-    |  #[code nr_class], before a logistic activation is applied elementwise.
-    |  The value of each output neuron is the probability that some class is
-    |  present.
-
-//- This class inherits from Pipe, so this page uses the template in pipe.jade.
-!=partial("pipe", { subclass: "TextCategorizer", short: "textcat", pipeline_id: "textcat" })
diff --git a/website/api/token.jade b/website/api/token.jade
deleted file mode 100644
index 1303a37ec..000000000
--- a/website/api/token.jade
+++ /dev/null
@@ -1,890 +0,0 @@
-
-//- 💫 DOCS > API > TOKEN
-
-include ../_includes/_mixins
-
-p An individual token — i.e. a word, punctuation symbol, whitespace, etc.
-
-+h(2, "init") Token.__init__
-    +tag method
-
-p Construct a #[code Token] object.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    token = doc[0]
-    assert token.text == u'Give'
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell A storage container for lexical types.
-
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The parent document.
-
-    +row
-        +cell #[code offset]
-        +cell int
-        +cell The index of the token within the document.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Token]
-        +cell The newly constructed object.
-
-+h(2, "len") Token.__len__
-    +tag method
-
-p The number of unicode characters in the token, i.e. #[code token.text].
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    token = doc[0]
-    assert len(token) == 4
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of unicode characters in the token.
-
-+h(2, "set_extension") Token.set_extension
-    +tag classmethod
-    +tag-new(2)
-
-p
-    |  Define a custom attribute on the #[code Token] which becomes available
-    |  via #[code Token._]. For details, see the documentation on
-    |  #[+a("/usage/processing-pipelines#custom-components-attributes") custom attributes].
-
-+aside-code("Example").
-    from spacy.tokens import Token
-    fruit_getter = lambda token: token.text in ('apple', 'pear', 'banana')
-    Token.set_extension('is_fruit', getter=fruit_getter)
-    doc = nlp(u'I have an apple')
-    assert doc[3]._.is_fruit
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell
-            |  Name of the attribute to set by the extension. For example,
-            |  #[code 'my_attr'] will be available as #[code token._.my_attr].
-
-    +row
-        +cell #[code default]
-        +cell -
-        +cell
-            |  Optional default value of the attribute if no getter or method
-            |  is defined.
-
-    +row
-        +cell #[code method]
-        +cell callable
-        +cell
-            |  Set a custom method on the object, for example
-            |  #[code token._.compare(other_token)].
-
-    +row
-        +cell #[code getter]
-        +cell callable
-        +cell
-            |  Getter function that takes the object and returns an attribute
-            |  value. Is called when the user accesses the #[code ._] attribute.
-
-    +row
-        +cell #[code setter]
-        +cell callable
-        +cell
-            |  Setter function that takes the #[code Token] and a value, and
-            |  modifies the object. Is called when the user writes to the
-            |  #[code Token._] attribute.
-
-+h(2, "get_extension") Token.get_extension
-    +tag classmethod
-    +tag-new(2)
-
-p
-    |  Look up a previously registered extension by name. Returns a 4-tuple
-    |  #[code.u-break (default, method, getter, setter)] if the extension is
-    |  registered. Raises a #[code KeyError] otherwise.
-
-+aside-code("Example").
-    from spacy.tokens import Token
-    Token.set_extension('is_fruit', default=False)
-    extension = Token.get_extension('is_fruit')
-    assert extension == (False, None, None, None)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of the extension.
-
-    +row("foot")
-        +cell returns
-        +cell tuple
-        +cell
-            |  A #[code.u-break (default, method, getter, setter)] tuple of the
-            |  extension.
-
-+h(2, "has_extension") Token.has_extension
-    +tag classmethod
-    +tag-new(2)
-
-p Check whether an extension has been registered on the #[code Token] class.
-
-+aside-code("Example").
-    from spacy.tokens import Token
-    Token.set_extension('is_fruit', default=False)
-    assert Token.has_extension('is_fruit')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of the extension to check.
-
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the extension has been registered.
-
-+h(2, "remove_extension") Token.remove_extension
-    +tag classmethod
-    +tag-new("2.0.11")
-
-p Remove a previously registered extension.
-
-+aside-code("Example").
-    from spacy.tokens import Token
-    Token.set_extension('is_fruit', default=False)
-    removed = Token.remove_extension('is_fruit')
-    assert not Token.has_extension('is_fruit')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code name]
-        +cell unicode
-        +cell Name of the extension.
-
-    +row("foot")
-        +cell returns
-        +cell tuple
-        +cell
-            |  A #[code.u-break (default, method, getter, setter)] tuple of the
-            |  removed extension.
-
-+h(2, "check_flag") Token.check_flag
-    +tag method
-
-p Check the value of a boolean flag.
-
-+aside-code("Example").
-    from spacy.attrs import IS_TITLE
-    doc = nlp(u'Give it back! He pleaded.')
-    token = doc[0]
-    assert token.check_flag(IS_TITLE) == True
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code flag_id]
-        +cell int
-        +cell The attribute ID of the flag to check.
-
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the flag is set.
-
-+h(2, "similarity") Token.similarity
-    +tag method
-    +tag-model("vectors")
-
-p Compute a semantic similarity estimate. Defaults to cosine over vectors.
-
-+aside-code("Example").
-    apples, _, oranges = nlp(u'apples and oranges')
-    apples_oranges = apples.similarity(oranges)
-    oranges_apples = oranges.similarity(apples)
-    assert apples_oranges == oranges_apples
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell other
-        +cell -
-        +cell
-            |  The object to compare with. By default, accepts #[code Doc],
-            |  #[code Span], #[code Token] and #[code Lexeme] objects.
-
-    +row("foot")
-        +cell returns
-        +cell float
-        +cell A scalar similarity score. Higher is more similar.
-
-+h(2, "nbor") Token.nbor
-    +tag method
-
-p Get a neighboring token.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    give_nbor = doc[0].nbor()
-    assert give_nbor.text == u'it'
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code i]
-        +cell int
-        +cell The relative position of the token to get. Defaults to #[code 1].
-
-    +row("foot")
-        +cell returns
-        +cell #[code Token]
-        +cell The token at position #[code self.doc[self.i+i]].
-
-+h(2, "is_ancestor") Token.is_ancestor
-    +tag method
-    +tag-model("parse")
-
-p
-    |  Check whether this token is a parent, grandparent, etc. of another
-    |  in the dependency tree.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    give = doc[0]
-    it = doc[1]
-    assert give.is_ancestor(it)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell descendant
-        +cell #[code Token]
-        +cell Another token.
-
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether this token is the ancestor of the descendant.
-
-+h(2, "ancestors") Token.ancestors
-    +tag property
-    +tag-model("parse")
-
-p The rightmost token of this token's syntactic descendants.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    it_ancestors = doc[1].ancestors
-    assert [t.text for t in it_ancestors] == [u'Give']
-    he_ancestors = doc[4].ancestors
-    assert [t.text for t in he_ancestors] == [u'pleaded']
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Token]
-        +cell
-            |  A sequence of ancestor tokens such that
-            |  #[code ancestor.is_ancestor(self)].
-
-+h(2, "conjuncts") Token.conjuncts
-    +tag property
-    +tag-model("parse")
-
-p A sequence of coordinated tokens, including the token itself.
-
-+aside-code("Example").
-    doc = nlp(u'I like apples and oranges')
-    apples_conjuncts = doc[2].conjuncts
-    assert [t.text for t in apples_conjuncts] == [u'oranges']
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Token]
-        +cell A coordinated token.
-
-+h(2, "children") Token.children
-    +tag property
-    +tag-model("parse")
-
-p A sequence of the token's immediate syntactic children.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    give_children = doc[0].children
-    assert [t.text for t in give_children] == [u'it', u'back', u'!']
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Token]
-        +cell A child token such that #[code child.head==self].
-
-+h(2, "lefts") Token.lefts
-    +tag property
-    +tag-model("parse")
-
-p
-    |  The leftward immediate children of the word, in the syntactic dependency
-    |  parse.
-
-+aside-code("Example").
-    doc = nlp(u'I like New York in Autumn.')
-    lefts = [t.text for t in doc[3].lefts]
-    assert lefts == [u'New']
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Token]
-        +cell A left-child of the token.
-
-+h(2, "rights") Token.rights
-    +tag property
-    +tag-model("parse")
-
-p
-    |  The rightward immediate children of the word, in the syntactic
-    |  dependency parse.
-
-+aside-code("Example").
-    doc = nlp(u'I like New York in Autumn.')
-    rights = [t.text for t in doc[3].rights]
-    assert rights == [u'in']
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Token]
-        +cell A right-child of the token.
-
-+h(2, "n_lefts") Token.n_lefts
-    +tag property
-    +tag-model("parse")
-
-p
-    |  The number of leftward immediate children of the word, in the syntactic
-    |  dependency parse.
-
-+aside-code("Example").
-    doc = nlp(u'I like New York in Autumn.')
-    assert doc[3].n_lefts == 1
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of left-child tokens.
-
-+h(2, "n_rights") Token.n_rights
-    +tag property
-    +tag-model("parse")
-
-p
-    |  The number of rightward immediate children of the word, in the syntactic
-    |  dependency parse.
-
-+aside-code("Example").
-    doc = nlp(u'I like New York in Autumn.')
-    assert doc[3].n_rights == 1
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of right-child tokens.
-
-+h(2, "subtree") Token.subtree
-    +tag property
-    +tag-model("parse")
-
-p A sequence containing the token and all the token's syntactic descendants.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    give_subtree = doc[0].subtree
-    assert [t.text for t in give_subtree] == [u'Give', u'it', u'back', u'!']
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Token]
-        +cell A descendant token such that #[code self.is_ancestor(token) or token == self].
-
-+h(2, "is_sent_start") Token.is_sent_start
-    +tag property
-    +tag-new(2)
-
-p
-    |  A boolean value indicating whether the token starts a sentence.
-    |  #[code None] if unknown.
-
-+aside-code("Example").
-    doc = nlp(u'Give it back! He pleaded.')
-    assert doc[4].is_sent_start
-    assert not doc[5].is_sent_start
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the token starts a sentence.
-
-+infobox("Changed in v2.0", "⚠️")
-    |  As of spaCy v2.0, the #[code Token.sent_start] property is deprecated and
-    |  has been replaced with #[code Token.is_sent_start], which returns a
-    |  boolean value instead of a misleading #[code 0] for #[code False] and
-    |  #[code 1] for #[code True]. It also now returns #[code None] if the
-    |  answer is unknown, and fixes a quirk in the old logic that would always
-    |  set the property to #[code 0] for the first word of the document.
-
-    +code-wrapper
-        +code-new assert doc[4].is_sent_start == True
-        +code-old assert doc[4].sent_start == 1
-
-+h(2, "has_vector") Token.has_vector
-    +tag property
-    +tag-model("vectors")
-
-p
-    |  A boolean value indicating whether a word vector is associated with the
-    |  token.
-
-+aside-code("Example").
-    doc = nlp(u'I like apples')
-    apples = doc[2]
-    assert apples.has_vector
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the token has a vector data attached.
-
-+h(2, "vector") Token.vector
-    +tag property
-    +tag-model("vectors")
-
-p A real-valued meaning representation.
-
-+aside-code("Example").
-    doc = nlp(u'I like apples')
-    apples = doc[2]
-    assert apples.vector.dtype == 'float32'
-    assert apples.vector.shape == (300,)
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
-        +cell A 1D numpy array representing the token's semantics.
-
-+h(2, "vector_norm") Token.vector_norm
-    +tag property
-    +tag-model("vectors")
-
-p The L2 norm of the token's vector representation.
-
-+aside-code("Example").
-    doc = nlp(u'I like apples and pasta')
-    apples = doc[2]
-    pasta = doc[4]
-    apples.vector_norm # 6.89589786529541
-    pasta.vector_norm # 7.759851932525635
-    assert apples.vector_norm != pasta.vector_norm
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell float
-        +cell The L2 norm of the vector representation.
-
-+h(2, "attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The parent document.
-
-    +row
-        +cell #[code sent]
-            +tag-new("2.0.12")
-        +cell #[code Span]
-        +cell The sentence span that this token is a part of.
-
-    +row
-        +cell #[code text]
-        +cell unicode
-        +cell Verbatim text content.
-
-    +row
-        +cell #[code text_with_ws]
-        +cell unicode
-        +cell Text content, with trailing space character if present.
-
-    +row
-        +cell #[code whitespace_]
-        +cell unicode
-        +cell Trailing space character if present.
-
-    +row
-        +cell #[code orth]
-        +cell int
-        +cell ID of the verbatim text content.
-
-    +row
-        +cell #[code orth_]
-        +cell unicode
-        +cell
-            |  Verbatim text content (identical to #[code Token.text]). Exists
-            |  mostly for consistency with the other attributes.
-
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell The vocab object of the parent #[code Doc].
-
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The parent document.
-
-    +row
-        +cell #[code head]
-        +cell #[code Token]
-        +cell The syntactic parent, or "governor", of this token.
-
-    +row
-        +cell #[code left_edge]
-        +cell #[code Token]
-        +cell The leftmost token of this token's syntactic descendants.
-
-    +row
-        +cell #[code right_edge]
-        +cell #[code Token]
-        +cell The rightmost token of this token's syntactic descendants.
-
-    +row
-        +cell #[code i]
-        +cell int
-        +cell The index of the token within the parent document.
-
-    +row
-        +cell #[code ent_type]
-        +cell int
-        +cell Named entity type.
-
-    +row
-        +cell #[code ent_type_]
-        +cell unicode
-        +cell Named entity type.
-
-    +row
-        +cell #[code ent_iob]
-        +cell int
-        +cell
-            |  IOB code of named entity tag. #[code "B"]
-            |  means the token begins an entity, #[code "I"] means it is inside
-            |  an entity, #[code "O"] means it is outside an entity, and
-            |  #[code ""] means no entity tag is set.
-
-    +row
-        +cell #[code ent_iob_]
-        +cell unicode
-        +cell
-            |  IOB code of named entity tag. #[code "B"]
-            |  means the token begins an entity, #[code "I"] means it is inside
-            |  an entity, #[code "O"] means it is outside an entity, and
-            |  #[code ""] means no entity tag is set.
-
-    +row
-        +cell #[code ent_id]
-        +cell int
-        +cell
-            |  ID of the entity the token is an instance of, if any. Currently
-            |  not used, but potentially for coreference resolution.
-
-    +row
-        +cell #[code ent_id_]
-        +cell unicode
-        +cell
-            |  ID of the entity the token is an instance of, if any. Currently
-            |  not used, but potentially for coreference resolution.
-
-    +row
-        +cell #[code lemma]
-        +cell int
-        +cell
-            |  Base form of the token, with no inflectional suffixes.
-
-    +row
-        +cell #[code lemma_]
-        +cell unicode
-        +cell Base form of the token, with no inflectional suffixes.
-
-    +row
-        +cell #[code norm]
-        +cell int
-        +cell
-            |  The token's norm, i.e. a normalised form of the token text.
-            |  Usually set in the language's
-            |  #[+a("/usage/adding-languages#tokenizer-exceptions") tokenizer exceptions] or
-            |  #[+a("/usage/adding-languages#norm-exceptions") norm exceptions].
-
-    +row
-        +cell #[code norm_]
-        +cell unicode
-        +cell
-            |  The token's norm, i.e. a normalised form of the token text.
-            |  Usually set in the language's
-            |  #[+a("/usage/adding-languages#tokenizer-exceptions") tokenizer exceptions] or
-            |  #[+a("/usage/adding-languages#norm-exceptions") norm exceptions].
-
-    +row
-        +cell #[code lower]
-        +cell int
-        +cell Lowercase form of the token.
-
-    +row
-        +cell #[code lower_]
-        +cell unicode
-        +cell
-            |  Lowercase form of the token text. Equivalent to
-            |  #[code Token.text.lower()].
-
-    +row
-        +cell #[code shape]
-        +cell int
-        +cell
-            |  Transform of the tokens's string, to show orthographic features.
-            |  For example, "Xxxx" or "dd".
-
-    +row
-        +cell #[code shape_]
-        +cell unicode
-        +cell
-            |  Transform of the tokens's string, to show orthographic features.
-            |  For example, "Xxxx" or "dd".
-
-    +row
-        +cell #[code prefix]
-        +cell int
-        +cell
-            |  Hash value of a length-N substring from the start of the
-            |  token. Defaults to #[code N=1].
-
-    +row
-        +cell #[code prefix_]
-        +cell unicode
-        +cell
-            |  A length-N substring from the start of the token. Defaults to
-            |  #[code N=1].
-
-    +row
-        +cell #[code suffix]
-        +cell int
-        +cell
-            |  Hash value of a length-N substring from the end of the token.
-            |  Defaults to #[code N=3].
-
-    +row
-        +cell #[code suffix_]
-        +cell unicode
-        +cell
-            |  Length-N substring from the end of the token. Defaults to
-            |  #[code N=3].
-
-    +row
-        +cell #[code is_alpha]
-        +cell bool
-        +cell
-            |  Does the token consist of alphabetic characters? Equivalent to
-            |  #[code token.text.isalpha()].
-
-    +row
-        +cell #[code is_ascii]
-        +cell bool
-        +cell
-            |  Does the token consist of ASCII characters? Equivalent to
-            |  #[code all(ord(c) &lt; 128 for c in token.text)].
-
-    +row
-        +cell #[code is_digit]
-        +cell bool
-        +cell
-            |  Does the token consist of digits? Equivalent to
-            |  #[code token.text.isdigit()].
-
-    +row
-        +cell #[code is_lower]
-        +cell bool
-        +cell
-            |  Is the token in lowercase? Equivalent to
-            |  #[code token.text.islower()].
-
-    +row
-        +cell #[code is_upper]
-        +cell bool
-        +cell
-            |  Is the token in uppercase? Equivalent to
-            |  #[code token.text.isupper()].
-
-    +row
-        +cell #[code is_title]
-        +cell bool
-        +cell
-            |  Is the token in titlecase? Equivalent to
-            |  #[code token.text.istitle()].
-
-    +row
-        +cell #[code is_punct]
-        +cell bool
-        +cell Is the token punctuation?
-
-    +row
-        +cell #[code is_left_punct]
-        +cell bool
-        +cell Is the token a left punctuation mark, e.g. #[code (]?
-
-    +row
-        +cell #[code is_right_punct]
-        +cell bool
-        +cell Is the token a right punctuation mark, e.g. #[code )]?
-
-    +row
-        +cell #[code is_space]
-        +cell bool
-        +cell
-            |  Does the token consist of whitespace characters? Equivalent to
-            |  #[code token.text.isspace()].
-
-    +row
-        +cell #[code is_bracket]
-        +cell bool
-        +cell Is the token a bracket?
-
-    +row
-        +cell #[code is_quote]
-        +cell bool
-        +cell Is the token a quotation mark?
-
-    +row
-        +cell #[code is_currency]
-            +tag-new("2.0.8")
-        +cell bool
-        +cell Is the token a currency symbol?
-
-    +row
-        +cell #[code like_url]
-        +cell bool
-        +cell Does the token resemble a URL?
-
-    +row
-        +cell #[code like_num]
-        +cell bool
-        +cell Does the token represent a number? e.g. "10.9", "10", "ten", etc.
-
-    +row
-        +cell #[code like_email]
-        +cell bool
-        +cell Does the token resemble an email address?
-
-    +row
-        +cell #[code is_oov]
-        +cell bool
-        +cell Is the token out-of-vocabulary?
-
-    +row
-        +cell #[code is_stop]
-        +cell bool
-        +cell Is the token part of a "stop list"?
-
-    +row
-        +cell #[code pos]
-        +cell int
-        +cell Coarse-grained part-of-speech.
-
-    +row
-        +cell #[code pos_]
-        +cell unicode
-        +cell Coarse-grained part-of-speech.
-
-    +row
-        +cell #[code tag]
-        +cell int
-        +cell Fine-grained part-of-speech.
-
-    +row
-        +cell #[code tag_]
-        +cell unicode
-        +cell Fine-grained part-of-speech.
-
-    +row
-        +cell #[code dep]
-        +cell int
-        +cell Syntactic dependency relation.
-
-    +row
-        +cell #[code dep_]
-        +cell unicode
-        +cell Syntactic dependency relation.
-
-    +row
-        +cell #[code lang]
-        +cell int
-        +cell Language of the parent document's vocabulary.
-
-    +row
-        +cell #[code lang_]
-        +cell unicode
-        +cell Language of the parent document's vocabulary.
-
-    +row
-        +cell #[code prob]
-        +cell float
-        +cell Smoothed log probability estimate of token's type.
-
-    +row
-        +cell #[code idx]
-        +cell int
-        +cell The character offset of the token within the parent document.
-
-    +row
-        +cell #[code sentiment]
-        +cell float
-        +cell
-            |  A scalar value indicating the positivity or negativity of the
-            |  token.
-
-    +row
-        +cell #[code lex_id]
-        +cell int
-        +cell Sequential ID of the token's lexical type.
-
-    +row
-        +cell #[code rank]
-        +cell int
-        +cell
-            |  Sequential ID of the token's lexical type, used to index into
-            |  tables, e.g. for word vectors.
-
-    +row
-        +cell #[code cluster]
-        +cell int
-        +cell Brown cluster ID.
-
-    +row
-        +cell #[code _]
-        +cell #[code Underscore]
-        +cell
-            |  User space for adding custom
-            |  #[+a("/usage/processing-pipelines#custom-components-attributes") attribute extensions].
diff --git a/website/api/tokenizer.jade b/website/api/tokenizer.jade
deleted file mode 100644
index 7a8a34838..000000000
--- a/website/api/tokenizer.jade
+++ /dev/null
@@ -1,229 +0,0 @@
-//- 💫 DOCS > API > TOKENIZER
-
-include ../_includes/_mixins
-
-p
-    |  Segment text, and create #[code Doc] objects with the discovered segment
-    |  boundaries.
-
-+h(2, "init") Tokenizer.__init__
-    +tag method
-
-p Create a #[code Tokenizer], to create #[code Doc] objects given unicode text.
-
-+aside-code("Example").
-    # Construction 1
-    from spacy.tokenizer import Tokenizer
-    tokenizer = Tokenizer(nlp.vocab)
-
-    # Construction 2
-    from spacy.lang.en import English
-    tokenizer = English().Defaults.create_tokenizer(nlp)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell A storage container for lexical types.
-
-    +row
-        +cell #[code rules]
-        +cell dict
-        +cell Exceptions and special-cases for the tokenizer.
-
-    +row
-        +cell #[code prefix_search]
-        +cell callable
-        +cell
-            |  A function matching the signature of
-            |  #[code re.compile(string).search] to match prefixes.
-
-    +row
-        +cell #[code suffix_search]
-        +cell callable
-        +cell
-            |  A function matching the signature of
-            |  #[code re.compile(string).search] to match suffixes.
-
-    +row
-        +cell #[code infix_finditer]
-        +cell callable
-        +cell
-            |  A function matching the signature of
-            |  #[code re.compile(string).finditer] to find infixes.
-
-    +row
-        +cell #[code token_match]
-        +cell callable
-        +cell A boolean function matching strings to be recognised as tokens.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Tokenizer]
-        +cell The newly constructed object.
-
-+h(2, "call") Tokenizer.__call__
-    +tag method
-
-p Tokenize a string.
-
-+aside-code("Example").
-    tokens = tokenizer(u'This is a sentence')
-    assert len(tokens) == 4
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code string]
-        +cell unicode
-        +cell The string to tokenize.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Doc]
-        +cell A container for linguistic annotations.
-
-+h(2, "pipe") Tokenizer.pipe
-    +tag method
-
-p Tokenize a stream of texts.
-
-+aside-code("Example").
-    texts = [u'One document.', u'...', u'Lots of documents']
-    for doc in tokenizer.pipe(texts, batch_size=50):
-        pass
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code texts]
-        +cell -
-        +cell A sequence of unicode texts.
-
-    +row
-        +cell #[code batch_size]
-        +cell int
-        +cell The number of texts to accumulate in an internal buffer.
-
-    +row
-        +cell #[code n_threads]
-        +cell int
-        +cell
-            |  The number of threads to use, if the implementation supports
-            |  multi-threading. The default tokenizer is single-threaded.
-
-    +row("foot")
-        +cell yields
-        +cell #[code Doc]
-        +cell A sequence of Doc objects, in order.
-
-+h(2, "find_infix") Tokenizer.find_infix
-    +tag method
-
-p Find internal split points of the string.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code string]
-        +cell unicode
-        +cell The string to split.
-
-    +row("foot")
-        +cell returns
-        +cell list
-        +cell
-            |  A list of #[code re.MatchObject] objects that have #[code .start()]
-            |  and #[code .end()] methods, denoting the placement of internal
-            |  segment separators, e.g. hyphens.
-
-+h(2, "find_prefix") Tokenizer.find_prefix
-    +tag method
-
-p
-    |  Find the length of a prefix that should be segmented from the string, or
-    |  #[code None] if no prefix rules match.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code string]
-        +cell unicode
-        +cell The string to segment.
-
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The length of the prefix if present, otherwise #[code None].
-
-+h(2, "find_suffix") Tokenizer.find_suffix
-    +tag method
-
-p
-    |  Find the length of a suffix that should be segmented from the string, or
-    |  #[code None] if no suffix rules match.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code string]
-        +cell unicode
-        +cell The string to segment.
-
-    +row("foot")
-        +cell returns
-        +cell int / #[code None]
-        +cell The length of the suffix if present, otherwise #[code None].
-
-+h(2, "add_special_case") Tokenizer.add_special_case
-    +tag method
-
-p
-    |  Add a special-case tokenization rule. This mechanism is also used to add
-    |  custom tokenizer exceptions to the language data. See the usage guide
-    |  on #[+a("/usage/adding-languages#tokenizer-exceptions") adding languages]
-    |  for more details and examples.
-
-+aside-code("Example").
-    from spacy.attrs import ORTH, LEMMA
-    case = [{"don't": [{ORTH: "do"}, {ORTH: "n't", LEMMA: "not"}]}]
-    tokenizer.add_special_case(case)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code string]
-        +cell unicode
-        +cell The string to specially tokenize.
-
-    +row
-        +cell #[code token_attrs]
-        +cell iterable
-        +cell
-            |  A sequence of dicts, where each dict describes a token and its
-            |  attributes. The #[code ORTH] fields of the attributes must
-            |  exactly match the string when they are concatenated.
-
-+h(2, "attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell The vocab object of the parent #[code Doc].
-
-    +row
-        +cell #[code prefix_search]
-        +cell -
-        +cell
-            |  A function to find segment boundaries from the start of a
-            |  string. Returns the length of the segment, or #[code None].
-
-    +row
-        +cell #[code suffix_search]
-        +cell -
-        +cell
-            |  A function to find segment boundaries from the end of a string.
-            |  Returns the length of the segment, or #[code None].
-
-    +row
-        +cell #[code infix_finditer]
-        +cell -
-        +cell
-            |  A function to find internal segment separators, e.g. hyphens.
-            |  Returns a (possibly empty) list of #[code re.MatchObject]
-            |  objects.
diff --git a/website/api/top-level.jade b/website/api/top-level.jade
deleted file mode 100644
index f16daae23..000000000
--- a/website/api/top-level.jade
+++ /dev/null
@@ -1,20 +0,0 @@
-//- 💫 DOCS > API > TOP-LEVEL
-
-include ../_includes/_mixins
-
-+section("spacy")
-    //-+h(2, "spacy") spaCy
-    //- spacy/__init__.py
-    include _top-level/_spacy
-
-+section("displacy")
-    +h(2, "displacy", "spacy/displacy") displaCy
-    include _top-level/_displacy
-
-+section("util")
-    +h(2, "util", "spacy/util.py") Utility functions
-    include _top-level/_util
-
-+section("compat")
-    +h(2, "compat", "spacy/compaty.py") Compatibility functions
-    include _top-level/_compat
diff --git a/website/api/vectors.jade b/website/api/vectors.jade
deleted file mode 100644
index e1976713e..000000000
--- a/website/api/vectors.jade
+++ /dev/null
@@ -1,476 +0,0 @@
-//- 💫 DOCS > API > VECTORS
-
-include ../_includes/_mixins
-
-p
-    |  Vectors data is kept in the #[code Vectors.data] attribute, which should
-    |  be an instance of #[code numpy.ndarray] (for CPU vectors) or
-    |  #[code cupy.ndarray] (for GPU vectors). Multiple keys can be mapped to
-    |  the same vector, and not all of the rows in the table need to be
-    |  assigned – so #[code vectors.n_keys] may be greater or smaller than
-    |  #[code vectors.shape[0]].
-
-+h(2, "init") Vectors.__init__
-    +tag method
-
-p
-    |  Create a new vector store. You can set the vector values and keys
-    |  directly on initialisation, or supply a #[code shape] keyword argument
-    |  to create an empty table you can add vectors to later.
-
-+aside-code("Example").
-    from spacy.vectors import Vectors
-
-    empty_vectors = Vectors(shape=(10000, 300))
-
-    data = numpy.zeros((3, 300), dtype='f')
-    keys = [u'cat', u'dog', u'rat']
-    vectors = Vectors(data=data, keys=keys)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code data]
-        +cell #[code.u-break ndarray[ndim=1, dtype='float32']]
-        +cell The vector data.
-
-    +row
-        +cell #[code keys]
-        +cell iterable
-        +cell A sequence of keys aligned with the data.
-
-    +row
-        +cell #[code shape]
-        +cell tuple
-        +cell
-            |  Size of the table as #[code (n_entries, n_columns)], the number
-            |  of entries and number of columns. Not required if you're
-            |  initialising the object with #[code data] and #[code keys].
-
-    +row("foot")
-        +cell returns
-        +cell #[code Vectors]
-        +cell The newly created object.
-
-+h(2, "getitem") Vectors.__getitem__
-    +tag method
-
-p
-    |  Get a vector by key. If the key is not found in the table, a
-    |  #[code KeyError] is raised.
-
-+aside-code("Example").
-    cat_id = nlp.vocab.strings[u'cat']
-    cat_vector = nlp.vocab.vectors[cat_id]
-    assert cat_vector == nlp.vocab[u'cat'].vector
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code key]
-        +cell int
-        +cell The key to get the vector for.
-
-    +row
-        +cell returns
-        +cell #[code.u-break ndarray[ndim=1, dtype='float32']]
-        +cell The vector for the key.
-
-+h(2, "setitem") Vectors.__setitem__
-    +tag method
-
-p
-    |  Set a vector for the given key.
-
-+aside-code("Example").
-    cat_id = nlp.vocab.strings[u'cat']
-    vector = numpy.random.uniform(-1, 1, (300,))
-    nlp.vocab.vectors[cat_id] = vector
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code key]
-        +cell int
-        +cell The key to set the vector for.
-
-    +row
-        +cell #[code vector]
-        +cell #[code.u-break ndarray[ndim=1, dtype='float32']]
-        +cell The vector to set.
-
-+h(2, "iter") Vectors.__iter__
-    +tag method
-
-p Iterate over the keys in the table.
-
-+aside-code("Example").
-    for key in nlp.vocab.vectors:
-        print(key, nlp.vocab.strings[key])
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell int
-        +cell A key in the table.
-
-+h(2, "len") Vectors.__len__
-    +tag method
-
-p Return the number of vectors in the table.
-
-+aside-code("Example").
-    vectors = Vectors(shape=(3, 300))
-    assert len(vectors) == 3
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of vectors in the table.
-
-+h(2, "contains") Vectors.__contains__
-    +tag method
-
-p
-    |  Check whether a key has been mapped to a vector entry in the table.
-
-+aside-code("Example").
-    cat_id = nlp.vocab.strings[u'cat']
-    nlp.vectors.add(cat_id, numpy.random.uniform(-1, 1, (300,)))
-    assert cat_id in vectors
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code key]
-        +cell int
-        +cell The key to check.
-
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the key has a vector entry.
-
-+h(2, "add") Vectors.add
-    +tag method
-
-p
-    |  Add a key to the table, optionally setting a vector value as well. Keys
-    |  can be mapped to an existing vector by setting #[code row], or a new
-    |  vector can be added. When adding unicode keys, keep in mind that the
-    |  #[code Vectors] class itself has no
-    |  #[+api("stringstore") #[code StringStore]], so you have to store the
-    |  hash-to-string mapping separately. If you need to manage the strings,
-    |  you should use the #[code Vectors] via the
-    |  #[+api("vocab") #[code Vocab]] class, e.g. #[code vocab.vectors].
-
-+aside-code("Example").
-    vector = numpy.random.uniform(-1, 1, (300,))
-    cat_id = nlp.vocab.strings[u'cat']
-    nlp.vocab.vectors.add(cat_id, vector=vector)
-    nlp.vocab.vectors.add(u'dog', row=0)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code key]
-        +cell unicode / int
-        +cell The key to add.
-
-    +row
-        +cell #[code vector]
-        +cell #[code.u-break ndarray[ndim=1, dtype='float32']]
-        +cell An optional vector to add for the key.
-
-    +row
-        +cell #[code row]
-        +cell int
-        +cell An optional row number of a vector to map the key to.
-
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The row the vector was added to.
-
-+h(2, "resize") Vectors.resize
-    +tag method
-
-p
-    |  Resize the underlying vectors array. If #[code inplace=True], the memory
-    |  is reallocated. This may cause other references to the data to become
-    |  invalid, so only use #[code inplace=True] if you're sure that's what you
-    |  want. If the number of vectors is reduced, keys mapped to rows that have
-    |  been deleted are removed. These removed items are returned as a list of
-    |  #[code (key, row)] tuples.
-
-+aside-code("Example").
-    removed = nlp.vocab.vectors.resize((10000, 300))
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code shape]
-        +cell tuple
-        +cell
-            |  A #[code (rows, dims)] tuple describing the number of rows and
-            |  dimensions.
-
-    +row
-        +cell #[code inplace]
-        +cell bool
-        +cell Reallocate the memory.
-
-    +row("foot")
-        +cell returns
-        +cell list
-        +cell The removed items as a list of #[code (key, row)] tuples.
-
-+h(2, "keys") Vectors.keys
-    +tag method
-
-p A sequence of the keys in the table.
-
-+aside-code("Example").
-    for key in nlp.vocab.vectors.keys():
-        print(key, nlp.vocab.strings[key])
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell iterable
-        +cell The keys.
-
-+h(2, "values") Vectors.values
-    +tag method
-
-p
-    |  Iterate over vectors that have been assigned to at least one key. Note
-    |  that some vectors may be unassigned, so the number of vectors returned
-    |  may be less than the length of the vectors table.
-
-+aside-code("Example").
-    for vector in nlp.vocab.vectors.values():
-        print(vector)
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code.u-break ndarray[ndim=1, dtype='float32']]
-        +cell A vector in the table.
-
-+h(2, "items") Vectors.items
-    +tag method
-
-p Iterate over #[code (key, vector)] pairs, in order.
-
-+aside-code("Example").
-    for key, vector in nlp.vocab.vectors.items():
-        print(key, nlp.vocab.strings[key], vector)
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell tuple
-        +cell #[code (key, vector)] pairs, in order.
-
-+h(2, "shape") Vectors.shape
-    +tag property
-
-p
-    |  Get #[code (rows, dims)] tuples of number of rows and number of
-    |  dimensions in the vector table.
-
-+aside-code("Example").
-    vectors = Vectors(shape(1, 300))
-    vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
-    rows, dims = vectors.shape
-    assert rows == 1
-    assert dims == 300
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell tuple
-        +cell A #[code (rows, dims)] pair.
-
-+h(2, "size") Vectors.size
-    +tag property
-
-p The vector size, i.e. #[code rows * dims].
-
-+aside-code("Example").
-    vectors = Vectors(shape=(500, 300))
-    assert vectors.size == 150000
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The vector size.
-
-+h(2, "is_full") Vectors.is_full
-    +tag property
-
-p
-    |  Whether the vectors table is full and has no slots are available for new
-    |  keys. If a table is full, it can be resized using
-    |  #[+api("vectors#resize") #[code Vectors.resize]].
-
-+aside-code("Example").
-    vectors = Vectors(shape=(1, 300))
-    vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
-    assert vectors.is_full
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the vectors table is full.
-
-+h(2, "n_keys") Vectors.n_keys
-    +tag property
-
-p
-    |  Get the number of keys in the table. Note that this is the number of
-    |  #[em all] keys, not just unique vectors. If several keys are mapped
-    |  are mapped to the same vectors, they will be counted individually.
-
-+aside-code("Example").
-    vectors = Vectors(shape=(10, 300))
-    assert len(vectors) == 10
-    assert vectors.n_keys == 0
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of all keys in the table.
-
-+h(2, "from_glove") Vectors.from_glove
-    +tag method
-
-p
-    |  Load #[+a("https://nlp.stanford.edu/projects/glove/") GloVe] vectors from
-    |  a directory. Assumes binary format, that the vocab is in a
-    |  #[code vocab.txt], and that vectors are named
-    |  #[code vectors.{size}.[fd].bin], e.g. #[code vectors.128.f.bin] for 128d
-    |  float32 vectors, #[code vectors.300.d.bin] for 300d float64 (double)
-    |  vectors, etc. By default GloVe outputs 64-bit vectors.
-
-+aside-code("Example").
-    vectors = Vectors()
-    vectors.from_glove('/path/to/glove_vectors')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode / #[code Path]
-        +cell The path to load the GloVe vectors from.
-
-+h(2, "to_disk") Vectors.to_disk
-    +tag method
-
-p Save the current state to a directory.
-
-+aside-code("Example").
-    vectors.to_disk('/path/to/vectors')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode / #[code Path]
-        +cell
-            |  A path to a directory, which will be created if it doesn't exist.
-            |  Paths may be either strings or #[code Path]-like objects.
-
-    +row
-        +cell #[code **exclude]
-        +cell -
-        +cell Named attributes to prevent from being saved.
-
-+h(2, "from_disk") Vectors.from_disk
-    +tag method
-
-p Loads state from a directory. Modifies the object in place and returns it.
-
-+aside-code("Example").
-    vectors = Vectors(StringStore())
-    vectors.from_disk('/path/to/vectors')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode / #[code Path]
-        +cell
-            |  A path to a directory. Paths may be either strings or
-            |  #[code Path]-like objects.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Vectors]
-        +cell The modified #[code Vectors] object.
-
-+h(2, "to_bytes") Vectors.to_bytes
-    +tag method
-
-p Serialize the current state to a binary string.
-
-+aside-code("Example").
-    vectors_bytes = vectors.to_bytes()
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code **exclude]
-        +cell -
-        +cell Named attributes to prevent from being serialized.
-
-    +row("foot")
-        +cell returns
-        +cell bytes
-        +cell The serialized form of the #[code Vectors] object.
-
-+h(2, "from_bytes") Vectors.from_bytes
-    +tag method
-
-p Load state from a binary string.
-
-+aside-code("Example").
-    fron spacy.vectors import Vectors
-    vectors_bytes = vectors.to_bytes()
-    new_vectors = Vectors(StringStore())
-    new_vectors.from_bytes(vectors_bytes)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code data]
-        +cell bytes
-        +cell The data to load from.
-
-    +row
-        +cell #[code **exclude]
-        +cell -
-        +cell Named attributes to prevent from being loaded.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Vectors]
-        +cell The #[code Vectors] object.
-
-+h(2, "attributes") Attributes
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code data]
-        +cell #[code.u-break ndarray[ndim=1, dtype='float32']]
-        +cell
-            |  Stored vectors data. #[code numpy] is used for CPU vectors,
-            |  #[code cupy] for GPU vectors.
-
-    +row
-        +cell #[code key2row]
-        +cell dict
-        +cell
-            |  Dictionary mapping word hashes to rows in the
-            |  #[code Vectors.data] table.
-
-    +row
-        +cell #[code keys]
-        +cell #[code.u-break ndarray[ndim=1, dtype='float32']]
-        +cell
-            |  Array keeping the keys in order, such that
-            |  #[code keys[vectors.key2row[key]] == key]
diff --git a/website/api/vocab.jade b/website/api/vocab.jade
deleted file mode 100644
index 0e3d1b5e2..000000000
--- a/website/api/vocab.jade
+++ /dev/null
@@ -1,411 +0,0 @@
-//- 💫 DOCS > API > VOCAB
-
-include ../_includes/_mixins
-
-p
-    |  The #[code Vocab] object provides a lookup table that allows you to
-    |  access #[+api("lexeme") #[code Lexeme]] objects, as well as the
-    |  #[+api("stringstore") #[code StringStore]]. It also owns underlying
-    |  C-data that is shared between #[code Doc] objects.
-
-+h(2, "init") Vocab.__init__
-    +tag method
-
-p Create the vocabulary.
-
-+aside-code("Example").
-    from spacy.vocab import Vocab
-    vocab = Vocab(strings=[u'hello', u'world'])
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code lex_attr_getters]
-        +cell dict
-        +cell
-            |  A dictionary mapping attribute IDs to functions to compute them.
-            |  Defaults to #[code None].
-
-    +row
-        +cell #[code tag_map]
-        +cell dict
-        +cell
-            |  A dictionary mapping fine-grained tags to coarse-grained
-            |  parts-of-speech, and optionally morphological attributes.
-
-    +row
-        +cell #[code lemmatizer]
-        +cell object
-        +cell A lemmatizer. Defaults to #[code None].
-
-    +row
-        +cell #[code strings]
-        +cell #[code StringStore] or list
-        +cell
-            |  A #[+api("stringstore") #[code StringStore]] that maps
-            |  strings to hash values, and vice versa, or a list of strings.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Vocab]
-        +cell The newly constructed object.
-
-+h(2, "len") Vocab.__len__
-    +tag method
-
-p Get the current number of lexemes in the vocabulary.
-
-+aside-code("Example").
-    doc = nlp(u'This is a sentence.')
-    assert len(nlp.vocab) > 0
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The number of lexems in the vocabulary.
-
-+h(2, "getitem") Vocab.__getitem__
-    +tag method
-
-p
-    |  Retrieve a lexeme, given an int ID or a unicode string. If a previously
-    |  unseen unicode string is given, a new lexeme is created and stored.
-
-+aside-code("Example").
-    apple = nlp.vocab.strings['apple']
-    assert nlp.vocab[apple] == nlp.vocab[u'apple']
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code id_or_string]
-        +cell int / unicode
-        +cell The hash value of a word, or its unicode string.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Lexeme]
-        +cell The lexeme indicated by the given ID.
-
-+h(2, "iter") Vocab.__iter__
-    +tag method
-
-p Iterate over the lexemes in the vocabulary.
-
-+aside-code("Example").
-    stop_words = (lex for lex in nlp.vocab if lex.is_stop)
-
-+table(["Name", "Type", "Description"])
-    +row("foot")
-        +cell yields
-        +cell #[code Lexeme]
-        +cell An entry in the vocabulary.
-
-+h(2, "contains") Vocab.__contains__
-    +tag method
-
-p
-    |  Check whether the string has an entry in the vocabulary. To get the ID
-    |  for a given string, you need to look it up in
-    |  #[+api("vocab#attributes") #[code vocab.strings]].
-
-+aside-code("Example").
-    apple = nlp.vocab.strings['apple']
-    oov = nlp.vocab.strings['dskfodkfos']
-    assert apple in nlp.vocab
-    assert oov not in nlp.vocab
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code string]
-        +cell unicode
-        +cell The ID string.
-
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the string has an entry in the vocabulary.
-
-+h(2, "add_flag") Vocab.add_flag
-    +tag method
-
-p
-    |  Set a new boolean flag to words in the vocabulary. The #[code flag_getter]
-    |  function will be called over the words currently in the vocab, and then
-    |  applied to new words as they occur. You'll then be able to access the flag
-    |  value on each token, using #[code token.check_flag(flag_id)].
-
-+aside-code("Example").
-    def is_my_product(text):
-        products = [u'spaCy', u'Thinc', u'displaCy']
-        return text in products
-
-    MY_PRODUCT = nlp.vocab.add_flag(is_my_product)
-    doc = nlp(u'I like spaCy')
-    assert doc[2].check_flag(MY_PRODUCT) == True
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code flag_getter]
-        +cell dict
-        +cell A function #[code f(unicode) -> bool], to get the flag value.
-
-    +row
-        +cell #[code flag_id]
-        +cell int
-        +cell
-            |  An integer between 1 and 63 (inclusive), specifying the bit at
-            |  which the flag will be stored. If #[code -1], the lowest
-            |  available bit will be chosen.
-
-    +row("foot")
-        +cell returns
-        +cell int
-        +cell The integer ID by which the flag value can be checked.
-
-+h(2, "reset_vectors") Vocab.reset_vectors
-    +tag method
-    +tag-new(2)
-
-p
-    |  Drop the current vector table. Because all vectors must be the same
-    |  width, you have to call this to change the size of the vectors. Only
-    |  one of the #[code width] and #[code shape] keyword arguments can be
-    |  specified.
-
-+aside-code("Example").
-    nlp.vocab.reset_vectors(width=300)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code width]
-        +cell int
-        +cell The new width (keyword argument only).
-
-    +row
-        +cell #[code shape]
-        +cell int
-        +cell The new shape (keyword argument only).
-
-+h(2, "prune_vectors") Vocab.prune_vectors
-    +tag method
-    +tag-new(2)
-
-p
-    |  Reduce the current vector table to #[code nr_row] unique entries. Words
-    |  mapped to the discarded vectors will be remapped to the closest vector
-    |  among those remaining. For example, suppose the original table had
-    |  vectors for the words:
-    |  #[code.u-break ['sat', 'cat', 'feline', 'reclined']]. If we prune the
-    |  vector table to, two rows, we would discard the vectors for "feline"
-    |  and "reclined". These words would then be remapped to the closest
-    |  remaining vector – so "feline" would have the same vector as "cat",
-    |  and "reclined" would have the same vector as "sat". The similarities are
-    |  judged by cosine. The original vectors may be large, so the cosines are
-    |  calculated in minibatches, to reduce memory usage.
-
-+aside-code("Example").
-    nlp.vocab.prune_vectors(10000)
-    assert len(nlp.vocab.vectors) &lt;= 1000
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code nr_row]
-        +cell int
-        +cell The number of rows to keep in the vector table.
-
-    +row
-        +cell #[code batch_size]
-        +cell int
-        +cell
-            |  Batch of vectors for calculating the similarities. Larger batch
-            |  sizes might be faster, while temporarily requiring more memory.
-
-    +row("foot")
-        +cell returns
-        +cell dict
-        +cell
-            |  A dictionary keyed by removed words mapped to
-            |  #[code (string, score)] tuples, where #[code string] is the entry
-            |  the removed word was mapped to, and #[code score] the similarity
-            |  score between the two words.
-
-+h(2, "get_vector") Vocab.get_vector
-    +tag method
-    +tag-new(2)
-
-p
-    |  Retrieve a vector for a word in the vocabulary. Words can be looked up
-    |  by string or hash value. If no vectors data is loaded, a
-    |  #[code ValueError] is raised.
-
-+aside-code("Example").
-    nlp.vocab.get_vector(u'apple')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code orth]
-        +cell int / unicode
-        +cell The hash value of a word, or its unicode string.
-
-    +row("foot")
-        +cell returns
-        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
-        +cell
-            |  A word vector. Size and shape are determined by the
-            |  #[code Vocab.vectors] instance.
-
-+h(2, "set_vector") Vocab.set_vector
-    +tag method
-    +tag-new(2)
-
-p
-    |  Set a vector for a word in the vocabulary. Words can be referenced by
-    |  by string or hash value.
-
-+aside-code("Example").
-    nlp.vocab.set_vector(u'apple', array([...]))
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code orth]
-        +cell int / unicode
-        +cell The hash value of a word, or its unicode string.
-
-    +row
-        +cell #[code vector]
-        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
-        +cell The vector to set.
-
-+h(2, "has_vector") Vocab.has_vector
-    +tag method
-    +tag-new(2)
-
-p
-    |  Check whether a word has a vector. Returns #[code False] if no vectors
-    |  are loaded. Words can be looked up by string or hash value.
-
-+aside-code("Example").
-    if nlp.vocab.has_vector(u'apple'):
-        vector = nlp.vocab.get_vector(u'apple')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code orth]
-        +cell int / unicode
-        +cell The hash value of a word, or its unicode string.
-
-    +row("foot")
-        +cell returns
-        +cell bool
-        +cell Whether the word has a vector.
-
-+h(2, "to_disk") Vocab.to_disk
-    +tag method
-    +tag-new(2)
-
-p Save the current state to a directory.
-
-+aside-code("Example").
-    nlp.vocab.to_disk('/path/to/vocab')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode or #[code Path]
-        +cell
-            |  A path to a directory, which will be created if it doesn't exist.
-            |  Paths may be either strings or #[code Path]-like objects.
-
-+h(2, "from_disk") Vocab.from_disk
-    +tag method
-    +tag-new(2)
-
-p Loads state from a directory. Modifies the object in place and returns it.
-
-+aside-code("Example").
-    from spacy.vocab import Vocab
-    vocab = Vocab().from_disk('/path/to/vocab')
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code path]
-        +cell unicode or #[code Path]
-        +cell
-            |  A path to a directory. Paths may be either strings or
-            |  #[code Path]-like objects.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Vocab]
-        +cell The modified #[code Vocab] object.
-
-+h(2, "to_bytes") Vocab.to_bytes
-    +tag method
-
-p Serialize the current state to a binary string.
-
-+aside-code("Example").
-    vocab_bytes = nlp.vocab.to_bytes()
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code **exclude]
-        +cell -
-        +cell Named attributes to prevent from being serialized.
-
-    +row("foot")
-        +cell returns
-        +cell bytes
-        +cell The serialized form of the #[code Vocab] object.
-
-+h(2, "from_bytes") Vocab.from_bytes
-    +tag method
-
-p Load state from a binary string.
-
-+aside-code("Example").
-    fron spacy.vocab import Vocab
-    vocab_bytes = nlp.vocab.to_bytes()
-    vocab = Vocab()
-    vocab.from_bytes(vocab_bytes)
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code bytes_data]
-        +cell bytes
-        +cell The data to load from.
-
-    +row
-        +cell #[code **exclude]
-        +cell -
-        +cell Named attributes to prevent from being loaded.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Vocab]
-        +cell The #[code Vocab] object.
-
-+h(2, "attributes") Attributes
-
-+aside-code("Example").
-    apple_id = nlp.vocab.strings['apple']
-    assert type(apple_id) == int
-    PERSON = nlp.vocab.strings['PERSON']
-    assert type(PERSON) == int
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code strings]
-        +cell #[code StringStore]
-        +cell A table managing the string-to-int mapping.
-
-    +row
-        +cell #[code vectors]
-            +tag-new(2)
-        +cell #[code Vectors]
-        +cell A table associating word IDs to word vectors.
-
-    +row
-        +cell #[code vectors_length]
-        +cell int
-        +cell Number of dimensions for each word vector.
diff --git a/website/assets/css/_base/_animations.sass b/website/assets/css/_base/_animations.sass
deleted file mode 100644
index 5c82a4fcc..000000000
--- a/website/assets/css/_base/_animations.sass
+++ /dev/null
@@ -1,28 +0,0 @@
-//- 💫 CSS > BASE > ANIMATIONS
-
-//- Fade in
-
-@keyframes fadeIn
-    from
-        opacity: 0
-
-    to
-        opacity: 1
-
-
-//- Element slides in from the top
-
-@keyframes slideInDown
-    from
-        transform: translate3d(0, -100%, 0)
-        visibility: visible
-
-    to
-        transform: translate3d(0, 0, 0)
-
-
-//- Element rotates
-
-@keyframes rotate
-    to
-        transform: rotate(360deg)
diff --git a/website/assets/css/_base/_fonts.sass b/website/assets/css/_base/_fonts.sass
deleted file mode 100644
index c1af115a7..000000000
--- a/website/assets/css/_base/_fonts.sass
+++ /dev/null
@@ -1,27 +0,0 @@
-//- 💫 CSS > BASE > FONTS
-
-//  HK Grotesk
-
-@font-face
-    font-family: "HK Grotesk"
-    font-style: normal
-    font-weight: 500
-    src: url("/assets/fonts/hkgrotesk-semibold.woff2") format("woff2"), url("/assets/fonts/hkgrotesk-semibold.woff") format("woff")
-
-@font-face
-    font-family: "HK Grotesk"
-    font-style: italic
-    font-weight: 500
-    src: url("/assets/fonts/hkgrotesk-semibolditalic.woff2") format("woff2"), url("/assets/fonts/hkgrotesk-semibolditalic.woff") format("woff")
-
-@font-face
-    font-family: "HK Grotesk"
-    font-style: normal
-    font-weight: 600
-    src: url("/assets/fonts/hkgrotesk-bold.woff2") format("woff2"), url("/assets/fonts/hkgrotesk-bold.woff") format("woff")
-
-@font-face
-    font-family: "HK Grotesk"
-    font-style: italic
-    font-weight: 600
-    src: url("/assets/fonts/hkgrotesk-bolditalic.woff2") format("woff2"), url("/assets/fonts/hkgrotesk-bolditalic.woff") format("woff")
diff --git a/website/assets/css/_base/_grid.sass b/website/assets/css/_base/_grid.sass
deleted file mode 100644
index 7f9ed9344..000000000
--- a/website/assets/css/_base/_grid.sass
+++ /dev/null
@@ -1,59 +0,0 @@
-//- 💫 CSS > BASE > GRID
-
-//- Grid container
-
-.o-grid
-    display: flex
-    flex-wrap: wrap
-
-    @include breakpoint(min, sm)
-        flex-direction: row
-        align-items: stretch
-        justify-content: space-between
-
-    &.o-grid--center
-        align-items: center
-        justify-content: center
-
-    &.o-grid--vcenter
-        align-items: center
-
-    &.o-grid--space
-        justify-content: space-between
-
-    &.o-grid--nowrap
-        flex-wrap: nowrap
-
-
-//- Grid column
-
-.o-grid__col
-    $grid-gutter: 2rem
-
-    margin-top: $grid-gutter
-    min-width: 0  // hack to prevent overflow
-
-    @include breakpoint(min, lg)
-        display: flex
-        flex: 0 0 100%
-        flex-direction: column
-        flex-wrap: wrap
-
-        @each $mode, $count in $grid
-            &.o-grid__col--#{$mode}
-                $percentage: calc(#{100% / $count} - #{$grid-gutter})
-                flex: 0 0 $percentage
-                max-width: $percentage
-
-    @include breakpoint(max, md)
-        flex: 0 0 100%
-        flex-flow: column wrap
-
-    &.o-grid__col--no-gutter
-        margin-top: 0
-
-    // Fix overflow issue in old browsers
-
-    & > *
-        flex-shrink: 1
-        max-width: 100%
diff --git a/website/assets/css/_base/_layout.sass b/website/assets/css/_base/_layout.sass
deleted file mode 100644
index 145b42789..000000000
--- a/website/assets/css/_base/_layout.sass
+++ /dev/null
@@ -1,43 +0,0 @@
-//- 💫 CSS > BASE > LAYOUT
-
-//- HTML
-
-html
-    font-size: $type-base
-
-
-//- Body
-
-body
-    animation: fadeIn 0.25s ease
-    background: $color-back
-    color: $color-front
-
-
-//- Paragraphs
-
-p
-    @extend .o-block, .u-text
-
-p:empty
-    margin-bottom: 0
-
-
-//- Links
-
-main p a,
-main table a,
-main > *:not(footer) li a,
-main aside a
-    @extend .u-link
-
-a:focus
-    outline: 1px dotted $color-theme
-
-
-//- Selection
-
-::selection
-    background: $color-theme
-    color: $color-back
-    text-shadow: none
diff --git a/website/assets/css/_base/_objects.sass b/website/assets/css/_base/_objects.sass
deleted file mode 100644
index ef463870e..000000000
--- a/website/assets/css/_base/_objects.sass
+++ /dev/null
@@ -1,249 +0,0 @@
-//- 💫 CSS > BASE > OBJECTS
-
-//- Main container
-
-.o-main
-    padding: $nav-height 0 0 0
-    max-width: 100%
-    min-height: 100vh
-
-    @include breakpoint(min, md)
-        &.o-main--sidebar
-            margin-left: $sidebar-width
-
-        &.o-main--aside
-            margin-right: $aside-width
-            position: relative
-
-            &:after
-                @include position(absolute, top, left, 0, 100%)
-                @include size($aside-width, 100%)
-                content: ""
-                display: block
-                background: $pattern
-                z-index: -1
-                min-height: 100vh
-
-
-//- Content container
-
-.o-content
-    padding: 3rem 7.5rem
-    margin: 0 auto
-    width: $content-width
-    max-width: 100%
-
-    @include breakpoint(max, sm)
-        padding: 3rem
-
-
-//- Footer
-
-.o-footer
-    position: relative
-    padding: 2.5rem 0
-    overflow: auto
-    background: $color-subtle-light
-
-    .o-main &
-        border-top-left-radius: $border-radius
-
-
-//- Blocks
-
-.o-section
-    width: 100%
-    max-width: 100%
-
-    &:not(:last-child)
-        margin-bottom: 7rem
-        padding-bottom: 4rem
-        border-bottom: 1px dotted $color-subtle
-
-    &.o-section--small
-        overflow: auto
-
-        &:not(:last-child)
-            margin-bottom: 3.5rem
-            padding-bottom: 2rem
-
-.o-block
-    margin-bottom: 4rem
-
-.o-block-small
-    margin-bottom: 2rem
-
-.o-no-block.o-no-block
-    margin-bottom: 0
-
-.o-card
-    background: $color-back
-    border-radius: $border-radius
-    box-shadow: $box-shadow
-
-
-//- Accordion
-
-.o-accordion
-    &:not(:last-child)
-        margin-bottom: 2rem
-
-.o-accordion__content
-    margin-top: 3rem
-
-.o-accordion__button
-    font: inherit
-    border-radius: $border-radius
-    width: 100%
-    padding: 1.5rem 2rem
-    background: $color-subtle-light
-
-    &[aria-expanded="true"]
-        border-bottom: 3px solid $color-subtle
-        border-bottom-left-radius: 0
-        border-bottom-right-radius: 0
-
-        .o-accordion__hide
-            display: none
-
-    &:focus:not([aria-expanded="true"])
-        background: $color-subtle
-
-.o-accordion__icon
-    @include size(2.5rem)
-    background: $color-theme
-    color: $color-back
-    border-radius: 50%
-    padding: 0.35rem
-    pointer-events: none
-
-//- Box
-
-.o-box
-    background: $color-subtle-light
-    padding: 2rem
-    border-radius: $border-radius
-
-.o-box__logos
-    padding-bottom: 1rem
-
-
-//- Icons
-
-.o-icon
-    vertical-align: middle
-
-    &.o-icon--inline
-        margin: 0 0.5rem 0 0.1rem
-
-    &.o-icon--tag
-        vertical-align: bottom
-        height: 100%
-        position: relative
-        top: 1px
-
-.o-emoji
-    margin-right: 0.75rem
-    vertical-align: text-bottom
-
-.o-badge
-    border-radius: 1em
-
-.o-thumb
-    @include size(100px)
-    overflow: hidden
-    border-radius: 50%
-
-    &.o-thumb--small
-        @include size(35px)
-
-
-//- SVG
-
-.o-svg
-    height: auto
-
-
-//- Inline List
-
-.o-inline-list > *
-    display: inline
-
-    &:not(:last-child)
-        margin-right: 3rem
-
-
-//- Logo
-
-.o-logo
-    @include size($logo-width, $logo-height)
-    fill: currentColor
-    vertical-align: middle
-    margin: 0 0.5rem
-
-
-//- Embeds
-
-.o-chart
-    max-width: 100%
-
-.cp_embed_iframe
-    border: 1px solid $color-subtle
-    border-radius: $border-radius
-
-
-//- Responsive Video embeds
-
-.o-video
-    position: relative
-    height: 0
-
-    @each $ratio1, $ratio2 in (16, 9), (4, 3)
-        &.o-video--#{$ratio1}x#{$ratio2}
-            padding-bottom: (100% * $ratio2 / $ratio1)
-
-.o-video__iframe
-    @include position(absolute, top, left, 0, 0)
-    @include size(100%)
-    border-radius: var(--border-radius)
-
-
-//- Form fields
-
-.o-field
-    background: $color-back
-    padding: 0 0.25em
-    border-radius: 2em
-    border: 1px solid $color-subtle
-    margin-bottom: 0.25rem
-
-.o-field__input,
-.o-field__button
-    padding: 0 0.35em
-
-.o-field__input
-    width: 100%
-
-.o-field__select
-     background: transparent
-     color: $color-dark
-     height: 1.4em
-     border: none
-     text-align-last: center
-     width: 100%
-
-//- Abbreviations
-
-.o-abbr
-    +breakpoint(min, md)
-        cursor: help
-        border-bottom: 2px dotted $color-theme
-        padding-bottom: 3px
-
-    +breakpoint(max, sm)
-        &[data-tooltip]:before
-            content: none
-
-        &:after
-            content: " (" attr(aria-label) ")"
-            color: $color-subtle-dark
diff --git a/website/assets/css/_base/_reset.sass b/website/assets/css/_base/_reset.sass
deleted file mode 100644
index 0ff1432d0..000000000
--- a/website/assets/css/_base/_reset.sass
+++ /dev/null
@@ -1,103 +0,0 @@
-//- 💫 CSS > BASE > RESET
-
-*, *:before, *:after
-    box-sizing: border-box
-    padding: 0
-    margin: 0
-    border: 0
-    outline: 0
-
-html
-    font-family: sans-serif
-    text-rendering: optimizeSpeed
-    -ms-text-size-adjust: 100%
-    -webkit-text-size-adjust: 100%
-    -webkit-font-smoothing: antialiased
-    -moz-osx-font-smoothing: grayscale
-
-body
-    margin: 0
-
-article, aside, details, figcaption, figure, footer, header, main, menu, nav,
-section, summary, progress
-    display: block
-
-a
-    background-color: transparent
-    color: inherit
-    text-decoration: none
-
-    &:active,
-    &:hover
-        outline: 0
-
-abbr[title]
-    border-bottom: none
-    text-decoration: underline
-    text-decoration: underline dotted
-
-b, strong
-    font-weight: inherit
-    font-weight: bolder
-
-small
-    font-size: 80%
-
-sub, sup
-    position: relative
-    font-size: 65%
-    line-height: 0
-    vertical-align: baseline
-
-sup
-    top: -0.5em
-
-sub
-    bottom: -0.15em
-
-img
-    border: 0
-    height: auto
-    max-width: 100%
-
-svg
-    max-width: 100%
-    color-interpolation-filters: sRGB
-    fill: currentColor
-
-    &:not(:root)
-        overflow: hidden
-
-hr
-    box-sizing: content-box
-    overflow: visible
-    height: 0
-
-pre
-    overflow: auto
-
-code, pre
-    font-family: monospace, monospace
-    font-size: 1em
-
-table
-    text-align: left
-    width: 100%
-    max-width: 100%
-    border-collapse: collapse
-
-    td, th
-        vertical-align: top
-
-ul, ol
-    list-style: none
-
-input, button
-    appearance: none
-    background: transparent
-
-button
-    cursor: pointer
-
-progress
-    appearance: none
diff --git a/website/assets/css/_base/_utilities.sass b/website/assets/css/_base/_utilities.sass
deleted file mode 100644
index 10fe843a4..000000000
--- a/website/assets/css/_base/_utilities.sass
+++ /dev/null
@@ -1,267 +0,0 @@
-//- 💫 CSS > BASE > UTILITIES
-
-//- Text
-
-.u-text,
-.u-text-small,
-.u-text-tiny
-    font-family: $font-primary
-
-.u-text
-    font-size: 1.35rem
-    line-height: 1.5
-
-.u-text-small
-    font-size: 1.3rem
-    line-height: 1.375
-
-.u-text-tiny
-    font-size: 1.1rem
-    line-height: 1.375
-
-//- Labels & Tags
-
-.u-text-label
-    font: normal 600 1.4rem/#{1.5} $font-secondary
-    text-transform: uppercase
-
-    &.u-text-label--light,
-    &.u-text-label--dark
-        display: inline-block
-        border-radius: 1em
-        padding: 0 1rem 0.15rem
-
-    &.u-text-label--dark
-        background: $color-dark
-        box-shadow: inset 1px 1px 1px rgba($color-front, 0.25)
-        color: $color-back
-        margin: 1.5rem 0 0 2rem
-
-    &.u-text-label--light
-        background: $color-back
-        color: $color-theme
-        margin-bottom: 1rem
-
-.u-text-tag
-    display: inline-block
-    font: 600 1.1rem/#{1} $font-secondary
-    background: $color-theme
-    color: $color-back
-    padding: 2px 6px 4px
-    border-radius: 1em
-    text-transform: uppercase
-    vertical-align: middle
-
-    &.u-text-tag--spaced
-        margin-left: 0.75em
-        margin-right: 0.5em
-
-
-//- Headings
-
-.u-heading
-    margin-bottom: 1em
-
-    @include breakpoint(max, md)
-        word-wrap: break-word
-
-    &:not(:first-child)
-        padding-top: 3.5rem
-
-    &.u-heading--title:after
-        content: ""
-        display: block
-        width: 10%
-        min-width: 6rem
-        height: 6px
-        background: $color-theme
-        margin-top: 3rem
-
-.u-heading-0
-    font: normal 600 7rem/#{1} $font-secondary
-
-    @include breakpoint(max, sm)
-        font-size: 6rem
-
-
-@each $level, $size in $headings
-    .u-heading-#{$level}
-        font: normal 500 #{$size}rem/#{1.1} $font-secondary
-
-.u-heading__teaser
-    margin-top: 2rem
-    font-weight: normal
-
-
-//- Links
-
-.u-link
-    color: $color-theme
-    border-bottom: 1px solid
-    transition: color 0.2s ease
-
-    &:hover
-        color: $color-theme-dark
-
-.u-hand
-    cursor: pointer
-
-.u-hide-link.u-hide-link
-    border: none
-    color: inherit
-
-    &:hover
-        color: inherit
-
-.u-permalink
-    position: relative
-
-    &:before
-        content: "\00b6"
-        font-size: 0.9em
-        font-weight: normal
-        color: $color-subtle
-        @include position(absolute, top, left, 0.15em, -2.85rem)
-        opacity: 0
-        transition: opacity 0.2s ease
-
-    &:hover:before
-        opacity: 1
-
-    &:active:before
-        color: $color-theme
-
-    &:target
-        display: inline-block
-
-        &:before
-            bottom: 0.15em
-            top: initial
-
-
-[id]:target
-    padding-top: $nav-height * 1.25
-
-
-
-//- Layout
-
-.u-width-full
-    width: 100%
-
-.u-float-left
-    float: left
-    margin-right: 1rem
-
-.u-float-right
-    float: right
-    margin-left: 1rem
-
-.u-text-center
-    text-align: center
-
-.u-text-right
-    text-align: right
-
-.u-padding
-    padding: 5rem
-
-.u-padding-small
-    padding: 0.5em 0.75em
-
-.u-padding-medium
-    padding: 1.8rem
-
-.u-padding-top
-    padding-top: 2rem
-
-.u-inline-block
-    display: inline-block
-
-.u-flex-full
-    flex: 1
-
-.u-nowrap
-    white-space: nowrap
-
-.u-wrap
-    white-space: pre-wrap
-
-.u-break.u-break
-    word-wrap: break-word
-    white-space: initial
-
-    &.u-break--all
-        word-break: break-all
-
-.u-no-border
-    border: none
-
-.u-border
-    border: 1px solid $color-subtle
-    border-radius: 2px
-
-.u-border-dotted
-    border-bottom: 1px dotted $color-subtle
-
-@each $name, $color in (theme: $color-theme, dark: $color-dark, subtle: $color-subtle-dark, light: $color-back, red: $color-red, green: $color-green, yellow: $color-yellow)
-    .u-color-#{$name}
-        color: $color
-
-.u-grayscale
-    filter: grayscale(100%)
-    transition: filter 0.15s ease
-    user-select: none
-
-    &:hover
-        filter: none
-
-.u-pattern
-    background: $pattern
-
-
-//- Loaders
-
-.u-loading,
-[data-loading]
-    $spinner-size: 75px
-    $spinner-bar: 8px
-
-    min-height: $spinner-size * 2
-    position: relative
-
-    & > *
-        opacity: 0.35
-
-    &:before
-        @include position(absolute, top, left, 0, 0)
-        @include size($spinner-size)
-        right: 0
-        bottom: 0
-        margin: auto
-        content: ""
-        border: $spinner-bar solid $color-subtle
-        border-right: $spinner-bar solid $color-theme
-        border-radius: 50%
-        animation: rotate 1s linear infinite
-        z-index: 10
-
-
-//- Hidden elements
-
-.u-hidden,
-[v-cloak]
-    display: none !important
-
-@each $breakpoint in (xs, sm, md)
-    .u-hidden-#{$breakpoint}.u-hidden-#{$breakpoint}
-        @include breakpoint(max, $breakpoint)
-            display: none
-
-//- Transitions
-
-.u-fade-enter-active
-    transition: opacity 0.5s
-
-.u-fade-enter
-    opacity: 0
diff --git a/website/assets/css/_components/_asides.sass b/website/assets/css/_components/_asides.sass
deleted file mode 100644
index c59590c29..000000000
--- a/website/assets/css/_components/_asides.sass
+++ /dev/null
@@ -1,43 +0,0 @@
-//- 💫 CSS > COMPONENTS > ASIDES
-
-//- Aside container
-
-.c-aside
-    position: relative
-
-
-//- Aside content
-
-.c-aside__content
-    background: $color-front
-    border-top-left-radius: $border-radius
-    border-bottom-left-radius: $border-radius
-    z-index: 10
-
-    @include breakpoint(min, md)
-        @include position(absolute, top, left, -3rem, calc(100% + 5.5rem))
-        width: calc(#{$aside-width} + 2rem)
-
-        // Banner effect
-
-        &:after
-            $triangle-size: 2rem
-
-            @include position(absolute, bottom, left, -$triangle-size / 2, $border-radius / 2)
-            @include size(0)
-            border-color: transparent
-            border-style: solid
-            border-top-color: $color-dark
-            border-width: $triangle-size / 2 0 0 calc(#{$triangle-size} - #{$border-radius / 2})
-            content: ""
-
-    @include breakpoint(max, sm)
-        display: block
-        margin: 2rem 0
-
-
-//- Aside text
-
-.c-aside__text
-    color: $color-back
-    padding: 1.5rem 2.5rem 3rem 2rem
diff --git a/website/assets/css/_components/_buttons.sass b/website/assets/css/_components/_buttons.sass
deleted file mode 100644
index d3ff4b037..000000000
--- a/website/assets/css/_components/_buttons.sass
+++ /dev/null
@@ -1,52 +0,0 @@
-//- 💫 CSS > COMPONENTS > BUTTONS
-
-.c-button
-    display: inline-block
-    font-weight: bold
-    padding: 0.8em 1.1em 1em
-    margin-bottom: 1px
-    border: 2px solid $color-theme
-    border-radius: 2em
-    text-align: center
-    transition: background-color, color 0.25s ease
-
-    &:hover
-        border-color: $color-theme-dark
-
-    &.c-button--small
-        font-size: 1.1rem
-        padding: 0.65rem 1.1rem 0.825rem
-
-    &.c-button--primary
-        background: $color-theme
-        color: $color-back
-
-        &:hover
-            background: $color-theme-dark
-
-    &.c-button--secondary
-        background: $color-back
-        color: $color-theme
-
-        &:hover
-            color: $color-theme-dark
-
-    &.c-button--secondary-light
-        background: transparent
-        color: $color-back
-        border-color: $color-back
-
-.c-icon-button
-    @include size(35px)
-    background: $color-subtle-light
-    color: $color-subtle-dark
-    border-radius: 50%
-    padding: 0.5rem
-    transition: color 0.2s ease
-
-    &:hover
-        color: $color-theme
-
-    &.c-icon-button--right
-        float: right
-        margin-left: 3rem
diff --git a/website/assets/css/_components/_chat.sass b/website/assets/css/_components/_chat.sass
deleted file mode 100644
index 659f80364..000000000
--- a/website/assets/css/_components/_chat.sass
+++ /dev/null
@@ -1,105 +0,0 @@
-//- 💫 CSS > COMPONENTS > CHAT
-
-.c-chat
-    @include position(fixed, top, left, 0, 60%)
-    bottom: 0
-    right: 0
-    display: flex
-    flex-flow: column nowrap
-    background: $color-back
-    transition: transform 0.3s cubic-bezier(0.16, 0.22, 0.22, 1.7)
-    box-shadow: -0.25rem 0 1rem 0 rgba($color-front, 0.25)
-    z-index: 100
-
-    @include breakpoint(min, md)
-        left: calc(100% - #{$aside-width} - #{$aside-padding})
-
-    @include breakpoint(max, sm)
-        left: 50%
-
-    @include breakpoint(max, xs)
-        left: 0
-
-    &.is-collapsed:not(.is-loading)
-        transform: translateX(110%)
-
-    &:before
-        @include position(absolute, top, left, 1.25rem, 2rem)
-        content: attr(data-title)
-        font: bold 1.4rem $font-secondary
-        text-transform: uppercase
-        color: $color-back
-
-    &:after
-        @include position(absolute, top, left, 0, 100%)
-        content: ""
-        z-index: -1
-        bottom: 0
-        right: -100%
-        background: $color-back
-
-    & > iframe
-        width: 100%
-        flex: 1 1 calc(100% - #{$nav-height})
-        border: 0
-
-    .gitter-chat-embed-loading-wrapper
-        @include position(absolute, top, left, 0, 0)
-        right: 0
-        bottom: 0
-        display: none
-        justify-content: center
-        align-items: center
-
-        .is-loading &
-            display: flex
-
-    .gitter-chat-embed-action-bar,
-    .gitter-chat-embed-action-bar-item
-        display: flex
-
-    .gitter-chat-embed-action-bar
-        align-items: center
-        justify-content: flex-end
-        background: $color-theme
-        padding: 0 1rem 0 2rem
-        flex: 0 0 $nav-height
-
-    .gitter-chat-embed-action-bar-item
-        @include size(40px)
-        padding: 0
-        opacity: 0.75
-        background-position: 50%
-        background-repeat: no-repeat
-        background-size: 22px 22px
-        border: 0
-        cursor: pointer
-        transition: all 0.2s ease
-
-        &:focus,
-        &:hover
-            opacity: 1
-
-        &.gitter-chat-embed-action-bar-item-pop-out
-            background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyMCIgaGVpZ2h0PSIyMCIgdmlld0JveD0iMCAwIDIwIDIwIj48cGF0aCBmaWxsPSIjZmZmIiBkPSJNMTYgMmgtOC4wMjFjLTEuMDk5IDAtMS45NzkgMC44OC0xLjk3OSAxLjk4djguMDIwYzAgMS4xIDAuOSAyIDIgMmg4YzEuMSAwIDItMC45IDItMnYtOGMwLTEuMS0wLjktMi0yLTJ6TTE2IDEyaC04di04aDh2OHpNNCAxMGgtMnY2YzAgMS4xIDAuOSAyIDIgMmg2di0yaC02di02eiI+PC9wYXRoPjwvc3ZnPg==)
-            margin-right: -4px
-
-        &.gitter-chat-embed-action-bar-item-collapse-chat
-            background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0Ij48cGF0aCBmaWxsPSIjZmZmIiBkPSJNMTguOTg0IDYuNDIybC01LjU3OCA1LjU3OCA1LjU3OCA1LjU3OC0xLjQwNiAxLjQwNi01LjU3OC01LjU3OC01LjU3OCA1LjU3OC0xLjQwNi0xLjQwNiA1LjU3OC01LjU3OC01LjU3OC01LjU3OCAxLjQwNi0xLjQwNiA1LjU3OCA1LjU3OCA1LjU3OC01LjU3OHoiPjwvcGF0aD48L3N2Zz4=)
-
-.c-chat__button
-    @include position(fixed, bottom, right, 1.5rem, 1.5rem)
-    z-index: 5
-    color: $color-back
-    background: $color-front
-    border-radius: 1em
-    padding: 0.5rem 1.15rem 0.35rem
-    opacity: 0.7
-    transition: opacity 0.2s ease
-
-    &:hover
-        opacity: 1
-
-
-.gitter-open-chat-button
-    display: none
diff --git a/website/assets/css/_components/_code.sass b/website/assets/css/_components/_code.sass
deleted file mode 100644
index 598ab4655..000000000
--- a/website/assets/css/_components/_code.sass
+++ /dev/null
@@ -1,202 +0,0 @@
-//- 💫 CSS > COMPONENTS > CODE
-
-//- Code block
-
-.c-code-block,
-.juniper-cell
-    background: $color-front
-    color: darken($color-back, 20)
-    padding: 0.75em 0
-    border-radius: $border-radius
-    overflow: auto
-    width: 100%
-    max-width: 100%
-    white-space: pre
-    direction: ltr
-
-.c-code-block--has-icon
-    padding: 0
-    display: flex
-    border-top-left-radius: 0
-    border-bottom-left-radius: 0
-
-.c-code-block__icon
-    padding: 0 0 0 1rem
-    display: flex
-    justify-content: center
-    align-items: center
-
-    &.c-code-block__icon--border
-        border-left: 6px solid
-
-//- Code block content
-
-.c-code-block__content,
-.juniper-input,
-.jp-OutputArea
-    display: block
-    font: normal normal 1.1rem/#{1.9} $font-code
-    padding: 1em 2em
-
-.c-code-block__content[data-prompt]:before,
-    content: attr(data-prompt)
-    margin-right: 0.65em
-    display: inline-block
-    vertical-align: middle
-    opacity: 0.5
-
-//- Juniper
-
-[data-executable]
-    margin-bottom: 0
-
-.juniper-cell
-    border: 0
-
-.juniper-input
-    padding: 0
-
-.juniper-output
-    color: inherit
-    background: inherit
-    padding: 0
-
-.jp-OutputArea
-    &:not(:empty)
-        padding: 2rem 2rem 1rem
-        border-top: 1px solid $color-dark
-        margin-top: 2rem
-
-    .entities, svg
-        white-space: initial
-        font-family: inherit
-
-    .entities
-        font-size: 1.35rem
-
-.jp-OutputArea pre
-    font: inherit
-
-.jp-OutputPrompt.jp-OutputArea-prompt
-    padding-top: 0.5em
-    margin-right: 1rem
-    font-family: inherit
-    font-weight: bold
-
-.juniper-button
-    @extend .u-text-label, .u-text-label--dark
-    position: static
-
-.juniper-wrapper
-    position: relative
-
-.juniper-wrapper__text
-    @include position(absolute, top, right, 1.25rem, 1.25rem)
-    color: $color-subtle-dark
-    z-index: 10
-
-//- Code
-
-code, .CodeMirror, .jp-RenderedText, .jp-OutputArea
-    -webkit-font-smoothing: subpixel-antialiased
-    -moz-osx-font-smoothing: auto
-
-
-//- Inline code
-
-*:not(a):not(.c-code-block) > code
-    color: $color-dark
-
-*:not(.c-code-block) > code
-    font-size: 90%
-    background-color: $color-subtle-light
-    padding: 0.2rem 0.4rem
-    border-radius: 0.25rem
-    font-family: $font-code
-    margin: 0
-    box-decoration-break: clone
-    white-space: nowrap
-
-    .c-aside__content &
-        background: lighten($color-front, 10)
-        color: $color-back
-        text-shadow: none
-
-
-//- Syntax Highlighting (Prism)
-
-[class*="language-"] .token
-    &.comment, &.prolog, &.doctype, &.cdata, &.punctuation
-        color: map-get($syntax-highlighting, comment)
-
-    &.property, &.tag, &.constant, &.symbol, &.deleted
-        color: map-get($syntax-highlighting, tag)
-
-    &.boolean, &.number
-        color: map-get($syntax-highlighting, number)
-
-    &.selector, &.attr-name, &.string, &.char, &.builtin, &.inserted
-        color: map-get($syntax-highlighting, selector)
-
-    @at-root .language-css .token.string,
-    &.operator, &.entity, &.url, &.variable
-        color: map-get($syntax-highlighting, operator)
-
-    &.atrule, &.attr-value, &.function
-        color: map-get($syntax-highlighting, function)
-
-    &.regex, &.important
-        color: map-get($syntax-highlighting, regex)
-
-    &.keyword
-        color: map-get($syntax-highlighting, keyword)
-
-    &.italic
-        font-style: italic
-
-//- Syntax Highlighting (CodeMirror)
-
-.CodeMirror.cm-s-default
-    background: $color-front
-    color: darken($color-back, 20)
-
-    .CodeMirror-selected
-        background: $color-theme
-        color: $color-back
-
-    .CodeMirror-cursor
-        border-left-color: currentColor
-
-    .cm-variable-2
-        color: inherit
-        font-style: italic
-
-    .cm-comment
-        color: map-get($syntax-highlighting, comment)
-
-    .cm-keyword, .cm-builtin
-        color: map-get($syntax-highlighting, keyword)
-
-    .cm-operator
-        color: map-get($syntax-highlighting, operator)
-
-    .cm-string
-        color: map-get($syntax-highlighting, selector)
-
-    .cm-number
-        color: map-get($syntax-highlighting, number)
-
-    .cm-def
-        color: map-get($syntax-highlighting, function)
-
-//- Syntax highlighting (Jupyter)
-
-.jp-RenderedText pre
-    .ansi-cyan-fg
-        color: map-get($syntax-highlighting, function)
-
-    .ansi-green-fg
-        color: $color-green
-
-    .ansi-red-fg
-        color: map-get($syntax-highlighting, operator)
diff --git a/website/assets/css/_components/_landing.sass b/website/assets/css/_components/_landing.sass
deleted file mode 100644
index 4c15e4a39..000000000
--- a/website/assets/css/_components/_landing.sass
+++ /dev/null
@@ -1,63 +0,0 @@
-//- 💫 CSS > COMPONENTS > LANDING
-
-.c-landing
-    background: $color-theme
-    padding-top: $nav-height * 1.5
-    width: 100%
-
-.c-landing__wrapper
-    background: $pattern
-    width: 100%
-
-.c-landing__content
-    background: $pattern-overlay
-    width: 100%
-    min-height: 573px
-
-.c-landing__headlines
-    position: relative
-    top: -1.5rem
-    left: 1rem
-
-.c-landing__title
-    color: $color-back
-    text-align: center
-    margin-bottom: 0.75rem
-
-.c-landing__blocks
-    @include breakpoint(min, sm)
-        position: relative
-        top: -25rem
-        margin-bottom: -25rem
-
-.c-landing__card
-    padding: 3rem 2.5rem
-
-.c-landing__banner
-    background: $color-theme
-
-.c-landing__banner__content
-    @include breakpoint(min, md)
-        border: 4px solid
-        padding: 1rem 6.5rem 2rem 4rem
-
-
-.c-landing__banner__text
-    font-weight: 500
-
-    strong
-        font-weight: 800
-
-    p
-        font-size: 1.5rem
-
-    @include breakpoint(min, md)
-        padding-top: 7rem
-
-.c-landing__badge
-    transform: rotate(7deg)
-    display: block
-    text-align: center
-
-    @include breakpoint(min, md)
-        @include position(absolute, top, right, 16rem, 6rem)
diff --git a/website/assets/css/_components/_lists.sass b/website/assets/css/_components/_lists.sass
deleted file mode 100644
index 553af6578..000000000
--- a/website/assets/css/_components/_lists.sass
+++ /dev/null
@@ -1,39 +0,0 @@
-//- 💫 CSS > COMPONENTS > LISTS
-
-//- List Container
-
-.c-list
-    @each $type, $counter in (numbers: decimal, letters: upper-latin, roman: lower-roman)
-        &.c-list--#{$type}
-            counter-reset: li
-
-            .c-list__item:before
-                content: counter(li, #{$counter}) '.'
-                font-size: 1em
-                padding-right: 1rem
-
-
-//- List Item
-
-.c-list__item
-    padding-left: 2rem
-    margin-bottom: 0.5em
-    margin-left: 1.25rem
-
-    &:before
-        content: '\25CF'
-        display: inline-block
-        font-size: 0.6em
-        font-weight: bold
-        padding-right: 1em
-        margin-left: -3.75rem
-        text-align: right
-        width: 2.5rem
-        counter-increment: li
-        box-sizing: content-box
-
-
-//- List icon
-
-.c-list__icon
-    margin-right: 1rem
diff --git a/website/assets/css/_components/_misc.sass b/website/assets/css/_components/_misc.sass
deleted file mode 100644
index 8d131aa80..000000000
--- a/website/assets/css/_components/_misc.sass
+++ /dev/null
@@ -1,68 +0,0 @@
-//- 💫 CSS > COMPONENTS > MISC
-
-.x-terminal
-    background: $color-subtle-light
-    color: $color-front
-    padding: $border-radius
-    border-radius: 1em
-    width: 100%
-    position: relative
-
-    &.x-terminal--small
-        background: $color-dark
-        color: $color-subtle
-        border-radius: 4px
-        margin-bottom: 4rem
-
-.x-terminal__icons
-    display: none
-    position: absolute
-    padding: 10px
-
-    @include breakpoint(min, sm)
-        display: block
-
-    &:before,
-    &:after,
-    span
-        @include size(15px)
-        display: inline-block
-        float: left
-        border-radius: 50%
-        margin-right: 10px
-
-    &:before
-        content: ""
-        background: $color-red
-
-    span
-        background: $color-green
-
-    &:after
-        content: ""
-        background: $color-yellow
-
-    &.x-terminal__icons--small
-        &:before,
-        &:after,
-        span
-            @include size(10px)
-
-.x-terminal__code
-    margin: 0
-    border: none
-    border-bottom-left-radius: 5px
-    border-bottom-right-radius: 5px
-    width: 100%
-    max-width: 100%
-    white-space: pre-wrap
-
-
-.x-terminal__button.x-terminal__button
-    @include position(absolute, bottom, right, 2.65rem, 2.6rem)
-    background: $color-dark
-    border-color: $color-dark
-
-    &:hover
-        background: darken($color-dark, 5)
-        border-color: darken($color-dark, 5)
diff --git a/website/assets/css/_components/_navigation.sass b/website/assets/css/_components/_navigation.sass
deleted file mode 100644
index eaeb33957..000000000
--- a/website/assets/css/_components/_navigation.sass
+++ /dev/null
@@ -1,61 +0,0 @@
-//- 💫 CSS > COMPONENTS > NAVIGATION
-
-.c-nav
-    @include position(fixed, top, left, 0, 0)
-    @include size(100%, $nav-height)
-    background: $color-back
-    color: $color-theme
-    align-items: center
-    display: flex
-    justify-content: space-between
-    flex-flow: row nowrap
-    padding: 0 0 0 1rem
-    z-index: 30
-    width: 100%
-    box-shadow: $box-shadow
-
-    &.is-fixed
-        animation: slideInDown 0.5s ease-in-out
-        position: fixed
-
-.c-nav__menu
-    @include size(100%)
-    display: flex
-    flex-flow: row nowrap
-    border-color: inherit
-    flex: 1
-
-
-    @include breakpoint(max, sm)
-        @include scroll-shadow-base($color-front)
-        overflow-x: auto
-        overflow-y: hidden
-        -webkit-overflow-scrolling: touch
-
-    @include breakpoint(min, md)
-        justify-content: flex-end
-
-.c-nav__menu__item
-    display: flex
-    align-items: center
-    height: 100%
-    text-transform: uppercase
-    font-family: $font-secondary
-    font-size: 1.6rem
-    font-weight: bold
-    color: $color-theme
-
-    &:not(:first-child)
-        margin-left: 2em
-
-    &:last-child
-        @include scroll-shadow-cover(right, $color-back)
-        padding-right: 2rem
-
-    &:first-child
-        @include scroll-shadow-cover(left, $color-back)
-        padding-left: 2rem
-
-    &.is-active
-        color: $color-dark
-        pointer-events: none
diff --git a/website/assets/css/_components/_quickstart.sass b/website/assets/css/_components/_quickstart.sass
deleted file mode 100644
index d853d7569..000000000
--- a/website/assets/css/_components/_quickstart.sass
+++ /dev/null
@@ -1,100 +0,0 @@
-//- 💫 CSS > COMPONENTS > QUICKSTART
-
-.c-quickstart
-    border-radius: $border-radius
-    display: none
-    background: $color-subtle-light
-
-    &:not([style]) + .c-quickstart__info
-        display: none
-
-    .c-code-block
-        border-top-left-radius: 0
-        border-top-right-radius: 0
-
-.c-quickstart__content
-    padding: 2rem 3rem
-
-.c-quickstart__input
-    @include size(0)
-    opacity: 0
-    position: absolute
-    left: -9999px
-
-.c-quickstart__label
-    cursor: pointer
-    background: $color-back
-    border: 1px solid $color-subtle
-    border-radius: 2px
-    display: inline-block
-    padding: 0.75rem 1.25rem
-    margin: 0 0.5rem 0.5rem 0
-    font-weight: bold
-
-    &:hover
-        background: lighten($color-theme-light, 5)
-
-    .c-quickstart__input:focus + &
-        border: 1px solid $color-theme
-
-    .c-quickstart__input--radio:checked + &
-        color: $color-back
-        border-color: $color-theme
-        background: $color-theme
-
-    .c-quickstart__input--check + &:before
-        content: ""
-        background: $color-back
-        display: inline-block
-        width: 20px
-        height: 20px
-        border: 1px solid $color-subtle
-        vertical-align: middle
-        margin-right: 1rem
-        cursor: pointer
-        border-radius: 2px
-
-    .c-quickstart__input--check:checked + &:before
-        background: $color-theme url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0Ij4gICAgPHBhdGggZmlsbD0iI2ZmZiIgZD0iTTkgMTYuMTcybDEwLjU5NC0xMC41OTQgMS40MDYgMS40MDYtMTIgMTItNS41NzgtNS41NzggMS40MDYtMS40MDZ6Ii8+PC9zdmc+)
-        background-size: contain
-        border-color: $color-theme
-
-.c-quickstart__label__meta
-    font-weight: normal
-    color: $color-subtle-dark
-
-.c-quickstart__group
-    @include breakpoint(min, md)
-        display: flex
-        flex-flow: row nowrap
-
-    &:not(:last-child)
-        margin-bottom: 1rem
-
-.c-quickstart__fields
-    flex: 100%
-
-.c-quickstart__legend
-    margin-right: 2rem
-    padding-top: 0.75rem
-    flex: 1 1 35%
-    font-weight: bold
-
-.c-quickstart__line
-    display: block
-
-    &:before
-        color: $color-theme
-        margin-right: 1em
-
-    &.c-quickstart__line--bash:before
-        content: "$"
-
-    &.c-quickstart__line--python:before
-        content: ">>>"
-
-    &.c-quickstart__line--divider
-        padding: 1.5rem 0
-
-.c-quickstart__code
-    font-size: 1.4rem
diff --git a/website/assets/css/_components/_sidebar.sass b/website/assets/css/_components/_sidebar.sass
deleted file mode 100644
index be3e34147..000000000
--- a/website/assets/css/_components/_sidebar.sass
+++ /dev/null
@@ -1,95 +0,0 @@
-//- 💫 CSS > COMPONENTS > SIDEBAR
-
-//- Sidebar container
-
-.c-sidebar
-    overflow-y: auto
-
-    @include breakpoint(min, md)
-        @include position(fixed, top, left, 0, 0)
-        @include size($sidebar-width, calc(100vh - 3px))
-        @include scroll-shadow($color-back, $color-front, $nav-height)
-        flex: 0 0 $sidebar-width
-        padding: calc(#{$nav-height} + 1.5rem) 0 0
-        z-index: 10
-
-    @include breakpoint(max, sm)
-        flex: 100%
-        width: 100%
-        margin-top: $nav-height
-        display: flex
-        flex-flow: row wrap
-        width: 100%
-
-
-//- Sidebar section
-
-.c-sidebar__section
-    & > *
-        padding: 0 2rem 0.35rem
-
-    @include breakpoint(max, sm)
-        flex: 1 1 0
-        padding: 1.25rem 0
-        border-bottom: 1px solid $color-subtle
-        margin: 0
-
-        &:not(:last-child)
-            border-right: 1px solid $color-subtle
-
-.c-sidebar__item
-    color: $color-theme
-
-    &:hover
-        color: $color-theme-dark
-
-    & > .is-active
-        font-weight: bold
-        color: $color-dark
-        margin-top: 1rem
-
-
-//- Sidebar subsections
-
-$crumb-bullet: 14px
-$crumb-bar: 2px
-
-.c-sidebar__crumb
-    display: block
-    padding-top: 1rem
-    padding-left: 1rem
-    position: relative
-
-.c-sidebar__crumb__item
-    margin-bottom: $crumb-bullet / 2
-    position: relative
-    padding-left: 2rem
-    color: $color-theme
-    font-size: 1.2rem
-
-    &:hover
-        color: $color-theme-dark
-
-    &:after
-        @include size($crumb-bullet)
-        @include position(absolute, top, left, $crumb-bullet / 4, 0)
-        content: ""
-        border-radius: 50%
-        background: $color-theme
-        z-index: 10
-
-    &:not(:last-child):before
-        @include size($crumb-bar, 100%)
-        @include position(absolute, top, left, $crumb-bullet, ($crumb-bullet - $crumb-bar) / 2)
-        content: ""
-        background: $color-subtle
-
-    &:first-child:before
-        height: calc(100% + #{$crumb-bullet * 2})
-        top: -$crumb-bullet / 2
-
-    &.is-active
-        color: $color-dark
-
-        &:after
-            background: $color-dark
diff --git a/website/assets/css/_components/_tables.sass b/website/assets/css/_components/_tables.sass
deleted file mode 100644
index 3d0060b42..000000000
--- a/website/assets/css/_components/_tables.sass
+++ /dev/null
@@ -1,86 +0,0 @@
-//- 💫 CSS > COMPONENTS > TABLES
-
-//- Table container
-
-.c-table
-    vertical-align: top
-
-
-//- Table row
-
-.c-table__row
-    &:nth-child(odd):not(.c-table__row--head)
-        background: rgba($color-subtle-light, 0.35)
-
-    &.c-table__row--foot
-        background: $color-theme-light
-        border-top: 2px solid $color-theme
-
-        .c-table__cell:first-child
-            @extend .u-text-label
-            color: $color-theme
-
-    &.c-table__row--divider
-        border-top: 2px solid $color-theme
-
-
-//- Table cell
-
-.c-table__cell
-    padding: 1rem
-
-    &:not(:last-child)
-        border-right: 1px solid $color-subtle
-
-    &.c-table__cell--num
-        text-align: right
-        font-feature-settings: "tnum"
-        font-variant-numeric: tabular-nums
-
-        & > strong
-            font-feature-settings: initial
-            font-variant-numeric: initial
-
-
-//- Table head cell
-
-.c-table__head-cell
-    font-weight: bold
-    color: $color-theme
-    padding: 1rem 0.5rem
-    border-bottom: 2px solid $color-theme
-
-
-//- Responsive table
-//- Shadows adapted from "CSS only Responsive Tables" by David Bushell
-//- http://codepen.io/dbushell/pen/wGaamR
-
-@include breakpoint(max, md)
-    .c-table
-        @include scroll-shadow-base($color-front)
-        display: inline-block
-        overflow-x: auto
-        overflow-y: hidden
-        width: auto
-        -webkit-overflow-scrolling: touch
-
-    .c-table__cell,
-    .c-table__head-cell
-        &:first-child
-            @include scroll-shadow-cover(left, $color-back)
-
-        &:last-child
-            @include scroll-shadow-cover(right, $color-back)
-
-        &:first-child:last-child
-            @include scroll-shadow-cover(both, $color-back)
-
-    .c-table__row--foot .c-table__cell
-        &:first-child
-            @include scroll-shadow-cover(left, lighten($color-subtle-light, 2))
-
-        &:last-child
-            @include scroll-shadow-cover(right, lighten($color-subtle-light, 2))
-
-        &:first-child:last-child
-            @include scroll-shadow-cover(both, lighten($color-subtle-light, 2))
diff --git a/website/assets/css/_components/_tooltips.sass b/website/assets/css/_components/_tooltips.sass
deleted file mode 100644
index f9284dcdb..000000000
--- a/website/assets/css/_components/_tooltips.sass
+++ /dev/null
@@ -1,39 +0,0 @@
-//- 💫 CSS > COMPONENTS > TOOLTIPS
-
-[data-tooltip]
-    position: relative
-
-    @include breakpoint(min, sm)
-        &[data-tooltip-style="code"]:before
-            -webkit-font-smoothing: subpixel-antialiased
-            -moz-osx-font-smoothing: auto
-            padding: 0.35em 0.85em 0.45em
-            font: normal 1rem/#{1.25} $font-code
-            white-space: nowrap
-            min-width: auto
-
-        &:before
-            @include position(absolute, top, left, 125%, 50%)
-            display: inline-block
-            content: attr(data-tooltip)
-            background: $color-front
-            border-radius: $border-radius
-            border: 1px solid rgba($color-subtle-dark, 0.5)
-            color: $color-back
-            font: normal 1.2rem/#{1.25} $font-primary
-            text-transform: none
-            text-align: left
-            opacity: 0
-            transform: translateX(-50%) translateY(-2px)
-            transition: opacity 0.1s ease-out, transform 0.1s ease-out
-            visibility: hidden
-            max-width: 300px
-            min-width: 200px
-            padding: 0.75em 1em 1em
-            z-index: 200
-            white-space: pre-wrap
-
-        &:hover:before
-            opacity: 1
-            transform: translateX(-50%) translateY(0)
-            visibility: visible
diff --git a/website/assets/css/_mixins.sass b/website/assets/css/_mixins.sass
deleted file mode 100644
index d1ea9c5d5..000000000
--- a/website/assets/css/_mixins.sass
+++ /dev/null
@@ -1,80 +0,0 @@
-//- 💫 CSS > MIXINS
-
-//  Helper for position
-//  $position    - valid position value (static, absolute, fixed, relative)
-//  $pos-y       - position direction Y (top, bottom)
-//  $pos-x       - position direction X (left, right)
-//  $pos-y-value - value of position Y direction
-//  $pos-x-value - value of position X direction
-
-@mixin position($position, $pos-y, $pos-x, $pos-y-value, $pos-x-value)
-    position: $position
-    #{$pos-y}: $pos-y-value
-    #{$pos-x}: $pos-x-value
-
-
-//  Helper for width and height
-//  $width  - width of element
-//  $height - height of element (default: $width)
-
-@mixin size($width, $height: $width)
-    width: $width
-    height: $height
-
-
-//- Responsive Breakpoint utility
-
-@mixin breakpoint($limit, $size)
-    $breakpoints-max: ( xs: map-get($breakpoints, sm) - 1, sm: map-get($breakpoints, md) - 1, md: map-get($breakpoints, lg) - 1 )
-
-    @if $limit == "min"
-        @media(min-width: #{map-get($breakpoints, $size)})
-            @content
-
-    @else if $limit == "max"
-        @media(max-width: #{map-get($breakpoints-max, $size)})
-            @content
-
-
-// Scroll shadows for reponsive tables
-// adapted from David Bushell, http://codepen.io/dbushell/pen/wGaamR
-// $scroll-shadow-color      - color of shadow
-// $scroll-shadow-side       - side to cover shadow (left or right)
-// $scroll-shadow-background - original background color to match
-
-@function scroll-shadow-gradient($scroll-gradient-direction, $scroll-shadow-background)
-    @return linear-gradient(to #{$scroll-gradient-direction}, rgba($scroll-shadow-background, 1) 50%, rgba($scroll-shadow-background, 0) 100%)
-
-@mixin scroll-shadow-base($scroll-shadow-color, $scroll-shadow-intensity: 0.2)
-    background: radial-gradient(ellipse at 0 50%, rgba($scroll-shadow-color, $scroll-shadow-intensity) 0%, rgba(0,0,0,0) 75%) 0 center, radial-gradient(ellipse at 100% 50%, rgba($scroll-shadow-color, $scroll-shadow-intensity) 0%, transparent 75%) 100% center
-    background-attachment: scroll, scroll
-    background-repeat: no-repeat
-    background-size: 10px 100%, 10px 100%
-
-@mixin scroll-shadow-cover($scroll-shadow-side, $scroll-shadow-background)
-    $scroll-gradient-direction: right !default
-    background-repeat: no-repeat
-
-    @if $scroll-shadow-side == right
-        $scroll-gradient-direction: left
-        background-position: 100% 0
-
-    @if $scroll-shadow-side == both
-        background-image: scroll-shadow-gradient(left, $scroll-shadow-background), scroll-shadow-gradient(right, $scroll-shadow-background)
-        background-position: 100% 0, 0 0
-        background-size: 20px 100%, 20px 100%
-    @else
-        background-image: scroll-shadow-gradient($scroll-gradient-direction, $scroll-shadow-background)
-        background-size: 20px 100%
-
-// Full vertical scroll shadows
-// adapted from: https://codepen.io/laustdeleuran/pen/DBaAu
-
-@mixin scroll-shadow($background-color, $shadow-color, $shadow-offset: 0, $shadow-intensity: 0.4, $cover-size: 40px, $shadow-size: 15px)
-    background: linear-gradient($background-color 30%, rgba($background-color,0)) 0 $shadow-offset, linear-gradient(rgba($background-color,0), $background-color 70%) 0 100%, radial-gradient(50% 0, farthest-side, rgba($shadow-color,$shadow-intensity), rgba($shadow-color,0)) 0 $shadow-offset, radial-gradient(50% 100%,farthest-side, rgba($shadow-color,$shadow-intensity), rgba($shadow-color,0)) 0 100%
-
-    background: linear-gradient($background-color 30%, rgba($background-color,0)) 0 $shadow-offset, linear-gradient(rgba($background-color,0), $background-color 70%) 0 100%, radial-gradient(farthest-side at 50% 0, rgba($shadow-color,$shadow-intensity), rgba($shadow-color,0)) -20px $shadow-offset, radial-gradient(farthest-side at 50% 100%, rgba($shadow-color, $shadow-intensity), rgba($shadow-color,0)) 0 100%
-    background-repeat: no-repeat
-    background-color: $background-color
-    background-size: 100% $cover-size, 100% $cover-size, 100% $shadow-size, 100% $shadow-size
-    background-attachment: local, local, scroll, scroll
diff --git a/website/assets/css/_variables.sass b/website/assets/css/_variables.sass
deleted file mode 100644
index bac6c4f69..000000000
--- a/website/assets/css/_variables.sass
+++ /dev/null
@@ -1,51 +0,0 @@
-//- 💫 CSS > VARIABLES
-
-// Settings and Sizes
-
-$type-base: 11px
-
-$nav-height: 55px
-$content-width: 1250px
-$sidebar-width: 235px
-$aside-width: 27.5vw
-$aside-padding: 25px
-$border-radius: 6px
-
-$logo-width: 85px
-$logo-height: 27px
-
-$grid: ( quarter: 4, third: 3, half: 2, two-thirds: 1.5, three-quarters: 1.33 )
-$breakpoints: ( sm: 768px, md: 992px, lg: 1200px )
-$headings: (1: 4.4, 2: 3.4, 3: 2.6, 4: 2.2, 5: 1.8)
-
-//  Fonts
-
-$font-primary: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol" !default
-$font-secondary: "HK Grotesk", Roboto, Helvetica, Arial, sans-serif !default
-$font-code: Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace !default
-
-// Colors
-
-$colors: ( blue: #09a3d5, green: #05b083, purple: #6542d1 )
-
-$color-back: #fff !default
-$color-front: #1a1e23 !default
-$color-dark: lighten($color-front, 20) !default
-
-$color-theme: map-get($colors, $theme)
-$color-theme-dark: darken(map-get($colors, $theme), 10)
-$color-theme-light: rgba($color-theme, 0.05)
-
-$color-subtle: #ddd !default
-$color-subtle-light: #f6f6f6 !default
-$color-subtle-dark: #949e9b !default
-
-$color-red: #ef476f
-$color-green: #7ddf64
-$color-yellow: #f4c025
-
-$syntax-highlighting: ( comment: #949e9b, tag: #b084eb, number: #b084eb, selector: #ffb86c, operator: #ff2c6d, function: #35b3dc, keyword: #ff2c6d, regex: #f4c025 )
-
-$pattern: $color-theme url("/assets/img/pattern_#{$theme}.jpg") center top repeat
-$pattern-overlay: transparent url("/assets/img/pattern_landing.jpg") center -138px no-repeat
-$box-shadow: 0 1px 5px rgba(0, 0, 0, 0.2)
diff --git a/website/assets/css/style.sass b/website/assets/css/style.sass
deleted file mode 100644
index 47cf3f1b5..000000000
--- a/website/assets/css/style.sass
+++ /dev/null
@@ -1,37 +0,0 @@
-//- 💫 STYLESHEET
-
-$theme: blue !default
-
-
-// Variables
-
-@import variables
-@import mixins
-
-
-// Base
-
-@import _base/reset
-@import _base/fonts
-@import _base/animations
-@import _base/grid
-@import _base/layout
-@import _base/objects
-@import _base/utilities
-
-
-// Components
-
-@import _components/asides
-@import _components/buttons
-@import _components/chat
-@import _components/code
-@import _components/landing
-@import _components/lists
-@import _components/misc
-@import _components/navigation
-@import _components/progress
-@import _components/sidebar
-@import _components/tables
-@import _components/quickstart
-@import _components/tooltips
diff --git a/website/assets/css/style_green.sass b/website/assets/css/style_green.sass
deleted file mode 100644
index c7369f990..000000000
--- a/website/assets/css/style_green.sass
+++ /dev/null
@@ -1,4 +0,0 @@
-//- 💫 STYLESHEET (GREEN)
-
-$theme: green
-@import style
diff --git a/website/assets/css/style_purple.sass b/website/assets/css/style_purple.sass
deleted file mode 100644
index f00573cc7..000000000
--- a/website/assets/css/style_purple.sass
+++ /dev/null
@@ -1,4 +0,0 @@
-//- 💫 STYLESHEET (PURPLE)
-
-$theme: purple
-@import style
diff --git a/website/assets/img/favicon.ico b/website/assets/img/favicon.ico
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diff --git a/website/assets/img/logo.svg b/website/assets/img/logo.svg
deleted file mode 100644
index 89b61e132..000000000
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diff --git a/website/assets/js/changelog.vue.js b/website/assets/js/changelog.vue.js
deleted file mode 100644
index a68042d3e..000000000
--- a/website/assets/js/changelog.vue.js
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * Initialise changelog table for releases and prereleases
- * @param {string} selector - The element selector to initialise the app.
- * @param {string} repo - Repository to load from, in the format user/repo.
- */
-export default function(selector, repo) {
-    new Vue({
-        el: selector,
-        data: {
-            url: `https://api.github.com/repos/${repo}/releases`,
-            releases: [],
-            prereleases: [],
-            error: false
-        },
-        beforeMount() {
-            fetch(this.url)
-                .then(res => res.json())
-                .then(json => this.$_update(json))
-                .catch(err => { this.error = true });
-        },
-        updated() {
-            window.dispatchEvent(new Event('resize'));  // scroll position for progress
-        },
-        methods: {
-            $_update(json) {
-                const allReleases = Object.values(json)
-                    .filter(release => release.name)
-                    .map(release => ({
-                        title: (release.name.split(': ').length == 2) ? release.name.split(': ')[1] : release.name,
-                        url: release.html_url,
-                        date: release.published_at.split('T')[0],
-                        tag: release.tag_name,
-                        pre: release.prerelease
-                    }));
-                this.releases = allReleases.filter(release => !release.pre);
-                this.prereleases = allReleases.filter(release => release.pre);
-            }
-        }
-    });
-}
diff --git a/website/assets/js/main.js b/website/assets/js/main.js
deleted file mode 100644
index 529840847..000000000
--- a/website/assets/js/main.js
+++ /dev/null
@@ -1,158 +0,0 @@
-/**
- * Initialise changelog
- */
-import initChangelog from './changelog.vue.js';
-
-{
-    const selector = '[data-vue="changelog"]';
-    if (window.Vue && document.querySelector(selector)) {
-        initChangelog(selector, 'explosion/spacy');
-    }
-}
-
-/**
- * Initialise models
- */
-import initModels from './models.vue.js';
-
-{
-    if (window.Vue && document.querySelector('[data-model]')) {
-        initModels('explosion/spacy-models')
-    }
-}
-
-/**
- * Initialise Universe
- */
-import initUniverse from './universe.vue.js';
-
-{
-    const selector = '[data-vue="universe"]';
-    if (window.Vue && document.querySelector(selector)) {
-        initUniverse(selector, '/universe/universe.json');
-    }
-}
-
-/**
- * Initialise Quickstart
- */
-{
-    if (document.querySelector('#qs') && window.Quickstart) {
-        new Quickstart('#qs');
-    }
-}
-
-/**
- * Initialise Juniper
- */
-{
-    if (window.Juniper) {
-        new Juniper({
-            repo: 'ines/spacy-io-binder',
-            branch: 'live',
-            storageExpire: 60
-        });
-    }
-}
-
-/**
- * Highlight section in viewport in sidebar, using in-view library
- */
-{
-    const sectionAttr = 'data-section';
-    const navAttr = 'data-nav';
-    const activeClass = 'is-active';
-    const sidebarAttr = 'data-sidebar-active';
-    const sections = [...document.querySelectorAll(`[${navAttr}]`)];
-    const currentItem = document.querySelector(`[${sidebarAttr}]`);
-    if (window.inView) {
-        if (currentItem && Element.prototype.scrollIntoView && !inView.is(currentItem)) {
-            currentItem.scrollIntoView();
-        }
-        if (sections.length) {  // highlight first item regardless
-            sections[0].classList.add(activeClass);
-        }
-        inView(`[${sectionAttr}]`).on('enter', section => {
-            const id = section.getAttribute(sectionAttr);
-            const el = document.querySelector(`[${navAttr}="${id}"]`);
-            if (el) {
-                sections.forEach(el => el.classList.remove(activeClass));
-                el.classList.add(activeClass);
-                if (Element.prototype.scrollIntoView && !inView.is(el)) {
-                    el.scrollIntoView();
-                }
-            }
-        });
-    }
-}
-
-/**
- * Simple, collapsible accordion sections.
- * Inspired by: https://inclusive-components.design/collapsible-sections/
- */
-{
-    const elements = [...document.querySelectorAll('.js-accordion')];
-    elements.forEach(el => el.addEventListener('click', ({ target }) => {
-        const exp = target.getAttribute('aria-expanded') === 'true' || false;
-        target.setAttribute('aria-expanded', !exp);
-        target.parentElement.nextElementSibling.hidden = exp;
-    }));
-}
-
-/**
- * Reading indicator as progress bar
- * @param {string} selector - Selector of <progress> element.
- */
-class ProgressBar {
-    constructor(selector) {
-        this.scrollY = 0;
-        this.sizes = this.updateSizes();
-        this.el = document.querySelector(selector);
-        this.el.setAttribute('max', 100);
-        window.addEventListener('scroll', ev => {
-            this.scrollY = (window.pageYOffset || document.scrollTop) - (document.clientTop || 0);
-            requestAnimationFrame(this.update.bind(this));
-        });
-        window.addEventListener('resize', ev => {
-            this.sizes = this.updateSizes();
-            requestAnimationFrame(this.update.bind(this));
-        });
-    }
-
-    update() {
-        const offset = 100 - ((this.sizes.height - this.scrollY - this.sizes.vh) / this.sizes.height * 100);
-        this.el.setAttribute('value', (this.scrollY == 0) ? 0 : offset || 0);
-    }
-
-    updateSizes() {
-        return {
-            height: Math.max(document.body.scrollHeight, document.body.offsetHeight, document.documentElement.clientHeight, document.documentElement.scrollHeight, document.documentElement.offsetHeight),
-            vh: Math.max(document.documentElement.clientHeight, window.innerHeight || 0)
-        }
-    }
-}
-
-new ProgressBar('.js-progress');
-
-/**
- * Embed code from GitHub repositories, similar to Gist embeds. Fetches the
- * raw text and places it inside element.
- * Usage: <pre><code data-gh-embed="spacy/master/examples/x.py"></code><pre>
- */
-{
-    const attr = 'data-gh-embed';
-    const url = 'https://raw.githubusercontent.com/explosion';
-    const elements = [...document.querySelectorAll(`[${attr}]`)];
-    elements.forEach(el => {
-        el.parentElement.setAttribute('data-loading', '');
-        fetch(`${url}/${el.getAttribute(attr)}`)
-            .then(res => res.text().then(text => ({ text, ok: res.ok })))
-            .then(({ text, ok }) => {
-                if (ok) {
-                    el.textContent = text;
-                    if (window.Prism) Prism.highlightElement(el);
-                }
-                el.parentElement.removeAttribute('data-loading');
-            })
-    });
-}
diff --git a/website/assets/js/models.vue.js b/website/assets/js/models.vue.js
deleted file mode 100644
index 2b5f53973..000000000
--- a/website/assets/js/models.vue.js
+++ /dev/null
@@ -1,150 +0,0 @@
-/**
- * Initialise model overviews
- * @param {string} repo - Repository to load from, in the format user/repo.
- */
-export default function(repo) {
-    const LICENSES = {
-        'CC BY 4.0':       'https://creativecommons.org/licenses/by/4.0/',
-        'CC BY-SA':        'https://creativecommons.org/licenses/by-sa/3.0/',
-        'CC BY-SA 3.0':    'https://creativecommons.org/licenses/by-sa/3.0/',
-        'CC BY-SA 4.0':    'https://creativecommons.org/licenses/by-sa/4.0/',
-        'CC BY-NC':        'https://creativecommons.org/licenses/by-nc/3.0/',
-        'CC BY-NC 3.0':    'https://creativecommons.org/licenses/by-nc/3.0/',
-        'CC BY-NC 4.0':    'https://creativecommons.org/licenses/by-nc/4.0/',
-        'CC-BY-NC-SA 3.0': 'https://creativecommons.org/licenses/by-nc-sa/3.0/',
-        'GPL':             'https://www.gnu.org/licenses/gpl.html',
-        'LGPL':            'https://www.gnu.org/licenses/lgpl.html',
-        'MIT':             'https://opensource.org/licenses/MIT'
-    };
-    const URL = `https://raw.githubusercontent.com/${repo}/master`;
-    const models = [...document.querySelectorAll('[data-vue]')]
-        .map(el => el.getAttribute('data-vue'));
-
-    document.addEventListener('DOMContentLoaded', ev => {
-        fetch(`${URL}/compatibility.json`)
-            .then(res => res.json())
-            .then(json => models.forEach(modelId => new Vue({
-                el: `[data-vue="${modelId}"]`,
-                data: {
-                    repo: `https://github.com/${repo}`,
-                    compat: json.spacy,
-                    loading: false,
-                    error: false,
-                    id: modelId,
-                    version: 'n/a',
-                    notes: null,
-                    sizeFull: null,
-                    fullName: null,
-                    pipeline: null,
-                    description: null,
-                    license: null,
-                    author: null,
-                    url: null,
-                    vectors: null,
-                    sources: null,
-                    uas: null,
-                    las: null,
-                    tags_acc: null,
-                    ents_f: null,
-                    ents_p: null,
-                    ents_r: null,
-                    modelLicenses: LICENSES,
-                    spacyVersion: Object.keys(json.spacy)[0]
-                },
-                computed: {
-                    compatVersion() {
-                        const res = this.compat[this.spacyVersion][this.id];
-                        return res ? `${this.id}-${res[0]}` : false;
-                    },
-                    orderedCompat() {
-                        return Object.keys(this.compat)
-                            .filter(v => this.$_isStableVersion(v));
-                    },
-                    hasAccuracy() {
-                        return this.uas || this.las || this.tags_acc || this.ents_f || this.ents_p || this.ents_r;
-                    },
-                    releaseUrl() {
-                        const baseUrl = `${this.repo}/releases`;
-                        return `${baseUrl}/${this.fullName ? `/tag/${this.fullName}` : ''}`
-                    }
-                },
-                beforeMount() {
-                    const version = this.$_getLatestVersion(this.id);
-                    if (version) {
-                        this.loading = true;
-                        fetch(`${URL}/meta/${this.id}-${version}.json`)
-                            .then(res => res.json())
-                            .then(json => this.$_updateData(json))
-                            .catch(err => { this.error = true });
-                    }
-                },
-                updated() {
-                    window.dispatchEvent(new Event('resize'));  // scroll position for progress
-                },
-                methods: {
-                    $_updateData(data) {
-                        this.fullName = `${data.lang}_${data.name}-${data.version}`;
-                        this.version = data.version;
-                        this.sizeFull = data.size;
-                        this.pipeline = data.pipeline;
-                        this.notes = data.notes;
-                        this.description = data.description;
-                        this.vectors = this.$_formatVectors(data.vectors);
-                        this.sources = data.sources;
-                        this.author = data.author;
-                        this.url = data.url;
-                        this.license = data.license;
-                        const accuracy = data.accuracy || {};
-                        for (let key of Object.keys(accuracy)) {
-                            this[key] = accuracy[key].toFixed(2);
-                        }
-                        this.loading = false;
-                    },
-
-                    $_getLatestVersion(modelId) {
-                        for (let [version, models] of Object.entries(this.compat)) {
-                            if (this.$_isStableVersion(version) && models[modelId]) {
-                                return models[modelId][0];
-                            }
-                        }
-                    },
-
-                    $_formatVectors(data) {
-                        if (!data) {
-                            return 'n/a';
-                        }
-                        if (Object.values(data).every(n => n == 0)) {
-                            return 'context vectors only';
-                        }
-                        const { keys, vectors, width } = data;
-                        const nKeys = this.$_abbrNum(keys);
-                        const nVectors = this.$_abbrNum(vectors);
-                        return `${nKeys} keys, ${nVectors} unique vectors (${width} dimensions)`;
-                    },
-
-                    /**
-                     * Hacky check if a version number is stable and not alpha,
-                     * dev or a release candidate
-                     */
-                    $_isStableVersion(v) {
-                        return !v.includes('a') && !v.includes('b') && !v.includes('dev') && !v.includes('rc');
-                    },
-
-                    /**
-                     * Abbreviate a number, e.g. 14249930 --> 14.25m.
-                     * @param {number|string} num - The number to convert.
-                     * @param {number} fixed - Number of decimals.
-                     */
-                    $_abbrNum: function(num = 0, fixed = 1) {
-                        const suffixes = ['', 'k', 'm', 'b', 't'];
-                        if (num === null || num === 0) return 0;
-                        const b = num.toPrecision(2).split('e');
-                        const k = (b.length === 1) ? 0 : Math.floor(Math.min(b[1].slice(1), 14) / 3);
-                        const n = (k < 1) ? num : num / Math.pow(10, k * 3);
-                        const c = (k >= 1 && n >= 100 ) ? Math.round(n) : n.toFixed(fixed);
-                        return (c < 0 ? c : Math.abs(c)) + suffixes[k];
-                    }
-                }
-            })))
-    });
-}
diff --git a/website/assets/js/universe.vue.js b/website/assets/js/universe.vue.js
deleted file mode 100644
index beeec82af..000000000
--- a/website/assets/js/universe.vue.js
+++ /dev/null
@@ -1,125 +0,0 @@
-export default function(selector, dataPath) {
-    Vue.use(VueMarkdown);
-
-    new Vue({
-        el: selector,
-        data: {
-            filteredResources: [],
-            allResources: [],
-            projectCats: {},
-            educationCats: {},
-            filterVals: ['category'],
-            activeMenu: 'all',
-            selected: null,
-            loading: false
-        },
-        computed: {
-            resources() {
-                return this.filteredResources.sort((a, b) => a.id.localeCompare(b.id));
-            },
-            categories() {
-                return Object.assign({}, this.projectCats, this.educationCats);
-            }
-        },
-
-        beforeMount() {
-            this.loading = true;
-            window.addEventListener('popstate', this.$_init);
-            fetch(dataPath)
-                .then(res => res.json())
-                .then(({ resources, projectCats, educationCats }) => {
-                    this.allResources = resources || [];
-                    this.filteredResources = resources || [];
-                    this.projectCats = projectCats || {};
-                    this.educationCats = educationCats || {};
-                    this.$_init();
-                    this.loading = false;
-                });
-        },
-        updated() {
-            if (window.Prism) Prism.highlightAll();
-            // make sure scroll positions for progress bar etc. are recalculated
-            window.dispatchEvent(new Event('resize'));
-        },
-        methods: {
-            getAuthorLink(id, link) {
-                if (id == 'twitter') return `https://twitter.com/${link}`;
-                else if (id == 'github') return `https://github.com/${link}`;
-                return link;
-            },
-
-            filterBy(id, selector = 'category') {
-                window.scrollTo(0, 0);
-                if (!this.filterVals.includes(selector)) {
-                    return;
-                }
-                const resources = this.$_filterResources(id, selector);
-                if (!resources.length) return;
-                this.selected = null;
-                this.activeMenu = id;
-                this.filteredResources = resources;
-            },
-
-            viewResource(id) {
-                const res = this.allResources.find(r => r.id == id);
-                if (!res) return;
-                this.selected = res;
-                this.activeMenu = null;
-                if (this.$_getQueryVar('id') != res.id) {
-                    this.$_updateUrl({ id: res.id });
-                }
-                window.scrollTo(0, 0);
-            },
-
-            $_filterResources(id, selector) {
-                if (id == 'all') {
-                    if (window.location.search != '') {
-                        this.$_updateUrl({});
-                    }
-                    return this.allResources;
-                }
-                const resources = this.allResources
-                    .filter(res => (res[selector] || []).includes(id));
-                if (resources.length && this.$_getQueryVar(selector) != id) {
-                    this.$_updateUrl({ [selector]: id });
-                }
-                return resources;
-            },
-
-            $_init() {
-                const current = this.$_getQueryVar('id');
-                if (current) {
-                    this.viewResource(current);
-                    return;
-                }
-                for (let filterVal of this.filterVals) {
-                    const queryVar = this.$_getQueryVar(filterVal);
-                    if (queryVar) {
-                        this.filterBy(queryVar, filterVal);
-                        return;
-                    }
-                }
-                this.filterBy('all');
-            },
-
-            $_getQueryVar(key) {
-                const query = window.location.search.substring(1);
-                const params = query.split('&').map(param => param.split('='));
-                for(let param of params) {
-                    if (param[0] == key) {
-                        return decodeURIComponent(param[1]);
-                    }
-                }
-                return false;
-            },
-
-            $_updateUrl(params) {
-                const loc = Object.keys(params)
-                    .map(param => `${param}=${encodeURIComponent(params[param])}`);
-                const url = loc.length ? '?' + loc.join('&')
-                    : window.location.origin + window.location.pathname;
-                window.history.pushState(params, null, url);
-            }
-        }
-    })
-}
diff --git a/website/assets/js/vendor/in-view.min.js b/website/assets/js/vendor/in-view.min.js
deleted file mode 100644
index f23f1d946..000000000
--- a/website/assets/js/vendor/in-view.min.js
+++ /dev/null
@@ -1,6 +0,0 @@
-/*!
- * in-view 0.6.1 - Get notified when a DOM element enters or exits the viewport.
- * Copyright (c) 2016 Cam Wiegert <cam@camwiegert.com> - https://camwiegert.github.io/in-view
- * License: MIT
- */
-!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.inView=e():t.inView=e()}(this,function(){return function(t){function e(r){if(n[r])return n[r].exports;var i=n[r]={exports:{},id:r,loaded:!1};return t[r].call(i.exports,i,i.exports,e),i.loaded=!0,i.exports}var n={};return e.m=t,e.c=n,e.p="",e(0)}([function(t,e,n){"use strict";function r(t){return t&&t.__esModule?t:{"default":t}}var i=n(2),o=r(i);t.exports=o["default"]},function(t,e){function n(t){var e=typeof t;return null!=t&&("object"==e||"function"==e)}t.exports=n},function(t,e,n){"use strict";function r(t){return t&&t.__esModule?t:{"default":t}}Object.defineProperty(e,"__esModule",{value:!0});var i=n(9),o=r(i),u=n(3),f=r(u),s=n(4),c=function(){if("undefined"!=typeof window){var 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diff --git a/website/assets/js/vendor/juniper.min.js b/website/assets/js/vendor/juniper.min.js
deleted file mode 100644
index 67420ee3e..000000000
--- a/website/assets/js/vendor/juniper.min.js
+++ /dev/null
@@ -1,9 +0,0 @@
-/*!
- * juniper-js
- * Edit and execute code snippets in the browser using Jupyter kernels
- *
- * @author Ines Montani
- * @version 0.0.3
- * @license MIT
- */
-!function(e){function t(t){for(var n,i,o=t[0],a=t[1],s=0,c=[];s<o.length;s++)i=o[s],r[i]&&c.push(r[i][0]),r[i]=0;for(n in a)Object.prototype.hasOwnProperty.call(a,n)&&(e[n]=a[n]);for(l&&l(t);c.length;)c.shift()()}var n={},r={2:0};function i(t){if(n[t])return n[t].exports;var r=n[t]={i:t,l:!1,exports:{}};return e[t].call(r.exports,r,r.exports,i),r.l=!0,r.exports}i.e=function(e){var t=[],n=r[e];if(0!==n)if(n)t.push(n[2]);else{var o=new Promise(function(t,i){n=r[e]=[t,i]});t.push(n[2]=o);var a,s=document.getElementsByTagName("head")[0],l=document.createElement("script");l.charset="utf-8",l.timeout=120,i.nc&&l.setAttribute("nonce",i.nc),l.src=function(e){return i.p+""+e+".juniper.min.js"}(e),a=function(t){l.onerror=l.onload=null,clearTimeout(c);var n=r[e];if(0!==n){if(n){var i=t&&("load"===t.type?"missing":t.type),o=t&&t.target&&t.target.src,a=new Error("Loading chunk "+e+" failed.\n("+i+": "+o+")");a.type=i,a.request=o,n[1](a)}r[e]=void 0}};var c=setTimeout(function(){a({type:"timeout",target:l})},12e4);l.onerror=l.onload=a,s.appendChild(l)}return Promise.all(t)},i.m=e,i.c=n,i.d=function(e,t,n){i.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:n})},i.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.t=function(e,t){if(1&t&&(e=i(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var n=Object.create(null);if(i.r(n),Object.defineProperty(n,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var r in e)i.d(n,r,function(t){return e[t]}.bind(null,r));return n},i.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return i.d(t,"a",t),t},i.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},i.p="",i.oe=function(e){throw console.error(e),e};var o=window.webpackJsonp=window.webpackJsonp||[],a=o.push.bind(o);o.push=t,o=o.slice();for(var s=0;s<o.length;s++)t(o[s]);var l=a;i(i.s=173)}([function(e,t,n){"use strict";var r,i,o,a="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e};o=function(){var e=navigator.userAgent,t=navigator.platform,n=/gecko\/\d/i.test(e),r=/MSIE \d/.test(e),i=/Trident\/(?:[7-9]|\d{2,})\..*rv:(\d+)/.exec(e),o=/Edge\/(\d+)/.exec(e),s=r||i||o,l=s&&(r?document.documentMode||6:+(o||i)[1]),c=!o&&/WebKit\//.test(e),u=c&&/Qt\/\d+\.\d+/.test(e),d=!o&&/Chrome\//.test(e),f=/Opera\//.test(e),h=/Apple Computer/.test(navigator.vendor),p=/Mac OS X 1\d\D([8-9]|\d\d)\D/.test(e),m=/PhantomJS/.test(e),g=!o&&/AppleWebKit/.test(e)&&/Mobile\/\w+/.test(e),v=/Android/.test(e),y=g||v||/webOS|BlackBerry|Opera Mini|Opera Mobi|IEMobile/i.test(e),b=g||/Mac/.test(t),x=/\bCrOS\b/.test(e),w=/win/i.test(t),_=f&&e.match(/Version\/(\d*\.\d*)/);_&&(_=Number(_[1])),_&&_>=15&&(f=!1,c=!0);var k=b&&(u||f&&(null==_||_<12.11)),C=n||s&&l>=9;function S(e){return new RegExp("(^|\\s)"+e+"(?:$|\\s)\\s*")}var M,L=function(e,t){var n=e.className,r=S(t).exec(n);if(r){var i=n.slice(r.index+r[0].length);e.className=n.slice(0,r.index)+(i?r[1]+i:"")}};function T(e){for(var t=e.childNodes.length;t>0;--t)e.removeChild(e.firstChild);return e}function O(e,t){return T(e).appendChild(t)}function A(e,t,n,r){var i=document.createElement(e);if(n&&(i.className=n),r&&(i.style.cssText=r),"string"==typeof t)i.appendChild(document.createTextNode(t));else if(t)for(var o=0;o<t.length;++o)i.appendChild(t[o]);return i}function P(e,t,n,r){var i=A(e,t,n,r);return i.setAttribute("role","presentation"),i}function E(e,t){if(3==t.nodeType&&(t=t.parentNode),e.contains)return e.contains(t);do{if(11==t.nodeType&&(t=t.host),t==e)return!0}while(t=t.parentNode)}function D(){var e;try{e=document.activeElement}catch(t){e=document.body||null}for(;e&&e.shadowRoot&&e.shadowRoot.activeElement;)e=e.shadowRoot.activeElement;return e}function j(e,t){var n=e.className;S(t).test(n)||(e.className+=(n?" 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n=t.text.className,r=dn(e,t);t.text==t.node&&(t.node=r.pre),t.text.parentNode.replaceChild(r.pre,t.text),t.text=r.pre,r.bgClass!=t.bgClass||r.textClass!=t.textClass?(t.bgClass=r.bgClass,t.textClass=r.textClass,hn(e,t)):n&&(t.text.className=n)}function hn(e,t){!function(e,t){var n=t.bgClass?t.bgClass+" "+(t.line.bgClass||""):t.line.bgClass;if(n&&(n+=" CodeMirror-linebackground"),t.background)n?t.background.className=n:(t.background.parentNode.removeChild(t.background),t.background=null);else if(n){var r=un(t);t.background=r.insertBefore(A("div",null,n),r.firstChild),e.display.input.setUneditable(t.background)}}(e,t),t.line.wrapClass?un(t).className=t.line.wrapClass:t.node!=t.text&&(t.node.className="");var n=t.textClass?t.textClass+" "+(t.line.textClass||""):t.line.textClass;t.text.className=n||""}function pn(e,t,n,r){if(t.gutter&&(t.node.removeChild(t.gutter),t.gutter=null),t.gutterBackground&&(t.node.removeChild(t.gutterBackground),t.gutterBackground=null),t.line.gutterClass){var i=un(t);t.gutterBackground=A("div",null,"CodeMirror-gutter-background "+t.line.gutterClass,"left: "+(e.options.fixedGutter?r.fixedPos:-r.gutterTotalWidth)+"px; width: "+r.gutterTotalWidth+"px"),e.display.input.setUneditable(t.gutterBackground),i.insertBefore(t.gutterBackground,t.text)}var o=t.line.gutterMarkers;if(e.options.lineNumbers||o){var a=un(t),s=t.gutter=A("div",null,"CodeMirror-gutter-wrapper","left: "+(e.options.fixedGutter?r.fixedPos:-r.gutterTotalWidth)+"px");if(e.display.input.setUneditable(s),a.insertBefore(s,t.text),t.line.gutterClass&&(s.className+=" "+t.line.gutterClass),!e.options.lineNumbers||o&&o["CodeMirror-linenumbers"]||(t.lineNumber=s.appendChild(A("div",me(e.options,n),"CodeMirror-linenumber CodeMirror-gutter-elt","left: "+r.gutterLeft["CodeMirror-linenumbers"]+"px; width: "+e.display.lineNumInnerWidth+"px"))),o)for(var l=0;l<e.options.gutters.length;++l){var 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r=Vn(e,t,"div",null,null,!e.options.singleCursorHeightPerLine),i=n.appendChild(A("div"," ","CodeMirror-cursor"));if(i.style.left=r.left+"px",i.style.top=r.top+"px",i.style.height=Math.max(0,r.bottom-r.top)*e.options.cursorHeight+"px",r.other){var o=n.appendChild(A("div"," ","CodeMirror-cursor CodeMirror-secondarycursor"));o.style.display="",o.style.left=r.other.left+"px",o.style.top=r.other.top+"px",o.style.height=.85*(r.other.bottom-r.other.top)+"px"}}function dr(e,t){return e.top-t.top||e.left-t.left}function fr(e,t,n){var r=e.display,i=e.doc,o=document.createDocumentFragment(),a=Cn(e.display),s=a.left,l=Math.max(r.sizerWidth,Mn(e)-r.sizer.offsetLeft)-a.right,c="ltr"==i.direction;function u(e,t,n,r){t<0&&(t=0),t=Math.round(t),r=Math.round(r),o.appendChild(A("div",null,"CodeMirror-selected","position: absolute; left: "+e+"px;\n                             top: "+t+"px; width: "+(null==n?l-e:n)+"px;\n                             height: "+(r-t)+"px"))}function d(t,n,r){var o,a,d=le(i,t),f=d.text.length;function h(n,r){return Kn(e,ge(t,n),"div",d,r)}function p(t,n,r){var i=Zn(e,d,null,t),o="ltr"==n==("after"==r)?"left":"right";return h("after"==r?i.begin:i.end-(/\s/.test(d.text.charAt(i.end-1))?2:1),o)[o]}var m=Ye(d,i.direction);return function(e,t,n,r){if(!e)return r(t,n,"ltr",0);for(var i=!1,o=0;o<e.length;++o){var a=e[o];(a.from<n&&a.to>t||t==n&&a.to==t)&&(r(Math.max(a.from,t),Math.min(a.to,n),1==a.level?"rtl":"ltr",o),i=!0)}i||r(t,n,"ltr")}(m,n||0,null==r?f:r,function(e,t,i,d){var g="ltr"==i,v=h(e,g?"left":"right"),y=h(t-1,g?"right":"left"),b=null==n&&0==e,x=null==r&&t==f,w=0==d,_=!m||d==m.length-1;if(y.top-v.top<=3){var k=(c?x:b)&&_,C=(c?b:x)&&w?s:(g?v:y).left,S=k?l:(g?y:v).right;u(C,v.top,S-C,v.bottom)}else{var M,L,T,O;g?(M=c&&b&&w?s:v.left,L=c?l:p(e,i,"before"),T=c?s:p(t,i,"after"),O=c&&x&&_?l:y.right):(M=c?p(e,i,"before"):s,L=!c&&b&&w?l:v.right,T=!c&&x&&_?s:y.left,O=c?p(t,i,"after"):l),u(M,v.top,L-M,v.bottom),v.bottom<y.top&&u(s,v.bottom,null,y.top),u(T,y.top,O-T,y.bottom)}(!o||dr(v,o)<0)&&(o=v),dr(y,o)<0&&(o=y),(!a||dr(v,a)<0)&&(a=v),dr(y,a)<0&&(a=y)}),{start:o,end:a}}var f=t.from(),h=t.to();if(f.line==h.line)d(f.line,f.ch,h.ch);else{var p=le(i,f.line),m=le(i,h.line),g=Be(p)==Be(m),v=d(f.line,f.ch,g?p.text.length+1:null).end,y=d(h.line,g?0:null,h.ch).start;g&&(v.top<y.top-2?(u(v.right,v.top,null,v.bottom),u(s,y.top,y.left,y.bottom)):u(v.right,v.top,y.left-v.right,v.bottom)),v.bottom<y.top&&u(s,v.bottom,null,y.top)}n.appendChild(o)}function hr(e){if(e.state.focused){var t=e.display;clearInterval(t.blinker);var n=!0;t.cursorDiv.style.visibility="",e.options.cursorBlinkRate>0?t.blinker=setInterval(function(){return t.cursorDiv.style.visibility=(n=!n)?"":"hidden"},e.options.cursorBlinkRate):e.options.cursorBlinkRate<0&&(t.cursorDiv.style.visibility="hidden")}}function pr(e){e.state.focused||(e.display.input.focus(),gr(e))}function mr(e){e.state.delayingBlurEvent=!0,setTimeout(function(){e.state.delayingBlurEvent&&(e.state.delayingBlurEvent=!1,vr(e))},100)}function gr(e,t){e.state.delayingBlurEvent&&(e.state.delayingBlurEvent=!1),"nocursor"!=e.options.readOnly&&(e.state.focused||(rt(e,"focus",e,t),e.state.focused=!0,j(e.display.wrapper,"CodeMirror-focused"),e.curOp||e.display.selForContextMenu==e.doc.sel||(e.display.input.reset(),c&&setTimeout(function(){return e.display.input.reset(!0)},20)),e.display.input.receivedFocus()),hr(e))}function vr(e,t){e.state.delayingBlurEvent||(e.state.focused&&(rt(e,"blur",e,t),e.state.focused=!1,L(e.display.wrapper,"CodeMirror-focused")),clearInterval(e.display.blinker),setTimeout(function(){e.state.focused||(e.display.shift=!1)},150))}function yr(e){for(var t=e.display,n=t.lineDiv.offsetTop,r=0;r<t.view.length;r++){var i=t.view[r],o=void 0;if(!i.hidden){if(s&&l<8){var a=i.node.offsetTop+i.node.offsetHeight;o=a-n,n=a}else{var c=i.node.getBoundingClientRect();o=c.bottom-c.top}var u=i.line.height-o;if(o<2&&(o=er(t)),(u>.005||u<-.005)&&(de(i.line,o),br(i.line),i.rest))for(var d=0;d<i.rest.length;d++)br(i.rest[d])}}}function br(e){if(e.widgets)for(var t=0;t<e.widgets.length;++t){var n=e.widgets[t],r=n.node.parentNode;r&&(n.height=r.offsetHeight)}}function xr(e,t,n){var r=n&&null!=n.top?Math.max(0,n.top):e.scroller.scrollTop;r=Math.floor(r-_n(e));var i=n&&null!=n.bottom?n.bottom:r+e.wrapper.clientHeight,o=he(t,r),a=he(t,i);if(n&&n.ensure){var s=n.ensure.from.line,l=n.ensure.to.line;s<o?(o=s,a=he(t,Ve(le(t,s))+e.wrapper.clientHeight)):Math.min(l,t.lastLine())>=a&&(o=he(t,Ve(le(t,l))-e.wrapper.clientHeight),a=l)}return{from:o,to:Math.max(a,o+1)}}function wr(e){var t=e.display,n=t.view;if(t.alignWidgets||t.gutters.firstChild&&e.options.fixedGutter){for(var r=rr(t)-t.scroller.scrollLeft+e.doc.scrollLeft,i=t.gutters.offsetWidth,o=r+"px",a=0;a<n.length;a++)if(!n[a].hidden){e.options.fixedGutter&&(n[a].gutter&&(n[a].gutter.style.left=o),n[a].gutterBackground&&(n[a].gutterBackground.style.left=o));var s=n[a].alignable;if(s)for(var l=0;l<s.length;l++)s[l].style.left=o}e.options.fixedGutter&&(t.gutters.style.left=r+i+"px")}}function _r(e){if(!e.options.lineNumbers)return!1;var t=e.doc,n=me(e.options,t.first+t.size-1),r=e.display;if(n.length!=r.lineNumChars){var i=r.measure.appendChild(A("div",[A("div",n)],"CodeMirror-linenumber CodeMirror-gutter-elt")),o=i.firstChild.offsetWidth,a=i.offsetWidth-o;return r.lineGutter.style.width="",r.lineNumInnerWidth=Math.max(o,r.lineGutter.offsetWidth-a)+1,r.lineNumWidth=r.lineNumInnerWidth+a,r.lineNumChars=r.lineNumInnerWidth?n.length:-1,r.lineGutter.style.width=r.lineNumWidth+"px",si(e),!0}return!1}function kr(e,t){var n=e.display,r=er(e.display);t.top<0&&(t.top=0);var i=e.curOp&&null!=e.curOp.scrollTop?e.curOp.scrollTop:n.scroller.scrollTop,o=Ln(e),a={};t.bottom-t.top>o&&(t.bottom=t.top+o);var s=e.doc.height+kn(n),l=t.top<r,c=t.bottom>s-r;if(t.top<i)a.scrollTop=l?0:t.top;else if(t.bottom>i+o){var u=Math.min(t.top,(c?s:t.bottom)-o);u!=i&&(a.scrollTop=u)}var d=e.curOp&&null!=e.curOp.scrollLeft?e.curOp.scrollLeft:n.scroller.scrollLeft,f=Mn(e)-(e.options.fixedGutter?n.gutters.offsetWidth:0),h=t.right-t.left>f;return h&&(t.right=t.left+f),t.left<10?a.scrollLeft=0:t.left<d?a.scrollLeft=Math.max(0,t.left-(h?0:10)):t.right>f+d-3&&(a.scrollLeft=t.right+(h?0:10)-f),a}function Cr(e,t){null!=t&&(Lr(e),e.curOp.scrollTop=(null==e.curOp.scrollTop?e.doc.scrollTop:e.curOp.scrollTop)+t)}function Sr(e){Lr(e);var t=e.getCursor();e.curOp.scrollToPos={from:t,to:t,margin:e.options.cursorScrollMargin}}function Mr(e,t,n){null==t&&null==n||Lr(e),null!=t&&(e.curOp.scrollLeft=t),null!=n&&(e.curOp.scrollTop=n)}function Lr(e){var t=e.curOp.scrollToPos;t&&(e.curOp.scrollToPos=null,Tr(e,$n(e,t.from),$n(e,t.to),t.margin))}function Tr(e,t,n,r){var i=kr(e,{left:Math.min(t.left,n.left),top:Math.min(t.top,n.top)-r,right:Math.max(t.right,n.right),bottom:Math.max(t.bottom,n.bottom)+r});Mr(e,i.scrollLeft,i.scrollTop)}function Or(e,t){Math.abs(e.doc.scrollTop-t)<2||(n||ai(e,{top:t}),Ar(e,t,!0),n&&ai(e),ti(e,100))}function Ar(e,t,n){t=Math.min(e.display.scroller.scrollHeight-e.display.scroller.clientHeight,t),(e.display.scroller.scrollTop!=t||n)&&(e.doc.scrollTop=t,e.display.scrollbars.setScrollTop(t),e.display.scroller.scrollTop!=t&&(e.display.scroller.scrollTop=t))}function Pr(e,t,n,r){t=Math.min(t,e.display.scroller.scrollWidth-e.display.scroller.clientWidth),(n?t==e.doc.scrollLeft:Math.abs(e.doc.scrollLeft-t)<2)&&!r||(e.doc.scrollLeft=t,wr(e),e.display.scroller.scrollLeft!=t&&(e.display.scroller.scrollLeft=t),e.display.scrollbars.setScrollLeft(t))}function Er(e){var t=e.display,n=t.gutters.offsetWidth,r=Math.round(e.doc.height+kn(e.display));return{clientHeight:t.scroller.clientHeight,viewHeight:t.wrapper.clientHeight,scrollWidth:t.scroller.scrollWidth,clientWidth:t.scroller.clientWidth,viewWidth:t.wrapper.clientWidth,barLeft:e.options.fixedGutter?n:0,docHeight:r,scrollHeight:r+Sn(e)+t.barHeight,nativeBarWidth:t.nativeBarWidth,gutterWidth:n}}var Dr=function(e,t,n){this.cm=n;var r=this.vert=A("div",[A("div",null,null,"min-width: 1px")],"CodeMirror-vscrollbar"),i=this.horiz=A("div",[A("div",null,null,"height: 100%; min-height: 1px")],"CodeMirror-hscrollbar");e(r),e(i),et(r,"scroll",function(){r.clientHeight&&t(r.scrollTop,"vertical")}),et(i,"scroll",function(){i.clientWidth&&t(i.scrollLeft,"horizontal")}),this.checkedZeroWidth=!1,s&&l<8&&(this.horiz.style.minHeight=this.vert.style.minWidth="18px")};Dr.prototype.update=function(e){var t=e.scrollWidth>e.clientWidth+1,n=e.scrollHeight>e.clientHeight+1,r=e.nativeBarWidth;if(n){this.vert.style.display="block",this.vert.style.bottom=t?r+"px":"0";var i=e.viewHeight-(t?r:0);this.vert.firstChild.style.height=Math.max(0,e.scrollHeight-e.clientHeight+i)+"px"}else this.vert.style.display="",this.vert.firstChild.style.height="0";if(t){this.horiz.style.display="block",this.horiz.style.right=n?r+"px":"0",this.horiz.style.left=e.barLeft+"px";var o=e.viewWidth-e.barLeft-(n?r:0);this.horiz.firstChild.style.width=Math.max(0,e.scrollWidth-e.clientWidth+o)+"px"}else this.horiz.style.display="",this.horiz.firstChild.style.width="0";return!this.checkedZeroWidth&&e.clientHeight>0&&(0==r&&this.zeroWidthHack(),this.checkedZeroWidth=!0),{right:n?r:0,bottom:t?r:0}},Dr.prototype.setScrollLeft=function(e){this.horiz.scrollLeft!=e&&(this.horiz.scrollLeft=e),this.disableHoriz&&this.enableZeroWidthBar(this.horiz,this.disableHoriz,"horiz")},Dr.prototype.setScrollTop=function(e){this.vert.scrollTop!=e&&(this.vert.scrollTop=e),this.disableVert&&this.enableZeroWidthBar(this.vert,this.disableVert,"vert")},Dr.prototype.zeroWidthHack=function(){var e=b&&!p?"12px":"18px";this.horiz.style.height=this.vert.style.width=e,this.horiz.style.pointerEvents=this.vert.style.pointerEvents="none",this.disableHoriz=new W,this.disableVert=new W},Dr.prototype.enableZeroWidthBar=function(e,t,n){e.style.pointerEvents="auto",t.set(1e3,function r(){var i=e.getBoundingClientRect();("vert"==n?document.elementFromPoint(i.right-1,(i.top+i.bottom)/2):document.elementFromPoint((i.right+i.left)/2,i.bottom-1))!=e?e.style.pointerEvents="none":t.set(1e3,r)})},Dr.prototype.clear=function(){var e=this.horiz.parentNode;e.removeChild(this.horiz),e.removeChild(this.vert)};var jr=function(){};function Nr(e,t){t||(t=Er(e));var n=e.display.barWidth,r=e.display.barHeight;Ir(e,t);for(var i=0;i<4&&n!=e.display.barWidth||r!=e.display.barHeight;i++)n!=e.display.barWidth&&e.options.lineWrapping&&yr(e),Ir(e,Er(e)),n=e.display.barWidth,r=e.display.barHeight}function Ir(e,t){var n=e.display,r=n.scrollbars.update(t);n.sizer.style.paddingRight=(n.barWidth=r.right)+"px",n.sizer.style.paddingBottom=(n.barHeight=r.bottom)+"px",n.heightForcer.style.borderBottom=r.bottom+"px solid transparent",r.right&&r.bottom?(n.scrollbarFiller.style.display="block",n.scrollbarFiller.style.height=r.bottom+"px",n.scrollbarFiller.style.width=r.right+"px"):n.scrollbarFiller.style.display="",r.bottom&&e.options.coverGutterNextToScrollbar&&e.options.fixedGutter?(n.gutterFiller.style.display="block",n.gutterFiller.style.height=r.bottom+"px",n.gutterFiller.style.width=t.gutterWidth+"px"):n.gutterFiller.style.display=""}jr.prototype.update=function(){return{bottom:0,right:0}},jr.prototype.setScrollLeft=function(){},jr.prototype.setScrollTop=function(){},jr.prototype.clear=function(){};var Rr={native:Dr,null:jr};function zr(e){e.display.scrollbars&&(e.display.scrollbars.clear(),e.display.scrollbars.addClass&&L(e.display.wrapper,e.display.scrollbars.addClass)),e.display.scrollbars=new Rr[e.options.scrollbarStyle](function(t){e.display.wrapper.insertBefore(t,e.display.scrollbarFiller),et(t,"mousedown",function(){e.state.focused&&setTimeout(function(){return e.display.input.focus()},0)}),t.setAttribute("cm-not-content","true")},function(t,n){"horizontal"==n?Pr(e,t):Or(e,t)},e),e.display.scrollbars.addClass&&j(e.display.wrapper,e.display.scrollbars.addClass)}var Fr=0;function Wr(e){var t;e.curOp={cm:e,viewChanged:!1,startHeight:e.doc.height,forceUpdate:!1,updateInput:null,typing:!1,changeObjs:null,cursorActivityHandlers:null,cursorActivityCalled:0,selectionChanged:!1,updateMaxLine:!1,scrollLeft:null,scrollTop:null,scrollToPos:null,focus:!1,id:++Fr},t=e.curOp,on?on.ops.push(t):t.ownsGroup=on={ops:[t],delayedCallbacks:[]}}function Br(e){!function(e,t){var n=e.ownsGroup;if(n)try{!function(e){var t=e.delayedCallbacks,n=0;do{for(;n<t.length;n++)t[n].call(null);for(var r=0;r<e.ops.length;r++){var i=e.ops[r];if(i.cursorActivityHandlers)for(;i.cursorActivityCalled<i.cursorActivityHandlers.length;)i.cursorActivityHandlers[i.cursorActivityCalled++].call(null,i.cm)}}while(n<t.length)}(n)}finally{on=null,function(e){for(var t=0;t<e.ops.length;t++)e.ops[t].cm.curOp=null;!function(e){for(var t=e.ops,n=0;n<t.length;n++)Hr(t[n]);for(var r=0;r<t.length;r++)(i=t[r]).updatedDisplay=i.mustUpdate&&ii(i.cm,i.update);for(var i,o=0;o<t.length;o++)qr(t[o]);for(var a=0;a<t.length;a++)Ur(t[a]);for(var s=0;s<t.length;s++)Kr(t[s])}(e)}(n)}}(e.curOp)}function Hr(e){var t=e.cm,n=t.display;!function(e){var t=e.display;!t.scrollbarsClipped&&t.scroller.offsetWidth&&(t.nativeBarWidth=t.scroller.offsetWidth-t.scroller.clientWidth,t.heightForcer.style.height=Sn(e)+"px",t.sizer.style.marginBottom=-t.nativeBarWidth+"px",t.sizer.style.borderRightWidth=Sn(e)+"px",t.scrollbarsClipped=!0)}(t),e.updateMaxLine&&Ge(t),e.mustUpdate=e.viewChanged||e.forceUpdate||null!=e.scrollTop||e.scrollToPos&&(e.scrollToPos.from.line<n.viewFrom||e.scrollToPos.to.line>=n.viewTo)||n.maxLineChanged&&t.options.lineWrapping,e.update=e.mustUpdate&&new ri(t,e.mustUpdate&&{top:e.scrollTop,ensure:e.scrollToPos},e.forceUpdate)}function qr(e){var t=e.cm,n=t.display;e.updatedDisplay&&yr(t),e.barMeasure=Er(t),n.maxLineChanged&&!t.options.lineWrapping&&(e.adjustWidthTo=On(t,n.maxLine,n.maxLine.text.length).left+3,t.display.sizerWidth=e.adjustWidthTo,e.barMeasure.scrollWidth=Math.max(n.scroller.clientWidth,n.sizer.offsetLeft+e.adjustWidthTo+Sn(t)+t.display.barWidth),e.maxScrollLeft=Math.max(0,n.sizer.offsetLeft+e.adjustWidthTo-Mn(t))),(e.updatedDisplay||e.selectionChanged)&&(e.preparedSelection=n.input.prepareSelection())}function Ur(e){var t=e.cm;null!=e.adjustWidthTo&&(t.display.sizer.style.minWidth=e.adjustWidthTo+"px",e.maxScrollLeft<t.doc.scrollLeft&&Pr(t,Math.min(t.display.scroller.scrollLeft,e.maxScrollLeft),!0),t.display.maxLineChanged=!1);var n=e.focus&&e.focus==D();e.preparedSelection&&t.display.input.showSelection(e.preparedSelection,n),(e.updatedDisplay||e.startHeight!=t.doc.height)&&Nr(t,e.barMeasure),e.updatedDisplay&&li(t,e.barMeasure),e.selectionChanged&&hr(t),t.state.focused&&e.updateInput&&t.display.input.reset(e.typing),n&&pr(e.cm)}function Kr(e){var t=e.cm,n=t.display,r=t.doc;e.updatedDisplay&&oi(t,e.update),null==n.wheelStartX||null==e.scrollTop&&null==e.scrollLeft&&!e.scrollToPos||(n.wheelStartX=n.wheelStartY=null),null!=e.scrollTop&&Ar(t,e.scrollTop,e.forceScroll),null!=e.scrollLeft&&Pr(t,e.scrollLeft,!0,!0),e.scrollToPos&&function(e,t){if(!it(e,"scrollCursorIntoView")){var n=e.display,r=n.sizer.getBoundingClientRect(),i=null;if(t.top+r.top<0?i=!0:t.bottom+r.top>(window.innerHeight||document.documentElement.clientHeight)&&(i=!1),null!=i&&!m){var o=A("div","​",null,"position: absolute;\n                         top: "+(t.top-n.viewOffset-_n(e.display))+"px;\n                         height: "+(t.bottom-t.top+Sn(e)+n.barHeight)+"px;\n                         left: "+t.left+"px; width: "+Math.max(2,t.right-t.left)+"px;");e.display.lineSpace.appendChild(o),o.scrollIntoView(i),e.display.lineSpace.removeChild(o)}}}(t,function(e,t,n,r){var i;null==r&&(r=0),e.options.lineWrapping||t!=n||(n="before"==(t=t.ch?ge(t.line,"before"==t.sticky?t.ch-1:t.ch,"after"):t).sticky?ge(t.line,t.ch+1,"before"):t);for(var o=0;o<5;o++){var a=!1,s=Vn(e,t),l=n&&n!=t?Vn(e,n):s,c=kr(e,i={left:Math.min(s.left,l.left),top:Math.min(s.top,l.top)-r,right:Math.max(s.left,l.left),bottom:Math.max(s.bottom,l.bottom)+r}),u=e.doc.scrollTop,d=e.doc.scrollLeft;if(null!=c.scrollTop&&(Or(e,c.scrollTop),Math.abs(e.doc.scrollTop-u)>1&&(a=!0)),null!=c.scrollLeft&&(Pr(e,c.scrollLeft),Math.abs(e.doc.scrollLeft-d)>1&&(a=!0)),!a)break}return i}(t,ke(r,e.scrollToPos.from),ke(r,e.scrollToPos.to),e.scrollToPos.margin));var i=e.maybeHiddenMarkers,o=e.maybeUnhiddenMarkers;if(i)for(var a=0;a<i.length;++a)i[a].lines.length||rt(i[a],"hide");if(o)for(var s=0;s<o.length;++s)o[s].lines.length&&rt(o[s],"unhide");n.wrapper.offsetHeight&&(r.scrollTop=t.display.scroller.scrollTop),e.changeObjs&&rt(t,"changes",t,e.changeObjs),e.update&&e.update.finish()}function Vr(e,t){if(e.curOp)return t();Wr(e);try{return t()}finally{Br(e)}}function $r(e,t){return function(){if(e.curOp)return t.apply(e,arguments);Wr(e);try{return t.apply(e,arguments)}finally{Br(e)}}}function Gr(e){return function(){if(this.curOp)return e.apply(this,arguments);Wr(this);try{return e.apply(this,arguments)}finally{Br(this)}}}function Jr(e){return function(){var t=this.cm;if(!t||t.curOp)return e.apply(this,arguments);Wr(t);try{return e.apply(this,arguments)}finally{Br(t)}}}function Xr(e,t,n,r){null==t&&(t=e.doc.first),null==n&&(n=e.doc.first+e.doc.size),r||(r=0);var i=e.display;if(r&&n<i.viewTo&&(null==i.updateLineNumbers||i.updateLineNumbers>t)&&(i.updateLineNumbers=t),e.curOp.viewChanged=!0,t>=i.viewTo)Me&&He(e.doc,t)<i.viewTo&&Yr(e);else if(n<=i.viewFrom)Me&&qe(e.doc,n+r)>i.viewFrom?Yr(e):(i.viewFrom+=r,i.viewTo+=r);else if(t<=i.viewFrom&&n>=i.viewTo)Yr(e);else if(t<=i.viewFrom){var o=Qr(e,n,n+r,1);o?(i.view=i.view.slice(o.index),i.viewFrom=o.lineN,i.viewTo+=r):Yr(e)}else if(n>=i.viewTo){var a=Qr(e,t,t,-1);a?(i.view=i.view.slice(0,a.index),i.viewTo=a.lineN):Yr(e)}else{var s=Qr(e,t,t,-1),l=Qr(e,n,n+r,1);s&&l?(i.view=i.view.slice(0,s.index).concat(rn(e,s.lineN,l.lineN)).concat(i.view.slice(l.index)),i.viewTo+=r):Yr(e)}var c=i.externalMeasured;c&&(n<c.lineN?c.lineN+=r:t<c.lineN+c.size&&(i.externalMeasured=null))}function Zr(e,t,n){e.curOp.viewChanged=!0;var r=e.display,i=e.display.externalMeasured;if(i&&t>=i.lineN&&t<i.lineN+i.size&&(r.externalMeasured=null),!(t<r.viewFrom||t>=r.viewTo)){var o=r.view[sr(e,t)];if(null!=o.node){var 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c=t.text.length-(a.line-o.line)-1;t.full?Xr(e):o.line!=a.line||1!=t.text.length||Si(e.doc,t)?Xr(e,o.line,a.line+1,c):Zr(e,o.line,"text");var u=at(e,"changes"),d=at(e,"change");if(d||u){var f={from:o,to:a,text:t.text,removed:t.removed,origin:t.origin};d&&sn(e,"change",e,f),u&&(e.curOp.changeObjs||(e.curOp.changeObjs=[])).push(f)}e.display.selForContextMenu=null}(e.cm,t,r):Mi(e,t,r),Vi(e,n,U)}}function ao(e,t,n,r,i){var o;r||(r=n),ve(r,n)<0&&(n=(o=[r,n])[0],r=o[1]),"string"==typeof t&&(t=e.splitLines(t)),to(e,{from:n,to:r,text:t,origin:i})}function so(e,t,n,r){n<e.line?e.line+=r:t<e.line&&(e.line=t,e.ch=0)}function lo(e,t,n,r){for(var i=0;i<e.length;++i){var o=e[i],a=!0;if(o.ranges){o.copied||((o=e[i]=o.deepCopy()).copied=!0);for(var s=0;s<o.ranges.length;s++)so(o.ranges[s].anchor,t,n,r),so(o.ranges[s].head,t,n,r)}else{for(var l=0;l<o.changes.length;++l){var c=o.changes[l];if(n<c.from.line)c.from=ge(c.from.line+r,c.from.ch),c.to=ge(c.to.line+r,c.to.ch);else 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t=0;t<this.children.length;++t)this.children[t].collapse(e)},insertInner:function(e,t,n){this.size+=t.length,this.height+=n;for(var r=0;r<this.children.length;++r){var i=this.children[r],o=i.chunkSize();if(e<=o){if(i.insertInner(e,t,n),i.lines&&i.lines.length>50){for(var a=i.lines.length%25+25,s=a;s<i.lines.length;){var l=new fo(i.lines.slice(s,s+=25));i.height-=l.height,this.children.splice(++r,0,l),l.parent=this}i.lines=i.lines.slice(0,a),this.maybeSpill()}break}e-=o}},maybeSpill:function(){if(!(this.children.length<=10)){var e=this;do{var t=new ho(e.children.splice(e.children.length-5,5));if(e.parent){e.size-=t.size,e.height-=t.height;var n=B(e.parent.children,e);e.parent.children.splice(n+1,0,t)}else{var r=new ho(e.children);r.parent=e,e.children=[r,t],e=r}t.parent=e.parent}while(e.children.length>10);e.parent.maybeSpill()}},iterN:function(e,t,n){for(var r=0;r<this.children.length;++r){var i=this.children[r],o=i.chunkSize();if(e<o){var 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s,l=t.line,c=e.cm;if(e.iter(l,n.line+1,function(e){c&&o.collapsed&&!c.options.lineWrapping&&Be(e)==c.display.maxLine&&(s=!0),o.collapsed&&l!=t.line&&de(e,0),function(e,t){e.markedSpans=e.markedSpans?e.markedSpans.concat([t]):[t],t.marker.attachLine(e)}(e,new Le(o,l==t.line?t.ch:null,l==n.line?n.ch:null)),++l}),o.collapsed&&e.iter(t.line,n.line+1,function(t){Ue(e,t)&&de(t,0)}),o.clearOnEnter&&et(o,"beforeCursorEnter",function(){return o.clear()}),o.readOnly&&(Se=!0,(e.history.done.length||e.history.undone.length)&&e.clearHistory()),o.collapsed&&(o.id=++go,o.atomic=!0),c){if(s&&(c.curOp.updateMaxLine=!0),o.collapsed)Xr(c,t.line,n.line+1);else if(o.className||o.title||o.startStyle||o.endStyle||o.css)for(var u=t.line;u<=n.line;u++)Zr(c,u,"text");o.atomic&&Gi(c.doc),sn(c,"markerAdded",c,o)}return o}vo.prototype.clear=function(){if(!this.explicitlyCleared){var e=this.doc.cm,t=e&&!e.curOp;if(t&&Wr(e),at(this,"clear")){var n=this.find();n&&sn(this,"clear",n.from,n.to)}for(var r=null,i=null,o=0;o<this.lines.length;++o){var a=this.lines[o],s=Te(a.markedSpans,this);e&&!this.collapsed?Zr(e,fe(a),"text"):e&&(null!=s.to&&(i=fe(a)),null!=s.from&&(r=fe(a))),a.markedSpans=Oe(a.markedSpans,s),null==s.from&&this.collapsed&&!Ue(this.doc,a)&&e&&de(a,er(e.display))}if(e&&this.collapsed&&!e.options.lineWrapping)for(var l=0;l<this.lines.length;++l){var c=Be(this.lines[l]),u=$e(c);u>e.display.maxLineLength&&(e.display.maxLine=c,e.display.maxLineLength=u,e.display.maxLineChanged=!0)}null!=r&&e&&this.collapsed&&Xr(e,r,i+1),this.lines.length=0,this.explicitlyCleared=!0,this.atomic&&this.doc.cantEdit&&(this.doc.cantEdit=!1,e&&Gi(e.doc)),e&&sn(e,"markerCleared",e,this,r,i),t&&Br(e),this.parent&&this.parent.clear()}},vo.prototype.find=function(e,t){var n,r;null==e&&"bookmark"==this.type&&(e=1);for(var i=0;i<this.lines.length;++i){var o=this.lines[i],a=Te(o.markedSpans,this);if(null!=a.from&&(n=ge(t?o:fe(o),a.from),-1==e))return n;if(null!=a.to&&(r=ge(t?o:fe(o),a.to),1==e))return r}return n&&{from:n,to:r}},vo.prototype.changed=function(){var e=this,t=this.find(-1,!0),n=this,r=this.doc.cm;t&&r&&Vr(r,function(){var i=t.line,o=fe(t.line),a=An(r,o);if(a&&(Rn(a),r.curOp.selectionChanged=r.curOp.forceUpdate=!0),r.curOp.updateMaxLine=!0,!Ue(n.doc,i)&&null!=n.height){var s=n.height;n.height=null;var l=xn(n)-s;l&&de(i,i.height+l)}sn(r,"markerChanged",r,e)})},vo.prototype.attachLine=function(e){if(!this.lines.length&&this.doc.cm){var t=this.doc.cm.curOp;t.maybeHiddenMarkers&&-1!=B(t.maybeHiddenMarkers,this)||(t.maybeUnhiddenMarkers||(t.maybeUnhiddenMarkers=[])).push(this)}this.lines.push(e)},vo.prototype.detachLine=function(e){if(this.lines.splice(B(this.lines,e),1),!this.lines.length&&this.doc.cm){var t=this.doc.cm.curOp;(t.maybeHiddenMarkers||(t.maybeHiddenMarkers=[])).push(this)}},st(vo);var bo=function(e,t){this.markers=e,this.primary=t;for(var n=0;n<e.length;++n)e[n].parent=this};function xo(e){return e.findMarks(ge(e.first,0),e.clipPos(ge(e.lastLine())),function(e){return e.parent})}function wo(e){for(var t=function(t){var n=e[t],r=[n.primary.doc];Li(n.primary.doc,function(e){return r.push(e)});for(var i=0;i<n.markers.length;i++){var o=n.markers[i];-1==B(r,o.doc)&&(o.parent=null,n.markers.splice(i--,1))}},n=0;n<e.length;n++)t(n)}bo.prototype.clear=function(){if(!this.explicitlyCleared){this.explicitlyCleared=!0;for(var e=0;e<this.markers.length;++e)this.markers[e].clear();sn(this,"clear")}},bo.prototype.find=function(e,t){return this.primary.find(e,t)},st(bo);var _o=0,ko=function e(t,n,r,i,o){if(!(this instanceof e))return new e(t,n,r,i,o);null==r&&(r=0),ho.call(this,[new fo([new Kt("",null)])]),this.first=r,this.scrollTop=this.scrollLeft=0,this.cantEdit=!1,this.cleanGeneration=1,this.modeFrontier=this.highlightFrontier=r;var a=ge(r,0);this.sel=yi(a),this.history=new Ai(null),this.id=++_o,this.modeOption=n,this.lineSep=i,this.direction="rtl"==o?"rtl":"ltr",this.extend=!1,"string"==typeof t&&(t=this.splitLines(t)),Mi(this,{from:a,to:a,text:t}),Ki(this,yi(a),U)};ko.prototype=Q(ho.prototype,{constructor:ko,iter:function(e,t,n){n?this.iterN(e-this.first,t-e,n):this.iterN(this.first,this.first+this.size,e)},insert:function(e,t){for(var n=0,r=0;r<t.length;++r)n+=t[r].height;this.insertInner(e-this.first,t,n)},remove:function(e,t){this.removeInner(e-this.first,t)},getValue:function(e){var t=ue(this,this.first,this.first+this.size);return!1===e?t:t.join(e||this.lineSeparator())},setValue:Jr(function(e){var t=ge(this.first,0),n=this.first+this.size-1;to(this,{from:t,to:ge(n,le(this,n).text.length),text:this.splitLines(e),origin:"setValue",full:!0},!0),this.cm&&Mr(this.cm,0,0),Ki(this,yi(t),U)}),replaceRange:function(e,t,n,r){ao(this,e,t=ke(this,t),n=n?ke(this,n):t,r)},getRange:function(e,t,n){var r=ce(this,ke(this,e),ke(this,t));return!1===n?r:r.join(n||this.lineSeparator())},getLine:function(e){var t=this.getLineHandle(e);return t&&t.text},getLineHandle:function(e){if(pe(this,e))return le(this,e)},getLineNumber:function(e){return fe(e)},getLineHandleVisualStart:function(e){return"number"==typeof e&&(e=le(this,e)),Be(e)},lineCount:function(){return this.size},firstLine:function(){return this.first},lastLine:function(){return this.first+this.size-1},clipPos:function(e){return ke(this,e)},getCursor:function(e){var t=this.sel.primary();return null==e||"head"==e?t.head:"anchor"==e?t.anchor:"end"==e||"to"==e||!1===e?t.to():t.from()},listSelections:function(){return this.sel.ranges},somethingSelected:function(){return this.sel.somethingSelected()},setCursor:Jr(function(e,t,n){qi(this,ke(this,"number"==typeof e?ge(e,t||0):e),null,n)}),setSelection:Jr(function(e,t,n){qi(this,ke(this,e),ke(this,t||e),n)}),extendSelection:Jr(function(e,t,n){Wi(this,ke(this,e),t&&ke(this,t),n)}),extendSelections:Jr(function(e,t){Bi(this,Ce(this,e),t)}),extendSelectionsBy:Jr(function(e,t){Bi(this,Ce(this,Z(this.sel.ranges,e)),t)}),setSelections:Jr(function(e,t,n){if(e.length){for(var r=[],i=0;i<e.length;i++)r[i]=new gi(ke(this,e[i].anchor),ke(this,e[i].head));null==t&&(t=Math.min(e.length-1,this.sel.primIndex)),Ki(this,vi(r,t),n)}}),addSelection:Jr(function(e,t,n){var r=this.sel.ranges.slice(0);r.push(new gi(ke(this,e),ke(this,t||e))),Ki(this,vi(r,r.length-1),n)}),getSelection:function(e){for(var t,n=this.sel.ranges,r=0;r<n.length;r++){var i=ce(this,n[r].from(),n[r].to());t=t?t.concat(i):i}return!1===e?t:t.join(e||this.lineSeparator())},getSelections:function(e){for(var t=[],n=this.sel.ranges,r=0;r<n.length;r++){var i=ce(this,n[r].from(),n[r].to());!1!==e&&(i=i.join(e||this.lineSeparator())),t[r]=i}return t},replaceSelection:function(e,t,n){for(var r=[],i=0;i<this.sel.ranges.length;i++)r[i]=e;this.replaceSelections(r,t,n||"+input")},replaceSelections:Jr(function(e,t,n){for(var r=[],i=this.sel,o=0;o<i.ranges.length;o++){var a=i.ranges[o];r[o]={from:a.from(),to:a.to(),text:this.splitLines(e[o]),origin:n}}for(var s=t&&"end"!=t&&function(e,t,n){for(var r=[],i=ge(e.first,0),o=i,a=0;a<t.length;a++){var s=t[a],l=_i(s.from,i,o),c=_i(bi(s),i,o);if(i=s.to,o=c,"around"==n){var u=e.sel.ranges[a],d=ve(u.head,u.anchor)<0;r[a]=new gi(d?c:l,d?l:c)}else r[a]=new gi(l,l)}return new mi(r,e.sel.primIndex)}(this,r,t),l=r.length-1;l>=0;l--)to(this,r[l]);s?Ui(this,s):this.cm&&Sr(this.cm)}),undo:Jr(function(){ro(this,"undo")}),redo:Jr(function(){ro(this,"redo")}),undoSelection:Jr(function(){ro(this,"undo",!0)}),redoSelection:Jr(function(){ro(this,"redo",!0)}),setExtending:function(e){this.extend=e},getExtending:function(){return this.extend},historySize:function(){for(var e=this.history,t=0,n=0,r=0;r<e.done.length;r++)e.done[r].ranges||++t;for(var i=0;i<e.undone.length;i++)e.undone[i].ranges||++n;return{undo:t,redo:n}},clearHistory:function(){this.history=new Ai(this.history.maxGeneration)},markClean:function(){this.cleanGeneration=this.changeGeneration(!0)},changeGeneration:function(e){return e&&(this.history.lastOp=this.history.lastSelOp=this.history.lastOrigin=null),this.history.generation},isClean:function(e){return this.history.generation==(e||this.cleanGeneration)},getHistory:function(){return{done:zi(this.history.done),undone:zi(this.history.undone)}},setHistory:function(e){var t=this.history=new Ai(this.history.maxGeneration);t.done=zi(e.done.slice(0),null,!0),t.undone=zi(e.undone.slice(0),null,!0)},setGutterMarker:Jr(function(e,t,n){return uo(this,e,"gutter",function(e){var r=e.gutterMarkers||(e.gutterMarkers={});return r[t]=n,!n&&re(r)&&(e.gutterMarkers=null),!0})}),clearGutter:Jr(function(e){var t=this;this.iter(function(n){n.gutterMarkers&&n.gutterMarkers[e]&&uo(t,n,"gutter",function(){return n.gutterMarkers[e]=null,re(n.gutterMarkers)&&(n.gutterMarkers=null),!0})})}),lineInfo:function(e){var t;if("number"==typeof e){if(!pe(this,e))return null;if(t=e,!(e=le(this,e)))return null}else if(null==(t=fe(e)))return null;return{line:t,handle:e,text:e.text,gutterMarkers:e.gutterMarkers,textClass:e.textClass,bgClass:e.bgClass,wrapClass:e.wrapClass,widgets:e.widgets}},addLineClass:Jr(function(e,t,n){return uo(this,e,"gutter"==t?"gutter":"class",function(e){var r="text"==t?"textClass":"background"==t?"bgClass":"gutter"==t?"gutterClass":"wrapClass";if(e[r]){if(S(n).test(e[r]))return!1;e[r]+=" "+n}else e[r]=n;return!0})}),removeLineClass:Jr(function(e,t,n){return uo(this,e,"gutter"==t?"gutter":"class",function(e){var r="text"==t?"textClass":"background"==t?"bgClass":"gutter"==t?"gutterClass":"wrapClass",i=e[r];if(!i)return!1;if(null==n)e[r]=null;else{var o=i.match(S(n));if(!o)return!1;var a=o.index+o[0].length;e[r]=i.slice(0,o.index)+(o.index&&a!=i.length?" ":"")+i.slice(a)||null}return!0})}),addLineWidget:Jr(function(e,t,n){return function(e,t,n,r){var i=new po(e,n,r),o=e.cm;return o&&i.noHScroll&&(o.display.alignWidgets=!0),uo(e,t,"widget",function(t){var n=t.widgets||(t.widgets=[]);if(null==i.insertAt?n.push(i):n.splice(Math.min(n.length-1,Math.max(0,i.insertAt)),0,i),i.line=t,o&&!Ue(e,t)){var r=Ve(t)<e.scrollTop;de(t,t.height+xn(i)),r&&Cr(o,i.height),o.curOp.forceUpdate=!0}return!0}),o&&sn(o,"lineWidgetAdded",o,i,"number"==typeof t?t:fe(t)),i}(this,e,t,n)}),removeLineWidget:function(e){e.clear()},markText:function(e,t,n){return yo(this,ke(this,e),ke(this,t),n,n&&n.type||"range")},setBookmark:function(e,t){var n={replacedWith:t&&(null==t.nodeType?t.widget:t),insertLeft:t&&t.insertLeft,clearWhenEmpty:!1,shared:t&&t.shared,handleMouseEvents:t&&t.handleMouseEvents};return yo(this,e=ke(this,e),e,n,"bookmark")},findMarksAt:function(e){var t=[],n=le(this,(e=ke(this,e)).line).markedSpans;if(n)for(var r=0;r<n.length;++r){var i=n[r];(null==i.from||i.from<=e.ch)&&(null==i.to||i.to>=e.ch)&&t.push(i.marker.parent||i.marker)}return t},findMarks:function(e,t,n){e=ke(this,e),t=ke(this,t);var r=[],i=e.line;return this.iter(e.line,t.line+1,function(o){var a=o.markedSpans;if(a)for(var s=0;s<a.length;s++){var l=a[s];null!=l.to&&i==e.line&&e.ch>=l.to||null==l.from&&i!=e.line||null!=l.from&&i==t.line&&l.from>=t.ch||n&&!n(l.marker)||r.push(l.marker.parent||l.marker)}++i}),r},getAllMarks:function(){var e=[];return this.iter(function(t){var n=t.markedSpans;if(n)for(var r=0;r<n.length;++r)null!=n[r].from&&e.push(n[r].marker)}),e},posFromIndex:function(e){var t,n=this.first,r=this.lineSeparator().length;return this.iter(function(i){var o=i.text.length+r;if(o>e)return t=e,!0;e-=o,++n}),ke(this,ge(n,t))},indexFromPos:function(e){var t=(e=ke(this,e)).ch;if(e.line<this.first||e.ch<0)return 0;var n=this.lineSeparator().length;return this.iter(this.first,e.line,function(e){t+=e.text.length+n}),t},copy:function(e){var t=new ko(ue(this,this.first,this.first+this.size),this.modeOption,this.first,this.lineSep,this.direction);return t.scrollTop=this.scrollTop,t.scrollLeft=this.scrollLeft,t.sel=this.sel,t.extend=!1,e&&(t.history.undoDepth=this.history.undoDepth,t.setHistory(this.getHistory())),t},linkedDoc:function(e){e||(e={});var t=this.first,n=this.first+this.size;null!=e.from&&e.from>t&&(t=e.from),null!=e.to&&e.to<n&&(n=e.to);var r=new ko(ue(this,t,n),e.mode||this.modeOption,t,this.lineSep,this.direction);return e.sharedHist&&(r.history=this.history),(this.linked||(this.linked=[])).push({doc:r,sharedHist:e.sharedHist}),r.linked=[{doc:this,isParent:!0,sharedHist:e.sharedHist}],function(e,t){for(var n=0;n<t.length;n++){var r=t[n],i=r.find(),o=e.clipPos(i.from),a=e.clipPos(i.to);if(ve(o,a)){var s=yo(e,o,a,r.primary,r.primary.type);r.markers.push(s),s.parent=r}}}(r,xo(this)),r},unlinkDoc:function(e){if(e instanceof ya&&(e=e.doc),this.linked)for(var t=0;t<this.linked.length;++t)if(this.linked[t].doc==e){this.linked.splice(t,1),e.unlinkDoc(this),wo(xo(this));break}if(e.history==this.history){var n=[e.id];Li(e,function(e){return n.push(e.id)},!0),e.history=new Ai(null),e.history.done=zi(this.history.done,n),e.history.undone=zi(this.history.undone,n)}},iterLinkedDocs:function(e){Li(this,e)},getMode:function(){return this.mode},getEditor:function(){return this.cm},splitLines:function(e){return this.lineSep?e.split(this.lineSep):xt(e)},lineSeparator:function(){return this.lineSep||"\n"},setDirection:Jr(function(e){var t;"rtl"!=e&&(e="ltr"),e!=this.direction&&(this.direction=e,this.iter(function(e){return e.order=null}),this.cm&&Vr(t=this.cm,function(){Oi(t),Xr(t)}))})}),ko.prototype.eachLine=ko.prototype.iter;var Co=0;function So(e){var t=this;if(Mo(t),!it(t,e)&&!wn(t.display,e)){lt(e),s&&(Co=+new Date);var n=ar(t,e,!0),r=e.dataTransfer.files;if(n&&!t.isReadOnly())if(r&&r.length&&window.FileReader&&window.File)for(var i=r.length,o=Array(i),a=0,l=function(e,r){if(!t.options.allowDropFileTypes||-1!=B(t.options.allowDropFileTypes,e.type)){var s=new FileReader;s.onload=$r(t,function(){var e=s.result;if(/[\x00-\x08\x0e-\x1f]{2}/.test(e)&&(e=""),o[r]=e,++a==i){var l={from:n=ke(t.doc,n),to:n,text:t.doc.splitLines(o.join(t.doc.lineSeparator())),origin:"paste"};to(t.doc,l),Ui(t.doc,yi(n,bi(l)))}}),s.readAsText(e)}},c=0;c<i;++c)l(r[c],c);else{if(t.state.draggingText&&t.doc.sel.contains(n)>-1)return t.state.draggingText(e),void setTimeout(function(){return t.display.input.focus()},20);try{var u=e.dataTransfer.getData("Text");if(u){var d;if(t.state.draggingText&&!t.state.draggingText.copy&&(d=t.listSelections()),Vi(t.doc,yi(n,n)),d)for(var f=0;f<d.length;++f)ao(t.doc,"",d[f].anchor,d[f].head,"drag");t.replaceSelection(u,"around","paste"),t.display.input.focus()}}catch(e){}}}}function Mo(e){e.display.dragCursor&&(e.display.lineSpace.removeChild(e.display.dragCursor),e.display.dragCursor=null)}function Lo(e){if(document.getElementsByClassName)for(var t=document.getElementsByClassName("CodeMirror"),n=0;n<t.length;n++){var r=t[n].CodeMirror;r&&e(r)}}var To=!1;function Oo(e){var t=e.display;t.lastWrapHeight==t.wrapper.clientHeight&&t.lastWrapWidth==t.wrapper.clientWidth||(t.cachedCharWidth=t.cachedTextHeight=t.cachedPaddingH=null,t.scrollbarsClipped=!1,e.setSize())}for(var Ao={3:"Pause",8:"Backspace",9:"Tab",13:"Enter",16:"Shift",17:"Ctrl",18:"Alt",19:"Pause",20:"CapsLock",27:"Esc",32:"Space",33:"PageUp",34:"PageDown",35:"End",36:"Home",37:"Left",38:"Up",39:"Right",40:"Down",44:"PrintScrn",45:"Insert",46:"Delete",59:";",61:"=",91:"Mod",92:"Mod",93:"Mod",106:"*",107:"=",109:"-",110:".",111:"/",127:"Delete",145:"ScrollLock",173:"-",186:";",187:"=",188:",",189:"-",190:".",191:"/",192:"`",219:"[",220:"\\",221:"]",222:"'",63232:"Up",63233:"Down",63234:"Left",63235:"Right",63272:"Delete",63273:"Home",63275:"End",63276:"PageUp",63277:"PageDown",63302:"Insert"},Po=0;Po<10;Po++)Ao[Po+48]=Ao[Po+96]=String(Po);for(var Eo=65;Eo<=90;Eo++)Ao[Eo]=String.fromCharCode(Eo);for(var Do=1;Do<=12;Do++)Ao[Do+111]=Ao[Do+63235]="F"+Do;var jo={};function No(e){var t,n,r,i,o=e.split(/-(?!$)/);e=o[o.length-1];for(var a=0;a<o.length-1;a++){var s=o[a];if(/^(cmd|meta|m)$/i.test(s))i=!0;else if(/^a(lt)?$/i.test(s))t=!0;else 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k=l.ranges.slice(0);k[s]=function(e,t){var n=t.anchor,r=t.head,i=le(e.doc,n.line);if(0==ve(n,r)&&n.sticky==r.sticky)return t;var o=Ye(i);if(!o)return t;var a=Xe(o,n.ch,n.sticky),s=o[a];if(s.from!=n.ch&&s.to!=n.ch)return t;var l,c=a+(s.from==n.ch==(1!=s.level)?0:1);if(0==c||c==o.length)return t;if(r.line!=n.line)l=(r.line-n.line)*("ltr"==e.doc.direction?1:-1)>0;else{var u=Xe(o,r.ch,r.sticky),d=u-a||(r.ch-n.ch)*(1==s.level?-1:1);l=u==c-1||u==c?d<0:d>0}var f=o[c+(l?-1:0)],h=l==(1==f.level),p=h?f.from:f.to,m=h?"after":"before";return n.ch==p&&n.sticky==m?t:new gi(new ge(n.line,p,m),r)}(e,new gi(ke(o,_),b)),Ki(o,vi(k,s),K)}}var h=i.wrapper.getBoundingClientRect(),p=0;function m(t){e.state.selectingText=!1,p=1/0,lt(t),i.input.focus(),nt(document,"mousemove",g),nt(document,"mouseup",v),o.history.lastSelOrigin=null}var g=$r(e,function(t){ht(t)?function t(n){var a=++p,s=ar(e,n,!0,"rectangle"==r.unit);if(s)if(0!=ve(s,d)){e.curOp.focus=D(),f(s);var 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n*n+r*r>400}et(t.scroller,"touchstart",function(i){if(!it(e,i)&&!function(e){if(1!=e.touches.length)return!1;var t=e.touches[0];return t.radiusX<=1&&t.radiusY<=1}(i)&&!ca(e,i)){t.input.ensurePolled(),clearTimeout(n);var o=+new Date;t.activeTouch={start:o,moved:!1,prev:o-r.end<=300?r:null},1==i.touches.length&&(t.activeTouch.left=i.touches[0].pageX,t.activeTouch.top=i.touches[0].pageY)}}),et(t.scroller,"touchmove",function(){t.activeTouch&&(t.activeTouch.moved=!0)}),et(t.scroller,"touchend",function(n){var r=t.activeTouch;if(r&&!wn(t,n)&&null!=r.left&&!r.moved&&new Date-r.start<300){var a,s=e.coordsChar(t.activeTouch,"page");a=!r.prev||o(r,r.prev)?new gi(s,s):!r.prev.prev||o(r,r.prev.prev)?e.findWordAt(s):new 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p=s.previousSibling,m=n;p;p=p.previousSibling){if(d=u(p,p.firstChild,-1))return Da(ge(d.line,d.ch+m),i);m+=p.textContent.length}}Pa.prototype.init=function(e){var t=this,n=this,r=n.cm,i=n.div=e.lineDiv;function o(e){if(!it(r,e)){if(r.somethingSelected())_a({lineWise:!1,text:r.getSelections()}),"cut"==e.type&&r.replaceSelection("",null,"cut");else{if(!r.options.lineWiseCopyCut)return;var t=Ma(r);_a({lineWise:!0,text:t.text}),"cut"==e.type&&r.operation(function(){r.setSelections(t.ranges,0,U),r.replaceSelection("",null,"cut")})}if(e.clipboardData){e.clipboardData.clearData();var o=wa.text.join("\n");if(e.clipboardData.setData("Text",o),e.clipboardData.getData("Text")==o)return void e.preventDefault()}var a=Ta(),s=a.firstChild;r.display.lineSpace.insertBefore(a,r.display.lineSpace.firstChild),s.value=wa.text.join("\n");var l=document.activeElement;I(s),setTimeout(function(){r.display.lineSpace.removeChild(a),l.focus(),l==i&&n.showPrimarySelection()},50)}}La(i,r.options.spellcheck),et(i,"paste",function(e){it(r,e)||Ca(e,r)||l<=11&&setTimeout($r(r,function(){return t.updateFromDOM()}),20)}),et(i,"compositionstart",function(e){t.composing={data:e.data,done:!1}}),et(i,"compositionupdate",function(e){t.composing||(t.composing={data:e.data,done:!1})}),et(i,"compositionend",function(e){t.composing&&(e.data!=t.composing.data&&t.readFromDOMSoon(),t.composing.done=!0)}),et(i,"touchstart",function(){return n.forceCompositionEnd()}),et(i,"input",function(){t.composing||t.readFromDOMSoon()}),et(i,"copy",o),et(i,"cut",o)},Pa.prototype.prepareSelection=function(){var e=cr(this.cm,!1);return e.focus=this.cm.state.focused,e},Pa.prototype.showSelection=function(e,t){e&&this.cm.display.view.length&&((e.focus||t)&&this.showPrimarySelection(),this.showMultipleSelections(e))},Pa.prototype.showPrimarySelection=function(){var e=window.getSelection(),t=this.cm,r=t.doc.sel.primary(),i=r.from(),o=r.to();if(t.display.viewTo==t.display.viewFrom||i.line>=t.display.viewTo||o.line<t.display.viewFrom)e.removeAllRanges();else{var a=ja(t,e.anchorNode,e.anchorOffset),s=ja(t,e.focusNode,e.focusOffset);if(!a||a.bad||!s||s.bad||0!=ve(we(a,s),i)||0!=ve(xe(a,s),o)){var l=t.display.view,c=i.line>=t.display.viewFrom&&Ea(t,i)||{node:l[0].measure.map[2],offset:0},u=o.line<t.display.viewTo&&Ea(t,o);if(!u){var d=l[l.length-1].measure,f=d.maps?d.maps[d.maps.length-1]:d.map;u={node:f[f.length-1],offset:f[f.length-2]-f[f.length-3]}}if(c&&u){var h,p=e.rangeCount&&e.getRangeAt(0);try{h=M(c.node,c.offset,u.offset,u.node)}catch(e){}h&&(!n&&t.state.focused?(e.collapse(c.node,c.offset),h.collapsed||(e.removeAllRanges(),e.addRange(h))):(e.removeAllRanges(),e.addRange(h)),p&&null==e.anchorNode?e.addRange(p):n&&this.startGracePeriod()),this.rememberSelection()}else e.removeAllRanges()}}},Pa.prototype.startGracePeriod=function(){var e=this;clearTimeout(this.gracePeriod),this.gracePeriod=setTimeout(function(){e.gracePeriod=!1,e.selectionChanged()&&e.cm.operation(function(){return e.cm.curOp.selectionChanged=!0})},20)},Pa.prototype.showMultipleSelections=function(e){O(this.cm.display.cursorDiv,e.cursors),O(this.cm.display.selectionDiv,e.selection)},Pa.prototype.rememberSelection=function(){var e=window.getSelection();this.lastAnchorNode=e.anchorNode,this.lastAnchorOffset=e.anchorOffset,this.lastFocusNode=e.focusNode,this.lastFocusOffset=e.focusOffset},Pa.prototype.selectionInEditor=function(){var e=window.getSelection();if(!e.rangeCount)return!1;var t=e.getRangeAt(0).commonAncestorContainer;return E(this.div,t)},Pa.prototype.focus=function(){"nocursor"!=this.cm.options.readOnly&&(this.selectionInEditor()||this.showSelection(this.prepareSelection(),!0),this.div.focus())},Pa.prototype.blur=function(){this.div.blur()},Pa.prototype.getField=function(){return this.div},Pa.prototype.supportsTouch=function(){return!0},Pa.prototype.receivedFocus=function(){var e=this;this.selectionInEditor()?this.pollSelection():Vr(this.cm,function(){return e.cm.curOp.selectionChanged=!0}),this.polling.set(this.cm.options.pollInterval,function t(){e.cm.state.focused&&(e.pollSelection(),e.polling.set(e.cm.options.pollInterval,t))})},Pa.prototype.selectionChanged=function(){var e=window.getSelection();return e.anchorNode!=this.lastAnchorNode||e.anchorOffset!=this.lastAnchorOffset||e.focusNode!=this.lastFocusNode||e.focusOffset!=this.lastFocusOffset},Pa.prototype.pollSelection=function(){if(null==this.readDOMTimeout&&!this.gracePeriod&&this.selectionChanged()){var e=window.getSelection(),t=this.cm;if(v&&d&&this.cm.options.gutters.length&&function(e){for(var t=e;t;t=t.parentNode)if(/CodeMirror-gutter-wrapper/.test(t.className))return!0;return!1}(e.anchorNode))return this.cm.triggerOnKeyDown({type:"keydown",keyCode:8,preventDefault:Math.abs}),this.blur(),void this.focus();if(!this.composing){this.rememberSelection();var n=ja(t,e.anchorNode,e.anchorOffset),r=ja(t,e.focusNode,e.focusOffset);n&&r&&Vr(t,function(){Ki(t.doc,yi(n,r),U),(n.bad||r.bad)&&(t.curOp.selectionChanged=!0)})}}},Pa.prototype.pollContent=function(){null!=this.readDOMTimeout&&(clearTimeout(this.readDOMTimeout),this.readDOMTimeout=null);var e,t,n,r=this.cm,i=r.display,o=r.doc.sel.primary(),a=o.from(),s=o.to();if(0==a.ch&&a.line>r.firstLine()&&(a=ge(a.line-1,le(r.doc,a.line-1).length)),s.ch==le(r.doc,s.line).text.length&&s.line<r.lastLine()&&(s=ge(s.line+1,0)),a.line<i.viewFrom||s.line>i.viewTo-1)return!1;a.line==i.viewFrom||0==(e=sr(r,a.line))?(t=fe(i.view[0].line),n=i.view[0].node):(t=fe(i.view[e].line),n=i.view[e-1].node.nextSibling);var l,c,u=sr(r,s.line);if(u==i.view.length-1?(l=i.viewTo-1,c=i.lineDiv.lastChild):(l=fe(i.view[u+1].line)-1,c=i.view[u+1].node.previousSibling),!n)return!1;for(var d=r.doc.splitLines(function(e,t,n,r,i){var o="",a=!1,s=e.doc.lineSeparator();function l(){a&&(o+=s,a=!1)}function c(e){e&&(l(),o+=e)}function u(t){if(1==t.nodeType){var n=t.getAttribute("cm-text");if(null!=n)return void c(n||t.textContent.replace(/\u200b/g,""));var o,d=t.getAttribute("cm-marker");if(d){var f=e.findMarks(ge(r,0),ge(i+1,0),(m=+d,function(e){return e.id==m}));return void(f.length&&(o=f[0].find(0))&&c(ce(e.doc,o.from,o.to).join(s)))}if("false"==t.getAttribute("contenteditable"))return;var h=/^(pre|div|p)$/i.test(t.nodeName);h&&l();for(var p=0;p<t.childNodes.length;p++)u(t.childNodes[p]);h&&(a=!0)}else 3==t.nodeType&&c(t.nodeValue);var m}for(;u(t),t!=n;)t=t.nextSibling;return o}(r,n,c,t,l)),f=ce(r.doc,ge(t,0),ge(l,le(r.doc,l).text.length));d.length>1&&f.length>1;)if(X(d)==X(f))d.pop(),f.pop(),l--;else{if(d[0]!=f[0])break;d.shift(),f.shift(),t++}for(var h=0,p=0,m=d[0],g=f[0],v=Math.min(m.length,g.length);h<v&&m.charCodeAt(h)==g.charCodeAt(h);)++h;for(var y=X(d),b=X(f),x=Math.min(y.length-(1==d.length?h:0),b.length-(1==f.length?h:0));p<x&&y.charCodeAt(y.length-p-1)==b.charCodeAt(b.length-p-1);)++p;if(1==d.length&&1==f.length&&t==a.line)for(;h&&h>a.ch&&y.charCodeAt(y.length-p-1)==b.charCodeAt(b.length-p-1);)h--,p++;d[d.length-1]=y.slice(0,y.length-p).replace(/^\u200b+/,""),d[0]=d[0].slice(h).replace(/\u200b+$/,"");var w=ge(t,h),_=ge(l,f.length?X(f).length-p:0);return d.length>1||d[0]||ve(w,_)?(ao(r.doc,d,w,_,"+input"),!0):void 0},Pa.prototype.ensurePolled=function(){this.forceCompositionEnd()},Pa.prototype.reset=function(){this.forceCompositionEnd()},Pa.prototype.forceCompositionEnd=function(){this.composing&&(clearTimeout(this.readDOMTimeout),this.composing=null,this.updateFromDOM(),this.div.blur(),this.div.focus())},Pa.prototype.readFromDOMSoon=function(){var e=this;null==this.readDOMTimeout&&(this.readDOMTimeout=setTimeout(function(){if(e.readDOMTimeout=null,e.composing){if(!e.composing.done)return;e.composing=null}e.updateFromDOM()},80))},Pa.prototype.updateFromDOM=function(){var e=this;!this.cm.isReadOnly()&&this.pollContent()||Vr(this.cm,function(){return Xr(e.cm)})},Pa.prototype.setUneditable=function(e){e.contentEditable="false"},Pa.prototype.onKeyPress=function(e){0!=e.charCode&&(e.preventDefault(),this.cm.isReadOnly()||$r(this.cm,ka)(this.cm,String.fromCharCode(null==e.charCode?e.keyCode:e.charCode),0))},Pa.prototype.readOnlyChanged=function(e){this.div.contentEditable=String("nocursor"!=e)},Pa.prototype.onContextMenu=function(){},Pa.prototype.resetPosition=function(){},Pa.prototype.needsContentAttribute=!0;var Ia=function(e){this.cm=e,this.prevInput="",this.pollingFast=!1,this.polling=new W,this.hasSelection=!1,this.composing=null};Ia.prototype.init=function(e){var t=this,n=this,r=this.cm,i=this.wrapper=Ta(),o=this.textarea=i.firstChild;function a(e){if(!it(r,e)){if(r.somethingSelected())_a({lineWise:!1,text:r.getSelections()});else{if(!r.options.lineWiseCopyCut)return;var t=Ma(r);_a({lineWise:!0,text:t.text}),"cut"==e.type?r.setSelections(t.ranges,null,U):(n.prevInput="",o.value=t.text.join("\n"),I(o))}"cut"==e.type&&(r.state.cutIncoming=!0)}}e.wrapper.insertBefore(i,e.wrapper.firstChild),g&&(o.style.width="0px"),et(o,"input",function(){s&&l>=9&&t.hasSelection&&(t.hasSelection=null),n.poll()}),et(o,"paste",function(e){it(r,e)||Ca(e,r)||(r.state.pasteIncoming=!0,n.fastPoll())}),et(o,"cut",a),et(o,"copy",a),et(e.scroller,"paste",function(t){wn(e,t)||it(r,t)||(r.state.pasteIncoming=!0,n.focus())}),et(e.lineSpace,"selectstart",function(t){wn(e,t)||lt(t)}),et(o,"compositionstart",function(){var e=r.getCursor("from");n.composing&&n.composing.range.clear(),n.composing={start:e,range:r.markText(e,r.getCursor("to"),{className:"CodeMirror-composing"})}}),et(o,"compositionend",function(){n.composing&&(n.poll(),n.composing.range.clear(),n.composing=null)})},Ia.prototype.prepareSelection=function(){var e=this.cm,t=e.display,n=e.doc,r=cr(e);if(e.options.moveInputWithCursor){var i=Vn(e,n.sel.primary().head,"div"),o=t.wrapper.getBoundingClientRect(),a=t.lineDiv.getBoundingClientRect();r.teTop=Math.max(0,Math.min(t.wrapper.clientHeight-10,i.top+a.top-o.top)),r.teLeft=Math.max(0,Math.min(t.wrapper.clientWidth-10,i.left+a.left-o.left))}return r},Ia.prototype.showSelection=function(e){var t=this.cm.display;O(t.cursorDiv,e.cursors),O(t.selectionDiv,e.selection),null!=e.teTop&&(this.wrapper.style.top=e.teTop+"px",this.wrapper.style.left=e.teLeft+"px")},Ia.prototype.reset=function(e){if(!this.contextMenuPending&&!this.composing){var t=this.cm;if(t.somethingSelected()){this.prevInput="";var n=t.getSelection();this.textarea.value=n,t.state.focused&&I(this.textarea),s&&l>=9&&(this.hasSelection=n)}else e||(this.prevInput=this.textarea.value="",s&&l>=9&&(this.hasSelection=null))}},Ia.prototype.getField=function(){return this.textarea},Ia.prototype.supportsTouch=function(){return!1},Ia.prototype.focus=function(){if("nocursor"!=this.cm.options.readOnly&&(!y||D()!=this.textarea))try{this.textarea.focus()}catch(e){}},Ia.prototype.blur=function(){this.textarea.blur()},Ia.prototype.resetPosition=function(){this.wrapper.style.top=this.wrapper.style.left=0},Ia.prototype.receivedFocus=function(){this.slowPoll()},Ia.prototype.slowPoll=function(){var e=this;this.pollingFast||this.polling.set(this.cm.options.pollInterval,function(){e.poll(),e.cm.state.focused&&e.slowPoll()})},Ia.prototype.fastPoll=function(){var e=!1,t=this;t.pollingFast=!0,t.polling.set(20,function n(){t.poll()||e?(t.pollingFast=!1,t.slowPoll()):(e=!0,t.polling.set(60,n))})},Ia.prototype.poll=function(){var e=this,t=this.cm,n=this.textarea,r=this.prevInput;if(this.contextMenuPending||!t.state.focused||wt(n)&&!r&&!this.composing||t.isReadOnly()||t.options.disableInput||t.state.keySeq)return!1;var i=n.value;if(i==r&&!t.somethingSelected())return!1;if(s&&l>=9&&this.hasSelection===i||b&&/[\uf700-\uf7ff]/.test(i))return t.display.input.reset(),!1;if(t.doc.sel==t.display.selForContextMenu){var o=i.charCodeAt(0);if(8203!=o||r||(r="​"),8666==o)return this.reset(),this.cm.execCommand("undo")}for(var a=0,c=Math.min(r.length,i.length);a<c&&r.charCodeAt(a)==i.charCodeAt(a);)++a;return Vr(t,function(){ka(t,i.slice(a),r.length-a,null,e.composing?"*compose":null),i.length>1e3||i.indexOf("\n")>-1?n.value=e.prevInput="":e.prevInput=i,e.composing&&(e.composing.range.clear(),e.composing.range=t.markText(e.composing.start,t.getCursor("to"),{className:"CodeMirror-composing"}))}),!0},Ia.prototype.ensurePolled=function(){this.pollingFast&&this.poll()&&(this.pollingFast=!1)},Ia.prototype.onKeyPress=function(){s&&l>=9&&(this.hasSelection=null),this.fastPoll()},Ia.prototype.onContextMenu=function(e){var t=this,n=t.cm,r=n.display,i=t.textarea,o=ar(n,e),a=r.scroller.scrollTop;if(o&&!f){n.options.resetSelectionOnContextMenu&&-1==n.doc.sel.contains(o)&&$r(n,Ki)(n.doc,yi(o),U);var u=i.style.cssText,d=t.wrapper.style.cssText;t.wrapper.style.cssText="position: absolute";var h,p=t.wrapper.getBoundingClientRect();i.style.cssText="position: absolute; width: 30px; height: 30px;\n      top: "+(e.clientY-p.top-5)+"px; left: "+(e.clientX-p.left-5)+"px;\n      z-index: 1000; background: "+(s?"rgba(255, 255, 255, .05)":"transparent")+";\n      outline: none; border-width: 0; outline: none; overflow: hidden; opacity: .05; filter: alpha(opacity=5);",c&&(h=window.scrollY),r.input.focus(),c&&window.scrollTo(null,h),r.input.reset(),n.somethingSelected()||(i.value=t.prevInput=" "),t.contextMenuPending=!0,r.selForContextMenu=n.doc.sel,clearTimeout(r.detectingSelectAll),s&&l>=9&&m(),C?(dt(e),et(window,"mouseup",function e(){nt(window,"mouseup",e),setTimeout(g,20)})):setTimeout(g,50)}function m(){if(null!=i.selectionStart){var e=n.somethingSelected(),o="​"+(e?i.value:"");i.value="⇚",i.value=o,t.prevInput=e?"":"​",i.selectionStart=1,i.selectionEnd=o.length,r.selForContextMenu=n.doc.sel}}function g(){if(t.contextMenuPending=!1,t.wrapper.style.cssText=d,i.style.cssText=u,s&&l<9&&r.scrollbars.setScrollTop(r.scroller.scrollTop=a),null!=i.selectionStart){(!s||s&&l<9)&&m();var e=0;r.detectingSelectAll=setTimeout(function o(){r.selForContextMenu==n.doc.sel&&0==i.selectionStart&&i.selectionEnd>0&&"​"==t.prevInput?$r(n,Qi)(n):e++<10?r.detectingSelectAll=setTimeout(o,500):(r.selForContextMenu=null,r.input.reset())},200)}}},Ia.prototype.readOnlyChanged=function(e){e||this.reset(),this.textarea.disabled="nocursor"==e},Ia.prototype.setUneditable=function(){},Ia.prototype.needsContentAttribute=!1,function(e){var t=e.optionHandlers;function n(n,r,i,o){e.defaults[n]=r,i&&(t[n]=o?function(e,t,n){n!=fa&&i(e,t,n)}:i)}e.defineOption=n,e.Init=fa,n("value","",function(e,t){return e.setValue(t)},!0),n("mode",null,function(e,t){e.doc.modeOption=t,ki(e)},!0),n("indentUnit",2,ki,!0),n("indentWithTabs",!1),n("smartIndent",!0),n("tabSize",4,function(e){Ci(e),Fn(e),Xr(e)},!0),n("lineSeparator",null,function(e,t){if(e.doc.lineSep=t,t){var n=[],r=e.doc.first;e.doc.iter(function(e){for(var i=0;;){var o=e.text.indexOf(t,i);if(-1==o)break;i=o+t.length,n.push(ge(r,o))}r++});for(var i=n.length-1;i>=0;i--)ao(e.doc,t,n[i],ge(n[i].line,n[i].ch+t.length))}}),n("specialChars",/[\u0000-\u001f\u007f-\u009f\u00ad\u061c\u200b-\u200f\u2028\u2029\ufeff]/g,function(e,t,n){e.state.specialChars=new RegExp(t.source+(t.test("\t")?"":"|\t"),"g"),n!=fa&&e.refresh()}),n("specialCharPlaceholder",Zt,function(e){return e.refresh()},!0),n("electricChars",!0),n("inputStyle",y?"contenteditable":"textarea",function(){throw new Error("inputStyle can not (yet) be changed in a running editor")},!0),n("spellcheck",!1,function(e,t){return e.getInputField().spellcheck=t},!0),n("rtlMoveVisually",!w),n("wholeLineUpdateBefore",!0),n("theme","default",function(e){da(e),ma(e)},!0),n("keyMap","default",function(e,t,n){var r=Wo(t),i=n!=fa&&Wo(n);i&&i.detach&&i.detach(e,r),r.attach&&r.attach(e,i||null)}),n("extraKeys",null),n("configureMouse",null),n("lineWrapping",!1,va,!0),n("gutters",[],function(e){ui(e.options),ma(e)},!0),n("fixedGutter",!0,function(e,t){e.display.gutters.style.left=t?rr(e.display)+"px":"0",e.refresh()},!0),n("coverGutterNextToScrollbar",!1,function(e){return Nr(e)},!0),n("scrollbarStyle","native",function(e){zr(e),Nr(e),e.display.scrollbars.setScrollTop(e.doc.scrollTop),e.display.scrollbars.setScrollLeft(e.doc.scrollLeft)},!0),n("lineNumbers",!1,function(e){ui(e.options),ma(e)},!0),n("firstLineNumber",1,ma,!0),n("lineNumberFormatter",function(e){return e},ma,!0),n("showCursorWhenSelecting",!1,lr,!0),n("resetSelectionOnContextMenu",!0),n("lineWiseCopyCut",!0),n("pasteLinesPerSelection",!0),n("readOnly",!1,function(e,t){"nocursor"==t&&(vr(e),e.display.input.blur()),e.display.input.readOnlyChanged(t)}),n("disableInput",!1,function(e,t){t||e.display.input.reset()},!0),n("dragDrop",!0,ga),n("allowDropFileTypes",null),n("cursorBlinkRate",530),n("cursorScrollMargin",0),n("cursorHeight",1,lr,!0),n("singleCursorHeightPerLine",!0,lr,!0),n("workTime",100),n("workDelay",100),n("flattenSpans",!0,Ci,!0),n("addModeClass",!1,Ci,!0),n("pollInterval",100),n("undoDepth",200,function(e,t){return e.doc.history.undoDepth=t}),n("historyEventDelay",1250),n("viewportMargin",10,function(e){return e.refresh()},!0),n("maxHighlightLength",1e4,Ci,!0),n("moveInputWithCursor",!0,function(e,t){t||e.display.input.resetPosition()}),n("tabindex",null,function(e,t){return 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0;if("\t"==p[0]){var y=e.cm.options.tabSize,b=y-e.col%y;(v=c.appendChild(A("span",J(b),"cm-tab"))).setAttribute("role","presentation"),v.setAttribute("cm-text","\t"),e.col+=b}else"\r"==p[0]||"\n"==p[0]?((v=c.appendChild(A("span","\r"==p[0]?"␍":"␤","cm-invalidchar"))).setAttribute("cm-text",p[0]),e.col+=1):((v=e.cm.options.specialCharPlaceholder(p[0])).setAttribute("cm-text",p[0]),s&&l<9?c.appendChild(A("span",[v])):c.appendChild(v),e.col+=1);e.map.push(e.pos,e.pos+1,v),e.pos++}}else e.col+=t.length,c=document.createTextNode(u),e.map.push(e.pos,e.pos+t.length,c),s&&l<9&&(f=!0),e.pos+=t.length;if(e.trailingSpace=32==u.charCodeAt(t.length-1),n||r||i||f||a){var x=n||"";r&&(x+=r),i&&(x+=i);var w=A("span",[c],x,a);return o&&(w.title=o),e.content.appendChild(w)}e.content.appendChild(c)}}function en(e,t){return function(n,r,i,o,a,s,l){i=i?i+" cm-force-border":"cm-force-border";for(var c=n.pos,u=c+r.length;;){for(var d=void 0,f=0;f<t.length&&!((d=t[f]).to>c&&d.from<=c);f++);if(d.to>=u)return 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rn(e.doc,le(e.doc,o),o);r=o+a.size,i.push(a)}return i}var an=null,sn=null;function ln(e,t){var n=nt(e,t);if(n.length){var r,i=Array.prototype.slice.call(arguments,2);an?r=an.delayedCallbacks:sn?r=sn:(r=sn=[],setTimeout(cn,0));for(var o=function(e){r.push(function(){return n[e].apply(null,i)})},a=0;a<n.length;++a)o(a)}}function cn(){var e=sn;sn=null;for(var t=0;t<e.length;++t)e[t]()}function un(e,t,n,r){for(var i=0;i<t.changes.length;i++){var o=t.changes[i];"text"==o?hn(e,t):"gutter"==o?mn(e,t,n,r):"class"==o?pn(e,t):"widget"==o&&gn(e,t,r)}t.changes=null}function dn(e){return e.node==e.text&&(e.node=A("div",null,null,"position: relative"),e.text.parentNode&&e.text.parentNode.replaceChild(e.node,e.text),e.node.appendChild(e.text),s&&l<8&&(e.node.style.zIndex=2)),e.node}function fn(e,t){var n=e.display.externalMeasured;return n&&n.line==t.line?(e.display.externalMeasured=null,t.measure=n.measure,n.built):Zt(e,t)}function hn(e,t){var n=t.text.className,r=fn(e,t);t.text==t.node&&(t.node=r.pre),t.text.parentNode.replaceChild(r.pre,t.text),t.text=r.pre,r.bgClass!=t.bgClass||r.textClass!=t.textClass?(t.bgClass=r.bgClass,t.textClass=r.textClass,pn(e,t)):n&&(t.text.className=n)}function pn(e,t){!function(e,t){var n=t.bgClass?t.bgClass+" "+(t.line.bgClass||""):t.line.bgClass;if(n&&(n+=" CodeMirror-linebackground"),t.background)n?t.background.className=n:(t.background.parentNode.removeChild(t.background),t.background=null);else if(n){var r=dn(t);t.background=r.insertBefore(A("div",null,n),r.firstChild),e.display.input.setUneditable(t.background)}}(e,t),t.line.wrapClass?dn(t).className=t.line.wrapClass:t.node!=t.text&&(t.node.className="");var n=t.textClass?t.textClass+" "+(t.line.textClass||""):t.line.textClass;t.text.className=n||""}function mn(e,t,n,r){if(t.gutter&&(t.node.removeChild(t.gutter),t.gutter=null),t.gutterBackground&&(t.node.removeChild(t.gutterBackground),t.gutterBackground=null),t.line.gutterClass){var i=dn(t);t.gutterBackground=A("div",null,"CodeMirror-gutter-background "+t.line.gutterClass,"left: "+(e.options.fixedGutter?r.fixedPos:-r.gutterTotalWidth)+"px; width: "+r.gutterTotalWidth+"px"),e.display.input.setUneditable(t.gutterBackground),i.insertBefore(t.gutterBackground,t.text)}var o=t.line.gutterMarkers;if(e.options.lineNumbers||o){var a=dn(t),s=t.gutter=A("div",null,"CodeMirror-gutter-wrapper","left: "+(e.options.fixedGutter?r.fixedPos:-r.gutterTotalWidth)+"px");if(e.display.input.setUneditable(s),a.insertBefore(s,t.text),t.line.gutterClass&&(s.className+=" "+t.line.gutterClass),!e.options.lineNumbers||o&&o["CodeMirror-linenumbers"]||(t.lineNumber=s.appendChild(A("div",me(e.options,n),"CodeMirror-linenumber CodeMirror-gutter-elt","left: "+r.gutterLeft["CodeMirror-linenumbers"]+"px; width: "+e.display.lineNumInnerWidth+"px"))),o)for(var l=0;l<e.options.gutters.length;++l){var c=e.options.gutters[l],u=o.hasOwnProperty(c)&&o[c];u&&s.appendChild(A("div",[u],"CodeMirror-gutter-elt","left: "+r.gutterLeft[c]+"px; width: "+r.gutterWidth[c]+"px"))}}}function gn(e,t,n){t.alignable&&(t.alignable=null);for(var r=t.node.firstChild,i=void 0;r;r=i)i=r.nextSibling,"CodeMirror-linewidget"==r.className&&t.node.removeChild(r);yn(e,t,n)}function vn(e,t,n,r){var i=fn(e,t);return t.text=t.node=i.pre,i.bgClass&&(t.bgClass=i.bgClass),i.textClass&&(t.textClass=i.textClass),pn(e,t),mn(e,t,n,r),yn(e,t,r),t.node}function yn(e,t,n){if(bn(e,t.line,t,n,!0),t.rest)for(var r=0;r<t.rest.length;r++)bn(e,t.rest[r],t,n,!1)}function bn(e,t,n,r,i){if(t.widgets)for(var o=dn(n),a=0,s=t.widgets;a<s.length;++a){var l=s[a],c=A("div",[l.node],"CodeMirror-linewidget");l.handleMouseEvents||c.setAttribute("cm-ignore-events","true"),xn(l,c,n,r),e.display.input.setUneditable(c),i&&l.above?o.insertBefore(c,n.gutter||n.text):o.appendChild(c),ln(l,"redraw")}}function xn(e,t,n,r){if(e.noHScroll){(n.alignable||(n.alignable=[])).push(t);var i=r.wrapperWidth;t.style.left=r.fixedPos+"px",e.coverGutter||(i-=r.gutterTotalWidth,t.style.paddingLeft=r.gutterTotalWidth+"px"),t.style.width=i+"px"}e.coverGutter&&(t.style.zIndex=5,t.style.position="relative",e.noHScroll||(t.style.marginLeft=-r.gutterTotalWidth+"px"))}function wn(e){if(null!=e.height)return e.height;var t=e.doc.cm;if(!t)return 0;if(!E(document.body,e.node)){var n="position: relative;";e.coverGutter&&(n+="margin-left: -"+t.display.gutters.offsetWidth+"px;"),e.noHScroll&&(n+="width: "+t.display.wrapper.clientWidth+"px;"),O(t.display.measure,A("div",[e.node],null,n))}return e.height=e.node.parentNode.offsetHeight}function _n(e,t){for(var n=ht(t);n!=e.wrapper;n=n.parentNode)if(!n||1==n.nodeType&&"true"==n.getAttribute("cm-ignore-events")||n.parentNode==e.sizer&&n!=e.mover)return!0}function kn(e){return e.lineSpace.offsetTop}function Cn(e){return e.mover.offsetHeight-e.lineSpace.offsetHeight}function 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t=e.display,n={},r={},i=t.gutters.clientLeft,o=t.gutters.firstChild,a=0;o;o=o.nextSibling,++a)n[e.options.gutters[a]]=o.offsetLeft+o.clientLeft+i,r[e.options.gutters[a]]=o.clientWidth;return{fixedPos:ir(t),gutterTotalWidth:t.gutters.offsetWidth,gutterLeft:n,gutterWidth:r,wrapperWidth:t.wrapper.clientWidth}}function ir(e){return e.scroller.getBoundingClientRect().left-e.sizer.getBoundingClientRect().left}function or(e){var t=tr(e.display),n=e.options.lineWrapping,r=n&&Math.max(5,e.display.scroller.clientWidth/nr(e.display)-3);return function(i){if(Ke(e.doc,i))return 0;var o=0;if(i.widgets)for(var a=0;a<i.widgets.length;a++)i.widgets[a].height&&(o+=i.widgets[a].height);return n?o+(Math.ceil(i.text.length/r)||1)*t:o+t}}function ar(e){var t=e.doc,n=or(e);t.iter(function(e){var t=n(e);t!=e.height&&de(e,t)})}function sr(e,t,n,r){var i=e.display;if(!n&&"true"==ht(t).getAttribute("cm-not-content"))return null;var 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o,a,d=le(i,t),f=d.text.length;function h(n,r){return Vn(e,ge(t,n),"div",d,r)}function p(t,n,r){var i=Yn(e,d,null,t),o="ltr"==n==("after"==r)?"left":"right";return h("after"==r?i.begin:i.end-(/\s/.test(d.text.charAt(i.end-1))?2:1),o)[o]}var m=Qe(d,i.direction);return function(e,t,n,r){if(!e)return r(t,n,"ltr",0);for(var i=!1,o=0;o<e.length;++o){var a=e[o];(a.from<n&&a.to>t||t==n&&a.to==t)&&(r(Math.max(a.from,t),Math.min(a.to,n),1==a.level?"rtl":"ltr",o),i=!0)}i||r(t,n,"ltr")}(m,n||0,null==r?f:r,function(e,t,i,d){var g="ltr"==i,v=h(e,g?"left":"right"),y=h(t-1,g?"right":"left"),b=null==n&&0==e,x=null==r&&t==f,w=0==d,_=!m||d==m.length-1;if(y.top-v.top<=3){var k=(c?x:b)&&_,C=(c?b:x)&&w?s:(g?v:y).left,S=k?l:(g?y:v).right;u(C,v.top,S-C,v.bottom)}else{var M,L,T,O;g?(M=c&&b&&w?s:v.left,L=c?l:p(e,i,"before"),T=c?s:p(t,i,"after"),O=c&&x&&_?l:y.right):(M=c?p(e,i,"before"):s,L=!c&&b&&w?l:v.right,T=!c&&x&&_?s:y.left,O=c?p(t,i,"after"):l),u(M,v.top,L-M,v.bottom),v.bottom<y.top&&u(s,v.bottom,null,y.top),u(T,y.top,O-T,y.bottom)}(!o||fr(v,o)<0)&&(o=v),fr(y,o)<0&&(o=y),(!a||fr(v,a)<0)&&(a=v),fr(y,a)<0&&(a=y)}),{start:o,end:a}}var f=t.from(),h=t.to();if(f.line==h.line)d(f.line,f.ch,h.ch);else{var p=le(i,f.line),m=le(i,h.line),g=He(p)==He(m),v=d(f.line,f.ch,g?p.text.length+1:null).end,y=d(h.line,g?0:null,h.ch).start;g&&(v.top<y.top-2?(u(v.right,v.top,null,v.bottom),u(s,y.top,y.left,y.bottom)):u(v.right,v.top,y.left-v.right,v.bottom)),v.bottom<y.top&&u(s,v.bottom,null,y.top)}n.appendChild(o)}function pr(e){if(e.state.focused){var t=e.display;clearInterval(t.blinker);var n=!0;t.cursorDiv.style.visibility="",e.options.cursorBlinkRate>0?t.blinker=setInterval(function(){return t.cursorDiv.style.visibility=(n=!n)?"":"hidden"},e.options.cursorBlinkRate):e.options.cursorBlinkRate<0&&(t.cursorDiv.style.visibility="hidden")}}function mr(e){e.state.focused||(e.display.input.focus(),vr(e))}function gr(e){e.state.delayingBlurEvent=!0,setTimeout(function(){e.state.delayingBlurEvent&&(e.state.delayingBlurEvent=!1,yr(e))},100)}function vr(e,t){e.state.delayingBlurEvent&&(e.state.delayingBlurEvent=!1),"nocursor"!=e.options.readOnly&&(e.state.focused||(it(e,"focus",e,t),e.state.focused=!0,j(e.display.wrapper,"CodeMirror-focused"),e.curOp||e.display.selForContextMenu==e.doc.sel||(e.display.input.reset(),c&&setTimeout(function(){return e.display.input.reset(!0)},20)),e.display.input.receivedFocus()),pr(e))}function yr(e,t){e.state.delayingBlurEvent||(e.state.focused&&(it(e,"blur",e,t),e.state.focused=!1,L(e.display.wrapper,"CodeMirror-focused")),clearInterval(e.display.blinker),setTimeout(function(){e.state.focused||(e.display.shift=!1)},150))}function br(e){for(var t=e.display,n=t.lineDiv.offsetTop,r=0;r<t.view.length;r++){var i=t.view[r],o=void 0;if(!i.hidden){if(s&&l<8){var a=i.node.offsetTop+i.node.offsetHeight;o=a-n,n=a}else{var c=i.node.getBoundingClientRect();o=c.bottom-c.top}var u=i.line.height-o;if(o<2&&(o=tr(t)),(u>.005||u<-.005)&&(de(i.line,o),xr(i.line),i.rest))for(var d=0;d<i.rest.length;d++)xr(i.rest[d])}}}function xr(e){if(e.widgets)for(var t=0;t<e.widgets.length;++t){var n=e.widgets[t],r=n.node.parentNode;r&&(n.height=r.offsetHeight)}}function wr(e,t,n){var r=n&&null!=n.top?Math.max(0,n.top):e.scroller.scrollTop;r=Math.floor(r-kn(e));var i=n&&null!=n.bottom?n.bottom:r+e.wrapper.clientHeight,o=he(t,r),a=he(t,i);if(n&&n.ensure){var s=n.ensure.from.line,l=n.ensure.to.line;s<o?(o=s,a=he(t,$e(le(t,s))+e.wrapper.clientHeight)):Math.min(l,t.lastLine())>=a&&(o=he(t,$e(le(t,l))-e.wrapper.clientHeight),a=l)}return{from:o,to:Math.max(a,o+1)}}function _r(e){var t=e.display,n=t.view;if(t.alignWidgets||t.gutters.firstChild&&e.options.fixedGutter){for(var r=ir(t)-t.scroller.scrollLeft+e.doc.scrollLeft,i=t.gutters.offsetWidth,o=r+"px",a=0;a<n.length;a++)if(!n[a].hidden){e.options.fixedGutter&&(n[a].gutter&&(n[a].gutter.style.left=o),n[a].gutterBackground&&(n[a].gutterBackground.style.left=o));var s=n[a].alignable;if(s)for(var l=0;l<s.length;l++)s[l].style.left=o}e.options.fixedGutter&&(t.gutters.style.left=r+i+"px")}}function kr(e){if(!e.options.lineNumbers)return!1;var t=e.doc,n=me(e.options,t.first+t.size-1),r=e.display;if(n.length!=r.lineNumChars){var i=r.measure.appendChild(A("div",[A("div",n)],"CodeMirror-linenumber CodeMirror-gutter-elt")),o=i.firstChild.offsetWidth,a=i.offsetWidth-o;return r.lineGutter.style.width="",r.lineNumInnerWidth=Math.max(o,r.lineGutter.offsetWidth-a)+1,r.lineNumWidth=r.lineNumInnerWidth+a,r.lineNumChars=r.lineNumInnerWidth?n.length:-1,r.lineGutter.style.width=r.lineNumWidth+"px",li(e),!0}return!1}function Cr(e,t){var n=e.display,r=tr(e.display);t.top<0&&(t.top=0);var i=e.curOp&&null!=e.curOp.scrollTop?e.curOp.scrollTop:n.scroller.scrollTop,o=Tn(e),a={};t.bottom-t.top>o&&(t.bottom=t.top+o);var s=e.doc.height+Cn(n),l=t.top<r,c=t.bottom>s-r;if(t.top<i)a.scrollTop=l?0:t.top;else if(t.bottom>i+o){var u=Math.min(t.top,(c?s:t.bottom)-o);u!=i&&(a.scrollTop=u)}var d=e.curOp&&null!=e.curOp.scrollLeft?e.curOp.scrollLeft:n.scroller.scrollLeft,f=Ln(e)-(e.options.fixedGutter?n.gutters.offsetWidth:0),h=t.right-t.left>f;return h&&(t.right=t.left+f),t.left<10?a.scrollLeft=0:t.left<d?a.scrollLeft=Math.max(0,t.left-(h?0:10)):t.right>f+d-3&&(a.scrollLeft=t.right+(h?0:10)-f),a}function Sr(e,t){null!=t&&(Tr(e),e.curOp.scrollTop=(null==e.curOp.scrollTop?e.doc.scrollTop:e.curOp.scrollTop)+t)}function Mr(e){Tr(e);var t=e.getCursor();e.curOp.scrollToPos={from:t,to:t,margin:e.options.cursorScrollMargin}}function Lr(e,t,n){null==t&&null==n||Tr(e),null!=t&&(e.curOp.scrollLeft=t),null!=n&&(e.curOp.scrollTop=n)}function Tr(e){var t=e.curOp.scrollToPos;t&&(e.curOp.scrollToPos=null,Or(e,Gn(e,t.from),Gn(e,t.to),t.margin))}function Or(e,t,n,r){var i=Cr(e,{left:Math.min(t.left,n.left),top:Math.min(t.top,n.top)-r,right:Math.max(t.right,n.right),bottom:Math.max(t.bottom,n.bottom)+r});Lr(e,i.scrollLeft,i.scrollTop)}function Ar(e,t){Math.abs(e.doc.scrollTop-t)<2||(n||si(e,{top:t}),Pr(e,t,!0),n&&si(e),ni(e,100))}function Pr(e,t,n){t=Math.min(e.display.scroller.scrollHeight-e.display.scroller.clientHeight,t),(e.display.scroller.scrollTop!=t||n)&&(e.doc.scrollTop=t,e.display.scrollbars.setScrollTop(t),e.display.scroller.scrollTop!=t&&(e.display.scroller.scrollTop=t))}function Er(e,t,n,r){t=Math.min(t,e.display.scroller.scrollWidth-e.display.scroller.clientWidth),(n?t==e.doc.scrollLeft:Math.abs(e.doc.scrollLeft-t)<2)&&!r||(e.doc.scrollLeft=t,_r(e),e.display.scroller.scrollLeft!=t&&(e.display.scroller.scrollLeft=t),e.display.scrollbars.setScrollLeft(t))}function Dr(e){var t=e.display,n=t.gutters.offsetWidth,r=Math.round(e.doc.height+Cn(e.display));return{clientHeight:t.scroller.clientHeight,viewHeight:t.wrapper.clientHeight,scrollWidth:t.scroller.scrollWidth,clientWidth:t.scroller.clientWidth,viewWidth:t.wrapper.clientWidth,barLeft:e.options.fixedGutter?n:0,docHeight:r,scrollHeight:r+Mn(e)+t.barHeight,nativeBarWidth:t.nativeBarWidth,gutterWidth:n}}var jr=function(e,t,n){this.cm=n;var r=this.vert=A("div",[A("div",null,null,"min-width: 1px")],"CodeMirror-vscrollbar"),i=this.horiz=A("div",[A("div",null,null,"height: 100%; min-height: 1px")],"CodeMirror-hscrollbar");r.tabIndex=i.tabIndex=-1,e(r),e(i),tt(r,"scroll",function(){r.clientHeight&&t(r.scrollTop,"vertical")}),tt(i,"scroll",function(){i.clientWidth&&t(i.scrollLeft,"horizontal")}),this.checkedZeroWidth=!1,s&&l<8&&(this.horiz.style.minHeight=this.vert.style.minWidth="18px")};jr.prototype.update=function(e){var t=e.scrollWidth>e.clientWidth+1,n=e.scrollHeight>e.clientHeight+1,r=e.nativeBarWidth;if(n){this.vert.style.display="block",this.vert.style.bottom=t?r+"px":"0";var i=e.viewHeight-(t?r:0);this.vert.firstChild.style.height=Math.max(0,e.scrollHeight-e.clientHeight+i)+"px"}else this.vert.style.display="",this.vert.firstChild.style.height="0";if(t){this.horiz.style.display="block",this.horiz.style.right=n?r+"px":"0",this.horiz.style.left=e.barLeft+"px";var o=e.viewWidth-e.barLeft-(n?r:0);this.horiz.firstChild.style.width=Math.max(0,e.scrollWidth-e.clientWidth+o)+"px"}else this.horiz.style.display="",this.horiz.firstChild.style.width="0";return!this.checkedZeroWidth&&e.clientHeight>0&&(0==r&&this.zeroWidthHack(),this.checkedZeroWidth=!0),{right:n?r:0,bottom:t?r:0}},jr.prototype.setScrollLeft=function(e){this.horiz.scrollLeft!=e&&(this.horiz.scrollLeft=e),this.disableHoriz&&this.enableZeroWidthBar(this.horiz,this.disableHoriz,"horiz")},jr.prototype.setScrollTop=function(e){this.vert.scrollTop!=e&&(this.vert.scrollTop=e),this.disableVert&&this.enableZeroWidthBar(this.vert,this.disableVert,"vert")},jr.prototype.zeroWidthHack=function(){var e=b&&!p?"12px":"18px";this.horiz.style.height=this.vert.style.width=e,this.horiz.style.pointerEvents=this.vert.style.pointerEvents="none",this.disableHoriz=new W,this.disableVert=new W},jr.prototype.enableZeroWidthBar=function(e,t,n){e.style.pointerEvents="auto",t.set(1e3,function r(){var i=e.getBoundingClientRect();("vert"==n?document.elementFromPoint(i.right-1,(i.top+i.bottom)/2):document.elementFromPoint((i.right+i.left)/2,i.bottom-1))!=e?e.style.pointerEvents="none":t.set(1e3,r)})},jr.prototype.clear=function(){var e=this.horiz.parentNode;e.removeChild(this.horiz),e.removeChild(this.vert)};var Nr=function(){};function Ir(e,t){t||(t=Dr(e));var n=e.display.barWidth,r=e.display.barHeight;Rr(e,t);for(var i=0;i<4&&n!=e.display.barWidth||r!=e.display.barHeight;i++)n!=e.display.barWidth&&e.options.lineWrapping&&br(e),Rr(e,Dr(e)),n=e.display.barWidth,r=e.display.barHeight}function Rr(e,t){var n=e.display,r=n.scrollbars.update(t);n.sizer.style.paddingRight=(n.barWidth=r.right)+"px",n.sizer.style.paddingBottom=(n.barHeight=r.bottom)+"px",n.heightForcer.style.borderBottom=r.bottom+"px solid transparent",r.right&&r.bottom?(n.scrollbarFiller.style.display="block",n.scrollbarFiller.style.height=r.bottom+"px",n.scrollbarFiller.style.width=r.right+"px"):n.scrollbarFiller.style.display="",r.bottom&&e.options.coverGutterNextToScrollbar&&e.options.fixedGutter?(n.gutterFiller.style.display="block",n.gutterFiller.style.height=r.bottom+"px",n.gutterFiller.style.width=t.gutterWidth+"px"):n.gutterFiller.style.display=""}Nr.prototype.update=function(){return{bottom:0,right:0}},Nr.prototype.setScrollLeft=function(){},Nr.prototype.setScrollTop=function(){},Nr.prototype.clear=function(){};var zr={native:jr,null:Nr};function Fr(e){e.display.scrollbars&&(e.display.scrollbars.clear(),e.display.scrollbars.addClass&&L(e.display.wrapper,e.display.scrollbars.addClass)),e.display.scrollbars=new zr[e.options.scrollbarStyle](function(t){e.display.wrapper.insertBefore(t,e.display.scrollbarFiller),tt(t,"mousedown",function(){e.state.focused&&setTimeout(function(){return e.display.input.focus()},0)}),t.setAttribute("cm-not-content","true")},function(t,n){"horizontal"==n?Er(e,t):Ar(e,t)},e),e.display.scrollbars.addClass&&j(e.display.wrapper,e.display.scrollbars.addClass)}var Wr=0;function Br(e){var t;e.curOp={cm:e,viewChanged:!1,startHeight:e.doc.height,forceUpdate:!1,updateInput:null,typing:!1,changeObjs:null,cursorActivityHandlers:null,cursorActivityCalled:0,selectionChanged:!1,updateMaxLine:!1,scrollLeft:null,scrollTop:null,scrollToPos:null,focus:!1,id:++Wr},t=e.curOp,an?an.ops.push(t):t.ownsGroup=an={ops:[t],delayedCallbacks:[]}}function Hr(e){!function(e,t){var n=e.ownsGroup;if(n)try{!function(e){var t=e.delayedCallbacks,n=0;do{for(;n<t.length;n++)t[n].call(null);for(var r=0;r<e.ops.length;r++){var i=e.ops[r];if(i.cursorActivityHandlers)for(;i.cursorActivityCalled<i.cursorActivityHandlers.length;)i.cursorActivityHandlers[i.cursorActivityCalled++].call(null,i.cm)}}while(n<t.length)}(n)}finally{an=null,function(e){for(var t=0;t<e.ops.length;t++)e.ops[t].cm.curOp=null;!function(e){for(var t=e.ops,n=0;n<t.length;n++)qr(t[n]);for(var r=0;r<t.length;r++)(i=t[r]).updatedDisplay=i.mustUpdate&&oi(i.cm,i.update);for(var i,o=0;o<t.length;o++)Ur(t[o]);for(var a=0;a<t.length;a++)Kr(t[a]);for(var s=0;s<t.length;s++)Vr(t[s])}(e)}(n)}}(e.curOp)}function qr(e){var t=e.cm,n=t.display;!function(e){var t=e.display;!t.scrollbarsClipped&&t.scroller.offsetWidth&&(t.nativeBarWidth=t.scroller.offsetWidth-t.scroller.clientWidth,t.heightForcer.style.height=Mn(e)+"px",t.sizer.style.marginBottom=-t.nativeBarWidth+"px",t.sizer.style.borderRightWidth=Mn(e)+"px",t.scrollbarsClipped=!0)}(t),e.updateMaxLine&&Je(t),e.mustUpdate=e.viewChanged||e.forceUpdate||null!=e.scrollTop||e.scrollToPos&&(e.scrollToPos.from.line<n.viewFrom||e.scrollToPos.to.line>=n.viewTo)||n.maxLineChanged&&t.options.lineWrapping,e.update=e.mustUpdate&&new ii(t,e.mustUpdate&&{top:e.scrollTop,ensure:e.scrollToPos},e.forceUpdate)}function Ur(e){var t=e.cm,n=t.display;e.updatedDisplay&&br(t),e.barMeasure=Dr(t),n.maxLineChanged&&!t.options.lineWrapping&&(e.adjustWidthTo=An(t,n.maxLine,n.maxLine.text.length).left+3,t.display.sizerWidth=e.adjustWidthTo,e.barMeasure.scrollWidth=Math.max(n.scroller.clientWidth,n.sizer.offsetLeft+e.adjustWidthTo+Mn(t)+t.display.barWidth),e.maxScrollLeft=Math.max(0,n.sizer.offsetLeft+e.adjustWidthTo-Ln(t))),(e.updatedDisplay||e.selectionChanged)&&(e.preparedSelection=n.input.prepareSelection())}function Kr(e){var t=e.cm;null!=e.adjustWidthTo&&(t.display.sizer.style.minWidth=e.adjustWidthTo+"px",e.maxScrollLeft<t.doc.scrollLeft&&Er(t,Math.min(t.display.scroller.scrollLeft,e.maxScrollLeft),!0),t.display.maxLineChanged=!1);var n=e.focus&&e.focus==D();e.preparedSelection&&t.display.input.showSelection(e.preparedSelection,n),(e.updatedDisplay||e.startHeight!=t.doc.height)&&Ir(t,e.barMeasure),e.updatedDisplay&&ci(t,e.barMeasure),e.selectionChanged&&pr(t),t.state.focused&&e.updateInput&&t.display.input.reset(e.typing),n&&mr(e.cm)}function Vr(e){var t=e.cm,n=t.display,r=t.doc;e.updatedDisplay&&ai(t,e.update),null==n.wheelStartX||null==e.scrollTop&&null==e.scrollLeft&&!e.scrollToPos||(n.wheelStartX=n.wheelStartY=null),null!=e.scrollTop&&Pr(t,e.scrollTop,e.forceScroll),null!=e.scrollLeft&&Er(t,e.scrollLeft,!0,!0),e.scrollToPos&&function(e,t){if(!ot(e,"scrollCursorIntoView")){var n=e.display,r=n.sizer.getBoundingClientRect(),i=null;if(t.top+r.top<0?i=!0:t.bottom+r.top>(window.innerHeight||document.documentElement.clientHeight)&&(i=!1),null!=i&&!m){var o=A("div","​",null,"position: absolute;\n                         top: "+(t.top-n.viewOffset-kn(e.display))+"px;\n                         height: "+(t.bottom-t.top+Mn(e)+n.barHeight)+"px;\n                         left: "+t.left+"px; width: "+Math.max(2,t.right-t.left)+"px;");e.display.lineSpace.appendChild(o),o.scrollIntoView(i),e.display.lineSpace.removeChild(o)}}}(t,function(e,t,n,r){var i;null==r&&(r=0),e.options.lineWrapping||t!=n||(n="before"==(t=t.ch?ge(t.line,"before"==t.sticky?t.ch-1:t.ch,"after"):t).sticky?ge(t.line,t.ch+1,"before"):t);for(var o=0;o<5;o++){var a=!1,s=$n(e,t),l=n&&n!=t?$n(e,n):s,c=Cr(e,i={left:Math.min(s.left,l.left),top:Math.min(s.top,l.top)-r,right:Math.max(s.left,l.left),bottom:Math.max(s.bottom,l.bottom)+r}),u=e.doc.scrollTop,d=e.doc.scrollLeft;if(null!=c.scrollTop&&(Ar(e,c.scrollTop),Math.abs(e.doc.scrollTop-u)>1&&(a=!0)),null!=c.scrollLeft&&(Er(e,c.scrollLeft),Math.abs(e.doc.scrollLeft-d)>1&&(a=!0)),!a)break}return i}(t,ke(r,e.scrollToPos.from),ke(r,e.scrollToPos.to),e.scrollToPos.margin));var i=e.maybeHiddenMarkers,o=e.maybeUnhiddenMarkers;if(i)for(var a=0;a<i.length;++a)i[a].lines.length||it(i[a],"hide");if(o)for(var s=0;s<o.length;++s)o[s].lines.length&&it(o[s],"unhide");n.wrapper.offsetHeight&&(r.scrollTop=t.display.scroller.scrollTop),e.changeObjs&&it(t,"changes",t,e.changeObjs),e.update&&e.update.finish()}function $r(e,t){if(e.curOp)return t();Br(e);try{return t()}finally{Hr(e)}}function Gr(e,t){return function(){if(e.curOp)return t.apply(e,arguments);Br(e);try{return t.apply(e,arguments)}finally{Hr(e)}}}function Jr(e){return function(){if(this.curOp)return e.apply(this,arguments);Br(this);try{return e.apply(this,arguments)}finally{Hr(this)}}}function Xr(e){return function(){var t=this.cm;if(!t||t.curOp)return e.apply(this,arguments);Br(t);try{return e.apply(this,arguments)}finally{Hr(t)}}}function Zr(e,t,n,r){null==t&&(t=e.doc.first),null==n&&(n=e.doc.first+e.doc.size),r||(r=0);var i=e.display;if(r&&n<i.viewTo&&(null==i.updateLineNumbers||i.updateLineNumbers>t)&&(i.updateLineNumbers=t),e.curOp.viewChanged=!0,t>=i.viewTo)Me&&qe(e.doc,t)<i.viewTo&&Qr(e);else if(n<=i.viewFrom)Me&&Ue(e.doc,n+r)>i.viewFrom?Qr(e):(i.viewFrom+=r,i.viewTo+=r);else if(t<=i.viewFrom&&n>=i.viewTo)Qr(e);else if(t<=i.viewFrom){var o=ei(e,n,n+r,1);o?(i.view=i.view.slice(o.index),i.viewFrom=o.lineN,i.viewTo+=r):Qr(e)}else if(n>=i.viewTo){var a=ei(e,t,t,-1);a?(i.view=i.view.slice(0,a.index),i.viewTo=a.lineN):Qr(e)}else{var s=ei(e,t,t,-1),l=ei(e,n,n+r,1);s&&l?(i.view=i.view.slice(0,s.index).concat(on(e,s.lineN,l.lineN)).concat(i.view.slice(l.index)),i.viewTo+=r):Qr(e)}var c=i.externalMeasured;c&&(n<c.lineN?c.lineN+=r:t<c.lineN+c.size&&(i.externalMeasured=null))}function Yr(e,t,n){e.curOp.viewChanged=!0;var r=e.display,i=e.display.externalMeasured;if(i&&t>=i.lineN&&t<i.lineN+i.size&&(r.externalMeasured=null),!(t<r.viewFrom||t>=r.viewTo)){var o=r.view[lr(e,t)];if(null!=o.node){var a=o.changes||(o.changes=[]);-1==B(a,n)&&a.push(n)}}}function Qr(e){e.display.viewFrom=e.display.viewTo=e.doc.first,e.display.view=[],e.display.viewOffset=0}function ei(e,t,n,r){var i,o=lr(e,t),a=e.display.view;if(!Me||n==e.doc.first+e.doc.size)return{index:o,lineN:n};for(var s=e.display.viewFrom,l=0;l<o;l++)s+=a[l].size;if(s!=t){if(r>0){if(o==a.length-1)return null;i=s+a[o].size-t,o++}else i=s-t;t+=i,n+=i}for(;qe(e.doc,n)!=n;){if(o==(r<0?0:a.length-1))return null;n+=r*a[o-(r<0?1:0)].size,o+=r}return{index:o,lineN:n}}function ti(e){for(var t=e.display.view,n=0,r=0;r<t.length;r++){var i=t[r];i.hidden||i.node&&!i.changes||++n}return n}function ni(e,t){e.doc.highlightFrontier<e.display.viewTo&&e.state.highlight.set(t,R(ri,e))}function ri(e){var t=e.doc;if(!(t.highlightFrontier>=e.display.viewTo)){var n=+new Date+e.options.workTime,r=zt(e,t.highlightFrontier),i=[];t.iter(r.line,Math.min(t.first+t.size,e.display.viewTo+500),function(o){if(r.line>=e.display.viewFrom){var 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r=e.display;this.viewport=t,this.visible=wr(r,e.doc,t),this.editorIsHidden=!r.wrapper.offsetWidth,this.wrapperHeight=r.wrapper.clientHeight,this.wrapperWidth=r.wrapper.clientWidth,this.oldDisplayWidth=Ln(e),this.force=n,this.dims=rr(e),this.events=[]};function oi(e,t){var n=e.display,r=e.doc;if(t.editorIsHidden)return Qr(e),!1;if(!t.force&&t.visible.from>=n.viewFrom&&t.visible.to<=n.viewTo&&(null==n.updateLineNumbers||n.updateLineNumbers>=n.viewTo)&&n.renderedView==n.view&&0==ti(e))return!1;kr(e)&&(Qr(e),t.dims=rr(e));var i=r.first+r.size,o=Math.max(t.visible.from-e.options.viewportMargin,r.first),a=Math.min(i,t.visible.to+e.options.viewportMargin);n.viewFrom<o&&o-n.viewFrom<20&&(o=Math.max(r.first,n.viewFrom)),n.viewTo>a&&n.viewTo-a<20&&(a=Math.min(i,n.viewTo)),Me&&(o=qe(e.doc,o),a=Ue(e.doc,a));var s=o!=n.viewFrom||a!=n.viewTo||n.lastWrapHeight!=t.wrapperHeight||n.lastWrapWidth!=t.wrapperWidth;!function(e,t,n){var 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r=e.display,i=e.options.lineNumbers,o=r.lineDiv,a=o.firstChild;function s(t){var n=t.nextSibling;return c&&b&&e.display.currentWheelTarget==t?t.style.display="none":t.parentNode.removeChild(t),n}for(var l=r.view,u=r.viewFrom,d=0;d<l.length;d++){var f=l[d];if(f.hidden);else if(f.node&&f.node.parentNode==o){for(;a!=f.node;)a=s(a);var h=i&&null!=t&&t<=u&&f.lineNumber;f.changes&&(B(f.changes,"gutter")>-1&&(h=!1),un(e,f,u,n)),h&&(T(f.lineNumber),f.lineNumber.appendChild(document.createTextNode(me(e.options,u)))),a=f.node.nextSibling}else{var p=vn(e,f,u,n);o.insertBefore(p,a)}u+=f.size}for(;a;)a=s(a)}(e,n.updateLineNumbers,t.dims),l>4&&(n.lineDiv.style.display=""),n.renderedView=n.view,function(e){if(e&&e.activeElt&&e.activeElt!=D()&&(e.activeElt.focus(),e.anchorNode&&E(document.body,e.anchorNode)&&E(document.body,e.focusNode))){var t=window.getSelection(),n=document.createRange();n.setEnd(e.anchorNode,e.anchorOffset),n.collapse(!1),t.removeAllRanges(),t.addRange(n),t.extend(e.focusNode,e.focusOffset)}}(u),T(n.cursorDiv),T(n.selectionDiv),n.gutters.style.height=n.sizer.style.minHeight=0,s&&(n.lastWrapHeight=t.wrapperHeight,n.lastWrapWidth=t.wrapperWidth,ni(e,400)),n.updateLineNumbers=null,!0}function ai(e,t){for(var n=t.viewport,r=!0;(r&&e.options.lineWrapping&&t.oldDisplayWidth!=Ln(e)||(n&&null!=n.top&&(n={top:Math.min(e.doc.height+Cn(e.display)-Tn(e),n.top)}),t.visible=wr(e.display,e.doc,n),!(t.visible.from>=e.display.viewFrom&&t.visible.to<=e.display.viewTo)))&&oi(e,t);r=!1){br(e);var i=Dr(e);cr(e),Ir(e,i),ci(e,i),t.force=!1}t.signal(e,"update",e),e.display.viewFrom==e.display.reportedViewFrom&&e.display.viewTo==e.display.reportedViewTo||(t.signal(e,"viewportChange",e,e.display.viewFrom,e.display.viewTo),e.display.reportedViewFrom=e.display.viewFrom,e.display.reportedViewTo=e.display.viewTo)}function si(e,t){var n=new ii(e,t);if(oi(e,n)){br(e),ai(e,n);var r=Dr(e);cr(e),Ir(e,r),ci(e,r),n.finish()}}function li(e){var t=e.display.gutters.offsetWidth;e.display.sizer.style.marginLeft=t+"px"}function ci(e,t){e.display.sizer.style.minHeight=t.docHeight+"px",e.display.heightForcer.style.top=t.docHeight+"px",e.display.gutters.style.height=t.docHeight+e.display.barHeight+Mn(e)+"px"}function ui(e){var t=e.display.gutters,n=e.options.gutters;T(t);for(var r=0;r<n.length;++r){var i=n[r],o=t.appendChild(A("div",null,"CodeMirror-gutter "+i));"CodeMirror-linenumbers"==i&&(e.display.lineGutter=o,o.style.width=(e.display.lineNumWidth||1)+"px")}t.style.display=r?"":"none",li(e)}function di(e){var t=B(e.gutters,"CodeMirror-linenumbers");-1==t&&e.lineNumbers?e.gutters=e.gutters.concat(["CodeMirror-linenumbers"]):t>-1&&!e.lineNumbers&&(e.gutters=e.gutters.slice(0),e.gutters.splice(t,1))}ii.prototype.signal=function(e,t){st(e,t)&&this.events.push(arguments)},ii.prototype.finish=function(){for(var e=0;e<this.events.length;e++)it.apply(null,this.events[e])};var fi=0,hi=null;function pi(e){var t=e.wheelDeltaX,n=e.wheelDeltaY;return null==t&&e.detail&&e.axis==e.HORIZONTAL_AXIS&&(t=e.detail),null==n&&e.detail&&e.axis==e.VERTICAL_AXIS?n=e.detail:null==n&&(n=e.wheelDelta),{x:t,y:n}}function mi(e,t){var r=pi(t),i=r.x,o=r.y,a=e.display,s=a.scroller,l=s.scrollWidth>s.clientWidth,u=s.scrollHeight>s.clientHeight;if(i&&l||o&&u){if(o&&b&&c)e:for(var d=t.target,h=a.view;d!=s;d=d.parentNode)for(var p=0;p<h.length;p++)if(h[p].node==d){e.display.currentWheelTarget=d;break e}if(i&&!n&&!f&&null!=hi)return o&&u&&Ar(e,Math.max(0,s.scrollTop+o*hi)),Er(e,Math.max(0,s.scrollLeft+i*hi)),(!o||o&&u)&&ct(t),void(a.wheelStartX=null);if(o&&null!=hi){var 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o=e.lastLine();t.to.line>o&&(t={from:t.from,to:ge(o,le(e,o).text.length),text:[t.text[0]],origin:t.origin}),t.removed=ce(e,t.from,t.to),n||(n=_i(e,t)),e.cm?function(e,t,n){var r=e.doc,i=e.display,o=t.from,a=t.to,s=!1,l=o.line;e.options.lineWrapping||(l=fe(He(le(r,o.line))),r.iter(l,a.line+1,function(e){if(e==i.maxLine)return s=!0,!0})),r.sel.contains(t.from,t.to)>-1&&at(e),Li(r,t,n,or(e)),e.options.lineWrapping||(r.iter(l,o.line+t.text.length,function(e){var t=Ge(e);t>i.maxLineLength&&(i.maxLine=e,i.maxLineLength=t,i.maxLineChanged=!0,s=!1)}),s&&(e.curOp.updateMaxLine=!0)),function(e,t){if(e.modeFrontier=Math.min(e.modeFrontier,t),!(e.highlightFrontier<t-10)){for(var n=e.first,r=t-1;r>n;r--){var i=le(e,r).stateAfter;if(i&&(!(i instanceof jt)||r+i.lookAhead<t)){n=r+1;break}}e.highlightFrontier=Math.min(e.highlightFrontier,n)}}(r,o.line),ni(e,400);var c=t.text.length-(a.line-o.line)-1;t.full?Zr(e):o.line!=a.line||1!=t.text.length||Mi(e.doc,t)?Zr(e,o.line,a.line+1,c):Yr(e,o.line,"text");var u=st(e,"changes"),d=st(e,"change");if(d||u){var f={from:o,to:a,text:t.text,removed:t.removed,origin:t.origin};d&&ln(e,"change",e,f),u&&(e.curOp.changeObjs||(e.curOp.changeObjs=[])).push(f)}e.display.selForContextMenu=null}(e.cm,t,r):Li(e,t,r),$i(e,n,U)}}function so(e,t,n,r,i){var o;r||(r=n),ve(r,n)<0&&(n=(o=[r,n])[0],r=o[1]),"string"==typeof t&&(t=e.splitLines(t)),no(e,{from:n,to:r,text:t,origin:i})}function lo(e,t,n,r){n<e.line?e.line+=r:t<e.line&&(e.line=t,e.ch=0)}function co(e,t,n,r){for(var i=0;i<e.length;++i){var o=e[i],a=!0;if(o.ranges){o.copied||((o=e[i]=o.deepCopy()).copied=!0);for(var s=0;s<o.ranges.length;s++)lo(o.ranges[s].anchor,t,n,r),lo(o.ranges[s].head,t,n,r)}else{for(var l=0;l<o.changes.length;++l){var c=o.changes[l];if(n<c.from.line)c.from=ge(c.from.line+r,c.from.ch),c.to=ge(c.to.line+r,c.to.ch);else 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i=this.lines[n];this.height-=i.height,$t(i),ln(i,"delete")}this.lines.splice(e,t)},collapse:function(e){e.push.apply(e,this.lines)},insertInner:function(e,t,n){this.height+=n,this.lines=this.lines.slice(0,e).concat(t).concat(this.lines.slice(e));for(var r=0;r<t.length;++r)t[r].parent=this},iterN:function(e,t,n){for(var r=e+t;e<r;++e)if(n(this.lines[e]))return!0}},po.prototype={chunkSize:function(){return this.size},removeInner:function(e,t){this.size-=t;for(var n=0;n<this.children.length;++n){var r=this.children[n],i=r.chunkSize();if(e<i){var o=Math.min(t,i-e),a=r.height;if(r.removeInner(e,o),this.height-=a-r.height,i==o&&(this.children.splice(n--,1),r.parent=null),0==(t-=o))break;e=0}else e-=i}if(this.size-t<25&&(this.children.length>1||!(this.children[0]instanceof ho))){var s=[];this.collapse(s),this.children=[new ho(s)],this.children[0].parent=this}},collapse:function(e){for(var t=0;t<this.children.length;++t)this.children[t].collapse(e)},insertInner:function(e,t,n){this.size+=t.length,this.height+=n;for(var r=0;r<this.children.length;++r){var i=this.children[r],o=i.chunkSize();if(e<=o){if(i.insertInner(e,t,n),i.lines&&i.lines.length>50){for(var a=i.lines.length%25+25,s=a;s<i.lines.length;){var l=new ho(i.lines.slice(s,s+=25));i.height-=l.height,this.children.splice(++r,0,l),l.parent=this}i.lines=i.lines.slice(0,a),this.maybeSpill()}break}e-=o}},maybeSpill:function(){if(!(this.children.length<=10)){var e=this;do{var t=new po(e.children.splice(e.children.length-5,5));if(e.parent){e.size-=t.size,e.height-=t.height;var n=B(e.parent.children,e);e.parent.children.splice(n+1,0,t)}else{var r=new po(e.children);r.parent=e,e.children=[r,t],e=r}t.parent=e.parent}while(e.children.length>10);e.parent.maybeSpill()}},iterN:function(e,t,n){for(var r=0;r<this.children.length;++r){var i=this.children[r],o=i.chunkSize();if(e<o){var a=Math.min(t,o-e);if(i.iterN(e,a,n))return!0;if(0==(t-=a))break;e=0}else e-=o}}};var mo=function(e,t,n){if(n)for(var r in n)n.hasOwnProperty(r)&&(this[r]=n[r]);this.doc=e,this.node=t};function go(e,t,n){$e(t)<(e.curOp&&e.curOp.scrollTop||e.doc.scrollTop)&&Sr(e,n)}mo.prototype.clear=function(){var e=this.doc.cm,t=this.line.widgets,n=this.line,r=fe(n);if(null!=r&&t){for(var i=0;i<t.length;++i)t[i]==this&&t.splice(i--,1);t.length||(n.widgets=null);var o=wn(this);de(n,Math.max(0,n.height-o)),e&&($r(e,function(){go(e,n,-o),Yr(e,r,"widget")}),ln(e,"lineWidgetCleared",e,this,r))}},mo.prototype.changed=function(){var e=this,t=this.height,n=this.doc.cm,r=this.line;this.height=null;var i=wn(this)-t;i&&(de(r,r.height+i),n&&$r(n,function(){n.curOp.forceUpdate=!0,go(n,r,i),ln(n,"lineWidgetChanged",n,e,fe(r))}))},lt(mo);var vo=0,yo=function(e,t){this.lines=[],this.type=t,this.doc=e,this.id=++vo};function bo(e,t,n,r,i){if(r&&r.shared)return function(e,t,n,r,i){(r=z(r)).shared=!1;var o=[bo(e,t,n,r,i)],a=o[0],s=r.widgetNode;return Ti(e,function(e){s&&(r.widgetNode=s.cloneNode(!0)),o.push(bo(e,ke(e,t),ke(e,n),r,i));for(var l=0;l<e.linked.length;++l)if(e.linked[l].isParent)return;a=X(o)}),new xo(o,a)}(e,t,n,r,i);if(e.cm&&!e.cm.curOp)return Gr(e.cm,bo)(e,t,n,r,i);var o=new yo(e,i),a=ve(t,n);if(r&&z(r,o,!1),a>0||0==a&&!1!==o.clearWhenEmpty)return o;if(o.replacedWith&&(o.collapsed=!0,o.widgetNode=P("span",[o.replacedWith],"CodeMirror-widget"),r.handleMouseEvents||o.widgetNode.setAttribute("cm-ignore-events","true"),r.insertLeft&&(o.widgetNode.insertLeft=!0)),o.collapsed){if(Be(e,t.line,t,n,o)||t.line!=n.line&&Be(e,n.line,t,n,o))throw new Error("Inserting collapsed marker partially overlapping an existing one");Me=!0}o.addToHistory&&ji(e,{from:t,to:n,origin:"markText"},e.sel,NaN);var s,l=t.line,c=e.cm;if(e.iter(l,n.line+1,function(e){c&&o.collapsed&&!c.options.lineWrapping&&He(e)==c.display.maxLine&&(s=!0),o.collapsed&&l!=t.line&&de(e,0),function(e,t){e.markedSpans=e.markedSpans?e.markedSpans.concat([t]):[t],t.marker.attachLine(e)}(e,new Le(o,l==t.line?t.ch:null,l==n.line?n.ch:null)),++l}),o.collapsed&&e.iter(t.line,n.line+1,function(t){Ke(e,t)&&de(t,0)}),o.clearOnEnter&&tt(o,"beforeCursorEnter",function(){return o.clear()}),o.readOnly&&(Se=!0,(e.history.done.length||e.history.undone.length)&&e.clearHistory()),o.collapsed&&(o.id=++vo,o.atomic=!0),c){if(s&&(c.curOp.updateMaxLine=!0),o.collapsed)Zr(c,t.line,n.line+1);else if(o.className||o.title||o.startStyle||o.endStyle||o.css)for(var u=t.line;u<=n.line;u++)Yr(c,u,"text");o.atomic&&Ji(c.doc),ln(c,"markerAdded",c,o)}return o}yo.prototype.clear=function(){if(!this.explicitlyCleared){var e=this.doc.cm,t=e&&!e.curOp;if(t&&Br(e),st(this,"clear")){var n=this.find();n&&ln(this,"clear",n.from,n.to)}for(var r=null,i=null,o=0;o<this.lines.length;++o){var a=this.lines[o],s=Te(a.markedSpans,this);e&&!this.collapsed?Yr(e,fe(a),"text"):e&&(null!=s.to&&(i=fe(a)),null!=s.from&&(r=fe(a))),a.markedSpans=Oe(a.markedSpans,s),null==s.from&&this.collapsed&&!Ke(this.doc,a)&&e&&de(a,tr(e.display))}if(e&&this.collapsed&&!e.options.lineWrapping)for(var l=0;l<this.lines.length;++l){var c=He(this.lines[l]),u=Ge(c);u>e.display.maxLineLength&&(e.display.maxLine=c,e.display.maxLineLength=u,e.display.maxLineChanged=!0)}null!=r&&e&&this.collapsed&&Zr(e,r,i+1),this.lines.length=0,this.explicitlyCleared=!0,this.atomic&&this.doc.cantEdit&&(this.doc.cantEdit=!1,e&&Ji(e.doc)),e&&ln(e,"markerCleared",e,this,r,i),t&&Hr(e),this.parent&&this.parent.clear()}},yo.prototype.find=function(e,t){var n,r;null==e&&"bookmark"==this.type&&(e=1);for(var i=0;i<this.lines.length;++i){var o=this.lines[i],a=Te(o.markedSpans,this);if(null!=a.from&&(n=ge(t?o:fe(o),a.from),-1==e))return n;if(null!=a.to&&(r=ge(t?o:fe(o),a.to),1==e))return r}return n&&{from:n,to:r}},yo.prototype.changed=function(){var e=this,t=this.find(-1,!0),n=this,r=this.doc.cm;t&&r&&$r(r,function(){var i=t.line,o=fe(t.line),a=Pn(r,o);if(a&&(zn(a),r.curOp.selectionChanged=r.curOp.forceUpdate=!0),r.curOp.updateMaxLine=!0,!Ke(n.doc,i)&&null!=n.height){var s=n.height;n.height=null;var l=wn(n)-s;l&&de(i,i.height+l)}ln(r,"markerChanged",r,e)})},yo.prototype.attachLine=function(e){if(!this.lines.length&&this.doc.cm){var t=this.doc.cm.curOp;t.maybeHiddenMarkers&&-1!=B(t.maybeHiddenMarkers,this)||(t.maybeUnhiddenMarkers||(t.maybeUnhiddenMarkers=[])).push(this)}this.lines.push(e)},yo.prototype.detachLine=function(e){if(this.lines.splice(B(this.lines,e),1),!this.lines.length&&this.doc.cm){var t=this.doc.cm.curOp;(t.maybeHiddenMarkers||(t.maybeHiddenMarkers=[])).push(this)}},lt(yo);var xo=function(e,t){this.markers=e,this.primary=t;for(var n=0;n<e.length;++n)e[n].parent=this};function wo(e){return e.findMarks(ge(e.first,0),e.clipPos(ge(e.lastLine())),function(e){return e.parent})}function _o(e){for(var t=function(t){var n=e[t],r=[n.primary.doc];Ti(n.primary.doc,function(e){return r.push(e)});for(var i=0;i<n.markers.length;i++){var o=n.markers[i];-1==B(r,o.doc)&&(o.parent=null,n.markers.splice(i--,1))}},n=0;n<e.length;n++)t(n)}xo.prototype.clear=function(){if(!this.explicitlyCleared){this.explicitlyCleared=!0;for(var e=0;e<this.markers.length;++e)this.markers[e].clear();ln(this,"clear")}},xo.prototype.find=function(e,t){return this.primary.find(e,t)},lt(xo);var ko=0,Co=function e(t,n,r,i,o){if(!(this instanceof e))return new e(t,n,r,i,o);null==r&&(r=0),po.call(this,[new ho([new Vt("",null)])]),this.first=r,this.scrollTop=this.scrollLeft=0,this.cantEdit=!1,this.cleanGeneration=1,this.modeFrontier=this.highlightFrontier=r;var a=ge(r,0);this.sel=bi(a),this.history=new Pi(null),this.id=++ko,this.modeOption=n,this.lineSep=i,this.direction="rtl"==o?"rtl":"ltr",this.extend=!1,"string"==typeof t&&(t=this.splitLines(t)),Li(this,{from:a,to:a,text:t}),Vi(this,bi(a),U)};Co.prototype=Q(po.prototype,{constructor:Co,iter:function(e,t,n){n?this.iterN(e-this.first,t-e,n):this.iterN(this.first,this.first+this.size,e)},insert:function(e,t){for(var n=0,r=0;r<t.length;++r)n+=t[r].height;this.insertInner(e-this.first,t,n)},remove:function(e,t){this.removeInner(e-this.first,t)},getValue:function(e){var t=ue(this,this.first,this.first+this.size);return!1===e?t:t.join(e||this.lineSeparator())},setValue:Xr(function(e){var t=ge(this.first,0),n=this.first+this.size-1;no(this,{from:t,to:ge(n,le(this,n).text.length),text:this.splitLines(e),origin:"setValue",full:!0},!0),this.cm&&Lr(this.cm,0,0),Vi(this,bi(t),U)}),replaceRange:function(e,t,n,r){so(this,e,t=ke(this,t),n=n?ke(this,n):t,r)},getRange:function(e,t,n){var r=ce(this,ke(this,e),ke(this,t));return!1===n?r:r.join(n||this.lineSeparator())},getLine:function(e){var t=this.getLineHandle(e);return t&&t.text},getLineHandle:function(e){if(pe(this,e))return le(this,e)},getLineNumber:function(e){return fe(e)},getLineHandleVisualStart:function(e){return"number"==typeof e&&(e=le(this,e)),He(e)},lineCount:function(){return this.size},firstLine:function(){return this.first},lastLine:function(){return this.first+this.size-1},clipPos:function(e){return ke(this,e)},getCursor:function(e){var t=this.sel.primary();return null==e||"head"==e?t.head:"anchor"==e?t.anchor:"end"==e||"to"==e||!1===e?t.to():t.from()},listSelections:function(){return this.sel.ranges},somethingSelected:function(){return this.sel.somethingSelected()},setCursor:Xr(function(e,t,n){Ui(this,ke(this,"number"==typeof 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t},replaceSelection:function(e,t,n){for(var r=[],i=0;i<this.sel.ranges.length;i++)r[i]=e;this.replaceSelections(r,t,n||"+input")},replaceSelections:Xr(function(e,t,n){for(var r=[],i=this.sel,o=0;o<i.ranges.length;o++){var a=i.ranges[o];r[o]={from:a.from(),to:a.to(),text:this.splitLines(e[o]),origin:n}}for(var s=t&&"end"!=t&&function(e,t,n){for(var r=[],i=ge(e.first,0),o=i,a=0;a<t.length;a++){var s=t[a],l=ki(s.from,i,o),c=ki(xi(s),i,o);if(i=s.to,o=c,"around"==n){var u=e.sel.ranges[a],d=ve(u.head,u.anchor)<0;r[a]=new vi(d?c:l,d?l:c)}else r[a]=new vi(l,l)}return new gi(r,e.sel.primIndex)}(this,r,t),l=r.length-1;l>=0;l--)no(this,r[l]);s?Ki(this,s):this.cm&&Mr(this.cm)}),undo:Xr(function(){io(this,"undo")}),redo:Xr(function(){io(this,"redo")}),undoSelection:Xr(function(){io(this,"undo",!0)}),redoSelection:Xr(function(){io(this,"redo",!0)}),setExtending:function(e){this.extend=e},getExtending:function(){return this.extend},historySize:function(){for(var e=this.history,t=0,n=0,r=0;r<e.done.length;r++)e.done[r].ranges||++t;for(var i=0;i<e.undone.length;i++)e.undone[i].ranges||++n;return{undo:t,redo:n}},clearHistory:function(){this.history=new Pi(this.history.maxGeneration)},markClean:function(){this.cleanGeneration=this.changeGeneration(!0)},changeGeneration:function(e){return e&&(this.history.lastOp=this.history.lastSelOp=this.history.lastOrigin=null),this.history.generation},isClean:function(e){return this.history.generation==(e||this.cleanGeneration)},getHistory:function(){return{done:Fi(this.history.done),undone:Fi(this.history.undone)}},setHistory:function(e){var t=this.history=new Pi(this.history.maxGeneration);t.done=Fi(e.done.slice(0),null,!0),t.undone=Fi(e.undone.slice(0),null,!0)},setGutterMarker:Xr(function(e,t,n){return fo(this,e,"gutter",function(e){var r=e.gutterMarkers||(e.gutterMarkers={});return r[t]=n,!n&&re(r)&&(e.gutterMarkers=null),!0})}),clearGutter:Xr(function(e){var t=this;this.iter(function(n){n.gutterMarkers&&n.gutterMarkers[e]&&fo(t,n,"gutter",function(){return n.gutterMarkers[e]=null,re(n.gutterMarkers)&&(n.gutterMarkers=null),!0})})}),lineInfo:function(e){var t;if("number"==typeof e){if(!pe(this,e))return null;if(t=e,!(e=le(this,e)))return null}else if(null==(t=fe(e)))return null;return{line:t,handle:e,text:e.text,gutterMarkers:e.gutterMarkers,textClass:e.textClass,bgClass:e.bgClass,wrapClass:e.wrapClass,widgets:e.widgets}},addLineClass:Xr(function(e,t,n){return fo(this,e,"gutter"==t?"gutter":"class",function(e){var r="text"==t?"textClass":"background"==t?"bgClass":"gutter"==t?"gutterClass":"wrapClass";if(e[r]){if(S(n).test(e[r]))return!1;e[r]+=" "+n}else e[r]=n;return!0})}),removeLineClass:Xr(function(e,t,n){return fo(this,e,"gutter"==t?"gutter":"class",function(e){var r="text"==t?"textClass":"background"==t?"bgClass":"gutter"==t?"gutterClass":"wrapClass",i=e[r];if(!i)return!1;if(null==n)e[r]=null;else{var o=i.match(S(n));if(!o)return!1;var a=o.index+o[0].length;e[r]=i.slice(0,o.index)+(o.index&&a!=i.length?" 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i=n[r];(null==i.from||i.from<=e.ch)&&(null==i.to||i.to>=e.ch)&&t.push(i.marker.parent||i.marker)}return t},findMarks:function(e,t,n){e=ke(this,e),t=ke(this,t);var r=[],i=e.line;return this.iter(e.line,t.line+1,function(o){var a=o.markedSpans;if(a)for(var s=0;s<a.length;s++){var l=a[s];null!=l.to&&i==e.line&&e.ch>=l.to||null==l.from&&i!=e.line||null!=l.from&&i==t.line&&l.from>=t.ch||n&&!n(l.marker)||r.push(l.marker.parent||l.marker)}++i}),r},getAllMarks:function(){var e=[];return this.iter(function(t){var n=t.markedSpans;if(n)for(var r=0;r<n.length;++r)null!=n[r].from&&e.push(n[r].marker)}),e},posFromIndex:function(e){var t,n=this.first,r=this.lineSeparator().length;return this.iter(function(i){var o=i.text.length+r;if(o>e)return t=e,!0;e-=o,++n}),ke(this,ge(n,t))},indexFromPos:function(e){var t=(e=ke(this,e)).ch;if(e.line<this.first||e.ch<0)return 0;var n=this.lineSeparator().length;return this.iter(this.first,e.line,function(e){t+=e.text.length+n}),t},copy:function(e){var t=new Co(ue(this,this.first,this.first+this.size),this.modeOption,this.first,this.lineSep,this.direction);return t.scrollTop=this.scrollTop,t.scrollLeft=this.scrollLeft,t.sel=this.sel,t.extend=!1,e&&(t.history.undoDepth=this.history.undoDepth,t.setHistory(this.getHistory())),t},linkedDoc:function(e){e||(e={});var t=this.first,n=this.first+this.size;null!=e.from&&e.from>t&&(t=e.from),null!=e.to&&e.to<n&&(n=e.to);var r=new Co(ue(this,t,n),e.mode||this.modeOption,t,this.lineSep,this.direction);return e.sharedHist&&(r.history=this.history),(this.linked||(this.linked=[])).push({doc:r,sharedHist:e.sharedHist}),r.linked=[{doc:this,isParent:!0,sharedHist:e.sharedHist}],function(e,t){for(var n=0;n<t.length;n++){var r=t[n],i=r.find(),o=e.clipPos(i.from),a=e.clipPos(i.to);if(ve(o,a)){var s=bo(e,o,a,r.primary,r.primary.type);r.markers.push(s),s.parent=r}}}(r,wo(this)),r},unlinkDoc:function(e){if(e instanceof ba&&(e=e.doc),this.linked)for(var t=0;t<this.linked.length;++t)if(this.linked[t].doc==e){this.linked.splice(t,1),e.unlinkDoc(this),_o(wo(this));break}if(e.history==this.history){var n=[e.id];Ti(e,function(e){return n.push(e.id)},!0),e.history=new Pi(null),e.history.done=Fi(this.history.done,n),e.history.undone=Fi(this.history.undone,n)}},iterLinkedDocs:function(e){Ti(this,e)},getMode:function(){return this.mode},getEditor:function(){return this.cm},splitLines:function(e){return this.lineSep?e.split(this.lineSep):wt(e)},lineSeparator:function(){return this.lineSep||"\n"},setDirection:Xr(function(e){var t;"rtl"!=e&&(e="ltr"),e!=this.direction&&(this.direction=e,this.iter(function(e){return e.order=null}),this.cm&&$r(t=this.cm,function(){Ai(t),Zr(t)}))})}),Co.prototype.eachLine=Co.prototype.iter;var So=0;function Mo(e){var t=this;if(Lo(t),!ot(t,e)&&!_n(t.display,e)){ct(e),s&&(So=+new Date);var n=sr(t,e,!0),r=e.dataTransfer.files;if(n&&!t.isReadOnly())if(r&&r.length&&window.FileReader&&window.File)for(var i=r.length,o=Array(i),a=0,l=function(e,r){if(!t.options.allowDropFileTypes||-1!=B(t.options.allowDropFileTypes,e.type)){var s=new FileReader;s.onload=Gr(t,function(){var e=s.result;if(/[\x00-\x08\x0e-\x1f]{2}/.test(e)&&(e=""),o[r]=e,++a==i){var l={from:n=ke(t.doc,n),to:n,text:t.doc.splitLines(o.join(t.doc.lineSeparator())),origin:"paste"};no(t.doc,l),Ki(t.doc,bi(n,xi(l)))}}),s.readAsText(e)}},c=0;c<i;++c)l(r[c],c);else{if(t.state.draggingText&&t.doc.sel.contains(n)>-1)return t.state.draggingText(e),void setTimeout(function(){return t.display.input.focus()},20);try{var u=e.dataTransfer.getData("Text");if(u){var d;if(t.state.draggingText&&!t.state.draggingText.copy&&(d=t.listSelections()),$i(t.doc,bi(n,n)),d)for(var f=0;f<d.length;++f)so(t.doc,"",d[f].anchor,d[f].head,"drag");t.replaceSelection(u,"around","paste"),t.display.input.focus()}}catch(e){}}}}function Lo(e){e.display.dragCursor&&(e.display.lineSpace.removeChild(e.display.dragCursor),e.display.dragCursor=null)}function 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u in a.wrapper.CodeMirror=this,ui(this),fa(this),t.lineWrapping&&(this.display.wrapper.className+=" CodeMirror-wrap"),Fr(this),this.state={keyMaps:[],overlays:[],modeGen:0,overwrite:!1,delayingBlurEvent:!1,focused:!1,suppressEdits:!1,pasteIncoming:!1,cutIncoming:!1,selectingText:!1,draggingText:!1,highlight:new W,keySeq:null,specialChars:null},t.autofocus&&!y&&a.input.focus(),s&&l<11&&setTimeout(function(){return r.display.input.reset(!0)},20),function(e){var t=e.display;tt(t.scroller,"mousedown",Gr(e,sa)),tt(t.scroller,"dblclick",s&&l<11?Gr(e,function(t){if(!ot(e,t)){var n=sr(e,t);if(n&&!ua(e,t)&&!_n(e.display,t)){ct(t);var r=e.findWordAt(n);Bi(e.doc,r.anchor,r.head)}}}):function(t){return ot(e,t)||ct(t)}),C||tt(t.scroller,"contextmenu",function(t){return da(e,t)});var n,r={end:0};function i(){t.activeTouch&&(n=setTimeout(function(){return t.activeTouch=null},1e3),(r=t.activeTouch).end=+new Date)}function o(e,t){if(null==t.left)return!0;var n=t.left-e.left,r=t.top-e.top;return 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d="",f=0;if(e.options.indentWithTabs)for(var h=Math.floor(c/a);h;--h)f+=a,d+="\t";if(f<c&&(d+=J(c-f)),d!=u)return so(o,d,ge(t,0),ge(t,u.length),"+input"),s.stateAfter=null,!0;for(var p=0;p<o.sel.ranges.length;p++){var m=o.sel.ranges[p];if(m.head.line==t&&m.head.ch<u.length){var g=ge(t,u.length);qi(o,p,new vi(g,g));break}}}ba.defineInitHook=function(e){return xa.push(e)};var _a=null;function ka(e){_a=e}function Ca(e,t,n,r,i){var o=e.doc;e.display.shift=!1,r||(r=o.sel);var a,s=e.state.pasteIncoming||"paste"==i,l=wt(t),c=null;if(s&&r.ranges.length>1)if(_a&&_a.text.join("\n")==t){if(r.ranges.length%_a.text.length==0){c=[];for(var u=0;u<_a.text.length;u++)c.push(o.splitLines(_a.text[u]))}}else l.length==r.ranges.length&&e.options.pasteLinesPerSelection&&(c=Z(l,function(e){return[e]}));for(var d=r.ranges.length-1;d>=0;d--){var 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l=Math.min(e.display.wrapper.clientHeight,window.innerHeight||document.documentElement.clientHeight),c=Math.max(l-.5*tr(e.display),3);i=(n>0?t.bottom:t.top)+n*c}else"line"==r&&(i=n>0?t.bottom+3:t.top-3);for(;(o=Xn(e,s,i)).outside;){if(n<0?i<=0:i>=a.height){o.hitSide=!0;break}i+=5*n}return o}var Ea=function(e){this.cm=e,this.lastAnchorNode=this.lastAnchorOffset=this.lastFocusNode=this.lastFocusOffset=null,this.polling=new W,this.composing=null,this.gracePeriod=!1,this.readDOMTimeout=null};function Da(e,t){var n=Pn(e,t.line);if(!n||n.hidden)return null;var r=le(e.doc,t.line),i=On(n,r,t.line),o=Qe(r,e.doc.direction),a="left";o&&(a=Ze(o,t.ch)%2?"right":"left");var s=In(i.map,t.ch,a);return s.offset="right"==s.collapse?s.end:s.start,s}function ja(e,t){return t&&(e.bad=!0),e}function Na(e,t,n){var r;if(t==e.display.lineDiv){if(!(r=e.display.lineDiv.childNodes[n]))return ja(e.clipPos(ge(e.display.viewTo-1)),!0);t=null,n=0}else for(r=t;;r=r.parentNode){if(!r||r==e.display.lineDiv)return null;if(r.parentNode&&r.parentNode==e.display.lineDiv)break}for(var i=0;i<e.display.view.length;i++){var o=e.display.view[i];if(o.node==r)return Ia(o,t,n)}}function Ia(e,t,n){var r=e.text.firstChild,i=!1;if(!t||!E(r,t))return ja(ge(fe(e.line),0),!0);if(t==r&&(i=!0,t=r.childNodes[n],n=0,!t)){var o=e.rest?X(e.rest):e.line;return ja(ge(fe(o),o.text.length),i)}var a=3==t.nodeType?t:null,s=t;for(a||1!=t.childNodes.length||3!=t.firstChild.nodeType||(a=t.firstChild,n&&(n=a.nodeValue.length));s.parentNode!=r;)s=s.parentNode;var l=e.measure,c=l.maps;function u(t,n,r){for(var i=-1;i<(c?c.length:0);i++)for(var o=i<0?l.map:c[i],a=0;a<o.length;a+=3){var s=o[a+2];if(s==t||s==n){var u=fe(i<0?e.line:e.rest[i]),d=o[a]+r;return(r<0||s!=t)&&(d=o[a+(r?1:0)]),ge(u,d)}}}var d=u(a,s,n);if(d)return ja(d,i);for(var f=s.nextSibling,h=a?a.nodeValue.length-n:0;f;f=f.nextSibling){if(d=u(f,f.firstChild,0))return ja(ge(d.line,d.ch-h),i);h+=f.textContent.length}for(var p=s.previousSibling,m=n;p;p=p.previousSibling){if(d=u(p,p.firstChild,-1))return ja(ge(d.line,d.ch+m),i);m+=p.textContent.length}}Ea.prototype.init=function(e){var t=this,n=this,r=n.cm,i=n.div=e.lineDiv;function o(e){if(!ot(r,e)){if(r.somethingSelected())ka({lineWise:!1,text:r.getSelections()}),"cut"==e.type&&r.replaceSelection("",null,"cut");else{if(!r.options.lineWiseCopyCut)return;var t=La(r);ka({lineWise:!0,text:t.text}),"cut"==e.type&&r.operation(function(){r.setSelections(t.ranges,0,U),r.replaceSelection("",null,"cut")})}if(e.clipboardData){e.clipboardData.clearData();var o=_a.text.join("\n");if(e.clipboardData.setData("Text",o),e.clipboardData.getData("Text")==o)return void e.preventDefault()}var a=Oa(),s=a.firstChild;r.display.lineSpace.insertBefore(a,r.display.lineSpace.firstChild),s.value=_a.text.join("\n");var l=document.activeElement;I(s),setTimeout(function(){r.display.lineSpace.removeChild(a),l.focus(),l==i&&n.showPrimarySelection()},50)}}Ta(i,r.options.spellcheck),tt(i,"paste",function(e){ot(r,e)||Sa(e,r)||l<=11&&setTimeout(Gr(r,function(){return t.updateFromDOM()}),20)}),tt(i,"compositionstart",function(e){t.composing={data:e.data,done:!1}}),tt(i,"compositionupdate",function(e){t.composing||(t.composing={data:e.data,done:!1})}),tt(i,"compositionend",function(e){t.composing&&(e.data!=t.composing.data&&t.readFromDOMSoon(),t.composing.done=!0)}),tt(i,"touchstart",function(){return n.forceCompositionEnd()}),tt(i,"input",function(){t.composing||t.readFromDOMSoon()}),tt(i,"copy",o),tt(i,"cut",o)},Ea.prototype.prepareSelection=function(){var e=ur(this.cm,!1);return e.focus=this.cm.state.focused,e},Ea.prototype.showSelection=function(e,t){e&&this.cm.display.view.length&&((e.focus||t)&&this.showPrimarySelection(),this.showMultipleSelections(e))},Ea.prototype.getSelection=function(){return this.cm.display.wrapper.ownerDocument.getSelection()},Ea.prototype.showPrimarySelection=function(){var e=this.getSelection(),t=this.cm,r=t.doc.sel.primary(),i=r.from(),o=r.to();if(t.display.viewTo==t.display.viewFrom||i.line>=t.display.viewTo||o.line<t.display.viewFrom)e.removeAllRanges();else{var a=Na(t,e.anchorNode,e.anchorOffset),s=Na(t,e.focusNode,e.focusOffset);if(!a||a.bad||!s||s.bad||0!=ve(we(a,s),i)||0!=ve(xe(a,s),o)){var l=t.display.view,c=i.line>=t.display.viewFrom&&Da(t,i)||{node:l[0].measure.map[2],offset:0},u=o.line<t.display.viewTo&&Da(t,o);if(!u){var d=l[l.length-1].measure,f=d.maps?d.maps[d.maps.length-1]:d.map;u={node:f[f.length-1],offset:f[f.length-2]-f[f.length-3]}}if(c&&u){var h,p=e.rangeCount&&e.getRangeAt(0);try{h=M(c.node,c.offset,u.offset,u.node)}catch(e){}h&&(!n&&t.state.focused?(e.collapse(c.node,c.offset),h.collapsed||(e.removeAllRanges(),e.addRange(h))):(e.removeAllRanges(),e.addRange(h)),p&&null==e.anchorNode?e.addRange(p):n&&this.startGracePeriod()),this.rememberSelection()}else e.removeAllRanges()}}},Ea.prototype.startGracePeriod=function(){var e=this;clearTimeout(this.gracePeriod),this.gracePeriod=setTimeout(function(){e.gracePeriod=!1,e.selectionChanged()&&e.cm.operation(function(){return e.cm.curOp.selectionChanged=!0})},20)},Ea.prototype.showMultipleSelections=function(e){O(this.cm.display.cursorDiv,e.cursors),O(this.cm.display.selectionDiv,e.selection)},Ea.prototype.rememberSelection=function(){var e=this.getSelection();this.lastAnchorNode=e.anchorNode,this.lastAnchorOffset=e.anchorOffset,this.lastFocusNode=e.focusNode,this.lastFocusOffset=e.focusOffset},Ea.prototype.selectionInEditor=function(){var e=this.getSelection();if(!e.rangeCount)return!1;var t=e.getRangeAt(0).commonAncestorContainer;return E(this.div,t)},Ea.prototype.focus=function(){"nocursor"!=this.cm.options.readOnly&&(this.selectionInEditor()||this.showSelection(this.prepareSelection(),!0),this.div.focus())},Ea.prototype.blur=function(){this.div.blur()},Ea.prototype.getField=function(){return this.div},Ea.prototype.supportsTouch=function(){return!0},Ea.prototype.receivedFocus=function(){var e=this;this.selectionInEditor()?this.pollSelection():$r(this.cm,function(){return e.cm.curOp.selectionChanged=!0}),this.polling.set(this.cm.options.pollInterval,function t(){e.cm.state.focused&&(e.pollSelection(),e.polling.set(e.cm.options.pollInterval,t))})},Ea.prototype.selectionChanged=function(){var e=this.getSelection();return e.anchorNode!=this.lastAnchorNode||e.anchorOffset!=this.lastAnchorOffset||e.focusNode!=this.lastFocusNode||e.focusOffset!=this.lastFocusOffset},Ea.prototype.pollSelection=function(){if(null==this.readDOMTimeout&&!this.gracePeriod&&this.selectionChanged()){var e=this.getSelection(),t=this.cm;if(v&&d&&this.cm.options.gutters.length&&function(e){for(var t=e;t;t=t.parentNode)if(/CodeMirror-gutter-wrapper/.test(t.className))return!0;return!1}(e.anchorNode))return this.cm.triggerOnKeyDown({type:"keydown",keyCode:8,preventDefault:Math.abs}),this.blur(),void this.focus();if(!this.composing){this.rememberSelection();var n=Na(t,e.anchorNode,e.anchorOffset),r=Na(t,e.focusNode,e.focusOffset);n&&r&&$r(t,function(){Vi(t.doc,bi(n,r),U),(n.bad||r.bad)&&(t.curOp.selectionChanged=!0)})}}},Ea.prototype.pollContent=function(){null!=this.readDOMTimeout&&(clearTimeout(this.readDOMTimeout),this.readDOMTimeout=null);var e,t,n,r=this.cm,i=r.display,o=r.doc.sel.primary(),a=o.from(),s=o.to();if(0==a.ch&&a.line>r.firstLine()&&(a=ge(a.line-1,le(r.doc,a.line-1).length)),s.ch==le(r.doc,s.line).text.length&&s.line<r.lastLine()&&(s=ge(s.line+1,0)),a.line<i.viewFrom||s.line>i.viewTo-1)return!1;a.line==i.viewFrom||0==(e=lr(r,a.line))?(t=fe(i.view[0].line),n=i.view[0].node):(t=fe(i.view[e].line),n=i.view[e-1].node.nextSibling);var l,c,u=lr(r,s.line);if(u==i.view.length-1?(l=i.viewTo-1,c=i.lineDiv.lastChild):(l=fe(i.view[u+1].line)-1,c=i.view[u+1].node.previousSibling),!n)return!1;for(var d=r.doc.splitLines(function(e,t,n,r,i){var o="",a=!1,s=e.doc.lineSeparator(),l=!1;function c(){a&&(o+=s,l&&(o+=s),a=l=!1)}function u(e){e&&(c(),o+=e)}function d(t){if(1==t.nodeType){var n=t.getAttribute("cm-text");if(n)return void u(n);var o,f=t.getAttribute("cm-marker");if(f){var h=e.findMarks(ge(r,0),ge(i+1,0),(g=+f,function(e){return e.id==g}));return void(h.length&&(o=h[0].find(0))&&u(ce(e.doc,o.from,o.to).join(s)))}if("false"==t.getAttribute("contenteditable"))return;var p=/^(pre|div|p|li|table|br)$/i.test(t.nodeName);if(!/^br$/i.test(t.nodeName)&&0==t.textContent.length)return;p&&c();for(var m=0;m<t.childNodes.length;m++)d(t.childNodes[m]);/^(pre|p)$/i.test(t.nodeName)&&(l=!0),p&&(a=!0)}else 3==t.nodeType&&u(t.nodeValue.replace(/\u200b/g,"").replace(/\u00a0/g," "));var g}for(;d(t),t!=n;)t=t.nextSibling,l=!1;return o}(r,n,c,t,l)),f=ce(r.doc,ge(t,0),ge(l,le(r.doc,l).text.length));d.length>1&&f.length>1;)if(X(d)==X(f))d.pop(),f.pop(),l--;else{if(d[0]!=f[0])break;d.shift(),f.shift(),t++}for(var h=0,p=0,m=d[0],g=f[0],v=Math.min(m.length,g.length);h<v&&m.charCodeAt(h)==g.charCodeAt(h);)++h;for(var y=X(d),b=X(f),x=Math.min(y.length-(1==d.length?h:0),b.length-(1==f.length?h:0));p<x&&y.charCodeAt(y.length-p-1)==b.charCodeAt(b.length-p-1);)++p;if(1==d.length&&1==f.length&&t==a.line)for(;h&&h>a.ch&&y.charCodeAt(y.length-p-1)==b.charCodeAt(b.length-p-1);)h--,p++;d[d.length-1]=y.slice(0,y.length-p).replace(/^\u200b+/,""),d[0]=d[0].slice(h).replace(/\u200b+$/,"");var w=ge(t,h),_=ge(l,f.length?X(f).length-p:0);return d.length>1||d[0]||ve(w,_)?(so(r.doc,d,w,_,"+input"),!0):void 0},Ea.prototype.ensurePolled=function(){this.forceCompositionEnd()},Ea.prototype.reset=function(){this.forceCompositionEnd()},Ea.prototype.forceCompositionEnd=function(){this.composing&&(clearTimeout(this.readDOMTimeout),this.composing=null,this.updateFromDOM(),this.div.blur(),this.div.focus())},Ea.prototype.readFromDOMSoon=function(){var e=this;null==this.readDOMTimeout&&(this.readDOMTimeout=setTimeout(function(){if(e.readDOMTimeout=null,e.composing){if(!e.composing.done)return;e.composing=null}e.updateFromDOM()},80))},Ea.prototype.updateFromDOM=function(){var e=this;!this.cm.isReadOnly()&&this.pollContent()||$r(this.cm,function(){return Zr(e.cm)})},Ea.prototype.setUneditable=function(e){e.contentEditable="false"},Ea.prototype.onKeyPress=function(e){0==e.charCode||this.composing||(e.preventDefault(),this.cm.isReadOnly()||Gr(this.cm,Ca)(this.cm,String.fromCharCode(null==e.charCode?e.keyCode:e.charCode),0))},Ea.prototype.readOnlyChanged=function(e){this.div.contentEditable=String("nocursor"!=e)},Ea.prototype.onContextMenu=function(){},Ea.prototype.resetPosition=function(){},Ea.prototype.needsContentAttribute=!0;var Ra=function(e){this.cm=e,this.prevInput="",this.pollingFast=!1,this.polling=new W,this.hasSelection=!1,this.composing=null};Ra.prototype.init=function(e){var t=this,n=this,r=this.cm;this.createField(e);var i=this.textarea;function o(e){if(!ot(r,e)){if(r.somethingSelected())ka({lineWise:!1,text:r.getSelections()});else{if(!r.options.lineWiseCopyCut)return;var t=La(r);ka({lineWise:!0,text:t.text}),"cut"==e.type?r.setSelections(t.ranges,null,U):(n.prevInput="",i.value=t.text.join("\n"),I(i))}"cut"==e.type&&(r.state.cutIncoming=!0)}}e.wrapper.insertBefore(this.wrapper,e.wrapper.firstChild),g&&(i.style.width="0px"),tt(i,"input",function(){s&&l>=9&&t.hasSelection&&(t.hasSelection=null),n.poll()}),tt(i,"paste",function(e){ot(r,e)||Sa(e,r)||(r.state.pasteIncoming=!0,n.fastPoll())}),tt(i,"cut",o),tt(i,"copy",o),tt(e.scroller,"paste",function(t){_n(e,t)||ot(r,t)||(r.state.pasteIncoming=!0,n.focus())}),tt(e.lineSpace,"selectstart",function(t){_n(e,t)||ct(t)}),tt(i,"compositionstart",function(){var e=r.getCursor("from");n.composing&&n.composing.range.clear(),n.composing={start:e,range:r.markText(e,r.getCursor("to"),{className:"CodeMirror-composing"})}}),tt(i,"compositionend",function(){n.composing&&(n.poll(),n.composing.range.clear(),n.composing=null)})},Ra.prototype.createField=function(e){this.wrapper=Oa(),this.textarea=this.wrapper.firstChild},Ra.prototype.prepareSelection=function(){var e=this.cm,t=e.display,n=e.doc,r=ur(e);if(e.options.moveInputWithCursor){var i=$n(e,n.sel.primary().head,"div"),o=t.wrapper.getBoundingClientRect(),a=t.lineDiv.getBoundingClientRect();r.teTop=Math.max(0,Math.min(t.wrapper.clientHeight-10,i.top+a.top-o.top)),r.teLeft=Math.max(0,Math.min(t.wrapper.clientWidth-10,i.left+a.left-o.left))}return r},Ra.prototype.showSelection=function(e){var t=this.cm.display;O(t.cursorDiv,e.cursors),O(t.selectionDiv,e.selection),null!=e.teTop&&(this.wrapper.style.top=e.teTop+"px",this.wrapper.style.left=e.teLeft+"px")},Ra.prototype.reset=function(e){if(!this.contextMenuPending&&!this.composing){var t=this.cm;if(t.somethingSelected()){this.prevInput="";var n=t.getSelection();this.textarea.value=n,t.state.focused&&I(this.textarea),s&&l>=9&&(this.hasSelection=n)}else e||(this.prevInput=this.textarea.value="",s&&l>=9&&(this.hasSelection=null))}},Ra.prototype.getField=function(){return this.textarea},Ra.prototype.supportsTouch=function(){return!1},Ra.prototype.focus=function(){if("nocursor"!=this.cm.options.readOnly&&(!y||D()!=this.textarea))try{this.textarea.focus()}catch(e){}},Ra.prototype.blur=function(){this.textarea.blur()},Ra.prototype.resetPosition=function(){this.wrapper.style.top=this.wrapper.style.left=0},Ra.prototype.receivedFocus=function(){this.slowPoll()},Ra.prototype.slowPoll=function(){var e=this;this.pollingFast||this.polling.set(this.cm.options.pollInterval,function(){e.poll(),e.cm.state.focused&&e.slowPoll()})},Ra.prototype.fastPoll=function(){var e=!1,t=this;t.pollingFast=!0,t.polling.set(20,function n(){t.poll()||e?(t.pollingFast=!1,t.slowPoll()):(e=!0,t.polling.set(60,n))})},Ra.prototype.poll=function(){var e=this,t=this.cm,n=this.textarea,r=this.prevInput;if(this.contextMenuPending||!t.state.focused||_t(n)&&!r&&!this.composing||t.isReadOnly()||t.options.disableInput||t.state.keySeq)return!1;var i=n.value;if(i==r&&!t.somethingSelected())return!1;if(s&&l>=9&&this.hasSelection===i||b&&/[\uf700-\uf7ff]/.test(i))return t.display.input.reset(),!1;if(t.doc.sel==t.display.selForContextMenu){var o=i.charCodeAt(0);if(8203!=o||r||(r="​"),8666==o)return this.reset(),this.cm.execCommand("undo")}for(var a=0,c=Math.min(r.length,i.length);a<c&&r.charCodeAt(a)==i.charCodeAt(a);)++a;return $r(t,function(){Ca(t,i.slice(a),r.length-a,null,e.composing?"*compose":null),i.length>1e3||i.indexOf("\n")>-1?n.value=e.prevInput="":e.prevInput=i,e.composing&&(e.composing.range.clear(),e.composing.range=t.markText(e.composing.start,t.getCursor("to"),{className:"CodeMirror-composing"}))}),!0},Ra.prototype.ensurePolled=function(){this.pollingFast&&this.poll()&&(this.pollingFast=!1)},Ra.prototype.onKeyPress=function(){s&&l>=9&&(this.hasSelection=null),this.fastPoll()},Ra.prototype.onContextMenu=function(e){var t=this,n=t.cm,r=n.display,i=t.textarea,o=sr(n,e),a=r.scroller.scrollTop;if(o&&!f){n.options.resetSelectionOnContextMenu&&-1==n.doc.sel.contains(o)&&Gr(n,Vi)(n.doc,bi(o),U);var u=i.style.cssText,d=t.wrapper.style.cssText;t.wrapper.style.cssText="position: absolute";var h,p=t.wrapper.getBoundingClientRect();i.style.cssText="position: absolute; width: 30px; height: 30px;\n      top: "+(e.clientY-p.top-5)+"px; left: "+(e.clientX-p.left-5)+"px;\n      z-index: 1000; background: "+(s?"rgba(255, 255, 255, .05)":"transparent")+";\n      outline: none; border-width: 0; outline: none; overflow: hidden; opacity: .05; filter: alpha(opacity=5);",c&&(h=window.scrollY),r.input.focus(),c&&window.scrollTo(null,h),r.input.reset(),n.somethingSelected()||(i.value=t.prevInput=" "),t.contextMenuPending=!0,r.selForContextMenu=n.doc.sel,clearTimeout(r.detectingSelectAll),s&&l>=9&&m(),C?(ft(e),tt(window,"mouseup",function e(){rt(window,"mouseup",e),setTimeout(g,20)})):setTimeout(g,50)}function m(){if(null!=i.selectionStart){var e=n.somethingSelected(),o="​"+(e?i.value:"");i.value="⇚",i.value=o,t.prevInput=e?"":"​",i.selectionStart=1,i.selectionEnd=o.length,r.selForContextMenu=n.doc.sel}}function g(){if(t.contextMenuPending=!1,t.wrapper.style.cssText=d,i.style.cssText=u,s&&l<9&&r.scrollbars.setScrollTop(r.scroller.scrollTop=a),null!=i.selectionStart){(!s||s&&l<9)&&m();var e=0;r.detectingSelectAll=setTimeout(function o(){r.selForContextMenu==n.doc.sel&&0==i.selectionStart&&i.selectionEnd>0&&"​"==t.prevInput?Gr(n,eo)(n):e++<10?r.detectingSelectAll=setTimeout(o,500):(r.selForContextMenu=null,r.input.reset())},200)}}},Ra.prototype.readOnlyChanged=function(e){e||this.reset(),this.textarea.disabled="nocursor"==e},Ra.prototype.setUneditable=function(){},Ra.prototype.needsContentAttribute=!1,function(e){var t=e.optionHandlers;function n(n,r,i,o){e.defaults[n]=r,i&&(t[n]=o?function(e,t,n){n!=ha&&i(e,t,n)}:i)}e.defineOption=n,e.Init=ha,n("value","",function(e,t){return e.setValue(t)},!0),n("mode",null,function(e,t){e.doc.modeOption=t,Ci(e)},!0),n("indentUnit",2,Ci,!0),n("indentWithTabs",!1),n("smartIndent",!0),n("tabSize",4,function(e){Si(e),Wn(e),Zr(e)},!0),n("lineSeparator",null,function(e,t){if(e.doc.lineSep=t,t){var n=[],r=e.doc.first;e.doc.iter(function(e){for(var i=0;;){var o=e.text.indexOf(t,i);if(-1==o)break;i=o+t.length,n.push(ge(r,o))}r++});for(var i=n.length-1;i>=0;i--)so(e.doc,t,n[i],ge(n[i].line,n[i].ch+t.length))}}),n("specialChars",/[\u0000-\u001f\u007f-\u009f\u00ad\u061c\u200b-\u200f\u2028\u2029\ufeff]/g,function(e,t,n){e.state.specialChars=new RegExp(t.source+(t.test("\t")?"":"|\t"),"g"),n!=ha&&e.refresh()}),n("specialCharPlaceholder",Yt,function(e){return e.refresh()},!0),n("electricChars",!0),n("inputStyle",y?"contenteditable":"textarea",function(){throw new Error("inputStyle can not (yet) be changed in a running editor")},!0),n("spellcheck",!1,function(e,t){return e.getInputField().spellcheck=t},!0),n("rtlMoveVisually",!w),n("wholeLineUpdateBefore",!0),n("theme","default",function(e){fa(e),ga(e)},!0),n("keyMap","default",function(e,t,n){var r=Bo(t),i=n!=ha&&Bo(n);i&&i.detach&&i.detach(e,r),r.attach&&r.attach(e,i||null)}),n("extraKeys",null),n("configureMouse",null),n("lineWrapping",!1,ya,!0),n("gutters",[],function(e){di(e.options),ga(e)},!0),n("fixedGutter",!0,function(e,t){e.display.gutters.style.left=t?ir(e.display)+"px":"0",e.refresh()},!0),n("coverGutterNextToScrollbar",!1,function(e){return Ir(e)},!0),n("scrollbarStyle","native",function(e){Fr(e),Ir(e),e.display.scrollbars.setScrollTop(e.doc.scrollTop),e.display.scrollbars.setScrollLeft(e.doc.scrollLeft)},!0),n("lineNumbers",!1,function(e){di(e.options),ga(e)},!0),n("firstLineNumber",1,ga,!0),n("lineNumberFormatter",function(e){return e},ga,!0),n("showCursorWhenSelecting",!1,cr,!0),n("resetSelectionOnContextMenu",!0),n("lineWiseCopyCut",!0),n("pasteLinesPerSelection",!0),n("readOnly",!1,function(e,t){"nocursor"==t&&(yr(e),e.display.input.blur()),e.display.input.readOnlyChanged(t)}),n("disableInput",!1,function(e,t){t||e.display.input.reset()},!0),n("dragDrop",!0,va),n("allowDropFileTypes",null),n("cursorBlinkRate",530),n("cursorScrollMargin",0),n("cursorHeight",1,cr,!0),n("singleCursorHeightPerLine",!0,cr,!0),n("workTime",100),n("workDelay",100),n("flattenSpans",!0,Si,!0),n("addModeClass",!1,Si,!0),n("pollInterval",100),n("undoDepth",200,function(e,t){return e.doc.history.undoDepth=t}),n("historyEventDelay",1250),n("viewportMargin",10,function(e){return e.refresh()},!0),n("maxHighlightLength",1e4,Si,!0),n("moveInputWithCursor",!0,function(e,t){t||e.display.input.resetPosition()}),n("tabindex",null,function(e,t){return e.display.input.getField().tabIndex=t||""}),n("autofocus",null),n("direction","ltr",function(e,t){return e.doc.setDirection(t)},!0)}(ba),function(e){var t=e.optionHandlers,n=e.helpers={};e.prototype={constructor:e,focus:function(){window.focus(),this.display.input.focus()},setOption:function(e,n){var r=this.options,i=r[e];r[e]==n&&"mode"!=e||(r[e]=n,t.hasOwnProperty(e)&&Gr(this,t[e])(this,n,i),it(this,"optionChange",this,e))},getOption:function(e){return this.options[e]},getDoc:function(){return this.doc},addKeyMap:function(e,t){this.state.keyMaps[t?"push":"unshift"](Bo(e))},removeKeyMap:function(e){for(var t=this.state.keyMaps,n=0;n<t.length;++n)if(t[n]==e||t[n].name==e)return t.splice(n,1),!0},addOverlay:Jr(function(t,n){var r=t.token?t:e.getMode(this.options,t);if(r.startState)throw new Error("Overlays may not be stateful.");!function(e,t,n){for(var r=0,i=n(t);r<e.length&&n(e[r])<=i;)r++;e.splice(r,0,t)}(this.state.overlays,{mode:r,modeSpec:t,opaque:n&&n.opaque,priority:n&&n.priority||0},function(e){return e.priority}),this.state.modeGen++,Zr(this)}),removeOverlay:Jr(function(e){for(var t=this.state.overlays,n=0;n<t.length;++n){var r=t[n].modeSpec;if(r==e||"string"==typeof e&&r.name==e)return t.splice(n,1),this.state.modeGen++,void Zr(this)}}),indentLine:Jr(function(e,t,n){"string"!=typeof t&&"number"!=typeof t&&(t=null==t?this.options.smartIndent?"smart":"prev":t?"add":"subtract"),pe(this.doc,e)&&wa(this,e,t,n)}),indentSelection:Jr(function(e){for(var t=this.doc.sel.ranges,n=-1,r=0;r<t.length;r++){var i=t[r];if(i.empty())i.head.line>n&&(wa(this,i.head.line,e,!0),n=i.head.line,r==this.doc.sel.primIndex&&Mr(this));else{var o=i.from(),a=i.to(),s=Math.max(n,o.line);n=Math.min(this.lastLine(),a.line-(a.ch?0:1))+1;for(var l=s;l<n;++l)wa(this,l,e);var c=this.doc.sel.ranges;0==o.ch&&t.length==c.length&&c[r].from().ch>0&&qi(this.doc,r,new 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this._ranks[e],this._types=null},e.prototype.getRank=function(e){var t=this._ranks[e];return t&&t.rank},e.prototype.setRank=function(e,t){if(this._ranks[e]){var n=this._id++;this._ranks[e]={rank:t,id:n},this._types=null}},e}();t.RenderMimeRegistry=c,function(e){var t=function(){function e(e){this._session=e.session,this._contents=e.contents}return e.prototype.resolveUrl=function(e){if(i.URLExt.isLocal(e)){var t=i.PathExt.dirname(this._session.path);e=i.PathExt.resolve(t,e)}return Promise.resolve(e)},e.prototype.getDownloadUrl=function(e){return i.URLExt.isLocal(e)?this._contents.getDownloadUrl(e):Promise.resolve(e)},e}();e.UrlResolver=t}(c=t.RenderMimeRegistry||(t.RenderMimeRegistry={})),t.RenderMimeRegistry=c,(l||(l={})).sortedTypes=function(e){return Object.keys(e).sort(function(t,n){var r=e[t],i=e[n];return r.rank!==i.rank?r.rank-i.rank:r.id-i.id})}},function(e,t,n){"use strict";Object.defineProperty(t,"__esModule",{value:!0});var r,i=n(5),o=n(2),a=n(4),s=n(25),l=function(){function 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t={};if(a.nbformat.isExecuteResult(e)||a.nbformat.isDisplayData(e)||a.nbformat.isDisplayUpdate(e))t=e.data;else if(a.nbformat.isStream(e))"stderr"===e.name?t["application/vnd.jupyter.stderr"]=e.text:t["application/vnd.jupyter.stdout"]=e.text;else if(a.nbformat.isError(e)){var n=e.traceback.join("\n");t["application/vnd.jupyter.stderr"]=n||e.ename+": "+e.evalue}return function(e){var t=Object.create(null);for(var n in e){var r=e[n];i.JSONExt.isArray(r)?r=r.join("\n"):i.JSONExt.isPrimitive(r)||(r=JSON.parse(JSON.stringify(r))),t[n]=r}return t}(t)}function n(e){var t=Object.create(null);if(a.nbformat.isExecuteResult(e)||a.nbformat.isDisplayData(e))for(var n in e.metadata)t[n]=r(e.metadata,n);return t}function r(e,t){var n=e[t];return i.JSONExt.isPrimitive(n)?n:JSON.parse(JSON.stringify(n))}e.getData=t,e.getMetadata=n,e.getBundleOptions=function(e){return{data:t(e.value),metadata:n(e.value),trusted:!!e.trusted}},e.extract=r}(r||(r={}))},function(e,t,n){var r=n(18);"string"==typeof r&&(r=[[e.i,r,""]]);n(17)(r,{singleton:!0,insertAt:"top",attrs:{"data-added-by":"juniper"},hmr:!0,transform:void 0,insertInto:void 0}),r.locals&&(e.exports=r.locals)},function(e,t,n){"use strict";(function(e){var r,i,o,a,s="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e};a=function(e){var t=e.commands,n=e.Pos;function r(t,r){t.extendSelectionsBy(function(i){return t.display.shift||t.doc.extend||i.empty()?function(t,r,i){if(i<0&&0==r.ch)return t.clipPos(n(r.line-1));var o=t.getLine(r.line);if(i>0&&r.ch>=o.length)return t.clipPos(n(r.line+1,0));for(var a,s="start",l=r.ch,c=i<0?0:o.length,u=0;l!=c;l+=i,u++){var d=o.charAt(i<0?l-1:l),f="_"!=d&&e.isWordChar(d)?"w":"o";if("w"==f&&d.toUpperCase()==d&&(f="W"),"start"==s)"o"!=f&&(s="in",a=f);else if("in"==s&&a!=f){if("w"==a&&"W"==f&&i<0&&l--,"W"==a&&"w"==f&&i>0){a="w";continue}break}}return n(r.line,l)}(t.doc,i.head,r):r<0?i.from():i.to()})}function i(t,r){if(t.isReadOnly())return e.Pass;t.operation(function(){for(var e=t.listSelections().length,i=[],o=-1,a=0;a<e;a++){var s=t.listSelections()[a].head;if(!(s.line<=o)){var l=n(s.line+(r?0:1),0);t.replaceRange("\n",l,null,"+insertLine"),t.indentLine(l.line,null,!0),i.push({head:l,anchor:l}),o=s.line+1}}t.setSelections(i)}),t.execCommand("indentAuto")}function o(t,r){for(var i=r.ch,o=i,a=t.getLine(r.line);i&&e.isWordChar(a.charAt(i-1));)--i;for(;o<a.length&&e.isWordChar(a.charAt(o));)++o;return{from:n(r.line,i),to:n(r.line,o),word:a.slice(i,o)}}function a(e,t){for(var n=e.listSelections(),r=[],i=0;i<n.length;i++){var o=n[i],a=e.findPosV(o.anchor,t,"line",o.anchor.goalColumn),s=e.findPosV(o.head,t,"line",o.head.goalColumn);a.goalColumn=null!=o.anchor.goalColumn?o.anchor.goalColumn:e.cursorCoords(o.anchor,"div").left,s.goalColumn=null!=o.head.goalColumn?o.head.goalColumn:e.cursorCoords(o.head,"div").left;var l={anchor:a,head:s};r.push(o),r.push(l)}e.setSelections(r)}t.goSubwordLeft=function(e){r(e,-1)},t.goSubwordRight=function(e){r(e,1)},t.scrollLineUp=function(e){var t=e.getScrollInfo();if(!e.somethingSelected()){var n=e.lineAtHeight(t.top+t.clientHeight,"local");e.getCursor().line>=n&&e.execCommand("goLineUp")}e.scrollTo(null,t.top-e.defaultTextHeight())},t.scrollLineDown=function(e){var t=e.getScrollInfo();if(!e.somethingSelected()){var n=e.lineAtHeight(t.top,"local")+1;e.getCursor().line<=n&&e.execCommand("goLineDown")}e.scrollTo(null,t.top+e.defaultTextHeight())},t.splitSelectionByLine=function(e){for(var t=e.listSelections(),r=[],i=0;i<t.length;i++)for(var o=t[i].from(),a=t[i].to(),s=o.line;s<=a.line;++s)a.line>o.line&&s==a.line&&0==a.ch||r.push({anchor:s==o.line?o:n(s,0),head:s==a.line?a:n(s)});e.setSelections(r,0)},t.singleSelectionTop=function(e){var t=e.listSelections()[0];e.setSelection(t.anchor,t.head,{scroll:!1})},t.selectLine=function(e){for(var t=e.listSelections(),r=[],i=0;i<t.length;i++){var o=t[i];r.push({anchor:n(o.from().line,0),head:n(o.to().line+1,0)})}e.setSelections(r)},t.insertLineAfter=function(e){return i(e,!1)},t.insertLineBefore=function(e){return i(e,!0)},t.selectNextOccurrence=function(t){var r=t.getCursor("from"),i=t.getCursor("to"),a=t.state.sublimeFindFullWord==t.doc.sel;if(0==e.cmpPos(r,i)){var s=o(t,r);if(!s.word)return;t.setSelection(s.from,s.to),a=!0}else{var l=t.getRange(r,i),c=a?new RegExp("\\b"+l+"\\b"):l,u=t.getSearchCursor(c,i),d=u.findNext();if(d||(d=(u=t.getSearchCursor(c,n(t.firstLine(),0))).findNext()),!d||function(e,t,n){for(var r=0;r<e.length;r++)if(e[r].from()==t&&e[r].to()==n)return!0;return!1}(t.listSelections(),u.from(),u.to()))return e.Pass;t.addSelection(u.from(),u.to())}a&&(t.state.sublimeFindFullWord=t.doc.sel)},t.addCursorToPrevLine=function(e){a(e,-1)},t.addCursorToNextLine=function(e){a(e,1)};var s="(){}[]";function l(t){for(var r=t.listSelections(),i=[],o=0;o<r.length;o++){var a=r[o],l=a.head,c=t.scanForBracket(l,-1);if(!c)return!1;for(;;){var u=t.scanForBracket(l,1);if(!u)return!1;if(u.ch==s.charAt(s.indexOf(c.ch)+1)){var d=n(c.pos.line,c.pos.ch+1);if(0!=e.cmpPos(d,a.from())||0!=e.cmpPos(u.pos,a.to())){i.push({anchor:d,head:u.pos});break}if(!(c=t.scanForBracket(c.pos,-1)))return!1}l=n(u.pos.line,u.pos.ch+1)}}return t.setSelections(i),!0}function c(t,r){if(t.isReadOnly())return e.Pass;for(var i,o=t.listSelections(),a=[],s=0;s<o.length;s++){var l=o[s];if(!l.empty()){for(var c=l.from().line,u=l.to().line;s<o.length-1&&o[s+1].from().line==u;)u=o[++s].to().line;o[s].to().ch||u--,a.push(c,u)}}a.length?i=!0:a.push(t.firstLine(),t.lastLine()),t.operation(function(){for(var e=[],o=0;o<a.length;o+=2){var s=a[o],l=a[o+1],c=n(s,0),u=n(l),d=t.getRange(c,u,!1);r?d.sort():d.sort(function(e,t){var n=e.toUpperCase(),r=t.toUpperCase();return n!=r&&(e=n,t=r),e<t?-1:e==t?0:1}),t.replaceRange(d,c,u),i&&e.push({anchor:c,head:n(l+1,0)})}i&&t.setSelections(e,0)})}function u(t,n){t.operation(function(){for(var r=t.listSelections(),i=[],a=[],s=0;s<r.length;s++)(c=r[s]).empty()?(i.push(s),a.push("")):a.push(n(t.getRange(c.from(),c.to())));var l;for(t.replaceSelections(a,"around","case"),s=i.length-1;s>=0;s--){var c=r[i[s]];if(!(l&&e.cmpPos(c.head,l)>0)){var u=o(t,c.head);l=u.from,t.replaceRange(n(u.word),u.from,u.to)}}})}function d(t){var n=t.getCursor("from"),r=t.getCursor("to");if(0==e.cmpPos(n,r)){var i=o(t,n);if(!i.word)return;n=i.from,r=i.to}return{from:n,to:r,query:t.getRange(n,r),word:i}}function f(e,t){var r=d(e);if(r){var i=r.query,o=e.getSearchCursor(i,t?r.to:r.from);(t?o.findNext():o.findPrevious())?e.setSelection(o.from(),o.to()):(o=e.getSearchCursor(i,t?n(e.firstLine(),0):e.clipPos(n(e.lastLine()))),(t?o.findNext():o.findPrevious())?e.setSelection(o.from(),o.to()):r.word&&e.setSelection(r.from,r.to))}}t.selectScope=function(e){l(e)||e.execCommand("selectAll")},t.selectBetweenBrackets=function(t){if(!l(t))return e.Pass},t.goToBracket=function(t){t.extendSelectionsBy(function(r){var i=t.scanForBracket(r.head,1);if(i&&0!=e.cmpPos(i.pos,r.head))return i.pos;var o=t.scanForBracket(r.head,-1);return o&&n(o.pos.line,o.pos.ch+1)||r.head})},t.swapLineUp=function(t){if(t.isReadOnly())return e.Pass;for(var r=t.listSelections(),i=[],o=t.firstLine()-1,a=[],s=0;s<r.length;s++){var l=r[s],c=l.from().line-1,u=l.to().line;a.push({anchor:n(l.anchor.line-1,l.anchor.ch),head:n(l.head.line-1,l.head.ch)}),0!=l.to().ch||l.empty()||--u,c>o?i.push(c,u):i.length&&(i[i.length-1]=u),o=u}t.operation(function(){for(var e=0;e<i.length;e+=2){var r=i[e],o=i[e+1],s=t.getLine(r);t.replaceRange("",n(r,0),n(r+1,0),"+swapLine"),o>t.lastLine()?t.replaceRange("\n"+s,n(t.lastLine()),null,"+swapLine"):t.replaceRange(s+"\n",n(o,0),null,"+swapLine")}t.setSelections(a),t.scrollIntoView()})},t.swapLineDown=function(t){if(t.isReadOnly())return e.Pass;for(var r=t.listSelections(),i=[],o=t.lastLine()+1,a=r.length-1;a>=0;a--){var s=r[a],l=s.to().line+1,c=s.from().line;0!=s.to().ch||s.empty()||l--,l<o?i.push(l,c):i.length&&(i[i.length-1]=c),o=c}t.operation(function(){for(var e=i.length-2;e>=0;e-=2){var r=i[e],o=i[e+1],a=t.getLine(r);r==t.lastLine()?t.replaceRange("",n(r-1),n(r),"+swapLine"):t.replaceRange("",n(r,0),n(r+1,0),"+swapLine"),t.replaceRange(a+"\n",n(o,0),null,"+swapLine")}t.scrollIntoView()})},t.toggleCommentIndented=function(e){e.toggleComment({indent:!0})},t.joinLines=function(e){for(var t=e.listSelections(),r=[],i=0;i<t.length;i++){for(var o=t[i],a=o.from(),s=a.line,l=o.to().line;i<t.length-1&&t[i+1].from().line==l;)l=t[++i].to().line;r.push({start:s,end:l,anchor:!o.empty()&&a})}e.operation(function(){for(var t=0,i=[],o=0;o<r.length;o++){for(var a,s=r[o],l=s.anchor&&n(s.anchor.line-t,s.anchor.ch),c=s.start;c<=s.end;c++){var u=c-t;c==s.end&&(a=n(u,e.getLine(u).length+1)),u<e.lastLine()&&(e.replaceRange(" 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i=t[r].from(),o=t[r].to(),a=t[r].empty()?e.findMarksAt(i):e.findMarks(i,o),s=0;s<a.length;s++)if(a[s].sublimeBookmark){a[s].clear();for(var l=0;l<n.length;l++)n[l]==a[s]&&n.splice(l--,1);break}s==a.length&&n.push(e.markText(i,o,{sublimeBookmark:!0,clearWhenEmpty:!1}))}},t.clearBookmarks=function(e){var t=e.state.sublimeBookmarks;if(t)for(var n=0;n<t.length;n++)t[n].clear();t.length=0},t.selectBookmarks=function(e){var t=e.state.sublimeBookmarks,n=[];if(t)for(var r=0;r<t.length;r++){var i=t[r].find();i?n.push({anchor:i.from,head:i.to}):t.splice(r--,0)}n.length&&e.setSelections(n,0)},t.smartBackspace=function(t){if(t.somethingSelected())return e.Pass;t.operation(function(){for(var r=t.listSelections(),i=t.getOption("indentUnit"),o=r.length-1;o>=0;o--){var a=r[o].head,s=t.getRange({line:a.line,ch:0},a),l=e.countColumn(s,null,t.getOption("tabSize")),c=t.findPosH(a,-1,"char",!1);if(s&&!/\S/.test(s)&&l%i==0){var u=new n(a.line,e.findColumn(s,l-i,i));u.ch!=a.ch&&(c=u)}t.replaceRange("",c,a,"+delete")}})},t.delLineRight=function(e){e.operation(function(){for(var t=e.listSelections(),r=t.length-1;r>=0;r--)e.replaceRange("",t[r].anchor,n(t[r].to().line),"+delete");e.scrollIntoView()})},t.upcaseAtCursor=function(e){u(e,function(e){return e.toUpperCase()})},t.downcaseAtCursor=function(e){u(e,function(e){return e.toLowerCase()})},t.setSublimeMark=function(e){e.state.sublimeMark&&e.state.sublimeMark.clear(),e.state.sublimeMark=e.setBookmark(e.getCursor())},t.selectToSublimeMark=function(e){var t=e.state.sublimeMark&&e.state.sublimeMark.find();t&&e.setSelection(e.getCursor(),t)},t.deleteToSublimeMark=function(t){var n=t.state.sublimeMark&&t.state.sublimeMark.find();if(n){var r=t.getCursor(),i=n;if(e.cmpPos(r,i)>0){var o=i;i=r,r=o}t.state.sublimeKilled=t.getRange(r,i),t.replaceRange("",r,i)}},t.swapWithSublimeMark=function(e){var t=e.state.sublimeMark&&e.state.sublimeMark.find();t&&(e.state.sublimeMark.clear(),e.state.sublimeMark=e.setBookmark(e.getCursor()),e.setCursor(t))},t.sublimeYank=function(e){null!=e.state.sublimeKilled&&e.replaceSelection(e.state.sublimeKilled,null,"paste")},t.showInCenter=function(e){var t=e.cursorCoords(null,"local");e.scrollTo(null,(t.top+t.bottom)/2-e.getScrollInfo().clientHeight/2)},t.findUnder=function(e){f(e,!0)},t.findUnderPrevious=function(e){f(e,!1)},t.findAllUnder=function(e){var t=d(e);if(t){for(var n=e.getSearchCursor(t.query),r=[],i=-1;n.findNext();)r.push({anchor:n.from(),head:n.to()}),n.from().line<=t.from.line&&n.from().ch<=t.from.ch&&i++;e.setSelections(r,i)}};var 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this.ansi_to(e,this.textFormatter)},e.prototype.with_state=function(e){return{bold:this.bold,fg:this.fg,bg:this.bg,text:e}},e.prototype.handle_incomplete_sequences=function(e){var t=e[e.length-1];t.length>0&&this.detect_incomplete_ansi(t)?(this._buffer="["+t,e.pop(),e.push("")):(""===t.slice(-1)&&(this._buffer="",console.log("raw",e),e.pop(),e.push(t.substr(0,t.length-1)),console.log(e),console.log(t)),2===e.length&&""===e[1]&&""===e[0].slice(-1)&&(this._buffer="",t=e.shift(),e.unshift(t.substr(0,t.length-1))))},e.prototype.process_ansi=function(e){this._sgr_regex||(this._sgr_regex=((r=["\n            ^                           # beginning of line\n            ([!<-?]?)             # a private-mode char (!, <, =, >, ?)\n            ([d;]*)                    # any digits or semicolons\n            ([ -/]?               # an intermediate modifier\n            [@-~])                # the command\n            ([sS]*)                   # any text following this CSI sequence\n          "]).raw=["\n            ^                           # beginning of line\n            ([!\\x3c-\\x3f]?)             # a private-mode char (!, <, =, >, ?)\n            ([\\d;]*)                    # any digits or semicolons\n            ([\\x20-\\x2f]?               # an intermediate modifier\n            [\\x40-\\x7e])                # the command\n            ([\\s\\S]*)                   # any text following this CSI sequence\n          "],function(e){for(var t=[],n=1;n<arguments.length;n++)t[n-1]=arguments[n];var r=e.raw[0].replace(/^\s+|\s+\n|\s+#[\s\S]+?\n/gm,"");return new RegExp(r,"m")}(r)));var t=e.match(this._sgr_regex);if(!t)return this.with_state(e);var n=t[4];if(""!==t[1]||"m"!==t[3])return this.with_state(n);for(var r,i=t[2].split(";");i.length>0;){var o=i.shift(),a=parseInt(o,10);if(isNaN(a)||0===a)this.fg=this.bg=null,this.bold=!1;else if(1===a)this.bold=!0;else if(22===a)this.bold=!1;else if(39===a)this.fg=null;else if(49===a)this.bg=null;else if(a>=30&&a<38)this.fg=this.ansi_colors[0][a-30];else if(a>=40&&a<48)this.bg=this.ansi_colors[0][a-40];else if(a>=90&&a<98)this.fg=this.ansi_colors[1][a-90];else if(a>=100&&a<108)this.bg=this.ansi_colors[1][a-100];else if((38===a||48===a)&&i.length>0){var s=38===a,l=i.shift();if("5"===l&&i.length>0){var c=parseInt(i.shift(),10);c>=0&&c<=255&&(s?this.fg=this.palette_256[c]:this.bg=this.palette_256[c])}if("2"===l&&i.length>2){var u=parseInt(i.shift(),10),d=parseInt(i.shift(),10),f=parseInt(i.shift(),10);if(u>=0&&u<=255&&d>=0&&d<=255&&f>=0&&f<=255){var h={rgb:[u,d,f],class_name:"truecolor"};s?this.fg=h:this.bg=h}}}}return this.with_state(n)},e}();Object.defineProperty(e,"__esModule",{value:!0}),e.default=t})?r.apply(t,[t]):r)||(e.exports=i)},function(e,t,n){"use strict";Object.defineProperty(t,"__esModule",{value:!0});var r=n(38);t.htmlRendererFactory={safe:!0,mimeTypes:["text/html"],defaultRank:50,createRenderer:function(e){return new r.RenderedHTML(e)}},t.imageRendererFactory={safe:!0,mimeTypes:["image/bmp","image/png","image/jpeg","image/gif"],defaultRank:90,createRenderer:function(e){return new r.RenderedImage(e)}},t.latexRendererFactory={safe:!0,mimeTypes:["text/latex"],defaultRank:70,createRenderer:function(e){return new r.RenderedLatex(e)}},t.markdownRendererFactory={safe:!0,mimeTypes:["text/markdown"],defaultRank:60,createRenderer:function(e){return new r.RenderedMarkdown(e)}},t.svgRendererFactory={safe:!1,mimeTypes:["image/svg+xml"],defaultRank:80,createRenderer:function(e){return new r.RenderedSVG(e)}},t.textRendererFactory={safe:!0,mimeTypes:["text/plain","application/vnd.jupyter.stdout","application/vnd.jupyter.stderr"],defaultRank:120,createRenderer:function(e){return new r.RenderedText(e)}},t.javaScriptRendererFactory={safe:!1,mimeTypes:["text/javascript","application/javascript"],defaultRank:110,createRenderer:function(e){return new r.RenderedJavaScript(e)}},t.standardRendererFactories=[t.htmlRendererFactory,t.markdownRendererFactory,t.latexRendererFactory,t.svgRendererFactory,t.imageRendererFactory,t.javaScriptRendererFactory,t.textRendererFactory]},function(e,t,n){var r=n(18);"string"==typeof r&&(r=[[e.i,r,""]]);n(17)(r,{singleton:!0,insertAt:"top",attrs:{"data-added-by":"juniper"},hmr:!0,transform:void 0,insertInto:void 0}),r.locals&&(e.exports=r.locals)},function(e,t,n){"use strict";Object.defineProperty(t,"__esModule",{value:!0});var r=n(8),i=n(2),o=n(5),a=n(24),s=n(43),l=n(42),c=n(41),u=function(){function e(e){void 0===e&&(e=null),this._value=null,this._changed=new i.Signal(this),this._isDisposed=!1,this._value=e}return Object.defineProperty(e.prototype,"type",{get:function(){return"Value"},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"isDisposed",{get:function(){return this._isDisposed},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"changed",{get:function(){return 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a.ObservableList,t.values&&r.each(t.values,function(e){n._add(e)}),this.list.changed.connect(this._onListChanged,this),t.modelDB&&(this._modelDB=t.modelDB,this._serialized=this._modelDB.createValue("outputs"),this._serialized.get()?this.fromJSON(this._serialized.get()):this._serialized.set(this.toJSON()),this._serialized.changed.connect(this._onSerializedChanged,this))}return Object.defineProperty(e.prototype,"stateChanged",{get:function(){return this._stateChanged},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"changed",{get:function(){return this._changed},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"length",{get:function(){return this.list?this.list.length:0},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"trusted",{get:function(){return this._trusted},set:function(e){if(e!==this._trusted)for(var t=this._trusted=e,n=0;n<this.list.length;n++){var r=this.list.get(n),i=r.toJSON();r.dispose(),r=this._createItem({value:i,trusted:t}),this.list.set(n,r)}},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"isDisposed",{get:function(){return this._isDisposed},enumerable:!0,configurable:!0}),e.prototype.dispose=function(){this.isDisposed||(this._isDisposed=!0,this.list.dispose(),i.Signal.clearData(this))},e.prototype.get=function(e){return this.list.get(e)},e.prototype.set=function(e,t){this._normalize(t);var n=this._createItem({value:t,trusted:this._trusted});this.list.set(e,n)},e.prototype.add=function(e){return this.clearNext&&(this.clear(),this.clearNext=!1),this._add(e)},e.prototype.clear=function(e){void 0===e&&(e=!1),this._lastStream="",e?this.clearNext=!0:(r.each(this.list,function(e){e.dispose()}),this.list.clear())},e.prototype.fromJSON=function(e){var t=this;this.clear(),r.each(e,function(e){t._add(e)})},e.prototype.toJSON=function(){return r.toArray(r.map(this.list,function(e){return e.toJSON()}))},e.prototype._add=function(e){var t=this._trusted;if(this._normalize(e),o.nbformat.isStream(e)&&this._lastStream&&e.name===this._lastName){this._lastStream+=e.text,e.text=this._lastStream,this._removeOverwrittenChars(e);var n=this._createItem({value:e,trusted:t}),r=this.length-1;return this.list.get(r).dispose(),this.list.set(r,n),r}o.nbformat.isStream(e)&&this._removeOverwrittenChars(e);var i=this._createItem({value:e,trusted:t});return o.nbformat.isStream(e)?(this._lastStream=e.text,this._lastName=e.name):this._lastStream="",this.list.push(i)},e.prototype._normalize=function(e){o.nbformat.isStream(e)&&Array.isArray(e.text)&&(e.text=e.text.join("\n"))},e.prototype._fixBackspace=function(e){var t=e;do{t=(e=t).replace(/[^\n]\x08/gm,"")}while(t.length<e.length);return e},e.prototype._fixCarriageReturn=function(e){var t=e;t=t.replace(/\r\r+\n/gm,"\r\n");do{t=t.replace(/^[^\n]*(?:\r(?!\n))+/gm,"")}while(t.search(/\r(?!\n)/)>-1);do{t=t.replace(/\r\n/gm,"\n")}while(t.indexOf("\r\n")>-1);return t},e.prototype._removeOverwrittenChars=function(e){var t=e.text;e.text=this._fixCarriageReturn(this._fixBackspace(t))},e.prototype._createItem=function(e){var t=this.contentFactory.createOutputModel(e);return t.changed.connect(this._onGenericChange,this),t.changed.connect(this._onGenericChange,this),t},e.prototype._onListChanged=function(e,t){this._serialized&&!this._changeGuard&&(this._changeGuard=!0,this._serialized.set(this.toJSON()),this._changeGuard=!1),this._changed.emit(t),this._stateChanged.emit(void 0)},e.prototype._onSerializedChanged=function(e,t){this._changeGuard||(this._changeGuard=!0,this.fromJSON(t.newValue),this._changeGuard=!1)},e.prototype._onGenericChange=function(){this._stateChanged.emit(void 0)},e}();t.OutputAreaModel=l,function(e){var t=function(){function e(){}return e.prototype.createOutputModel=function(e){return new s.OutputModel(e)},e}();e.ContentFactory=t,e.defaultContentFactory=new t}(l=t.OutputAreaModel||(t.OutputAreaModel={})),t.OutputAreaModel=l},function(e,t,n){"use strict";e.exports=function(e){var t="undefined"!=typeof window&&window.location;if(!t)throw new Error("fixUrls requires window.location");if(!e||"string"!=typeof e)return e;var n=t.protocol+"//"+t.host,r=n+t.pathname.replace(/\/[^\/]*$/,"/");return e.replace(/url\s*\(((?:[^)(]|\((?:[^)(]+|\([^)(]*\))*\))*)\)/gi,function(e,t){var i,o=t.trim().replace(/^"(.*)"$/,function(e,t){return t}).replace(/^'(.*)'$/,function(e,t){return t});return/^(#|data:|http:\/\/|https:\/\/|file:\/\/\/|\s*$)/i.test(o)?e:(i=0===o.indexOf("//")?o:0===o.indexOf("/")?n+o:r+o.replace(/^\.\//,""),"url("+JSON.stringify(i)+")")})}},function(e,t,n){(e.exports=n(44)(!1)).push([e.i,"/* BASICS */\n\n.CodeMirror {\n  /* Set height, width, borders, and global font properties here */\n  font-family: monospace;\n  height: 300px;\n  direction: ltr;\n}\n\n/* PADDING */\n\n.CodeMirror-lines {\n  padding: 4px 0; /* Vertical padding around content */\n}\n.CodeMirror pre {\n  padding: 0 4px; /* Horizontal padding of content */\n}\n\n.CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {\n  background-color: white; /* The little square between H and V scrollbars */\n}\n\n/* GUTTER */\n\n.CodeMirror-gutters {\n  border-right: 1px solid #ddd;\n  background-color: #f7f7f7;\n  white-space: nowrap;\n}\n.CodeMirror-linenumbers {}\n.CodeMirror-linenumber {\n  padding: 0 3px 0 5px;\n  min-width: 20px;\n  text-align: right;\n  color: #999;\n  white-space: nowrap;\n}\n\n.CodeMirror-guttermarker { color: black; }\n.CodeMirror-guttermarker-subtle { color: #999; }\n\n/* CURSOR */\n\n.CodeMirror-cursor {\n  border-left: 1px solid black;\n  border-right: none;\n  width: 0;\n}\n/* Shown when moving in bi-directional text */\n.CodeMirror div.CodeMirror-secondarycursor {\n  border-left: 1px solid silver;\n}\n.cm-fat-cursor .CodeMirror-cursor {\n  width: auto;\n  border: 0 !important;\n  background: #7e7;\n}\n.cm-fat-cursor div.CodeMirror-cursors {\n  z-index: 1;\n}\n.cm-fat-cursor-mark {\n  background-color: rgba(20, 255, 20, 0.5);\n  -webkit-animation: blink 1.06s steps(1) infinite;\n  -moz-animation: blink 1.06s steps(1) infinite;\n  animation: blink 1.06s steps(1) infinite;\n}\n.cm-animate-fat-cursor {\n  width: auto;\n  border: 0;\n  -webkit-animation: blink 1.06s steps(1) infinite;\n  -moz-animation: blink 1.06s steps(1) infinite;\n  animation: blink 1.06s steps(1) infinite;\n  background-color: #7e7;\n}\n@-moz-keyframes blink {\n  0% {}\n  50% { background-color: transparent; }\n  100% {}\n}\n@-webkit-keyframes blink {\n  0% {}\n  50% { background-color: transparent; }\n  100% {}\n}\n@keyframes blink {\n  0% {}\n  50% { background-color: transparent; }\n  100% {}\n}\n\n/* Can style cursor different in overwrite (non-insert) mode */\n.CodeMirror-overwrite .CodeMirror-cursor {}\n\n.cm-tab { display: inline-block; text-decoration: inherit; }\n\n.CodeMirror-rulers {\n  position: absolute;\n  left: 0; right: 0; top: -50px; bottom: -20px;\n  overflow: hidden;\n}\n.CodeMirror-ruler {\n  border-left: 1px solid #ccc;\n  top: 0; bottom: 0;\n  position: absolute;\n}\n\n/* DEFAULT THEME */\n\n.cm-s-default .cm-header {color: blue;}\n.cm-s-default .cm-quote {color: #090;}\n.cm-negative {color: #d44;}\n.cm-positive {color: #292;}\n.cm-header, .cm-strong {font-weight: bold;}\n.cm-em {font-style: italic;}\n.cm-link {text-decoration: underline;}\n.cm-strikethrough {text-decoration: line-through;}\n\n.cm-s-default .cm-keyword {color: #708;}\n.cm-s-default .cm-atom {color: #219;}\n.cm-s-default .cm-number {color: #164;}\n.cm-s-default .cm-def {color: #00f;}\n.cm-s-default .cm-variable,\n.cm-s-default .cm-punctuation,\n.cm-s-default .cm-property,\n.cm-s-default .cm-operator {}\n.cm-s-default .cm-variable-2 {color: #05a;}\n.cm-s-default .cm-variable-3, .cm-s-default .cm-type {color: #085;}\n.cm-s-default .cm-comment {color: #a50;}\n.cm-s-default .cm-string {color: #a11;}\n.cm-s-default .cm-string-2 {color: #f50;}\n.cm-s-default .cm-meta {color: #555;}\n.cm-s-default .cm-qualifier {color: #555;}\n.cm-s-default .cm-builtin {color: #30a;}\n.cm-s-default .cm-bracket {color: #997;}\n.cm-s-default .cm-tag {color: #170;}\n.cm-s-default .cm-attribute {color: #00c;}\n.cm-s-default .cm-hr {color: #999;}\n.cm-s-default .cm-link {color: #00c;}\n\n.cm-s-default .cm-error {color: #f00;}\n.cm-invalidchar {color: #f00;}\n\n.CodeMirror-composing { border-bottom: 2px solid; }\n\n/* Default styles for common addons */\n\ndiv.CodeMirror span.CodeMirror-matchingbracket {color: #0b0;}\ndiv.CodeMirror span.CodeMirror-nonmatchingbracket {color: #a22;}\n.CodeMirror-matchingtag { background: rgba(255, 150, 0, .3); }\n.CodeMirror-activeline-background {background: #e8f2ff;}\n\n/* STOP */\n\n/* The rest of this file contains styles related to the mechanics of\n   the editor. You probably shouldn't touch them. */\n\n.CodeMirror {\n  position: relative;\n  overflow: hidden;\n}\n\n.CodeMirror-scroll {\n  overflow: scroll !important; /* Things will break if this is overridden */\n  /* 30px is the magic margin used to hide the element's real scrollbars */\n  /* See overflow: hidden in .CodeMirror */\n  margin-bottom: -30px; margin-right: -30px;\n  padding-bottom: 30px;\n  height: 100%;\n  outline: none; /* Prevent dragging from highlighting the element */\n  position: relative;\n}\n.CodeMirror-sizer {\n  position: relative;\n  border-right: 30px solid transparent;\n}\n\n/* The fake, visible scrollbars. Used to force redraw during scrolling\n   before actual scrolling happens, thus preventing shaking and\n   flickering artifacts. */\n.CodeMirror-vscrollbar, .CodeMirror-hscrollbar, .CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {\n  position: absolute;\n  z-index: 6;\n  display: none;\n}\n.CodeMirror-vscrollbar {\n  right: 0; top: 0;\n  overflow-x: hidden;\n  overflow-y: scroll;\n}\n.CodeMirror-hscrollbar {\n  bottom: 0; left: 0;\n  overflow-y: hidden;\n  overflow-x: scroll;\n}\n.CodeMirror-scrollbar-filler {\n  right: 0; bottom: 0;\n}\n.CodeMirror-gutter-filler {\n  left: 0; bottom: 0;\n}\n\n.CodeMirror-gutters {\n  position: absolute; left: 0; top: 0;\n  min-height: 100%;\n  z-index: 3;\n}\n.CodeMirror-gutter {\n  white-space: normal;\n  height: 100%;\n  display: inline-block;\n  vertical-align: top;\n  margin-bottom: -30px;\n}\n.CodeMirror-gutter-wrapper {\n  position: absolute;\n  z-index: 4;\n  background: none !important;\n  border: none !important;\n}\n.CodeMirror-gutter-background {\n  position: absolute;\n  top: 0; bottom: 0;\n  z-index: 4;\n}\n.CodeMirror-gutter-elt {\n  position: absolute;\n  cursor: default;\n  z-index: 4;\n}\n.CodeMirror-gutter-wrapper ::selection { background-color: transparent }\n.CodeMirror-gutter-wrapper ::-moz-selection { background-color: transparent }\n\n.CodeMirror-lines {\n  cursor: text;\n  min-height: 1px; /* prevents collapsing before first draw */\n}\n.CodeMirror pre {\n  /* Reset some styles that the rest of the page might have set */\n  -moz-border-radius: 0; -webkit-border-radius: 0; border-radius: 0;\n  border-width: 0;\n  background: transparent;\n  font-family: inherit;\n  font-size: inherit;\n  margin: 0;\n  white-space: pre;\n  word-wrap: normal;\n  line-height: inherit;\n  color: inherit;\n  z-index: 2;\n  position: relative;\n  overflow: visible;\n  -webkit-tap-highlight-color: transparent;\n  -webkit-font-variant-ligatures: contextual;\n  font-variant-ligatures: contextual;\n}\n.CodeMirror-wrap pre {\n  word-wrap: break-word;\n  white-space: pre-wrap;\n  word-break: normal;\n}\n\n.CodeMirror-linebackground {\n  position: absolute;\n  left: 0; right: 0; top: 0; bottom: 0;\n  z-index: 0;\n}\n\n.CodeMirror-linewidget {\n  position: relative;\n  z-index: 2;\n  padding: 0.1px; /* Force widget margins to stay inside of the container */\n}\n\n.CodeMirror-widget {}\n\n.CodeMirror-rtl pre { direction: rtl; }\n\n.CodeMirror-code {\n  outline: none;\n}\n\n/* Force content-box sizing for the elements where we expect it */\n.CodeMirror-scroll,\n.CodeMirror-sizer,\n.CodeMirror-gutter,\n.CodeMirror-gutters,\n.CodeMirror-linenumber {\n  -moz-box-sizing: content-box;\n  box-sizing: content-box;\n}\n\n.CodeMirror-measure {\n  position: absolute;\n  width: 100%;\n  height: 0;\n  overflow: hidden;\n  visibility: hidden;\n}\n\n.CodeMirror-cursor {\n  position: absolute;\n  pointer-events: none;\n}\n.CodeMirror-measure pre { position: static; 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r={msgType:"comm_open",channel:"shell",username:this._kernel.username,session:this._kernel.clientId},i={comm_id:this._id,target_name:this._target,data:e||{}},o=a.KernelMessage.createShellMessage(r,i,t,n);return this._kernel.sendShellMessage(o,!1,!0)},t.prototype.send=function(e,t,n,r){if(void 0===n&&(n=[]),void 0===r&&(r=!0),this.isDisposed||this._kernel.isDisposed)throw new Error("Cannot send");var i={msgType:"comm_msg",channel:"shell",username:this._kernel.username,session:this._kernel.clientId},o={comm_id:this._id,data:e},s=a.KernelMessage.createShellMessage(i,o,t,n);return this._kernel.sendShellMessage(s,!1,!0)},t.prototype.close=function(e,t,n){if(void 0===n&&(n=[]),this.isDisposed||this._kernel.isDisposed)throw new Error("Cannot close");var r={msgType:"comm_msg",channel:"shell",username:this._kernel.username,session:this._kernel.clientId},i={comm_id:this._id,data:e||{}},o=a.KernelMessage.createShellMessage(r,i,t,n),s=this._kernel.sendShellMessage(o,!1,!0);r.channel="iopub";var 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s=o.session===n.clientId;"iopub"!==t.channel&&s&&n._unhandledMessage.emit(t)}}if("iopub"===t.channel){switch(t.header.msg_type){case"status":n._updateStatus(t.content.execution_state);break;case"comm_open":n._handleCommOpen(t);break;case"comm_msg":n._handleCommMsg(t);break;case"comm_close":n._handleCommClose(t)}n._iopubMessage.emit(t)}}},this._onWSClose=function(e){if(!n._wsStopped&&n._ws)if(n._ws.onclose=n._noOp,n._ws.onerror=n._noOp,n._ws=null,n._reconnectAttempt<n._reconnectLimit){n._updateStatus("reconnecting");var t=Math.pow(2,n._reconnectAttempt);console.error("Connection lost, reconnecting in "+t+" seconds."),setTimeout(n._createSocket,1e3*t),n._reconnectAttempt+=1}else n._updateStatus("dead"),n._connectionPromise.reject(new Error("Could not establish connection"))},this._id="",this._name="",this._status="unknown",this._clientId="",this._isDisposed=!1,this._wsStopped=!1,this._ws=null,this._username="",this._reconnectLimit=7,this._reconnectAttempt=0,this._isReady=!1,this._targetRegistry=Object.create(null),this._info=null,this._pendingMessages=[],this._statusChanged=new c.Signal(this),this._iopubMessage=new c.Signal(this),this._unhandledMessage=new c.Signal(this),this._displayIdToParentIds=new Map,this._msgIdToDisplayIds=new Map,this._terminated=new c.Signal(this),this._noOp=function(){},this._name=e.name,this._id=t,this.serverSettings=e.serverSettings||u.ServerConnection.makeSettings(),this._clientId=e.clientId||o.uuid(),this._username=e.username||"",this._futures=new Map,this._commPromises=new Map,this._createSocket(),i.runningKernels.push(this)}return Object.defineProperty(e.prototype,"terminated",{get:function(){return this._terminated},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"statusChanged",{get:function(){return this._statusChanged},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"iopubMessage",{get:function(){return this._iopubMessage},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"unhandledMessage",{get:function(){return this._unhandledMessage},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"id",{get:function(){return this._id},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"name",{get:function(){return this._name},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"model",{get:function(){return{name:this.name,id:this.id}},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"username",{get:function(){return this._username},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"clientId",{get:function(){return 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e({name:this._name,username:this._username,serverSettings:this.serverSettings},this._id)},e.prototype.dispose=function(){this.isDisposed||(this._isDisposed=!0,this._terminated.emit(void 0),this._status="dead",this._clearSocket(),this._futures.forEach(function(e,t){e.dispose()}),this._commPromises.forEach(function(e,t){e.then(function(e){e.dispose()})}),this._displayIdToParentIds.clear(),this._msgIdToDisplayIds.clear(),a.ArrayExt.removeFirstOf(i.runningKernels,this),c.Signal.clearData(this))},e.prototype.sendShellMessage=function(e,t,n){var r=this;if(void 0===t&&(t=!1),void 0===n&&(n=!0),"dead"===this.status)throw new Error("Kernel is dead");this._isReady&&this._ws?this._ws.send(p.serialize(e)):this._pendingMessages.push(e);var i=new h.KernelFutureHandler(function(){var t=e.header.msg_id;r._futures.delete(t);var n=r._msgIdToDisplayIds.get(t);n&&(n.forEach(function(e){var n=r._displayIdToParentIds.get(e);if(n){var 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n={msgType:"execute_request",channel:"shell",username:this._username,session:this._clientId};e=r({},{silent:!1,store_history:!0,user_expressions:{},allow_stdin:!0,stop_on_error:!1},e);var i=f.KernelMessage.createShellMessage(n,e);return this.sendShellMessage(i,!0,t)},e.prototype.requestIsComplete=function(e){var t={msgType:"is_complete_request",channel:"shell",username:this._username,session:this._clientId},n=f.KernelMessage.createShellMessage(t,e);return i.handleShellMessage(this,n)},e.prototype.requestCommInfo=function(e){var t={msgType:"comm_info_request",channel:"shell",username:this._username,session:this._clientId},n=f.KernelMessage.createShellMessage(t,e);return i.handleShellMessage(this,n)},e.prototype.sendInputReply=function(e){if("dead"===this.status)throw new Error("Kernel is dead");var t={msgType:"input_reply",channel:"stdin",username:this._username,session:this._clientId},n=f.KernelMessage.createMessage(t,e);this._isReady&&this._ws?this._ws.send(p.serialize(n)):this._pendingMessages.push(n)},e.prototype.registerMessageHook=function(e,t){var n=this,r=this._futures&&this._futures.get(e);return r&&r.registerMessageHook(t),new l.DisposableDelegate(function(){(r=n._futures&&n._futures.get(e))&&r.removeMessageHook(t)})},e.prototype.registerCommTarget=function(e,t){var n=this;return this._targetRegistry[e]=t,new l.DisposableDelegate(function(){n.isDisposed||delete n._targetRegistry[e]})},e.prototype.connectToComm=function(e,t){var n=this,r=t||o.uuid();if(this._commPromises.has(r))return this._commPromises.get(r);var i=Promise.resolve(void 0).then(function(){return new d.CommHandler(e,r,n,function(){n._unregisterComm(r)})});return this._commPromises.set(r,i),i},e.prototype._handleDisplayId=function(e,t){var n=this,r=t.parent_header.msg_id,i=this._displayIdToParentIds.get(e);if(i){var o={header:s.JSONExt.deepCopy(t.header),parent_header:s.JSONExt.deepCopy(t.parent_header),metadata:s.JSONExt.deepCopy(t.metadata),content:s.JSONExt.deepCopy(t.content),channel:t.channel,buffers:t.buffers?t.buffers.slice():[]};o.header.msg_type="update_display_data",i.map(function(e){var t=n._futures&&n._futures.get(e);t&&t.handleMsg(o)})}if("update_display_data"===t.header.msg_type)return!0;-1===(i=this._displayIdToParentIds.get(e)||[]).indexOf(r)&&i.push(r),this._displayIdToParentIds.set(e,i);var a=this._msgIdToDisplayIds.get(r)||[];return-1===a.indexOf(r)&&a.push(r),this._msgIdToDisplayIds.set(r,a),!1},e.prototype._clearSocket=function(){this._wsStopped=!0,this._isReady=!1,null!==this._ws&&(this._ws.onopen=this._noOp,this._ws.onclose=this._noOp,this._ws.onerror=this._noOp,this._ws.onmessage=this._noOp,this._ws.close(),this._ws=null)},e.prototype._updateStatus=function(e){switch(e){case"starting":case"idle":case"busy":case"connected":this._isReady=!0;break;case"restarting":case"reconnecting":case"dead":this._isReady=!1;break;default:return void console.error("invalid kernel status:",e)}e!==this._status&&(this._status=e,i.logKernelStatus(this),this._statusChanged.emit(e),"dead"===e&&this.dispose()),this._isReady&&this._sendPending()},e.prototype._sendPending=function(){for(;this._ws&&this._pendingMessages.length>0;){var 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t=this,n=e.content,r=this._commPromises.get(n.comm_id);r?r.then(function(n){if(n){t._unregisterComm(n.commId);try{var r=n.onClose;r&&r(e),n.dispose()}catch(t){console.error("Exception closing comm: ",t,t.stack,e)}}}):console.error("Comm not found for comm id "+n.comm_id)},e.prototype._handleCommMsg=function(e){var t=e.content,n=this._commPromises.get(t.comm_id);n&&n.then(function(t){if(t)try{var n=t.onMsg;n&&n(e)}catch(t){console.error("Exception handling comm msg: ",t,t.stack,e)}})},e.prototype._unregisterComm=function(e){this._commPromises.delete(e)},e}();t.DefaultKernel=y,function(e){e.findById=function(e,t){return i.findById(e,t)},e.getSpecs=function(e){return i.getSpecs(e)},e.listRunning=function(e){return i.listRunning(e)},e.startNew=function(e){return i.startNew(e)},e.connectTo=function(e,t){return i.connectTo(e,t)},e.shutdown=function(e,t){return i.shutdownKernel(e,t)},e.shutdownAll=function(e){return 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r=o.URLExt.join(n.baseUrl,g,encodeURIComponent(t));return u.ServerConnection.makeRequest(r,{method:"DELETE"},n).then(function(n){if(404===n.status)n.json().then(function(e){var n=e.message||'The kernel "'+t+'"" does not exist on the server';console.warn(n)});else if(204!==n.status)throw new u.ServerConnection.ResponseError(n);!function(t){a.each(a.toArray(e.runningKernels),function(e){e.id===t&&e.dispose()})}(t)})}function l(e,t){t=t||u.ServerConnection.makeSettings();var n=o.URLExt.join(t.baseUrl,g,encodeURIComponent(e));return u.ServerConnection.makeRequest(n,{},t).then(function(e){if(200!==e.status)throw new u.ServerConnection.ResponseError(e);return e.json()}).then(function(e){return m.validateModel(e),e})}e.runningKernels=[],e.specs=Object.create(null),e.findById=function(t,n){var r=a.find(e.runningKernels,function(e){return e.id===t});return r?Promise.resolve(r.model):l(t,n).catch(function(){throw new Error("No running kernel with id: "+t)})},e.findSpecs=function(n){return 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diff --git a/website/assets/js/vendor/prism.min.js b/website/assets/js/vendor/prism.min.js
deleted file mode 100644
index 00fc29aab..000000000
--- a/website/assets/js/vendor/prism.min.js
+++ /dev/null
@@ -1,28 +0,0 @@
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-Prism.languages.javascript=Prism.languages.extend("clike",{keyword:/\b(as|async|await|break|case|catch|class|const|continue|debugger|default|delete|do|else|enum|export|extends|finally|for|from|function|get|if|implements|import|in|instanceof|interface|let|new|null|of|package|private|protected|public|return|set|static|super|switch|this|throw|try|typeof|var|void|while|with|yield)\b/,number:/\b-?(0x[\dA-Fa-f]+|0b[01]+|0o[0-7]+|\d*\.?\d+([Ee][+-]?\d+)?|NaN|Infinity)\b/,"function":/[_$a-zA-Z\xA0-\uFFFF][_$a-zA-Z0-9\xA0-\uFFFF]*(?=\()/i}),Prism.languages.insertBefore("javascript","keyword",{regex:{pattern:/(^|[^\/])\/(?!\/)(\[.+?]|\\.|[^\/\\\r\n])+\/[gimyu]{0,5}(?=\s*($|[\r\n,.;})]))/,lookbehind:!0,greedy:!0}}),Prism.languages.insertBefore("javascript","class-name",{"template-string":{pattern:/`(?:\\\\|\\?[^\\])*?`/,greedy:!0,inside:{interpolation:{pattern:/\$\{[^}]+\}/,inside:{"interpolation-punctuation":{pattern:/^\$\{|\}$/,alias:"punctuation"},rest:Prism.languages.javascript}},string:/[\s\S]+/}}}),Prism.languages.markup&&Prism.languages.insertBefore("markup","tag",{script:{pattern:/(<script[\w\W]*?>)[\w\W]*?(?=<\/script>)/i,lookbehind:!0,inside:Prism.languages.javascript,alias:"language-javascript"}}),Prism.languages.js=Prism.languages.javascript;
-!function(e){var t={variable:[{pattern:/\$?\(\([\w\W]+?\)\)/,inside:{variable:[{pattern:/(^\$\(\([\w\W]+)\)\)/,lookbehind:!0},/^\$\(\(/],number:/\b-?(?:0x[\dA-Fa-f]+|\d*\.?\d+(?:[Ee]-?\d+)?)\b/,operator:/--?|-=|\+\+?|\+=|!=?|~|\*\*?|\*=|\/=?|%=?|<<=?|>>=?|<=?|>=?|==?|&&?|&=|\^=?|\|\|?|\|=|\?|:/,punctuation:/\(\(?|\)\)?|,|;/}},{pattern:/\$\([^)]+\)|`[^`]+`/,inside:{variable:/^\$\(|^`|\)$|`$/}},/\$(?:[a-z0-9_#\?\*!@]+|\{[^}]+\})/i]};e.languages.bash={shebang:{pattern:/^#!\s*\/bin\/bash|^#!\s*\/bin\/sh/,alias:"important"},comment:{pattern:/(^|[^"{\\])#.*/,lookbehind:!0},string:[{pattern:/((?:^|[^<])<<\s*)(?:"|')?(\w+?)(?:"|')?\s*\r?\n(?:[\s\S])*?\r?\n\2/g,lookbehind:!0,inside:t},{pattern:/(["'])(?:\\\\|\\?[^\\])*?\1/g,inside:t}],variable:t.variable,"function":{pattern:/(^|\s|;|\||&)(?:alias|apropos|apt-get|aptitude|aspell|awk|basename|bash|bc|bg|builtin|bzip2|cal|cat|cd|cfdisk|chgrp|chmod|chown|chroot|chkconfig|cksum|clear|cmp|comm|command|cp|cron|crontab|csplit|cut|date|dc|dd|ddrescue|df|diff|diff3|dig|dir|dircolors|dirname|dirs|dmesg|du|egrep|eject|enable|env|ethtool|eval|exec|expand|expect|export|expr|fdformat|fdisk|fg|fgrep|file|find|fmt|fold|format|free|fsck|ftp|fuser|gawk|getopts|git|grep|groupadd|groupdel|groupmod|groups|gzip|hash|head|help|hg|history|hostname|htop|iconv|id|ifconfig|ifdown|ifup|import|install|jobs|join|kill|killall|less|link|ln|locate|logname|logout|look|lpc|lpr|lprint|lprintd|lprintq|lprm|ls|lsof|make|man|mkdir|mkfifo|mkisofs|mknod|more|most|mount|mtools|mtr|mv|mmv|nano|netstat|nice|nl|nohup|notify-send|nslookup|open|op|passwd|paste|pathchk|ping|pkill|popd|pr|printcap|printenv|printf|ps|pushd|pv|pwd|quota|quotacheck|quotactl|ram|rar|rcp|read|readarray|readonly|reboot|rename|renice|remsync|rev|rm|rmdir|rsync|screen|scp|sdiff|sed|seq|service|sftp|shift|shopt|shutdown|sleep|slocate|sort|source|split|ssh|stat|strace|su|sudo|sum|suspend|sync|tail|tar|tee|test|time|timeout|times|touch|top|traceroute|trap|tr|tsort|tty|type|ulimit|umask|umount|unalias|uname|unexpand|uniq|units|unrar|unshar|uptime|useradd|userdel|usermod|users|uuencode|uudecode|v|vdir|vi|vmstat|wait|watch|wc|wget|whereis|which|who|whoami|write|xargs|xdg-open|yes|zip)(?=$|\s|;|\||&)/,lookbehind:!0},keyword:{pattern:/(^|\s|;|\||&)(?:let|:|\.|if|then|else|elif|fi|for|break|continue|while|in|case|function|select|do|done|until|echo|exit|return|set|declare)(?=$|\s|;|\||&)/,lookbehind:!0},"boolean":{pattern:/(^|\s|;|\||&)(?:true|false)(?=$|\s|;|\||&)/,lookbehind:!0},operator:/&&?|\|\|?|==?|!=?|<<<?|>>|<=?|>=?|=~/,punctuation:/\$?\(\(?|\)\)?|\.\.|[{}[\];]/};var a=t.variable[1].inside;a["function"]=e.languages.bash["function"],a.keyword=e.languages.bash.keyword,a.boolean=e.languages.bash.boolean,a.operator=e.languages.bash.operator,a.punctuation=e.languages.bash.punctuation}(Prism);
-Prism.languages.c=Prism.languages.extend("clike",{keyword:/\b(asm|typeof|inline|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|int|long|register|return|short|signed|sizeof|static|struct|switch|typedef|union|unsigned|void|volatile|while)\b/,operator:/\-[>-]?|\+\+?|!=?|<<?=?|>>?=?|==?|&&?|\|?\||[~^%?*\/]/,number:/\b-?(?:0x[\da-f]+|\d*\.?\d+(?:e[+-]?\d+)?)[ful]*\b/i}),Prism.languages.insertBefore("c","string",{macro:{pattern:/(^\s*)#\s*[a-z]+([^\r\n\\]|\\.|\\(?:\r\n?|\n))*/im,lookbehind:!0,alias:"property",inside:{string:{pattern:/(#\s*include\s*)(<.+?>|("|')(\\?.)+?\3)/,lookbehind:!0},directive:{pattern:/(#\s*)\b(define|elif|else|endif|error|ifdef|ifndef|if|import|include|line|pragma|undef|using)\b/,lookbehind:!0,alias:"keyword"}}},constant:/\b(__FILE__|__LINE__|__DATE__|__TIME__|__TIMESTAMP__|__func__|EOF|NULL|stdin|stdout|stderr)\b/}),delete Prism.languages.c["class-name"],delete Prism.languages.c["boolean"];
-Prism.languages.csharp=Prism.languages.extend("clike",{keyword:/\b(abstract|as|async|await|base|bool|break|byte|case|catch|char|checked|class|const|continue|decimal|default|delegate|do|double|else|enum|event|explicit|extern|false|finally|fixed|float|for|foreach|goto|if|implicit|in|int|interface|internal|is|lock|long|namespace|new|null|object|operator|out|override|params|private|protected|public|readonly|ref|return|sbyte|sealed|short|sizeof|stackalloc|static|string|struct|switch|this|throw|true|try|typeof|uint|ulong|unchecked|unsafe|ushort|using|virtual|void|volatile|while|add|alias|ascending|async|await|descending|dynamic|from|get|global|group|into|join|let|orderby|partial|remove|select|set|value|var|where|yield)\b/,string:[/@("|')(\1\1|\\\1|\\?(?!\1)[\s\S])*\1/,/("|')(\\?.)*?\1/],number:/\b-?(0x[\da-f]+|\d*\.?\d+f?)\b/i}),Prism.languages.insertBefore("csharp","keyword",{preprocessor:{pattern:/(^\s*)#.*/m,lookbehind:!0,alias:"property",inside:{directive:{pattern:/(\s*#)\b(define|elif|else|endif|endregion|error|if|line|pragma|region|undef|warning)\b/,lookbehind:!0,alias:"keyword"}}}});
-Prism.languages.cpp=Prism.languages.extend("c",{keyword:/\b(alignas|alignof|asm|auto|bool|break|case|catch|char|char16_t|char32_t|class|compl|const|constexpr|const_cast|continue|decltype|default|delete|do|double|dynamic_cast|else|enum|explicit|export|extern|float|for|friend|goto|if|inline|int|long|mutable|namespace|new|noexcept|nullptr|operator|private|protected|public|register|reinterpret_cast|return|short|signed|sizeof|static|static_assert|static_cast|struct|switch|template|this|thread_local|throw|try|typedef|typeid|typename|union|unsigned|using|virtual|void|volatile|wchar_t|while)\b/,"boolean":/\b(true|false)\b/,operator:/[-+]{1,2}|!=?|<{1,2}=?|>{1,2}=?|\->|:{1,2}|={1,2}|\^|~|%|&{1,2}|\|?\||\?|\*|\/|\b(and|and_eq|bitand|bitor|not|not_eq|or|or_eq|xor|xor_eq)\b/}),Prism.languages.insertBefore("cpp","keyword",{"class-name":{pattern:/(class\s+)[a-z0-9_]+/i,lookbehind:!0}});
-Prism.languages.css.selector={pattern:/[^\{\}\s][^\{\}]*(?=\s*\{)/,inside:{"pseudo-element":/:(?:after|before|first-letter|first-line|selection)|::[-\w]+/,"pseudo-class":/:[-\w]+(?:\(.*\))?/,"class":/\.[-:\.\w]+/,id:/#[-:\.\w]+/}},Prism.languages.insertBefore("css","function",{hexcode:/#[\da-f]{3,6}/i,entity:/\\[\da-f]{1,8}/i,number:/[\d%\.]+/});
-Prism.languages.git={comment:/^#.*/m,deleted:/^[-–].*/m,inserted:/^\+.*/m,string:/("|')(\\?.)*?\1/m,command:{pattern:/^.*\$ git .*$/m,inside:{parameter:/\s(--|-)\w+/m}},coord:/^@@.*@@$/m,commit_sha1:/^commit \w{40}$/m};
-!function(e){e.languages.jade={comment:{pattern:/(^([\t ]*))\/\/.*((?:\r?\n|\r)\2[\t ]+.+)*/m,lookbehind:!0},"multiline-script":{pattern:/(^([\t ]*)script\b.*\.[\t ]*)((?:\r?\n|\r(?!\n))(?:\2[\t ]+.+|\s*?(?=\r?\n|\r)))+/m,lookbehind:!0,inside:{rest:e.languages.javascript}},filter:{pattern:/(^([\t ]*)):.+((?:\r?\n|\r(?!\n))(?:\2[\t ]+.+|\s*?(?=\r?\n|\r)))+/m,lookbehind:!0,inside:{"filter-name":{pattern:/^:[\w-]+/,alias:"variable"}}},"multiline-plain-text":{pattern:/(^([\t ]*)[\w\-#.]+\.[\t ]*)((?:\r?\n|\r(?!\n))(?:\2[\t ]+.+|\s*?(?=\r?\n|\r)))+/m,lookbehind:!0},markup:{pattern:/(^[\t ]*)<.+/m,lookbehind:!0,inside:{rest:e.languages.markup}},doctype:{pattern:/((?:^|\n)[\t ]*)doctype(?: .+)?/,lookbehind:!0},"flow-control":{pattern:/(^[\t ]*)(?:if|unless|else|case|when|default|each|while)\b(?: .+)?/m,lookbehind:!0,inside:{each:{pattern:/^each .+? in\b/,inside:{keyword:/\b(?:each|in)\b/,punctuation:/,/}},branch:{pattern:/^(?:if|unless|else|case|when|default|while)\b/,alias:"keyword"},rest:e.languages.javascript}},keyword:{pattern:/(^[\t ]*)(?:block|extends|include|append|prepend)\b.+/m,lookbehind:!0},mixin:[{pattern:/(^[\t ]*)mixin .+/m,lookbehind:!0,inside:{keyword:/^mixin/,"function":/\w+(?=\s*\(|\s*$)/,punctuation:/[(),.]/}},{pattern:/(^[\t ]*)\+.+/m,lookbehind:!0,inside:{name:{pattern:/^\+\w+/,alias:"function"},rest:e.languages.javascript}}],script:{pattern:/(^[\t ]*script(?:(?:&[^(]+)?\([^)]+\))*[\t ]+).+/m,lookbehind:!0,inside:{rest:e.languages.javascript}},"plain-text":{pattern:/(^[\t ]*(?!-)[\w\-#.]*[\w\-](?:(?:&[^(]+)?\([^)]+\))*\/?[\t ]+).+/m,lookbehind:!0},tag:{pattern:/(^[\t ]*)(?!-)[\w\-#.]*[\w\-](?:(?:&[^(]+)?\([^)]+\))*\/?:?/m,lookbehind:!0,inside:{attributes:[{pattern:/&[^(]+\([^)]+\)/,inside:{rest:e.languages.javascript}},{pattern:/\([^)]+\)/,inside:{"attr-value":{pattern:/(=\s*)(?:\{[^}]*\}|[^,)\r\n]+)/,lookbehind:!0,inside:{rest:e.languages.javascript}},"attr-name":/[\w-]+(?=\s*!?=|\s*[,)])/,punctuation:/[!=(),]+/}}],punctuation:/:/}},code:[{pattern:/(^[\t ]*(?:-|!?=)).+/m,lookbehind:!0,inside:{rest:e.languages.javascript}}],punctuation:/[.\-!=|]+/};for(var t="(^([\\t ]*)):{{filter_name}}((?:\\r?\\n|\\r(?!\\n))(?:\\2[\\t ]+.+|\\s*?(?=\\r?\\n|\\r)))+",n=[{filter:"atpl",language:"twig"},{filter:"coffee",language:"coffeescript"},"ejs","handlebars","hogan","less","livescript","markdown","mustache","plates",{filter:"sass",language:"scss"},"stylus","swig"],a={},i=0,r=n.length;r>i;i++){var s=n[i];s="string"==typeof s?{filter:s,language:s}:s,e.languages[s.language]&&(a["filter-"+s.filter]={pattern:RegExp(t.replace("{{filter_name}}",s.filter),"m"),lookbehind:!0,inside:{"filter-name":{pattern:/^:[\w-]+/,alias:"variable"},rest:e.languages[s.language]}})}e.languages.insertBefore("jade","filter",a)}(Prism);
-Prism.languages.java=Prism.languages.extend("clike",{keyword:/\b(abstract|continue|for|new|switch|assert|default|goto|package|synchronized|boolean|do|if|private|this|break|double|implements|protected|throw|byte|else|import|public|throws|case|enum|instanceof|return|transient|catch|extends|int|short|try|char|final|interface|static|void|class|finally|long|strictfp|volatile|const|float|native|super|while)\b/,number:/\b0b[01]+\b|\b0x[\da-f]*\.?[\da-fp\-]+\b|\b\d*\.?\d+(?:e[+-]?\d+)?[df]?\b/i,operator:{pattern:/(^|[^.])(?:\+[+=]?|-[-=]?|!=?|<<?=?|>>?>?=?|==?|&[&=]?|\|[|=]?|\*=?|\/=?|%=?|\^=?|[?:~])/m,lookbehind:!0}}),Prism.languages.insertBefore("java","function",{annotation:{alias:"punctuation",pattern:/(^|[^.])@\w+/,lookbehind:!0}});
-Prism.languages.json={property:/".*?"(?=\s*:)/gi,string:/"(?!:)(\\?[^"])*?"(?!:)/g,number:/\b-?(0x[\dA-Fa-f]+|\d*\.?\d+([Ee]-?\d+)?)\b/g,punctuation:/[{}[\]);,]/g,operator:/:/g,"boolean":/\b(true|false)\b/gi,"null":/\bnull\b/gi},Prism.languages.jsonp=Prism.languages.json;
-!function(a){var e=/\\([^a-z()[\]]|[a-z\*]+)/i,n={"equation-command":{pattern:e,alias:"regex"}};a.languages.latex={comment:/%.*/m,cdata:{pattern:/(\\begin\{((?:verbatim|lstlisting)\*?)\})([\w\W]*?)(?=\\end\{\2\})/,lookbehind:!0},equation:[{pattern:/\$(?:\\?[\w\W])*?\$|\\\((?:\\?[\w\W])*?\\\)|\\\[(?:\\?[\w\W])*?\\\]/,inside:n,alias:"string"},{pattern:/(\\begin\{((?:equation|math|eqnarray|align|multline|gather)\*?)\})([\w\W]*?)(?=\\end\{\2\})/,lookbehind:!0,inside:n,alias:"string"}],keyword:{pattern:/(\\(?:begin|end|ref|cite|label|usepackage|documentclass)(?:\[[^\]]+\])?\{)[^}]+(?=\})/,lookbehind:!0},url:{pattern:/(\\url\{)[^}]+(?=\})/,lookbehind:!0},headline:{pattern:/(\\(?:part|chapter|section|subsection|frametitle|subsubsection|paragraph|subparagraph|subsubparagraph|subsubsubparagraph)\*?(?:\[[^\]]+\])?\{)[^}]+(?=\}(?:\[[^\]]+\])?)/,lookbehind:!0,alias:"class-name"},"function":{pattern:e,alias:"selector"},punctuation:/[[\]{}&]/}}(Prism);
-Prism.languages.makefile={comment:{pattern:/(^|[^\\])#(?:\\(?:\r\n|[\s\S])|.)*/,lookbehind:!0},string:/(["'])(?:\\(?:\r\n|[\s\S])|(?!\1)[^\\\r\n])*\1/,builtin:/\.[A-Z][^:#=\s]+(?=\s*:(?!=))/,symbol:{pattern:/^[^:=\r\n]+(?=\s*:(?!=))/m,inside:{variable:/\$+(?:[^(){}:#=\s]+|(?=[({]))/}},variable:/\$+(?:[^(){}:#=\s]+|\([@*%<^+?][DF]\)|(?=[({]))/,keyword:[/-include\b|\b(?:define|else|endef|endif|export|ifn?def|ifn?eq|include|override|private|sinclude|undefine|unexport|vpath)\b/,{pattern:/(\()(?:addsuffix|abspath|and|basename|call|dir|error|eval|file|filter(?:-out)?|findstring|firstword|flavor|foreach|guile|if|info|join|lastword|load|notdir|or|origin|patsubst|realpath|shell|sort|strip|subst|suffix|value|warning|wildcard|word(?:s|list)?)(?=[ \t])/,lookbehind:!0}],operator:/(?:::|[?:+!])?=|[|@]/,punctuation:/[:;(){}]/};
-Prism.languages.markdown=Prism.languages.extend("markup",{}),Prism.languages.insertBefore("markdown","prolog",{blockquote:{pattern:/^>(?:[\t ]*>)*/m,alias:"punctuation"},code:[{pattern:/^(?: {4}|\t).+/m,alias:"keyword"},{pattern:/``.+?``|`[^`\n]+`/,alias:"keyword"}],title:[{pattern:/\w+.*(?:\r?\n|\r)(?:==+|--+)/,alias:"important",inside:{punctuation:/==+$|--+$/}},{pattern:/(^\s*)#+.+/m,lookbehind:!0,alias:"important",inside:{punctuation:/^#+|#+$/}}],hr:{pattern:/(^\s*)([*-])([\t ]*\2){2,}(?=\s*$)/m,lookbehind:!0,alias:"punctuation"},list:{pattern:/(^\s*)(?:[*+-]|\d+\.)(?=[\t ].)/m,lookbehind:!0,alias:"punctuation"},"url-reference":{pattern:/!?\[[^\]]+\]:[\t ]+(?:\S+|<(?:\\.|[^>\\])+>)(?:[\t ]+(?:"(?:\\.|[^"\\])*"|'(?:\\.|[^'\\])*'|\((?:\\.|[^)\\])*\)))?/,inside:{variable:{pattern:/^(!?\[)[^\]]+/,lookbehind:!0},string:/(?:"(?:\\.|[^"\\])*"|'(?:\\.|[^'\\])*'|\((?:\\.|[^)\\])*\))$/,punctuation:/^[\[\]!:]|[<>]/},alias:"url"},bold:{pattern:/(^|[^\\])(\*\*|__)(?:(?:\r?\n|\r)(?!\r?\n|\r)|.)+?\2/,lookbehind:!0,inside:{punctuation:/^\*\*|^__|\*\*$|__$/}},italic:{pattern:/(^|[^\\])([*_])(?:(?:\r?\n|\r)(?!\r?\n|\r)|.)+?\2/,lookbehind:!0,inside:{punctuation:/^[*_]|[*_]$/}},url:{pattern:/!?\[[^\]]+\](?:\([^\s)]+(?:[\t ]+"(?:\\.|[^"\\])*")?\)| ?\[[^\]\n]*\])/,inside:{variable:{pattern:/(!?\[)[^\]]+(?=\]$)/,lookbehind:!0},string:{pattern:/"(?:\\.|[^"\\])*"(?=\)$)/}}}}),Prism.languages.markdown.bold.inside.url=Prism.util.clone(Prism.languages.markdown.url),Prism.languages.markdown.italic.inside.url=Prism.util.clone(Prism.languages.markdown.url),Prism.languages.markdown.bold.inside.italic=Prism.util.clone(Prism.languages.markdown.italic),Prism.languages.markdown.italic.inside.bold=Prism.util.clone(Prism.languages.markdown.bold);
-Prism.languages.python={"triple-quoted-string":{pattern:/"""[\s\S]+?"""|'''[\s\S]+?'''/,alias:"string"},comment:{pattern:/(^|[^\\])#.*/,lookbehind:!0},string:/("|')(?:\\?.)*?\1/,"function":{pattern:/((?:^|\s)def[ \t]+)[a-zA-Z_][a-zA-Z0-9_]*(?=\()/g,lookbehind:!0},"class-name":{pattern:/(\bclass\s+)[a-z0-9_]+/i,lookbehind:!0},keyword:/\b(?:as|assert|async|await|break|class|continue|def|del|elif|else|except|exec|finally|for|from|global|if|import|in|is|lambda|pass|print|raise|return|try|while|with|yield|cimport)\b/,"boolean":/\b(?:True|False|None)\b/,number:/\b-?(?:0[bo])?(?:(?:\d|0x[\da-f])[\da-f]*\.?\d*|\.\d+)(?:e[+-]?\d+)?j?\b/i,operator:/[-+%=]=?|!=|\*\*?=?|\/\/?=?|<[<=>]?|>[=>]?|[&|^~]|\b(?:or|and|not)\b/,punctuation:/[{}[\];(),.:]/,"constant":/\b[A-Z_]{2,}\b/};
-Prism.languages.rest={table:[{pattern:/(\s*)(?:\+[=-]+)+\+(?:\r?\n|\r)(?:\1(?:[+|].+)+[+|](?:\r?\n|\r))+\1(?:\+[=-]+)+\+/,lookbehind:!0,inside:{punctuation:/\||(?:\+[=-]+)+\+/}},{pattern:/(\s*)(?:=+ +)+=+((?:\r?\n|\r)\1.+)+(?:\r?\n|\r)\1(?:=+ +)+=+(?=(?:\r?\n|\r){2}|\s*$)/,lookbehind:!0,inside:{punctuation:/[=-]+/}}],"substitution-def":{pattern:/(^\s*\.\. )\|(?:[^|\s](?:[^|]*[^|\s])?)\| [^:]+::/m,lookbehind:!0,inside:{substitution:{pattern:/^\|(?:[^|\s]|[^|\s][^|]*[^|\s])\|/,alias:"attr-value",inside:{punctuation:/^\||\|$/}},directive:{pattern:/( +)[^:]+::/,lookbehind:!0,alias:"function",inside:{punctuation:/::$/}}}},"link-target":[{pattern:/(^\s*\.\. )\[[^\]]+\]/m,lookbehind:!0,alias:"string",inside:{punctuation:/^\[|\]$/}},{pattern:/(^\s*\.\. )_(?:`[^`]+`|(?:[^:\\]|\\.)+):/m,lookbehind:!0,alias:"string",inside:{punctuation:/^_|:$/}}],directive:{pattern:/(^\s*\.\. )[^:]+::/m,lookbehind:!0,alias:"function",inside:{punctuation:/::$/}},comment:{pattern:/(^\s*\.\.)(?:(?: .+)?(?:(?:\r?\n|\r).+)+| .+)(?=(?:\r?\n|\r){2}|$)/m,lookbehind:!0},title:[{pattern:/^(([!"#$%&'()*+,\-.\/:;<=>?@\[\\\]^_`{|}~])\2+)(?:\r?\n|\r).+(?:\r?\n|\r)\1$/m,inside:{punctuation:/^[!"#$%&'()*+,\-.\/:;<=>?@\[\\\]^_`{|}~]+|[!"#$%&'()*+,\-.\/:;<=>?@\[\\\]^_`{|}~]+$/,important:/.+/}},{pattern:/(^|(?:\r?\n|\r){2}).+(?:\r?\n|\r)([!"#$%&'()*+,\-.\/:;<=>?@\[\\\]^_`{|}~])\2+(?=\r?\n|\r|$)/,lookbehind:!0,inside:{punctuation:/[!"#$%&'()*+,\-.\/:;<=>?@\[\\\]^_`{|}~]+$/,important:/.+/}}],hr:{pattern:/((?:\r?\n|\r){2})([!"#$%&'()*+,\-.\/:;<=>?@\[\\\]^_`{|}~])\2{3,}(?=(?:\r?\n|\r){2})/,lookbehind:!0,alias:"punctuation"},field:{pattern:/(^\s*):[^:\r\n]+:(?= )/m,lookbehind:!0,alias:"attr-name"},"command-line-option":{pattern:/(^\s*)(?:[+-][a-z\d]|(?:\-\-|\/)[a-z\d-]+)(?:[ =](?:[a-z][a-z\d_-]*|<[^<>]+>))?(?:, (?:[+-][a-z\d]|(?:\-\-|\/)[a-z\d-]+)(?:[ =](?:[a-z][a-z\d_-]*|<[^<>]+>))?)*(?=(?:\r?\n|\r)? {2,}\S)/im,lookbehind:!0,alias:"symbol"},"literal-block":{pattern:/::(?:\r?\n|\r){2}([ \t]+).+(?:(?:\r?\n|\r)\1.+)*/,inside:{"literal-block-punctuation":{pattern:/^::/,alias:"punctuation"}}},"quoted-literal-block":{pattern:/::(?:\r?\n|\r){2}([!"#$%&'()*+,\-.\/:;<=>?@\[\\\]^_`{|}~]).*(?:(?:\r?\n|\r)\1.*)*/,inside:{"literal-block-punctuation":{pattern:/^(?:::|([!"#$%&'()*+,\-.\/:;<=>?@\[\\\]^_`{|}~])\1*)/m,alias:"punctuation"}}},"list-bullet":{pattern:/(^\s*)(?:[*+\-•‣⁃]|\(?(?:\d+|[a-z]|[ivxdclm]+)\)|(?:\d+|[a-z]|[ivxdclm]+)\.)(?= )/im,lookbehind:!0,alias:"punctuation"},"doctest-block":{pattern:/(^\s*)>>> .+(?:(?:\r?\n|\r).+)*/m,lookbehind:!0,inside:{punctuation:/^>>>/}},inline:[{pattern:/(^|[\s\-:\/'"<(\[{])(?::[^:]+:`.*?`|`.*?`:[^:]+:|(\*\*?|``?|\|)(?!\s).*?[^\s]\2(?=[\s\-.,:;!?\\\/'")\]}]|$))/m,lookbehind:!0,inside:{bold:{pattern:/(^\*\*).+(?=\*\*$)/,lookbehind:!0},italic:{pattern:/(^\*).+(?=\*$)/,lookbehind:!0},"inline-literal":{pattern:/(^``).+(?=``$)/,lookbehind:!0,alias:"symbol"},role:{pattern:/^:[^:]+:|:[^:]+:$/,alias:"function",inside:{punctuation:/^:|:$/}},"interpreted-text":{pattern:/(^`).+(?=`$)/,lookbehind:!0,alias:"attr-value"},substitution:{pattern:/(^\|).+(?=\|$)/,lookbehind:!0,alias:"attr-value"},punctuation:/\*\*?|``?|\|/}}],link:[{pattern:/\[[^\]]+\]_(?=[\s\-.,:;!?\\\/'")\]}]|$)/,alias:"string",inside:{punctuation:/^\[|\]_$/}},{pattern:/(?:\b[a-z\d](?:[_.:+]?[a-z\d]+)*_?_|`[^`]+`_?_|_`[^`]+`)(?=[\s\-.,:;!?\\\/'")\]}]|$)/i,alias:"string",inside:{punctuation:/^_?`|`$|`?_?_$/}}],punctuation:{pattern:/(^\s*)(?:\|(?= |$)|(?:---?|—|\.\.|__)(?= )|\.\.$)/m,lookbehind:!0}};
-!function(e){e.languages.sass=e.languages.extend("css",{comment:{pattern:/^([ \t]*)\/[\/*].*(?:(?:\r?\n|\r)\1[ \t]+.+)*/m,lookbehind:!0}}),e.languages.insertBefore("sass","atrule",{"atrule-line":{pattern:/^(?:[ \t]*)[@+=].+/m,inside:{atrule:/(?:@[\w-]+|[+=])/m}}}),delete e.languages.sass.atrule;var a=/((\$[-_\w]+)|(#\{\$[-_\w]+\}))/i,t=[/[+*\/%]|[=!]=|<=?|>=?|\b(?:and|or|not)\b/,{pattern:/(\s+)-(?=\s)/,lookbehind:!0}];e.languages.insertBefore("sass","property",{"variable-line":{pattern:/^[ \t]*\$.+/m,inside:{punctuation:/:/,variable:a,operator:t}},"property-line":{pattern:/^[ \t]*(?:[^:\s]+ *:.*|:[^:\s]+.*)/m,inside:{property:[/[^:\s]+(?=\s*:)/,{pattern:/(:)[^:\s]+/,lookbehind:!0}],punctuation:/:/,variable:a,operator:t,important:e.languages.sass.important}}}),delete e.languages.sass.property,delete e.languages.sass.important,delete e.languages.sass.selector,e.languages.insertBefore("sass","punctuation",{selector:{pattern:/([ \t]*)\S(?:,?[^,\r\n]+)*(?:,(?:\r?\n|\r)\1[ \t]+\S(?:,?[^,\r\n]+)*)*/,lookbehind:!0}})}(Prism);
-Prism.languages.scss=Prism.languages.extend("css",{comment:{pattern:/(^|[^\\])(?:\/\*[\w\W]*?\*\/|\/\/.*)/,lookbehind:!0},atrule:{pattern:/@[\w-]+(?:\([^()]+\)|[^(])*?(?=\s+[{;])/,inside:{rule:/@[\w-]+/}},url:/(?:[-a-z]+-)*url(?=\()/i,selector:{pattern:/(?=\S)[^@;\{\}\(\)]?([^@;\{\}\(\)]|&|#\{\$[-_\w]+\})+(?=\s*\{(\}|\s|[^\}]+(:|\{)[^\}]+))/m,inside:{placeholder:/%[-_\w]+/}}}),Prism.languages.insertBefore("scss","atrule",{keyword:[/@(?:if|else(?: if)?|for|each|while|import|extend|debug|warn|mixin|include|function|return|content)/i,{pattern:/( +)(?:from|through)(?= )/,lookbehind:!0}]}),Prism.languages.insertBefore("scss","property",{variable:/\$[-_\w]+|#\{\$[-_\w]+\}/}),Prism.languages.insertBefore("scss","function",{placeholder:{pattern:/%[-_\w]+/,alias:"selector"},statement:/\B!(?:default|optional)\b/i,"boolean":/\b(?:true|false)\b/,"null":/\bnull\b/,operator:{pattern:/(\s)(?:[-+*\/%]|[=!]=|<=?|>=?|and|or|not)(?=\s)/,lookbehind:!0}}),Prism.languages.scss.atrule.inside.rest=Prism.util.clone(Prism.languages.scss);
-Prism.languages.sql={comment:{pattern:/(^|[^\\])(?:\/\*[\w\W]*?\*\/|(?:--|\/\/|#).*)/,lookbehind:!0},string:{pattern:/(^|[^@\\])("|')(?:\\?[\s\S])*?\2/,lookbehind:!0},variable:/@[\w.$]+|@("|'|`)(?:\\?[\s\S])+?\1/,"function":/\b(?:COUNT|SUM|AVG|MIN|MAX|FIRST|LAST|UCASE|LCASE|MID|LEN|ROUND|NOW|FORMAT)(?=\s*\()/i,keyword:/\b(?:ACTION|ADD|AFTER|ALGORITHM|ALL|ALTER|ANALYZE|ANY|APPLY|AS|ASC|AUTHORIZATION|BACKUP|BDB|BEGIN|BERKELEYDB|BIGINT|BINARY|BIT|BLOB|BOOL|BOOLEAN|BREAK|BROWSE|BTREE|BULK|BY|CALL|CASCADED?|CASE|CHAIN|CHAR VARYING|CHARACTER (?:SET|VARYING)|CHARSET|CHECK|CHECKPOINT|CLOSE|CLUSTERED|COALESCE|COLLATE|COLUMN|COLUMNS|COMMENT|COMMIT|COMMITTED|COMPUTE|CONNECT|CONSISTENT|CONSTRAINT|CONTAINS|CONTAINSTABLE|CONTINUE|CONVERT|CREATE|CROSS|CURRENT(?:_DATE|_TIME|_TIMESTAMP|_USER)?|CURSOR|DATA(?:BASES?)?|DATETIME|DBCC|DEALLOCATE|DEC|DECIMAL|DECLARE|DEFAULT|DEFINER|DELAYED|DELETE|DENY|DESC|DESCRIBE|DETERMINISTIC|DISABLE|DISCARD|DISK|DISTINCT|DISTINCTROW|DISTRIBUTED|DO|DOUBLE(?: PRECISION)?|DROP|DUMMY|DUMP(?:FILE)?|DUPLICATE KEY|ELSE|ENABLE|ENCLOSED BY|END|ENGINE|ENUM|ERRLVL|ERRORS|ESCAPE(?:D BY)?|EXCEPT|EXEC(?:UTE)?|EXISTS|EXIT|EXPLAIN|EXTENDED|FETCH|FIELDS|FILE|FILLFACTOR|FIRST|FIXED|FLOAT|FOLLOWING|FOR(?: EACH ROW)?|FORCE|FOREIGN|FREETEXT(?:TABLE)?|FROM|FULL|FUNCTION|GEOMETRY(?:COLLECTION)?|GLOBAL|GOTO|GRANT|GROUP|HANDLER|HASH|HAVING|HOLDLOCK|IDENTITY(?:_INSERT|COL)?|IF|IGNORE|IMPORT|INDEX|INFILE|INNER|INNODB|INOUT|INSERT|INT|INTEGER|INTERSECT|INTO|INVOKER|ISOLATION LEVEL|JOIN|KEYS?|KILL|LANGUAGE SQL|LAST|LEFT|LIMIT|LINENO|LINES|LINESTRING|LOAD|LOCAL|LOCK|LONG(?:BLOB|TEXT)|MATCH(?:ED)?|MEDIUM(?:BLOB|INT|TEXT)|MERGE|MIDDLEINT|MODIFIES SQL DATA|MODIFY|MULTI(?:LINESTRING|POINT|POLYGON)|NATIONAL(?: CHAR VARYING| CHARACTER(?: VARYING)?| VARCHAR)?|NATURAL|NCHAR(?: VARCHAR)?|NEXT|NO(?: SQL|CHECK|CYCLE)?|NONCLUSTERED|NULLIF|NUMERIC|OFF?|OFFSETS?|ON|OPEN(?:DATASOURCE|QUERY|ROWSET)?|OPTIMIZE|OPTION(?:ALLY)?|ORDER|OUT(?:ER|FILE)?|OVER|PARTIAL|PARTITION|PERCENT|PIVOT|PLAN|POINT|POLYGON|PRECEDING|PRECISION|PREV|PRIMARY|PRINT|PRIVILEGES|PROC(?:EDURE)?|PUBLIC|PURGE|QUICK|RAISERROR|READ(?:S SQL DATA|TEXT)?|REAL|RECONFIGURE|REFERENCES|RELEASE|RENAME|REPEATABLE|REPLICATION|REQUIRE|RESTORE|RESTRICT|RETURNS?|REVOKE|RIGHT|ROLLBACK|ROUTINE|ROW(?:COUNT|GUIDCOL|S)?|RTREE|RULE|SAVE(?:POINT)?|SCHEMA|SELECT|SERIAL(?:IZABLE)?|SESSION(?:_USER)?|SET(?:USER)?|SHARE MODE|SHOW|SHUTDOWN|SIMPLE|SMALLINT|SNAPSHOT|SOME|SONAME|START(?:ING BY)?|STATISTICS|STATUS|STRIPED|SYSTEM_USER|TABLES?|TABLESPACE|TEMP(?:ORARY|TABLE)?|TERMINATED BY|TEXT(?:SIZE)?|THEN|TIMESTAMP|TINY(?:BLOB|INT|TEXT)|TOP?|TRAN(?:SACTIONS?)?|TRIGGER|TRUNCATE|TSEQUAL|TYPES?|UNBOUNDED|UNCOMMITTED|UNDEFINED|UNION|UNIQUE|UNPIVOT|UPDATE(?:TEXT)?|USAGE|USE|USER|USING|VALUES?|VAR(?:BINARY|CHAR|CHARACTER|YING)|VIEW|WAITFOR|WARNINGS|WHEN|WHERE|WHILE|WITH(?: ROLLUP|IN)?|WORK|WRITE(?:TEXT)?)\b/i,"boolean":/\b(?:TRUE|FALSE|NULL)\b/i,number:/\b-?(?:0x)?\d*\.?[\da-f]+\b/,operator:/[-+*\/=%^~]|&&?|\|?\||!=?|<(?:=>?|<|>)?|>[>=]?|\b(?:AND|BETWEEN|IN|LIKE|NOT|OR|IS|DIV|REGEXP|RLIKE|SOUNDS LIKE|XOR)\b/i,punctuation:/[;[\]()`,.]/};
-Prism.languages.wiki=Prism.languages.extend("markup",{"block-comment":{pattern:/(^|[^\\])\/\*[\w\W]*?\*\//,lookbehind:!0,alias:"comment"},heading:{pattern:/^(=+).+?\1/m,inside:{punctuation:/^=+|=+$/,important:/.+/}},emphasis:{pattern:/('{2,5}).+?\1/,inside:{"bold italic":{pattern:/(''''').+?(?=\1)/,lookbehind:!0},bold:{pattern:/(''')[^'](?:.*?[^'])?(?=\1)/,lookbehind:!0},italic:{pattern:/('')[^'](?:.*?[^'])?(?=\1)/,lookbehind:!0},punctuation:/^''+|''+$/}},hr:{pattern:/^-{4,}/m,alias:"punctuation"},url:[/ISBN +(?:97[89][ -]?)?(?:\d[ -]?){9}[\dx]\b|(?:RFC|PMID) +\d+/i,/\[\[.+?\]\]|\[.+?\]/],variable:[/__[A-Z]+__/,/\{{3}.+?\}{3}/,/\{\{.+?}}/],symbol:[/^#redirect/im,/~{3,5}/],"table-tag":{pattern:/((?:^|[|!])[|!])[^|\r\n]+\|(?!\|)/m,lookbehind:!0,inside:{"table-bar":{pattern:/\|$/,alias:"punctuation"},rest:Prism.languages.markup.tag.inside}},punctuation:/^(?:\{\||\|\}|\|-|[*#:;!|])|\|\||!!/m}),Prism.languages.insertBefore("wiki","tag",{nowiki:{pattern:/<(nowiki|pre|source)\b[\w\W]*?>[\w\W]*?<\/\1>/i,inside:{tag:{pattern:/<(?:nowiki|pre|source)\b[\w\W]*?>|<\/(?:nowiki|pre|source)>/i,inside:Prism.languages.markup.tag.inside}}}});
-Prism.languages.yaml={scalar:{pattern:/([\-:]\s*(![^\s]+)?[ \t]*[|>])[ \t]*(?:((?:\r?\n|\r)[ \t]+)[^\r\n]+(?:\3[^\r\n]+)*)/,lookbehind:!0,alias:"string"},comment:/#.*/,key:{pattern:/(\s*[:\-,[{\r\n?][ \t]*(![^\s]+)?[ \t]*)[^\r\n{[\]},#]+?(?=\s*:\s)/,lookbehind:!0,alias:"atrule"},directive:{pattern:/(^[ \t]*)%.+/m,lookbehind:!0,alias:"important"},datetime:{pattern:/([:\-,[{]\s*(![^\s]+)?[ \t]*)(\d{4}-\d\d?-\d\d?([tT]|[ \t]+)\d\d?:\d{2}:\d{2}(\.\d*)?[ \t]*(Z|[-+]\d\d?(:\d{2})?)?|\d{4}-\d{2}-\d{2}|\d\d?:\d{2}(:\d{2}(\.\d*)?)?)(?=[ \t]*($|,|]|}))/m,lookbehind:!0,alias:"number"},"boolean":{pattern:/([:\-,[{]\s*(![^\s]+)?[ \t]*)(true|false)[ \t]*(?=$|,|]|})/im,lookbehind:!0,alias:"important"},"null":{pattern:/([:\-,[{]\s*(![^\s]+)?[ \t]*)(null|~)[ \t]*(?=$|,|]|})/im,lookbehind:!0,alias:"important"},string:{pattern:/([:\-,[{]\s*(![^\s]+)?[ \t]*)("(?:[^"\\]|\\.)*"|'(?:[^'\\]|\\.)*')(?=[ \t]*($|,|]|}))/m,lookbehind:!0},number:{pattern:/([:\-,[{]\s*(![^\s]+)?[ \t]*)[+\-]?(0x[\da-f]+|0o[0-7]+|(\d+\.?\d*|\.?\d+)(e[\+\-]?\d+)?|\.inf|\.nan)[ \t]*(?=$|,|]|})/im,lookbehind:!0},tag:/![^\s]+/,important:/[&*][\w]+/,punctuation:/---|[:[\]{}\-,|>?]|\.\.\./};
-Prism.languages.julia={comment:{pattern:/(^|[^\\])#.*/,lookbehind:!0},string:/("""|''')[\s\S]+?\1|("|')(?:\\.|(?!\2)[^\\\r\n])*\2/,keyword:/\b(?:abstract|baremodule|begin|bitstype|break|catch|ccall|const|continue|do|else|elseif|end|export|finally|for|function|global|if|immutable|import|importall|let|local|macro|module|print|println|quote|return|try|type|typealias|using|while)\b/,"boolean":/\b(?:true|false)\b/,number:/(?:\b(?=\d)|\B(?=\.))(?:0[box])?(?:[\da-f]+\.?\d*|\.\d+)(?:[efp][+-]?\d+)?j?/i,operator:/[-+*^%÷&$\\]=?|\/[\/=]?|!=?=?|\|[=>]?|<(?:<=?|[=:])?|>(?:=|>>?=?)?|==?=?|[~≠≤≥]/,punctuation:/[{}[\];(),.:]/};
-Prism.languages.r={comment:/#.*/,string:{pattern:/(['"])(?:\\.|(?!\1)[^\\\r\n])*\1/,greedy:!0},"percent-operator":{pattern:/%[^%\s]*%/,alias:"operator"},"boolean":/\b(?:TRUE|FALSE)\b/,ellipsis:/\.\.(?:\.|\d+)/,number:[/\b(?:NaN|Inf)\b/,/(?:\b0x[\dA-Fa-f]+(?:\.\d*)?|\b\d+\.?\d*|\B\.\d+)(?:[EePp][+-]?\d+)?[iL]?/],keyword:/\b(?:if|else|repeat|while|function|for|in|next|break|NULL|NA|NA_integer_|NA_real_|NA_complex_|NA_character_)\b/,operator:/->?>?|<(?:=|<?-)?|[>=!]=?|::?|&&?|\|\|?|[+*\/^$@~]/,punctuation:/[(){}\[\],;]/};
-Prism.languages.docker={keyword:{pattern:/(^\s*)(?:ADD|ARG|CMD|COPY|ENTRYPOINT|ENV|EXPOSE|FROM|HEALTHCHECK|LABEL|MAINTAINER|ONBUILD|RUN|SHELL|STOPSIGNAL|USER|VOLUME|WORKDIR)(?=\s)/im,lookbehind:!0},string:/("|')(?:(?!\1)[^\\\r\n]|\\(?:\r\n|[\s\S]))*\1/,comment:/#.*/,punctuation:/---|\.\.\.|[:[\]{}\-,|>?]/},Prism.languages.dockerfile=Prism.languages.docker;
diff --git a/website/assets/js/vendor/quickstart.min.js b/website/assets/js/vendor/quickstart.min.js
deleted file mode 100644
index 5212c46fb..000000000
--- a/website/assets/js/vendor/quickstart.min.js
+++ /dev/null
@@ -1,7 +0,0 @@
-/**
- * quickstart.js
- *
- * @author Ines Montani <ines@ines.io>
- * @version 0.0.1
- * @license MIT
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diff --git a/website/assets/js/vendor/vue-markdown.min.js b/website/assets/js/vendor/vue-markdown.min.js
deleted file mode 100644
index 24686aad9..000000000
--- a/website/assets/js/vendor/vue-markdown.min.js
+++ /dev/null
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e.replace(m,"\\$&")},t.normalizeReference=function(e){return e.trim().replace(/\s+/g," ").toUpperCase()}},function(e,t,r){"use strict";e.exports=r(63)},function(e,t){e.exports={Aacute:"Á",aacute:"á",Abreve:"Ă",abreve:"ă",ac:"∾",acd:"∿",acE:"∾̳",Acirc:"Â",acirc:"â",acute:"´",Acy:"А",acy:"а",AElig:"Æ",aelig:"æ",af:"⁡",Afr:"𝔄",afr:"𝔞",Agrave:"À",agrave:"à",alefsym:"ℵ",aleph:"ℵ",Alpha:"Α",alpha:"α",Amacr:"Ā",amacr:"ā",amalg:"⨿",amp:"&",AMP:"&",andand:"⩕",And:"⩓",and:"∧",andd:"⩜",andslope:"⩘",andv:"⩚",ang:"∠",ange:"⦤",angle:"∠",angmsdaa:"⦨",angmsdab:"⦩",angmsdac:"⦪",angmsdad:"⦫",angmsdae:"⦬",angmsdaf:"⦭",angmsdag:"⦮",angmsdah:"⦯",angmsd:"∡",angrt:"∟",angrtvb:"⊾",angrtvbd:"⦝",angsph:"∢",angst:"Å",angzarr:"⍼",Aogon:"Ą",aogon:"ą",Aopf:"𝔸",aopf:"𝕒",apacir:"⩯",ap:"≈",apE:"⩰",ape:"≊",apid:"≋",apos:"'",ApplyFunction:"⁡",approx:"≈",approxeq:"≊",Aring:"Å",aring:"å",Ascr:"𝒜",ascr:"𝒶",Assign:"≔",ast:"*",asymp:"≈",asympeq:"≍",Atilde:"Ã",atilde:"ã",Auml:"Ä",auml:"ä",awconint:"∳",awint:"⨑",backcong:"≌",backepsilon:"϶",backprime:"‵",backsim:"∽",backsimeq:"⋍",Backslash:"∖",Barv:"⫧",barvee:"⊽",barwed:"⌅",Barwed:"⌆",barwedge:"⌅",bbrk:"⎵",bbrktbrk:"⎶",bcong:"≌",Bcy:"Б",bcy:"б",bdquo:"„",becaus:"∵",because:"∵",Because:"∵",bemptyv:"⦰",bepsi:"϶",bernou:"ℬ",Bernoullis:"ℬ",Beta:"Β",beta:"β",beth:"ℶ",between:"≬",Bfr:"𝔅",bfr:"𝔟",bigcap:"⋂",bigcirc:"◯",bigcup:"⋃",bigodot:"⨀",bigoplus:"⨁",bigotimes:"⨂",bigsqcup:"⨆",bigstar:"★",bigtriangledown:"▽",bigtriangleup:"△",biguplus:"⨄",bigvee:"⋁",bigwedge:"⋀",bkarow:"⤍",blacklozenge:"⧫",blacksquare:"▪",blacktriangle:"▴",blacktriangledown:"▾",blacktriangleleft:"◂",blacktriangleright:"▸",blank:"␣",blk12:"▒",blk14:"░",blk34:"▓",block:"█",bne:"=⃥",bnequiv:"≡⃥",bNot:"⫭",bnot:"⌐",Bopf:"𝔹",bopf:"𝕓",bot:"⊥",bottom:"⊥",bowtie:"⋈",boxbox:"⧉",boxdl:"┐",boxdL:"╕",boxDl:"╖",boxDL:"╗",boxdr:"┌",boxdR:"╒",boxDr:"╓",boxDR:"╔",boxh:"─",boxH:"═",boxhd:"┬",boxHd:"╤",boxhD:"╥",boxHD:"╦",boxhu:"┴",boxHu:"╧",boxhU:"╨",boxHU:"╩",boxminus:"⊟",boxplus:"⊞",boxtimes:"⊠",boxul:"┘",boxuL:"╛",boxUl:"╜",boxUL:"╝",boxur:"└",boxuR:"╘",boxUr:"╙",boxUR:"╚",boxv:"│",boxV:"║",boxvh:"┼",boxvH:"╪",boxVh:"╫",boxVH:"╬",boxvl:"┤",boxvL:"╡",boxVl:"╢",boxVL:"╣",boxvr:"├",boxvR:"╞",boxVr:"╟",boxVR:"╠",bprime:"‵",breve:"˘",Breve:"˘",brvbar:"¦",bscr:"𝒷",Bscr:"ℬ",bsemi:"⁏",bsim:"∽",bsime:"⋍",bsolb:"⧅",bsol:"\\",bsolhsub:"⟈",bull:"•",bullet:"•",bump:"≎",bumpE:"⪮",bumpe:"≏",Bumpeq:"≎",bumpeq:"≏",Cacute:"Ć",cacute:"ć",capand:"⩄",capbrcup:"⩉",capcap:"⩋",cap:"∩",Cap:"⋒",capcup:"⩇",capdot:"⩀",CapitalDifferentialD:"ⅅ",caps:"∩︀",caret:"⁁",caron:"ˇ",Cayleys:"ℭ",ccaps:"⩍",Ccaron:"Č",ccaron:"č",Ccedil:"Ç",ccedil:"ç",Ccirc:"Ĉ",ccirc:"ĉ",Cconint:"∰",ccups:"⩌",ccupssm:"⩐",Cdot:"Ċ",cdot:"ċ",cedil:"¸",Cedilla:"¸",cemptyv:"⦲",cent:"¢",centerdot:"·",CenterDot:"·",cfr:"𝔠",Cfr:"ℭ",CHcy:"Ч",chcy:"ч",check:"✓",checkmark:"✓",Chi:"Χ",chi:"χ",circ:"ˆ",circeq:"≗",circlearrowleft:"↺",circlearrowright:"↻",circledast:"⊛",circledcirc:"⊚",circleddash:"⊝",CircleDot:"⊙",circledR:"®",circledS:"Ⓢ",CircleMinus:"⊖",CirclePlus:"⊕",CircleTimes:"⊗",cir:"○",cirE:"⧃",cire:"≗",cirfnint:"⨐",cirmid:"⫯",cirscir:"⧂",ClockwiseContourIntegral:"∲",CloseCurlyDoubleQuote:"”",CloseCurlyQuote:"’",clubs:"♣",clubsuit:"♣",colon:":",Colon:"∷",Colone:"⩴",colone:"≔",coloneq:"≔",comma:",",commat:"@",comp:"∁",compfn:"∘",complement:"∁",complexes:"ℂ",cong:"≅",congdot:"⩭",Congruent:"≡",conint:"∮",Conint:"∯",ContourIntegral:"∮",copf:"𝕔",Copf:"ℂ",coprod:"∐",Coproduct:"∐",copy:"©",COPY:"©",copysr:"℗",CounterClockwiseContourIntegral:"∳",crarr:"↵",cross:"✗",Cross:"⨯",Cscr:"𝒞",cscr:"𝒸",csub:"⫏",csube:"⫑",csup:"⫐",csupe:"⫒",ctdot:"⋯",cudarrl:"⤸",cudarrr:"⤵",cuepr:"⋞",cuesc:"⋟",cularr:"↶",cularrp:"⤽",cupbrcap:"⩈",cupcap:"⩆",CupCap:"≍",cup:"∪",Cup:"⋓",cupcup:"⩊",cupdot:"⊍",cupor:"⩅",cups:"∪︀",curarr:"↷",curarrm:"⤼",curlyeqprec:"⋞",curlyeqsucc:"⋟",curlyvee:"⋎",curlywedge:"⋏",curren:"¤",curvearrowleft:"↶",curvearrowright:"↷",cuvee:"⋎",cuwed:"⋏",cwconint:"∲",cwint:"∱",cylcty:"⌭",dagger:"†",Dagger:"‡",daleth:"ℸ",darr:"↓",Darr:"↡",dArr:"⇓",dash:"‐",Dashv:"⫤",dashv:"⊣",dbkarow:"⤏",dblac:"˝",Dcaron:"Ď",dcaron:"ď",Dcy:"Д",dcy:"д",ddagger:"‡",ddarr:"⇊",DD:"ⅅ",dd:"ⅆ",DDotrahd:"⤑",ddotseq:"⩷",deg:"°",Del:"∇",Delta:"Δ",delta:"δ",demptyv:"⦱",dfisht:"⥿",Dfr:"𝔇",dfr:"𝔡",dHar:"⥥",dharl:"⇃",dharr:"⇂",DiacriticalAcute:"´",DiacriticalDot:"˙",DiacriticalDoubleAcute:"˝",DiacriticalGrave:"`",DiacriticalTilde:"˜",diam:"⋄",diamond:"⋄",Diamond:"⋄",diamondsuit:"♦",diams:"♦",die:"¨",DifferentialD:"ⅆ",digamma:"ϝ",disin:"⋲",div:"÷",divide:"÷",divideontimes:"⋇",divonx:"⋇",DJcy:"Ђ",djcy:"ђ",dlcorn:"⌞",dlcrop:"⌍",dollar:"$",Dopf:"𝔻",dopf:"𝕕",Dot:"¨",dot:"˙",DotDot:"⃜",doteq:"≐",doteqdot:"≑",DotEqual:"≐",dotminus:"∸",dotplus:"∔",dotsquare:"⊡",doublebarwedge:"⌆",DoubleContourIntegral:"∯",DoubleDot:"¨",DoubleDownArrow:"⇓",DoubleLeftArrow:"⇐",DoubleLeftRightArrow:"⇔",DoubleLeftTee:"⫤",DoubleLongLeftArrow:"⟸",DoubleLongLeftRightArrow:"⟺",DoubleLongRightArrow:"⟹",DoubleRightArrow:"⇒",DoubleRightTee:"⊨",DoubleUpArrow:"⇑",DoubleUpDownArrow:"⇕",DoubleVerticalBar:"∥",DownArrowBar:"⤓",downarrow:"↓",DownArrow:"↓",Downarrow:"⇓",DownArrowUpArrow:"⇵",DownBreve:"̑",downdownarrows:"⇊",downharpoonleft:"⇃",downharpoonright:"⇂",DownLeftRightVector:"⥐",DownLeftTeeVector:"⥞",DownLeftVectorBar:"⥖",DownLeftVector:"↽",DownRightTeeVector:"⥟",DownRightVectorBar:"⥗",DownRightVector:"⇁",DownTeeArrow:"↧",DownTee:"⊤",drbkarow:"⤐",drcorn:"⌟",drcrop:"⌌",Dscr:"𝒟",dscr:"𝒹",DScy:"Ѕ",dscy:"ѕ",dsol:"⧶",Dstrok:"Đ",dstrok:"đ",dtdot:"⋱",dtri:"▿",dtrif:"▾",duarr:"⇵",duhar:"⥯",dwangle:"⦦",DZcy:"Џ",dzcy:"џ",dzigrarr:"⟿",Eacute:"É",eacute:"é",easter:"⩮",Ecaron:"Ě",ecaron:"ě",Ecirc:"Ê",ecirc:"ê",ecir:"≖",ecolon:"≕",Ecy:"Э",ecy:"э",eDDot:"⩷",Edot:"Ė",edot:"ė",eDot:"≑",ee:"ⅇ",efDot:"≒",Efr:"𝔈",efr:"𝔢",eg:"⪚",Egrave:"È",egrave:"è",egs:"⪖",egsdot:"⪘",el:"⪙",Element:"∈",elinters:"⏧",ell:"ℓ",els:"⪕",elsdot:"⪗",Emacr:"Ē",emacr:"ē",empty:"∅",emptyset:"∅",EmptySmallSquare:"◻",emptyv:"∅",EmptyVerySmallSquare:"▫",emsp13:" ",emsp14:" ",emsp:" ",ENG:"Ŋ",eng:"ŋ",ensp:" ",Eogon:"Ę",eogon:"ę",Eopf:"𝔼",eopf:"𝕖",epar:"⋕",eparsl:"⧣",eplus:"⩱",epsi:"ε",Epsilon:"Ε",epsilon:"ε",epsiv:"ϵ",eqcirc:"≖",eqcolon:"≕",eqsim:"≂",eqslantgtr:"⪖",eqslantless:"⪕",Equal:"⩵",equals:"=",EqualTilde:"≂",equest:"≟",Equilibrium:"⇌",equiv:"≡",equivDD:"⩸",eqvparsl:"⧥",erarr:"⥱",erDot:"≓",escr:"ℯ",Escr:"ℰ",esdot:"≐",Esim:"⩳",esim:"≂",Eta:"Η",eta:"η",ETH:"Ð",eth:"ð",Euml:"Ë",euml:"ë",euro:"€",excl:"!",exist:"∃",Exists:"∃",expectation:"ℰ",exponentiale:"ⅇ",ExponentialE:"ⅇ",fallingdotseq:"≒",Fcy:"Ф",fcy:"ф",female:"♀",ffilig:"ffi",fflig:"ff",ffllig:"ffl",Ffr:"𝔉",ffr:"𝔣",filig:"fi",FilledSmallSquare:"◼",FilledVerySmallSquare:"▪",fjlig:"fj",flat:"♭",fllig:"fl",fltns:"▱",fnof:"ƒ",Fopf:"𝔽",fopf:"𝕗",forall:"∀",ForAll:"∀",fork:"⋔",forkv:"⫙",Fouriertrf:"ℱ",fpartint:"⨍",frac12:"½",frac13:"⅓",frac14:"¼",frac15:"⅕",frac16:"⅙",frac18:"⅛",frac23:"⅔",frac25:"⅖",frac34:"¾",frac35:"⅗",frac38:"⅜",frac45:"⅘",frac56:"⅚",frac58:"⅝",frac78:"⅞",frasl:"⁄",frown:"⌢",fscr:"𝒻",Fscr:"ℱ",gacute:"ǵ",Gamma:"Γ",gamma:"γ",Gammad:"Ϝ",gammad:"ϝ",gap:"⪆",Gbreve:"Ğ",gbreve:"ğ",Gcedil:"Ģ",Gcirc:"Ĝ",gcirc:"ĝ",Gcy:"Г",gcy:"г",Gdot:"Ġ",gdot:"ġ",ge:"≥",gE:"≧",gEl:"⪌",gel:"⋛",geq:"≥",geqq:"≧",geqslant:"⩾",gescc:"⪩",ges:"⩾",gesdot:"⪀",gesdoto:"⪂",gesdotol:"⪄",gesl:"⋛︀",gesles:"⪔",Gfr:"𝔊",gfr:"𝔤",gg:"≫",Gg:"⋙",ggg:"⋙",gimel:"ℷ",GJcy:"Ѓ",gjcy:"ѓ",gla:"⪥",gl:"≷",glE:"⪒",glj:"⪤",gnap:"⪊",gnapprox:"⪊",gne:"⪈",gnE:"≩",gneq:"⪈",gneqq:"≩",gnsim:"⋧",Gopf:"𝔾",gopf:"𝕘",grave:"`",GreaterEqual:"≥",GreaterEqualLess:"⋛",GreaterFullEqual:"≧",GreaterGreater:"⪢",GreaterLess:"≷",GreaterSlantEqual:"⩾",GreaterTilde:"≳",Gscr:"𝒢",gscr:"ℊ",gsim:"≳",gsime:"⪎",gsiml:"⪐",gtcc:"⪧",gtcir:"⩺",gt:">",GT:">",Gt:"≫",gtdot:"⋗",gtlPar:"⦕",gtquest:"⩼",gtrapprox:"⪆",gtrarr:"⥸",gtrdot:"⋗",gtreqless:"⋛",gtreqqless:"⪌",gtrless:"≷",gtrsim:"≳",gvertneqq:"≩︀",gvnE:"≩︀",Hacek:"ˇ",hairsp:" ",half:"½",hamilt:"ℋ",HARDcy:"Ъ",hardcy:"ъ",harrcir:"⥈",harr:"↔",hArr:"⇔",harrw:"↭",Hat:"^",hbar:"ℏ",Hcirc:"Ĥ",hcirc:"ĥ",hearts:"♥",heartsuit:"♥",hellip:"…",hercon:"⊹",hfr:"𝔥",Hfr:"ℌ",HilbertSpace:"ℋ",hksearow:"⤥",hkswarow:"⤦",hoarr:"⇿",homtht:"∻",hookleftarrow:"↩",hookrightarrow:"↪",hopf:"𝕙",Hopf:"ℍ",horbar:"―",HorizontalLine:"─",hscr:"𝒽",Hscr:"ℋ",hslash:"ℏ",Hstrok:"Ħ",hstrok:"ħ",HumpDownHump:"≎",HumpEqual:"≏",hybull:"⁃",hyphen:"‐",Iacute:"Í",iacute:"í",ic:"⁣",Icirc:"Î",icirc:"î",Icy:"И",icy:"и",Idot:"İ",IEcy:"Е",iecy:"е",iexcl:"¡",iff:"⇔",ifr:"𝔦",Ifr:"ℑ",Igrave:"Ì",igrave:"ì",ii:"ⅈ",iiiint:"⨌",iiint:"∭",iinfin:"⧜",iiota:"℩",IJlig:"IJ",ijlig:"ij",Imacr:"Ī",imacr:"ī",image:"ℑ",ImaginaryI:"ⅈ",imagline:"ℐ",imagpart:"ℑ",imath:"ı",Im:"ℑ",imof:"⊷",imped:"Ƶ",Implies:"⇒",incare:"℅",in:"∈",infin:"∞",infintie:"⧝",inodot:"ı",intcal:"⊺",int:"∫",Int:"∬",integers:"ℤ",Integral:"∫",intercal:"⊺",Intersection:"⋂",intlarhk:"⨗",intprod:"⨼",InvisibleComma:"⁣",InvisibleTimes:"⁢",IOcy:"Ё",iocy:"ё",Iogon:"Į",iogon:"į",Iopf:"𝕀",iopf:"𝕚",Iota:"Ι",iota:"ι",iprod:"⨼",iquest:"¿",iscr:"𝒾",Iscr:"ℐ",isin:"∈",isindot:"⋵",isinE:"⋹",isins:"⋴",isinsv:"⋳",isinv:"∈",it:"⁢",Itilde:"Ĩ",itilde:"ĩ",Iukcy:"І",iukcy:"і",Iuml:"Ï",iuml:"ï",Jcirc:"Ĵ",jcirc:"ĵ",Jcy:"Й",jcy:"й",Jfr:"𝔍",jfr:"𝔧",jmath:"ȷ",Jopf:"𝕁",jopf:"𝕛",Jscr:"𝒥",jscr:"𝒿",Jsercy:"Ј",jsercy:"ј",Jukcy:"Є",jukcy:"є",Kappa:"Κ",kappa:"κ",kappav:"ϰ",Kcedil:"Ķ",kcedil:"ķ",Kcy:"К",kcy:"к",Kfr:"𝔎",kfr:"𝔨",kgreen:"ĸ",KHcy:"Х",khcy:"х",KJcy:"Ќ",kjcy:"ќ",Kopf:"𝕂",kopf:"𝕜",Kscr:"𝒦",kscr:"𝓀",lAarr:"⇚",Lacute:"Ĺ",lacute:"ĺ",laemptyv:"⦴",lagran:"ℒ",Lambda:"Λ",lambda:"λ",lang:"⟨",Lang:"⟪",langd:"⦑",langle:"⟨",lap:"⪅",Laplacetrf:"ℒ",laquo:"«",larrb:"⇤",larrbfs:"⤟",larr:"←",Larr:"↞",lArr:"⇐",larrfs:"⤝",larrhk:"↩",larrlp:"↫",larrpl:"⤹",larrsim:"⥳",larrtl:"↢",latail:"⤙",lAtail:"⤛",lat:"⪫",late:"⪭",lates:"⪭︀",lbarr:"⤌",lBarr:"⤎",lbbrk:"❲",lbrace:"{",lbrack:"[",lbrke:"⦋",lbrksld:"⦏",lbrkslu:"⦍",Lcaron:"Ľ",lcaron:"ľ",Lcedil:"Ļ",lcedil:"ļ",lceil:"⌈",lcub:"{",Lcy:"Л",lcy:"л",ldca:"⤶",ldquo:"“",ldquor:"„",ldrdhar:"⥧",ldrushar:"⥋",ldsh:"↲",le:"≤",lE:"≦",LeftAngleBracket:"⟨",LeftArrowBar:"⇤",leftarrow:"←",LeftArrow:"←",Leftarrow:"⇐",LeftArrowRightArrow:"⇆",leftarrowtail:"↢",LeftCeiling:"⌈",LeftDoubleBracket:"⟦",LeftDownTeeVector:"⥡",LeftDownVectorBar:"⥙",LeftDownVector:"⇃",LeftFloor:"⌊",leftharpoondown:"↽",leftharpoonup:"↼",leftleftarrows:"⇇",leftrightarrow:"↔",LeftRightArrow:"↔",Leftrightarrow:"⇔",leftrightarrows:"⇆",leftrightharpoons:"⇋",leftrightsquigarrow:"↭",LeftRightVector:"⥎",LeftTeeArrow:"↤",LeftTee:"⊣",LeftTeeVector:"⥚",leftthreetimes:"⋋",LeftTriangleBar:"⧏",LeftTriangle:"⊲",LeftTriangleEqual:"⊴",LeftUpDownVector:"⥑",LeftUpTeeVector:"⥠",LeftUpVectorBar:"⥘",LeftUpVector:"↿",LeftVectorBar:"⥒",LeftVector:"↼",lEg:"⪋",leg:"⋚",leq:"≤",leqq:"≦",leqslant:"⩽",lescc:"⪨",les:"⩽",lesdot:"⩿",lesdoto:"⪁",lesdotor:"⪃",lesg:"⋚︀",lesges:"⪓",lessapprox:"⪅",lessdot:"⋖",lesseqgtr:"⋚",lesseqqgtr:"⪋",LessEqualGreater:"⋚",LessFullEqual:"≦",LessGreater:"≶",lessgtr:"≶",LessLess:"⪡",lesssim:"≲",LessSlantEqual:"⩽",LessTilde:"≲",lfisht:"⥼",lfloor:"⌊",Lfr:"𝔏",lfr:"𝔩",lg:"≶",lgE:"⪑",lHar:"⥢",lhard:"↽",lharu:"↼",lharul:"⥪",lhblk:"▄",LJcy:"Љ",ljcy:"љ",llarr:"⇇",ll:"≪",Ll:"⋘",llcorner:"⌞",Lleftarrow:"⇚",llhard:"⥫",lltri:"◺",Lmidot:"Ŀ",lmidot:"ŀ",lmoustache:"⎰",lmoust:"⎰",lnap:"⪉",lnapprox:"⪉",lne:"⪇",lnE:"≨",lneq:"⪇",lneqq:"≨",lnsim:"⋦",loang:"⟬",loarr:"⇽",lobrk:"⟦",longleftarrow:"⟵",LongLeftArrow:"⟵",Longleftarrow:"⟸",longleftrightarrow:"⟷",LongLeftRightArrow:"⟷",Longleftrightarrow:"⟺",longmapsto:"⟼",longrightarrow:"⟶",LongRightArrow:"⟶",Longrightarrow:"⟹",looparrowleft:"↫",looparrowright:"↬",lopar:"⦅",Lopf:"𝕃",lopf:"𝕝",loplus:"⨭",lotimes:"⨴",lowast:"∗",lowbar:"_",LowerLeftArrow:"↙",LowerRightArrow:"↘",loz:"◊",lozenge:"◊",lozf:"⧫",lpar:"(",lparlt:"⦓",lrarr:"⇆",lrcorner:"⌟",lrhar:"⇋",lrhard:"⥭",lrm:"‎",lrtri:"⊿",lsaquo:"‹",lscr:"𝓁",Lscr:"ℒ",lsh:"↰",Lsh:"↰",lsim:"≲",lsime:"⪍",lsimg:"⪏",lsqb:"[",lsquo:"‘",lsquor:"‚",Lstrok:"Ł",lstrok:"ł",ltcc:"⪦",ltcir:"⩹",lt:"<",LT:"<",Lt:"≪",ltdot:"⋖",lthree:"⋋",ltimes:"⋉",ltlarr:"⥶",ltquest:"⩻",ltri:"◃",ltrie:"⊴",ltrif:"◂",ltrPar:"⦖",lurdshar:"⥊",luruhar:"⥦",lvertneqq:"≨︀",lvnE:"≨︀",macr:"¯",male:"♂",malt:"✠",maltese:"✠",Map:"⤅",map:"↦",mapsto:"↦",mapstodown:"↧",mapstoleft:"↤",mapstoup:"↥",marker:"▮",mcomma:"⨩",Mcy:"М",mcy:"м",mdash:"—",mDDot:"∺",measuredangle:"∡",MediumSpace:" ",Mellintrf:"ℳ",Mfr:"𝔐",mfr:"𝔪",mho:"℧",micro:"µ",midast:"*",midcir:"⫰",mid:"∣",middot:"·",minusb:"⊟",minus:"−",minusd:"∸",minusdu:"⨪",MinusPlus:"∓",mlcp:"⫛",mldr:"…",mnplus:"∓",models:"⊧",Mopf:"𝕄",mopf:"𝕞",mp:"∓",mscr:"𝓂",Mscr:"ℳ",mstpos:"∾",Mu:"Μ",mu:"μ",multimap:"⊸",mumap:"⊸",nabla:"∇",Nacute:"Ń",nacute:"ń",nang:"∠⃒",nap:"≉",napE:"⩰̸",napid:"≋̸",napos:"ʼn",napprox:"≉",natural:"♮",naturals:"ℕ",natur:"♮",nbsp:" ",nbump:"≎̸",nbumpe:"≏̸",ncap:"⩃",Ncaron:"Ň",ncaron:"ň",Ncedil:"Ņ",ncedil:"ņ",ncong:"≇",ncongdot:"⩭̸",ncup:"⩂",Ncy:"Н",ncy:"н",ndash:"–",nearhk:"⤤",nearr:"↗",neArr:"⇗",nearrow:"↗",ne:"≠",nedot:"≐̸",NegativeMediumSpace:"​",NegativeThickSpace:"​",NegativeThinSpace:"​",NegativeVeryThinSpace:"​",nequiv:"≢",nesear:"⤨",nesim:"≂̸",NestedGreaterGreater:"≫",NestedLessLess:"≪",NewLine:"\n",nexist:"∄",nexists:"∄",Nfr:"𝔑",nfr:"𝔫",ngE:"≧̸",nge:"≱",ngeq:"≱",ngeqq:"≧̸",ngeqslant:"⩾̸",nges:"⩾̸",nGg:"⋙̸",ngsim:"≵",nGt:"≫⃒",ngt:"≯",ngtr:"≯",nGtv:"≫̸",nharr:"↮",nhArr:"⇎",nhpar:"⫲",ni:"∋",nis:"⋼",nisd:"⋺",niv:"∋",NJcy:"Њ",njcy:"њ",nlarr:"↚",nlArr:"⇍",nldr:"‥",nlE:"≦̸",nle:"≰",nleftarrow:"↚",nLeftarrow:"⇍",nleftrightarrow:"↮",nLeftrightarrow:"⇎",nleq:"≰",nleqq:"≦̸",nleqslant:"⩽̸",nles:"⩽̸",nless:"≮",nLl:"⋘̸",nlsim:"≴",nLt:"≪⃒",nlt:"≮",nltri:"⋪",nltrie:"⋬",nLtv:"≪̸",nmid:"∤",NoBreak:"⁠",NonBreakingSpace:" ",nopf:"𝕟",Nopf:"ℕ",Not:"⫬",not:"¬",NotCongruent:"≢",NotCupCap:"≭",NotDoubleVerticalBar:"∦",NotElement:"∉",NotEqual:"≠",NotEqualTilde:"≂̸",NotExists:"∄",NotGreater:"≯",NotGreaterEqual:"≱",NotGreaterFullEqual:"≧̸",NotGreaterGreater:"≫̸",NotGreaterLess:"≹",NotGreaterSlantEqual:"⩾̸",NotGreaterTilde:"≵",NotHumpDownHump:"≎̸",NotHumpEqual:"≏̸",notin:"∉",notindot:"⋵̸",notinE:"⋹̸",notinva:"∉",notinvb:"⋷",notinvc:"⋶",NotLeftTriangleBar:"⧏̸",NotLeftTriangle:"⋪",NotLeftTriangleEqual:"⋬",NotLess:"≮",NotLessEqual:"≰",NotLessGreater:"≸",NotLessLess:"≪̸",NotLessSlantEqual:"⩽̸",NotLessTilde:"≴",NotNestedGreaterGreater:"⪢̸",NotNestedLessLess:"⪡̸",notni:"∌",notniva:"∌",notnivb:"⋾",notnivc:"⋽",NotPrecedes:"⊀",NotPrecedesEqual:"⪯̸",NotPrecedesSlantEqual:"⋠",NotReverseElement:"∌",NotRightTriangleBar:"⧐̸",NotRightTriangle:"⋫",NotRightTriangleEqual:"⋭",NotSquareSubset:"⊏̸",NotSquareSubsetEqual:"⋢",NotSquareSuperset:"⊐̸",NotSquareSupersetEqual:"⋣",NotSubset:"⊂⃒",NotSubsetEqual:"⊈",NotSucceeds:"⊁",NotSucceedsEqual:"⪰̸",NotSucceedsSlantEqual:"⋡",NotSucceedsTilde:"≿̸",NotSuperset:"⊃⃒",NotSupersetEqual:"⊉",NotTilde:"≁",NotTildeEqual:"≄",NotTildeFullEqual:"≇",NotTildeTilde:"≉",NotVerticalBar:"∤",nparallel:"∦",npar:"∦",nparsl:"⫽⃥",npart:"∂̸",npolint:"⨔",npr:"⊀",nprcue:"⋠",nprec:"⊀",npreceq:"⪯̸",npre:"⪯̸",nrarrc:"⤳̸",nrarr:"↛",nrArr:"⇏",nrarrw:"↝̸",nrightarrow:"↛",nRightarrow:"⇏",nrtri:"⋫",nrtrie:"⋭",nsc:"⊁",nsccue:"⋡",nsce:"⪰̸",Nscr:"𝒩",nscr:"𝓃",nshortmid:"∤",nshortparallel:"∦",nsim:"≁",nsime:"≄",nsimeq:"≄",nsmid:"∤",nspar:"∦",nsqsube:"⋢",nsqsupe:"⋣",nsub:"⊄",nsubE:"⫅̸",nsube:"⊈",nsubset:"⊂⃒",nsubseteq:"⊈",nsubseteqq:"⫅̸",nsucc:"⊁",nsucceq:"⪰̸",nsup:"⊅",nsupE:"⫆̸",nsupe:"⊉",nsupset:"⊃⃒",nsupseteq:"⊉",nsupseteqq:"⫆̸",ntgl:"≹",Ntilde:"Ñ",ntilde:"ñ",ntlg:"≸",ntriangleleft:"⋪",ntrianglelefteq:"⋬",ntriangleright:"⋫",ntrianglerighteq:"⋭",Nu:"Ν",nu:"ν",num:"#",numero:"№",numsp:" ",nvap:"≍⃒",nvdash:"⊬",nvDash:"⊭",nVdash:"⊮",nVDash:"⊯",nvge:"≥⃒",nvgt:">⃒",nvHarr:"⤄",nvinfin:"⧞",nvlArr:"⤂",nvle:"≤⃒",nvlt:"<⃒",nvltrie:"⊴⃒",nvrArr:"⤃",nvrtrie:"⊵⃒",nvsim:"∼⃒",nwarhk:"⤣",nwarr:"↖",nwArr:"⇖",nwarrow:"↖",nwnear:"⤧",Oacute:"Ó",oacute:"ó",oast:"⊛",Ocirc:"Ô",ocirc:"ô",ocir:"⊚",Ocy:"О",ocy:"о",odash:"⊝",Odblac:"Ő",odblac:"ő",odiv:"⨸",odot:"⊙",odsold:"⦼",OElig:"Œ",oelig:"œ",ofcir:"⦿",Ofr:"𝔒",ofr:"𝔬",ogon:"˛",Ograve:"Ò",ograve:"ò",ogt:"⧁",ohbar:"⦵",ohm:"Ω",oint:"∮",olarr:"↺",olcir:"⦾",olcross:"⦻",oline:"‾",olt:"⧀",Omacr:"Ō",omacr:"ō",Omega:"Ω",omega:"ω",Omicron:"Ο",omicron:"ο",omid:"⦶",ominus:"⊖",Oopf:"𝕆",oopf:"𝕠",opar:"⦷",OpenCurlyDoubleQuote:"“",OpenCurlyQuote:"‘",operp:"⦹",oplus:"⊕",orarr:"↻",Or:"⩔",or:"∨",ord:"⩝",order:"ℴ",orderof:"ℴ",ordf:"ª",ordm:"º",origof:"⊶",oror:"⩖",orslope:"⩗",orv:"⩛",oS:"Ⓢ",Oscr:"𝒪",oscr:"ℴ",Oslash:"Ø",oslash:"ø",osol:"⊘",Otilde:"Õ",otilde:"õ",otimesas:"⨶",Otimes:"⨷",otimes:"⊗",Ouml:"Ö",ouml:"ö",ovbar:"⌽",OverBar:"‾",OverBrace:"⏞",OverBracket:"⎴",OverParenthesis:"⏜",para:"¶",parallel:"∥",par:"∥",parsim:"⫳",parsl:"⫽",part:"∂",PartialD:"∂",Pcy:"П",pcy:"п",percnt:"%",period:".",permil:"‰",perp:"⊥",pertenk:"‱",Pfr:"𝔓",pfr:"𝔭",Phi:"Φ",phi:"φ",phiv:"ϕ",phmmat:"ℳ",phone:"☎",Pi:"Π",pi:"π",pitchfork:"⋔",piv:"ϖ",planck:"ℏ",planckh:"ℎ",plankv:"ℏ",plusacir:"⨣",plusb:"⊞",pluscir:"⨢",plus:"+",plusdo:"∔",plusdu:"⨥",pluse:"⩲",PlusMinus:"±",plusmn:"±",plussim:"⨦",plustwo:"⨧",pm:"±",Poincareplane:"ℌ",pointint:"⨕",popf:"𝕡",Popf:"ℙ",pound:"£",prap:"⪷",Pr:"⪻",pr:"≺",prcue:"≼",precapprox:"⪷",prec:"≺",preccurlyeq:"≼",Precedes:"≺",PrecedesEqual:"⪯",PrecedesSlantEqual:"≼",PrecedesTilde:"≾",preceq:"⪯",precnapprox:"⪹",precneqq:"⪵",precnsim:"⋨",pre:"⪯",prE:"⪳",precsim:"≾",prime:"′",Prime:"″",primes:"ℙ",prnap:"⪹",prnE:"⪵",prnsim:"⋨",prod:"∏",Product:"∏",profalar:"⌮",profline:"⌒",profsurf:"⌓",prop:"∝",Proportional:"∝",Proportion:"∷",propto:"∝",prsim:"≾",prurel:"⊰",Pscr:"𝒫",pscr:"𝓅",Psi:"Ψ",psi:"ψ",puncsp:" ",Qfr:"𝔔",qfr:"𝔮",qint:"⨌",qopf:"𝕢",Qopf:"ℚ",qprime:"⁗",Qscr:"𝒬",qscr:"𝓆",quaternions:"ℍ",quatint:"⨖",quest:"?",questeq:"≟",quot:'"',QUOT:'"',rAarr:"⇛",race:"∽̱",Racute:"Ŕ",racute:"ŕ",radic:"√",raemptyv:"⦳",rang:"⟩",Rang:"⟫",rangd:"⦒",range:"⦥",rangle:"⟩",raquo:"»",rarrap:"⥵",rarrb:"⇥",rarrbfs:"⤠",rarrc:"⤳",rarr:"→",Rarr:"↠",rArr:"⇒",rarrfs:"⤞",rarrhk:"↪",rarrlp:"↬",rarrpl:"⥅",rarrsim:"⥴",Rarrtl:"⤖",rarrtl:"↣",rarrw:"↝",ratail:"⤚",rAtail:"⤜",ratio:"∶",rationals:"ℚ",rbarr:"⤍",rBarr:"⤏",RBarr:"⤐",rbbrk:"❳",rbrace:"}",rbrack:"]",rbrke:"⦌",rbrksld:"⦎",rbrkslu:"⦐",Rcaron:"Ř",rcaron:"ř",Rcedil:"Ŗ",rcedil:"ŗ",rceil:"⌉",rcub:"}",Rcy:"Р",rcy:"р",rdca:"⤷",rdldhar:"⥩",rdquo:"”",rdquor:"”",rdsh:"↳",real:"ℜ",realine:"ℛ",realpart:"ℜ",reals:"ℝ",Re:"ℜ",rect:"▭",reg:"®",REG:"®",ReverseElement:"∋",ReverseEquilibrium:"⇋",ReverseUpEquilibrium:"⥯",rfisht:"⥽",rfloor:"⌋",rfr:"𝔯",Rfr:"ℜ",rHar:"⥤",rhard:"⇁",rharu:"⇀",rharul:"⥬",Rho:"Ρ",rho:"ρ",rhov:"ϱ",RightAngleBracket:"⟩",RightArrowBar:"⇥",rightarrow:"→",RightArrow:"→",Rightarrow:"⇒",RightArrowLeftArrow:"⇄",rightarrowtail:"↣",RightCeiling:"⌉",RightDoubleBracket:"⟧",RightDownTeeVector:"⥝",RightDownVectorBar:"⥕",RightDownVector:"⇂",RightFloor:"⌋",rightharpoondown:"⇁",rightharpoonup:"⇀",rightleftarrows:"⇄",rightleftharpoons:"⇌",rightrightarrows:"⇉",rightsquigarrow:"↝",RightTeeArrow:"↦",RightTee:"⊢",RightTeeVector:"⥛",rightthreetimes:"⋌",RightTriangleBar:"⧐",RightTriangle:"⊳",RightTriangleEqual:"⊵",RightUpDownVector:"⥏",RightUpTeeVector:"⥜",RightUpVectorBar:"⥔",RightUpVector:"↾",RightVectorBar:"⥓",RightVector:"⇀",ring:"˚",risingdotseq:"≓",rlarr:"⇄",rlhar:"⇌",rlm:"‏",rmoustache:"⎱",rmoust:"⎱",rnmid:"⫮",roang:"⟭",roarr:"⇾",robrk:"⟧",ropar:"⦆",ropf:"𝕣",Ropf:"ℝ",roplus:"⨮",rotimes:"⨵",RoundImplies:"⥰",rpar:")",rpargt:"⦔",rppolint:"⨒",rrarr:"⇉",Rrightarrow:"⇛",rsaquo:"›",rscr:"𝓇",Rscr:"ℛ",rsh:"↱",Rsh:"↱",rsqb:"]",rsquo:"’",rsquor:"’",rthree:"⋌",rtimes:"⋊",rtri:"▹",rtrie:"⊵",rtrif:"▸",rtriltri:"⧎",RuleDelayed:"⧴",ruluhar:"⥨",rx:"℞",Sacute:"Ś",sacute:"ś",sbquo:"‚",scap:"⪸",Scaron:"Š",scaron:"š",Sc:"⪼",sc:"≻",sccue:"≽",sce:"⪰",scE:"⪴",Scedil:"Ş",scedil:"ş",Scirc:"Ŝ",scirc:"ŝ",scnap:"⪺",scnE:"⪶",scnsim:"⋩",scpolint:"⨓",scsim:"≿",Scy:"С",scy:"с",sdotb:"⊡",sdot:"⋅",sdote:"⩦",searhk:"⤥",searr:"↘",seArr:"⇘",searrow:"↘",sect:"§",semi:";",seswar:"⤩",setminus:"∖",setmn:"∖",sext:"✶",Sfr:"𝔖",sfr:"𝔰",sfrown:"⌢",sharp:"♯",SHCHcy:"Щ",shchcy:"щ",SHcy:"Ш",shcy:"ш",ShortDownArrow:"↓",ShortLeftArrow:"←",shortmid:"∣",shortparallel:"∥",ShortRightArrow:"→",ShortUpArrow:"↑",shy:"­",Sigma:"Σ",sigma:"σ",sigmaf:"ς",sigmav:"ς",sim:"∼",simdot:"⩪",sime:"≃",simeq:"≃",simg:"⪞",simgE:"⪠",siml:"⪝",simlE:"⪟",simne:"≆",simplus:"⨤",simrarr:"⥲",slarr:"←",SmallCircle:"∘",smallsetminus:"∖",smashp:"⨳",smeparsl:"⧤",smid:"∣",smile:"⌣",smt:"⪪",smte:"⪬",smtes:"⪬︀",SOFTcy:"Ь",softcy:"ь",solbar:"⌿",solb:"⧄",sol:"/",Sopf:"𝕊",sopf:"𝕤",spades:"♠",spadesuit:"♠",spar:"∥",sqcap:"⊓",sqcaps:"⊓︀",sqcup:"⊔",sqcups:"⊔︀",Sqrt:"√",sqsub:"⊏",sqsube:"⊑",sqsubset:"⊏",sqsubseteq:"⊑",sqsup:"⊐",sqsupe:"⊒",sqsupset:"⊐",sqsupseteq:"⊒",square:"□",Square:"□",SquareIntersection:"⊓",SquareSubset:"⊏",SquareSubsetEqual:"⊑",SquareSuperset:"⊐",SquareSupersetEqual:"⊒",SquareUnion:"⊔",squarf:"▪",squ:"□",squf:"▪",srarr:"→",Sscr:"𝒮",sscr:"𝓈",ssetmn:"∖",ssmile:"⌣",sstarf:"⋆",Star:"⋆",star:"☆",starf:"★",straightepsilon:"ϵ",straightphi:"ϕ",strns:"¯",sub:"⊂",Sub:"⋐",subdot:"⪽",subE:"⫅",sube:"⊆",subedot:"⫃",submult:"⫁",subnE:"⫋",subne:"⊊",subplus:"⪿",subrarr:"⥹",subset:"⊂",Subset:"⋐",subseteq:"⊆",subseteqq:"⫅",SubsetEqual:"⊆",subsetneq:"⊊",subsetneqq:"⫋",subsim:"⫇",subsub:"⫕",subsup:"⫓",succapprox:"⪸",succ:"≻",succcurlyeq:"≽",Succeeds:"≻",SucceedsEqual:"⪰",SucceedsSlantEqual:"≽",SucceedsTilde:"≿",succeq:"⪰",succnapprox:"⪺",succneqq:"⪶",succnsim:"⋩",succsim:"≿",SuchThat:"∋",sum:"∑",Sum:"∑",sung:"♪",sup1:"¹",sup2:"²",sup3:"³",sup:"⊃",Sup:"⋑",supdot:"⪾",supdsub:"⫘",supE:"⫆",supe:"⊇",supedot:"⫄",Superset:"⊃",SupersetEqual:"⊇",suphsol:"⟉",suphsub:"⫗",suplarr:"⥻",supmult:"⫂",supnE:"⫌",supne:"⊋",supplus:"⫀",supset:"⊃",Supset:"⋑",supseteq:"⊇",supseteqq:"⫆",supsetneq:"⊋",supsetneqq:"⫌",supsim:"⫈",supsub:"⫔",supsup:"⫖",swarhk:"⤦",swarr:"↙",swArr:"⇙",swarrow:"↙",swnwar:"⤪",szlig:"ß",Tab:"\t",target:"⌖",Tau:"Τ",tau:"τ",tbrk:"⎴",Tcaron:"Ť",tcaron:"ť",Tcedil:"Ţ",tcedil:"ţ",Tcy:"Т",tcy:"т",tdot:"⃛",telrec:"⌕",Tfr:"𝔗",tfr:"𝔱",there4:"∴",therefore:"∴",Therefore:"∴",Theta:"Θ",theta:"θ",thetasym:"ϑ",thetav:"ϑ",thickapprox:"≈",thicksim:"∼",ThickSpace:"  ",ThinSpace:" ",thinsp:" ",thkap:"≈",thksim:"∼",THORN:"Þ",thorn:"þ",tilde:"˜",Tilde:"∼",TildeEqual:"≃",TildeFullEqual:"≅",TildeTilde:"≈",timesbar:"⨱",timesb:"⊠",times:"×",timesd:"⨰",tint:"∭",toea:"⤨",topbot:"⌶",topcir:"⫱",top:"⊤",Topf:"𝕋",topf:"𝕥",topfork:"⫚",tosa:"⤩",tprime:"‴",trade:"™",TRADE:"™",triangle:"▵",triangledown:"▿",triangleleft:"◃",trianglelefteq:"⊴",triangleq:"≜",triangleright:"▹",trianglerighteq:"⊵",tridot:"◬",trie:"≜",triminus:"⨺",TripleDot:"⃛",triplus:"⨹",trisb:"⧍",tritime:"⨻",trpezium:"⏢",Tscr:"𝒯",tscr:"𝓉",TScy:"Ц",tscy:"ц",TSHcy:"Ћ",tshcy:"ћ",Tstrok:"Ŧ",tstrok:"ŧ",twixt:"≬",twoheadleftarrow:"↞",twoheadrightarrow:"↠",Uacute:"Ú",uacute:"ú",uarr:"↑",Uarr:"↟",uArr:"⇑",Uarrocir:"⥉",Ubrcy:"Ў",ubrcy:"ў",Ubreve:"Ŭ",ubreve:"ŭ",Ucirc:"Û",ucirc:"û",Ucy:"У",ucy:"у",udarr:"⇅",Udblac:"Ű",udblac:"ű",udhar:"⥮",ufisht:"⥾",Ufr:"𝔘",ufr:"𝔲",Ugrave:"Ù",ugrave:"ù",uHar:"⥣",uharl:"↿",uharr:"↾",uhblk:"▀",ulcorn:"⌜",ulcorner:"⌜",ulcrop:"⌏",ultri:"◸",Umacr:"Ū",umacr:"ū",uml:"¨",UnderBar:"_",UnderBrace:"⏟",UnderBracket:"⎵",UnderParenthesis:"⏝",Union:"⋃",UnionPlus:"⊎",Uogon:"Ų",uogon:"ų",Uopf:"𝕌",uopf:"𝕦",UpArrowBar:"⤒",uparrow:"↑",UpArrow:"↑",Uparrow:"⇑",UpArrowDownArrow:"⇅",updownarrow:"↕",UpDownArrow:"↕",Updownarrow:"⇕",UpEquilibrium:"⥮",upharpoonleft:"↿",upharpoonright:"↾",uplus:"⊎",UpperLeftArrow:"↖",UpperRightArrow:"↗",upsi:"υ",Upsi:"ϒ",upsih:"ϒ",Upsilon:"Υ",upsilon:"υ",UpTeeArrow:"↥",UpTee:"⊥",upuparrows:"⇈",urcorn:"⌝",urcorner:"⌝",urcrop:"⌎",Uring:"Ů",uring:"ů",urtri:"◹",Uscr:"𝒰",uscr:"𝓊",utdot:"⋰",Utilde:"Ũ",utilde:"ũ",utri:"▵",utrif:"▴",uuarr:"⇈",Uuml:"Ü",uuml:"ü",uwangle:"⦧",vangrt:"⦜",varepsilon:"ϵ",varkappa:"ϰ",varnothing:"∅",varphi:"ϕ",varpi:"ϖ",varpropto:"∝",varr:"↕",vArr:"⇕",varrho:"ϱ",varsigma:"ς",varsubsetneq:"⊊︀",varsubsetneqq:"⫋︀",varsupsetneq:"⊋︀",varsupsetneqq:"⫌︀",vartheta:"ϑ",vartriangleleft:"⊲",vartriangleright:"⊳",vBar:"⫨",Vbar:"⫫",vBarv:"⫩",Vcy:"В",vcy:"в",vdash:"⊢",vDash:"⊨",Vdash:"⊩",VDash:"⊫",Vdashl:"⫦",veebar:"⊻",vee:"∨",Vee:"⋁",veeeq:"≚",vellip:"⋮",verbar:"|",Verbar:"‖",vert:"|",Vert:"‖",VerticalBar:"∣",VerticalLine:"|",VerticalSeparator:"❘",VerticalTilde:"≀",VeryThinSpace:" ",Vfr:"𝔙",vfr:"𝔳",vltri:"⊲",vnsub:"⊂⃒",vnsup:"⊃⃒",Vopf:"𝕍",vopf:"𝕧",vprop:"∝",vrtri:"⊳",Vscr:"𝒱",vscr:"𝓋",vsubnE:"⫋︀",vsubne:"⊊︀",vsupnE:"⫌︀",vsupne:"⊋︀",Vvdash:"⊪",vzigzag:"⦚",Wcirc:"Ŵ",wcirc:"ŵ",wedbar:"⩟",wedge:"∧",Wedge:"⋀",wedgeq:"≙",weierp:"℘",Wfr:"𝔚",wfr:"𝔴",Wopf:"𝕎",wopf:"𝕨",wp:"℘",wr:"≀",wreath:"≀",Wscr:"𝒲",wscr:"𝓌",xcap:"⋂",xcirc:"◯",xcup:"⋃",xdtri:"▽",Xfr:"𝔛",xfr:"𝔵",xharr:"⟷",xhArr:"⟺",Xi:"Ξ",xi:"ξ",xlarr:"⟵",xlArr:"⟸",xmap:"⟼",xnis:"⋻",xodot:"⨀",Xopf:"𝕏",xopf:"𝕩",xoplus:"⨁",xotime:"⨂",xrarr:"⟶",xrArr:"⟹",Xscr:"𝒳",xscr:"𝓍",xsqcup:"⨆",xuplus:"⨄",xutri:"△",xvee:"⋁",xwedge:"⋀",Yacute:"Ý",yacute:"ý",YAcy:"Я",yacy:"я",Ycirc:"Ŷ",ycirc:"ŷ",Ycy:"Ы",ycy:"ы",yen:"¥",Yfr:"𝔜",yfr:"𝔶",YIcy:"Ї",yicy:"ї",Yopf:"𝕐",yopf:"𝕪",Yscr:"𝒴",yscr:"𝓎",YUcy:"Ю",yucy:"ю",yuml:"ÿ",Yuml:"Ÿ",Zacute:"Ź",zacute:"ź",Zcaron:"Ž",zcaron:"ž",Zcy:"З",zcy:"з",Zdot:"Ż",zdot:"ż",zeetrf:"ℨ",ZeroWidthSpace:"​",Zeta:"Ζ",zeta:"ζ",zfr:"𝔷",Zfr:"ℨ",ZHcy:"Ж",zhcy:"ж",zigrarr:"⇝",zopf:"𝕫",Zopf:"ℤ",Zscr:"𝒵",zscr:"𝓏",zwj:"‍",zwnj:"‌"}},function(e,t){e.exports=/[!-#%-\*,-/:;\?@\[-\]_\{\}\xA1\xA7\xAB\xB6\xB7\xBB\xBF\u037E\u0387\u055A-\u055F\u0589\u058A\u05BE\u05C0\u05C3\u05C6\u05F3\u05F4\u0609\u060A\u060C\u060D\u061B\u061E\u061F\u066A-\u066D\u06D4\u0700-\u070D\u07F7-\u07F9\u0830-\u083E\u085E\u0964\u0965\u0970\u0AF0\u0DF4\u0E4F\u0E5A\u0E5B\u0F04-\u0F12\u0F14\u0F3A-\u0F3D\u0F85\u0FD0-\u0FD4\u0FD9\u0FDA\u104A-\u104F\u10FB\u1360-\u1368\u1400\u166D\u166E\u169B\u169C\u16EB-\u16ED\u1735\u1736\u17D4-\u17D6\u17D8-\u17DA\u1800-\u180A\u1944\u1945\u1A1E\u1A1F\u1AA0-\u1AA6\u1AA8-\u1AAD\u1B5A-\u1B60\u1BFC-\u1BFF\u1C3B-\u1C3F\u1C7E\u1C7F\u1CC0-\u1CC7\u1CD3\u2010-\u2027\u2030-\u2043\u2045-\u2051\u2053-\u205E\u207D\u207E\u208D\u208E\u2308-\u230B\u2329\u232A\u2768-\u2775\u27C5\u27C6\u27E6-\u27EF\u2983-\u2998\u29D8-\u29DB\u29FC\u29FD\u2CF9-\u2CFC\u2CFE\u2CFF\u2D70\u2E00-\u2E2E\u2E30-\u2E44\u3001-\u3003\u3008-\u3011\u3014-\u301F\u3030\u303D\u30A0\u30FB\uA4FE\uA4FF\uA60D-\uA60F\uA673\uA67E\uA6F2-\uA6F7\uA874-\uA877\uA8CE\uA8CF\uA8F8-\uA8FA\uA8FC\uA92E\uA92F\uA95F\uA9C1-\uA9CD\uA9DE\uA9DF\uAA5C-\uAA5F\uAADE\uAADF\uAAF0\uAAF1\uABEB\uFD3E\uFD3F\uFE10-\uFE19\uFE30-\uFE52\uFE54-\uFE61\uFE63\uFE68\uFE6A\uFE6B\uFF01-\uFF03\uFF05-\uFF0A\uFF0C-\uFF0F\uFF1A\uFF1B\uFF1F\uFF20\uFF3B-\uFF3D\uFF3F\uFF5B\uFF5D\uFF5F-\uFF65]|\uD800[\uDD00-\uDD02\uDF9F\uDFD0]|\uD801\uDD6F|\uD802[\uDC57\uDD1F\uDD3F\uDE50-\uDE58\uDE7F\uDEF0-\uDEF6\uDF39-\uDF3F\uDF99-\uDF9C]|\uD804[\uDC47-\uDC4D\uDCBB\uDCBC\uDCBE-\uDCC1\uDD40-\uDD43\uDD74\uDD75\uDDC5-\uDDC9\uDDCD\uDDDB\uDDDD-\uDDDF\uDE38-\uDE3D\uDEA9]|\uD805[\uDC4B-\uDC4F\uDC5B\uDC5D\uDCC6\uDDC1-\uDDD7\uDE41-\uDE43\uDE60-\uDE6C\uDF3C-\uDF3E]|\uD807[\uDC41-\uDC45\uDC70\uDC71]|\uD809[\uDC70-\uDC74]|\uD81A[\uDE6E\uDE6F\uDEF5\uDF37-\uDF3B\uDF44]|\uD82F\uDC9F|\uD836[\uDE87-\uDE8B]|\uD83A[\uDD5E\uDD5F]/},function(e,t,r){"use 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e.blkIndent=c,!0}},function(e,t,r){"use strict";var n=r(61).isSpace;e.exports=function(e,t,r,o){var i,a,s,l,u=e.bMarks[t]+e.tShift[t],c=e.eMarks[t];if(42!==(i=e.src.charCodeAt(u++))&&45!==i&&95!==i)return!1;for(a=1;u<c;){if((s=e.src.charCodeAt(u++))!==i&&!n(s))return!1;s===i&&a++}return!(a<3)&&(!!o||(e.line=t+1,l=e.push("hr","hr",0),l.map=[t,e.line],l.markup=Array(a+1).join(String.fromCharCode(i)),!0))}},function(e,t,r){"use strict";function n(e,t){var r,n,o,a;return n=e.bMarks[t]+e.tShift[t],o=e.eMarks[t],42!==(r=e.src.charCodeAt(n++))&&45!==r&&43!==r?-1:n<o&&(a=e.src.charCodeAt(n),!i(a))?-1:n}function o(e,t){var r,n=e.bMarks[t]+e.tShift[t],o=n,a=e.eMarks[t];if(o+1>=a)return-1;if((r=e.src.charCodeAt(o++))<48||r>57)return-1;for(;;){if(o>=a)return-1;if(!((r=e.src.charCodeAt(o++))>=48&&r<=57)){if(41===r||46===r)break;return-1}if(o-n>=10)return-1}return o<a&&(r=e.src.charCodeAt(o),!i(r))?-1:o}var i=r(61).isSpace;e.exports=function(e,t,r,a){var 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i,a,s,l,u=e.bMarks[t]+e.tShift[t],c=e.eMarks[t];if(35!==(i=e.src.charCodeAt(u))||u>=c)return!1;for(a=1,i=e.src.charCodeAt(++u);35===i&&u<c&&a<=6;)a++,i=e.src.charCodeAt(++u);return!(a>6||u<c&&32!==i)&&(!!o||(c=e.skipSpacesBack(c,u),(s=e.skipCharsBack(c,35,u))>u&&n(e.src.charCodeAt(s-1))&&(c=s),e.line=t+1,l=e.push("heading_open","h"+String(a),1),l.markup="########".slice(0,a),l.map=[t,e.line],l=e.push("inline","",0),l.content=e.src.slice(u,c).trim(),l.map=[t,e.line],l.children=[],l=e.push("heading_close","h"+String(a),-1),l.markup="########".slice(0,a),!0))}},function(e,t){"use strict";e.exports=function(e,t,r){for(var n,o,i,a,s,l,u,c,p,h=t+1,f=e.md.block.ruler.getRules("paragraph");h<r&&!e.isEmpty(h);h++)if(!(e.sCount[h]-e.blkIndent>3)){if(e.sCount[h]>=e.blkIndent&&(l=e.bMarks[h]+e.tShift[h],u=e.eMarks[h],l<u&&(45===(p=e.src.charCodeAt(l))||61===p)&&(l=e.skipChars(l,p),(l=e.skipSpaces(l))>=u))){c=61===p?1:2;break}if(!(e.sCount[h]<0)){for(o=!1,i=0,a=f.length;i<a;i++)if(f[i](e,h,r,!0)){o=!0;break}if(o)break}}return!!c&&(n=e.getLines(t,h,e.blkIndent,!1).trim(),e.line=h+1,s=e.push("heading_open","h"+String(c),1),s.markup=String.fromCharCode(p),s.map=[t,e.line],s=e.push("inline","",0),s.content=n,s.map=[t,e.line-1],s.children=[],s=e.push("heading_close","h"+String(c),-1),s.markup=String.fromCharCode(p),!0)}},function(e,t,r){"use strict";var n=r(101),o=r(102).HTML_OPEN_CLOSE_TAG_RE,i=[[/^<(script|pre|style)(?=(\s|>|$))/i,/<\/(script|pre|style)>/i,!0],[/^<!--/,/-->/,!0],[/^<\?/,/\?>/,!0],[/^<![A-Z]/,/>/,!0],[/^<!\[CDATA\[/,/\]\]>/,!0],[new RegExp("^</?("+n.join("|")+")(?=(\\s|/?>|$))","i"),/^$/,!0],[new RegExp(o.source+"\\s*$"),/^$/,!1]];e.exports=function(e,t,r,n){var o,a,s,l,u=e.bMarks[t]+e.tShift[t],c=e.eMarks[t];if(!e.md.options.html)return!1;if(60!==e.src.charCodeAt(u))return!1;for(l=e.src.slice(u,c),o=0;o<i.length&&!i[o][0].test(l);o++);if(o===i.length)return!1;if(n)return i[o][2];if(a=t+1,!i[o][1].test(l))for(;a<r&&!(e.sCount[a]<e.blkIndent);a++)if(u=e.bMarks[a]+e.tShift[a],c=e.eMarks[a],l=e.src.slice(u,c),i[o][1].test(l)){0!==l.length&&a++;break}return e.line=a,s=e.push("html_block","",0),s.map=[t,a],s.content=e.getLines(t,a,e.blkIndent,!0),!0}},function(e,t){"use strict";e.exports=["address","article","aside","base","basefont","blockquote","body","caption","center","col","colgroup","dd","details","dialog","dir","div","dl","dt","fieldset","figcaption","figure","footer","form","frame","frameset","h1","head","header","hr","html","iframe","legend","li","link","main","menu","menuitem","meta","nav","noframes","ol","optgroup","option","p","param","pre","section","source","title","summary","table","tbody","td","tfoot","th","thead","title","tr","track","ul"]},function(e,t){"use strict";var r="<[A-Za-z][A-Za-z0-9\\-]*(?:\\s+[a-zA-Z_:][a-zA-Z0-9:._-]*(?:\\s*=\\s*(?:[^\"'=<>`\\x00-\\x20]+|'[^']*'|\"[^\"]*\"))?)*\\s*\\/?>",n="<\\/[A-Za-z][A-Za-z0-9\\-]*\\s*>",o=new RegExp("^(?:"+r+"|"+n+"|\x3c!----\x3e|\x3c!--(?:-?[^>-])(?:-?[^-])*--\x3e|<[?].*?[?]>|<![A-Z]+\\s+[^>]*>|<!\\[CDATA\\[[\\s\\S]*?\\]\\]>)"),i=new RegExp("^(?:"+r+"|"+n+")");e.exports.HTML_TAG_RE=o,e.exports.HTML_OPEN_CLOSE_TAG_RE=i},function(e,t){"use strict";e.exports=function(e,t){for(var r,n,o,i,a,s=t+1,l=e.md.block.ruler.getRules("paragraph"),u=e.lineMax;s<u&&!e.isEmpty(s);s++)if(!(e.sCount[s]-e.blkIndent>3||e.sCount[s]<0)){for(n=!1,o=0,i=l.length;o<i;o++)if(l[o](e,s,u,!0)){n=!0;break}if(n)break}return r=e.getLines(t,s,e.blkIndent,!1).trim(),e.line=s,a=e.push("paragraph_open","p",1),a.map=[t,e.line],a=e.push("inline","",0),a.content=r,a.map=[t,e.line],a.children=[],a=e.push("paragraph_close","p",-1),!0}},function(e,t,r){"use strict";function n(e,t,r,n){var o,a,s,l,u,c,p,h;for(this.src=e,this.md=t,this.env=r,this.tokens=n,this.bMarks=[],this.eMarks=[],this.tShift=[],this.sCount=[],this.blkIndent=0,this.line=0,this.lineMax=0,this.tight=!1,this.parentType="root",this.ddIndent=-1,this.level=0,this.result="",h=!1,s=l=c=p=0,u=(a=this.src).length;l<u;l++){if(o=a.charCodeAt(l),!h){if(i(o)){c++,9===o?p+=4-p%4:p++;continue}h=!0}10!==o&&l!==u-1||(10!==o&&l++,this.bMarks.push(s),this.eMarks.push(l),this.tShift.push(c),this.sCount.push(p),h=!1,c=0,p=0,s=l+1)}this.bMarks.push(a.length),this.eMarks.push(a.length),this.tShift.push(0),this.sCount.push(0),this.lineMax=this.bMarks.length-1}var o=r(89),i=r(61).isSpace;n.prototype.push=function(e,t,r){var n=new o(e,t,r);return n.block=!0,r<0&&this.level--,n.level=this.level,r>0&&this.level++,this.tokens.push(n),n},n.prototype.isEmpty=function(e){return this.bMarks[e]+this.tShift[e]>=this.eMarks[e]},n.prototype.skipEmptyLines=function(e){for(var t=this.lineMax;e<t&&!(this.bMarks[e]+this.tShift[e]<this.eMarks[e]);e++);return e},n.prototype.skipSpaces=function(e){for(var t,r=this.src.length;e<r&&(t=this.src.charCodeAt(e),i(t));e++);return e},n.prototype.skipSpacesBack=function(e,t){if(e<=t)return e;for(;e>t;)if(!i(this.src.charCodeAt(--e)))return e+1;return e},n.prototype.skipChars=function(e,t){for(var r=this.src.length;e<r&&this.src.charCodeAt(e)===t;e++);return e},n.prototype.skipCharsBack=function(e,t,r){if(e<=r)return e;for(;e>r;)if(t!==this.src.charCodeAt(--e))return e+1;return e},n.prototype.getLines=function(e,t,r,n){var o,a,s,l,u,c,p,h=e;if(e>=t)return"";for(c=new Array(t-e),o=0;h<t;h++,o++){for(a=0,p=l=this.bMarks[h],u=h+1<t||n?this.eMarks[h]+1:this.eMarks[h];l<u&&a<r;){if(s=this.src.charCodeAt(l),i(s))9===s?a+=4-a%4:a++;else{if(!(l-p<this.tShift[h]))break;a++}l++}c[o]=this.src.slice(l,u)}return c.join("")},n.prototype.Token=o,e.exports=n},function(e,t,r){"use strict";function n(){var e;for(this.ruler=new 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+$/,""),e.push("hardbreak","br",0)):(e.pending=e.pending.slice(0,-1),e.push("softbreak","br",0)):e.push("softbreak","br",0)),o++;o<n&&32===e.src.charCodeAt(o);)o++;return e.pos=o,!0}},function(e,t,r){"use strict";for(var n=r(61).isSpace,o=[],i=0;i<256;i++)o.push(0);"\\!\"#$%&'()*+,./:;<=>?@[]^_`{|}~-".split("").forEach(function(e){o[e.charCodeAt(0)]=1}),e.exports=function(e,t){var r,i=e.pos,a=e.posMax;if(92!==e.src.charCodeAt(i))return!1;if(++i<a){if((r=e.src.charCodeAt(i))<256&&0!==o[r])return t||(e.pending+=e.src[i]),e.pos+=2,!0;if(10===r){for(t||e.push("hardbreak","br",0),i++;i<a&&(r=e.src.charCodeAt(i),n(r));)i++;return e.pos=i,!0}}return t||(e.pending+="\\"),e.pos++,!0}},function(e,t){"use strict";e.exports=function(e,t){var r,n,o,i,a,s,l=e.pos;if(96!==e.src.charCodeAt(l))return!1;for(r=l,l++,n=e.posMax;l<n&&96===e.src.charCodeAt(l);)l++;for(o=e.src.slice(r,l),i=a=l;-1!==(i=e.src.indexOf("`",a));){for(a=i+1;a<n&&96===e.src.charCodeAt(a);)a++;if(a-i===o.length)return t||((s=e.push("code_inline","code",0)).markup=o,s.content=e.src.slice(l,i).replace(/[ \n]+/g," ").trim()),e.pos=a,!0}return t||(e.pending+=o),e.pos+=o.length,!0}},function(e,t){"use strict";e.exports.tokenize=function(e,t){var r,n,o,i,a=e.pos,s=e.src.charCodeAt(a);if(t)return!1;if(126!==s)return!1;if(n=e.scanDelims(e.pos,!0),o=n.length,i=String.fromCharCode(s),o<2)return!1;for(o%2&&(e.push("text","",0).content=i,o--),r=0;r<o;r+=2)e.push("text","",0).content=i+i,e.delimiters.push({marker:s,jump:r,token:e.tokens.length-1,level:e.level,end:-1,open:n.can_open,close:n.can_close});return e.pos+=n.length,!0},e.exports.postProcess=function(e){var t,r,n,o,i,a=[],s=e.delimiters,l=e.delimiters.length;for(t=0;t<l;t++)126===(n=s[t]).marker&&-1!==n.end&&(o=s[n.end],(i=e.tokens[n.token]).type="s_open",i.tag="s",i.nesting=1,i.markup="~~",i.content="",(i=e.tokens[o.token]).type="s_close",i.tag="s",i.nesting=-1,i.markup="~~",i.content="","text"===e.tokens[o.token-1].type&&"~"===e.tokens[o.token-1].content&&a.push(o.token-1));for(;a.length;){for(r=(t=a.pop())+1;r<e.tokens.length&&"s_close"===e.tokens[r].type;)r++;t!==--r&&(i=e.tokens[r],e.tokens[r]=e.tokens[t],e.tokens[t]=i)}}},function(e,t){"use strict";e.exports.tokenize=function(e,t){var r,n,o=e.pos,i=e.src.charCodeAt(o);if(t)return!1;if(95!==i&&42!==i)return!1;for(n=e.scanDelims(e.pos,42===i),r=0;r<n.length;r++)e.push("text","",0).content=String.fromCharCode(i),e.delimiters.push({marker:i,jump:r,token:e.tokens.length-1,level:e.level,end:-1,open:n.can_open,close:n.can_close});return e.pos+=n.length,!0},e.exports.postProcess=function(e){var t,r,n,o,i,a,s=e.delimiters,l=e.delimiters.length;for(t=0;t<l;t++)95!==(r=s[t]).marker&&42!==r.marker||-1!==r.end&&(n=s[r.end],a=t+1<l&&s[t+1].end===r.end-1&&s[t+1].token===r.token+1&&s[r.end-1].token===n.token-1&&s[t+1].marker===r.marker,i=String.fromCharCode(r.marker),(o=e.tokens[r.token]).type=a?"strong_open":"em_open",o.tag=a?"strong":"em",o.nesting=1,o.markup=a?i+i:i,o.content="",(o=e.tokens[n.token]).type=a?"strong_close":"em_close",o.tag=a?"strong":"em",o.nesting=-1,o.markup=a?i+i:i,o.content="",a&&(e.tokens[s[t+1].token].content="",e.tokens[s[r.end-1].token].content="",t++))}},function(e,t,r){"use strict";var n=r(76),o=r(77),i=r(78),a=r(61).normalizeReference,s=r(61).isSpace;e.exports=function(e,t){var r,l,u,c,p,h,f,d,m,g="",_=e.pos,b=e.posMax,y=e.pos;if(91!==e.src.charCodeAt(e.pos))return!1;if(p=e.pos+1,(c=n(e,e.pos,!0))<0)return!1;if((h=c+1)<b&&40===e.src.charCodeAt(h)){for(h++;h<b&&(l=e.src.charCodeAt(h),s(l)||10===l);h++);if(h>=b)return!1;for(y=h,(f=o(e.src,h,e.posMax)).ok&&(g=e.md.normalizeLink(f.str),e.md.validateLink(g)?h=f.pos:g=""),y=h;h<b&&(l=e.src.charCodeAt(h),s(l)||10===l);h++);if(f=i(e.src,h,e.posMax),h<b&&y!==h&&f.ok)for(m=f.str,h=f.pos;h<b&&(l=e.src.charCodeAt(h),s(l)||10===l);h++);else m="";if(h>=b||41!==e.src.charCodeAt(h))return e.pos=_,!1;h++}else{if(void 0===e.env.references)return!1;if(h<b&&91===e.src.charCodeAt(h)?(y=h+1,(h=n(e,h))>=0?u=e.src.slice(y,h++):h=c+1):h=c+1,u||(u=e.src.slice(p,c)),!(d=e.env.references[a(u)]))return e.pos=_,!1;g=d.href,m=d.title}return t||(e.pos=p,e.posMax=c,e.push("link_open","a",1).attrs=r=[["href",g]],m&&r.push(["title",m]),e.md.inline.tokenize(e),e.push("link_close","a",-1)),e.pos=h,e.posMax=b,!0}},function(e,t,r){"use strict";var n=r(76),o=r(77),i=r(78),a=r(61).normalizeReference,s=r(61).isSpace;e.exports=function(e,t){var r,l,u,c,p,h,f,d,m,g,_,b,y,v="",w=e.pos,k=e.posMax;if(33!==e.src.charCodeAt(e.pos))return!1;if(91!==e.src.charCodeAt(e.pos+1))return!1;if(h=e.pos+2,(p=n(e,e.pos+1,!1))<0)return!1;if((f=p+1)<k&&40===e.src.charCodeAt(f)){for(f++;f<k&&(l=e.src.charCodeAt(f),s(l)||10===l);f++);if(f>=k)return!1;for(y=f,(m=o(e.src,f,e.posMax)).ok&&(v=e.md.normalizeLink(m.str),e.md.validateLink(v)?f=m.pos:v=""),y=f;f<k&&(l=e.src.charCodeAt(f),s(l)||10===l);f++);if(m=i(e.src,f,e.posMax),f<k&&y!==f&&m.ok)for(g=m.str,f=m.pos;f<k&&(l=e.src.charCodeAt(f),s(l)||10===l);f++);else g="";if(f>=k||41!==e.src.charCodeAt(f))return e.pos=w,!1;f++}else{if(void 0===e.env.references)return!1;if(f<k&&91===e.src.charCodeAt(f)?(y=f+1,(f=n(e,f))>=0?c=e.src.slice(y,f++):f=p+1):f=p+1,c||(c=e.src.slice(h,p)),!(d=e.env.references[a(c)]))return e.pos=w,!1;v=d.href,g=d.title}return t||(u=e.src.slice(h,p),e.md.inline.parse(u,e.md,e.env,b=[]),(_=e.push("image","img",0)).attrs=r=[["src",v],["alt",""]],_.children=b,_.content=u,g&&r.push(["title",g])),e.pos=f,e.posMax=k,!0}},function(e,t){"use strict";var r=/^<([a-zA-Z0-9.!#$%&'*+\/=?^_`{|}~-]+@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*)>/,n=/^<([a-zA-Z][a-zA-Z0-9+.\-]{1,31}):([^<>\x00-\x20]*)>/;e.exports=function(e,t){var o,i,a,s,l,u,c=e.pos;return 60===e.src.charCodeAt(c)&&!((o=e.src.slice(c)).indexOf(">")<0||(n.test(o)?(i=o.match(n),s=i[0].slice(1,-1),l=e.md.normalizeLink(s),!e.md.validateLink(l)||(t||((u=e.push("link_open","a",1)).attrs=[["href",l]],u.markup="autolink",u.info="auto",(u=e.push("text","",0)).content=e.md.normalizeLinkText(s),(u=e.push("link_close","a",-1)).markup="autolink",u.info="auto"),e.pos+=i[0].length,0)):!r.test(o)||(a=o.match(r),s=a[0].slice(1,-1),l=e.md.normalizeLink("mailto:"+s),!e.md.validateLink(l)||(t||((u=e.push("link_open","a",1)).attrs=[["href",l]],u.markup="autolink",u.info="auto",(u=e.push("text","",0)).content=e.md.normalizeLinkText(s),(u=e.push("link_close","a",-1)).markup="autolink",u.info="auto"),e.pos+=a[0].length,0))))}},function(e,t,r){"use strict";var n=r(102).HTML_TAG_RE;e.exports=function(e,t){var r,o,i,a=e.pos;return!!e.md.options.html&&(i=e.posMax,!(60!==e.src.charCodeAt(a)||a+2>=i)&&(!(33!==(r=e.src.charCodeAt(a+1))&&63!==r&&47!==r&&!function(e){var t=32|e;return t>=97&&t<=122}(r))&&(!!(o=e.src.slice(a).match(n))&&(t||(e.push("html_inline","",0).content=e.src.slice(a,a+o[0].length)),e.pos+=o[0].length,!0))))}},function(e,t,r){"use strict";var n=r(62),o=r(61).has,i=r(61).isValidEntityCode,a=r(61).fromCodePoint,s=/^&#((?:x[a-f0-9]{1,8}|[0-9]{1,8}));/i,l=/^&([a-z][a-z0-9]{1,31});/i;e.exports=function(e,t){var r,u,c=e.pos,p=e.posMax;if(38!==e.src.charCodeAt(c))return!1;if(c+1<p)if(35===e.src.charCodeAt(c+1)){if(u=e.src.slice(c).match(s))return t||(r="x"===u[1][0].toLowerCase()?parseInt(u[1].slice(1),16):parseInt(u[1],10),e.pending+=a(i(r)?r:65533)),e.pos+=u[0].length,!0}else if((u=e.src.slice(c).match(l))&&o(n,u[1]))return t||(e.pending+=n[u[1]]),e.pos+=u[0].length,!0;return t||(e.pending+="&"),e.pos++,!0}},function(e,t){"use strict";e.exports=function(e){var t,r,n,o,i=e.delimiters,a=e.delimiters.length;for(t=0;t<a;t++)if((n=i[t]).close)for(r=t-n.jump-1;r>=0;){if((o=i[r]).open&&o.marker===n.marker&&o.end<0&&o.level===n.level){n.jump=t-r,n.open=!1,o.end=t,o.jump=0;break}r-=o.jump+1}}},function(e,t){"use strict";e.exports=function(e){var t,r,n=0,o=e.tokens,i=e.tokens.length;for(t=r=0;t<i;t++)n+=o[t].nesting,o[t].level=n,"text"===o[t].type&&t+1<i&&"text"===o[t+1].type?o[t+1].content=o[t].content+o[t+1].content:(t!==r&&(o[r]=o[t]),r++);t!==r&&(o.length=r)}},function(e,t,r){"use strict";function n(e,t,r,n){this.src=e,this.env=r,this.md=t,this.tokens=n,this.pos=0,this.posMax=this.src.length,this.level=0,this.pending="",this.pendingLevel=0,this.cache={},this.delimiters=[]}var o=r(89),i=r(61).isWhiteSpace,a=r(61).isPunctChar,s=r(61).isMdAsciiPunct;n.prototype.pushPending=function(){var e=new o("text","",0);return e.content=this.pending,e.level=this.pendingLevel,this.tokens.push(e),this.pending="",e},n.prototype.push=function(e,t,r){this.pending&&this.pushPending();var n=new o(e,t,r);return r<0&&this.level--,n.level=this.level,r>0&&this.level++,this.pendingLevel=this.level,this.tokens.push(n),n},n.prototype.scanDelims=function(e,t){var r,n,o,l,u,c,p,h,f,d=e,m=!0,g=!0,_=this.posMax,b=this.src.charCodeAt(e);for(r=e>0?this.src.charCodeAt(e-1):32;d<_&&this.src.charCodeAt(d)===b;)d++;return o=d-e,n=d<_?this.src.charCodeAt(d):32,p=s(r)||a(String.fromCharCode(r)),f=s(n)||a(String.fromCharCode(n)),c=i(r),(h=i(n))?m=!1:f&&(c||p||(m=!1)),c?g=!1:p&&(h||f||(g=!1)),t?(l=m,u=g):(l=m&&(!g||p),u=g&&(!m||f)),{can_open:l,can_close:u,length:o}},n.prototype.Token=o,e.exports=n},function(e,t,r){"use strict";function n(e){return Array.prototype.slice.call(arguments,1).forEach(function(t){t&&Object.keys(t).forEach(function(r){e[r]=t[r]})}),e}function o(e){return Object.prototype.toString.call(e)}function 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r={defs:n,shortcuts:o,enabled:[]},l=s(e.utils.assign({},r,t||{}));e.renderer.rules.emoji=i,e.core.ruler.push("emoji",a(e,l.defs,l.shortcuts,l.scanRE,l.replaceRE))}},function(e,t){e.exports={100:"💯",1234:"🔢",grinning:"😀",smiley:"😃",smile:"😄",grin:"😁",laughing:"😆",satisfied:"😆",sweat_smile:"😅",joy:"😂",rofl:"🤣",relaxed:"☺️",blush:"😊",innocent:"😇",slightly_smiling_face:"🙂",upside_down_face:"🙃",wink:"😉",relieved:"😌",heart_eyes:"😍",kissing_heart:"😘",kissing:"😗",kissing_smiling_eyes:"😙",kissing_closed_eyes:"😚",yum:"😋",stuck_out_tongue_winking_eye:"😜",stuck_out_tongue_closed_eyes:"😝",stuck_out_tongue:"😛",money_mouth_face:"🤑",hugs:"🤗",nerd_face:"🤓",sunglasses:"😎",clown_face:"🤡",cowboy_hat_face:"🤠",smirk:"😏",unamused:"😒",disappointed:"😞",pensive:"😔",worried:"😟",confused:"😕",slightly_frowning_face:"🙁",frowning_face:"☹️",persevere:"😣",confounded:"😖",tired_face:"😫",weary:"😩",triumph:"😤",angry:"😠",rage:"😡",pout:"😡",no_mouth:"😶",neutral_face:"😐",expressionless:"😑",hushed:"😯",frowning:"😦",anguished:"😧",open_mouth:"😮",astonished:"😲",dizzy_face:"😵",flushed:"😳",scream:"😱",fearful:"😨",cold_sweat:"😰",cry:"😢",disappointed_relieved:"😥",drooling_face:"🤤",sob:"😭",sweat:"😓",sleepy:"😪",sleeping:"😴",roll_eyes:"🙄",thinking:"🤔",lying_face:"🤥",grimacing:"😬",zipper_mouth_face:"🤐",nauseated_face:"🤢",sneezing_face:"🤧",mask:"😷",face_with_thermometer:"🤒",face_with_head_bandage:"🤕",smiling_imp:"😈",imp:"👿",japanese_ogre:"👹",japanese_goblin:"👺",hankey:"💩",poop:"💩",shit:"💩",ghost:"👻",skull:"💀",skull_and_crossbones:"☠️",alien:"👽",space_invader:"👾",robot:"🤖",jack_o_lantern:"🎃",smiley_cat:"😺",smile_cat:"😸",joy_cat:"😹",heart_eyes_cat:"😻",smirk_cat:"😼",kissing_cat:"😽",scream_cat:"🙀",crying_cat_face:"😿",pouting_cat:"😾",open_hands:"👐",raised_hands:"🙌",clap:"👏",pray:"🙏",handshake:"🤝","+1":"👍",thumbsup:"👍","-1":"👎",thumbsdown:"👎",fist_oncoming:"👊",facepunch:"👊",punch:"👊",fist_raised:"✊",fist:"✊",fist_left:"🤛",fist_right:"🤜",crossed_fingers:"🤞",v:"✌️",metal:"🤘",ok_hand:"👌",point_left:"👈",point_right:"👉",point_up_2:"👆",point_down:"👇",point_up:"☝️",hand:"✋",raised_hand:"✋",raised_back_of_hand:"🤚",raised_hand_with_fingers_splayed:"🖐",vulcan_salute:"🖖",wave:"👋",call_me_hand:"🤙",muscle:"💪",middle_finger:"🖕",fu:"🖕",writing_hand:"✍️",selfie:"🤳",nail_care:"💅",ring:"💍",lipstick:"💄",kiss:"💋",lips:"👄",tongue:"👅",ear:"👂",nose:"👃",footprints:"👣",eye:"👁",eyes:"👀",speaking_head:"🗣",bust_in_silhouette:"👤",busts_in_silhouette:"👥",baby:"👶",boy:"👦",girl:"👧",man:"👨",woman:"👩",blonde_woman:"👱‍♀",blonde_man:"👱",person_with_blond_hair:"👱",older_man:"👴",older_woman:"👵",man_with_gua_pi_mao:"👲",woman_with_turban:"👳‍♀",man_with_turban:"👳",policewoman:"👮‍♀",policeman:"👮",cop:"👮",construction_worker_woman:"👷‍♀",construction_worker_man:"👷",construction_worker:"👷",guardswoman:"💂‍♀",guardsman:"💂",female_detective:"🕵️‍♀️",male_detective:"🕵",detective:"🕵",woman_health_worker:"👩‍⚕",man_health_worker:"👨‍⚕",woman_farmer:"👩‍🌾",man_farmer:"👨‍🌾",woman_cook:"👩‍🍳",man_cook:"👨‍🍳",woman_student:"👩‍🎓",man_student:"👨‍🎓",woman_singer:"👩‍🎤",man_singer:"👨‍🎤",woman_teacher:"👩‍🏫",man_teacher:"👨‍🏫",woman_factory_worker:"👩‍🏭",man_factory_worker:"👨‍🏭",woman_technologist:"👩‍💻",man_technologist:"👨‍💻",woman_office_worker:"👩‍💼",man_office_worker:"👨‍💼",woman_mechanic:"👩‍🔧",man_mechanic:"👨‍🔧",woman_scientist:"👩‍🔬",man_scientist:"👨‍🔬",woman_artist:"👩‍🎨",man_artist:"👨‍🎨",woman_firefighter:"👩‍🚒",man_firefighter:"👨‍🚒",woman_pilot:"👩‍✈",man_pilot:"👨‍✈",woman_astronaut:"👩‍🚀",man_astronaut:"👨‍🚀",woman_judge:"👩‍⚖",man_judge:"👨‍⚖",mrs_claus:"🤶",santa:"🎅",princess:"👸",prince:"🤴",bride_with_veil:"👰",man_in_tuxedo:"🤵",angel:"👼",pregnant_woman:"🤰",bowing_woman:"🙇‍♀",bowing_man:"🙇",bow:"🙇",tipping_hand_woman:"💁",information_desk_person:"💁",sassy_woman:"💁",tipping_hand_man:"💁‍♂",sassy_man:"💁‍♂",no_good_woman:"🙅",no_good:"🙅",ng_woman:"🙅",no_good_man:"🙅‍♂",ng_man:"🙅‍♂",ok_woman:"🙆",ok_man:"🙆‍♂",raising_hand_woman:"🙋",raising_hand:"🙋",raising_hand_man:"🙋‍♂",woman_facepalming:"🤦‍♀",man_facepalming:"🤦‍♂",woman_shrugging:"🤷‍♀",man_shrugging:"🤷‍♂",pouting_woman:"🙎",person_with_pouting_face:"🙎",pouting_man:"🙎‍♂",frowning_woman:"🙍",person_frowning:"🙍",frowning_man:"🙍‍♂",haircut_woman:"💇",haircut:"💇",haircut_man:"💇‍♂",massage_woman:"💆",massage:"💆",massage_man:"💆‍♂",business_suit_levitating:"🕴",dancer:"💃",man_dancing:"🕺",dancing_women:"👯",dancers:"👯",dancing_men:"👯‍♂",walking_woman:"🚶‍♀",walking_man:"🚶",walking:"🚶",running_woman:"🏃‍♀",running_man:"🏃",runner:"🏃",running:"🏃",couple:"👫",two_women_holding_hands:"👭",two_men_holding_hands:"👬",couple_with_heart_woman_man:"💑",couple_with_heart:"💑",couple_with_heart_woman_woman:"👩‍❤️‍👩",couple_with_heart_man_man:"👨‍❤️‍👨",couplekiss_man_woman:"💏",couplekiss_woman_woman:"👩‍❤️‍💋‍👩",couplekiss_man_man:"👨‍❤️‍💋‍👨",family_man_woman_boy:"👪",family:"👪",family_man_woman_girl:"👨‍👩‍👧",family_man_woman_girl_boy:"👨‍👩‍👧‍👦",family_man_woman_boy_boy:"👨‍👩‍👦‍👦",family_man_woman_girl_girl:"👨‍👩‍👧‍👧",family_woman_woman_boy:"👩‍👩‍👦",family_woman_woman_girl:"👩‍👩‍👧",family_woman_woman_girl_boy:"👩‍👩‍👧‍👦",family_woman_woman_boy_boy:"👩‍👩‍👦‍👦",family_woman_woman_girl_girl:"👩‍👩‍👧‍👧",family_man_man_boy:"👨‍👨‍👦",family_man_man_girl:"👨‍👨‍👧",family_man_man_girl_boy:"👨‍👨‍👧‍👦",family_man_man_boy_boy:"👨‍👨‍👦‍👦",family_man_man_girl_girl:"👨‍👨‍👧‍👧",family_woman_boy:"👩‍👦",family_woman_girl:"👩‍👧",family_woman_girl_boy:"👩‍👧‍👦",family_woman_boy_boy:"👩‍👦‍👦",family_woman_girl_girl:"👩‍👧‍👧",family_man_boy:"👨‍👦",family_man_girl:"👨‍👧",family_man_girl_boy:"👨‍👧‍👦",family_man_boy_boy:"👨‍👦‍👦",family_man_girl_girl:"👨‍👧‍👧",womans_clothes:"👚",shirt:"👕",tshirt:"👕",jeans:"👖",necktie:"👔",dress:"👗",bikini:"👙",kimono:"👘",high_heel:"👠",sandal:"👡",boot:"👢",mans_shoe:"👞",shoe:"👞",athletic_shoe:"👟",womans_hat:"👒",tophat:"🎩",mortar_board:"🎓",crown:"👑",rescue_worker_helmet:"⛑",school_satchel:"🎒",pouch:"👝",purse:"👛",handbag:"👜",briefcase:"💼",eyeglasses:"👓",dark_sunglasses:"🕶",closed_umbrella:"🌂",open_umbrella:"☂️",dog:"🐶",cat:"🐱",mouse:"🐭",hamster:"🐹",rabbit:"🐰",fox_face:"🦊",bear:"🐻",panda_face:"🐼",koala:"🐨",tiger:"🐯",lion:"🦁",cow:"🐮",pig:"🐷",pig_nose:"🐽",frog:"🐸",monkey_face:"🐵",see_no_evil:"🙈",hear_no_evil:"🙉",speak_no_evil:"🙊",monkey:"🐒",chicken:"🐔",penguin:"🐧",bird:"🐦",baby_chick:"🐤",hatching_chick:"🐣",hatched_chick:"🐥",duck:"🦆",eagle:"🦅",owl:"🦉",bat:"🦇",wolf:"🐺",boar:"🐗",horse:"🐴",unicorn:"🦄",bee:"🐝",honeybee:"🐝",bug:"🐛",butterfly:"🦋",snail:"🐌",shell:"🐚",beetle:"🐞",ant:"🐜",spider:"🕷",spider_web:"🕸",turtle:"🐢",snake:"🐍",lizard:"🦎",scorpion:"🦂",crab:"🦀",squid:"🦑",octopus:"🐙",shrimp:"🦐",tropical_fish:"🐠",fish:"🐟",blowfish:"🐡",dolphin:"🐬",flipper:"🐬",shark:"🦈",whale:"🐳",whale2:"🐋",crocodile:"🐊",leopard:"🐆",tiger2:"🐅",water_buffalo:"🐃",ox:"🐂",cow2:"🐄",deer:"🦌",dromedary_camel:"🐪",camel:"🐫",elephant:"🐘",rhinoceros:"🦏",gorilla:"🦍",racehorse:"🐎",pig2:"🐖",goat:"🐐",ram:"🐏",sheep:"🐑",dog2:"🐕",poodle:"🐩",cat2:"🐈",rooster:"🐓",turkey:"🦃",dove:"🕊",rabbit2:"🐇",mouse2:"🐁",rat:"🐀",chipmunk:"🐿",feet:"🐾",paw_prints:"🐾",dragon:"🐉",dragon_face:"🐲",cactus:"🌵",christmas_tree:"🎄",evergreen_tree:"🌲",deciduous_tree:"🌳",palm_tree:"🌴",seedling:"🌱",herb:"🌿",shamrock:"☘️",four_leaf_clover:"🍀",bamboo:"🎍",tanabata_tree:"🎋",leaves:"🍃",fallen_leaf:"🍂",maple_leaf:"🍁",mushroom:"🍄",ear_of_rice:"🌾",bouquet:"💐",tulip:"🌷",rose:"🌹",wilted_flower:"🥀",sunflower:"🌻",blossom:"🌼",cherry_blossom:"🌸",hibiscus:"🌺",earth_americas:"🌎",earth_africa:"🌍",earth_asia:"🌏",full_moon:"🌕",waning_gibbous_moon:"🌖",last_quarter_moon:"🌗",waning_crescent_moon:"🌘",new_moon:"🌑",waxing_crescent_moon:"🌒",first_quarter_moon:"🌓",moon:"🌔",waxing_gibbous_moon:"🌔",new_moon_with_face:"🌚",full_moon_with_face:"🌝",sun_with_face:"🌞",first_quarter_moon_with_face:"🌛",last_quarter_moon_with_face:"🌜",crescent_moon:"🌙",dizzy:"💫",star:"⭐️",star2:"🌟",sparkles:"✨",zap:"⚡️",fire:"🔥",boom:"💥",collision:"💥",comet:"☄",sunny:"☀️",sun_behind_small_cloud:"🌤",partly_sunny:"⛅️",sun_behind_large_cloud:"🌥",sun_behind_rain_cloud:"🌦",rainbow:"🌈",cloud:"☁️",cloud_with_rain:"🌧",cloud_with_lightning_and_rain:"⛈",cloud_with_lightning:"🌩",cloud_with_snow:"🌨",snowman_with_snow:"☃️",snowman:"⛄️",snowflake:"❄️",wind_face:"🌬",dash:"💨",tornado:"🌪",fog:"🌫",ocean:"🌊",droplet:"💧",sweat_drops:"💦",umbrella:"☔️",green_apple:"🍏",apple:"🍎",pear:"🍐",tangerine:"🍊",orange:"🍊",mandarin:"🍊",lemon:"🍋",banana:"🍌",watermelon:"🍉",grapes:"🍇",strawberry:"🍓",melon:"🍈",cherries:"🍒",peach:"🍑",pineapple:"🍍",kiwi_fruit:"🥝",avocado:"🥑",tomato:"🍅",eggplant:"🍆",cucumber:"🥒",carrot:"🥕",corn:"🌽",hot_pepper:"🌶",potato:"🥔",sweet_potato:"🍠",chestnut:"🌰",peanuts:"🥜",honey_pot:"🍯",croissant:"🥐",bread:"🍞",baguette_bread:"🥖",cheese:"🧀",egg:"🥚",fried_egg:"🍳",bacon:"🥓",pancakes:"🥞",fried_shrimp:"🍤",poultry_leg:"🍗",meat_on_bone:"🍖",pizza:"🍕",hotdog:"🌭",hamburger:"🍔",fries:"🍟",stuffed_flatbread:"🥙",taco:"🌮",burrito:"🌯",green_salad:"🥗",shallow_pan_of_food:"🥘",spaghetti:"🍝",ramen:"🍜",stew:"🍲",fish_cake:"🍥",sushi:"🍣",bento:"🍱",curry:"🍛",rice:"🍚",rice_ball:"🍙",rice_cracker:"🍘",oden:"🍢",dango:"🍡",shaved_ice:"🍧",ice_cream:"🍨",icecream:"🍦",cake:"🍰",birthday:"🎂",custard:"🍮",lollipop:"🍭",candy:"🍬",chocolate_bar:"🍫",popcorn:"🍿",doughnut:"🍩",cookie:"🍪",milk_glass:"🥛",baby_bottle:"🍼",coffee:"☕️",tea:"🍵",sake:"🍶",beer:"🍺",beers:"🍻",clinking_glasses:"🥂",wine_glass:"🍷",tumbler_glass:"🥃",cocktail:"🍸",tropical_drink:"🍹",champagne:"🍾",spoon:"🥄",fork_and_knife:"🍴",plate_with_cutlery:"🍽",soccer:"⚽️",basketball:"🏀",football:"🏈",baseball:"⚾️",tennis:"🎾",volleyball:"🏐",rugby_football:"🏉","8ball":"🎱",ping_pong:"🏓",badminton:"🏸",goal_net:"🥅",ice_hockey:"🏒",field_hockey:"🏑",cricket:"🏏",golf:"⛳️",bow_and_arrow:"🏹",fishing_pole_and_fish:"🎣",boxing_glove:"🥊",martial_arts_uniform:"🥋",ice_skate:"⛸",ski:"🎿",skier:"⛷",snowboarder:"🏂",weight_lifting_woman:"🏋️‍♀️",weight_lifting_man:"🏋",person_fencing:"🤺",women_wrestling:"🤼‍♀",men_wrestling:"🤼‍♂",woman_cartwheeling:"🤸‍♀",man_cartwheeling:"🤸‍♂",basketball_woman:"⛹️‍♀️",basketball_man:"⛹",woman_playing_handball:"🤾‍♀",man_playing_handball:"🤾‍♂",golfing_woman:"🏌️‍♀️",golfing_man:"🏌",surfing_woman:"🏄‍♀",surfing_man:"🏄",surfer:"🏄",swimming_woman:"🏊‍♀",swimming_man:"🏊",swimmer:"🏊",woman_playing_water_polo:"🤽‍♀",man_playing_water_polo:"🤽‍♂",rowing_woman:"🚣‍♀",rowing_man:"🚣",rowboat:"🚣",horse_racing:"🏇",biking_woman:"🚴‍♀",biking_man:"🚴",bicyclist:"🚴",mountain_biking_woman:"🚵‍♀",mountain_biking_man:"🚵",mountain_bicyclist:"🚵",running_shirt_with_sash:"🎽",medal_sports:"🏅",medal_military:"🎖","1st_place_medal":"🥇","2nd_place_medal":"🥈","3rd_place_medal":"🥉",trophy:"🏆",rosette:"🏵",reminder_ribbon:"🎗",ticket:"🎫",tickets:"🎟",circus_tent:"🎪",woman_juggling:"🤹‍♀",man_juggling:"🤹‍♂",performing_arts:"🎭",art:"🎨",clapper:"🎬",microphone:"🎤",headphones:"🎧",musical_score:"🎼",musical_keyboard:"🎹",drum:"🥁",saxophone:"🎷",trumpet:"🎺",guitar:"🎸",violin:"🎻",game_die:"🎲",dart:"🎯",bowling:"🎳",video_game:"🎮",slot_machine:"🎰",car:"🚗",red_car:"🚗",taxi:"🚕",blue_car:"🚙",bus:"🚌",trolleybus:"🚎",racing_car:"🏎",police_car:"🚓",ambulance:"🚑",fire_engine:"🚒",minibus:"🚐",truck:"🚚",articulated_lorry:"🚛",tractor:"🚜",kick_scooter:"🛴",bike:"🚲",motor_scooter:"🛵",motorcycle:"🏍",rotating_light:"🚨",oncoming_police_car:"🚔",oncoming_bus:"🚍",oncoming_automobile:"🚘",oncoming_taxi:"🚖",aerial_tramway:"🚡",mountain_cableway:"🚠",suspension_railway:"🚟",railway_car:"🚃",train:"🚋",mountain_railway:"🚞",monorail:"🚝",bullettrain_side:"🚄",bullettrain_front:"🚅",light_rail:"🚈",steam_locomotive:"🚂",train2:"🚆",metro:"🚇",tram:"🚊",station:"🚉",helicopter:"🚁",small_airplane:"🛩",airplane:"✈️",flight_departure:"🛫",flight_arrival:"🛬",rocket:"🚀",artificial_satellite:"🛰",seat:"💺",canoe:"🛶",boat:"⛵️",sailboat:"⛵️",motor_boat:"🛥",speedboat:"🚤",passenger_ship:"🛳",ferry:"⛴",ship:"🚢",anchor:"⚓️",construction:"🚧",fuelpump:"⛽️",busstop:"🚏",vertical_traffic_light:"🚦",traffic_light:"🚥",world_map:"🗺",moyai:"🗿",statue_of_liberty:"🗽",fountain:"⛲️",tokyo_tower:"🗼",european_castle:"🏰",japanese_castle:"🏯",stadium:"🏟",ferris_wheel:"🎡",roller_coaster:"🎢",carousel_horse:"🎠",parasol_on_ground:"⛱",beach_umbrella:"🏖",desert_island:"🏝",mountain:"⛰",mountain_snow:"🏔",mount_fuji:"🗻",volcano:"🌋",desert:"🏜",camping:"🏕",tent:"⛺️",railway_track:"🛤",motorway:"🛣",building_construction:"🏗",factory:"🏭",house:"🏠",house_with_garden:"🏡",houses:"🏘",derelict_house:"🏚",office:"🏢",department_store:"🏬",post_office:"🏣",european_post_office:"🏤",hospital:"🏥",bank:"🏦",hotel:"🏨",convenience_store:"🏪",school:"🏫",love_hotel:"🏩",wedding:"💒",classical_building:"🏛",church:"⛪️",mosque:"🕌",synagogue:"🕍",kaaba:"🕋",shinto_shrine:"⛩",japan:"🗾",rice_scene:"🎑",national_park:"🏞",sunrise:"🌅",sunrise_over_mountains:"🌄",stars:"🌠",sparkler:"🎇",fireworks:"🎆",city_sunrise:"🌇",city_sunset:"🌆",cityscape:"🏙",night_with_stars:"🌃",milky_way:"🌌",bridge_at_night:"🌉",foggy:"🌁",watch:"⌚️",iphone:"📱",calling:"📲",computer:"💻",keyboard:"⌨️",desktop_computer:"🖥",printer:"🖨",computer_mouse:"🖱",trackball:"🖲",joystick:"🕹",clamp:"🗜",minidisc:"💽",floppy_disk:"💾",cd:"💿",dvd:"📀",vhs:"📼",camera:"📷",camera_flash:"📸",video_camera:"📹",movie_camera:"🎥",film_projector:"📽",film_strip:"🎞",telephone_receiver:"📞",phone:"☎️",telephone:"☎️",pager:"📟",fax:"📠",tv:"📺",radio:"📻",studio_microphone:"🎙",level_slider:"🎚",control_knobs:"🎛",stopwatch:"⏱",timer_clock:"⏲",alarm_clock:"⏰",mantelpiece_clock:"🕰",hourglass:"⌛️",hourglass_flowing_sand:"⏳",satellite:"📡",battery:"🔋",electric_plug:"🔌",bulb:"💡",flashlight:"🔦",candle:"🕯",wastebasket:"🗑",oil_drum:"🛢",money_with_wings:"💸",dollar:"💵",yen:"💴",euro:"💶",pound:"💷",moneybag:"💰",credit_card:"💳",gem:"💎",balance_scale:"⚖️",wrench:"🔧",hammer:"🔨",hammer_and_pick:"⚒",hammer_and_wrench:"🛠",pick:"⛏",nut_and_bolt:"🔩",gear:"⚙️",chains:"⛓",gun:"🔫",bomb:"💣",hocho:"🔪",knife:"🔪",dagger:"🗡",crossed_swords:"⚔️",shield:"🛡",smoking:"🚬",coffin:"⚰️",funeral_urn:"⚱️",amphora:"🏺",crystal_ball:"🔮",prayer_beads:"📿",barber:"💈",alembic:"⚗️",telescope:"🔭",microscope:"🔬",hole:"🕳",pill:"💊",syringe:"💉",thermometer:"🌡",toilet:"🚽",potable_water:"🚰",shower:"🚿",bathtub:"🛁",bath:"🛀",bellhop_bell:"🛎",key:"🔑",old_key:"🗝",door:"🚪",couch_and_lamp:"🛋",bed:"🛏",sleeping_bed:"🛌",framed_picture:"🖼",shopping:"🛍",shopping_cart:"🛒",gift:"🎁",balloon:"🎈",flags:"🎏",ribbon:"🎀",confetti_ball:"🎊",tada:"🎉",dolls:"🎎",izakaya_lantern:"🏮",lantern:"🏮",wind_chime:"🎐",email:"✉️",envelope:"✉️",envelope_with_arrow:"📩",incoming_envelope:"📨","e-mail":"📧",love_letter:"💌",inbox_tray:"📥",outbox_tray:"📤",package:"📦",label:"🏷",mailbox_closed:"📪",mailbox:"📫",mailbox_with_mail:"📬",mailbox_with_no_mail:"📭",postbox:"📮",postal_horn:"📯",scroll:"📜",page_with_curl:"📃",page_facing_up:"📄",bookmark_tabs:"📑",bar_chart:"📊",chart_with_upwards_trend:"📈",chart_with_downwards_trend:"📉",spiral_notepad:"🗒",spiral_calendar:"🗓",calendar:"📆",date:"📅",card_index:"📇",card_file_box:"🗃",ballot_box:"🗳",file_cabinet:"🗄",clipboard:"📋",file_folder:"📁",open_file_folder:"📂",card_index_dividers:"🗂",newspaper_roll:"🗞",newspaper:"📰",notebook:"📓",notebook_with_decorative_cover:"📔",ledger:"📒",closed_book:"📕",green_book:"📗",blue_book:"📘",orange_book:"📙",books:"📚",book:"📖",open_book:"📖",bookmark:"🔖",link:"🔗",paperclip:"📎",paperclips:"🖇",triangular_ruler:"📐",straight_ruler:"📏",pushpin:"📌",round_pushpin:"📍",scissors:"✂️",pen:"🖊",fountain_pen:"🖋",blac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i=this.parseFunction(e),s=i.value.name;if(!a.hasOwnProperty(s))throw new p("No such environment: "+s,this.lexer,i.value.namepos);var l=a[s],c=this.parseArguments("\\begin{"+s+"}",l),f={mode:this.mode,envName:s,parser:this,lexer:this.lexer,positions:c.pop()},g=l.handler(f,c);this.expect("\\end",!1);var _=this.parseFunction();if(_.value.name!==s)throw new p("Mismatch: \\begin{"+s+"} matched by \\end{"+_.value.name+"}",this.lexer);return g.position=_.position,g}return u.contains(d,r)?(t=this.parseExpression(!1),new h("sizing",{size:"size"+(u.indexOf(d,r)+1),value:t},this.mode)):u.contains(m,r)?(t=this.parseExpression(!0),new h("styling",{style:r.slice(1,r.length-5),value:t},this.mode)):this.parseFunction(e)},n.prototype.parseFunction=function(e){if(e||(e=this.parseGroup()),e){if(e.isFunction){var t=e.result,r=i[t];if("text"===this.mode&&!r.allowedInText)throw new p("Can't use function '"+t+"' in text mode",this.lexer,e.position);var n=this.parseArguments(t,r),o=this.callFunction(t,n,n.pop());return new h(o.type,o,this.mode)}return e.result}return null},n.prototype.callFunction=function(e,t,r){var n={funcName:e,parser:this,lexer:this.lexer,positions:r};return i[e].handler(n,t)},n.prototype.parseArguments=function(e,t){var r=t.numArgs+t.numOptionalArgs;if(0===r)return[[this.pos]];for(var n=t.greediness,a=[this.pos],s=[],l=0;l<r;l++){var u,c=t.argTypes&&t.argTypes[l];if(l<t.numOptionalArgs){if(!(u=c?this.parseSpecialGroup(c,!0):this.parseOptionalGroup())){s.push(null),a.push(this.pos);continue}}else if(!(u=c?this.parseSpecialGroup(c):this.parseGroup())){if(this.settings.throwOnError||"\\"!==this.nextToken.text[0])throw new p("Expected group after '"+e+"'",this.lexer,this.pos);u=new o(this.handleUnsupportedCmd(this.nextToken.text),!1)}var h;if(u.isFunction){if(!(i[u.result].greediness>n))throw new p("Got function '"+u.result+"' as argument to '"+e+"'",this.lexer,this.pos-1);h=this.parseFunction(u)}else h=u.result;s.push(h),a.push(this.pos)}return s.push(a),s},n.prototype.parseSpecialGroup=function(e,t){var r=this.mode;if("original"===e&&(e=r),"color"===e||"size"===e){var n=this.nextToken;if(t&&"["!==n.text)return null;this.mode=e,this.expect(t?"[":"{");var i=this.nextToken;this.mode=r;var a;return a="color"===e?i.text:i.data,this.consume(),this.expect(t?"]":"}"),new o(new h(e,a,r),!1)}if("text"===e){var s=this.lexer.lex(this.pos,"whitespace");this.pos=s.position}this.mode=e,this.nextToken=this.lexer.lex(this.pos,e);var l;return l=t?this.parseOptionalGroup():this.parseGroup(),this.mode=r,this.nextToken=this.lexer.lex(this.pos,r),l},n.prototype.parseGroup=function(){if("{"===this.nextToken.text){this.consume();var e=this.parseExpression(!1);return this.expect("}"),new o(new h("ordgroup",e,this.mode),!1)}return this.parseSymbol()},n.prototype.parseOptionalGroup=function(){if("["===this.nextToken.text){this.consume();var e=this.parseExpression(!1,"]");return this.expect("]"),new o(new 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a={"\\bigl":{type:"open",size:1},"\\Bigl":{type:"open",size:2},"\\biggl":{type:"open",size:3},"\\Biggl":{type:"open",size:4},"\\bigr":{type:"close",size:1},"\\Bigr":{type:"close",size:2},"\\biggr":{type:"close",size:3},"\\Biggr":{type:"close",size:4},"\\bigm":{type:"rel",size:1},"\\Bigm":{type:"rel",size:2},"\\biggm":{type:"rel",size:3},"\\Biggm":{type:"rel",size:4},"\\big":{type:"textord",size:1},"\\Big":{type:"textord",size:2},"\\bigg":{type:"textord",size:3},"\\Bigg":{type:"textord",size:4}},s=["(",")","[","\\lbrack","]","\\rbrack","\\{","\\lbrace","\\}","\\rbrace","\\lfloor","\\rfloor","\\lceil","\\rceil","<",">","\\langle","\\rangle","\\lt","\\gt","\\lvert","\\rvert","\\lVert","\\rVert","\\lgroup","\\rgroup","\\lmoustache","\\rmoustache","/","\\backslash","|","\\vert","\\|","\\Vert","\\uparrow","\\Uparrow","\\downarrow","\\Downarrow","\\updownarrow","\\Updownarrow","."],l={"\\Bbb":"\\mathbb","\\bold":"\\mathbf","\\frak":"\\mathfrak"};n(["\\blue","\\orange","\\pink","\\red","\\green","\\gray","\\purple","\\blueA","\\blueB","\\blueC","\\blueD","\\blueE","\\tealA","\\tealB","\\tealC","\\tealD","\\tealE","\\greenA","\\greenB","\\greenC","\\greenD","\\greenE","\\goldA","\\goldB","\\goldC","\\goldD","\\goldE","\\redA","\\redB","\\redC","\\redD","\\redE","\\maroonA","\\maroonB","\\maroonC","\\maroonD","\\maroonE","\\purpleA","\\purpleB","\\purpleC","\\purpleD","\\purpleE","\\mintA","\\mintB","\\mintC","\\grayA","\\grayB","\\grayC","\\grayD","\\grayE","\\grayF","\\grayG","\\grayH","\\grayI","\\kaBlue","\\kaGreen"],{numArgs:1,allowedInText:!0,greediness:3},function(e,t){var r,n=t[0];return r="ordgroup"===n.type?n.value:[n],{type:"color",color:"katex-"+e.funcName.slice(1),value:r}}),n(["\\arcsin","\\arccos","\\arctan","\\arg","\\cos","\\cosh","\\cot","\\coth","\\csc","\\deg","\\dim","\\exp","\\hom","\\ker","\\lg","\\ln","\\log","\\sec","\\sin","\\sinh","\\tan","\\tanh"],{numArgs:0},function(e){return{type:"op",limits:!1,symbol:!1,body:e.funcName}}),n(["\\det","\\gcd","\\inf","\\lim","\\liminf","\\limsup","\\max","\\min","\\Pr","\\sup"],{numArgs:0},function(e){return{type:"op",limits:!0,symbol:!1,body:e.funcName}}),n(["\\int","\\iint","\\iiint","\\oint"],{numArgs:0},function(e){return{type:"op",limits:!1,symbol:!0,body:e.funcName}}),n(["\\coprod","\\bigvee","\\bigwedge","\\biguplus","\\bigcap","\\bigcup","\\intop","\\prod","\\sum","\\bigotimes","\\bigoplus","\\bigodot","\\bigsqcup","\\smallint"],{numArgs:0},function(e){return{type:"op",limits:!0,symbol:!0,body:e.funcName}}),n(["\\dfrac","\\frac","\\tfrac","\\dbinom","\\binom","\\tbinom"],{numArgs:2,greediness:2},function(e,t){var r,n=t[0],o=t[1],i=null,a=null,s="auto";switch(e.funcName){case"\\dfrac":case"\\frac":case"\\tfrac":r=!0;break;case"\\dbinom":case"\\binom":case"\\tbinom":r=!1,i="(",a=")";break;default:throw new Error("Unrecognized genfrac command")}switch(e.funcName){case"\\dfrac":case"\\dbinom":s="display";break;case"\\tfrac":case"\\tbinom":s="text"}return{type:"genfrac",numer:n,denom:o,hasBarLine:r,leftDelim:i,rightDelim:a,size:s}}),n(["\\llap","\\rlap"],{numArgs:1,allowedInText:!0},function(e,t){var r=t[0];return{type:e.funcName.slice(1),body:r}}),n(["\\bigl","\\Bigl","\\biggl","\\Biggl","\\bigr","\\Bigr","\\biggr","\\Biggr","\\bigm","\\Bigm","\\biggm","\\Biggm","\\big","\\Big","\\bigg","\\Bigg","\\left","\\right"],{numArgs:1},function(e,t){var r=t[0];if(!o.contains(s,r.value))throw new i("Invalid delimiter: '"+r.value+"' after 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t;switch(e.funcName){case"\\over":t="\\frac";break;case"\\choose":t="\\binom";break;default:throw new Error("Unrecognized infix genfrac command")}return{type:"infix",replaceWith:t}}),n(["\\\\","\\cr"],{numArgs:0,numOptionalArgs:1,argTypes:["size"]},function(e,t){return{type:"cr",size:t[0]}}),n(["\\begin","\\end"],{numArgs:1,argTypes:["text"]},function(e,t){var r=t[0];if("ordgroup"!==r.type)throw new i("Invalid environment name",e.lexer,e.positions[1]);for(var n="",o=0;o<r.value.length;++o)n+=r.value[o].value;return{type:"environment",name:n,namepos:e.positions[1]}})},function(e,t,r){function n(e,t){for(var r=[],n=[r],o=[];;){var i=e.parseExpression(!1,null);r.push(new l("ordgroup",i,e.mode));var a=e.nextToken.text;if("&"===a)e.consume();else{if("\\end"===a)break;if("\\\\"!==a&&"\\cr"!==a){var u=Math.min(e.pos+1,e.lexer._input.length);throw new s("Expected & or \\\\ or \\end",e.lexer,u)}var c=e.parseFunction();o.push(c.value.size),r=[],n.push(r)}}return t.body=n,t.rowGaps=o,new 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diff --git a/website/docs/api/annotation.md b/website/docs/api/annotation.md
new file mode 100644
index 000000000..048af5f4c
--- /dev/null
+++ b/website/docs/api/annotation.md
@@ -0,0 +1,620 @@
+---
+title: Annotation Specifications
+teaser: Schemes used for labels, tags and training data
+menu:
+  - ['Text Processing', 'text-processing']
+  - ['POS Tagging', 'pos-tagging']
+  - ['Dependencies', 'dependency-parsing']
+  - ['Named Entities', 'named-entities']
+  - ['Models & Training', 'training']
+---
+
+## Text processing {#text-processing}
+
+> #### Example
+>
+> ```python
+> from spacy.lang.en import English
+> nlp = English()
+> tokens = nlp(u"Some\\nspaces  and\\ttab characters")
+> tokens_text = [t.text for t in tokens]
+> assert tokens_text == ["Some", "\\n", "spaces", " ", "and", "\\t", "tab", "characters"]
+> ```
+
+Tokenization standards are based on the
+[OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) corpus. The tokenizer
+differs from most by including **tokens for significant whitespace**. Any
+sequence of whitespace characters beyond a single space (`' '`) is included as a
+token. The whitespace tokens are useful for much the same reason punctuation is
+– it's often an important delimiter in the text. By preserving it in the token
+output, we are able to maintain a simple alignment between the tokens and the
+original string, and we ensure that **no information is lost** during
+processing.
+
+### Lemmatization {#lemmatization}
+
+> #### Examples
+>
+> In English, this means:
+>
+> - **Adjectives**: happier, happiest → happy
+> - **Adverbs**: worse, worst → badly
+> - **Nouns**: dogs, children → dog, child
+> - **Verbs**: writes, writing, wrote, written → write
+
+A lemma is the uninflected form of a word. The English lemmatization data is
+taken from [WordNet](https://wordnet.princeton.edu). Lookup tables are taken
+from [Lexiconista](http://www.lexiconista.com/datasets/lemmatization/). spaCy
+also adds a **special case for pronouns**: all pronouns are lemmatized to the
+special token `-PRON-`.
+
+<Infobox title="About spaCy's custom pronoun lemma" variant="warning">
+
+Unlike verbs and common nouns, there's no clear base form of a personal pronoun.
+Should the lemma of "me" be "I", or should we normalize person as well, giving
+"it" — or maybe "he"? spaCy's solution is to introduce a novel symbol, `-PRON-`,
+which is used as the lemma for all personal pronouns.
+
+</Infobox>
+
+### Sentence boundary detection {#sentence-boundary}
+
+Sentence boundaries are calculated from the syntactic parse tree, so features
+such as punctuation and capitalization play an important but non-decisive role
+in determining the sentence boundaries. Usually this means that the sentence
+boundaries will at least coincide with clause boundaries, even given poorly
+punctuated text.
+
+## Part-of-speech tagging {#pos-tagging}
+
+> #### Tip: Understanding tags
+>
+> You can also use `spacy.explain` to get the description for the string
+> representation of a tag. For example, `spacy.explain("RB")` will return
+> "adverb".
+
+This section lists the fine-grained and coarse-grained part-of-speech tags
+assigned by spaCy's [models](/models). The individual mapping is specific to the
+training corpus and can be defined in the respective language data's
+[`tag_map.py`](/usage/adding-languages#tag-map).
+
+<Accordion title="Universal Part-of-speech Tags">
+
+spaCy also maps all language-specific part-of-speech tags to a small, fixed set
+of word type tags following the
+[Universal Dependencies scheme](http://universaldependencies.org/u/pos/). The
+universal tags don't code for any morphological features and only cover the word
+type. They're available as the [`Token.pos`](/api/token#attributes) and
+[`Token.pos_`](/api/token#attributes) attributes.
+
+| POS     | Description               | Examples                                      |
+| ------- | ------------------------- | --------------------------------------------- |
+| `ADJ`   | adjective                 | big, old, green, incomprehensible, first      |
+| `ADP`   | adposition                | in, to, during                                |
+| `ADV`   | adverb                    | very, tomorrow, down, where, there            |
+| `AUX`   | auxiliary                 | is, has (done), will (do), should (do)        |
+| `CONJ`  | conjunction               | and, or, but                                  |
+| `CCONJ` | coordinating conjunction  | and, or, but                                  |
+| `DET`   | determiner                | a, an, the                                    |
+| `INTJ`  | interjection              | psst, ouch, bravo, hello                      |
+| `NOUN`  | noun                      | girl, cat, tree, air, beauty                  |
+| `NUM`   | numeral                   | 1, 2017, one, seventy-seven, IV, MMXIV        |
+| `PART`  | particle                  | 's, not,                                      |
+| `PRON`  | pronoun                   | I, you, he, she, myself, themselves, somebody |
+| `PROPN` | proper noun               | Mary, John, London, NATO, HBO                 |
+| `PUNCT` | punctuation               | ., (, ), ?                                    |
+| `SCONJ` | subordinating conjunction | if, while, that                               |
+| `SYM`   | symbol                    | \$, %, §, ©, +, −, ×, ÷, =, :), 😝            |
+| `VERB`  | verb                      | run, runs, running, eat, ate, eating          |
+| `X`     | other                     | sfpksdpsxmsa                                  |
+| `SPACE` | space                     |
+
+</Accordion>
+
+<Accordion title="English" id="pos-en">
+
+The English part-of-speech tagger uses the
+[OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) version of the Penn
+Treebank tag set. We also map the tags to the simpler Google Universal POS tag
+set.
+
+| Tag                                 |  POS    | Morphology                                     | Description                               |
+| ----------------------------------- | ------- | ---------------------------------------------- | ----------------------------------------- |
+| `-LRB-`                             | `PUNCT` | `PunctType=brck PunctSide=ini`                 | left round bracket                        |
+| `-RRB-`                             | `PUNCT` | `PunctType=brck PunctSide=fin`                 | right round bracket                       |
+| `,`                                 | `PUNCT` | `PunctType=comm`                               | punctuation mark, comma                   |
+| `:`                                 | `PUNCT` |                                                | punctuation mark, colon or ellipsis       |
+| `.`                                 | `PUNCT` | `PunctType=peri`                               | punctuation mark, sentence closer         |
+| `''`                                | `PUNCT` | `PunctType=quot PunctSide=fin`                 | closing quotation mark                    |
+| `""`                                | `PUNCT` | `PunctType=quot PunctSide=fin`                 | closing quotation mark                    |
+| <InlineCode>&#96;&#96;</InlineCode> | `PUNCT` | `PunctType=quot PunctSide=ini`                 | opening quotation mark                    |
+| `#`                                 | `SYM`   | `SymType=numbersign`                           | symbol, number sign                       |
+| `$`                                 | `SYM`   | `SymType=currency`                             | symbol, currency                          |
+| `ADD`                               | `X`     |                                                | email                                     |
+| `AFX`                               | `ADJ`   | `Hyph=yes`                                     | affix                                     |
+| `BES`                               | `VERB`  |                                                | auxiliary "be"                            |
+| `CC`                                | `CONJ`  | `ConjType=coor`                                | conjunction, coordinating                 |
+| `CD`                                | `NUM`   | `NumType=card`                                 | cardinal number                           |
+| `DT`                                | `DET`   |                                                | determiner                                |
+| `EX`                                | `ADV`   | `AdvType=ex`                                   | existential there                         |
+| `FW`                                | `X`     | `Foreign=yes`                                  | foreign word                              |
+| `GW`                                | `X`     |                                                | additional word in multi-word expression  |
+| `HVS`                               | `VERB`  |                                                | forms of "have"                           |
+| `HYPH`                              | `PUNCT` | `PunctType=dash`                               | punctuation mark, hyphen                  |
+| `IN`                                | `ADP`   |                                                | conjunction, subordinating or preposition |
+| `JJ`                                | `ADJ`   | `Degree=pos`                                   | adjective                                 |
+| `JJR`                               | `ADJ`   | `Degree=comp`                                  | adjective, comparative                    |
+| `JJS`                               | `ADJ`   | `Degree=sup`                                   | adjective, superlative                    |
+| `LS`                                | `PUNCT` | `NumType=ord`                                  | list item marker                          |
+| `MD`                                | `VERB`  | `VerbType=mod`                                 | verb, modal auxiliary                     |
+| `NFP`                               | `PUNCT` |                                                | superfluous punctuation                   |
+| `NIL`                               |         |                                                | missing tag                               |
+| `NN`                                | `NOUN`  | `Number=sing`                                  | noun, singular or mass                    |
+| `NNP`                               | `PROPN` | `NounType=prop Number=sign`                    | noun, proper singular                     |
+| `NNPS`                              | `PROPN` | `NounType=prop Number=plur`                    | noun, proper plural                       |
+| `NNS`                               | `NOUN`  | `Number=plur`                                  | noun, plural                              |
+| `PDT`                               | `ADJ`   | `AdjType=pdt PronType=prn`                     | predeterminer                             |
+| `POS`                               | `PART`  | `Poss=yes`                                     | possessive ending                         |
+| `PRP`                               | `PRON`  | `PronType=prs`                                 | pronoun, personal                         |
+| `PRP$`                              | `ADJ`   | `PronType=prs Poss=yes`                        | pronoun, possessive                       |
+| `RB`                                | `ADV`   | `Degree=pos`                                   | adverb                                    |
+| `RBR`                               | `ADV`   | `Degree=comp`                                  | adverb, comparative                       |
+| `RBS`                               | `ADV`   | `Degree=sup`                                   | adverb, superlative                       |
+| `RP`                                | `PART`  |                                                | adverb, particle                          |
+| `_SP`                               | `SPACE` |                                                | space                                     |
+| `SYM`                               | `SYM`   |                                                | symbol                                    |
+| `TO`                                | `PART`  | `PartType=inf VerbForm=inf`                    | infinitival "to"                          |
+| `UH`                                | `INTJ`  |                                                | interjection                              |
+| `VB`                                | `VERB`  | `VerbForm=inf`                                 | verb, base form                           |
+| `VBD`                               | `VERB`  | `VerbForm=fin Tense=past`                      | verb, past tense                          |
+| `VBG`                               | `VERB`  | `VerbForm=part Tense=pres Aspect=prog`         | verb, gerund or present participle        |
+| `VBN`                               | `VERB`  | `VerbForm=part Tense=past Aspect=perf`         | verb, past participle                     |
+| `VBP`                               | `VERB`  | `VerbForm=fin Tense=pres`                      | verb, non-3rd person singular present     |
+| `VBZ`                               | `VERB`  | `VerbForm=fin Tense=pres Number=sing Person=3` | verb, 3rd person singular present         |
+| `WDT`                               | `ADJ`   | `PronType=int|rel`                             | wh-determiner                             |
+| `WP`                                | `NOUN`  | `PronType=int|rel`                             | wh-pronoun, personal                      |
+| `WP$`                               | `ADJ`   | `Poss=yes PronType=int|rel`                    | wh-pronoun, possessive                    |
+| `WRB`                               | `ADV`   | `PronType=int|rel`                             | wh-adverb                                 |
+| `XX`                                | `X`     |                                                | unknown                                   |
+
+</Accordion>
+
+<Accordion title="German" id="pos-de">
+
+The German part-of-speech tagger uses the
+[TIGER Treebank](http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/TIGERCorpus/annotation/index.html)
+annotation scheme. We also map the tags to the simpler Google Universal POS tag
+set.
+
+| Tag       |  POS    | Morphology                                  | Description                                       |
+| --------- | ------- | ------------------------------------------- | ------------------------------------------------- |
+| `$(`      | `PUNCT` | `PunctType=brck`                            | other sentence-internal punctuation mark          |
+| `$,`      | `PUNCT` | `PunctType=comm`                            | comma                                             |
+| `$.`      | `PUNCT` | `PunctType=peri`                            | sentence-final punctuation mark                   |
+| `ADJA`    | `ADJ`   |                                             | adjective, attributive                            |
+| `ADJD`    | `ADJ`   | `Variant=short`                             | adjective, adverbial or predicative               |
+| `ADV`     | `ADV`   |                                             | adverb                                            |
+| `APPO`    | `ADP`   | `AdpType=post`                              | postposition                                      |
+| `APPR`    | `ADP`   | `AdpType=prep`                              | preposition; circumposition left                  |
+| `APPRART` | `ADP`   | `AdpType=prep PronType=art`                 | preposition with article                          |
+| `APZR`    | `ADP`   | `AdpType=circ`                              | circumposition right                              |
+| `ART`     | `DET`   | `PronType=art`                              | definite or indefinite article                    |
+| `CARD`    | `NUM`   | `NumType=card`                              | cardinal number                                   |
+| `FM`      | `X`     | `Foreign=yes`                               | foreign language material                         |
+| `ITJ`     | `INTJ`  |                                             | interjection                                      |
+| `KOKOM`   | `CONJ`  | `ConjType=comp`                             | comparative conjunction                           |
+| `KON`     | `CONJ`  |                                             | coordinate conjunction                            |
+| `KOUI`    | `SCONJ` |                                             | subordinate conjunction with "zu" and infinitive  |
+| `KOUS`    | `SCONJ` |                                             | subordinate conjunction with sentence             |
+| `NE`      | `PROPN` |                                             | proper noun                                       |
+| `NNE`     | `PROPN` |                                             | proper noun                                       |
+| `NN`      | `NOUN`  |                                             | noun, singular or mass                            |
+| `PAV`     | `ADV`   | `PronType=dem`                              | pronominal adverb                                 |
+| `PROAV`   | `ADV`   | `PronType=dem`                              | pronominal adverb                                 |
+| `PDAT`    | `DET`   | `PronType=dem`                              | attributive demonstrative pronoun                 |
+| `PDS`     | `PRON`  | `PronType=dem`                              | substituting demonstrative pronoun                |
+| `PIAT`    | `DET`   | `PronType=ind\|neg\|tot`                    | attributive indefinite pronoun without determiner |
+| `PIDAT`   | `DET`   | `AdjType=pdt PronType=ind\|neg\|tot`        | attributive indefinite pronoun with determiner    |
+| `PIS`     | `PRON`  | `PronType=ind\|neg\|tot`                    | substituting indefinite pronoun                   |
+| `PPER`    | `PRON`  | `PronType=prs`                              | non-reflexive personal pronoun                    |
+| `PPOSAT`  | `DET`   | `Poss=yes PronType=prs`                     | attributive possessive pronoun                    |
+| `PPOSS`   | `PRON`  | `PronType=rel`                              | substituting possessive pronoun                   |
+| `PRELAT`  | `DET`   | `PronType=rel`                              | attributive relative pronoun                      |
+| `PRELS`   | `PRON`  | `PronType=rel`                              | substituting relative pronoun                     |
+| `PRF`     | `PRON`  | `PronType=prs Reflex=yes`                   | reflexive personal pronoun                        |
+| `PTKA`    | `PART`  |                                             | particle with adjective or adverb                 |
+| `PTKANT`  | `PART`  | `PartType=res`                              | answer particle                                   |
+| `PTKNEG`  | `PART`  | `Negative=yes`                              | negative particle                                 |
+| `PTKVZ`   | `PART`  | `PartType=vbp`                              | separable verbal particle                         |
+| `PTKZU`   | `PART`  | `PartType=inf" | "zu" before infinitive     |
+| `PWAT`    | `DET`   | `PronType=int`                              | attributive interrogative pronoun                 |
+| `PWAV`    | `ADV`   | `PronType=int`                              | adverbial interrogative or relative pronoun       |
+| `PWS`     | `PRON`  | `PronType=int`                              | substituting interrogative pronoun                |
+| `TRUNC`   | `X`     | `Hyph=yes`                                  | word remnant                                      |
+| `VAFIN`   | `AUX`   | `Mood=ind VerbForm=fin`                     | finite verb, auxiliary                            |
+| `VAIMP`   | `AUX`   | `Mood=imp VerbForm=fin`                     | imperative, auxiliary                             |
+| `VAINF`   | `AUX`   | `VerbForm=inf`                              | infinitive, auxiliary                             |
+| `VAPP`    | `AUX`   | `Aspect=perf VerbForm=fin`                  | perfect participle, auxiliary                     |
+| `VMFIN`   | `VERB`  | `Mood=ind VerbForm=fin VerbType=mod`        | finite verb, modal                                |
+| `VMINF`   | `VERB`  | `VerbForm=fin VerbType=mod`                 | infinitive, modal                                 |
+| `VMPP`    | `VERB`  | `Aspect=perf VerbForm=part VerbType=mod`    | perfect participle, modal                         |
+| `VVFIN`   | `VERB`  | `Mood=ind VerbForm=fin`                     | finite verb, full                                 |
+| `VVIMP`   | `VERB`  | `Mood=imp VerbForm=fin`                     | imperative, full                                  |
+| `VVINF`   | `VERB`  | `VerbForm=inf`                              | infinitive, full                                  |
+| `VVIZU`   | `VERB`  | `VerbForm=inf" | infinitive with "zu", full |
+| `VVPP`    | `VERB`  | `Aspect=perf VerbForm=part`                 | perfect participle, full                          |
+| `XY`      | `X`     |                                             | non-word containing non-letter                    |
+| `SP`      | `SPACE` |                                             | space                                             |
+
+</Accordion>
+
+---
+
+<Infobox title="Annotation schemes for other models">
+
+For the label schemes used by the other models, see the respective `tag_map.py`
+in [`spacy/lang`](https://github.com/explosion/spaCy/tree/master/spacy/lang).
+
+</Infobox>
+
+## Syntactic Dependency Parsing {#dependency-parsing}
+
+> #### Tip: Understanding labels
+>
+> You can also use `spacy.explain` to get the description for the string
+> representation of a label. For example, `spacy.explain("prt")` will return
+> "particle".
+
+This section lists the syntactic dependency labels assigned by spaCy's
+[models](/models). The individual labels are language-specific and depend on the
+training corpus.
+
+<Accordion title="Universal Dependency Labels">
+
+The [Universal Dependencies scheme](http://universaldependencies.org/u/dep/) is
+used in all languages trained on Universal Dependency Corpora.
+
+| Label        | Description                                  |
+| ------------ | -------------------------------------------- |
+| `acl`        | clausal modifier of noun (adjectival clause) |
+| `advcl`      | adverbial clause modifier                    |
+| `advmod`     | adverbial modifier                           |
+| `amod`       | adjectival modifier                          |
+| `appos`      | appositional modifier                        |
+| `aux`        | auxiliary                                    |
+| `case`       | case marking                                 |
+| `cc`         | coordinating conjunction                     |
+| `ccomp`      | clausal complement                           |
+| `clf`        | classifier                                   |
+| `compound`   | compound                                     |
+| `conj`       | conjunct                                     |
+| `cop`        | copula                                       |
+| `csubj`      | clausal subject                              |
+| `dep`        | unspecified dependency                       |
+| `det`        | determiner                                   |
+| `discourse`  | discourse element                            |
+| `dislocated` | dislocated elements                          |
+| `expl`       | expletive                                    |
+| `fixed`      | fixed multiword expression                   |
+| `flat`       | flat multiword expression                    |
+| `goeswith`   | goes with                                    |
+| `iobj`       | indirect object                              |
+| `list`       | list                                         |
+| `mark`       | marker                                       |
+| `nmod`       | nominal modifier                             |
+| `nsubj`      | nominal subject                              |
+| `nummod`     | numeric modifier                             |
+| `obj`        | object                                       |
+| `obl`        | oblique nominal                              |
+| `orphan`     | orphan                                       |
+| `parataxis`  | parataxis                                    |
+| `punct`      | punctuation                                  |
+| `reparandum` | overridden disfluency                        |
+| `root`       | root                                         |
+| `vocative`   | vocative                                     |
+| `xcomp`      | open clausal complement                      |
+
+</Accordion>
+
+<Accordion title="English" id="dependency-parsing-english">
+
+The English dependency labels use the
+[CLEAR Style](https://github.com/clir/clearnlp-guidelines/blob/master/md/specifications/dependency_labels.md)
+by [ClearNLP](http://www.clearnlp.com).
+
+| Label       | Description                                  |
+| ----------- | -------------------------------------------- |
+| `acl`       | clausal modifier of noun (adjectival clause) |
+| `acomp`     | adjectival complement                        |
+| `advcl`     | adverbial clause modifier                    |
+| `advmod`    | adverbial modifier                           |
+| `agent`     | agent                                        |
+| `amod`      | adjectival modifier                          |
+| `appos`     | appositional modifier                        |
+| `attr`      | attribute                                    |
+| `aux`       | auxiliary                                    |
+| `auxpass`   | auxiliary (passive)                          |
+| `case`      | case marking                                 |
+| `cc`        | coordinating conjunction                     |
+| `ccomp`     | clausal complement                           |
+| `compound`  | compound                                     |
+| `conj`      | conjunct                                     |
+| `cop`       | copula                                       |
+| `csubj`     | clausal subject                              |
+| `csubjpass` | clausal subject (passive)                    |
+| `dative`    | dative                                       |
+| `dep`       | unclassified dependent                       |
+| `det`       | determiner                                   |
+| `dobj`      | direct object                                |
+| `expl`      | expletive                                    |
+| `intj`      | interjection                                 |
+| `mark`      | marker                                       |
+| `meta`      | meta modifier                                |
+| `neg`       | negation modifier                            |
+| `nn`        | noun compound modifier                       |
+| `nounmod`   | modifier of nominal                          |
+| `npmod`     | noun phrase as adverbial modifier            |
+| `nsubj`     | nominal subject                              |
+| `nsubjpass` | nominal subject (passive)                    |
+| `nummod`    | numeric modifier                             |
+| `oprd`      | object predicate                             |
+| `obj`       | object                                       |
+| `obl`       | oblique nominal                              |
+| `parataxis` | parataxis                                    |
+| `pcomp`     | complement of preposition                    |
+| `pobj`      | object of preposition                        |
+| `poss`      | possession modifier                          |
+| `preconj`   | pre-correlative conjunction                  |
+| `prep`      | prepositional modifier                       |
+| `prt`       | particle                                     |
+| `punct`     | punctuation                                  |
+| `quantmod`  | modifier of quantifier                       |
+| `relcl`     | relative clause modifier                     |
+| `root`      | root                                         |
+| `xcomp`     | open clausal complement                      |
+
+</Accordion>
+
+<Accordion title="German" id="dependency-parsing-german">
+
+The German dependency labels use the
+[TIGER Treebank](http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/TIGERCorpus/annotation/index.html)
+annotation scheme.
+
+| Label  | Description                     |
+| ------ | ------------------------------- |
+| `ac`   | adpositional case marker        |
+| `adc`  | adjective component             |
+| `ag`   | genitive attribute              |
+| `ams`  | measure argument of adjective   |
+| `app`  | apposition                      |
+| `avc`  | adverbial phrase component      |
+| `cc`   | comparative complement          |
+| `cd`   | coordinating conjunction        |
+| `cj`   | conjunct                        |
+| `cm`   | comparative conjunction         |
+| `cp`   | complementizer                  |
+| `cvc`  | collocational verb construction |
+| `da`   | dative                          |
+| `dh`   | discourse-level head            |
+| `dm`   | discourse marker                |
+| `ep`   | expletive es                    |
+| `hd`   | head                            |
+| `ju`   | junctor                         |
+| `mnr`  | postnominal modifier            |
+| `mo`   | modifier                        |
+| `ng`   | negation                        |
+| `nk`   | noun kernel element             |
+| `nmc`  | numerical component             |
+| `oa`   | accusative object               |
+| `oa`   | second accusative object        |
+| `oc`   | clausal object                  |
+| `og`   | genitive object                 |
+| `op`   | prepositional object            |
+| `par`  | parenthetical element           |
+| `pd`   | predicate                       |
+| `pg`   | phrasal genitive                |
+| `ph`   | placeholder                     |
+| `pm`   | morphological particle          |
+| `pnc`  | proper noun component           |
+| `rc`   | relative clause                 |
+| `re`   | repeated element                |
+| `rs`   | reported speech                 |
+| `sb`   | subject                         |
+| `sp`   | "subject or predicate           |
+| `svp`  | separable verb prefix           |
+| `uc`   | unit component                  |
+| `vo`   | vocative                        |
+| `ROOT` | root                            |
+
+</Accordion>
+
+## Named Entity Recognition {#named-entities}
+
+> #### Tip: Understanding entity types
+>
+> You can also use `spacy.explain` to get the description for the string
+> representation of an entity label. For example, `spacy.explain("LANGUAGE")`
+> will return "any named language".
+
+Models trained on the [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19)
+corpus support the following entity types:
+
+| Type          | Description                                          |
+| ------------- | ---------------------------------------------------- |
+| `PERSON`      | People, including fictional.                         |
+| `NORP`        | Nationalities or religious or political groups.      |
+| `FAC`         | Buildings, airports, highways, bridges, etc.         |
+| `ORG`         | Companies, agencies, institutions, etc.              |
+| `GPE`         | Countries, cities, states.                           |
+| `LOC`         | Non-GPE locations, mountain ranges, bodies of water. |
+| `PRODUCT`     | Objects, vehicles, foods, etc. (Not services.)       |
+| `EVENT`       | Named hurricanes, battles, wars, sports events, etc. |
+| `WORK_OF_ART` | Titles of books, songs, etc.                         |
+| `LAW`         | Named documents made into laws.                      |
+| `LANGUAGE`    | Any named language.                                  |
+| `DATE`        | Absolute or relative dates or periods.               |
+| `TIME`        | Times smaller than a day.                            |
+| `PERCENT`     | Percentage, including "%".                           |
+| `MONEY`       | Monetary values, including unit.                     |
+| `QUANTITY`    | Measurements, as of weight or distance.              |
+| `ORDINAL`     | "first", "second", etc.                              |
+| `CARDINAL`    | Numerals that do not fall under another type.        |
+
+### Wikipedia scheme {#ner-wikipedia-scheme}
+
+Models trained on Wikipedia corpus
+([Nothman et al., 2013](http://www.sciencedirect.com/science/article/pii/S0004370212000276))
+use a less fine-grained NER annotation scheme and recognise the following
+entities:
+
+| Type   | Description                                                                                                                               |
+| ------ | ----------------------------------------------------------------------------------------------------------------------------------------- |
+| `PER`  | Named person or family.                                                                                                                   |
+| `LOC`  | Name of politically or geographically defined location (cities, provinces, countries, international regions, bodies of water, mountains). |
+| `ORG`  | Named corporate, governmental, or other organizational entity.                                                                            |
+| `MISC` | Miscellaneous entities, e.g. events, nationalities, products or works of art.                                                             |
+
+### IOB Scheme {#iob}
+
+| Tag   | ID  | Description                           |
+| ----- | --- | ------------------------------------- |
+| `"I"` | `1` | Token is inside an entity.            |
+| `"O"` | `2` | Token is outside an entity.           |
+| `"B"` | `3` | Token begins an entity.               |
+| `""`  | `0` | No entity tag is set (missing value). |
+
+### BILUO Scheme {#biluo}
+
+| Tag         | Description                              |
+| ----------- | ---------------------------------------- |
+| **`B`**EGIN | The first token of a multi-token entity. |
+| **`I`**N    | An inner token of a multi-token entity.  |
+| **`L`**AST  | The final token of a multi-token entity. |
+| **`U`**NIT  | A single-token entity.                   |
+| **`O`**UT   | A non-entity token.                      |
+
+> #### Why BILUO, not IOB?
+>
+> There are several coding schemes for encoding entity annotations as token
+> tags. These coding schemes are equally expressive, but not necessarily equally
+> learnable. [Ratinov and Roth](http://www.aclweb.org/anthology/W09-1119) showed
+> that the minimal **Begin**, **In**, **Out** scheme was more difficult to learn
+> than the **BILUO** scheme that we use, which explicitly marks boundary tokens.
+
+spaCy translates the character offsets into this scheme, in order to decide the
+cost of each action given the current state of the entity recogniser. The costs
+are then used to calculate the gradient of the loss, to train the model. The
+exact algorithm is a pastiche of well-known methods, and is not currently
+described in any single publication. The model is a greedy transition-based
+parser guided by a linear model whose weights are learned using the averaged
+perceptron loss, via the
+[dynamic oracle](http://www.aclweb.org/anthology/C12-1059) imitation learning
+strategy. The transition system is equivalent to the BILOU tagging scheme.
+
+## Models and training data {#training}
+
+### JSON input format for training {#json-input}
+
+spaCy takes training data in JSON format. The built-in
+[`convert`](/api/cli#convert) command helps you convert the `.conllu` format
+used by the
+[Universal Dependencies corpora](https://github.com/UniversalDependencies) to
+spaCy's training format.
+
+> #### Annotating entities
+>
+> Named entities are provided in the [BILUO](#biluo) notation. Tokens outside an
+> entity are set to `"O"` and tokens that are part of an entity are set to the
+> entity label, prefixed by the BILUO marker. For example `"B-ORG"` describes
+> the first token of a multi-token `ORG` entity and `"U-PERSON"` a single token
+> representing a `PERSON` entity. The
+> [`biluo_tags_from_offsets`](/api/goldparse#biluo_tags_from_offsets) function
+> can help you convert entity offsets to the right format.
+
+```python
+### Example structure
+[{
+    "id": int,                      # ID of the document within the corpus
+    "paragraphs": [{                # list of paragraphs in the corpus
+        "raw": string,              # raw text of the paragraph
+        "sentences": [{             # list of sentences in the paragraph
+            "tokens": [{            # list of tokens in the sentence
+                "id": int,          # index of the token in the document
+                "dep": string,      # dependency label
+                "head": int,        # offset of token head relative to token index
+                "tag": string,      # part-of-speech tag
+                "orth": string,     # verbatim text of the token
+                "ner": string       # BILUO label, e.g. "O" or "B-ORG"
+            }],
+            "brackets": [{          # phrase structure (NOT USED by current models)
+                "first": int,       # index of first token
+                "last": int,        # index of last token
+                "label": string     # phrase label
+            }]
+        }]
+    }]
+}]
+```
+
+Here's an example of dependencies, part-of-speech tags and names entities, taken
+from the English Wall Street Journal portion of the Penn Treebank:
+
+```json
+https://github.com/explosion/spaCy/tree/master/examples/training/training-data.json
+```
+
+### Lexical data for vocabulary {#vocab-jsonl new="2"}
+
+To populate a model's vocabulary, you can use the
+[`spacy init-model`](/api/cli#init-model) command and load in a
+[newline-delimited JSON](http://jsonlines.org/) (JSONL) file containing one
+lexical entry per line via the `--jsonl-loc` option. The first line defines the
+language and vocabulary settings. All other lines are expected to be JSON
+objects describing an individual lexeme. The lexical attributes will be then set
+as attributes on spaCy's [`Lexeme`](/api/lexeme#attributes) object. The `vocab`
+command outputs a ready-to-use spaCy model with a `Vocab` containing the lexical
+data.
+
+```python
+### First line
+{"lang": "en", "settings": {"oov_prob": -20.502029418945312}}
+```
+
+```python
+### Entry structure
+{
+    "orth": string,
+    "id": int,
+    "lower": string,
+    "norm": string,
+    "shape": string
+    "prefix": string,
+    "suffix": string,
+    "length": int,
+    "cluster": string,
+    "prob": float,
+    "is_alpha": bool,
+    "is_ascii": bool,
+    "is_digit": bool,
+    "is_lower": bool,
+    "is_punct": bool,
+    "is_space": bool,
+    "is_title": bool,
+    "is_upper": bool,
+    "like_url": bool,
+    "like_num": bool,
+    "like_email": bool,
+    "is_stop": bool,
+    "is_oov": bool,
+    "is_quote": bool,
+    "is_left_punct": bool,
+    "is_right_punct": bool
+}
+```
+
+Here's an example of the 20 most frequent lexemes in the English training data:
+
+```json
+https://github.com/explosion/spaCy/tree/master/examples/training/vocab-data.jsonl
+```
diff --git a/website/docs/api/cli.md b/website/docs/api/cli.md
new file mode 100644
index 000000000..f30096fa1
--- /dev/null
+++ b/website/docs/api/cli.md
@@ -0,0 +1,361 @@
+---
+title: Command Line Interface
+teaser: Download, train and package models, and debug spaCy
+source: spacy/cli
+menu:
+  - ['Download', 'download']
+  - ['Link', 'link']
+  - ['Info', 'info']
+  - ['Validate', 'validate']
+  - ['Convert', 'convert']
+  - ['Train', 'train']
+  - ['Pretrain', 'pretrain']
+  - ['Init Model', 'init-model']
+  - ['Evaluate', 'evaluate']
+  - ['Package', 'package']
+---
+
+As of v1.7.0, spaCy comes with new command line helpers to download and link
+models and show useful debugging information. For a list of available commands,
+type `spacy --help`.
+
+## Download {#download}
+
+Download [models](/usage/models) for spaCy. The downloader finds the
+best-matching compatible version, uses pip to download the model as a package
+and automatically creates a [shortcut link](/usage/models#usage) to load the
+model by name. Direct downloads don't perform any compatibility checks and
+require the model name to be specified with its version (e.g.
+`en_core_web_sm-2.0.0`).
+
+> #### Downloading best practices
+>
+> The `download` command is mostly intended as a convenient, interactive wrapper
+> – it performs compatibility checks and prints detailed messages in case things
+> go wrong. It's **not recommended** to use this command as part of an automated
+> process. If you know which model your project needs, you should consider a
+> [direct download via pip](/usage/models#download-pip), or uploading the model
+> to a local PyPi installation and fetching it straight from there. This will
+> also allow you to add it as a versioned package dependency to your project.
+
+```bash
+$ python -m spacy download [model] [--direct]
+```
+
+| Argument                           | Type               | Description                                                                                                                                                   |
+| ---------------------------------- | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `model`                            | positional         | Model name or shortcut (`en`, `de`, `en_core_web_sm`).                                                                                                        |
+| `--direct`, `-d`                   | flag               | Force direct download of exact model version.                                                                                                                 |
+| other <Tag variant="new">2.1</Tag> | -                  | Additional installation options to be passed to `pip install` when installing the model package. For example, `--user` to install to the user home directory. |
+| `--help`, `-h`                     | flag               | Show help message and available arguments.                                                                                                                    |
+| **CREATES**                        | directory, symlink | The installed model package in your `site-packages` directory and a shortcut link as a symlink in `spacy/data`.                                               |
+
+## Link {#link}
+
+Create a [shortcut link](/usage/models#usage) for a model, either a Python
+package or a local directory. This will let you load models from any location
+using a custom name via [`spacy.load()`](/api/top-level#spacy.load).
+
+<Infobox title="Important note" variant="warning">
+
+In spaCy v1.x, you had to use the model data directory to set up a shortcut link
+for a local path. As of v2.0, spaCy expects all shortcut links to be **loadable
+model packages**. If you want to load a data directory, call
+[`spacy.load()`](/api/top-level#spacy.load) or
+[`Language.from_disk()`](/api/language#from_disk) with the path, or use the
+[`package`](/api/cli#package) command to create a model package.
+
+</Infobox>
+
+```bash
+$ python -m spacy link [origin] [link_name] [--force]
+```
+
+| Argument        | Type       | Description                                                     |
+| --------------- | ---------- | --------------------------------------------------------------- |
+| `origin`        | positional | Model name if package, or path to local directory.              |
+| `link_name`     | positional | Name of the shortcut link to create.                            |
+| `--force`, `-f` | flag       | Force overwriting of existing link.                             |
+| `--help`, `-h`  | flag       | Show help message and available arguments.                      |
+| **CREATES**     | symlink    | A shortcut link of the given name as a symlink in `spacy/data`. |
+
+## Info {#info}
+
+Print information about your spaCy installation, models and local setup, and
+generate [Markdown](https://en.wikipedia.org/wiki/Markdown)-formatted markup to
+copy-paste into [GitHub issues](https://github.com/explosion/spaCy/issues).
+
+```bash
+$ python -m spacy info [--markdown]
+```
+
+```bash
+$ python -m spacy info [model] [--markdown]
+```
+
+| Argument                                         | Type       | Description                                                   |
+| ------------------------------------------------ | ---------- | ------------------------------------------------------------- |
+| `model`                                          | positional | A model, i.e. shortcut link, package name or path (optional). |
+| `--markdown`, `-md`                              | flag       | Print information as Markdown.                                |
+| `--silent`, `-s` <Tag variant="new">2.0.12</Tag> | flag       | Don't print anything, just return the values.                 |
+| `--help`, `-h`                                   | flag       | Show help message and available arguments.                    |
+| **PRINTS**                                       | `stdout`   | Information about your spaCy installation.                    |
+
+## Validate {#validate new="2"}
+
+Find all models installed in the current environment (both packages and shortcut
+links) and check whether they are compatible with the currently installed
+version of spaCy. Should be run after upgrading spaCy via `pip install -U spacy`
+to ensure that all installed models are can be used with the new version. The
+command is also useful to detect out-of-sync model links resulting from links
+created in different virtual environments. It will a list of models, the
+installed versions, the latest compatible version (if out of date) and the
+commands for updating.
+
+> #### Automated validation
+>
+> You can also use the `validate` command as part of your build process or test
+> suite, to ensure all models are up to date before proceeding. If incompatible
+> models or shortcut links are found, it will return `1`.
+
+```bash
+$ python -m spacy validate
+```
+
+| Argument   | Type     | Description                                               |
+| ---------- | -------- | --------------------------------------------------------- |
+| **PRINTS** | `stdout` | Details about the compatibility of your installed models. |
+
+## Convert {#convert}
+
+Convert files into spaCy's [JSON format](/api/annotation#json-input) for use
+with the `train` command and other experiment management functions. The
+converter can be specified on the command line, or chosen based on the file
+extension of the input file.
+
+```bash
+$ python -m spacy convert [input_file] [output_dir] [--converter] [--n-sents]
+[--morphology]
+```
+
+| Argument                                     | Type       | Description                                                |
+| -------------------------------------------- | ---------- | ---------------------------------------------------------- |
+| `input_file`                                 | positional | Input file.                                                |
+| `output_dir`                                 | positional | Output directory for converted JSON file.                  |
+| `converter`, `-c` <Tag variant="new">2</Tag> | option     | Name of converter to use (see below).                      |
+| `--n-sents`, `-n`                            | option     | Number of sentences per document.                          |
+| `--morphology`, `-m`                         | option     | Enable appending morphology to tags.                       |
+| `--help`, `-h`                               | flag       | Show help message and available arguments.                 |
+| **CREATES**                                  | JSON       | Data in spaCy's [JSON format](/api/annotation#json-input). |
+
+The following file format converters are available:
+
+| ID                | Description                                                     |
+| ----------------- | --------------------------------------------------------------- |
+| `auto`            | Automatically pick converter based on file extension (default). |
+| `conllu`, `conll` | Universal Dependencies `.conllu` or `.conll` format.            |
+| `ner`             | Tab-based named entity recognition format.                      |
+| `iob`             | IOB or IOB2 named entity recognition format.                    |
+
+## Train {#train}
+
+Train a model. Expects data in spaCy's
+[JSON format](/api/annotation#json-input). On each epoch, a model will be saved
+out to the directory. Accuracy scores and model details will be added to a
+[`meta.json`](/usage/training#models-generating) to allow packaging the model
+using the [`package`](/api/cli#package) command.
+
+<Infobox title="Changed in v2.1" variant="warning">
+
+As of spaCy 2.1, the `--no-tagger`, `--no-parser` and `--no-parser` flags have
+been replaced by a `--pipeline` option, which lets you define comma-separated
+names of pipeline components to train. For example, `--pipeline tagger,parser`
+will only train the tagger and parser.
+
+</Infobox>
+
+```bash
+$ python -m spacy train [lang] [output_path] [train_path] [dev_path]
+[--base-model] [--pipeline] [--vectors] [--n-iter] [--n-examples] [--use-gpu]
+[--version] [--meta-path] [--init-tok2vec] [--parser-multitasks]
+[--entity-multitasks] [--gold-preproc] [--noise-level] [--learn-tokens]
+[--verbose]
+```
+
+| Argument                                              | Type          | Description                                                                                                                                                       |
+| ----------------------------------------------------- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `lang`                                                | positional    | Model language.                                                                                                                                                   |
+| `output_path`                                         | positional    | Directory to store model in. Will be created if it doesn't exist.                                                                                                 |
+| `train_path`                                          | positional    | Location of JSON-formatted training data.                                                                                                                         |
+| `dev_path`                                            | positional    | Location of JSON-formatted development data for evaluation.                                                                                                       |
+| `--base-model`, `-b`                                  | option        | Optional name of base model to update. Can be any loadable spaCy model.                                                                                           |
+| `--pipeline`, `-p` <Tag variant="new">2.1</Tag>       | option        | Comma-separated names of pipeline components to train. Defaults to `'tagger,parser,ner'`.                                                                         |
+| `--vectors`, `-v`                                     | option        | Model to load vectors from.                                                                                                                                       |
+| `--n-iter`, `-n`                                      | option        | Number of iterations (default: `30`).                                                                                                                             |
+| `--n-examples`, `-ns`                                 | option        | Number of examples to use (defaults to `0` for all examples).                                                                                                     |
+| `--use-gpu`, `-g`                                     | option        | Whether to use GPU. Can be either `0`, `1` or `-1`.                                                                                                               |
+| `--version`, `-V`                                     | option        | Model version. Will be written out to the model's `meta.json` after training.                                                                                     |
+| `--meta-path`, `-m` <Tag variant="new">2</Tag>        | option        | Optional path to model [`meta.json`](/usage/training#models-generating). All relevant properties like `lang`, `pipeline` and `spacy_version` will be overwritten. |
+| `--init-tok2vec`, `-t2v` <Tag variant="new">2.1</Tag> | option        | Path to pretrained weights for the token-to-vector parts of the models. See `spacy pretrain`. Experimental.                                                       |
+| `--parser-multitasks`, `-pt`                          | option        | Side objectives for parser CNN, e.g. `'dep'` or `'dep,tag'`                                                                                                       |
+| `--entity-multitasks`, `-et`                          | option        | Side objectives for NER CNN, e.g. `'dep'` or `'dep,tag'`                                                                                                          |
+| `--noise-level`, `-nl`                                | option        | Float indicating the amount of corruption for data augmentation.                                                                                                  |
+| `--gold-preproc`, `-G`                                | flag          | Use gold preprocessing.                                                                                                                                           |
+| `--learn-tokens`, `-T`                                | flag          | Make parser learn gold-standard tokenization by merging ] subtokens. Typically used for languages like Chinese.                                                   |
+| `--verbose`, `-VV` <Tag variant="new">2.0.13</Tag>    | flag          | Show more detailed messages during training.                                                                                                                      |
+| `--help`, `-h`                                        | flag          | Show help message and available arguments.                                                                                                                        |
+| **CREATES**                                           | model, pickle | A spaCy model on each epoch.                                                                                                                                      |
+
+### Environment variables for hyperparameters {#train-hyperparams new="2"}
+
+spaCy lets you set hyperparameters for training via environment variables. This
+is useful, because it keeps the command simple and allows you to
+[create an alias](https://askubuntu.com/questions/17536/how-do-i-create-a-permanent-bash-alias/17537#17537)
+for your custom `train` command while still being able to easily tweak the
+hyperparameters. For example:
+
+```bash
+$ parser_hidden_depth=2 parser_maxout_pieces=1 spacy train [...]
+```
+
+```bash
+### Usage with alias
+alias train-parser="spacy train en /output /data /train /dev -n 1000"
+parser_maxout_pieces=1 train-parser
+```
+
+| Name                 | Description                                         | Default |
+| -------------------- | --------------------------------------------------- | ------- |
+| `dropout_from`       | Initial dropout rate.                               | `0.2`   |
+| `dropout_to`         | Final dropout rate.                                 | `0.2`   |
+| `dropout_decay`      | Rate of dropout change.                             | `0.0`   |
+| `batch_from`         | Initial batch size.                                 | `1`     |
+| `batch_to`           | Final batch size.                                   | `64`    |
+| `batch_compound`     | Rate of batch size acceleration.                    | `1.001` |
+| `token_vector_width` | Width of embedding tables and convolutional layers. | `128`   |
+| `embed_size`         | Number of rows in embedding tables.                 | `7500`  |
+| `hidden_width`       | Size of the parser's and NER's hidden layers.       | `128`   |
+| `learn_rate`         | Learning rate.                                      | `0.001` |
+| `optimizer_B1`       | Momentum for the Adam solver.                       | `0.9`   |
+| `optimizer_B2`       | Adagrad-momentum for the Adam solver.               | `0.999` |
+| `optimizer_eps`      | Epsilon value for the Adam solver.                  | `1e-08` |
+| `L2_penalty`         | L2 regularization penalty.                          | `1e-06` |
+| `grad_norm_clip`     | Gradient L2 norm constraint.                        | `1.0`   |
+
+## Pretrain {#pretrain new="2.1" tag="experimental"}
+
+Pre-train the "token to vector" (`tok2vec`) layer of pipeline components, using
+an approximate language-modeling objective. Specifically, we load pre-trained
+vectors, and train a component like a CNN, BiLSTM, etc to predict vectors which
+match the pre-trained ones. The weights are saved to a directory after each
+epoch. You can then pass a path to one of these pre-trained weights files to the
+'spacy train' command.
+
+This technique may be especially helpful if you have little labelled data.
+However, it's still quite experimental, so your mileage may vary. To load the
+weights back in during `spacy train`, you need to ensure all settings are the
+same between pretraining and training. The API and errors around this need some
+improvement.
+
+```bash
+$ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir] [--width]
+[--depth] [--embed-rows] [--dropout] [--seed] [--n-iter] [--use-vectors]
+```
+
+| Argument               | Type       | Description                                                           |
+| ---------------------- | ---------- | --------------------------------------------------------------------- |
+| `texts_loc`            | positional | Path to JSONL file with raw texts to learn from.                      |
+| `vectors_model`        | positional | Name or path to spaCy model with vectors to learn from.               |
+| `output_dir`           | positional | Directory to write models to on each epoch.                           |
+| `--width`, `-cw`       | option     | Width of CNN layers.                                                  |
+| `--depth`, `-cd`       | option     | Depth of CNN layers.                                                  |
+| `--embed-rows`, `-er`  | option     | Number of embedding rows.                                             |
+| `--dropout`, `-d`      | option     | Dropout rate.                                                         |
+| `--seed`, `-s`         | option     | Seed for random number generators.                                    |
+| `--n-iter`, `-i`       | option     | Number of iterations to pretrain.                                     |
+| `--use-vectors`, `-uv` | flag       | Whether to use the static vectors as input features.                  |
+| **CREATES**            | weights    | The pre-trained weights that can be used to initialize `spacy train`. |
+
+## Init Model {#init-model new="2"}
+
+Create a new model directory from raw data, like word frequencies, Brown
+clusters and word vectors. This command is similar to the `spacy model` command
+in v1.x.
+
+<Infobox title="Deprecation note" variant="warning">
+
+As of v2.1.0, the `--freqs-loc` and `--clusters-loc` are deprecated and have
+been replaced with the `--jsonl-loc` argument, which lets you pass in a a
+[newline-delimited JSON](http://jsonlines.org/) (JSONL) file containing one
+lexical entry per line. For more details on the format, see the
+[annotation specs](/api/annotation#vocab-jsonl).
+
+</Infobox>
+
+```bash
+$ python -m spacy init-model [lang] [output_dir] [--jsonl-loc] [--vectors-loc]
+[--prune-vectors]
+```
+
+| Argument                | Type       | Description                                                                                                                                                                                                                                                       |
+| ----------------------- | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `lang`                  | positional | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes), e.g. `en`.                                                                                                                                                                      |
+| `output_dir`            | positional | Model output directory. Will be created if it doesn't exist.                                                                                                                                                                                                      |
+| `--jsonl-loc`, `-j`     | option     | Optional location of JSONL-formatted vocabulary file with lexical attributes.                                                                                                                                                                                     |
+| `--vectors-loc`, `-v`   | option     | Optional location of vectors file. Should be a tab-separated file in Word2Vec format where the first column contains the word and the remaining columns the values. File can be provided in `.txt` format or as a zipped text file in `.zip` or `.tar.gz` format. |
+| `--prune-vectors`, `-V` | flag       | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning.                                                                                                                                                                                    |
+| **CREATES**             | model      | A spaCy model containing the vocab and vectors.                                                                                                                                                                                                                   |
+
+## Evaluate {#evaluate new="2"}
+
+Evaluate a model's accuracy and speed on JSON-formatted annotated data. Will
+print the results and optionally export
+[displaCy visualizations](/usage/visualizers) of a sample set of parses to
+`.html` files. Visualizations for the dependency parse and NER will be exported
+as separate files if the respective component is present in the model's
+pipeline.
+
+```bash
+$ python -m spacy evaluate [model] [data_path] [--displacy-path] [--displacy-limit]
+[--gpu-id] [--gold-preproc]
+```
+
+| Argument                  | Type           | Description                                                                                                                                              |
+| ------------------------- | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `model`                   | positional     | Model to evaluate. Can be a package or shortcut link name, or a path to a model data directory.                                                          |
+| `data_path`               | positional     | Location of JSON-formatted evaluation data.                                                                                                              |
+| `--displacy-path`, `-dp`  | option         | Directory to output rendered parses as HTML. If not set, no visualizations will be generated.                                                            |
+| `--displacy-limit`, `-dl` | option         | Number of parses to generate per file. Defaults to `25`. Keep in mind that a significantly higher number might cause the `.html` files to render slowly. |
+| `--gpu-id`, `-g`          | option         | GPU to use, if any. Defaults to `-1` for CPU.                                                                                                            |
+| `--gold-preproc`, `-G`    | flag           | Use gold preprocessing.                                                                                                                                  |
+| **CREATES**               | `stdout`, HTML | Training results and optional displaCy visualizations.                                                                                                   |
+
+## Package {#package}
+
+Generate a [model Python package](/usage/training#models-generating) from an
+existing model data directory. All data files are copied over. If the path to a
+`meta.json` is supplied, or a `meta.json` is found in the input directory, this
+file is used. Otherwise, the data can be entered directly from the command line.
+After packaging, you can run `python setup.py sdist` from the newly created
+directory to turn your model into an installable archive file.
+
+```bash
+$ python -m spacy package [input_dir] [output_dir] [--meta-path] [--create-meta] [--force]
+```
+
+```bash
+### Example
+python -m spacy package /input /output
+cd /output/en_model-0.0.0
+python setup.py sdist
+pip install dist/en_model-0.0.0.tar.gz
+```
+
+| Argument                                         | Type       | Description                                                                                                                                                                                     |
+| ------------------------------------------------ | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `input_dir`                                      | positional | Path to directory containing model data.                                                                                                                                                        |
+| `output_dir`                                     | positional | Directory to create package folder in.                                                                                                                                                          |
+| `--meta-path`, `-m` <Tag variant="new">2</Tag>   | option     | Path to `meta.json` file (optional).                                                                                                                                                            |
+| `--create-meta`, `-c` <Tag variant="new">2</Tag> | flag       | Create a `meta.json` file on the command line, even if one already exists in the directory. If an existing file is found, its entries will be shown as the defaults in the command line prompt. | `--force`, `-f` | flag | Force overwriting of existing folder in output directory. |
+| `--help`, `-h`                                   | flag       | Show help message and available arguments.                                                                                                                                                      |
+| **CREATES**                                      | directory  | A Python package containing the spaCy model.                                                                                                                                                    |
diff --git a/website/docs/api/cython-classes.md b/website/docs/api/cython-classes.md
new file mode 100644
index 000000000..4d188d90f
--- /dev/null
+++ b/website/docs/api/cython-classes.md
@@ -0,0 +1,210 @@
+---
+title: Cython Classes
+menu:
+  - ['Doc', 'doc']
+  - ['Token', 'token']
+  - ['Span', 'span']
+  - ['Lexeme', 'lexeme']
+  - ['Vocab', 'vocab']
+  - ['StringStore', 'stringstore']
+---
+
+## Doc {#doc tag="cdef class" source="spacy/tokens/doc.pxd"}
+
+The `Doc` object holds an array of [`TokenC`](/api/cython-structs#tokenc)
+structs.
+
+<Infobox variant="warning">
+
+This section documents the extra C-level attributes and methods that can't be
+accessed from Python. For the Python documentation, see [`Doc`](/api/doc).
+
+</Infobox>
+
+### Attributes {#doc_attributes}
+
+| Name         | Type         | Description                                                                               |
+| ------------ | ------------ | ----------------------------------------------------------------------------------------- |
+| `mem`        | `cymem.Pool` | A memory pool. Allocated memory will be freed once the `Doc` object is garbage collected. |
+| `vocab`      | `Vocab`      | A reference to the shared `Vocab` object.                                                 |
+| `c`          | `TokenC*`    | A pointer to a [`TokenC`](/api/cython-structs#tokenc) struct.                             |
+| `length`     | `int`        | The number of tokens in the document.                                                     |
+| `max_length` | `int`        | The underlying size of the `Doc.c` array.                                                 |
+
+### Doc.push_back {#doc_push_back tag="method"}
+
+Append a token to the `Doc`. The token can be provided as a
+[`LexemeC`](/api/cython-structs#lexemec) or
+[`TokenC`](/api/cython-structs#tokenc) pointer, using Cython's
+[fused types](http://cython.readthedocs.io/en/latest/src/userguide/fusedtypes.html).
+
+> #### Example
+>
+> ```python
+> from spacy.tokens cimport Doc
+> from spacy.vocab cimport Vocab
+>
+> doc = Doc(Vocab())
+> lexeme = doc.vocab.get(u'hello')
+> doc.push_back(lexeme, True)
+> assert doc.text == u'hello '
+> ```
+
+| Name         | Type            | Description                               |
+| ------------ | --------------- | ----------------------------------------- |
+| `lex_or_tok` | `LexemeOrToken` | The word to append to the `Doc`.          |
+| `has_space`  | `bint`          | Whether the word has trailing whitespace. |
+
+## Token {#token tag="cdef class" source="spacy/tokens/token.pxd"}
+
+A Cython class providing access and methods for a
+[`TokenC`](/api/cython-structs#tokenc) struct. Note that the `Token` object does
+not own the struct. It only receives a pointer to it.
+
+<Infobox variant="warning">
+
+This section documents the extra C-level attributes and methods that can't be
+accessed from Python. For the Python documentation, see [`Token`](/api/token).
+
+</Infobox>
+
+### Attributes {#token_attributes}
+
+| Name    | Type      | Description                                                   |
+| ------- | --------- | ------------------------------------------------------------- |
+| `vocab` | `Vocab`   | A reference to the shared `Vocab` object.                     |
+| `c`     | `TokenC*` | A pointer to a [`TokenC`](/api/cython-structs#tokenc) struct. |
+| `i`     | `int`     | The offset of the token within the document.                  |
+| `doc`   | `Doc`     | The parent document.                                          |
+
+### Token.cinit {#token_cinit tag="method"}
+
+Create a `Token` object from a `TokenC*` pointer.
+
+> #### Example
+>
+> ```python
+> token = Token.cinit(&doc.c[3], doc, 3)
+> ```
+
+| Name        | Type      | Description                                                  |
+| ----------- | --------- | ------------------------------------------------------------ |
+| `vocab`     | `Vocab`   | A reference to the shared `Vocab`.                           |
+| `c`         | `TokenC*` | A pointer to a [`TokenC`](/api/cython-structs#tokenc)struct. |
+| `offset`    | `int`     | The offset of the token within the document.                 |
+| `doc`       | `Doc`     | The parent document.                                         |
+| **RETURNS** | `Token`   | The newly constructed object.                                |
+
+## Span {#span tag="cdef class" source="spacy/tokens/span.pxd"}
+
+A Cython class providing access and methods for a slice of a `Doc` object.
+
+<Infobox variant="warning">
+
+This section documents the extra C-level attributes and methods that can't be
+accessed from Python. For the Python documentation, see [`Span`](/api/span).
+
+</Infobox>
+
+### Attributes {#span_attributes}
+
+| Name         | Type                                   | Description                                             |
+| ------------ | -------------------------------------- | ------------------------------------------------------- |
+| `doc`        | `Doc`                                  | The parent document.                                    |
+| `start`      | `int`                                  | The index of the first token of the span.               |
+| `end`        | `int`                                  | The index of the first token after the span.            |
+| `start_char` | `int`                                  | The index of the first character of the span.           |
+| `end_char`   | `int`                                  | The index of the last character of the span.            |
+| `label`      | <Abbr title="uint64_t">`attr_t`</Abbr> | A label to attach to the span, e.g. for named entities. |
+
+## Lexeme {#lexeme tag="cdef class" source="spacy/lexeme.pxd"}
+
+A Cython class providing access and methods for an entry in the vocabulary.
+
+<Infobox variant="warning">
+
+This section documents the extra C-level attributes and methods that can't be
+accessed from Python. For the Python documentation, see [`Lexeme`](/api/lexeme).
+
+</Infobox>
+
+### Attributes {#lexeme_attributes}
+
+| Name    | Type                                   | Description                                                     |
+| ------- | -------------------------------------- | --------------------------------------------------------------- |
+| `c`     | `LexemeC*`                             | A pointer to a [`LexemeC`](/api/cython-structs#lexemec) struct. |
+| `vocab` | `Vocab`                                | A reference to the shared `Vocab` object.                       |
+| `orth`  | <Abbr title="uint64_t">`attr_t`</Abbr> | ID of the verbatim text content.                                |
+
+## Vocab {#vocab tag="cdef class" source="spacy/vocab.pxd"}
+
+A Cython class providing access and methods for a vocabulary and other data
+shared across a language.
+
+<Infobox variant="warning">
+
+This section documents the extra C-level attributes and methods that can't be
+accessed from Python. For the Python documentation, see [`Vocab`](/api/vocab).
+
+</Infobox>
+
+### Attributes {#vocab_attributes}
+
+| Name      | Type          | Description                                                                                 |
+| --------- | ------------- | ------------------------------------------------------------------------------------------- |
+| `mem`     | `cymem.Pool`  | A memory pool. Allocated memory will be freed once the `Vocab` object is garbage collected. |
+| `strings` | `StringStore` | A `StringStore` that maps string to hash values and vice versa.                             |
+| `length`  | `int`         | The number of entries in the vocabulary.                                                    |
+
+### Vocab.get {#vocab_get tag="method"}
+
+Retrieve a [`LexemeC*`](/api/cython-structs#lexemec) pointer from the
+vocabulary.
+
+> #### Example
+>
+> ```python
+> lexeme = vocab.get(vocab.mem, u'hello')
+> ```
+
+| Name        | Type             | Description                                                                                 |
+| ----------- | ---------------- | ------------------------------------------------------------------------------------------- |
+| `mem`       | `cymem.Pool`     | A memory pool. Allocated memory will be freed once the `Vocab` object is garbage collected. |
+| `string`    | unicode          | The string of the word to look up.                                                          |
+| **RETURNS** | `const LexemeC*` | The lexeme in the vocabulary.                                                               |
+
+### Vocab.get_by_orth {#vocab_get_by_orth tag="method"}
+
+Retrieve a [`LexemeC*`](/api/cython-structs#lexemec) pointer from the
+vocabulary.
+
+> #### Example
+>
+> ```python
+> lexeme = vocab.get_by_orth(doc[0].lex.norm)
+> ```
+
+| Name        | Type                                   | Description                                                                                 |
+| ----------- | -------------------------------------- | ------------------------------------------------------------------------------------------- |
+| `mem`       | `cymem.Pool`                           | A memory pool. Allocated memory will be freed once the `Vocab` object is garbage collected. |
+| `orth`      | <Abbr title="uint64_t">`attr_t`</Abbr> | ID of the verbatim text content.                                                            |
+| **RETURNS** | `const LexemeC*`                       | The lexeme in the vocabulary.                                                               |
+
+## StringStore {#stringstore tag="cdef class" source="spacy/strings.pxd"}
+
+A lookup table to retrieve strings by 64-bit hashes.
+
+<Infobox variant="warning">
+
+This section documents the extra C-level attributes and methods that can't be
+accessed from Python. For the Python documentation, see
+[`StringStore`](/api/stringstore).
+
+</Infobox>
+
+### Attributes {#stringstore_attributes}
+
+| Name   | Type                                                   | Description                                                                                      |
+| ------ | ------------------------------------------------------ | ------------------------------------------------------------------------------------------------ |
+| `mem`  | `cymem.Pool`                                           | A memory pool. Allocated memory will be freed once the`StringStore` object is garbage collected. |
+| `keys` | <Abbr title="vector[uint64_t]">`vector[hash_t]`</Abbr> | A list of hash values in the `StringStore`.                                                      |
diff --git a/website/docs/api/cython-structs.md b/website/docs/api/cython-structs.md
new file mode 100644
index 000000000..1d3139a96
--- /dev/null
+++ b/website/docs/api/cython-structs.md
@@ -0,0 +1,256 @@
+---
+title: Cython Structs
+teaser: C-language objects that let you group variables together
+next: /api/cython-classes
+menu:
+  - ['TokenC', 'tokenc']
+  - ['LexemeC', 'lexemec']
+---
+
+## TokenC {#tokenc tag="C struct" source="spacy/structs.pxd"}
+
+Cython data container for the `Token` object.
+
+> #### Example
+>
+> ```python
+> token = &doc.c[3]
+> token_ptr = &doc.c[3]
+> ```
+
+| Name         | Type                                   | Description                                                                                                                                                                                                                                                                                                                         |
+| ------------ | -------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `lex`        | `const LexemeC*`                       | A pointer to the lexeme for the token.                                                                                                                                                                                                                                                                                              |
+| `morph`      | `uint64_t`                             | An ID allowing lookup of morphological attributes.                                                                                                                                                                                                                                                                                  |
+| `pos`        | `univ_pos_t`                           | Coarse-grained part-of-speech tag.                                                                                                                                                                                                                                                                                                  |
+| `spacy`      | `bint`                                 | A binary value indicating whether the token has trailing whitespace.                                                                                                                                                                                                                                                                |
+| `tag`        | <Abbr title="uint64_t">`attr_t`</Abbr> | Fine-grained part-of-speech tag.                                                                                                                                                                                                                                                                                                    |
+| `idx`        | `int`                                  | The character offset of the token within the parent document.                                                                                                                                                                                                                                                                       |
+| `lemma`      | <Abbr title="uint64_t">`attr_t`</Abbr> | Base form of the token, with no inflectional suffixes.                                                                                                                                                                                                                                                                              |
+| `sense`      | <Abbr title="uint64_t">`attr_t`</Abbr> | Space for storing a word sense ID, currently unused.                                                                                                                                                                                                                                                                                |
+| `head`       | `int`                                  | Offset of the syntactic parent relative to the token.                                                                                                                                                                                                                                                                               |
+| `dep`        | <Abbr title="uint64_t">`attr_t`</Abbr> | Syntactic dependency relation.                                                                                                                                                                                                                                                                                                      |
+| `l_kids`     | `uint32_t`                             | Number of left children.                                                                                                                                                                                                                                                                                                            |
+| `r_kids`     | `uint32_t`                             | Number of right children.                                                                                                                                                                                                                                                                                                           |
+| `l_edge`     | `uint32_t`                             | Offset of the leftmost token of this token's syntactic descendants.                                                                                                                                                                                                                                                                 |
+| `r_edge`     | `uint32_t`                             | Offset of the rightmost token of this token's syntactic descendants.                                                                                                                                                                                                                                                                |
+| `sent_start` | `int`                                  | Ternary value indicating whether the token is the first word of a sentence. `0` indicates a missing value, `-1` indicates `False` and `1` indicates `True`. The default value, 0, is interpreted as no sentence break. Sentence boundary detectors will usually set 0 for all tokens except tokens that follow a sentence boundary. |
+| `ent_iob`    | `int`                                  | IOB code of named entity tag. `0` indicates a missing value, `1` indicates `I`, `2` indicates `0` and `3` indicates `B`.                                                                                                                                                                                                            |
+| `ent_type`   | <Abbr title="uint64_t">`attr_t`</Abbr> | Named entity type.                                                                                                                                                                                                                                                                                                                  |
+| `ent_id`     | <Abbr title="uint64_t">`attr_t`</Abbr> | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution.                                                                                                                                                                                                               |
+
+### Token.get_struct_attr {#token_get_struct_attr tag="staticmethod, nogil" source="spacy/tokens/token.pxd"}
+
+Get the value of an attribute from the `TokenC` struct by attribute ID.
+
+> #### Example
+>
+> ```python
+> from spacy.attrs cimport IS_ALPHA
+> from spacy.tokens cimport Token
+>
+> is_alpha = Token.get_struct_attr(&doc.c[3], IS_ALPHA)
+> ```
+
+| Name        | Type                                   | Description                                                                            |
+| ----------- | -------------------------------------- | -------------------------------------------------------------------------------------- |
+| `token`     | `const TokenC*`                        | A pointer to a `TokenC` struct.                                                        |
+| `feat_name` | `attr_id_t`                            | The ID of the attribute to look up. The attributes are enumerated in `spacy.typedefs`. |
+| **RETURNS** | <Abbr title="uint64_t">`attr_t`</Abbr> | The value of the attribute.                                                            |
+
+### Token.set_struct_attr {#token_set_struct_attr tag="staticmethod, nogil" source="spacy/tokens/token.pxd"}
+
+Set the value of an attribute of the `TokenC` struct by attribute ID.
+
+> #### Example
+>
+> ```python
+> from spacy.attrs cimport TAG
+> from spacy.tokens cimport Token
+>
+> token = &doc.c[3]
+> Token.set_struct_attr(token, TAG, 0)
+> ```
+
+| Name        | Type                                   | Description                                                                            |
+| ----------- | -------------------------------------- | -------------------------------------------------------------------------------------- |
+| `token`     | `const TokenC*`                        | A pointer to a `TokenC` struct.                                                        |
+| `feat_name` | `attr_id_t`                            | The ID of the attribute to look up. The attributes are enumerated in `spacy.typedefs`. |
+| `value`     | <Abbr title="uint64_t">`attr_t`</Abbr> | The value to set.                                                                      |
+
+### token_by_start {#token_by_start tag="function" source="spacy/tokens/doc.pxd"}
+
+Find a token in a `TokenC*` array by the offset of its first character.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens.doc cimport Doc, token_by_start
+> from spacy.vocab cimport Vocab
+>
+> doc = Doc(Vocab(), words=[u'hello', u'world'])
+> assert token_by_start(doc.c, doc.length, 6) == 1
+> assert token_by_start(doc.c, doc.length, 4) == -1
+> ```
+
+| Name         | Type            | Description                                               |
+| ------------ | --------------- | --------------------------------------------------------- |
+| `tokens`     | `const TokenC*` | A `TokenC*` array.                                        |
+| `length`     | `int`           | The number of tokens in the array.                        |
+| `start_char` | `int`           | The start index to search for.                            |
+| **RETURNS**  | `int`           | The index of the token in the array or `-1` if not found. |
+
+### token_by_end {#token_by_end tag="function" source="spacy/tokens/doc.pxd"}
+
+Find a token in a `TokenC*` array by the offset of its final character.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens.doc cimport Doc, token_by_end
+> from spacy.vocab cimport Vocab
+>
+> doc = Doc(Vocab(), words=[u'hello', u'world'])
+> assert token_by_end(doc.c, doc.length, 5) == 0
+> assert token_by_end(doc.c, doc.length, 1) == -1
+> ```
+
+| Name        | Type            | Description                                               |
+| ----------- | --------------- | --------------------------------------------------------- |
+| `tokens`    | `const TokenC*` | A `TokenC*` array.                                        |
+| `length`    | `int`           | The number of tokens in the array.                        |
+| `end_char`  | `int`           | The end index to search for.                              |
+| **RETURNS** | `int`           | The index of the token in the array or `-1` if not found. |
+
+### set_children_from_heads {#set_children_from_heads tag="function" source="spacy/tokens/doc.pxd"}
+
+Set attributes that allow lookup of syntactic children on a `TokenC*` array.
+This function must be called after making changes to the `TokenC.head`
+attribute, in order to make the parse tree navigation consistent.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens.doc cimport Doc, set_children_from_heads
+> from spacy.vocab cimport Vocab
+>
+> doc = Doc(Vocab(), words=[u'Baileys', u'from', u'a', u'shoe'])
+> doc.c[0].head = 0
+> doc.c[1].head = 0
+> doc.c[2].head = 3
+> doc.c[3].head = 1
+> set_children_from_heads(doc.c, doc.length)
+> assert doc.c[3].l_kids == 1
+> ```
+
+| Name     | Type            | Description                        |
+| -------- | --------------- | ---------------------------------- |
+| `tokens` | `const TokenC*` | A `TokenC*` array.                 |
+| `length` | `int`           | The number of tokens in the array. |
+
+## LexemeC {#lexemec tag="C struct" source="spacy/structs.pxd"}
+
+Struct holding information about a lexical type. `LexemeC` structs are usually
+owned by the `Vocab`, and accessed through a read-only pointer on the `TokenC`
+struct.
+
+> #### Example
+>
+> ```python
+> lex = doc.c[3].lex
+> ```
+
+| Name        | Type                                    | Description                                                                                                                |
+| ----------- | --------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- |
+| `flags`     | <Abbr title="uint64_t">`flags_t`</Abbr> | Bit-field for binary lexical flag values.                                                                                  |
+| `id`        | <Abbr title="uint64_t">`attr_t`</Abbr>  | Usually used to map lexemes to rows in a matrix, e.g. for word vectors. Does not need to be unique, so currently misnamed. |
+| `length`    | <Abbr title="uint64_t">`attr_t`</Abbr>  | Number of unicode characters in the lexeme.                                                                                |
+| `orth`      | <Abbr title="uint64_t">`attr_t`</Abbr>  | ID of the verbatim text content.                                                                                           |
+| `lower`     | <Abbr title="uint64_t">`attr_t`</Abbr>  | ID of the lowercase form of the lexeme.                                                                                    |
+| `norm`      | <Abbr title="uint64_t">`attr_t`</Abbr>  | ID of the lexeme's norm, i.e. a normalized form of the text.                                                               |
+| `shape`     | <Abbr title="uint64_t">`attr_t`</Abbr>  | Transform of the lexeme's string, to show orthographic features.                                                           |
+| `prefix`    | <Abbr title="uint64_t">`attr_t`</Abbr>  | Length-N substring from the start of the lexeme. Defaults to `N=1`.                                                        |
+| `suffix`    | <Abbr title="uint64_t">`attr_t`</Abbr>  | Length-N substring from the end of the lexeme. Defaults to `N=3`.                                                          |
+| `cluster`   | <Abbr title="uint64_t">`attr_t`</Abbr>  | Brown cluster ID.                                                                                                          |
+| `prob`      | `float`                                 | Smoothed log probability estimate of the lexeme's type.                                                                    |
+| `sentiment` | `float`                                 | A scalar value indicating positivity or negativity.                                                                        |
+
+### Lexeme.get_struct_attr {#lexeme_get_struct_attr tag="staticmethod, nogil" source="spacy/lexeme.pxd"}
+
+Get the value of an attribute from the `LexemeC` struct by attribute ID.
+
+> #### Example
+>
+> ```python
+> from spacy.attrs cimport IS_ALPHA
+> from spacy.lexeme cimport Lexeme
+>
+> lexeme = doc.c[3].lex
+> is_alpha = Lexeme.get_struct_attr(lexeme, IS_ALPHA)
+> ```
+
+| Name        | Type                                   | Description                                                                            |
+| ----------- | -------------------------------------- | -------------------------------------------------------------------------------------- |
+| `lex`       | `const LexemeC*`                       | A pointer to a `LexemeC` struct.                                                       |
+| `feat_name` | `attr_id_t`                            | The ID of the attribute to look up. The attributes are enumerated in `spacy.typedefs`. |
+| **RETURNS** | <Abbr title="uint64_t">`attr_t`</Abbr> | The value of the attribute.                                                            |
+
+### Lexeme.set_struct_attr {#lexeme_set_struct_attr tag="staticmethod, nogil" source="spacy/lexeme.pxd"}
+
+Set the value of an attribute of the `LexemeC` struct by attribute ID.
+
+> #### Example
+>
+> ```python
+> from spacy.attrs cimport NORM
+> from spacy.lexeme cimport Lexeme
+>
+> lexeme = doc.c[3].lex
+> Lexeme.set_struct_attr(lexeme, NORM, lexeme.lower)
+> ```
+
+| Name        | Type                                   | Description                                                                            |
+| ----------- | -------------------------------------- | -------------------------------------------------------------------------------------- |
+| `lex`       | `const LexemeC*`                       | A pointer to a `LexemeC` struct.                                                       |
+| `feat_name` | `attr_id_t`                            | The ID of the attribute to look up. The attributes are enumerated in `spacy.typedefs`. |
+| `value`     | <Abbr title="uint64_t">`attr_t`</Abbr> | The value to set.                                                                      |
+
+### Lexeme.c_check_flag {#lexeme_c_check_flag tag="staticmethod, nogil" source="spacy/lexeme.pxd"}
+
+Check the value of a binary flag attribute.
+
+> #### Example
+>
+> ```python
+> from spacy.attrs cimport IS_STOP
+> from spacy.lexeme cimport Lexeme
+>
+> lexeme = doc.c[3].lex
+> is_stop = Lexeme.c_check_flag(lexeme, IS_STOP)
+> ```
+
+| Name        | Type             | Description                                                                     |
+| ----------- | ---------------- | ------------------------------------------------------------------------------- |
+| `lexeme`    | `const LexemeC*` | A pointer to a `LexemeC` struct.                                                |
+| `flag_id`   | `attr_id_t`      | The ID of the flag to look up. The flag IDs are enumerated in `spacy.typedefs`. |
+| **RETURNS** | `bint`           | The boolean value of the flag.                                                  |
+
+### Lexeme.c_set_flag {#lexeme_c_set_flag tag="staticmethod, nogil" source="spacy/lexeme.pxd"}
+
+Set the value of a binary flag attribute.
+
+> #### Example
+>
+> ```python
+> from spacy.attrs cimport IS_STOP
+> from spacy.lexeme cimport Lexeme
+>
+> lexeme = doc.c[3].lex
+> Lexeme.c_set_flag(lexeme, IS_STOP, 0)
+> ```
+
+| Name      | Type             | Description                                                                     |
+| --------- | ---------------- | ------------------------------------------------------------------------------- |
+| `lexeme`  | `const LexemeC*` | A pointer to a `LexemeC` struct.                                                |
+| `flag_id` | `attr_id_t`      | The ID of the flag to look up. The flag IDs are enumerated in `spacy.typedefs`. |
+| `value`   | `bint`           | The value to set.                                                               |
diff --git a/website/docs/api/cython.md b/website/docs/api/cython.md
new file mode 100644
index 000000000..f91909747
--- /dev/null
+++ b/website/docs/api/cython.md
@@ -0,0 +1,134 @@
+---
+title: Cython Architecture
+next: /api/cython-structs
+menu:
+  - ['Overview', 'overview']
+  - ['Conventions', 'conventions']
+---
+
+## Overview {#overview hidden="true"}
+
+> #### What's Cython?
+>
+> [Cython](http://cython.org/) is a language for writing C extensions for
+> Python. Most Python code is also valid Cython, but you can add type
+> declarations to get efficient memory-managed code just like C or C++.
+
+This section documents spaCy's C-level data structures and interfaces, intended
+for use from Cython. Some of the attributes are primarily for internal use, and
+all C-level functions and methods are designed for speed over safety – if you
+make a mistake and access an array out-of-bounds, the program may crash
+abruptly.
+
+With Cython there are four ways of declaring complex data types. Unfortunately
+we use all four in different places, as they all have different utility:
+
+| Declaration     | Description                                                                                                                                                                                                                                                                      | Example                                                                            |
+| --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------- |
+| `class`         | A normal Python class.                                                                                                                                                                                                                                                           | [`Language`](/api/language)                                                        |
+| `cdef class`    | A Python extension type. Differs from a normal Python class in that its attributes can be defined on the underlying struct. Can have C-level objects as attributes (notably structs and pointers), and can have methods which have C-level objects as arguments or return types. | [`Lexeme`](/api/cython-classes#lexeme)                                             |
+| `cdef struct`   | A struct is just a collection of variables, sort of like a named tuple, except the memory is contiguous. Structs can't have methods, only attributes.                                                                                                                            | [`LexemeC`](/api/cython-structs#lexemec)                                           |
+| `cdef cppclass` | A C++ class. Like a struct, this can be allocated on the stack, but can have methods, a constructor and a destructor. Differs from `cdef class` in that it can be created and destroyed without acquiring the Python global interpreter lock. This style is the most obscure.    | [`StateC`](https://github.com/explosion/spaCy/tree/master/spacy/syntax/_state.pxd) |
+
+The most important classes in spaCy are defined as `cdef class` objects. The
+underlying data for these objects is usually gathered into a struct, which is
+usually named `c`. For instance, the [`Lexeme`](/api/cython-classses#lexeme)
+class holds a [`LexemeC`](/api/cython-structs#lexemec) struct, at `Lexeme.c`.
+This lets you shed the Python container, and pass a pointer to the underlying
+data into C-level functions.
+
+## Conventions {#conventions}
+
+spaCy's core data structures are implemented as [Cython](http://cython.org/)
+`cdef` classes. Memory is managed through the
+[`cymem`](https://github.com/explosion/cymem) `cymem.Pool` class, which allows
+you to allocate memory which will be freed when the `Pool` object is garbage
+collected. This means you usually don't have to worry about freeing memory. You
+just have to decide which Python object owns the memory, and make it own the
+`Pool`. When that object goes out of scope, the memory will be freed. You do
+have to take care that no pointers outlive the object that owns them — but this
+is generally quite easy.
+
+All Cython modules should have the `# cython: infer_types=True` compiler
+directive at the top of the file. This makes the code much cleaner, as it avoids
+the need for many type declarations. If possible, you should prefer to declare
+your functions `nogil`, even if you don't especially care about multi-threading.
+The reason is that `nogil` functions help the Cython compiler reason about your
+code quite a lot — you're telling the compiler that no Python dynamics are
+possible. This lets many errors be raised, and ensures your function will run at
+C speed.
+
+Cython gives you many choices of sequences: you could have a Python list, a
+numpy array, a memory view, a C++ vector, or a pointer. Pointers are preferred,
+because they are fastest, have the most explicit semantics, and let the compiler
+check your code more strictly. C++ vectors are also great — but you should only
+use them internally in functions. It's less friendly to accept a vector as an
+argument, because that asks the user to do much more work. Here's how to get a
+pointer from a numpy array, memory view or vector:
+
+```python
+cdef void get_pointers(np.ndarray[int, mode='c'] numpy_array, vector[int] cpp_vector, int[::1] memory_view) nogil:
+pointer1 = <int*>numpy_array.data
+pointer2 = cpp_vector.data()
+pointer3 = &memory_view[0]
+```
+
+Both C arrays and C++ vectors reassure the compiler that no Python operations
+are possible on your variable. This is a big advantage: it lets the Cython
+compiler raise many more errors for you.
+
+When getting a pointer from a numpy array or memoryview, take care that the data
+is actually stored in C-contiguous order — otherwise you'll get a pointer to
+nonsense. The type-declarations in the code above should generate runtime errors
+if buffers with incorrect memory layouts are passed in. To iterate over the
+array, the following style is preferred:
+
+```python
+cdef int c_total(const int* int_array, int length) nogil:
+    total = 0
+    for item in int_array[:length]:
+        total += item
+    return total
+```
+
+If this is confusing, consider that the compiler couldn't deal with
+`for item in int_array:` — there's no length attached to a raw pointer, so how
+could we figure out where to stop? The length is provided in the slice notation
+as a solution to this. Note that we don't have to declare the type of `item` in
+the code above — the compiler can easily infer it. This gives us tidy code that
+looks quite like Python, but is exactly as fast as C — because we've made sure
+the compilation to C is trivial.
+
+Your functions cannot be declared `nogil` if they need to create Python objects
+or call Python functions. This is perfectly okay — you shouldn't torture your
+code just to get `nogil` functions. However, if your function isn't `nogil`, you
+should compile your module with `cython -a --cplus my_module.pyx` and open the
+resulting `my_module.html` file in a browser. This will let you see how Cython
+is compiling your code. Calls into the Python run-time will be in bright yellow.
+This lets you easily see whether Cython is able to correctly type your code, or
+whether there are unexpected problems.
+
+Working in Cython is very rewarding once you're over the initial learning curve.
+As with C and C++, the first way you write something in Cython will often be the
+performance-optimal approach. In contrast, Python optimization generally
+requires a lot of experimentation. Is it faster to have an `if item in my_dict`
+check, or to use `.get()`? What about `try`/`except`? Does this numpy operation
+create a copy? There's no way to guess the answers to these questions, and
+you'll usually be dissatisfied with your results — so there's no way to know
+when to stop this process. In the worst case, you'll make a mess that invites
+the next reader to try their luck too. This is like one of those
+[volcanic gas-traps](http://www.wemjournal.org/article/S1080-6032%2809%2970088-2/abstract),
+where the rescuers keep passing out from low oxygen, causing another rescuer to
+follow — only to succumb themselves. In short, just say no to optimizing your
+Python. If it's not fast enough the first time, just switch to Cython.
+
+<Infobox title="📖 Resources">
+
+- [Official Cython documentation](http://docs.cython.org/en/latest/)
+  (cython.org)
+- [Writing C in Cython](https://explosion.ai/blog/writing-c-in-cython)
+  (explosion.ai)
+- [Multi-threading spaCy’s parser and named entity recognizer](https://explosion.ai/blog/multithreading-with-cython)
+  (explosion.ai)
+
+</Infobox>
diff --git a/website/docs/api/dependencyparser.md b/website/docs/api/dependencyparser.md
new file mode 100644
index 000000000..84ea707f0
--- /dev/null
+++ b/website/docs/api/dependencyparser.md
@@ -0,0 +1,311 @@
+---
+title: DependencyParser
+tag: class
+source: spacy/pipeline.pyx
+---
+
+This class is a subclass of `Pipe` and follows the same API. The pipeline
+component is available in the [processing pipeline](/usage/processing-pipelines)
+via the ID `"parser"`.
+
+## DependencyParser.Model {#model tag="classmethod"}
+
+Initialize a model for the pipe. The model should implement the
+`thinc.neural.Model` API. Wrappers are under development for most major machine
+learning libraries.
+
+| Name        | Type   | Description                           |
+| ----------- | ------ | ------------------------------------- |
+| `**kwargs`  | -      | Parameters for initializing the model |
+| **RETURNS** | object | The initialized model.                |
+
+## DependencyParser.\_\_init\_\_ {#init tag="method"}
+
+Create a new pipeline instance. In your application, you would normally use a
+shortcut for this and instantiate the component using its string name and
+[`nlp.create_pipe`](/api/language#create_pipe).
+
+> #### Example
+>
+> ```python
+> # Construction via create_pipe
+> parser = nlp.create_pipe("parser")
+>
+> # Construction from class
+> from spacy.pipeline import DependencyParser
+> parser = DependencyParser(nlp.vocab)
+> parser.from_disk("/path/to/model")
+> ```
+
+| Name        | Type                           | Description                                                                                                                                           |
+| ----------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `vocab`     | `Vocab`                        | The shared vocabulary.                                                                                                                                |
+| `model`     | `thinc.neural.Model` or `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. |
+| `**cfg`     | -                              | Configuration parameters.                                                                                                                             |
+| **RETURNS** | `DependencyParser`             | The newly constructed object.                                                                                                                         |
+
+## DependencyParser.\_\_call\_\_ {#call tag="method"}
+
+Apply the pipe to one document. The document is modified in place, and returned.
+Both [`__call__`](/api/dependencyparser#call) and
+[`pipe`](/api/dependencyparser#pipe) delegate to the
+[`predict`](/api/dependencyparser#predict) and
+[`set_annotations`](/api/dependencyparser#set_annotations) methods.
+
+> #### Example
+>
+> ```python
+> parser = DependencyParser(nlp.vocab)
+> doc = nlp(u"This is a sentence.")
+> processed = parser(doc)
+> ```
+
+| Name        | Type  | Description              |
+| ----------- | ----- | ------------------------ |
+| `doc`       | `Doc` | The document to process. |
+| **RETURNS** | `Doc` | The processed document.  |
+
+## DependencyParser.pipe {#pipe tag="method"}
+
+Apply the pipe to a stream of documents. Both
+[`__call__`](/api/dependencyparser#call) and
+[`pipe`](/api/dependencyparser#pipe) delegate to the
+[`predict`](/api/dependencyparser#predict) and
+[`set_annotations`](/api/dependencyparser#set_annotations) methods.
+
+> #### Example
+>
+> ```python
+> texts = [u"One doc", u"...", u"Lots of docs"]
+> parser = DependencyParser(nlp.vocab)
+> for doc in parser.pipe(texts, batch_size=50):
+>     pass
+> ```
+
+| Name         | Type     | Description                                                                                                    |
+| ------------ | -------- | -------------------------------------------------------------------------------------------------------------- |
+| `stream`     | iterable | A stream of documents.                                                                                         |
+| `batch_size` | int      | The number of texts to buffer. Defaults to `128`.                                                              |
+| **YIELDS**   | `Doc`    | Processed documents in the order of the original text.                                                         |
+
+## DependencyParser.predict {#predict tag="method"}
+
+Apply the pipeline's model to a batch of docs, without modifying them.
+
+> #### Example
+>
+> ```python
+> parser = DependencyParser(nlp.vocab)
+> scores = parser.predict([doc1, doc2])
+> ```
+
+| Name        | Type     | Description               |
+| ----------- | -------- | ------------------------- |
+| `docs`      | iterable | The documents to predict. |
+| **RETURNS** | -        | Scores from the model.    |
+
+## DependencyParser.set_annotations {#set_annotations tag="method"}
+
+Modify a batch of documents, using pre-computed scores.
+
+> #### Example
+>
+> ```python
+> parser = DependencyParser(nlp.vocab)
+> scores = parser.predict([doc1, doc2])
+> parser.set_annotations([doc1, doc2], scores)
+> ```
+
+| Name     | Type     | Description                                                |
+| -------- | -------- | ---------------------------------------------------------- |
+| `docs`   | iterable | The documents to modify.                                   |
+| `scores` | -        | The scores to set, produced by `DependencyParser.predict`. |
+
+## DependencyParser.update {#update tag="method"}
+
+Learn from a batch of documents and gold-standard information, updating the
+pipe's model. Delegates to [`predict`](/api/dependencyparser#predict) and
+[`get_loss`](/api/dependencyparser#get_loss).
+
+> #### Example
+>
+> ```python
+> parser = DependencyParser(nlp.vocab)
+> losses = {}
+> optimizer = nlp.begin_training()
+> parser.update([doc1, doc2], [gold1, gold2], losses=losses, sgd=optimizer)
+> ```
+
+| Name     | Type     | Description                                                                                  |
+| -------- | -------- | -------------------------------------------------------------------------------------------- |
+| `docs`   | iterable | A batch of documents to learn from.                                                          |
+| `golds`  | iterable | The gold-standard data. Must have the same length as `docs`.                                 |
+| `drop`   | float    | The dropout rate.                                                                            |
+| `sgd`    | callable | The optimizer. Should take two arguments `weights` and `gradient`, and an optional ID.       |
+| `losses` | dict     | Optional record of the loss during training. The value keyed by the model's name is updated. |
+
+## DependencyParser.get_loss {#get_loss tag="method"}
+
+Find the loss and gradient of loss for the batch of documents and their
+predicted scores.
+
+> #### Example
+>
+> ```python
+> parser = DependencyParser(nlp.vocab)
+> scores = parser.predict([doc1, doc2])
+> loss, d_loss = parser.get_loss([doc1, doc2], [gold1, gold2], scores)
+> ```
+
+| Name        | Type     | Description                                                  |
+| ----------- | -------- | ------------------------------------------------------------ |
+| `docs`      | iterable | The batch of documents.                                      |
+| `golds`     | iterable | The gold-standard data. Must have the same length as `docs`. |
+| `scores`    | -        | Scores representing the model's predictions.                 |
+| **RETURNS** | tuple    | The loss and the gradient, i.e. `(loss, gradient)`.          |
+
+## DependencyParser.begin_training {#begin_training tag="method"}
+
+Initialize the pipe for training, using data examples if available. If no model
+has been initialized yet, the model is added.
+
+> #### Example
+>
+> ```python
+> parser = DependencyParser(nlp.vocab)
+> nlp.pipeline.append(parser)
+> optimizer = parser.begin_training(pipeline=nlp.pipeline)
+> ```
+
+| Name          | Type     | Description                                                                                                                                                                                 |
+| ------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `gold_tuples` | iterable | Optional gold-standard annotations from which to construct [`GoldParse`](/api/goldparse) objects.                                                                                           |
+| `pipeline`    | list     | Optional list of pipeline components that this component is part of.                                                                                                                        |
+| `sgd`         | callable | An optional optimizer. Should take two arguments `weights` and `gradient`, and an optional ID. Will be created via [`DependencyParser`](/api/dependencyparser#create_optimizer) if not set. |
+| **RETURNS**   | callable | An optimizer.                                                                                                                                                                               |
+
+## DependencyParser.create_optimizer {#create_optimizer tag="method"}
+
+Create an optimizer for the pipeline component.
+
+> #### Example
+>
+> ```python
+> parser = DependencyParser(nlp.vocab)
+> optimizer = parser.create_optimizer()
+> ```
+
+| Name        | Type     | Description    |
+| ----------- | -------- | -------------- |
+| **RETURNS** | callable | The optimizer. |
+
+## DependencyParser.use_params {#use_params tag="method, contextmanager"}
+
+Modify the pipe's model, to use the given parameter values.
+
+> #### Example
+>
+> ```python
+> parser = DependencyParser(nlp.vocab)
+> with parser.use_params():
+>     parser.to_disk('/best_model')
+> ```
+
+| Name     | Type | Description                                                                                                |
+| -------- | ---- | ---------------------------------------------------------------------------------------------------------- |
+| `params` | -    | The parameter values to use in the model. At the end of the context, the original parameters are restored. |
+
+## DependencyParser.add_label {#add_label tag="method"}
+
+Add a new label to the pipe.
+
+> #### Example
+>
+> ```python
+> parser = DependencyParser(nlp.vocab)
+> parser.add_label('MY_LABEL')
+> ```
+
+| Name    | Type    | Description       |
+| ------- | ------- | ----------------- |
+| `label` | unicode | The label to add. |
+
+## DependencyParser.to_disk {#to_disk tag="method"}
+
+Serialize the pipe to disk.
+
+> #### Example
+>
+> ```python
+> parser = DependencyParser(nlp.vocab)
+> parser.to_disk('/path/to/parser')
+> ```
+
+| Name   | Type             | Description                                                                                                           |
+| ------ | ---------------- | --------------------------------------------------------------------------------------------------------------------- |
+| `path` | unicode / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
+
+## DependencyParser.from_disk {#from_disk tag="method"}
+
+Load the pipe from disk. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> parser = DependencyParser(nlp.vocab)
+> parser.from_disk('/path/to/parser')
+> ```
+
+| Name        | Type               | Description                                                                |
+| ----------- | ------------------ | -------------------------------------------------------------------------- |
+| `path`      | unicode / `Path`   | A path to a directory. Paths may be either strings or `Path`-like objects. |
+| **RETURNS** | `DependencyParser` | The modified `DependencyParser` object.                                    |
+
+## DependencyParser.to_bytes {#to_bytes tag="method"}
+
+> #### example
+>
+> ```python
+> parser = DependencyParser(nlp.vocab)
+> parser_bytes = parser.to_bytes()
+> ```
+
+Serialize the pipe to a bytestring.
+
+| Name        | Type  | Description                                           |
+| ----------- | ----- | ----------------------------------------------------- |
+| `**exclude` | -     | Named attributes to prevent from being serialized.    |
+| **RETURNS** | bytes | The serialized form of the `DependencyParser` object. |
+
+## DependencyParser.from_bytes {#from_bytes tag="method"}
+
+Load the pipe from a bytestring. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> parser_bytes = parser.to_bytes()
+> parser = DependencyParser(nlp.vocab)
+> parser.from_bytes(parser_bytes)
+> ```
+
+| Name         | Type               | Description                                    |
+| ------------ | ------------------ | ---------------------------------------------- |
+| `bytes_data` | bytes              | The data to load from.                         |
+| `**exclude`  | -                  | Named attributes to prevent from being loaded. |
+| **RETURNS**  | `DependencyParser` | The `DependencyParser` object.                 |
+
+## DependencyParser.labels {#labels tag="property"}
+
+The labels currently added to the component.
+
+> #### Example
+>
+> ```python
+> parser.add_label("MY_LABEL")
+> assert "MY_LABEL" in parser.labels
+> ```
+
+| Name        | Type  | Description                        |
+| ----------- | ----- | ---------------------------------- |
+| **RETURNS** | tuple | The labels added to the component. |
diff --git a/website/docs/api/doc.md b/website/docs/api/doc.md
new file mode 100644
index 000000000..87b7b373e
--- /dev/null
+++ b/website/docs/api/doc.md
@@ -0,0 +1,608 @@
+---
+title: Doc
+tag: class
+teaser: A container for accessing linguistic annotations.
+source: spacy/tokens/doc.pyx
+---
+
+A `Doc` is a sequence of [`Token`](/api/token) objects. Access sentences and
+named entities, export annotations to numpy arrays, losslessly serialize to
+compressed binary strings. The `Doc` object holds an array of `TokenC]` structs.
+The Python-level `Token` and [`Span`](/api/span) objects are views of this
+array, i.e. they don't own the data themselves.
+
+> #### Example
+>
+> ```python
+> # Construction 1
+> doc = nlp(u"Some text")
+>
+> # Construction 2
+> from spacy.tokens import Doc
+> words = [u"hello", u"world", u"!"]
+> spaces = [True, False, False]
+> doc = Doc(nlp.vocab, words=words, spaces=spaces)
+> ```
+
+## Doc.\_\_init\_\_ {#init tag="method"}
+
+Construct a `Doc` object. The most common way to get a `Doc` object is via the
+`nlp` object.
+
+| Name        | Type     | Description                                                                                                                                                         |
+| ----------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `vocab`     | `Vocab`  | A storage container for lexical types.                                                                                                                              |
+| `words`     | iterable | A list of strings to add to the container.                                                                                                                          |
+| `spaces`    | iterable | A list of boolean values indicating whether each word has a subsequent space. Must have the same length as `words`, if specified. Defaults to a sequence of `True`. |
+| **RETURNS** | `Doc`    | The newly constructed object.                                                                                                                                       |
+
+## Doc.\_\_getitem\_\_ {#getitem tag="method"}
+
+Get a [`Token`](/api/token) object at position `i`, where `i` is an integer.
+Negative indexing is supported, and follows the usual Python semantics, i.e.
+`doc[-2]` is `doc[len(doc) - 2]`.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> assert doc[0].text == "Give"
+> assert doc[-1].text == "."
+> span = doc[1:3]
+> assert span.text == "it back"
+> ```
+
+| Name        | Type    | Description             |
+| ----------- | ------- | ----------------------- |
+| `i`         | int     | The index of the token. |
+| **RETURNS** | `Token` | The token at `doc[i]`.  |
+
+Get a [`Span`](/api/span) object, starting at position `start` (token index) and
+ending at position `end` (token index). For instance, `doc[2:5]` produces a span
+consisting of tokens 2, 3 and 4. Stepped slices (e.g. `doc[start : end : step]`)
+are not supported, as `Span` objects must be contiguous (cannot have gaps). You
+can use negative indices and open-ended ranges, which have their normal Python
+semantics.
+
+| Name        | Type   | Description                       |
+| ----------- | ------ | --------------------------------- |
+| `start_end` | tuple  | The slice of the document to get. |
+| **RETURNS** | `Span` | The span at `doc[start:end]`.     |
+
+## Doc.\_\_iter\_\_ {#iter tag="method"}
+
+Iterate over `Token` objects, from which the annotations can be easily accessed.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u'Give it back')
+> assert [t.text for t in doc] == [u'Give', u'it', u'back']
+> ```
+
+This is the main way of accessing [`Token`](/api/token) objects, which are the
+main way annotations are accessed from Python. If faster-than-Python speeds are
+required, you can instead access the annotations as a numpy array, or access the
+underlying C data directly from Cython.
+
+| Name       | Type    | Description       |
+| ---------- | ------- | ----------------- |
+| **YIELDS** | `Token` | A `Token` object. |
+
+## Doc.\_\_len\_\_ {#len tag="method"}
+
+Get the number of tokens in the document.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> assert len(doc) == 7
+> ```
+
+| Name        | Type | Description                           |
+| ----------- | ---- | ------------------------------------- |
+| **RETURNS** | int  | The number of tokens in the document. |
+
+## Doc.set_extension {#set_extension tag="classmethod" new="2"}
+
+Define a custom attribute on the `Doc` which becomes available via `Doc._`. For
+details, see the documentation on
+[custom attributes](/usage/processing-pipelines#custom-components-attributes).
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Doc
+> city_getter = lambda doc: any(city in doc.text for city in ('New York', 'Paris', 'Berlin'))
+> Doc.set_extension('has_city', getter=city_getter)
+> doc = nlp(u'I like New York')
+> assert doc._.has_city
+> ```
+
+| Name      | Type     | Description                                                                                                                         |
+| --------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------- |
+| `name`    | unicode  | Name of the attribute to set by the extension. For example, `'my_attr'` will be available as `doc._.my_attr`.                       |
+| `default` | -        | Optional default value of the attribute if no getter or method is defined.                                                          |
+| `method`  | callable | Set a custom method on the object, for example `doc._.compare(other_doc)`.                                                          |
+| `getter`  | callable | Getter function that takes the object and returns an attribute value. Is called when the user accesses the `._` attribute.          |
+| `setter`  | callable | Setter function that takes the `Doc` and a value, and modifies the object. Is called when the user writes to the `Doc._` attribute. |
+
+## Doc.get_extension {#get_extension tag="classmethod" new="2"}
+
+Look up a previously registered extension by name. Returns a 4-tuple
+`(default, method, getter, setter)` if the extension is registered. Raises a
+`KeyError` otherwise.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Doc
+> Doc.set_extension('has_city', default=False)
+> extension = Doc.get_extension('has_city')
+> assert extension == (False, None, None, None)
+> ```
+
+| Name        | Type    | Description                                                   |
+| ----------- | ------- | ------------------------------------------------------------- |
+| `name`      | unicode | Name of the extension.                                        |
+| **RETURNS** | tuple   | A `(default, method, getter, setter)` tuple of the extension. |
+
+## Doc.has_extension {#has_extension tag="classmethod" new="2"}
+
+Check whether an extension has been registered on the `Doc` class.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Doc
+> Doc.set_extension('has_city', default=False)
+> assert Doc.has_extension('has_city')
+> ```
+
+| Name        | Type    | Description                                |
+| ----------- | ------- | ------------------------------------------ |
+| `name`      | unicode | Name of the extension to check.            |
+| **RETURNS** | bool    | Whether the extension has been registered. |
+
+## Doc.remove_extension {#remove_extension tag="classmethod" new="2.0.12"}
+
+Remove a previously registered extension.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Doc
+> Doc.set_extension('has_city', default=False)
+> removed = Doc.remove_extension('has_city')
+> assert not Doc.has_extension('has_city')
+> ```
+
+| Name        | Type    | Description                                                           |
+| ----------- | ------- | --------------------------------------------------------------------- |
+| `name`      | unicode | Name of the extension.                                                |
+| **RETURNS** | tuple   | A `(default, method, getter, setter)` tuple of the removed extension. |
+
+## Doc.char_span {#char_span tag="method" new="2"}
+
+Create a `Span` object from the slice `doc.text[start:end]`. Returns `None` if
+the character indices don't map to a valid span.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like New York")
+> span = doc.char_span(7, 15, label=u"GPE")
+> assert span.text == "New York"
+> ```
+
+| Name        | Type                                     | Description                                             |
+| ----------- | ---------------------------------------- | ------------------------------------------------------- |
+| `start`     | int                                      | The index of the first character of the span.           |
+| `end`       | int                                      | The index of the last character after the span.         |
+| `label`     | uint64 / unicode                         | A label to attach to the Span, e.g. for named entities. |
+| `vector`    | `numpy.ndarray[ndim=1, dtype='float32']` | A meaning representation of the span.                   |
+| **RETURNS** | `Span`                                   | The newly constructed object or `None`.                 |
+
+## Doc.similarity {#similarity tag="method" model="vectors"}
+
+Make a semantic similarity estimate. The default estimate is cosine similarity
+using an average of word vectors.
+
+> #### Example
+>
+> ```python
+> apples = nlp(u"I like apples")
+> oranges = nlp(u"I like oranges")
+> apples_oranges = apples.similarity(oranges)
+> oranges_apples = oranges.similarity(apples)
+> assert apples_oranges == oranges_apples
+> ```
+
+| Name        | Type  | Description                                                                                  |
+| ----------- | ----- | -------------------------------------------------------------------------------------------- |
+| `other`     | -     | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. |
+| **RETURNS** | float | A scalar similarity score. Higher is more similar.                                           |
+
+## Doc.count_by {#count_by tag="method"}
+
+Count the frequencies of a given attribute. Produces a dict of
+`{attr (int): count (ints)}` frequencies, keyed by the values of the given
+attribute ID.
+
+> #### Example
+>
+> ```python
+> from spacy.attrs import ORTH
+> doc = nlp(u"apple apple orange banana")
+> assert doc.count_by(ORTH) == {7024L: 1, 119552L: 1, 2087L: 2}
+> doc.to_array([attrs.ORTH])
+> # array([[11880], [11880], [7561], [12800]])
+> ```
+
+| Name        | Type | Description                                        |
+| ----------- | ---- | -------------------------------------------------- |
+| `attr_id`   | int  | The attribute ID                                   |
+| **RETURNS** | dict | A dictionary mapping attributes to integer counts. |
+
+## Doc.get_lca_matrix {#get_lca_matrix tag="method"}
+
+Calculates the lowest common ancestor matrix for a given `Doc`. Returns LCA
+matrix containing the integer index of the ancestor, or `-1` if no common
+ancestor is found, e.g. if span excludes a necessary ancestor.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"This is a test")
+> matrix = doc.get_lca_matrix()
+> # array([[0, 1, 1, 1], [1, 1, 1, 1], [1, 1, 2, 3], [1, 1, 3, 3]], dtype=int32)
+> ```
+
+| Name        | Type                                   | Description                                     |
+| ----------- | -------------------------------------- | ----------------------------------------------- |
+| **RETURNS** | `numpy.ndarray[ndim=2, dtype='int32']` | The lowest common ancestor matrix of the `Doc`. |
+
+## Doc.to_array {#to_array tag="method"}
+
+Export given token attributes to a numpy `ndarray`. If `attr_ids` is a sequence
+of `M` attributes, the output array will be of shape `(N, M)`, where `N` is the
+length of the `Doc` (in tokens). If `attr_ids` is a single attribute, the output
+shape will be `(N,)`. You can specify attributes by integer ID (e.g.
+`spacy.attrs.LEMMA`) or string name (e.g. 'LEMMA' or 'lemma'). The values will
+be 64-bit integers.
+
+Returns a 2D array with one row per token and one column per attribute (when
+`attr_ids` is a list), or as a 1D numpy array, with one item per attribute (when
+`attr_ids` is a single value).
+
+> #### Example
+>
+> ```python
+> from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
+> doc = nlp(text)
+> # All strings mapped to integers, for easy export to numpy
+> np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
+> np_array = doc.to_array("POS")
+> ```
+
+| Name        | Type                                                                               | Description                                                                                  |
+| ----------- | ---------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- |
+| `attr_ids`  | list or int or string                                                              | A list of attributes (int IDs or string names) or a single attribute (int ID or string name) |
+| **RETURNS** | `numpy.ndarray[ndim=2, dtype='uint64']` or `numpy.ndarray[ndim=1, dtype='uint64']` | The exported attributes as a numpy array.                                                    |
+
+## Doc.from_array {#from_array tag="method"}
+
+Load attributes from a numpy array. Write to a `Doc` object, from an `(M, N)`
+array of attributes.
+
+> #### Example
+>
+> ```python
+> from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
+> from spacy.tokens import Doc
+> doc = nlp(u"Hello world!")
+> np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
+> doc2 = Doc(doc.vocab, words=[t.text for t in doc])
+> doc2.from_array([LOWER, POS, ENT_TYPE, IS_ALPHA], np_array)
+> assert doc[0].pos_ == doc2[0].pos_
+> ```
+
+| Name        | Type                                   | Description                   |
+| ----------- | -------------------------------------- | ----------------------------- |
+| `attrs`     | ints                                   | A list of attribute ID ints.  |
+| `array`     | `numpy.ndarray[ndim=2, dtype='int32']` | The attribute values to load. |
+| **RETURNS** | `Doc`                                  | Itself.                       |
+
+## Doc.to_disk {#to_disk tag="method" new="2"}
+
+Save the current state to a directory.
+
+> #### Example
+>
+> ```python
+> doc.to_disk("/path/to/doc")
+> ```
+
+| Name   | Type             | Description                                                                                                           |
+| ------ | ---------------- | --------------------------------------------------------------------------------------------------------------------- |
+| `path` | unicode / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
+
+## Doc.from_disk {#from_disk tag="method" new="2"}
+
+Loads state from a directory. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Doc
+> from spacy.vocab import Vocab
+> doc = Doc(Vocab()).from_disk("/path/to/doc")
+> ```
+
+| Name        | Type             | Description                                                                |
+| ----------- | ---------------- | -------------------------------------------------------------------------- |
+| `path`      | unicode / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
+| **RETURNS** | `Doc`            | The modified `Doc` object.                                                 |
+
+## Doc.to_bytes {#to_bytes tag="method"}
+
+Serialize, i.e. export the document contents to a binary string.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> doc_bytes = doc.to_bytes()
+> ```
+
+| Name        | Type  | Description                                                           |
+| ----------- | ----- | --------------------------------------------------------------------- |
+| **RETURNS** | bytes | A losslessly serialized copy of the `Doc`, including all annotations. |
+
+## Doc.from_bytes {#from_bytes tag="method"}
+
+Deserialize, i.e. import the document contents from a binary string.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Doc
+> text = u"Give it back! He pleaded."
+> doc = nlp(text)
+> bytes = doc.to_bytes()
+> doc2 = Doc(doc.vocab).from_bytes(bytes)
+> assert doc.text == doc2.text
+> ```
+
+| Name        | Type  | Description              |
+| ----------- | ----- | ------------------------ |
+| `data`      | bytes | The string to load from. |
+| **RETURNS** | `Doc` | The `Doc` object.        |
+
+## Doc.retokenize {#retokenize tag="contextmanager" new="2.1"}
+
+Context manager to handle retokenization of the `Doc`. Modifications to the
+`Doc`'s tokenization are stored, and then made all at once when the context
+manager exits. This is much more efficient, and less error-prone. All views of
+the `Doc` (`Span` and `Token`) created before the retokenization are
+invalidated, although they may accidentally continue to work.
+
+> #### Example
+>
+> ```python
+> doc = nlp("Hello world!")
+> with doc.retokenize() as retokenizer:
+>     retokenizer.merge(doc[0:2])
+> ```
+
+| Name        | Type          | Description      |
+| ----------- | ------------- | ---------------- |
+| **RETURNS** | `Retokenizer` | The retokenizer. |
+
+### Retokenizer.merge {#retokenizer.merge tag="method"}
+
+Mark a span for merging. The `attrs` will be applied to the resulting token.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like David Bowie")
+> with doc.retokenize() as retokenizer:
+>     attrs = {"LEMMA": u"David Bowie"}
+>     retokenizer.merge(doc[2:4], attrs=attrs)
+> ```
+
+| Name    | Type   | Description                            |
+| ------- | ------ | -------------------------------------- |
+| `span`  | `Span` | The span to merge.                     |
+| `attrs` | dict   | Attributes to set on the merged token. |
+
+### Retokenizer.split {#retokenizer.split tag="method"}
+
+Mark a token for splitting, into the specified `orths`. The `heads` are required
+to specify how the new subtokens should be integrated into the dependency tree.
+The list of per-token heads can either be a token in the original document, e.g.
+`doc[2]`, or a tuple consisting of the token in the original document and its
+subtoken index. For example, `(doc[3], 1)` will attach the subtoken to the
+second subtoken of `doc[3]`. This mechanism allows attaching subtokens to other
+newly created subtokens, without having to keep track of the changing token
+indices. If the specified head token will be split within the retokenizer block
+and no subtoken index is specified, it will default to `0`.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I live in NewYork")
+> with doc.retokenize() as retokenizer:
+>     heads = [(doc[3], 1), doc[2]]
+>     attrs = {"POS": ["PROPN", "PROPN"],
+>              "DEP": ["pobj", "compound"]}
+>     retokenizer.split(doc[3], ["New", "York"], heads=heads, attrs=attrs)
+> ```
+
+| Name    | Type    | Description                                                                                                 |
+| ------- | ------- | ----------------------------------------------------------------------------------------------------------- |
+| `token` | `Token` | The token to split.                                                                                         |
+| `orths` | list    | The verbatim text of the split tokens. Needs to match the text of the original token.                       |
+| `heads` | list    | List of `token` or `(token, subtoken)` tuples specifying the tokens to attach the newly split subtokens to. |
+| `attrs` | dict    | Attributes to set on all split tokens. Attribute names mapped to list of per-token attribute values.        |
+
+## Doc.merge {#merge tag="method"}
+
+<Infobox title="Deprecation note" variant="danger">
+
+As of v2.1.0, `Doc.merge` still works but is considered deprecated. You should
+use the new and less error-prone [`Doc.retokenize`](/api/doc#retokenize)
+instead.
+
+</Infobox>
+
+Retokenize the document, such that the span at `doc.text[start_idx : end_idx]`
+is merged into a single token. If `start_idx` and `end_idx` do not mark start
+and end token boundaries, the document remains unchanged.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Los Angeles start.")
+> doc.merge(0, len("Los Angeles"), "NNP", "Los Angeles", "GPE")
+> assert [t.text for t in doc] == [u"Los Angeles", u"start", u"."]
+> ```
+
+| Name           | Type    | Description                                                                                                               |
+| -------------- | ------- | ------------------------------------------------------------------------------------------------------------------------- |
+| `start_idx`    | int     | The character index of the start of the slice to merge.                                                                   |
+| `end_idx`      | int     | The character index after the end of the slice to merge.                                                                  |
+| `**attributes` | -       | Attributes to assign to the merged token. By default, attributes are inherited from the syntactic root token of the span. |
+| **RETURNS**    | `Token` | The newly merged token, or `None` if the start and end indices did not fall at token boundaries                           |
+
+## Doc.ents {#ents tag="property" model="NER"}
+
+Iterate over the entities in the document. Yields named-entity `Span` objects,
+if the entity recognizer has been applied to the document.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Mr. Best flew to New York on Saturday morning.")
+> ents = list(doc.ents)
+> assert ents[0].label == 346
+> assert ents[0].label_ == u"PERSON"
+> assert ents[0].text == u"Mr. Best"
+> ```
+
+| Name       | Type   | Description               |
+| ---------- | ------ | ------------------------- |
+| **YIELDS** | `Span` | Entities in the document. |
+
+## Doc.noun_chunks {#noun_chunks tag="property" model="parser"}
+
+Iterate over the base noun phrases in the document. Yields base noun-phrase
+`Span` objects, if the document has been syntactically parsed. A base noun
+phrase, or "NP chunk", is a noun phrase that does not permit other NPs to be
+nested within it – so no NP-level coordination, no prepositional phrases, and no
+relative clauses.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"A phrase with another phrase occurs.")
+> chunks = list(doc.noun_chunks)
+> assert chunks[0].text == u"A phrase"
+> assert chunks[1].text == u"another phrase"
+> ```
+
+| Name       | Type   | Description                  |
+| ---------- | ------ | ---------------------------- |
+| **YIELDS** | `Span` | Noun chunks in the document. |
+
+## Doc.sents {#sents tag="property" model="parser"}
+
+Iterate over the sentences in the document. Sentence spans have no label. To
+improve accuracy on informal texts, spaCy calculates sentence boundaries from
+the syntactic dependency parse. If the parser is disabled, the `sents` iterator
+will be unavailable.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"This is a sentence. Here's another...")
+> sents = list(doc.sents)
+> assert len(sents) == 2
+> assert [s.root.text for s in sents] == [u"is", u"'s"]
+> ```
+
+| Name       | Type                               | Description |
+| ---------- | ---------------------------------- | ----------- |
+| **YIELDS** | `Span | Sentences in the document. |
+
+## Doc.has_vector {#has_vector tag="property" model="vectors"}
+
+A boolean value indicating whether a word vector is associated with the object.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like apples")
+> assert doc.has_vector
+> ```
+
+| Name        | Type | Description                                      |
+| ----------- | ---- | ------------------------------------------------ |
+| **RETURNS** | bool | Whether the document has a vector data attached. |
+
+## Doc.vector {#vector tag="property" model="vectors"}
+
+A real-valued meaning representation. Defaults to an average of the token
+vectors.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like apples")
+> assert doc.vector.dtype == 'float32'
+> assert doc.vector.shape == (300,)
+> ```
+
+| Name        | Type                                     | Description                                             |
+| ----------- | ---------------------------------------- | ------------------------------------------------------- |
+| **RETURNS** | `numpy.ndarray[ndim=1, dtype='float32']` | A 1D numpy array representing the document's semantics. |
+
+## Doc.vector_norm {#vector_norm tag="property" model="vectors"}
+
+The L2 norm of the document's vector representation.
+
+> #### Example
+>
+> ```python
+> doc1 = nlp(u"I like apples")
+> doc2 = nlp(u"I like oranges")
+> doc1.vector_norm  # 4.54232424414368
+> doc2.vector_norm  # 3.304373298575751
+> assert doc1.vector_norm != doc2.vector_norm
+> ```
+
+| Name        | Type  | Description                               |
+| ----------- | ----- | ----------------------------------------- |
+| **RETURNS** | float | The L2 norm of the vector representation. |
+
+## Attributes {#attributes}
+
+| Name                                | Type         | Description                                                                                                                                                                                                                                                                                |
+| ----------------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `text`                              | unicode      | A unicode representation of the document text.                                                                                                                                                                                                                                             |
+| `text_with_ws`                      | unicode      | An alias of `Doc.text`, provided for duck-type compatibility with `Span` and `Token`.                                                                                                                                                                                                      |
+| `mem`                               | `Pool`       | The document's local memory heap, for all C data it owns.                                                                                                                                                                                                                                  |
+| `vocab`                             | `Vocab`      | The store of lexical types.                                                                                                                                                                                                                                                                |
+| `tensor` <Tag variant="new">2</Tag> | object       | Container for dense vector representations.                                                                                                                                                                                                                                                |
+| `cats` <Tag variant="new">2</Tag>   | dictionary   | Maps either a label to a score for categories applied to whole document, or `(start_char, end_char, label)` to score for categories applied to spans. `start_char` and `end_char` should be character offsets, label can be either a string or an integer ID, and score should be a float. |
+| `user_data`                         | -            | A generic storage area, for user custom data.                                                                                                                                                                                                                                              |
+| `is_tagged`                         | bool         | A flag indicating that the document has been part-of-speech tagged.                                                                                                                                                                                                                        |
+| `is_parsed`                         | bool         | A flag indicating that the document has been syntactically parsed.                                                                                                                                                                                                                         |
+| `is_sentenced`                      | bool         | A flag indicating that sentence boundaries have been applied to the document.                                                                                                                                                                                                              |
+| `sentiment`                         | float        | The document's positivity/negativity score, if available.                                                                                                                                                                                                                                  |
+| `user_hooks`                        | dict         | A dictionary that allows customization of the `Doc`'s properties.                                                                                                                                                                                                                          |
+| `user_token_hooks`                  | dict         | A dictionary that allows customization of properties of `Token` children.                                                                                                                                                                                                                  |
+| `user_span_hooks`                   | dict         | A dictionary that allows customization of properties of `Span` children.                                                                                                                                                                                                                   |
+| `_`                                 | `Underscore` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes).                                                                                                                                                                             |
diff --git a/website/docs/api/entityrecognizer.md b/website/docs/api/entityrecognizer.md
new file mode 100644
index 000000000..e24d2b408
--- /dev/null
+++ b/website/docs/api/entityrecognizer.md
@@ -0,0 +1,311 @@
+---
+title: EntityRecognizer
+tag: class
+source: spacy/pipeline.pyx
+---
+
+This class is a subclass of `Pipe` and follows the same API. The pipeline
+component is available in the [processing pipeline](/usage/processing-pipelines)
+via the ID `"ner"`.
+
+## EntityRecognizer.Model {#model tag="classmethod"}
+
+Initialize a model for the pipe. The model should implement the
+`thinc.neural.Model` API. Wrappers are under development for most major machine
+learning libraries.
+
+| Name        | Type   | Description                           |
+| ----------- | ------ | ------------------------------------- |
+| `**kwargs`  | -      | Parameters for initializing the model |
+| **RETURNS** | object | The initialized model.                |
+
+## EntityRecognizer.\_\_init\_\_ {#init tag="method"}
+
+Create a new pipeline instance. In your application, you would normally use a
+shortcut for this and instantiate the component using its string name and
+[`nlp.create_pipe`](/api/language#create_pipe).
+
+> #### Example
+>
+> ```python
+> # Construction via create_pipe
+> ner = nlp.create_pipe("ner")
+>
+> # Construction from class
+> from spacy.pipeline import EntityRecognizer
+> ner = EntityRecognizer(nlp.vocab)
+> ner.from_disk("/path/to/model")
+> ```
+
+| Name        | Type                           | Description                                                                                                                                           |
+| ----------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `vocab`     | `Vocab`                        | The shared vocabulary.                                                                                                                                |
+| `model`     | `thinc.neural.Model` or `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. |
+| `**cfg`     | -                              | Configuration parameters.                                                                                                                             |
+| **RETURNS** | `EntityRecognizer`             | The newly constructed object.                                                                                                                         |
+
+## EntityRecognizer.\_\_call\_\_ {#call tag="method"}
+
+Apply the pipe to one document. The document is modified in place, and returned.
+Both [`__call__`](/api/entityrecognizer#call) and
+[`pipe`](/api/entityrecognizer#pipe) delegate to the
+[`predict`](/api/entityrecognizer#predict) and
+[`set_annotations`](/api/entityrecognizer#set_annotations) methods.
+
+> #### Example
+>
+> ```python
+> ner = EntityRecognizer(nlp.vocab)
+> doc = nlp(u"This is a sentence.")
+> processed = ner(doc)
+> ```
+
+| Name        | Type  | Description              |
+| ----------- | ----- | ------------------------ |
+| `doc`       | `Doc` | The document to process. |
+| **RETURNS** | `Doc` | The processed document.  |
+
+## EntityRecognizer.pipe {#pipe tag="method"}
+
+Apply the pipe to a stream of documents. Both
+[`__call__`](/api/entityrecognizer#call) and
+[`pipe`](/api/entityrecognizer#pipe) delegate to the
+[`predict`](/api/entityrecognizer#predict) and
+[`set_annotations`](/api/entityrecognizer#set_annotations) methods.
+
+> #### Example
+>
+> ```python
+> texts = [u"One doc", u"...", u"Lots of docs"]
+> ner = EntityRecognizer(nlp.vocab)
+> for doc in ner.pipe(texts, batch_size=50):
+>     pass
+> ```
+
+| Name         | Type     | Description                                                                                                    |
+| ------------ | -------- | -------------------------------------------------------------------------------------------------------------- |
+| `stream`     | iterable | A stream of documents.                                                                                         |
+| `batch_size` | int      | The number of texts to buffer. Defaults to `128`.                                                              |
+| **YIELDS**   | `Doc`    | Processed documents in the order of the original text.                                                         |
+
+## EntityRecognizer.predict {#predict tag="method"}
+
+Apply the pipeline's model to a batch of docs, without modifying them.
+
+> #### Example
+>
+> ```python
+> ner = EntityRecognizer(nlp.vocab)
+> scores = ner.predict([doc1, doc2])
+> ```
+
+| Name        | Type     | Description               |
+| ----------- | -------- | ------------------------- |
+| `docs`      | iterable | The documents to predict. |
+| **RETURNS** | -        | Scores from the model.    |
+
+## EntityRecognizer.set_annotations {#set_annotations tag="method"}
+
+Modify a batch of documents, using pre-computed scores.
+
+> #### Example
+>
+> ```python
+> ner = EntityRecognizer(nlp.vocab)
+> scores = ner.predict([doc1, doc2])
+> ner.set_annotations([doc1, doc2], scores)
+> ```
+
+| Name     | Type     | Description                                                |
+| -------- | -------- | ---------------------------------------------------------- |
+| `docs`   | iterable | The documents to modify.                                   |
+| `scores` | -        | The scores to set, produced by `EntityRecognizer.predict`. |
+
+## EntityRecognizer.update {#update tag="method"}
+
+Learn from a batch of documents and gold-standard information, updating the
+pipe's model. Delegates to [`predict`](/api/entityrecognizer#predict) and
+[`get_loss`](/api/entityrecognizer#get_loss).
+
+> #### Example
+>
+> ```python
+> ner = EntityRecognizer(nlp.vocab)
+> losses = {}
+> optimizer = nlp.begin_training()
+> ner.update([doc1, doc2], [gold1, gold2], losses=losses, sgd=optimizer)
+> ```
+
+| Name     | Type     | Description                                                                                  |
+| -------- | -------- | -------------------------------------------------------------------------------------------- |
+| `docs`   | iterable | A batch of documents to learn from.                                                          |
+| `golds`  | iterable | The gold-standard data. Must have the same length as `docs`.                                 |
+| `drop`   | float    | The dropout rate.                                                                            |
+| `sgd`    | callable | The optimizer. Should take two arguments `weights` and `gradient`, and an optional ID.       |
+| `losses` | dict     | Optional record of the loss during training. The value keyed by the model's name is updated. |
+
+## EntityRecognizer.get_loss {#get_loss tag="method"}
+
+Find the loss and gradient of loss for the batch of documents and their
+predicted scores.
+
+> #### Example
+>
+> ```python
+> ner = EntityRecognizer(nlp.vocab)
+> scores = ner.predict([doc1, doc2])
+> loss, d_loss = ner.get_loss([doc1, doc2], [gold1, gold2], scores)
+> ```
+
+| Name        | Type     | Description                                                  |
+| ----------- | -------- | ------------------------------------------------------------ |
+| `docs`      | iterable | The batch of documents.                                      |
+| `golds`     | iterable | The gold-standard data. Must have the same length as `docs`. |
+| `scores`    | -        | Scores representing the model's predictions.                 |
+| **RETURNS** | tuple    | The loss and the gradient, i.e. `(loss, gradient)`.          |
+
+## EntityRecognizer.begin_training {#begin_training tag="method"}
+
+Initialize the pipe for training, using data examples if available. If no model
+has been initialized yet, the model is added.
+
+> #### Example
+>
+> ```python
+> ner = EntityRecognizer(nlp.vocab)
+> nlp.pipeline.append(ner)
+> optimizer = ner.begin_training(pipeline=nlp.pipeline)
+> ```
+
+| Name          | Type     | Description                                                                                                                                                                                 |
+| ------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `gold_tuples` | iterable | Optional gold-standard annotations from which to construct [`GoldParse`](/api/goldparse) objects.                                                                                           |
+| `pipeline`    | list     | Optional list of pipeline components that this component is part of.                                                                                                                        |
+| `sgd`         | callable | An optional optimizer. Should take two arguments `weights` and `gradient`, and an optional ID. Will be created via [`EntityRecognizer`](/api/entityrecognizer#create_optimizer) if not set. |
+| **RETURNS**   | callable | An optimizer.                                                                                                                                                                               |
+
+## EntityRecognizer.create_optimizer {#create_optimizer tag="method"}
+
+Create an optimizer for the pipeline component.
+
+> #### Example
+>
+> ```python
+> ner = EntityRecognizer(nlp.vocab)
+> optimizer = ner.create_optimizer()
+> ```
+
+| Name        | Type     | Description    |
+| ----------- | -------- | -------------- |
+| **RETURNS** | callable | The optimizer. |
+
+## EntityRecognizer.use_params {#use_params tag="method, contextmanager"}
+
+Modify the pipe's model, to use the given parameter values.
+
+> #### Example
+>
+> ```python
+> ner = EntityRecognizer(nlp.vocab)
+> with ner.use_params():
+>     ner.to_disk('/best_model')
+> ```
+
+| Name     | Type | Description                                                                                                |
+| -------- | ---- | ---------------------------------------------------------------------------------------------------------- |
+| `params` | -    | The parameter values to use in the model. At the end of the context, the original parameters are restored. |
+
+## EntityRecognizer.add_label {#add_label tag="method"}
+
+Add a new label to the pipe.
+
+> #### Example
+>
+> ```python
+> ner = EntityRecognizer(nlp.vocab)
+> ner.add_label('MY_LABEL')
+> ```
+
+| Name    | Type    | Description       |
+| ------- | ------- | ----------------- |
+| `label` | unicode | The label to add. |
+
+## EntityRecognizer.to_disk {#to_disk tag="method"}
+
+Serialize the pipe to disk.
+
+> #### Example
+>
+> ```python
+> ner = EntityRecognizer(nlp.vocab)
+> ner.to_disk('/path/to/ner')
+> ```
+
+| Name   | Type             | Description                                                                                                           |
+| ------ | ---------------- | --------------------------------------------------------------------------------------------------------------------- |
+| `path` | unicode / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
+
+## EntityRecognizer.from_disk {#from_disk tag="method"}
+
+Load the pipe from disk. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> ner = EntityRecognizer(nlp.vocab)
+> ner.from_disk('/path/to/ner')
+> ```
+
+| Name        | Type               | Description                                                                |
+| ----------- | ------------------ | -------------------------------------------------------------------------- |
+| `path`      | unicode / `Path`   | A path to a directory. Paths may be either strings or `Path`-like objects. |
+| **RETURNS** | `EntityRecognizer` | The modified `EntityRecognizer` object.                                    |
+
+## EntityRecognizer.to_bytes {#to_bytes tag="method"}
+
+> #### example
+>
+> ```python
+> ner = EntityRecognizer(nlp.vocab)
+> ner_bytes = ner.to_bytes()
+> ```
+
+Serialize the pipe to a bytestring.
+
+| Name        | Type  | Description                                           |
+| ----------- | ----- | ----------------------------------------------------- |
+| `**exclude` | -     | Named attributes to prevent from being serialized.    |
+| **RETURNS** | bytes | The serialized form of the `EntityRecognizer` object. |
+
+## EntityRecognizer.from_bytes {#from_bytes tag="method"}
+
+Load the pipe from a bytestring. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> ner_bytes = ner.to_bytes()
+> ner = EntityRecognizer(nlp.vocab)
+> ner.from_bytes(ner_bytes)
+> ```
+
+| Name         | Type               | Description                                    |
+| ------------ | ------------------ | ---------------------------------------------- |
+| `bytes_data` | bytes              | The data to load from.                         |
+| `**exclude`  | -                  | Named attributes to prevent from being loaded. |
+| **RETURNS**  | `EntityRecognizer` | The `EntityRecognizer` object.                 |
+
+## EntityRecognizer.labels {#labels tag="property"}
+
+The labels currently added to the component.
+
+> #### Example
+>
+> ```python
+> ner.add_label("MY_LABEL")
+> assert "MY_LABEL" in ner.labels
+> ```
+
+| Name        | Type  | Description                        |
+| ----------- | ----- | ---------------------------------- |
+| **RETURNS** | tuple | The labels added to the component. |
diff --git a/website/docs/api/entityruler.md b/website/docs/api/entityruler.md
new file mode 100644
index 000000000..df2db1a23
--- /dev/null
+++ b/website/docs/api/entityruler.md
@@ -0,0 +1,210 @@
+---
+title: EntityRuler
+tag: class
+source: spacy/pipeline.pyx
+new: 2.1
+---
+
+The EntityRuler lets you add spans to the [`Doc.ents`](/api/doc#ents) using
+token-based rules or exact phrase matches. It can be combined with the
+statistical [`EntityRecognizer`](/api/entityrecognizer) to boost accuracy, or
+used on its own to implement a purely rule-based entity recognition system.
+After initialization, the component is typically added to the processing
+pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
+
+## EntityRuler.\_\_init\_\_ {#init tag="method"}
+
+Initialize the entity ruler. If patterns are supplied here, they need to be a
+list of dictionaries with a `"label"` and `"pattern"` key. A pattern can either
+be a token pattern (list) or a phrase pattern (string). For example:
+`{'label': 'ORG', 'pattern': 'Apple'}`.
+
+> #### Example
+>
+> ```python
+> # Construction via create_pipe
+> ruler = nlp.create_pipe("entityruler")
+>
+> # Construction from class
+> from spacy.pipeline import EntityRuler
+> ruler = EntityRuler(nlp, overwrite_ents=True)
+> ```
+
+| Name             | Type          | Description                                                                                                                                           |
+| ---------------- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `nlp`            | `Language`    | The shared nlp object to pass the vocab to the matchers and process phrase patterns.                                                                  |
+| `patterns`       | iterable      | Optional patterns to load in.                                                                                                                         |
+| `overwrite_ents` | bool          | If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to `False`.                      |
+| `**cfg`          | -             | Other config parameters. If pipeline component is loaded as part of a model pipeline, this will include all keyword arguments passed to `spacy.load`. |
+| **RETURNS**      | `EntityRuler` | The newly constructed object.                                                                                                                         |
+
+## EntityRuler.\_\len\_\_ {#len tag="method"}
+
+The number of all patterns added to the entity ruler.
+
+> #### Example
+>
+> ```python
+> ruler = EntityRuler(nlp)
+> assert len(ruler) == 0
+> ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
+> assert len(ruler) == 1
+> ```
+
+| Name        | Type | Description             |
+| ----------- | ---- | ----------------------- |
+| **RETURNS** | int  | The number of patterns. |
+
+## EntityRuler.\_\_contains\_\_ {#contains tag="method"}
+
+Whether a label is present in the patterns.
+
+> #### Example
+>
+> ```python
+> ruler = EntityRuler(nlp)
+> ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
+> assert "ORG" in ruler
+> assert not "PERSON" in ruler
+> ```
+
+| Name        | Type    | Description                                  |
+| ----------- | ------- | -------------------------------------------- |
+| `label`     | unicode | The label to check.                          |
+| **RETURNS** | bool    | Whether the entity ruler contains the label. |
+
+## EntityRuler.\_\_call\_\_ {#call tag="method"}
+
+Find matches in the `Doc` and add them to the `doc.ents`. Typically, this
+happens automatically after the component has been added to the pipeline using
+[`nlp.add_pipe`](/api/language#add_pipe). If the entity ruler was initialized
+with `overwrite_ents=True`, existing entities will be replaced if they overlap
+with the matches.
+
+> #### Example
+>
+> ```python
+> ruler = EntityRuler(nlp)
+> ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
+> nlp.add_pipe(ruler)
+>
+> doc = nlp("A text about Apple.")
+> ents = [(ent.text, ent.label_) for ent in doc.ents]
+> assert ents == [("Apple", "ORG")]
+> ```
+
+| Name        | Type  | Description                                                  |
+| ----------- | ----- | ------------------------------------------------------------ |
+| `doc`       | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
+| **RETURNS** | `Doc` | The modified `Doc` with added entities, if available.        |
+
+## EntityRuler.add_patterns {#add_patterns tag="method"}
+
+Add patterns to the entity ruler. A pattern can either be a token pattern (list
+of dicts) or a phrase pattern (string). For more details, see the usage guide on
+[rule-based matching](/usage/rule-based-matching).
+
+> #### Example
+>
+> ```python
+> patterns = [
+>     {"label": "ORG", "pattern": "Apple"},
+>     {"label": "GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]}
+> ]
+> ruler = EntityRuler(nlp)
+> ruler.add_patterns(patterns)
+> ```
+
+| Name       | Type | Description          |
+| ---------- | ---- | -------------------- |
+| `patterns` | list | The patterns to add. |
+
+## EntityRuler.to_disk {#to_disk tag="method"}
+
+Save the entity ruler patterns to a directory. The patterns will be saved as
+newline-delimited JSON (JSONL).
+
+> #### Example
+>
+> ```python
+> ruler = EntityRuler(nlp)
+> ruler.to_disk('/path/to/rules.jsonl')
+> ```
+
+| Name   | Type             | Description                                                                                                      |
+| ------ | ---------------- | ---------------------------------------------------------------------------------------------------------------- |
+| `path` | unicode / `Path` | A path to a file, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
+
+## EntityRuler.from_disk {#from_disk tag="method"}
+
+Load the entity ruler from a file. Expects a file containing newline-delimited
+JSON (JSONL) with one entry per line.
+
+> #### Example
+>
+> ```python
+> ruler = EntityRuler(nlp)
+> ruler.from_disk('/path/to/rules.jsonl')
+> ```
+
+| Name        | Type             | Description                                                                 |
+| ----------- | ---------------- | --------------------------------------------------------------------------- |
+| `path`      | unicode / `Path` | A path to a JSONL file. Paths may be either strings or `Path`-like objects. |
+| **RETURNS** | `EntityRuler`    | The modified `EntityRuler` object.                                          |
+
+## EntityRuler.to_bytes {#to_bytes tag="method"}
+
+Serialize the entity ruler patterns to a bytestring.
+
+> #### Example
+>
+> ```python
+> ruler = EntityRuler(nlp)
+> ruler_bytes = ruler.to_bytes()
+> ```
+
+| Name        | Type  | Description              |
+| ----------- | ----- | ------------------------ |
+| **RETURNS** | bytes | The serialized patterns. |
+
+## EntityRuler.from_bytes {#from_bytes tag="method"}
+
+Load the pipe from a bytestring. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> ruler_bytes = ruler.to_bytes()
+> ruler = EntityRuler(nlp)
+> ruler.from_bytes(ruler_bytes)
+> ```
+
+| Name             | Type          | Description                        |
+| ---------------- | ------------- | ---------------------------------- |
+| `patterns_bytes` | bytes         | The bytestring to load.            |
+| **RETURNS**      | `EntityRuler` | The modified `EntityRuler` object. |
+
+## EntityRuler.labels {#labels tag="property"}
+
+All labels present in the match patterns.
+
+| Name        | Type  | Description        |
+| ----------- | ----- | ------------------ |
+| **RETURNS** | tuple | The string labels. |
+
+## EntityRuler.patterns {#patterns tag="property"}
+
+Get all patterns that were added to the entity ruler.
+
+| Name        | Type | Description                                        |
+| ----------- | ---- | -------------------------------------------------- |
+| **RETURNS** | list | The original patterns, one dictionary per pattern. |
+
+## Attributes {#attributes}
+
+| Name              | Type                                  | Description                                                      |
+| ----------------- | ------------------------------------- | ---------------------------------------------------------------- |
+| `matcher`         | [`Matcher`](/api/matcher)             | The underlying matcher used to process token patterns.           |
+| `phrase_matcher`  | [`PhraseMatcher`](/api/phtasematcher) | The underlying phrase matcher, used to process phrase patterns.  |
+| `token_patterns`  | dict                                  | The token patterns present in the entity ruler, keyed by label.  |
+| `phrase_patterns` | dict                                  | The phrase patterns present in the entity ruler, keyed by label. |
diff --git a/website/docs/api/goldcorpus.md b/website/docs/api/goldcorpus.md
new file mode 100644
index 000000000..a18ef4d32
--- /dev/null
+++ b/website/docs/api/goldcorpus.md
@@ -0,0 +1,24 @@
+---
+title: GoldCorpus
+teaser: An annotated corpus, using the JSON file format
+tag: class
+source: spacy/gold.pyx
+new: 2
+---
+
+This class manages annotations for tagging, dependency parsing and NER.
+
+## GoldCorpus.\_\_init\_\_ {#init tag="method"}
+
+Create a `GoldCorpus`. IF the input data is an iterable, each item should be a
+`(text, paragraphs)` tuple, where each paragraph is a tuple
+`(sentences, brackets)`, and each sentence is a tuple
+`(ids, words, tags, heads, ner)`. See the implementation of
+[`gold.read_json_file`](https://github.com/explosion/spaCy/tree/master/spacy/gold.pyx)
+for further details.
+
+| Name        | Type                        | Description                                                  |
+| ----------- | --------------------------- | ------------------------------------------------------------ |
+| `train`     | unicode / `Path` / iterable | Training data, as a path (file or directory) or iterable.    |
+| `dev`       | unicode / `Path` / iterable | Development data, as a path (file or directory) or iterable. |
+| **RETURNS** | `GoldCorpus`                | The newly constructed object.                                |
diff --git a/website/docs/api/goldparse.md b/website/docs/api/goldparse.md
new file mode 100644
index 000000000..f46aa4216
--- /dev/null
+++ b/website/docs/api/goldparse.md
@@ -0,0 +1,125 @@
+---
+title: GoldParse
+teaser: A collection for training annotations
+tag: class
+source: spacy/gold.pyx
+---
+
+## GoldParse.\_\_init\_\_ {#init tag="method"}
+
+Create a `GoldParse`.
+
+| Name        | Type        | Description                                                                                                                                           |
+| ----------- | ----------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `doc`       | `Doc`       | The document the annotations refer to.                                                                                                                |
+| `words`     | iterable    | A sequence of unicode word strings.                                                                                                                   |
+| `tags`      | iterable    | A sequence of strings, representing tag annotations.                                                                                                  |
+| `heads`     | iterable    | A sequence of integers, representing syntactic head offsets.                                                                                          |
+| `deps`      | iterable    | A sequence of strings, representing the syntactic relation types.                                                                                     |
+| `entities`  | iterable    | A sequence of named entity annotations, either as BILUO tag strings, or as `(start_char, end_char, label)` tuples, representing the entity positions. |
+| **RETURNS** | `GoldParse` | The newly constructed object.                                                                                                                         |
+
+## GoldParse.\_\_len\_\_ {#len tag="method"}
+
+Get the number of gold-standard tokens.
+
+| Name        | Type | Description                         |
+| ----------- | ---- | ----------------------------------- |
+| **RETURNS** | int  | The number of gold-standard tokens. |
+
+## GoldParse.is_projective {#is_projective tag="property"}
+
+Whether the provided syntactic annotations form a projective dependency tree.
+
+| Name        | Type | Description                               |
+| ----------- | ---- | ----------------------------------------- |
+| **RETURNS** | bool | Whether annotations form projective tree. |
+
+## Attributes {#attributes}
+
+| Name                              | Type | Description                                                                                                                                              |
+| --------------------------------- | ---- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `tags`                            | list | The part-of-speech tag annotations.                                                                                                                      |
+| `heads`                           | list | The syntactic head annotations.                                                                                                                          |
+| `labels`                          | list | The syntactic relation-type annotations.                                                                                                                 |
+| `ents`                            | list | The named entity annotations.                                                                                                                            |
+| `cand_to_gold`                    | list | The alignment from candidate tokenization to gold tokenization.                                                                                          |
+| `gold_to_cand`                    | list | The alignment from gold tokenization to candidate tokenization.                                                                                          |
+| `cats` <Tag variant="new">2</Tag> | list | Entries in the list should be either a label, or a `(start, end, label)` triple. The tuple form is used for categories applied to spans of the document. |
+
+## Utilities {#util}
+
+### gold.biluo_tags_from_offsets {#biluo_tags_from_offsets tag="function"}
+
+Encode labelled spans into per-token tags, using the
+[BILUO scheme](/api/annotation#biluo) (Begin/In/Last/Unit/Out).
+
+Returns a list of unicode strings, describing the tags. Each tag string will be
+of the form of either `""`, `"O"` or `"{action}-{label}"`, where action is one
+of `"B"`, `"I"`, `"L"`, `"U"`. The string `"-"` is used where the entity offsets
+don't align with the tokenization in the `Doc` object. The training algorithm
+will view these as missing values. `O` denotes a non-entity token. `B` denotes
+the beginning of a multi-token entity, `I` the inside of an entity of three or
+more tokens, and `L` the end of an entity of two or more tokens. `U` denotes a
+single-token entity.
+
+> #### Example
+>
+> ```python
+> from spacy.gold import biluo_tags_from_offsets
+>
+> doc = nlp(u"I like London.")
+> entities = [(7, 13, "LOC")]
+> tags = biluo_tags_from_offsets(doc, entities)
+> assert tags == ["O", "O", "U-LOC", "O"]
+> ```
+
+| Name        | Type     | Description                                                                                                                                     |
+| ----------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
+| `doc`       | `Doc`    | The document that the entity offsets refer to. The output tags will refer to the token boundaries within the document.                          |
+| `entities`  | iterable | A sequence of `(start, end, label)` triples. `start` and `end` should be character-offset integers denoting the slice into the original string. |
+| **RETURNS** | list     | Unicode strings, describing the [BILUO](/api/annotation#biluo) tags.                                                                            |
+
+### gold.offsets_from_biluo_tags {#offsets_from_biluo_tags tag="function"}
+
+Encode per-token tags following the [BILUO scheme](/api/annotation#biluo) into
+entity offsets.
+
+> #### Example
+>
+> ```python
+> from spacy.gold import offsets_from_biluo_tags
+>
+> doc = nlp(u"I like London.")
+> tags = ["O", "O", "U-LOC", "O"]
+> entities = offsets_from_biluo_tags(doc, tags)
+> assert entities == [(7, 13, "LOC")]
+> ```
+
+| Name        | Type     | Description                                                                                                                                                                                                                 |
+| ----------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `doc`       | `Doc`    | The document that the BILUO tags refer to.                                                                                                                                                                                  |
+| `entities`  | iterable | A sequence of [BILUO](/api/annotation#biluo) tags with each tag describing one token. Each tag string will be of the form of either `""`, `"O"` or `"{action}-{label}"`, where action is one of `"B"`, `"I"`, `"L"`, `"U"`. |
+| **RETURNS** | list     | A sequence of `(start, end, label)` triples. `start` and `end` will be character-offset integers denoting the slice into the original string.                                                                               |
+
+### gold.spans_from_biluo_tags {#spans_from_biluo_tags tag="function" new="2.1"}
+
+Encode per-token tags following the [BILUO scheme](/api/annotation#biluo) into
+[`Span`](/api/span) objects. This can be used to create entity spans from
+token-based tags, e.g. to overwrite the `doc.ents`.
+
+> #### Example
+>
+> ```python
+> from spacy.gold import offsets_from_biluo_tags
+>
+> doc = nlp(u"I like London.")
+> tags = ["O", "O", "U-LOC", "O"]
+> doc.ents = spans_from_biluo_tags(doc, tags)
+> ```
+
+| Name        | Type     | Description                                                                                                                                                                                                                 |
+| ----------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `doc`       | `Doc`    | The document that the BILUO tags refer to.                                                                                                                                                                                  |
+| `entities`  | iterable | A sequence of [BILUO](/api/annotation#biluo) tags with each tag describing one token. Each tag string will be of the form of either `""`, `"O"` or `"{action}-{label}"`, where action is one of `"B"`, `"I"`, `"L"`, `"U"`. |
+| **RETURNS** | list     | A sequence of `Span` objects with added entity labels.                                                                                                                                                                      |
diff --git a/website/docs/api/index.md b/website/docs/api/index.md
new file mode 100644
index 000000000..97a7f57c4
--- /dev/null
+++ b/website/docs/api/index.md
@@ -0,0 +1,10 @@
+---
+title: Architecture
+next: /api/annotation
+---
+
+## Library architecture {#architecture}
+
+import Architecture101 from 'usage/101/\_architecture.md'
+
+<Architecture101 />
diff --git a/website/docs/api/language.md b/website/docs/api/language.md
new file mode 100644
index 000000000..1c35082ab
--- /dev/null
+++ b/website/docs/api/language.md
@@ -0,0 +1,440 @@
+---
+title: Language
+teaser: A text-processing pipeline
+tag: class
+source: spacy/language.py
+---
+
+Usually you'll load this once per process as `nlp` and pass the instance around
+your application. The `Language` class is created when you call
+[`spacy.load()`](/api/top-level#spacy.load) and contains the shared vocabulary
+and [language data](/usage/adding-languages), optional model data loaded from a
+[model package](/models) or a path, and a
+[processing pipeline](/usage/processing-pipelines) containing components like
+the tagger or parser that are called on a document in order. You can also add
+your own processing pipeline components that take a `Doc` object, modify it and
+return it.
+
+## Language.\_\_init\_\_ {#init tag="method"}
+
+Initialize a `Language` object.
+
+> #### Example
+>
+> ```python
+> from spacy.vocab import Vocab
+> from spacy.language import Language
+> nlp = Language(Vocab())
+>
+> from spacy.lang.en import English
+> nlp = English()
+> ```
+
+| Name        | Type       | Description                                                                                |
+| ----------- | ---------- | ------------------------------------------------------------------------------------------ |
+| `vocab`     | `Vocab`    | A `Vocab` object. If `True`, a vocab is created via `Language.Defaults.create_vocab`.      |
+| `make_doc`  | callable   | A function that takes text and returns a `Doc` object. Usually a `Tokenizer`.              |
+| `meta`      | dict       | Custom meta data for the `Language` class. Is written to by models to add model meta data. |
+| **RETURNS** | `Language` | The newly constructed object.                                                              |
+
+## Language.\_\_call\_\_ {#call tag="method"}
+
+Apply the pipeline to some text. The text can span multiple sentences, and can
+contain arbitrary whitespace. Alignment into the original string is preserved.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"An example sentence. Another sentence.")
+> assert (doc[0].text, doc[0].head.tag_) == ("An", "NN")
+> ```
+
+| Name        | Type    | Description                                                                       |
+| ----------- | ------- | --------------------------------------------------------------------------------- |
+| `text`      | unicode | The text to be processed.                                                         |
+| `disable`   | list    | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
+| **RETURNS** | `Doc`   | A container for accessing the annotations.                                        |
+
+<Infobox title="Changed in v2.0" variant="warning">
+
+Pipeline components to prevent from being loaded can now be added as a list to
+`disable`, instead of specifying one keyword argument per component.
+
+```diff
+- doc = nlp(u"I don't want parsed", parse=False)
++ doc = nlp(u"I don't want parsed", disable=["parser"])
+```
+
+</Infobox>
+
+## Language.pipe {#pipe tag="method"}
+
+Process texts as a stream, and yield `Doc` objects in order. This is usually
+more efficient than processing texts one-by-one.
+
+<Infobox title="Important note for spaCy v2.0.x" variant="danger">
+
+Early versions of spaCy used simple statistical models that could be efficiently
+multi-threaded, as we were able to entirely release Python's global interpreter
+lock. The multi-threading was controlled using the `n_threads` keyword argument
+to the `.pipe` method. This keyword argument is now deprecated as of v2.1.0.
+Future versions may introduce a `n_process` argument for parallel inference via
+multiprocessing.
+
+</Infobox>
+
+> #### Example
+>
+> ```python
+> texts = [u"One document.", u"...", u"Lots of documents"]
+> for doc in nlp.pipe(texts, batch_size=50):
+>     assert doc.is_parsed
+> ```
+
+| Name         | Type  | Description                                                                                                                                                |
+| ------------ | ----- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `texts`      | -     | A sequence of unicode objects.                                                                                                                             |
+| `as_tuples`  | bool  | If set to `True`, inputs should be a sequence of `(text, context)` tuples. Output will then be a sequence of `(doc, context)` tuples. Defaults to `False`. |
+| `n_threads`  | int   | The number of worker threads to use. If `-1`, OpenMP will decide how many to use at run time. Default is `2`.                                              |
+| `batch_size` | int   | The number of texts to buffer.                                                                                                                             |
+| `disable`    | list  | Names of pipeline components to [disable](/usage/processing-pipelines#disabling).                                                                          |
+| **YIELDS**   | `Doc` | Documents in the order of the original text.                                                                                                               |
+
+## Language.update {#update tag="method"}
+
+Update the models in the pipeline.
+
+> #### Example
+>
+> ```python
+> for raw_text, entity_offsets in train_data:
+>     doc = nlp.make_doc(raw_text)
+>     gold = GoldParse(doc, entities=entity_offsets)
+>     nlp.update([doc], [gold], drop=0.5, sgd=optimizer)
+> ```
+
+| Name        | Type     | Description                                                                                                                                                                                                         |
+| ----------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `docs`      | iterable | A batch of `Doc` objects or unicode. If unicode, a `Doc` object will be created from the text.                                                                                                                      |
+| `golds`     | iterable | A batch of `GoldParse` objects or dictionaries. Dictionaries will be used to create [`GoldParse`](/api/goldparse) objects. For the available keys and their usage, see [`GoldParse.__init__`](/api/goldparse#init). |
+| `drop`      | float    | The dropout rate.                                                                                                                                                                                                   |
+| `sgd`       | callable | An optimizer.                                                                                                                                                                                                       |
+| **RETURNS** | dict     | Results from the update.                                                                                                                                                                                            |
+
+## Language.begin_training {#begin_training tag="method"}
+
+Allocate models, pre-process training data and acquire an optimizer.
+
+> #### Example
+>
+> ```python
+> optimizer = nlp.begin_training(gold_tuples)
+> ```
+
+| Name          | Type     | Description                  |
+| ------------- | -------- | ---------------------------- |
+| `gold_tuples` | iterable | Gold-standard training data. |
+| `**cfg`       | -        | Config parameters.           |
+| **RETURNS**   | callable | An optimizer.                |
+
+## Language.use_params {#use_params tag="contextmanager, method"}
+
+Replace weights of models in the pipeline with those provided in the params
+dictionary. Can be used as a context manager, in which case, models go back to
+their original weights after the block.
+
+> #### Example
+>
+> ```python
+> with nlp.use_params(optimizer.averages):
+>     nlp.to_disk("/tmp/checkpoint")
+> ```
+
+| Name     | Type | Description                                   |
+| -------- | ---- | --------------------------------------------- |
+| `params` | dict | A dictionary of parameters keyed by model ID. |
+| `**cfg`  | -    | Config parameters.                            |
+
+## Language.preprocess_gold {#preprocess_gold tag="method"}
+
+Can be called before training to pre-process gold data. By default, it handles
+nonprojectivity and adds missing tags to the tag map.
+
+| Name         | Type     | Description                              |
+| ------------ | -------- | ---------------------------------------- |
+| `docs_golds` | iterable | Tuples of `Doc` and `GoldParse` objects. |
+| **YIELDS**   | tuple    | Tuples of `Doc` and `GoldParse` objects. |
+
+## Language.create_pipe {#create_pipe tag="method" new="2"}
+
+Create a pipeline component from a factory.
+
+> #### Example
+>
+> ```python
+> parser = nlp.create_pipe("parser")
+> nlp.add_pipe(parser)
+> ```
+
+| Name        | Type     | Description                                                                        |
+| ----------- | -------- | ---------------------------------------------------------------------------------- |
+| `name`      | unicode  | Factory name to look up in [`Language.factories`](/api/language#class-attributes). |
+| `config`    | dict     | Configuration parameters to initialize component.                                  |
+| **RETURNS** | callable | The pipeline component.                                                            |
+
+## Language.add_pipe {#add_pipe tag="method" new="2"}
+
+Add a component to the processing pipeline. Valid components are callables that
+take a `Doc` object, modify it and return it. Only one of `before`, `after`,
+`first` or `last` can be set. Default behavior is `last=True`.
+
+> #### Example
+>
+> ```python
+> def component(doc):
+>     # modify Doc and return it return doc
+>
+> nlp.add_pipe(component, before="ner")
+> nlp.add_pipe(component, name="custom_name", last=True)
+> ```
+
+| Name        | Type     | Description                                                                                                                                                                                                                                            |
+| ----------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `component` | callable | The pipeline component.                                                                                                                                                                                                                                |
+| `name`      | unicode  | Name of pipeline component. Overwrites existing `component.name` attribute if available. If no `name` is set and the component exposes no name attribute, `component.__name__` is used. An error is raised if the name already exists in the pipeline. |
+| `before`    | unicode  | Component name to insert component directly before.                                                                                                                                                                                                    |
+| `after`     | unicode  | Component name to insert component directly after:                                                                                                                                                                                                     |
+| `first`     | bool     | Insert component first / not first in the pipeline.                                                                                                                                                                                                    |
+| `last`      | bool     | Insert component last / not last in the pipeline.                                                                                                                                                                                                      |
+
+## Language.has_pipe {#has_pipe tag="method" new="2"}
+
+Check whether a component is present in the pipeline. Equivalent to
+`name in nlp.pipe_names`.
+
+> #### Example
+>
+> ```python
+> nlp.add_pipe(lambda doc: doc, name="component")
+> assert "component" in nlp.pipe_names
+> assert nlp.has_pipe("component")
+> ```
+
+| Name        | Type    | Description                                              |
+| ----------- | ------- | -------------------------------------------------------- |
+| `name`      | unicode | Name of the pipeline component to check.                 |
+| **RETURNS** | bool    | Whether a component of that name exists in the pipeline. |
+
+## Language.get_pipe {#get_pipe tag="method" new="2"}
+
+Get a pipeline component for a given component name.
+
+> #### Example
+>
+> ```python
+> parser = nlp.get_pipe("parser")
+> custom_component = nlp.get_pipe("custom_component")
+> ```
+
+| Name        | Type     | Description                            |
+| ----------- | -------- | -------------------------------------- |
+| `name`      | unicode  | Name of the pipeline component to get. |
+| **RETURNS** | callable | The pipeline component.                |
+
+## Language.replace_pipe {#replace_pipe tag="method" new="2"}
+
+Replace a component in the pipeline.
+
+> #### Example
+>
+> ```python
+> nlp.replace_pipe("parser", my_custom_parser)
+> ```
+
+| Name        | Type     | Description                       |
+| ----------- | -------- | --------------------------------- |
+| `name`      | unicode  | Name of the component to replace. |
+| `component` | callable | The pipeline component to insert. |
+
+## Language.rename_pipe {#rename_pipe tag="method" new="2"}
+
+Rename a component in the pipeline. Useful to create custom names for
+pre-defined and pre-loaded components. To change the default name of a component
+added to the pipeline, you can also use the `name` argument on
+[`add_pipe`](/api/language#add_pipe).
+
+> #### Example
+>
+> ```python
+> nlp.rename_pipe("parser", "spacy_parser")
+> ```
+
+| Name       | Type    | Description                      |
+| ---------- | ------- | -------------------------------- |
+| `old_name` | unicode | Name of the component to rename. |
+| `new_name` | unicode | New name of the component.       |
+
+## Language.remove_pipe {#remove_pipe tag="method" new="2"}
+
+Remove a component from the pipeline. Returns the removed component name and
+component function.
+
+> #### Example
+>
+> ```python
+> name, component = nlp.remove_pipe("parser")
+> assert name == "parser"
+> ```
+
+| Name        | Type    | Description                                           |
+| ----------- | ------- | ----------------------------------------------------- |
+| `name`      | unicode | Name of the component to remove.                      |
+| **RETURNS** | tuple   | A `(name, component)` tuple of the removed component. |
+
+## Language.disable_pipes {#disable_pipes tag="contextmanager, method" new="2"}
+
+Disable one or more pipeline components. If used as a context manager, the
+pipeline will be restored to the initial state at the end of the block.
+Otherwise, a `DisabledPipes` object is returned, that has a `.restore()` method
+you can use to undo your changes.
+
+> #### Example
+>
+> ```python
+> with nlp.disable_pipes('tagger', 'parser'):
+>     nlp.begin_training()
+>
+> disabled = nlp.disable_pipes('tagger', 'parser')
+> nlp.begin_training()
+> disabled.restore()
+> ```
+
+| Name        | Type            | Description                                                                          |
+| ----------- | --------------- | ------------------------------------------------------------------------------------ |
+| `*disabled` | unicode         | Names of pipeline components to disable.                                             |
+| **RETURNS** | `DisabledPipes` | The disabled pipes that can be restored by calling the object's `.restore()` method. |
+
+## Language.to_disk {#to_disk tag="method" new="2"}
+
+Save the current state to a directory. If a model is loaded, this will **include
+the model**.
+
+> #### Example
+>
+> ```python
+> nlp.to_disk("/path/to/models")
+> ```
+
+| Name      | Type             | Description                                                                                                           |
+| --------- | ---------------- | --------------------------------------------------------------------------------------------------------------------- |
+| `path`    | unicode / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
+| `disable` | list             | Names of pipeline components to [disable](/usage/processing-pipelines#disabling) and prevent from being saved.        |
+
+## Language.from_disk {#from_disk tag="method" new="2"}
+
+Loads state from a directory. Modifies the object in place and returns it. If
+the saved `Language` object contains a model, the model will be loaded. Note
+that this method is commonly used via the subclasses like `English` or `German`
+to make language-specific functionality like the
+[lexical attribute getters](/usage/adding-languages#lex-attrs) available to the
+loaded object.
+
+> #### Example
+>
+> ```python
+> from spacy.language import Language
+> nlp = Language().from_disk("/path/to/model")
+>
+> # using language-specific subclass
+> from spacy.lang.en import English
+> nlp = English().from_disk("/path/to/en_model")
+> ```
+
+| Name        | Type             | Description                                                                       |
+| ----------- | ---------------- | --------------------------------------------------------------------------------- |
+| `path`      | unicode / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects.        |
+| `disable`   | list             | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
+| **RETURNS** | `Language`       | The modified `Language` object.                                                   |
+
+<Infobox title="Changed in v2.0" variant="warning">
+
+As of spaCy v2.0, the `save_to_directory` method has been renamed to `to_disk`,
+to improve consistency across classes. Pipeline components to prevent from being
+loaded can now be added as a list to `disable`, instead of specifying one
+keyword argument per component.
+
+```diff
+- nlp = spacy.load("en", tagger=False, entity=False)
++ nlp = English().from_disk("/model", disable=["tagger', 'ner"])
+```
+
+</Infobox>
+
+## Language.to_bytes {#to_bytes tag="method"}
+
+Serialize the current state to a binary string.
+
+> #### Example
+>
+> ```python
+> nlp_bytes = nlp.to_bytes()
+> ```
+
+| Name        | Type  | Description                                                                                                         |
+| ----------- | ----- | ------------------------------------------------------------------------------------------------------------------- |
+| `disable`   | list  | Names of pipeline components to [disable](/usage/processing-pipelines#disabling) and prevent from being serialized. |
+| **RETURNS** | bytes | The serialized form of the `Language` object.                                                                       |
+
+## Language.from_bytes {#from_bytes tag="method"}
+
+Load state from a binary string. Note that this method is commonly used via the
+subclasses like `English` or `German` to make language-specific functionality
+like the [lexical attribute getters](/usage/adding-languages#lex-attrs)
+available to the loaded object.
+
+> #### Example
+>
+> ```python
+> from spacy.lang.en import English
+> nlp_bytes = nlp.to_bytes()
+> nlp2 = English()
+> nlp2.from_bytes(nlp_bytes)
+> ```
+
+| Name         | Type       | Description                                                                       |
+| ------------ | ---------- | --------------------------------------------------------------------------------- |
+| `bytes_data` | bytes      | The data to load from.                                                            |
+| `disable`    | list       | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
+| **RETURNS**  | `Language` | The `Language` object.                                                            |
+
+<Infobox title="Changed in v2.0" variant="warning">
+
+Pipeline components to prevent from being loaded can now be added as a list to
+`disable`, instead of specifying one keyword argument per component.
+
+```diff
+- nlp = English().from_bytes(bytes, tagger=False, entity=False)
++ nlp = English().from_bytes(bytes, disable=["tagger", "ner"])
+```
+
+</Infobox>
+
+## Attributes {#attributes}
+
+| Name                                    | Type               | Description                                                                                     |
+| --------------------------------------- | ------------------ | ----------------------------------------------------------------------------------------------- |
+| `vocab`                                 | `Vocab`            | A container for the lexical types.                                                              |
+| `tokenizer`                             | `Tokenizer`        | The tokenizer.                                                                                  |
+| `make_doc`                              | `lambda text: Doc` | Create a `Doc` object from unicode text.                                                        |
+| `pipeline`                              | list               | List of `(name, component)` tuples describing the current processing pipeline, in order.        |
+| `pipe_names` <Tag variant="new">2</Tag> | list               | List of pipeline component names, in order.                                                     |
+| `meta`                                  | dict               | Custom meta data for the Language class. If a model is loaded, contains meta data of the model. |
+| `path` <Tag variant="new">2</Tag>       | `Path`             | Path to the model data directory, if a model is loaded. Otherwise `None`.                       |
+
+## Class attributes {#class-attributes}
+
+| Name                                   | Type    | Description                                                                                                                         |
+| -------------------------------------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------- |
+| `Defaults`                             | class   | Settings, data and factory methods for creating the `nlp` object and processing pipeline.                                           |
+| `lang`                                 | unicode | Two-letter language ID, i.e. [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes).                                     |
+| `factories` <Tag variant="new">2</Tag> | dict    | Factories that create pre-defined pipeline components, e.g. the tagger, parser or entity recognizer, keyed by their component name. |
diff --git a/website/docs/api/lemmatizer.md b/website/docs/api/lemmatizer.md
new file mode 100644
index 000000000..7bc2691e5
--- /dev/null
+++ b/website/docs/api/lemmatizer.md
@@ -0,0 +1,98 @@
+---
+title: Lemmatizer
+teaser: Assign the base forms of words
+tag: class
+source: spacy/lemmatizer.py
+---
+
+The `Lemmatizer` supports simple part-of-speech-sensitive suffix rules and
+lookup tables.
+
+## Lemmatizer.\_\_init\_\_ {#init tag="method"}
+
+Create a `Lemmatizer`.
+
+> #### Example
+>
+> ```python
+> from spacy.lemmatizer import Lemmatizer
+> lemmatizer = Lemmatizer()
+> ```
+
+| Name         | Type          | Description                                                |
+| ------------ | ------------- | ---------------------------------------------------------- |
+| `index`      | dict / `None` | Inventory of lemmas in the language.                       |
+| `exceptions` | dict / `None` | Mapping of string forms to lemmas that bypass the `rules`. |
+| `rules`      | dict / `None` | List of suffix rewrite rules.                              |
+| `lookup`     | dict / `None` | Lookup table mapping string to their lemmas.               |
+| **RETURNS**  | `Lemmatizer`  | The newly created object.                                  |
+
+## Lemmatizer.\_\_call\_\_ {#call tag="method"}
+
+Lemmatize a string.
+
+> #### Example
+>
+> ```python
+> from spacy.lemmatizer import Lemmatizer
+> from spacy.lang.en import LEMMA_INDEX, LEMMA_EXC, LEMMA_RULES
+> lemmatizer = Lemmatizer(LEMMA_INDEX, LEMMA_EXC, LEMMA_RULES)
+> lemmas = lemmatizer(u"ducks", u"NOUN")
+> assert lemmas == [u"duck"]
+> ```
+
+| Name         | Type          | Description                                                                                              |
+| ------------ | ------------- | -------------------------------------------------------------------------------------------------------- |
+| `string`     | unicode       | The string to lemmatize, e.g. the token text.                                                            |
+| `univ_pos`   | unicode / int | The token's universal part-of-speech tag.                                                                |
+| `morphology` | dict / `None` | Morphological features following the [Universal Dependencies](http://universaldependencies.org/) scheme. |
+| **RETURNS**  | list          | The available lemmas for the string.                                                                     |
+
+## Lemmatizer.lookup {#lookup tag="method" new="2"}
+
+Look up a lemma in the lookup table, if available. If no lemma is found, the
+original string is returned. Languages can provide a
+[lookup table](/usage/adding-languages#lemmatizer) via the `lemma_lookup`
+variable, set on the individual `Language` class.
+
+> #### Example
+>
+> ```python
+> lookup = {u"going": u"go"}
+> lemmatizer = Lemmatizer(lookup=lookup)
+> assert lemmatizer.lookup(u"going") == u"go"
+> ```
+
+| Name        | Type    | Description                                                       |
+| ----------- | ------- | ----------------------------------------------------------------- |
+| `string`    | unicode | The string to look up.                                            |
+| **RETURNS** | unicode | The lemma if the string was found, otherwise the original string. |
+
+## Lemmatizer.is_base_form {#is_base_form tag="method"}
+
+Check whether we're dealing with an uninflected paradigm, so we can avoid
+lemmatization entirely.
+
+> #### Example
+>
+> ```python
+> pos = "verb"
+> morph = {"VerbForm": "inf"}
+> is_base_form = lemmatizer.is_base_form(pos, morph)
+> assert is_base_form == True
+> ```
+
+| Name         | Type          | Description                                                                             |
+| ------------ | ------------- | --------------------------------------------------------------------------------------- |
+| `univ_pos`   | unicode / int | The token's universal part-of-speech tag.                                               |
+| `morphology` | dict          | The token's morphological features.                                                     |
+| **RETURNS**  | bool          | Whether the token's part-of-speech tag and morphological features describe a base form. |
+
+## Attributes {#attributes}
+
+| Name                                      | Type          | Description                                                |
+| ----------------------------------------- | ------------- | ---------------------------------------------------------- |
+| `index`                                   | dict / `None` | Inventory of lemmas in the language.                       |
+| `exc`                                     | dict / `None` | Mapping of string forms to lemmas that bypass the `rules`. |
+| `rules`                                   | dict / `None` | List of suffix rewrite rules.                              |
+| `lookup_table` <Tag variant="new">2</Tag> | dict / `None` | The lemma lookup table, if available.                      |
diff --git a/website/docs/api/lexeme.md b/website/docs/api/lexeme.md
new file mode 100644
index 000000000..d5e5c54b8
--- /dev/null
+++ b/website/docs/api/lexeme.md
@@ -0,0 +1,166 @@
+---
+title: Lexeme
+teaser: An entry in the vocabulary
+tag: class
+source: spacy/lexeme.pyx
+---
+
+A `Lexeme` has no string context – it's a word type, as opposed to a word token.
+It therefore has no part-of-speech tag, dependency parse, or lemma (if
+lemmatization depends on the part-of-speech tag).
+
+## Lexeme.\_\_init\_\_ {#init tag="method"}
+
+Create a `Lexeme` object.
+
+| Name        | Type     | Description                   |
+| ----------- | -------- | ----------------------------- |
+| `vocab`     | `Vocab`  | The parent vocabulary.        |
+| `orth`      | int      | The orth id of the lexeme.    |
+| **RETURNS** | `Lexeme` | The newly constructed object. |
+
+## Lexeme.set_flag {#set_flag tag="method"}
+
+Change the value of a boolean flag.
+
+> #### Example
+>
+> ```python
+> COOL_FLAG = nlp.vocab.add_flag(lambda text: False)
+> nlp.vocab[u'spaCy'].set_flag(COOL_FLAG, True)
+> ```
+
+| Name      | Type | Description                          |
+| --------- | ---- | ------------------------------------ |
+| `flag_id` | int  | The attribute ID of the flag to set. |
+| `value`   | bool | The new value of the flag.           |
+
+## Lexeme.check_flag {#check_flag tag="method"}
+
+Check the value of a boolean flag.
+
+> #### Example
+>
+> ```python
+> is_my_library = lambda text: text in [u"spaCy", u"Thinc"]
+> MY_LIBRARY = nlp.vocab.add_flag(is_my_library)
+> assert nlp.vocab[u"spaCy"].check_flag(MY_LIBRARY) == True
+> ```
+
+| Name        | Type | Description                            |
+| ----------- | ---- | -------------------------------------- |
+| `flag_id`   | int  | The attribute ID of the flag to query. |
+| **RETURNS** | bool | The value of the flag.                 |
+
+## Lexeme.similarity {#similarity tag="method" model="vectors"}
+
+Compute a semantic similarity estimate. Defaults to cosine over vectors.
+
+> #### Example
+>
+> ```python
+> apple = nlp.vocab[u"apple"]
+> orange = nlp.vocab[u"orange"]
+> apple_orange = apple.similarity(orange)
+> orange_apple = orange.similarity(apple)
+> assert apple_orange == orange_apple
+> ```
+
+| Name        | Type  | Description                                                                                  |
+| ----------- | ----- | -------------------------------------------------------------------------------------------- |
+| other       | -     | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. |
+| **RETURNS** | float | A scalar similarity score. Higher is more similar.                                           |
+
+## Lexeme.has_vector {#has_vector tag="property" model="vectors"}
+
+A boolean value indicating whether a word vector is associated with the lexeme.
+
+> #### Example
+>
+> ```python
+> apple = nlp.vocab[u"apple"]
+> assert apple.has_vector
+> ```
+
+| Name        | Type | Description                                    |
+| ----------- | ---- | ---------------------------------------------- |
+| **RETURNS** | bool | Whether the lexeme has a vector data attached. |
+
+## Lexeme.vector {#vector tag="property" model="vectors"}
+
+A real-valued meaning representation.
+
+> #### Example
+>
+> ```python
+> apple = nlp.vocab[u"apple"]
+> assert apple.vector.dtype == "float32"
+> assert apple.vector.shape == (300,)
+> ```
+
+| Name        | Type                                     | Description                                           |
+| ----------- | ---------------------------------------- | ----------------------------------------------------- |
+| **RETURNS** | `numpy.ndarray[ndim=1, dtype='float32']` | A 1D numpy array representing the lexeme's semantics. |
+
+## Lexeme.vector_norm {#vector_norm tag="property" model="vectors"}
+
+The L2 norm of the lexeme's vector representation.
+
+> #### Example
+>
+> ```python
+> apple = nlp.vocab[u"apple"]
+> pasta = nlp.vocab[u"pasta"]
+> apple.vector_norm  # 7.1346845626831055
+> pasta.vector_norm  # 7.759851932525635
+> assert apple.vector_norm != pasta.vector_norm
+> ```
+
+| Name        | Type  | Description                               |
+| ----------- | ----- | ----------------------------------------- |
+| **RETURNS** | float | The L2 norm of the vector representation. |
+
+## Attributes {#attributes}
+
+| Name                                         | Type    | Description                                                                                                  |
+| -------------------------------------------- | ------- | ------------------------------------------------------------------------------------------------------------ |
+| `vocab`                                      | `Vocab` | The lexeme's vocabulary.                                                                                     |
+| `text`                                       | unicode | Verbatim text content.                                                                                       |
+| `orth`                                       | int     | ID of the verbatim text content.                                                                             |
+| `orth_`                                      | unicode | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. |
+| `lex_id`                                     | int     | ID of the lexeme's lexical type.                                                                             |
+| `rank`                                       | int     | Sequential ID of the lexemes's lexical type, used to index into tables, e.g. for word vectors.               |
+| `flags`                                      | int     | Container of the lexeme's binary flags.                                                                      |
+| `norm`                                       | int     | The lexemes's norm, i.e. a normalized form of the lexeme text.                                               |
+| `norm_`                                      | unicode | The lexemes's norm, i.e. a normalized form of the lexeme text.                                               |
+| `lower`                                      | int     | Lowercase form of the word.                                                                                  |
+| `lower_`                                     | unicode | Lowercase form of the word.                                                                                  |
+| `shape`                                      | int     | Transform of the word's string, to show orthographic features.                                               |
+| `shape_`                                     | unicode | Transform of the word's string, to show orthographic features.                                               |
+| `prefix`                                     | int     | Length-N substring from the start of the word. Defaults to `N=1`.                                            |
+| `prefix_`                                    | unicode | Length-N substring from the start of the word. Defaults to `N=1`.                                            |
+| `suffix`                                     | int     | Length-N substring from the end of the word. Defaults to `N=3`.                                              |
+| `suffix_`                                    | unicode | Length-N substring from the start of the word. Defaults to `N=3`.                                            |
+| `is_alpha`                                   | bool    | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`.                     |
+| `is_ascii`                                   | bool    | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`.      |
+| `is_digit`                                   | bool    | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`.                                    |
+| `is_lower`                                   | bool    | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`.                                           |
+| `is_upper`                                   | bool    | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`.                                           |
+| `is_title`                                   | bool    | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`.                                           |
+| `is_punct`                                   | bool    | Is the lexeme punctuation?                                                                                   |
+| `is_left_punct`                              | bool    | Is the lexeme a left punctuation mark, e.g. `(`?                                                             |
+| `is_right_punct`                             | bool    | Is the lexeme a right punctuation mark, e.g. `)`?                                                            |
+| `is_space`                                   | bool    | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`.                     |
+| `is_bracket`                                 | bool    | Is the lexeme a bracket?                                                                                     |
+| `is_quote`                                   | bool    | Is the lexeme a quotation mark?                                                                              |
+| `is_currency` <Tag variant="new">2.0.8</Tag> | bool    | Is the lexeme a currency symbol?                                                                             |
+| `like_url`                                   | bool    | Does the lexeme resemble a URL?                                                                              |
+| `like_num`                                   | bool    | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc.                                           |
+| `like_email`                                 | bool    | Does the lexeme resemble an email address?                                                                   |
+| `is_oov`                                     | bool    | Is the lexeme out-of-vocabulary?                                                                             |
+| `is_stop`                                    | bool    | Is the lexeme part of a "stop list"?                                                                         |
+| `lang`                                       | int     | Language of the parent vocabulary.                                                                           |
+| `lang_`                                      | unicode | Language of the parent vocabulary.                                                                           |
+| `prob`                                       | float   | Smoothed log probability estimate of the lexeme's type.                                                      |
+| `cluster`                                    | int     | Brown cluster ID.                                                                                            |
+| `sentiment`                                  | float   | A scalar value indicating the positivity or negativity of the lexeme.                                        |
diff --git a/website/docs/api/matcher.md b/website/docs/api/matcher.md
new file mode 100644
index 000000000..d48d38904
--- /dev/null
+++ b/website/docs/api/matcher.md
@@ -0,0 +1,208 @@
+---
+title: Matcher
+teaser: Match sequences of tokens, based on pattern rules
+tag: class
+source: spacy/matcher/matcher.pyx
+---
+
+<Infobox title="Changed in v2.0" variant="warning">
+
+As of spaCy 2.0, `Matcher.add_pattern` and `Matcher.add_entity` are deprecated
+and have been replaced with a simpler [`Matcher.add`](/api/matcher#add) that
+lets you add a list of patterns and a callback for a given match ID.
+`Matcher.get_entity` is now called [`matcher.get`](/api/matcher#get).
+`Matcher.load` (not useful, as it didn't allow specifying callbacks), and
+`Matcher.has_entity` (now redundant) have been removed. The concept of "acceptor
+functions" has also been retired – this logic can now be handled in the callback
+functions.
+
+</Infobox>
+
+## Matcher.\_\_init\_\_ {#init tag="method"}
+
+Create the rule-based `Matcher`. If `validate=True` is set, all patterns added
+to the matcher will be validated against a JSON schema and a `MatchPatternError`
+is raised if problems are found. Those can include incorrect types (e.g. a
+string where an integer is expected) or unexpected property names.
+
+> #### Example
+>
+> ```python
+> from spacy.matcher import Matcher
+> matcher = Matcher(nlp.vocab)
+> ```
+
+| Name                                    | Type      | Description                                                                                 |
+| --------------------------------------- | --------- | ------------------------------------------------------------------------------------------- |
+| `vocab`                                 | `Vocab`   | The vocabulary object, which must be shared with the documents the matcher will operate on. |
+| `validate` <Tag variant="new">2.1</Tag> | bool      | Validate all patterns added to this matcher.                                                |
+| **RETURNS**                             | `Matcher` | The newly constructed object.                                                               |
+
+## Matcher.\_\_call\_\_ {#call tag="method"}
+
+Find all token sequences matching the supplied patterns on the `Doc`.
+
+> #### Example
+>
+> ```python
+> from spacy.matcher import Matcher
+>
+> matcher = Matcher(nlp.vocab)
+> pattern = [{"LOWER": "hello"}, {"LOWER": "world"}]
+> matcher.add("HelloWorld", None, pattern)
+> doc = nlp(u'hello world!')
+> matches = matcher(doc)
+> ```
+
+| Name        | Type  | Description                                                                                                                                                              |
+| ----------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `doc`       | `Doc` | The document to match over.                                                                                                                                              |
+| **RETURNS** | list  | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. The `match_id` is the ID of the added match pattern. |
+
+<Infobox title="Important note" variant="warning">
+
+By default, the matcher **does not perform any action** on matches, like tagging
+matched phrases with entity types. Instead, actions need to be specified when
+**adding patterns or entities**, by passing in a callback function as the
+`on_match` argument on [`add`](/api/matcher#add). This allows you to define
+custom actions per pattern within the same matcher. For example, you might only
+want to merge some entity types, and set custom flags for other matched
+patterns. For more details and examples, see the usage guide on
+[rule-based matching](/usage/linguistic-features#rule-based-matching).
+
+</Infobox>
+
+## Matcher.pipe {#pipe tag="method"}
+
+Match a stream of documents, yielding them in turn.
+
+> #### Example
+>
+> ```python
+> from spacy.matcher import Matcher
+> matcher = Matcher(nlp.vocab)
+> for doc in matcher.pipe(docs, batch_size=50, n_threads=4):
+>     pass
+> ```
+
+| Name                                          | Type     | Description                                                                                                                                                                                                                |
+| --------------------------------------------- | -------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `docs`                                        | iterable | A stream of documents.                                                                                                                                                                                                     |
+| `batch_size`                                  | int      | The number of documents to accumulate into a working set.                                                                                                                                                                  |
+| `n_threads`                                   | int      | The number of threads with which to work on the buffer in parallel, if the `Matcher` implementation supports multi-threading.                                                                                              |
+| `return_matches` <Tag variant="new">2.1</Tag> | bool     | Yield the match lists along with the docs, making results `(doc, matches)` tuples.                                                                                                                                         |
+| `as_tuples`                                   | bool     | Interpret the input stream as `(doc, context)` tuples, and yield `(result, context)` tuples out. If both `return_matches` and `as_tuples` are `True`, the output will be a sequence of `((doc, matches), context)` tuples. |
+| **YIELDS**                                    | `Doc`    | Documents, in order.                                                                                                                                                                                                       |
+
+## Matcher.\_\_len\_\_ {#len tag="method" new="2"}
+
+Get the number of rules added to the matcher. Note that this only returns the
+number of rules (identical with the number of IDs), not the number of individual
+patterns.
+
+> #### Example
+>
+> ```python
+> matcher = Matcher(nlp.vocab)
+> assert len(matcher) == 0
+> matcher.add("Rule", None, [{"ORTH": "test"}])
+> assert len(matcher) == 1
+> ```
+
+| Name        | Type | Description          |
+| ----------- | ---- | -------------------- |
+| **RETURNS** | int  | The number of rules. |
+
+## Matcher.\_\_contains\_\_ {#contains tag="method" new="2"}
+
+Check whether the matcher contains rules for a match ID.
+
+> #### Example
+>
+> ```python
+> matcher = Matcher(nlp.vocab)
+> assert 'Rule' not in matcher
+> matcher.add('Rule', None, [{'ORTH': 'test'}])
+> assert 'Rule' in matcher
+> ```
+
+| Name        | Type    | Description                                           |
+| ----------- | ------- | ----------------------------------------------------- |
+| `key`       | unicode | The match ID.                                         |
+| **RETURNS** | bool    | Whether the matcher contains rules for this match ID. |
+
+## Matcher.add {#add tag="method" new="2"}
+
+Add a rule to the matcher, consisting of an ID key, one or more patterns, and a
+callback function to act on the matches. The callback function will receive the
+arguments `matcher`, `doc`, `i` and `matches`. If a pattern already exists for
+the given ID, the patterns will be extended. An `on_match` callback will be
+overwritten.
+
+> #### Example
+>
+> ```python
+>   def on_match(matcher, doc, id, matches):
+>       print('Matched!', matches)
+>
+>   matcher = Matcher(nlp.vocab)
+>   matcher.add("HelloWorld", on_match, [{"LOWER": "hello"}, {"LOWER": "world"}])
+>   matcher.add("GoogleMaps", on_match, [{"ORTH": "Google"}, {"ORTH": "Maps"}])
+>   doc = nlp(u"HELLO WORLD on Google Maps.")
+>   matches = matcher(doc)
+> ```
+
+| Name        | Type               | Description                                                                                   |
+| ----------- | ------------------ | --------------------------------------------------------------------------------------------- |
+| `match_id`  | unicode            | An ID for the thing you're matching.                                                          |
+| `on_match`  | callable or `None` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. |
+| `*patterns` | list               | Match pattern. A pattern consists of a list of dicts, where each dict describes a token.      |
+
+<Infobox title="Changed in v2.0" variant="warning">
+
+As of spaCy 2.0, `Matcher.add_pattern` and `Matcher.add_entity` are deprecated
+and have been replaced with a simpler [`Matcher.add`](/api/matcher#add) that
+lets you add a list of patterns and a callback for a given match ID.
+
+```diff
+- matcher.add_entity("GoogleNow", on_match=merge_phrases)
+- matcher.add_pattern("GoogleNow", [{ORTH: "Google"}, {ORTH: "Now"}])
++ matcher.add('GoogleNow', merge_phrases, [{"ORTH": "Google"}, {"ORTH": "Now"}])
+```
+
+</Infobox>
+
+## Matcher.remove {#remove tag="method" new="2"}
+
+Remove a rule from the matcher. A `KeyError` is raised if the match ID does not
+exist.
+
+> #### Example
+>
+> ```python
+> matcher.add("Rule", None, [{"ORTH": "test"}])
+> assert "Rule" in matcher
+> matcher.remove("Rule")
+> assert "Rule" not in matcher
+> ```
+
+| Name  | Type    | Description               |
+| ----- | ------- | ------------------------- |
+| `key` | unicode | The ID of the match rule. |
+
+## Matcher.get {#get tag="method" new="2"}
+
+Retrieve the pattern stored for a key. Returns the rule as an
+`(on_match, patterns)` tuple containing the callback and available patterns.
+
+> #### Example
+>
+> ```python
+> matcher.add("Rule", None, [{"ORTH": "test"}])
+> on_match, patterns = matcher.get("Rule")
+> ```
+
+| Name        | Type    | Description                                   |
+| ----------- | ------- | --------------------------------------------- |
+| `key`       | unicode | The ID of the match rule.                     |
+| **RETURNS** | tuple   | The rule, as an `(on_match, patterns)` tuple. |
diff --git a/website/docs/api/phrasematcher.md b/website/docs/api/phrasematcher.md
new file mode 100644
index 000000000..078ba49fc
--- /dev/null
+++ b/website/docs/api/phrasematcher.md
@@ -0,0 +1,155 @@
+---
+title: PhraseMatcher
+teaser: Match sequences of tokens, based on documents
+tag: class
+source: spacy/matcher/phrasematcher.pyx
+new: 2
+---
+
+The `PhraseMatcher` lets you efficiently match large terminology lists. While
+the [`Matcher`](/api/matcher) lets you match sequences based on lists of token
+descriptions, the `PhraseMatcher` accepts match patterns in the form of `Doc`
+objects.
+
+## PhraseMatcher.\_\_init\_\_ {#init tag="method"}
+
+Create the rule-based `PhraseMatcher`. Setting a different `attr` to match on
+will change the token attributes that will be compared to determine a match. By
+default, the incoming `Doc` is checked for sequences of tokens with the same
+`ORTH` value, i.e. the verbatim token text. Matching on the attribute `LOWER`
+will result in case-insensitive matching, since only the lowercase token texts
+are compared. In theory, it's also possible to match on sequences of the same
+part-of-speech tags or dependency labels.
+
+If `validate=True` is set, additional validation is performed when pattern are
+added. At the moment, it will check whether a `Doc` has attributes assigned that
+aren't necessary to produce the matches (for example, part-of-speech tags if the
+`PhraseMatcher` matches on the token text). Since this can often lead to
+significantly worse performance when creating the pattern, a `UserWarning` will
+be shown.
+
+> #### Example
+>
+> ```python
+> from spacy.matcher import PhraseMatcher
+> matcher = PhraseMatcher(nlp.vocab)
+> ```
+
+| Name                                    | Type            | Description                                                                                 |
+| --------------------------------------- | --------------- | ------------------------------------------------------------------------------------------- |
+| `vocab`                                 | `Vocab`         | The vocabulary object, which must be shared with the documents the matcher will operate on. |
+| `attr` <Tag variant="new">2.1</Tag>     | int / unicode   | The token attribute to match on. Defaults to `ORTH`, i.e. the verbatim token text.          |
+| `validate` <Tag variant="new">2.1</Tag> | bool            | Validate patterns added to the matcher.                                                     |
+| **RETURNS**                             | `PhraseMatcher` | The newly constructed object.                                                               |
+
+<Infobox title="Changed in v2.1" variant="warning">
+
+As of v2.1, the `PhraseMatcher` doesn't have a phrase length limit anymore, so
+the `max_length` argument is now deprecated.
+
+</Infobox>
+
+## PhraseMatcher.\_\_call\_\_ {#call tag="method"}
+
+Find all token sequences matching the supplied patterns on the `Doc`.
+
+> #### Example
+>
+> ```python
+> from spacy.matcher import PhraseMatcher
+>
+> matcher = PhraseMatcher(nlp.vocab)
+> matcher.add("OBAMA", None, nlp(u"Barack Obama"))
+> doc = nlp(u"Barack Obama lifts America one last time in emotional farewell")
+> matches = matcher(doc)
+> ```
+
+| Name        | Type  | Description                                                                                                                                                              |
+| ----------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `doc`       | `Doc` | The document to match over.                                                                                                                                              |
+| **RETURNS** | list  | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end]`. The `match_id` is the ID of the added match pattern. |
+
+## PhraseMatcher.pipe {#pipe tag="method"}
+
+Match a stream of documents, yielding them in turn.
+
+> #### Example
+>
+> ```python
+>   from spacy.matcher import PhraseMatcher
+>   matcher = PhraseMatcher(nlp.vocab)
+>   for doc in matcher.pipe(texts, batch_size=50, n_threads=4):
+>       pass
+> ```
+
+| Name         | Type     | Description                                                                                                                         |
+| ------------ | -------- | ----------------------------------------------------------------------------------------------------------------------------------- |
+| `docs`       | iterable | A stream of documents.                                                                                                              |
+| `batch_size` | int      | The number of documents to accumulate into a working set.                                                                           |
+| `n_threads`  | int      | The number of threads with which to work on the buffer in parallel, if the `PhraseMatcher` implementation supports multi-threading. |
+| **YIELDS**   | `Doc`    | Documents, in order.                                                                                                                |
+
+## PhraseMatcher.\_\_len\_\_ {#len tag="method"}
+
+Get the number of rules added to the matcher. Note that this only returns the
+number of rules (identical with the number of IDs), not the number of individual
+patterns.
+
+> #### Example
+>
+> ```python
+>   matcher = PhraseMatcher(nlp.vocab)
+>   assert len(matcher) == 0
+>   matcher.add("OBAMA", None, nlp(u"Barack Obama"))
+>   assert len(matcher) == 1
+> ```
+
+| Name        | Type | Description          |
+| ----------- | ---- | -------------------- |
+| **RETURNS** | int  | The number of rules. |
+
+## PhraseMatcher.\_\_contains\_\_ {#contains tag="method"}
+
+Check whether the matcher contains rules for a match ID.
+
+> #### Example
+>
+> ```python
+>   matcher = PhraseMatcher(nlp.vocab)
+>   assert "OBAMA" not in matcher
+>   matcher.add("OBAMA", None, nlp(u"Barack Obama"))
+>   assert "OBAMA" in matcher
+> ```
+
+| Name        | Type    | Description                                           |
+| ----------- | ------- | ----------------------------------------------------- |
+| `key`       | unicode | The match ID.                                         |
+| **RETURNS** | bool    | Whether the matcher contains rules for this match ID. |
+
+## PhraseMatcher.add {#add tag="method"}
+
+Add a rule to the matcher, consisting of an ID key, one or more patterns, and a
+callback function to act on the matches. The callback function will receive the
+arguments `matcher`, `doc`, `i` and `matches`. If a pattern already exists for
+the given ID, the patterns will be extended. An `on_match` callback will be
+overwritten.
+
+> #### Example
+>
+> ```python
+>   def on_match(matcher, doc, id, matches):
+>       print('Matched!', matches)
+>
+>   matcher = PhraseMatcher(nlp.vocab)
+>   matcher.add("OBAMA", on_match, nlp(u"Barack Obama"))
+>   matcher.add("HEALTH", on_match, nlp(u"health care reform"),
+>                                   nlp(u"healthcare reform"))
+>   doc = nlp(u"Barack Obama urges Congress to find courage to defend his healthcare reforms")
+>   matches = matcher(doc)
+> ```
+
+| Name       | Type               | Description                                                                                   |
+| ---------- | ------------------ | --------------------------------------------------------------------------------------------- |
+| `match_id` | unicode            | An ID for the thing you're matching.                                                          |
+| `on_match` | callable or `None` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. |
+| `*docs`    | list               | `Doc` objects of the phrases to match.                                                        |
diff --git a/website/docs/api/pipeline-functions.md b/website/docs/api/pipeline-functions.md
new file mode 100644
index 000000000..9c0e6ad97
--- /dev/null
+++ b/website/docs/api/pipeline-functions.md
@@ -0,0 +1,119 @@
+---
+title: Pipeline Functions
+teaser: Other built-in pipeline components and helpers
+source: spacy/pipeline.pyx
+menu:
+  - ['merge_noun_chunks', 'merge_noun_chunks']
+  - ['merge_entities', 'merge_entities']
+  - ['merge_subtokens', 'merge_subtokens']
+---
+
+## merge_noun_chunks {#merge_noun_chunks tag="function"}
+
+Merge noun chunks into a single token. Also available via the string name
+`"merge_noun_chunks"`. After initialization, the component is typically added to
+the processing pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
+
+> #### Example
+>
+> ```python
+> texts = [t.token for t in nlp(u"I have a blue car")]
+> assert texts = ["I", "have", "a", "blue", "car"]
+>
+> merge_nps = nlp.create_pipe("merge_noun_chunks")
+> nlp.add_pipe(merge_nps)
+>
+> texts = [t.token for t in nlp(u"I have a blue car")]
+> assert texts == ["I", "have", "a blue car"]
+> ```
+
+<Infobox variant="warning">
+
+Since noun chunks require part-of-speech tags and the dependency parse, make
+sure to add this component _after_ the `"tagger"` and `"parser"` components. By
+default, `nlp.add_pipe` will add components to the end of the pipeline and after
+all other components.
+
+</Infobox>
+
+| Name        | Type  | Description                                                  |
+| ----------- | ----- | ------------------------------------------------------------ |
+| `doc`       | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
+| **RETURNS** | `Doc` | The modified `Doc` with merged noun chunks.                  |
+
+## merge_entities {#merge_entities tag="function"}
+
+Merge named entities into a single token. Also available via the string name
+`"merge_entities"`. After initialization, the component is typically added to
+the processing pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
+
+> #### Example
+>
+> ```python
+> texts = [t.token for t in nlp(u"I like David Bowie")]
+> assert texts = ["I", "like", "David", "Bowie"]
+>
+> merge_ents = nlp.create_pipe("merge_entities")
+> nlp.add_pipe(merge_ents)
+>
+> texts = [t.token for t in nlp(u"I like David Bowie")]
+> assert texts == ["I", "like", "David Bowie"]
+> ```
+
+<Infobox variant="warning">
+
+Since named entities are set by the entity recognizer, make sure to add this
+component _after_ the `"ner"` component. By default, `nlp.add_pipe` will add
+components to the end of the pipeline and after all other components.
+
+</Infobox>
+
+| Name        | Type  | Description                                                  |
+| ----------- | ----- | ------------------------------------------------------------ |
+| `doc`       | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
+| **RETURNS** | `Doc` | The modified `Doc` with merged entities.                     |
+
+## merge_subtokens {#merge_entities tag="function" new="2.1"}
+
+Merge subtokens into a single token. Also available via the string name
+`"merge_entities"`. After initialization, the component is typically added to
+the processing pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
+
+As of v2.1, the parser is able to predict "subtokens" that should be merged into
+one single token later on. This is especially relevant for languages like
+Chinese, Japanese or Korean, where a "word" isn't defined as a
+whitespace-delimited sequence of characters. Under the hood, this component uses
+the [`Matcher`](/api/matcher) to find sequences of tokens with the dependency
+label `"subtok"` and then merges them into a single token.
+
+> #### Example
+>
+> Note that this example assumes a custom Chinese model that oversegments and
+> was trained to predict subtokens.
+>
+> ```python
+> doc = nlp("拜托")
+> print([(token.text, token.dep_) for token in doc])
+> # [('拜', 'subtok'), ('托', 'subtok')]
+>
+> merge_subtok = nlp.create_pipe("merge_subtokens")
+> nlp.add_pipe(merge_subtok)
+>
+> doc = nlp("拜托")
+> print([token.text for token in doc])
+> # ['拜托']
+> ```
+
+<Infobox variant="warning">
+
+Since subtokens are set by the parser, make sure to add this component _after_
+the `"parser"` component. By default, `nlp.add_pipe` will add components to the
+end of the pipeline and after all other components.
+
+</Infobox>
+
+| Name        | Type    | Description                                                  |
+| ----------- | ------- | ------------------------------------------------------------ |
+| `doc`       | `Doc`   | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
+| `label`     | unicode | The subtoken dependency label. Defaults to `"subtok"`.       |
+| **RETURNS** | `Doc`   | The modified `Doc` with merged subtokens.                    |
diff --git a/website/docs/api/sentencesegmenter.md b/website/docs/api/sentencesegmenter.md
new file mode 100644
index 000000000..8090b060d
--- /dev/null
+++ b/website/docs/api/sentencesegmenter.md
@@ -0,0 +1,78 @@
+---
+title: SentenceSegmenter
+tag: class
+source: spacy/pipeline.pyx
+---
+
+A simple spaCy hook, to allow custom sentence boundary detection logic that
+doesn't require the dependency parse. By default, sentence segmentation is
+performed by the [`DependencyParser`](/api/dependencyparser), so the
+`SentenceSegmenter` lets you implement a simpler, rule-based strategy that
+doesn't require a statistical model to be loaded. The component is also
+available via the string name `"sentencizer"`. After initialization, it is
+typically added to the processing pipeline using
+[`nlp.add_pipe`](/api/language#add_pipe).
+
+## SentenceSegmenter.\_\_init\_\_ {#init tag="method"}
+
+Initialize the sentence segmenter. To change the sentence boundary detection
+strategy, pass a generator function `strategy` on initialization, or assign a
+new strategy to the `.strategy` attribute. Sentence detection strategies should
+be generators that take `Doc` objects and yield `Span` objects for each
+sentence.
+
+> #### Example
+>
+> ```python
+> # Construction via create_pipe
+> sentencizer = nlp.create_pipe("sentencizer")
+>
+> # Construction from class
+> from spacy.pipeline import SentenceSegmenter
+> sentencizer = SentenceSegmenter(nlp.vocab)
+> ```
+
+| Name        | Type                | Description                                                 |
+| ----------- | ------------------- | ----------------------------------------------------------- |
+| `vocab`     | `Vocab`             | The shared vocabulary.                                      |
+| `strategy`  | unicode / callable  | The segmentation strategy to use. Defaults to `"on_punct"`. |
+| **RETURNS** | `SentenceSegmenter` | The newly constructed object.                               |
+
+## SentenceSegmenter.\_\_call\_\_ {#call tag="method"}
+
+Apply the sentence segmenter on a `Doc`. Typically, this happens automatically
+after the component has been added to the pipeline using
+[`nlp.add_pipe`](/api/language#add_pipe).
+
+> #### Example
+>
+> ```python
+> from spacy.lang.en import English
+>
+> nlp = English()
+> sentencizer = nlp.create_pipe("sentencizer")
+> nlp.add_pipe(sentencizer)
+> doc = nlp(u"This is a sentence. This is another sentence.")
+> assert list(doc.sents) == 2
+> ```
+
+| Name        | Type  | Description                                                  |
+| ----------- | ----- | ------------------------------------------------------------ |
+| `doc`       | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
+| **RETURNS** | `Doc` | The modified `Doc` with added sentence boundaries.           |
+
+## SentenceSegmenter.split_on_punct {#split_on_punct tag="staticmethod"}
+
+Split the `Doc` on punctuation characters `.`, `!` and `?`. This is the default
+strategy used by the `SentenceSegmenter.`
+
+| Name       | Type   | Description                    |
+| ---------- | ------ | ------------------------------ |
+| `doc`      | `Doc`  | The `Doc` object to process.   |
+| **YIELDS** | `Span` | The sentences in the document. |
+
+## Attributes {#attributes}
+
+| Name       | Type     | Description                                                         |
+| ---------- | -------- | ------------------------------------------------------------------- |
+| `strategy` | callable | The segmentation strategy. Can be overwritten after initialization. |
diff --git a/website/docs/api/span.md b/website/docs/api/span.md
new file mode 100644
index 000000000..bf95bd6f2
--- /dev/null
+++ b/website/docs/api/span.md
@@ -0,0 +1,467 @@
+---
+title: Span
+tag: class
+source: spacy/tokens/span.pyx
+---
+
+A slice from a [`Doc`](/api/doc) object.
+
+## Span.\_\_init\_\_ {#init tag="method"}
+
+Create a Span object from the `slice doc[start : end]`.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> span = doc[1:4]
+> assert [t.text for t in span] ==  [u"it", u"back", u"!"]
+> ```
+
+| Name        | Type                                     | Description                                             |
+| ----------- | ---------------------------------------- | ------------------------------------------------------- |
+| `doc`       | `Doc`                                    | The parent document.                                    |
+| `start`     | int                                      | The index of the first token of the span.               |
+| `end`       | int                                      | The index of the first token after the span.            |
+| `label`     | int                                      | A label to attach to the span, e.g. for named entities. |
+| `vector`    | `numpy.ndarray[ndim=1, dtype='float32']` | A meaning representation of the span.                   |
+| **RETURNS** | `Span`                                   | The newly constructed object.                           |
+
+## Span.\_\_getitem\_\_ {#getitem tag="method"}
+
+Get a `Token` object.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> span = doc[1:4]
+> assert span[1].text == "back"
+> ```
+
+| Name        | Type    | Description                             |
+| ----------- | ------- | --------------------------------------- |
+| `i`         | int     | The index of the token within the span. |
+| **RETURNS** | `Token` | The token at `span[i]`.                 |
+
+Get a `Span` object.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> span = doc[1:4]
+> assert span[1:3].text == u"back!"
+> ```
+
+| Name        | Type   | Description                      |
+| ----------- | ------ | -------------------------------- |
+| `start_end` | tuple  | The slice of the span to get.    |
+| **RETURNS** | `Span` | The span at `span[start : end]`. |
+
+## Span.\_\_iter\_\_ {#iter tag="method"}
+
+Iterate over `Token` objects.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> span = doc[1:4]
+> assert [t.text for t in span] == [u"it", u"back", u"!"]
+> ```
+
+| Name       | Type    | Description       |
+| ---------- | ------- | ----------------- |
+| **YIELDS** | `Token` | A `Token` object. |
+
+## Span.\_\_len\_\_ {#len tag="method"}
+
+Get the number of tokens in the span.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> span = doc[1:4]
+> assert len(span) == 3
+> ```
+
+| Name        | Type | Description                       |
+| ----------- | ---- | --------------------------------- |
+| **RETURNS** | int  | The number of tokens in the span. |
+
+## Span.set_extension {#set_extension tag="classmethod" new="2"}
+
+Define a custom attribute on the `Span` which becomes available via `Span._`.
+For details, see the documentation on
+[custom attributes](/usage/processing-pipelines#custom-components-attributes).
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Span
+> city_getter = lambda span: any(city in span.text for city in (u"New York", u"Paris", u"Berlin"))
+> Span.set_extension("has_city", getter=city_getter)
+> doc = nlp(u"I like New York in Autumn")
+> assert doc[1:4]._.has_city
+> ```
+
+| Name      | Type     | Description                                                                                                                           |
+| --------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------- |
+| `name`    | unicode  | Name of the attribute to set by the extension. For example, `'my_attr'` will be available as `span._.my_attr`.                        |
+| `default` | -        | Optional default value of the attribute if no getter or method is defined.                                                            |
+| `method`  | callable | Set a custom method on the object, for example `span._.compare(other_span)`.                                                          |
+| `getter`  | callable | Getter function that takes the object and returns an attribute value. Is called when the user accesses the `._` attribute.            |
+| `setter`  | callable | Setter function that takes the `Span` and a value, and modifies the object. Is called when the user writes to the `Span._` attribute. |
+
+## Span.get_extension {#get_extension tag="classmethod" new="2"}
+
+Look up a previously registered extension by name. Returns a 4-tuple
+`(default, method, getter, setter)` if the extension is registered. Raises a
+`KeyError` otherwise.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Span
+> Span.set_extension("is_city", default=False)
+> extension = Span.get_extension("is_city")
+> assert extension == (False, None, None, None)
+> ```
+
+| Name        | Type    | Description                                                   |
+| ----------- | ------- | ------------------------------------------------------------- |
+| `name`      | unicode | Name of the extension.                                        |
+| **RETURNS** | tuple   | A `(default, method, getter, setter)` tuple of the extension. |
+
+## Span.has_extension {#has_extension tag="classmethod" new="2"}
+
+Check whether an extension has been registered on the `Span` class.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Span
+> Span.set_extension("is_city", default=False)
+> assert Span.has_extension("is_city")
+> ```
+
+| Name        | Type    | Description                                |
+| ----------- | ------- | ------------------------------------------ |
+| `name`      | unicode | Name of the extension to check.            |
+| **RETURNS** | bool    | Whether the extension has been registered. |
+
+## Span.remove_extension {#remove_extension tag="classmethod" new="2.0.12"}
+
+Remove a previously registered extension.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Span
+> Span.set_extension("is_city", default=False)
+> removed = Span.remove_extension("is_city")
+> assert not Span.has_extension("is_city")
+> ```
+
+| Name        | Type    | Description                                                           |
+| ----------- | ------- | --------------------------------------------------------------------- |
+| `name`      | unicode | Name of the extension.                                                |
+| **RETURNS** | tuple   | A `(default, method, getter, setter)` tuple of the removed extension. |
+
+## Span.similarity {#similarity tag="method" model="vectors"}
+
+Make a semantic similarity estimate. The default estimate is cosine similarity
+using an average of word vectors.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"green apples and red oranges")
+> green_apples = doc[:2]
+> red_oranges = doc[3:]
+> apples_oranges = green_apples.similarity(red_oranges)
+> oranges_apples = red_oranges.similarity(green_apples)
+> assert apples_oranges == oranges_apples
+> ```
+
+| Name        | Type  | Description                                                                                  |
+| ----------- | ----- | -------------------------------------------------------------------------------------------- |
+| `other`     | -     | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. |
+| **RETURNS** | float | A scalar similarity score. Higher is more similar.                                           |
+
+## Span.get_lca_matrix {#get_lca_matrix tag="method"}
+
+Calculates the lowest common ancestor matrix for a given `Span`. Returns LCA
+matrix containing the integer index of the ancestor, or `-1` if no common
+ancestor is found, e.g. if span excludes a necessary ancestor.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like New York in Autumn")
+> span = doc[1:4]
+> matrix = span.get_lca_matrix()
+> # array([[0, 0, 0], [0, 1, 2], [0, 2, 2]], dtype=int32)
+> ```
+
+| Name        | Type                                   | Description                                      |
+| ----------- | -------------------------------------- | ------------------------------------------------ |
+| **RETURNS** | `numpy.ndarray[ndim=2, dtype='int32']` | The lowest common ancestor matrix of the `Span`. |
+
+## Span.to_array {#to_array tag="method" new="2"}
+
+Given a list of `M` attribute IDs, export the tokens to a numpy `ndarray` of
+shape `(N, M)`, where `N` is the length of the document. The values will be
+32-bit integers.
+
+> #### Example
+>
+> ```python
+> from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
+> doc = nlp(u"I like New York in Autumn.")
+> span = doc[2:3]
+> # All strings mapped to integers, for easy export to numpy
+> np_array = span.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
+> ```
+
+| Name        | Type                          | Description                                                                                              |
+| ----------- | ----------------------------- | -------------------------------------------------------------------------------------------------------- |
+| `attr_ids`  | list                          | A list of attribute ID ints.                                                                             |
+| **RETURNS** | `numpy.ndarray[long, ndim=2]` | A feature matrix, with one row per word, and one column per attribute indicated in the input `attr_ids`. |
+
+## Span.merge {#merge tag="method"}
+
+<Infobox title="Deprecation note" variant="danger">
+
+As of v2.1.0, `Span.merge` still works but is considered deprecated. You should
+use the new and less error-prone [`Doc.retokenize`](/api/doc#retokenize)
+instead.
+
+</Infobox>
+
+Retokenize the document, such that the span is merged into a single token.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like New York in Autumn.")
+> span = doc[2:4]
+> span.merge()
+> assert len(doc) == 6
+> assert doc[2].text == u"New York"
+> ```
+
+| Name           | Type    | Description                                                                                                               |
+| -------------- | ------- | ------------------------------------------------------------------------------------------------------------------------- |
+| `**attributes` | -       | Attributes to assign to the merged token. By default, attributes are inherited from the syntactic root token of the span. |
+| **RETURNS**    | `Token` | The newly merged token.                                                                                                   |
+
+## Span.ents {#ents tag="property" new="2.0.12" model="ner"}
+
+Iterate over the entities in the span. Yields named-entity `Span` objects, if
+the entity recognizer has been applied to the parent document.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Mr. Best flew to New York on Saturday morning.")
+> span = doc[0:6]
+> ents = list(span.ents)
+> assert ents[0].label == 346
+> assert ents[0].label_ == "PERSON"
+> assert ents[0].text == u"Mr. Best"
+> ```
+
+| Name       | Type   | Description               |
+| ---------- | ------ | ------------------------- |
+| **YIELDS** | `Span` | Entities in the document. |
+
+## Span.as_doc {#as_doc tag="method"}
+
+Create a new `Doc` object corresponding to the `Span`, with a copy of the data.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like New York in Autumn.")
+> span = doc[2:4]
+> doc2 = span.as_doc()
+> assert doc2.text == u"New York"
+> ```
+
+| Name        | Type  | Description                             |
+| ----------- | ----- | --------------------------------------- |
+| **RETURNS** | `Doc` | A `Doc` object of the `Span`'s content. |
+
+## Span.root {#root tag="property" model="parser"}
+
+The token within the span that's highest in the parse tree. If there's a tie,
+the earliest is preferred.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like New York in Autumn.")
+> i, like, new, york, in_, autumn, dot = range(len(doc))
+> assert doc[new].head.text == u"York"
+> assert doc[york].head.text == u"like"
+> new_york = doc[new:york+1]
+> assert new_york.root.text == u"York"
+> ```
+
+| Name        | Type    | Description     |
+| ----------- | ------- | --------------- |
+| **RETURNS** | `Token` | The root token. |
+
+## Span.lefts {#lefts tag="property" model="parser"}
+
+Tokens that are to the left of the span, whose heads are within the span.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like New York in Autumn.")
+> lefts = [t.text for t in doc[3:7].lefts]
+> assert lefts == [u"New"]
+> ```
+
+| Name       | Type    | Description                          |
+| ---------- | ------- | ------------------------------------ |
+| **YIELDS** | `Token` | A left-child of a token of the span. |
+
+## Span.rights {#rights tag="property" model="parser"}
+
+Tokens that are to the right of the span, whose heads are within the span.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like New York in Autumn.")
+> rights = [t.text for t in doc[2:4].rights]
+> assert rights == [u"in"]
+> ```
+
+| Name       | Type    | Description                           |
+| ---------- | ------- | ------------------------------------- |
+| **YIELDS** | `Token` | A right-child of a token of the span. |
+
+## Span.n_lefts {#n_lefts tag="property" model="parser"}
+
+The number of tokens that are to the left of the span, whose heads are within
+the span.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like New York in Autumn.")
+> assert doc[3:7].n_lefts == 1
+> ```
+
+| Name        | Type | Description                      |
+| ----------- | ---- | -------------------------------- |
+| **RETURNS** | int  | The number of left-child tokens. |
+
+## Span.n_rights {#n_rights tag="property" model="parser"}
+
+The number of tokens that are to the right of the span, whose heads are within
+the span.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like New York in Autumn.")
+> assert doc[2:4].n_rights == 1
+> ```
+
+| Name        | Type | Description                       |
+| ----------- | ---- | --------------------------------- |
+| **RETURNS** | int  | The number of right-child tokens. |
+
+## Span.subtree {#subtree tag="property" model="parser"}
+
+Tokens within the span and tokens which descend from them.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> subtree = [t.text for t in doc[:3].subtree]
+> assert subtree == [u"Give", u"it", u"back", u"!"]
+> ```
+
+| Name       | Type    | Description                                       |
+| ---------- | ------- | ------------------------------------------------- |
+| **YIELDS** | `Token` | A token within the span, or a descendant from it. |
+
+## Span.has_vector {#has_vector tag="property" model="vectors"}
+
+A boolean value indicating whether a word vector is associated with the object.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like apples")
+> assert doc[1:].has_vector
+> ```
+
+| Name        | Type | Description                                  |
+| ----------- | ---- | -------------------------------------------- |
+| **RETURNS** | bool | Whether the span has a vector data attached. |
+
+## Span.vector {#vector tag="property" model="vectors"}
+
+A real-valued meaning representation. Defaults to an average of the token
+vectors.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like apples")
+> assert doc[1:].vector.dtype == "float32"
+> assert doc[1:].vector.shape == (300,)
+> ```
+
+| Name        | Type                                     | Description                                         |
+| ----------- | ---------------------------------------- | --------------------------------------------------- |
+| **RETURNS** | `numpy.ndarray[ndim=1, dtype='float32']` | A 1D numpy array representing the span's semantics. |
+
+## Span.vector_norm {#vector_norm tag="property" model="vectors"}
+
+The L2 norm of the span's vector representation.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like apples")
+> doc[1:].vector_norm # 4.800883928527915
+> doc[2:].vector_norm # 6.895897646384268
+> assert doc[1:].vector_norm != doc[2:].vector_norm
+> ```
+
+| Name        | Type  | Description                               |
+| ----------- | ----- | ----------------------------------------- |
+| **RETURNS** | float | The L2 norm of the vector representation. |
+
+## Attributes {#attributes}
+
+| Name           | Type         | Description                                                                                                    |
+| -------------- | ------------ | -------------------------------------------------------------------------------------------------------------- |
+| `doc`          | `Doc`        | The parent document.                                                                                           |
+| `sent`         | `Span`       | The sentence span that this span is a part of.                                                                 |
+| `start`        | int          | The token offset for the start of the span.                                                                    |
+| `end`          | int          | The token offset for the end of the span.                                                                      |
+| `start_char`   | int          | The character offset for the start of the span.                                                                |
+| `end_char`     | int          | The character offset for the end of the span.                                                                  |
+| `text`         | unicode      | A unicode representation of the span text.                                                                     |
+| `text_with_ws` | unicode      | The text content of the span with a trailing whitespace character if the last token has one.                   |
+| `orth`         | int          | ID of the verbatim text content.                                                                               |
+| `orth_`        | unicode      | Verbatim text content (identical to `Span.text`). Exists mostly for consistency with the other attributes.     |
+| `label`        | int          | The span's label.                                                                                              |
+| `label_`       | unicode      | The span's label.                                                                                              |
+| `lemma_`       | unicode      | The span's lemma.                                                                                              |
+| `ent_id`       | int          | The hash value of the named entity the token is an instance of.                                                |
+| `ent_id_`      | unicode      | The string ID of the named entity the token is an instance of.                                                 |
+| `sentiment`    | float        | A scalar value indicating the positivity or negativity of the span.                                            |
+| `_`            | `Underscore` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). |
diff --git a/website/docs/api/stringstore.md b/website/docs/api/stringstore.md
new file mode 100644
index 000000000..e7184ea95
--- /dev/null
+++ b/website/docs/api/stringstore.md
@@ -0,0 +1,193 @@
+---
+title: StringStore
+tag: class
+source: spacy/strings.pyx
+---
+
+Look up strings by 64-bit hashes. As of v2.0, spaCy uses hash values instead of
+integer IDs. This ensures that strings always map to the same ID, even from
+different `StringStores`.
+
+## StringStore.\_\_init\_\_ {#init tag="method"}
+
+Create the `StringStore`.
+
+> #### Example
+>
+> ```python
+> from spacy.strings import StringStore
+> stringstore = StringStore([u"apple", u"orange"])
+> ```
+
+| Name        | Type          | Description                                        |
+| ----------- | ------------- | -------------------------------------------------- |
+| `strings`   | iterable      | A sequence of unicode strings to add to the store. |
+| **RETURNS** | `StringStore` | The newly constructed object.                      |
+
+## StringStore.\_\_len\_\_ {#len tag="method"}
+
+Get the number of strings in the store.
+
+> #### Example
+>
+> ```python
+> stringstore = StringStore([u"apple", u"orange"])
+> assert len(stringstore) == 2
+> ```
+
+| Name        | Type | Description                         |
+| ----------- | ---- | ----------------------------------- |
+| **RETURNS** | int  | The number of strings in the store. |
+
+## StringStore.\_\_getitem\_\_ {#getitem tag="method"}
+
+Retrieve a string from a given hash, or vice versa.
+
+> #### Example
+>
+> ```python
+> stringstore = StringStore([u"apple", u"orange"])
+> apple_hash = stringstore[u"apple"]
+> assert apple_hash == 8566208034543834098
+> assert stringstore[apple_hash] == u"apple"
+> ```
+
+| Name           | Type                     | Description                |
+| -------------- | ------------------------ | -------------------------- |
+| `string_or_id` | bytes, unicode or uint64 | The value to encode.       |
+| **RETURNS**    | unicode or int           | The value to be retrieved. |
+
+## StringStore.\_\_contains\_\_ {#contains tag="method"}
+
+Check whether a string is in the store.
+
+> #### Example
+>
+> ```python
+> stringstore = StringStore([u"apple", u"orange"])
+> assert u"apple" in stringstore
+> assert not u"cherry" in stringstore
+> ```
+
+| Name        | Type    | Description                            |
+| ----------- | ------- | -------------------------------------- |
+| `string`    | unicode | The string to check.                   |
+| **RETURNS** | bool    | Whether the store contains the string. |
+
+## StringStore.\_\_iter\_\_ {#iter tag="method"}
+
+Iterate over the strings in the store, in order. Note that a newly initialized
+store will always include an empty string `''` at position `0`.
+
+> #### Example
+>
+> ```python
+> stringstore = StringStore([u"apple", u"orange"])
+> all_strings = [s for s in stringstore]
+> assert all_strings == [u"apple", u"orange"]
+> ```
+
+| Name       | Type    | Description            |
+| ---------- | ------- | ---------------------- |
+| **YIELDS** | unicode | A string in the store. |
+
+## StringStore.add {#add tag="method" new="2"}
+
+Add a string to the `StringStore`.
+
+> #### Example
+>
+> ```python
+> stringstore = StringStore([u"apple", u"orange"])
+> banana_hash = stringstore.add(u"banana")
+> assert len(stringstore) == 3
+> assert banana_hash == 2525716904149915114
+> assert stringstore[banana_hash] == u"banana"
+> assert stringstore[u"banana"] == banana_hash
+> ```
+
+| Name        | Type    | Description              |
+| ----------- | ------- | ------------------------ |
+| `string`    | unicode | The string to add.       |
+| **RETURNS** | uint64  | The string's hash value. |
+
+## StringStore.to_disk {#to_disk tag="method" new="2"}
+
+Save the current state to a directory.
+
+> #### Example
+>
+> ```python
+> stringstore.to_disk("/path/to/strings")
+> ```
+
+| Name   | Type             | Description                                                                                                           |
+| ------ | ---------------- | --------------------------------------------------------------------------------------------------------------------- |
+| `path` | unicode / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
+
+## StringStore.from_disk {#from_disk tag="method" new="2"}
+
+Loads state from a directory. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> from spacy.strings import StringStore
+> stringstore = StringStore().from_disk("/path/to/strings")
+> ```
+
+| Name        | Type             | Description                                                                |
+| ----------- | ---------------- | -------------------------------------------------------------------------- |
+| `path`      | unicode / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
+| **RETURNS** | `StringStore`    | The modified `StringStore` object.                                         |
+
+## StringStore.to_bytes {#to_bytes tag="method"}
+
+Serialize the current state to a binary string.
+
+> #### Example
+>
+> ```python
+> store_bytes = stringstore.to_bytes()
+> ```
+
+| Name        | Type  | Description                                        |
+| ----------- | ----- | -------------------------------------------------- |
+| `**exclude` | -     | Named attributes to prevent from being serialized. |
+| **RETURNS** | bytes | The serialized form of the `StringStore` object.   |
+
+## StringStore.from_bytes {#from_bytes tag="method"}
+
+Load state from a binary string.
+
+> #### Example
+>
+> ```python
+> fron spacy.strings import StringStore
+> store_bytes = stringstore.to_bytes()
+> new_store = StringStore().from_bytes(store_bytes)
+> ```
+
+| Name         | Type          | Description                                    |
+| ------------ | ------------- | ---------------------------------------------- |
+| `bytes_data` | bytes         | The data to load from.                         |
+| `**exclude`  | -             | Named attributes to prevent from being loaded. |
+| **RETURNS**  | `StringStore` | The `StringStore` object.                      |
+
+## Utilities {#util}
+
+### strings.hash_string {#hash_string tag="function"}
+
+Get a 64-bit hash for a given string.
+
+> #### Example
+>
+> ```python
+> from spacy.strings import hash_string
+> assert hash_string(u"apple") == 8566208034543834098
+> ```
+
+| Name        | Type    | Description         |
+| ----------- | ------- | ------------------- |
+| `string`    | unicode | The string to hash. |
+| **RETURNS** | uint64  | The hash.           |
diff --git a/website/docs/api/tagger.md b/website/docs/api/tagger.md
new file mode 100644
index 000000000..e2d7c257f
--- /dev/null
+++ b/website/docs/api/tagger.md
@@ -0,0 +1,312 @@
+---
+title: Tagger
+tag: class
+source: spacy/pipeline.pyx
+---
+
+This class is a subclass of `Pipe` and follows the same API. The pipeline
+component is available in the [processing pipeline](/usage/processing-pipelines)
+via the ID `"tagger"`.
+
+## Tagger.Model {#model tag="classmethod"}
+
+Initialize a model for the pipe. The model should implement the
+`thinc.neural.Model` API. Wrappers are under development for most major machine
+learning libraries.
+
+| Name        | Type   | Description                           |
+| ----------- | ------ | ------------------------------------- |
+| `**kwargs`  | -      | Parameters for initializing the model |
+| **RETURNS** | object | The initialized model.                |
+
+## Tagger.\_\_init\_\_ {#init tag="method"}
+
+Create a new pipeline instance. In your application, you would normally use a
+shortcut for this and instantiate the component using its string name and
+[`nlp.create_pipe`](/api/language#create_pipe).
+
+> #### Example
+>
+> ```python
+> # Construction via create_pipe
+> tagger = nlp.create_pipe("tagger")
+>
+> # Construction from class
+> from spacy.pipeline import Tagger
+> tagger = Tagger(nlp.vocab)
+> tagger.from_disk("/path/to/model")
+> ```
+
+| Name        | Type                           | Description                                                                                                                                           |
+| ----------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `vocab`     | `Vocab`                        | The shared vocabulary.                                                                                                                                |
+| `model`     | `thinc.neural.Model` or `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. |
+| `**cfg`     | -                              | Configuration parameters.                                                                                                                             |
+| **RETURNS** | `Tagger`                       | The newly constructed object.                                                                                                                         |
+
+## Tagger.\_\_call\_\_ {#call tag="method"}
+
+Apply the pipe to one document. The document is modified in place, and returned.
+Both [`__call__`](/api/tagger#call) and [`pipe`](/api/tagger#pipe) delegate to
+the [`predict`](/api/tagger#predict) and
+[`set_annotations`](/api/tagger#set_annotations) methods.
+
+> #### Example
+>
+> ```python
+> tagger = Tagger(nlp.vocab)
+> doc = nlp(u"This is a sentence.")
+> processed = tagger(doc)
+> ```
+
+| Name        | Type  | Description              |
+| ----------- | ----- | ------------------------ |
+| `doc`       | `Doc` | The document to process. |
+| **RETURNS** | `Doc` | The processed document.  |
+
+## Tagger.pipe {#pipe tag="method"}
+
+Apply the pipe to a stream of documents. Both [`__call__`](/api/tagger#call) and
+[`pipe`](/api/tagger#pipe) delegate to the [`predict`](/api/tagger#predict) and
+[`set_annotations`](/api/tagger#set_annotations) methods.
+
+> #### Example
+>
+> ```python
+> texts = [u"One doc", u"...", u"Lots of docs"]
+> tagger = Tagger(nlp.vocab)
+> for doc in tagger.pipe(texts, batch_size=50):
+>     pass
+> ```
+
+| Name         | Type     | Description                                                                                                    |
+| ------------ | -------- | -------------------------------------------------------------------------------------------------------------- |
+| `stream`     | iterable | A stream of documents.                                                                                         |
+| `batch_size` | int      | The number of texts to buffer. Defaults to `128`.                                                              |
+| **YIELDS**   | `Doc`    | Processed documents in the order of the original text.                                                         |
+
+## Tagger.predict {#predict tag="method"}
+
+Apply the pipeline's model to a batch of docs, without modifying them.
+
+> #### Example
+>
+> ```python
+> tagger = Tagger(nlp.vocab)
+> scores = tagger.predict([doc1, doc2])
+> ```
+
+| Name        | Type     | Description               |
+| ----------- | -------- | ------------------------- |
+| `docs`      | iterable | The documents to predict. |
+| **RETURNS** | -        | Scores from the model.    |
+
+## Tagger.set_annotations {#set_annotations tag="method"}
+
+Modify a batch of documents, using pre-computed scores.
+
+> #### Example
+>
+> ```python
+> tagger = Tagger(nlp.vocab)
+> scores = tagger.predict([doc1, doc2])
+> tagger.set_annotations([doc1, doc2], scores)
+> ```
+
+| Name     | Type     | Description                                      |
+| -------- | -------- | ------------------------------------------------ |
+| `docs`   | iterable | The documents to modify.                         |
+| `scores` | -        | The scores to set, produced by `Tagger.predict`. |
+
+## Tagger.update {#update tag="method"}
+
+Learn from a batch of documents and gold-standard information, updating the
+pipe's model. Delegates to [`predict`](/api/tagger#predict) and
+[`get_loss`](/api/tagger#get_loss).
+
+> #### Example
+>
+> ```python
+> tagger = Tagger(nlp.vocab)
+> losses = {}
+> optimizer = nlp.begin_training()
+> tagger.update([doc1, doc2], [gold1, gold2], losses=losses, sgd=optimizer)
+> ```
+
+| Name     | Type     | Description                                                                                  |
+| -------- | -------- | -------------------------------------------------------------------------------------------- |
+| `docs`   | iterable | A batch of documents to learn from.                                                          |
+| `golds`  | iterable | The gold-standard data. Must have the same length as `docs`.                                 |
+| `drop`   | float    | The dropout rate.                                                                            |
+| `sgd`    | callable | The optimizer. Should take two arguments `weights` and `gradient`, and an optional ID.       |
+| `losses` | dict     | Optional record of the loss during training. The value keyed by the model's name is updated. |
+
+## Tagger.get_loss {#get_loss tag="method"}
+
+Find the loss and gradient of loss for the batch of documents and their
+predicted scores.
+
+> #### Example
+>
+> ```python
+> tagger = Tagger(nlp.vocab)
+> scores = tagger.predict([doc1, doc2])
+> loss, d_loss = tagger.get_loss([doc1, doc2], [gold1, gold2], scores)
+> ```
+
+| Name        | Type     | Description                                                  |
+| ----------- | -------- | ------------------------------------------------------------ |
+| `docs`      | iterable | The batch of documents.                                      |
+| `golds`     | iterable | The gold-standard data. Must have the same length as `docs`. |
+| `scores`    | -        | Scores representing the model's predictions.                 |
+| **RETURNS** | tuple    | The loss and the gradient, i.e. `(loss, gradient)`.          |
+
+## Tagger.begin_training {#begin_training tag="method"}
+
+Initialize the pipe for training, using data examples if available. If no model
+has been initialized yet, the model is added.
+
+> #### Example
+>
+> ```python
+> tagger = Tagger(nlp.vocab)
+> nlp.pipeline.append(tagger)
+> optimizer = tagger.begin_training(pipeline=nlp.pipeline)
+> ```
+
+| Name          | Type     | Description                                                                                                                                                             |
+| ------------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `gold_tuples` | iterable | Optional gold-standard annotations from which to construct [`GoldParse`](/api/goldparse) objects.                                                                       |
+| `pipeline`    | list     | Optional list of pipeline components that this component is part of.                                                                                                    |
+| `sgd`         | callable | An optional optimizer. Should take two arguments `weights` and `gradient`, and an optional ID. Will be created via [`Tagger`](/api/tagger#create_optimizer) if not set. |
+| **RETURNS**   | callable | An optimizer.                                                                                                                                                           |
+
+## Tagger.create_optimizer {#create_optimizer tag="method"}
+
+Create an optimizer for the pipeline component.
+
+> #### Example
+>
+> ```python
+> tagger = Tagger(nlp.vocab)
+> optimizer = tagger.create_optimizer()
+> ```
+
+| Name        | Type     | Description    |
+| ----------- | -------- | -------------- |
+| **RETURNS** | callable | The optimizer. |
+
+## Tagger.use_params {#use_params tag="method, contextmanager"}
+
+Modify the pipe's model, to use the given parameter values.
+
+> #### Example
+>
+> ```python
+> tagger = Tagger(nlp.vocab)
+> with tagger.use_params():
+>     tagger.to_disk('/best_model')
+> ```
+
+| Name     | Type | Description                                                                                                |
+| -------- | ---- | ---------------------------------------------------------------------------------------------------------- |
+| `params` | -    | The parameter values to use in the model. At the end of the context, the original parameters are restored. |
+
+## Tagger.add_label {#add_label tag="method"}
+
+Add a new label to the pipe.
+
+> #### Example
+>
+> ```python
+> from spacy.symbols import POS
+> tagger = Tagger(nlp.vocab)
+> tagger.add_label('MY_LABEL', {POS: 'NOUN'})
+> ```
+
+| Name     | Type    | Description                                                     |
+| -------- | ------- | --------------------------------------------------------------- |
+| `label`  | unicode | The label to add.                                               |
+| `values` | dict    | Optional values to map to the label, e.g. a tag map dictionary. |
+
+## Tagger.to_disk {#to_disk tag="method"}
+
+Serialize the pipe to disk.
+
+> #### Example
+>
+> ```python
+> tagger = Tagger(nlp.vocab)
+> tagger.to_disk('/path/to/tagger')
+> ```
+
+| Name   | Type             | Description                                                                                                           |
+| ------ | ---------------- | --------------------------------------------------------------------------------------------------------------------- |
+| `path` | unicode / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
+
+## Tagger.from_disk {#from_disk tag="method"}
+
+Load the pipe from disk. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> tagger = Tagger(nlp.vocab)
+> tagger.from_disk('/path/to/tagger')
+> ```
+
+| Name        | Type             | Description                                                                |
+| ----------- | ---------------- | -------------------------------------------------------------------------- |
+| `path`      | unicode / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
+| **RETURNS** | `Tagger`         | The modified `Tagger` object.                                              |
+
+## Tagger.to_bytes {#to_bytes tag="method"}
+
+> #### example
+>
+> ```python
+> tagger = Tagger(nlp.vocab)
+> tagger_bytes = tagger.to_bytes()
+> ```
+
+Serialize the pipe to a bytestring.
+
+| Name        | Type  | Description                                        |
+| ----------- | ----- | -------------------------------------------------- |
+| `**exclude` | -     | Named attributes to prevent from being serialized. |
+| **RETURNS** | bytes | The serialized form of the `Tagger` object.        |
+
+## Tagger.from_bytes {#from_bytes tag="method"}
+
+Load the pipe from a bytestring. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> tagger_bytes = tagger.to_bytes()
+> tagger = Tagger(nlp.vocab)
+> tagger.from_bytes(tagger_bytes)
+> ```
+
+| Name         | Type     | Description                                    |
+| ------------ | -------- | ---------------------------------------------- |
+| `bytes_data` | bytes    | The data to load from.                         |
+| `**exclude`  | -        | Named attributes to prevent from being loaded. |
+| **RETURNS**  | `Tagger` | The `Tagger` object.                           |
+
+## Tagger.labels {#labels tag="property"}
+
+The labels currently added to the component. Note that even for a blank
+component, this will always include the built-in coarse-grained part-of-speech
+tags by default, e.g. `VERB`, `NOUN` and so on.
+
+> #### Example
+>
+> ```python
+> tagger.add_label("MY_LABEL")
+> assert "MY_LABEL" in tagger.labels
+> ```
+
+| Name        | Type  | Description                        |
+| ----------- | ----- | ---------------------------------- |
+| **RETURNS** | tuple | The labels added to the component. |
diff --git a/website/docs/api/textcategorizer.md b/website/docs/api/textcategorizer.md
new file mode 100644
index 000000000..456a92f36
--- /dev/null
+++ b/website/docs/api/textcategorizer.md
@@ -0,0 +1,312 @@
+---
+title: TextCategorizer
+tag: class
+source: spacy/pipeline.pyx
+new: 2
+---
+
+This class is a subclass of `Pipe` and follows the same API. The pipeline
+component is available in the [processing pipeline](/usage/processing-pipelines)
+via the ID `"textcat"`.
+
+## TextCategorizer.Model {#model tag="classmethod"}
+
+Initialize a model for the pipe. The model should implement the
+`thinc.neural.Model` API. Wrappers are under development for most major machine
+learning libraries.
+
+| Name        | Type   | Description                           |
+| ----------- | ------ | ------------------------------------- |
+| `**kwargs`  | -      | Parameters for initializing the model |
+| **RETURNS** | object | The initialized model.                |
+
+## TextCategorizer.\_\_init\_\_ {#init tag="method"}
+
+Create a new pipeline instance. In your application, you would normally use a
+shortcut for this and instantiate the component using its string name and
+[`nlp.create_pipe`](/api/language#create_pipe).
+
+> #### Example
+>
+> ```python
+> # Construction via create_pipe
+> textcat = nlp.create_pipe("textcat")
+>
+> # Construction from class
+> from spacy.pipeline import TextCategorizer
+> textcat = TextCategorizer(nlp.vocab)
+> textcat.from_disk("/path/to/model")
+> ```
+
+| Name        | Type                           | Description                                                                                                                                           |
+| ----------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `vocab`     | `Vocab`                        | The shared vocabulary.                                                                                                                                |
+| `model`     | `thinc.neural.Model` or `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. |
+| `**cfg`     | -                              | Configuration parameters.                                                                                                                             |
+| **RETURNS** | `TextCategorizer`              | The newly constructed object.                                                                                                                         |
+
+## TextCategorizer.\_\_call\_\_ {#call tag="method"}
+
+Apply the pipe to one document. The document is modified in place, and returned.
+Both [`__call__`](/api/textcategorizer#call) and
+[`pipe`](/api/textcategorizer#pipe) delegate to the
+[`predict`](/api/textcategorizer#predict) and
+[`set_annotations`](/api/textcategorizer#set_annotations) methods.
+
+> #### Example
+>
+> ```python
+> textcat = TextCategorizer(nlp.vocab)
+> doc = nlp(u"This is a sentence.")
+> processed = textcat(doc)
+> ```
+
+| Name        | Type  | Description              |
+| ----------- | ----- | ------------------------ |
+| `doc`       | `Doc` | The document to process. |
+| **RETURNS** | `Doc` | The processed document.  |
+
+## TextCategorizer.pipe {#pipe tag="method"}
+
+Apply the pipe to a stream of documents. Both
+[`__call__`](/api/textcategorizer#call) and [`pipe`](/api/textcategorizer#pipe)
+delegate to the [`predict`](/api/textcategorizer#predict) and
+[`set_annotations`](/api/textcategorizer#set_annotations) methods.
+
+> #### Example
+>
+> ```python
+> texts = [u"One doc", u"...", u"Lots of docs"]
+> textcat = TextCategorizer(nlp.vocab)
+> for doc in textcat.pipe(texts, batch_size=50):
+>     pass
+> ```
+
+| Name         | Type     | Description                                                                                                    |
+| ------------ | -------- | -------------------------------------------------------------------------------------------------------------- |
+| `stream`     | iterable | A stream of documents.                                                                                         |
+| `batch_size` | int      | The number of texts to buffer. Defaults to `128`.                                                              |
+| `n_threads`  | int      | The number of worker threads to use. If `-1`, OpenMP will decide how many to use at run time. Default is `-1`. |
+| **YIELDS**   | `Doc`    | Processed documents in the order of the original text.                                                         |
+
+## TextCategorizer.predict {#predict tag="method"}
+
+Apply the pipeline's model to a batch of docs, without modifying them.
+
+> #### Example
+>
+> ```python
+> textcat = TextCategorizer(nlp.vocab)
+> scores = textcat.predict([doc1, doc2])
+> ```
+
+| Name        | Type     | Description               |
+| ----------- | -------- | ------------------------- |
+| `docs`      | iterable | The documents to predict. |
+| **RETURNS** | -        | Scores from the model.    |
+
+## TextCategorizer.set_annotations {#set_annotations tag="method"}
+
+Modify a batch of documents, using pre-computed scores.
+
+> #### Example
+>
+> ```python
+> textcat = TextCategorizer(nlp.vocab)
+> scores = textcat.predict([doc1, doc2])
+> textcat.set_annotations([doc1, doc2], scores)
+> ```
+
+| Name     | Type     | Description                                               |
+| -------- | -------- | --------------------------------------------------------- |
+| `docs`   | iterable | The documents to modify.                                  |
+| `scores` | -        | The scores to set, produced by `TextCategorizer.predict`. |
+
+## TextCategorizer.update {#update tag="method"}
+
+Learn from a batch of documents and gold-standard information, updating the
+pipe's model. Delegates to [`predict`](/api/textcategorizer#predict) and
+[`get_loss`](/api/textcategorizer#get_loss).
+
+> #### Example
+>
+> ```python
+> textcat = TextCategorizer(nlp.vocab)
+> losses = {}
+> optimizer = nlp.begin_training()
+> textcat.update([doc1, doc2], [gold1, gold2], losses=losses, sgd=optimizer)
+> ```
+
+| Name     | Type     | Description                                                                                  |
+| -------- | -------- | -------------------------------------------------------------------------------------------- |
+| `docs`   | iterable | A batch of documents to learn from.                                                          |
+| `golds`  | iterable | The gold-standard data. Must have the same length as `docs`.                                 |
+| `drop`   | float    | The dropout rate.                                                                            |
+| `sgd`    | callable | The optimizer. Should take two arguments `weights` and `gradient`, and an optional ID.       |
+| `losses` | dict     | Optional record of the loss during training. The value keyed by the model's name is updated. |
+
+## TextCategorizer.get_loss {#get_loss tag="method"}
+
+Find the loss and gradient of loss for the batch of documents and their
+predicted scores.
+
+> #### Example
+>
+> ```python
+> textcat = TextCategorizer(nlp.vocab)
+> scores = textcat.predict([doc1, doc2])
+> loss, d_loss = textcat.get_loss([doc1, doc2], [gold1, gold2], scores)
+> ```
+
+| Name        | Type     | Description                                                  |
+| ----------- | -------- | ------------------------------------------------------------ |
+| `docs`      | iterable | The batch of documents.                                      |
+| `golds`     | iterable | The gold-standard data. Must have the same length as `docs`. |
+| `scores`    | -        | Scores representing the model's predictions.                 |
+| **RETURNS** | tuple    | The loss and the gradient, i.e. `(loss, gradient)`.          |
+
+## TextCategorizer.begin_training {#begin_training tag="method"}
+
+Initialize the pipe for training, using data examples if available. If no model
+has been initialized yet, the model is added.
+
+> #### Example
+>
+> ```python
+> textcat = TextCategorizer(nlp.vocab)
+> nlp.pipeline.append(textcat)
+> optimizer = textcat.begin_training(pipeline=nlp.pipeline)
+> ```
+
+| Name          | Type     | Description                                                                                                                                                                               |
+| ------------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `gold_tuples` | iterable | Optional gold-standard annotations from which to construct [`GoldParse`](/api/goldparse) objects.                                                                                         |
+| `pipeline`    | list     | Optional list of pipeline components that this component is part of.                                                                                                                      |
+| `sgd`         | callable | An optional optimizer. Should take two arguments `weights` and `gradient`, and an optional ID. Will be created via [`TextCategorizer`](/api/textcategorizer#create_optimizer) if not set. |
+| **RETURNS**   | callable | An optimizer.                                                                                                                                                                             |
+
+## TextCategorizer.create_optimizer {#create_optimizer tag="method"}
+
+Create an optimizer for the pipeline component.
+
+> #### Example
+>
+> ```python
+> textcat = TextCategorizer(nlp.vocab)
+> optimizer = textcat.create_optimizer()
+> ```
+
+| Name        | Type     | Description    |
+| ----------- | -------- | -------------- |
+| **RETURNS** | callable | The optimizer. |
+
+## TextCategorizer.use_params {#use_params tag="method, contextmanager"}
+
+Modify the pipe's model, to use the given parameter values.
+
+> #### Example
+>
+> ```python
+> textcat = TextCategorizer(nlp.vocab)
+> with textcat.use_params():
+>     textcat.to_disk('/best_model')
+> ```
+
+| Name     | Type | Description                                                                                                |
+| -------- | ---- | ---------------------------------------------------------------------------------------------------------- |
+| `params` | -    | The parameter values to use in the model. At the end of the context, the original parameters are restored. |
+
+## TextCategorizer.add_label {#add_label tag="method"}
+
+Add a new label to the pipe.
+
+> #### Example
+>
+> ```python
+> textcat = TextCategorizer(nlp.vocab)
+> textcat.add_label('MY_LABEL')
+> ```
+
+| Name    | Type    | Description       |
+| ------- | ------- | ----------------- |
+| `label` | unicode | The label to add. |
+
+## TextCategorizer.to_disk {#to_disk tag="method"}
+
+Serialize the pipe to disk.
+
+> #### Example
+>
+> ```python
+> textcat = TextCategorizer(nlp.vocab)
+> textcat.to_disk('/path/to/textcat')
+> ```
+
+| Name   | Type             | Description                                                                                                           |
+| ------ | ---------------- | --------------------------------------------------------------------------------------------------------------------- |
+| `path` | unicode / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
+
+## TextCategorizer.from_disk {#from_disk tag="method"}
+
+Load the pipe from disk. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> textcat = TextCategorizer(nlp.vocab)
+> textcat.from_disk('/path/to/textcat')
+> ```
+
+| Name        | Type              | Description                                                                |
+| ----------- | ----------------- | -------------------------------------------------------------------------- |
+| `path`      | unicode / `Path`  | A path to a directory. Paths may be either strings or `Path`-like objects. |
+| **RETURNS** | `TextCategorizer` | The modified `TextCategorizer` object.                                     |
+
+## TextCategorizer.to_bytes {#to_bytes tag="method"}
+
+> #### example
+>
+> ```python
+> textcat = TextCategorizer(nlp.vocab)
+> textcat_bytes = textcat.to_bytes()
+> ```
+
+Serialize the pipe to a bytestring.
+
+| Name        | Type  | Description                                          |
+| ----------- | ----- | ---------------------------------------------------- |
+| `**exclude` | -     | Named attributes to prevent from being serialized.   |
+| **RETURNS** | bytes | The serialized form of the `TextCategorizer` object. |
+
+## TextCategorizer.from_bytes {#from_bytes tag="method"}
+
+Load the pipe from a bytestring. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> textcat_bytes = textcat.to_bytes()
+> textcat = TextCategorizer(nlp.vocab)
+> textcat.from_bytes(textcat_bytes)
+> ```
+
+| Name         | Type              | Description                                    |
+| ------------ | ----------------- | ---------------------------------------------- |
+| `bytes_data` | bytes             | The data to load from.                         |
+| `**exclude`  | -                 | Named attributes to prevent from being loaded. |
+| **RETURNS**  | `TextCategorizer` | The `TextCategorizer` object.                  |
+
+## TextCategorizer.labels {#labels tag="property"}
+
+The labels currently added to the component.
+
+> #### Example
+>
+> ```python
+> textcat.add_label("MY_LABEL")
+> assert "MY_LABEL" in textcat.labels
+> ```
+
+| Name        | Type  | Description                        |
+| ----------- | ----- | ---------------------------------- |
+| **RETURNS** | tuple | The labels added to the component. |
diff --git a/website/docs/api/token.md b/website/docs/api/token.md
new file mode 100644
index 000000000..5c22eaae3
--- /dev/null
+++ b/website/docs/api/token.md
@@ -0,0 +1,475 @@
+---
+title: Token
+teaser: An individual token — i.e. a word, punctuation symbol, whitespace, etc.
+tag: class
+source: spacy/tokens/token.pyx
+---
+
+## Token.\_\_init\_\_ {#init tag="method"}
+
+Construct a `Token` object.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> token = doc[0]
+> assert token.text == u"Give"
+> ```
+
+| Name        | Type    | Description                                 |
+| ----------- | ------- | ------------------------------------------- |
+| `vocab`     | `Vocab` | A storage container for lexical types.      |
+| `doc`       | `Doc`   | The parent document.                        |
+| `offset`    | int     | The index of the token within the document. |
+| **RETURNS** | `Token` | The newly constructed object.               |
+
+## Token.\_\_len\_\_ {#len tag="method"}
+
+The number of unicode characters in the token, i.e. `token.text`.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> token = doc[0]
+> assert len(token) == 4
+> ```
+
+| Name        | Type | Description                                    |
+| ----------- | ---- | ---------------------------------------------- |
+| **RETURNS** | int  | The number of unicode characters in the token. |
+
+## Token.set_extension {#set_extension tag="classmethod" new="2"}
+
+Define a custom attribute on the `Token` which becomes available via `Token._`.
+For details, see the documentation on
+[custom attributes](/usage/processing-pipelines#custom-components-attributes).
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Token
+> fruit_getter = lambda token: token.text in (u"apple", u"pear", u"banana")
+> Token.set_extension("is_fruit", getter=fruit_getter)
+> doc = nlp(u"I have an apple")
+> assert doc[3]._.is_fruit
+> ```
+
+| Name      | Type     | Description                                                                                                                             |
+| --------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------- |
+| `name`    | unicode  | Name of the attribute to set by the extension. For example, `'my_attr'` will be available as `token._.my_attr`.                         |
+| `default` | -        | Optional default value of the attribute if no getter or method is defined.                                                              |
+| `method`  | callable | Set a custom method on the object, for example `token._.compare(other_token)`.                                                          |
+| `getter`  | callable | Getter function that takes the object and returns an attribute value. Is called when the user accesses the `._` attribute.              |
+| `setter`  | callable | Setter function that takes the `Token` and a value, and modifies the object. Is called when the user writes to the `Token._` attribute. |
+
+## Token.get_extension {#get_extension tag="classmethod" new="2"}
+
+Look up a previously registered extension by name. Returns a 4-tuple
+`(default, method, getter, setter)` if the extension is registered. Raises a
+`KeyError` otherwise.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Token
+> Token.set_extension("is_fruit", default=False)
+> extension = Token.get_extension("is_fruit")
+> assert extension == (False, None, None, None)
+> ```
+
+| Name        | Type    | Description                                                   |
+| ----------- | ------- | ------------------------------------------------------------- |
+| `name`      | unicode | Name of the extension.                                        |
+| **RETURNS** | tuple   | A `(default, method, getter, setter)` tuple of the extension. |
+
+## Token.has_extension {#has_extension tag="classmethod" new="2"}
+
+Check whether an extension has been registered on the `Token` class.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Token
+> Token.set_extension("is_fruit", default=False)
+> assert Token.has_extension("is_fruit")
+> ```
+
+| Name        | Type    | Description                                |
+| ----------- | ------- | ------------------------------------------ |
+| `name`      | unicode | Name of the extension to check.            |
+| **RETURNS** | bool    | Whether the extension has been registered. |
+
+## Token.remove_extension {#remove_extension tag="classmethod" new=""2.0.11""}
+
+Remove a previously registered extension.
+
+> #### Example
+>
+> ```python
+> from spacy.tokens import Token
+> Token.set_extension("is_fruit", default=False)
+> removed = Token.remove_extension("is_fruit")
+> assert not Token.has_extension("is_fruit")
+> ```
+
+| Name        | Type    | Description                                                           |
+| ----------- | ------- | --------------------------------------------------------------------- |
+| `name`      | unicode | Name of the extension.                                                |
+| **RETURNS** | tuple   | A `(default, method, getter, setter)` tuple of the removed extension. |
+
+## Token.check_flag {#check_flag tag="method"}
+
+Check the value of a boolean flag.
+
+> #### Example
+>
+> ```python
+> from spacy.attrs import IS_TITLE
+> doc = nlp(u"Give it back! He pleaded.")
+> token = doc[0]
+> assert token.check_flag(IS_TITLE) == True
+> ```
+
+| Name        | Type | Description                            |
+| ----------- | ---- | -------------------------------------- |
+| `flag_id`   | int  | The attribute ID of the flag to check. |
+| **RETURNS** | bool | Whether the flag is set.               |
+
+## Token.similarity {#similarity tag="method" model="vectors"}
+
+Compute a semantic similarity estimate. Defaults to cosine over vectors.
+
+> #### Example
+>
+> ```python
+> apples, _, oranges = nlp(u"apples and oranges")
+> apples_oranges = apples.similarity(oranges)
+> oranges_apples = oranges.similarity(apples)
+> assert apples_oranges == oranges_apples
+> ```
+
+| Name        | Type  | Description                                                                                  |
+| ----------- | ----- | -------------------------------------------------------------------------------------------- |
+| other       | -     | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. |
+| **RETURNS** | float | A scalar similarity score. Higher is more similar.                                           |
+
+## Token.nbor {#nbor tag="method"}
+
+Get a neighboring token.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> give_nbor = doc[0].nbor()
+> assert give_nbor.text == u"it"
+> ```
+
+| Name        | Type    | Description                                                 |
+| ----------- | ------- | ----------------------------------------------------------- |
+| `i`         | int     | The relative position of the token to get. Defaults to `1`. |
+| **RETURNS** | `Token` | The token at position `self.doc[self.i+i]`.                 |
+
+## Token.is_ancestor {#is_ancestor tag="method" model="parser"}
+
+Check whether this token is a parent, grandparent, etc. of another in the
+dependency tree.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> give = doc[0]
+> it = doc[1]
+> assert give.is_ancestor(it)
+> ```
+
+| Name        | Type    | Description                                           |
+| ----------- | ------- | ----------------------------------------------------- |
+| descendant  | `Token` | Another token.                                        |
+| **RETURNS** | bool    | Whether this token is the ancestor of the descendant. |
+
+## Token.ancestors {#ancestors tag="property" model="parser"}
+
+The rightmost token of this token's syntactic descendants.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> it_ancestors = doc[1].ancestors
+> assert [t.text for t in it_ancestors] == [u"Give"]
+> he_ancestors = doc[4].ancestors
+> assert [t.text for t in he_ancestors] == [u"pleaded"]
+> ```
+
+| Name       | Type    | Description                                                           |
+| ---------- | ------- | --------------------------------------------------------------------- |
+| **YIELDS** | `Token` | A sequence of ancestor tokens such that `ancestor.is_ancestor(self)`. |
+
+## Token.conjuncts {#conjuncts tag="property" model="parser"}
+
+A sequence of coordinated tokens, including the token itself.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like apples and oranges")
+> apples_conjuncts = doc[2].conjuncts
+> assert [t.text for t in apples_conjuncts] == [u"oranges"]
+> ```
+
+| Name       | Type    | Description          |
+| ---------- | ------- | -------------------- |
+| **YIELDS** | `Token` | A coordinated token. |
+
+## Token.children {#children tag="property" model="parser"}
+
+A sequence of the token's immediate syntactic children.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> give_children = doc[0].children
+> assert [t.text for t in give_children] == [u"it", u"back", u"!"]
+> ```
+
+| Name       | Type    | Description                                 |
+| ---------- | ------- | ------------------------------------------- |
+| **YIELDS** | `Token` | A child token such that `child.head==self`. |
+
+## Token.lefts {#lefts tag="property" model="parser"}
+
+The leftward immediate children of the word, in the syntactic dependency parse.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like New York in Autumn.")
+> lefts = [t.text for t in doc[3].lefts]
+> assert lefts == [u'New']
+> ```
+
+| Name       | Type    | Description                |
+| ---------- | ------- | -------------------------- |
+| **YIELDS** | `Token` | A left-child of the token. |
+
+## Token.rights {#rights tag="property" model="parser"}
+
+The rightward immediate children of the word, in the syntactic dependency parse.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like New York in Autumn.")
+> rights = [t.text for t in doc[3].rights]
+> assert rights == [u"in"]
+> ```
+
+| Name       | Type    | Description                 |
+| ---------- | ------- | --------------------------- |
+| **YIELDS** | `Token` | A right-child of the token. |
+
+## Token.n_lefts {#n_lefts tag="property" model="parser"}
+
+The number of leftward immediate children of the word, in the syntactic
+dependency parse.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like New York in Autumn.")
+> assert doc[3].n_lefts == 1
+> ```
+
+| Name        | Type | Description                      |
+| ----------- | ---- | -------------------------------- |
+| **RETURNS** | int  | The number of left-child tokens. |
+
+## Token.n_rights {#n_rights tag="property" model="parser"}
+
+The number of rightward immediate children of the word, in the syntactic
+dependency parse.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like New York in Autumn.")
+> assert doc[3].n_rights == 1
+> ```
+
+| Name        | Type | Description                       |
+| ----------- | ---- | --------------------------------- |
+| **RETURNS** | int  | The number of right-child tokens. |
+
+## Token.subtree {#subtree tag="property" model="parser"}
+
+A sequence containing the token and all the token's syntactic descendants.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> give_subtree = doc[0].subtree
+> assert [t.text for t in give_subtree] == [u"Give", u"it", u"back", u"!"]
+> ```
+
+| Name       | Type    | Description                                                                |
+| ---------- | ------- | -------------------------------------------------------------------------- |
+| **YIELDS** | `Token` | A descendant token such that `self.is_ancestor(token)` or `token == self`. |
+
+## Token.is_sent_start {#is_sent_start tag="property" new="2"}
+
+A boolean value indicating whether the token starts a sentence. `None` if
+unknown.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"Give it back! He pleaded.")
+> assert doc[4].is_sent_start
+> assert not doc[5].is_sent_start
+> ```
+
+| Name        | Type | Description                          |
+| ----------- | ---- | ------------------------------------ |
+| **RETURNS** | bool | Whether the token starts a sentence. |
+
+<Infobox title="Changed in v2.0" variant="warning">
+
+As of spaCy v2.0, the `Token.sent_start` property is deprecated and has been
+replaced with `Token.is_sent_start`, which returns a boolean value instead of a
+misleading `0` for `False` and `1` for `True`. It also now returns `None` if the
+answer is unknown, and fixes a quirk in the old logic that would always set the
+property to `0` for the first word of the document.
+
+```diff
+- assert doc[4].sent_start == 1
++ assert doc[4].is_sent_start == True
+```
+
+</Infobox>
+
+## Token.has_vector {#has_vector tag="property" model="vectors"}
+
+A boolean value indicating whether a word vector is associated with the token.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like apples")
+> apples = doc[2]
+> assert apples.has_vector
+> ```
+
+| Name        | Type | Description                                   |
+| ----------- | ---- | --------------------------------------------- |
+| **RETURNS** | bool | Whether the token has a vector data attached. |
+
+## Token.vector {#vector tag="property" model="vectors"}
+
+A real-valued meaning representation.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like apples")
+> apples = doc[2]
+> assert apples.vector.dtype == "float32"
+> assert apples.vector.shape == (300,)
+> ```
+
+| Name        | Type                                     | Description                                          |
+| ----------- | ---------------------------------------- | ---------------------------------------------------- |
+| **RETURNS** | `numpy.ndarray[ndim=1, dtype='float32']` | A 1D numpy array representing the token's semantics. |
+
+## Token.vector_norm {#vector_norm tag="property" model="vectors"}
+
+The L2 norm of the token's vector representation.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like apples and pasta")
+> apples = doc[2]
+> pasta = doc[4]
+> apples.vector_norm  # 6.89589786529541
+> pasta.vector_norm  # 7.759851932525635
+> assert apples.vector_norm != pasta.vector_norm
+> ```
+
+| Name        | Type  | Description                               |
+| ----------- | ----- | ----------------------------------------- |
+| **RETURNS** | float | The L2 norm of the vector representation. |
+
+## Attributes {#attributes}
+
+| Name                                         | Type         | Description                                                                                                                                                                                                                   |
+| -------------------------------------------- | ------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `doc`                                        | `Doc`        | The parent document.                                                                                                                                                                                                          |
+| `sent` <Tag variant="new">2.0.12</Tag>       | `Span`       | The sentence span that this token is a part of.                                                                                                                                                                               |
+| `text`                                       | unicode      | Verbatim text content.                                                                                                                                                                                                        |
+| `text_with_ws`                               | unicode      | Text content, with trailing space character if present.                                                                                                                                                                       |
+| `whitespace_`                                | unicode      | Trailing space character if present.                                                                                                                                                                                          |
+| `orth`                                       | int          | ID of the verbatim text content.                                                                                                                                                                                              |
+| `orth_`                                      | unicode      | Verbatim text content (identical to `Token.text`). Exists mostly for consistency with the other attributes.                                                                                                                   |
+| `vocab`                                      | `Vocab`      | The vocab object of the parent `Doc`.                                                                                                                                                                                         |
+| `doc`                                        | `Doc`        | The parent document.                                                                                                                                                                                                          |
+| `head`                                       | `Token`      | The syntactic parent, or "governor", of this token.                                                                                                                                                                           |
+| `left_edge`                                  | `Token`      | The leftmost token of this token's syntactic descendants.                                                                                                                                                                     |
+| `right_edge`                                 | `Token`      | The rightmost token of this token's syntactic descendants.                                                                                                                                                                    |
+| `i`                                          | int          | The index of the token within the parent document.                                                                                                                                                                            |
+| `ent_type`                                   | int          | Named entity type.                                                                                                                                                                                                            |
+| `ent_type_`                                  | unicode      | Named entity type.                                                                                                                                                                                                            |
+| `ent_iob`                                    | int          | IOB code of named entity tag. `3` means the token begins an entity, `2` means it is outside an entity, `1` means it is inside an entity, and `0` means no entity tag is set.                                                  |  |
+| `ent_iob_`                                   | unicode      | IOB code of named entity tag. `3` means the token begins an entity, `2` means it is outside an entity, `1` means it is inside an entity, and `0` means no entity tag is set.                                                  |
+| `ent_id`                                     | int          | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution.                                                                                                         |
+| `ent_id_`                                    | unicode      | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution.                                                                                                         |
+| `lemma`                                      | int          | Base form of the token, with no inflectional suffixes.                                                                                                                                                                        |
+| `lemma_`                                     | unicode      | Base form of the token, with no inflectional suffixes.                                                                                                                                                                        |
+| `norm`                                       | int          | The token's norm, i.e. a normalized form of the token text. Usually set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions) or [norm exceptions](/usage/adding-languages#norm-exceptions). |
+| `norm_`                                      | unicode      | The token's norm, i.e. a normalized form of the token text. Usually set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions) or [norm exceptions](/usage/adding-languages#norm-exceptions). |
+| `lower`                                      | int          | Lowercase form of the token.                                                                                                                                                                                                  |
+| `lower_`                                     | unicode      | Lowercase form of the token text. Equivalent to `Token.text.lower()`.                                                                                                                                                         |
+| `shape`                                      | int          | Transform of the tokens's string, to show orthographic features. For example, "Xxxx" or "dd".                                                                                                                                 |
+| `shape_`                                     | unicode      | Transform of the tokens's string, to show orthographic features. For example, "Xxxx" or "dd".                                                                                                                                 |
+| `prefix`                                     | int          | Hash value of a length-N substring from the start of the token. Defaults to `N=1`.                                                                                                                                            |
+| `prefix_`                                    | unicode      | A length-N substring from the start of the token. Defaults to `N=1`.                                                                                                                                                          |
+| `suffix`                                     | int          | Hash value of a length-N substring from the end of the token. Defaults to `N=3`.                                                                                                                                              |
+| `suffix_`                                    | unicode      | Length-N substring from the end of the token. Defaults to `N=3`.                                                                                                                                                              |
+| `is_alpha`                                   | bool         | Does the token consist of alphabetic characters? Equivalent to `token.text.isalpha()`.                                                                                                                                        |
+| `is_ascii`                                   | bool         | Does the token consist of ASCII characters? Equivalent to `all(ord(c) < 128 for c in token.text)`.                                                                                                                            |
+| `is_digit`                                   | bool         | Does the token consist of digits? Equivalent to `token.text.isdigit()`.                                                                                                                                                       |
+| `is_lower`                                   | bool         | Is the token in lowercase? Equivalent to `token.text.islower()`.                                                                                                                                                              |
+| `is_upper`                                   | bool         | Is the token in uppercase? Equivalent to `token.text.isupper()`.                                                                                                                                                              |
+| `is_title`                                   | bool         | Is the token in titlecase? Equivalent to `token.text.istitle()`.                                                                                                                                                              |
+| `is_punct`                                   | bool         | Is the token punctuation?                                                                                                                                                                                                     |
+| `is_left_punct`                              | bool         | Is the token a left punctuation mark, e.g. `(`?                                                                                                                                                                               |
+| `is_right_punct`                             | bool         | Is the token a right punctuation mark, e.g. `)`?                                                                                                                                                                              |
+| `is_space`                                   | bool         | Does the token consist of whitespace characters? Equivalent to `token.text.isspace()`.                                                                                                                                        |
+| `is_bracket`                                 | bool         | Is the token a bracket?                                                                                                                                                                                                       |
+| `is_quote`                                   | bool         | Is the token a quotation mark?                                                                                                                                                                                                |
+| `is_currency` <Tag variant="new">2.0.8</Tag> | bool         | Is the token a currency symbol?                                                                                                                                                                                               |
+| `like_url`                                   | bool         | Does the token resemble a URL?                                                                                                                                                                                                |
+| `like_num`                                   | bool         | Does the token represent a number? e.g. "10.9", "10", "ten", etc.                                                                                                                                                             |
+| `like_email`                                 | bool         | Does the token resemble an email address?                                                                                                                                                                                     |
+| `is_oov`                                     | bool         | Is the token out-of-vocabulary?                                                                                                                                                                                               |
+| `is_stop`                                    | bool         | Is the token part of a "stop list"?                                                                                                                                                                                           |
+| `pos`                                        | int          | Coarse-grained part-of-speech.                                                                                                                                                                                                |
+| `pos_`                                       | unicode      | Coarse-grained part-of-speech.                                                                                                                                                                                                |
+| `tag`                                        | int          | Fine-grained part-of-speech.                                                                                                                                                                                                  |
+| `tag_`                                       | unicode      | Fine-grained part-of-speech.                                                                                                                                                                                                  |
+| `dep`                                        | int          | Syntactic dependency relation.                                                                                                                                                                                                |
+| `dep_`                                       | unicode      | Syntactic dependency relation.                                                                                                                                                                                                |
+| `lang`                                       | int          | Language of the parent document's vocabulary.                                                                                                                                                                                 |
+| `lang_`                                      | unicode      | Language of the parent document's vocabulary.                                                                                                                                                                                 |
+| `prob`                                       | float        | Smoothed log probability estimate of token's type.                                                                                                                                                                            |
+| `idx`                                        | int          | The character offset of the token within the parent document.                                                                                                                                                                 |
+| `sentiment`                                  | float        | A scalar value indicating the positivity or negativity of the token.                                                                                                                                                          |
+| `lex_id`                                     | int          | Sequential ID of the token's lexical type.                                                                                                                                                                                    |
+| `rank`                                       | int          | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors.                                                                                                                                  |
+| `cluster`                                    | int          | Brown cluster ID.                                                                                                                                                                                                             |
+| `_`                                          | `Underscore` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes).                                                                                                                |
diff --git a/website/docs/api/tokenizer.md b/website/docs/api/tokenizer.md
new file mode 100644
index 000000000..463a1c146
--- /dev/null
+++ b/website/docs/api/tokenizer.md
@@ -0,0 +1,127 @@
+---
+title: Tokenizer
+teaser: Segment text into words, punctuations marks etc.
+tag: class
+source: spacy/tokenizer.pyx
+---
+
+Segment text, and create `Doc` objects with the discovered segment boundaries.
+
+## Tokenizer.\_\_init\_\_ {#init tag="method"}
+
+Create a `Tokenizer`, to create `Doc` objects given unicode text.
+
+> #### Example
+>
+> ```python
+> # Construction 1
+> from spacy.tokenizer import Tokenizer
+> tokenizer = Tokenizer(nlp.vocab)
+>
+> # Construction 2
+> from spacy.lang.en import English
+> tokenizer = English().Defaults.create_tokenizer(nlp)
+> ```
+
+| Name             | Type        | Description                                                                         |
+| ---------------- | ----------- | ----------------------------------------------------------------------------------- |
+| `vocab`          | `Vocab`     | A storage container for lexical types.                                              |
+| `rules`          | dict        | Exceptions and special-cases for the tokenizer.                                     |
+| `prefix_search`  | callable    | A function matching the signature of `re.compile(string).search` to match prefixes. |
+| `suffix_search`  | callable    | A function matching the signature of `re.compile(string).search` to match suffixes. |
+| `infix_finditer` | callable    | A function matching the signature of `re.compile(string).finditer` to find infixes. |
+| `token_match`    | callable    | A boolean function matching strings to be recognized as tokens.                     |
+| **RETURNS**      | `Tokenizer` | The newly constructed object.                                                       |
+
+## Tokenizer.\_\_call\_\_ {#call tag="method"}
+
+Tokenize a string.
+
+> #### Example
+>
+> ```python
+> tokens = tokenizer(u"This is a sentence")
+> assert len(tokens) == 4
+> ```
+
+| Name        | Type    | Description                             |
+| ----------- | ------- | --------------------------------------- |
+| `string`    | unicode | The string to tokenize.                 |
+| **RETURNS** | `Doc`   | A container for linguistic annotations. |
+
+## Tokenizer.pipe {#pipe tag="method"}
+
+Tokenize a stream of texts.
+
+> #### Example
+>
+> ```python
+> texts = [u"One document.", u"...", u"Lots of documents"]
+> for doc in tokenizer.pipe(texts, batch_size=50):
+>     pass
+> ```
+
+| Name         | Type  | Description                                                                                                             |
+| ------------ | ----- | ----------------------------------------------------------------------------------------------------------------------- |
+| `texts`      | -     | A sequence of unicode texts.                                                                                            |
+| `batch_size` | int   | The number of texts to accumulate in an internal buffer.                                                                |
+| `n_threads`  | int   | The number of threads to use, if the implementation supports multi-threading. The default tokenizer is single-threaded. |
+| **YIELDS**   | `Doc` | A sequence of Doc objects, in order.                                                                                    |
+
+## Tokenizer.find_infix {#find_infix tag="method"}
+
+Find internal split points of the string.
+
+| Name        | Type    | Description                                                                                                                                        |
+| ----------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `string`    | unicode | The string to split.                                                                                                                               |
+| **RETURNS** | list    | A list of `re.MatchObject` objects that have `.start()` and `.end()` methods, denoting the placement of internal segment separators, e.g. hyphens. |
+
+## Tokenizer.find_prefix {#find_prefix tag="method"}
+
+Find the length of a prefix that should be segmented from the string, or `None`
+if no prefix rules match.
+
+| Name        | Type    | Description                                            |
+| ----------- | ------- | ------------------------------------------------------ |
+| `string`    | unicode | The string to segment.                                 |
+| **RETURNS** | int     | The length of the prefix if present, otherwise `None`. |
+
+## Tokenizer.find_suffix {#find_suffix tag="method"}
+
+Find the length of a suffix that should be segmented from the string, or `None`
+if no suffix rules match.
+
+| Name        | Type         | Description                                            |
+| ----------- | ------------ | ------------------------------------------------------ |
+| `string`    | unicode      | The string to segment.                                 |
+| **RETURNS** | int / `None` | The length of the suffix if present, otherwise `None`. |
+
+## Tokenizer.add_special_case {#add_special_case tag="method"}
+
+Add a special-case tokenization rule. This mechanism is also used to add custom
+tokenizer exceptions to the language data. See the usage guide on
+[adding languages](/usage/adding-languages#tokenizer-exceptions) for more
+details and examples.
+
+> #### Example
+>
+> ```python
+> from spacy.attrs import ORTH, LEMMA
+> case = [{"don't": [{ORTH: "do"}, {ORTH: "n't", LEMMA: "not"}]}]
+> tokenizer.add_special_case(case)
+> ```
+
+| Name          | Type     | Description                                                                                                                                                              |
+| ------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `string`      | unicode  | The string to specially tokenize.                                                                                                                                        |
+| `token_attrs` | iterable | A sequence of dicts, where each dict describes a token and its attributes. The `ORTH` fields of the attributes must exactly match the string when they are concatenated. |
+
+## Attributes {#attributes}
+
+| Name             | Type    | Description                                                                                                                |
+| ---------------- | ------- | -------------------------------------------------------------------------------------------------------------------------- |
+| `vocab`          | `Vocab` | The vocab object of the parent `Doc`.                                                                                      |
+| `prefix_search`  | -       | A function to find segment boundaries from the start of a string. Returns the length of the segment, or `None`.            |
+| `suffix_search`  | -       | A function to find segment boundaries from the end of a string. Returns the length of the segment, or `None`.              |
+| `infix_finditer` | -       | A function to find internal segment separators, e.g. hyphens. Returns a (possibly empty) list of `re.MatchObject` objects. |
diff --git a/website/docs/api/top-level.md b/website/docs/api/top-level.md
new file mode 100644
index 000000000..1e23cfbcc
--- /dev/null
+++ b/website/docs/api/top-level.md
@@ -0,0 +1,633 @@
+---
+title: Top-level Functions
+menu:
+  - ['spacy', 'spacy']
+  - ['displacy', 'displacy']
+  - ['Utility Functions', 'util']
+  - ['Compatibility', 'compat']
+---
+
+## spaCy {#spacy hidden="true"}
+
+### spacy.load {#spacy.load tag="function" model="any"}
+
+Load a model via its [shortcut link](/usage/models#usage), the name of an
+installed [model package](/usage/training#models-generating), a unicode path or
+a `Path`-like object. spaCy will try resolving the load argument in this order.
+If a model is loaded from a shortcut link or package name, spaCy will assume
+it's a Python package and import it and call the model's own `load()` method. If
+a model is loaded from a path, spaCy will assume it's a data directory, read the
+language and pipeline settings off the meta.json and initialize the `Language`
+class. The data will be loaded in via
+[`Language.from_disk`](/api/language#from_disk).
+
+> #### Example
+>
+> ```python
+> nlp = spacy.load("en") # shortcut link
+> nlp = spacy.load("en_core_web_sm") # package
+> nlp = spacy.load("/path/to/en") # unicode path
+> nlp = spacy.load(Path("/path/to/en")) # pathlib Path
+>
+> nlp = spacy.load("en", disable=["parser", "tagger"])
+> ```
+
+| Name        | Type             | Description                                                                       |
+| ----------- | ---------------- | --------------------------------------------------------------------------------- |
+| `name`      | unicode / `Path` | Model to load, i.e. shortcut link, package name or path.                          |
+| `disable`   | list             | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
+| **RETURNS** | `Language`       | A `Language` object with the loaded model.                                        |
+
+Essentially, `spacy.load()` is a convenience wrapper that reads the language ID
+and pipeline components from a model's `meta.json`, initializes the `Language`
+class, loads in the model data and returns it.
+
+```python
+### Abstract example
+cls = util.get_lang_class(lang)         #  get language for ID, e.g. 'en'
+nlp = cls()                             #  initialise the language
+for name in pipeline: component = nlp.create_pipe(name)   #  create each pipeline component nlp.add_pipe(component)             #  add component to pipeline
+nlp.from_disk(model_data_path)          #  load in model data
+```
+
+<Infobox title="Changed in v2.0" variant="warning">
+
+As of spaCy 2.0, the `path` keyword argument is deprecated. spaCy will also
+raise an error if no model could be loaded and never just return an empty
+`Language` object. If you need a blank language, you can use the new function
+[`spacy.blank()`](/api/top-level#spacy.blank) or import the class explicitly,
+e.g. `from spacy.lang.en import English`.
+
+```diff
+- nlp = spacy.load("en", path="/model")
++ nlp = spacy.load("/model")
+```
+
+</Infobox>
+
+### spacy.blank {#spacy.blank tag="function" new="2"}
+
+Create a blank model of a given language class. This function is the twin of
+`spacy.load()`.
+
+> #### Example
+>
+> ```python
+> nlp_en = spacy.blank("en")
+> nlp_de = spacy.blank("de")
+> ```
+
+| Name        | Type       | Description                                                                                      |
+| ----------- | ---------- | ------------------------------------------------------------------------------------------------ |
+| `name`      | unicode    | [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) of the language class to load. |
+| `disable`   | list       | Names of pipeline components to [disable](/usage/processing-pipelines#disabling).                |
+| **RETURNS** | `Language` | An empty `Language` object of the appropriate subclass.                                          |
+
+#### spacy.info {#spacy.info tag="function"}
+
+The same as the [`info` command](/api/cli#info). Pretty-print information about
+your installation, models and local setup from within spaCy. To get the model
+meta data as a dictionary instead, you can use the `meta` attribute on your
+`nlp` object with a loaded model, e.g. `nlp.meta`.
+
+> #### Example
+>
+> ```python
+> spacy.info()
+> spacy.info("en")
+> spacy.info("de", markdown=True)
+> ```
+
+| Name       | Type    | Description                                                   |
+| ---------- | ------- | ------------------------------------------------------------- |
+| `model`    | unicode | A model, i.e. shortcut link, package name or path (optional). |
+| `markdown` | bool    | Print information as Markdown.                                |
+
+### spacy.explain {#spacy.explain tag="function"}
+
+Get a description for a given POS tag, dependency label or entity type. For a
+list of available terms, see
+[`glossary.py`](https://github.com/explosion/spaCy/tree/master/spacy/glossary.py).
+
+> #### Example
+>
+> ```python
+> spacy.explain(u"NORP")
+> # Nationalities or religious or political groups
+>
+> doc = nlp(u"Hello world")
+> for word in doc:
+>    print(word.text, word.tag_, spacy.explain(word.tag_))
+> # Hello UH interjection
+> # world NN noun, singular or mass
+> ```
+
+| Name        | Type    | Description                                              |
+| ----------- | ------- | -------------------------------------------------------- |
+| `term`      | unicode | Term to explain.                                         |
+| **RETURNS** | unicode | The explanation, or `None` if not found in the glossary. |
+
+### spacy.prefer_gpu {#spacy.prefer_gpu tag="function" new="2.0.14"}
+
+Allocate data and perform operations on [GPU](/usage/#gpu), if available. If
+data has already been allocated on CPU, it will not be moved. Ideally, this
+function should be called right after importing spaCy and _before_ loading any
+models.
+
+> #### Example
+>
+> ```python
+> import spacy
+> activated = spacy.prefer_gpu()
+> nlp = spacy.load("en_core_web_sm")
+> ```
+
+| Name        | Type | Description                    |
+| ----------- | ---- | ------------------------------ |
+| **RETURNS** | bool | Whether the GPU was activated. |
+
+### spacy.require_gpu {#spacy.require_gpu tag="function" new="2.0.14"}
+
+Allocate data and perform operations on [GPU](/usage/#gpu). Will raise an error
+if no GPU is available. If data has already been allocated on CPU, it will not
+be moved. Ideally, this function should be called right after importing spaCy
+and _before_ loading any models.
+
+> #### Example
+>
+> ```python
+> import spacy
+> spacy.require_gpu()
+> nlp = spacy.load("en_core_web_sm")
+> ```
+
+| Name        | Type | Description |
+| ----------- | ---- | ----------- |
+| **RETURNS** | bool | `True`      |
+
+## displaCy {#displacy source="spacy/displacy"}
+
+As of v2.0, spaCy comes with a built-in visualization suite. For more info and
+examples, see the usage guide on [visualizing spaCy](/usage/visualizers).
+
+### displacy.serve {#displacy.serve tag="method" new="2"}
+
+Serve a dependency parse tree or named entity visualization to view it in your
+browser. Will run a simple web server.
+
+> #### Example
+>
+> ```python
+> import spacy
+> from spacy import displacy
+> nlp = spacy.load("en_core_web_sm")
+> doc1 = nlp(u"This is a sentence.")
+> doc2 = nlp(u"This is another sentence.")
+> displacy.serve([doc1, doc2], style="dep")
+> ```
+
+| Name      | Type                | Description                                                                                                                          | Default     |
+| --------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ----------- |
+| `docs`    | list, `Doc`, `Span` | Document(s) to visualize.                                                                                                            |
+| `style`   | unicode             | Visualization style, `'dep'` or `'ent'`.                                                                                             | `'dep'`     |
+| `page`    | bool                | Render markup as full HTML page.                                                                                                     | `True`      |
+| `minify`  | bool                | Minify HTML markup.                                                                                                                  | `False`     |
+| `options` | dict                | [Visualizer-specific options](#options), e.g. colors.                                                                                | `{}`        |
+| `manual`  | bool                | Don't parse `Doc` and instead, expect a dict or list of dicts. [See here](/usage/visualizers#manual-usage) for formats and examples. | `False`     |
+| `port`    | int                 | Port to serve visualization.                                                                                                         | `5000`      |
+| `host`    | unicode             | Host to serve visualization.                                                                                                         | `'0.0.0.0'` |
+
+### displacy.render {#displacy.render tag="method" new="2"}
+
+Render a dependency parse tree or named entity visualization.
+
+> #### Example
+>
+> ```python
+> import spacy
+> from spacy import displacy
+> nlp = spacy.load("en_core_web_sm")
+> doc = nlp(u"This is a sentence.")
+> html = displacy.render(doc, style="dep")
+> ```
+
+| Name        | Type                | Description                                                                                                                          | Default                |
+| ----------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ---------------------- |
+| `docs`      | list, `Doc`, `Span` | Document(s) to visualize.                                                                                                            |
+| `style`     | unicode             | Visualization style, `'dep'` or `'ent'`.                                                                                             | `'dep'`                |
+| `page`      | bool                | Render markup as full HTML page.                                                                                                     | `False`                |
+| `minify`    | bool                | Minify HTML markup.                                                                                                                  | `False`                |
+| `jupyter`   | bool                | Explicitly enable "[Jupyter](http://jupyter.org/) mode" to return markup ready to be rendered in a notebook.                         | detected automatically |
+| `options`   | dict                | [Visualizer-specific options](#options), e.g. colors.                                                                                | `{}`                   |
+| `manual`    | bool                | Don't parse `Doc` and instead, expect a dict or list of dicts. [See here](/usage/visualizers#manual-usage) for formats and examples. | `False`                |
+| **RETURNS** | unicode             | Rendered HTML markup.                                                                                                                |
+
+### Visualizer options {#displacy_options}
+
+The `options` argument lets you specify additional settings for each visualizer.
+If a setting is not present in the options, the default value will be used.
+
+#### Dependency Visualizer options {#options-dep}
+
+> #### Example
+>
+> ```python
+> options = {"compact": True, "color": "blue"}
+> displacy.serve(doc, style="dep", options=options)
+> ```
+
+| Name               | Type    | Description                                                                                                     | Default                 |
+| ------------------ | ------- | --------------------------------------------------------------------------------------------------------------- | ----------------------- |
+| `fine_grained`     | bool    | Use fine-grained part-of-speech tags (`Token.tag_`) instead of coarse-grained tags (`Token.pos_`).              | `False`                 |
+| `collapse_punct`   | bool    | Attach punctuation to tokens. Can make the parse more readable, as it prevents long arcs to attach punctuation. | `True`                  |
+| `collapse_phrases` | bool    | Merge noun phrases into one token.                                                                              | `False`                 |
+| `compact`          | bool    | "Compact mode" with square arrows that takes up less space.                                                     | `False`                 |
+| `color`            | unicode | Text color (HEX, RGB or color names).                                                                           | `'#000000'`             |
+| `bg`               | unicode | Background color (HEX, RGB or color names).                                                                     | `'#ffffff'`             |
+| `font`             | unicode | Font name or font family for all text.                                                                          | `'Arial'`               |
+| `offset_x`         | int     | Spacing on left side of the SVG in px.                                                                          | `50`                    |
+| `arrow_stroke`     | int     | Width of arrow path in px.                                                                                      | `2`                     |
+| `arrow_width`      | int     | Width of arrow head in px.                                                                                      | `10` / `8` (compact)    |
+| `arrow_spacing`    | int     | Spacing between arrows in px to avoid overlaps.                                                                 | `20` / `12` (compact)   |
+| `word_spacing`     | int     | Vertical spacing between words and arcs in px.                                                                  | `45`                    |
+| `distance`         | int     | Distance between words in px.                                                                                   | `175` / `150` (compact) |
+
+#### Named Entity Visualizer options {#displacy_options-ent}
+
+> #### Example
+>
+> ```python
+> options = {"ents": ["PERSON", "ORG", "PRODUCT"],
+>            "colors": {"ORG": "yellow"}}
+> displacy.serve(doc, style="ent", options=options)
+> ```
+
+| Name     | Type | Description                                                                           | Default |
+| -------- | ---- | ------------------------------------------------------------------------------------- | ------- |
+| `ents`   | list | Entity types to highlight (`None` for all types).                                     | `None`  |
+| `colors` | dict | Color overrides. Entity types in uppercase should be mapped to color names or values. | `{}`    |
+
+By default, displaCy comes with colors for all
+[entity types supported by spaCy](/api/annotation#named-entities). If you're
+using custom entity types, you can use the `colors` setting to add your own
+colors for them.
+
+## Utility functions {#util source="spacy/util.py"}
+
+spaCy comes with a small collection of utility functions located in
+[`spacy/util.py`](https://github.com/explosion/spaCy/tree/master/spacy/util.py).
+Because utility functions are mostly intended for **internal use within spaCy**,
+their behavior may change with future releases. The functions documented on this
+page should be safe to use and we'll try to ensure backwards compatibility.
+However, we recommend having additional tests in place if your application
+depends on any of spaCy's utilities.
+
+### util.get_data_path {#util.get_data_path tag="function"}
+
+Get path to the data directory where spaCy looks for models. Defaults to
+`spacy/data`.
+
+| Name             | Type            | Description                                             |
+| ---------------- | --------------- | ------------------------------------------------------- |
+| `require_exists` | bool            | Only return path if it exists, otherwise return `None`. |
+| **RETURNS**      | `Path` / `None` | Data path or `None`.                                    |
+
+### util.set_data_path {#util.set_data_path tag="function"}
+
+Set custom path to the data directory where spaCy looks for models.
+
+> #### Example
+>
+> ```python
+> util.set_data_path("/custom/path")
+> util.get_data_path()
+> # PosixPath('/custom/path')
+> ```
+
+| Name   | Type             | Description                 |
+| ------ | ---------------- | --------------------------- |
+| `path` | unicode / `Path` | Path to new data directory. |
+
+### util.get_lang_class {#util.get_lang_class tag="function"}
+
+Import and load a `Language` class. Allows lazy-loading
+[language data](/usage/adding-languages) and importing languages using the
+two-letter language code. To add a language code for a custom language class,
+you can use the [`set_lang_class`](/api/top-level#util.set_lang_class) helper.
+
+> #### Example
+>
+> ```python
+> for lang_id in ["en", "de"]:
+>     lang_class = util.get_lang_class(lang_id)
+>     lang = lang_class()
+>     tokenizer = lang.Defaults.create_tokenizer()
+> ```
+
+| Name        | Type       | Description                            |
+| ----------- | ---------- | -------------------------------------- |
+| `lang`      | unicode    | Two-letter language code, e.g. `'en'`. |
+| **RETURNS** | `Language` | Language class.                        |
+
+### util.set_lang_class {#util.set_lang_class tag="function"}
+
+Set a custom `Language` class name that can be loaded via
+[`get_lang_class`](/api/top-level#util.get_lang_class). If your model uses a
+custom language, this is required so that spaCy can load the correct class from
+the two-letter language code.
+
+> #### Example
+>
+> ```python
+> from spacy.lang.xy import CustomLanguage
+>
+> util.set_lang_class('xy', CustomLanguage)
+> lang_class = util.get_lang_class('xy')
+> nlp = lang_class()
+> ```
+
+| Name   | Type       | Description                            |
+| ------ | ---------- | -------------------------------------- |
+| `name` | unicode    | Two-letter language code, e.g. `'en'`. |
+| `cls`  | `Language` | The language class, e.g. `English`.    |
+
+### util.load_model {#util.load_model tag="function" new="2"}
+
+Load a model from a shortcut link, package or data path. If called with a
+shortcut link or package name, spaCy will assume the model is a Python package
+and import and call its `load()` method. If called with a path, spaCy will
+assume it's a data directory, read the language and pipeline settings from the
+meta.json and initialize a `Language` class. The model data will then be loaded
+in via [`Language.from_disk()`](/api/language#from_disk).
+
+> #### Example
+>
+> ```python
+> nlp = util.load_model("en")
+> nlp = util.load_model("en_core_web_sm", disable=["ner"])
+> nlp = util.load_model("/path/to/data")
+> ```
+
+| Name          | Type       | Description                                              |
+| ------------- | ---------- | -------------------------------------------------------- |
+| `name`        | unicode    | Package name, shortcut link or model path.               |
+| `**overrides` | -          | Specific overrides, like pipeline components to disable. |
+| **RETURNS**   | `Language` | `Language` class with the loaded model.                  |
+
+### util.load_model_from_path {#util.load_model_from_path tag="function" new="2"}
+
+Load a model from a data directory path. Creates the [`Language`](/api/language)
+class and pipeline based on the directory's meta.json and then calls
+[`from_disk()`](/api/language#from_disk) with the path. This function also makes
+it easy to test a new model that you haven't packaged yet.
+
+> #### Example
+>
+> ```python
+> nlp = load_model_from_path("/path/to/data")
+> ```
+
+| Name          | Type       | Description                                                                                          |
+| ------------- | ---------- | ---------------------------------------------------------------------------------------------------- |
+| `model_path`  | unicode    | Path to model data directory.                                                                        |
+| `meta`        | dict       | Model meta data. If `False`, spaCy will try to load the meta from a meta.json in the same directory. |
+| `**overrides` | -          | Specific overrides, like pipeline components to disable.                                             |
+| **RETURNS**   | `Language` | `Language` class with the loaded model.                                                              |
+
+### util.load_model_from_init_py {#util.load_model_from_init_py tag="function" new="2"}
+
+A helper function to use in the `load()` method of a model package's
+[`__init__.py`](https://github.com/explosion/spacy-models/tree/master/template/model/xx_model_name/__init__.py).
+
+> #### Example
+>
+> ```python
+> from spacy.util import load_model_from_init_py
+>
+> def load(**overrides):
+>     return load_model_from_init_py(__file__, **overrides)
+> ```
+
+| Name          | Type       | Description                                              |
+| ------------- | ---------- | -------------------------------------------------------- |
+| `init_file`   | unicode    | Path to model's `__init__.py`, i.e. `__file__`.          |
+| `**overrides` | -          | Specific overrides, like pipeline components to disable. |
+| **RETURNS**   | `Language` | `Language` class with the loaded model.                  |
+
+### util.get_model_meta {#util.get_model_meta tag="function" new="2"}
+
+Get a model's meta.json from a directory path and validate its contents.
+
+> #### Example
+>
+> ```python
+> meta = util.get_model_meta("/path/to/model")
+> ```
+
+| Name        | Type             | Description              |
+| ----------- | ---------------- | ------------------------ |
+| `path`      | unicode / `Path` | Path to model directory. |
+| **RETURNS** | dict             | The model's meta data.   |
+
+### util.is_package {#util.is_package tag="function"}
+
+Check if string maps to a package installed via pip. Mainly used to validate
+[model packages](/usage/models).
+
+> #### Example
+>
+> ```python
+> util.is_package("en_core_web_sm") # True
+> util.is_package("xyz") # False
+> ```
+
+| Name        | Type    | Description                                  |
+| ----------- | ------- | -------------------------------------------- |
+| `name`      | unicode | Name of package.                             |
+| **RETURNS** | `bool`  | `True` if installed package, `False` if not. |
+
+### util.get_package_path {#util.get_package_path tag="function" new="2"}
+
+Get path to an installed package. Mainly used to resolve the location of
+[model packages](/usage/models). Currently imports the package to find its path.
+
+> #### Example
+>
+> ```python
+> util.get_package_path("en_core_web_sm")
+> # /usr/lib/python3.6/site-packages/en_core_web_sm
+> ```
+
+| Name           | Type    | Description                      |
+| -------------- | ------- | -------------------------------- |
+| `package_name` | unicode | Name of installed package.       |
+| **RETURNS**    | `Path`  | Path to model package directory. |
+
+### util.is_in_jupyter {#util.is_in_jupyter tag="function" new="2"}
+
+Check if user is running spaCy from a [Jupyter](https://jupyter.org) notebook by
+detecting the IPython kernel. Mainly used for the
+[`displacy`](/api/top-level#displacy) visualizer.
+
+> #### Example
+>
+> ```python
+> html = "<h1>Hello world!</h1>"
+> if util.is_in_jupyter():
+>     from IPython.core.display import display, HTML
+>     display(HTML(html))
+> ```
+
+| Name        | Type | Description                           |
+| ----------- | ---- | ------------------------------------- |
+| **RETURNS** | bool | `True` if in Jupyter, `False` if not. |
+
+### util.update_exc {#util.update_exc tag="function"}
+
+Update, validate and overwrite
+[tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions). Used to
+combine global exceptions with custom, language-specific exceptions. Will raise
+an error if key doesn't match `ORTH` values.
+
+> #### Example
+>
+> ```python
+> BASE =  {"a.": [{ORTH: "a."}], ":)": [{ORTH: ":)"}]}
+> NEW = {"a.": [{ORTH: "a.", LEMMA: "all"}]}
+> exceptions = util.update_exc(BASE, NEW)
+> # {"a.": [{ORTH: "a.", LEMMA: "all"}], ":)": [{ORTH: ":)"}]}
+> ```
+
+| Name              | Type  | Description                                                     |
+| ----------------- | ----- | --------------------------------------------------------------- |
+| `base_exceptions` | dict  | Base tokenizer exceptions.                                      |
+| `*addition_dicts` | dicts | Exception dictionaries to add to the base exceptions, in order. |
+| **RETURNS**       | dict  | Combined tokenizer exceptions.                                  |
+
+### util.minibatch {#util.minibatch tag="function" new="2"}
+
+Iterate over batches of items. `size` may be an iterator, so that batch-size can
+vary on each step.
+
+> #### Example
+>
+> ```python
+> batches = minibatch(train_data)
+> for batch in batches:
+>     texts, annotations = zip(*batch)
+>     nlp.update(texts, annotations)
+> ```
+
+| Name       | Type           | Description                                                                                                                                                                                  |
+| ---------- | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `items`    | iterable       | The items to batch up.                                                                                                                                                                       |
+| `size`     | int / iterable | The batch size(s). Use [`util.compounding`](/api/top-level#util.compounding) or [`util.decaying`](/api/top-level#util.decaying) or for an infinite series of compounding or decaying values. |
+| **YIELDS** | list           | The batches.                                                                                                                                                                                 |
+
+### util.compounding {#util.compounding tag="function" new="2"}
+
+Yield an infinite series of compounding values. Each time the generator is
+called, a value is produced by multiplying the previous value by the compound
+rate.
+
+> #### Example
+>
+> ```python
+> sizes = compounding(1., 10., 1.5)
+> assert next(sizes) == 1.
+> assert next(sizes) == 1. * 1.5
+> assert next(sizes) == 1.5 * 1.5
+> ```
+
+| Name       | Type        | Description             |
+| ---------- | ----------- | ----------------------- |
+| `start`    | int / float | The first value.        |
+| `stop`     | int / float | The maximum value.      |
+| `compound` | int / float | The compounding factor. |
+| **YIELDS** | int         | Compounding values.     |
+
+### util.decaying {#util.decaying tag="function" new="2"}
+
+Yield an infinite series of linearly decaying values.
+
+> #### Example
+>
+> ```python
+> sizes = decaying(1., 10., 0.001)
+> assert next(sizes) == 1.
+> assert next(sizes) == 1. - 0.001
+> assert next(sizes) == 0.999 - 0.001
+> ```
+
+| Name       | Type        | Description          |
+| ---------- | ----------- | -------------------- |
+| `start`    | int / float | The first value.     |
+| `end`      | int / float | The maximum value.   |
+| `decay`    | int / float | The decaying factor. |
+| **YIELDS** | int         | The decaying values. |
+
+### util.itershuffle {#util.itershuffle tag="function" new="2"}
+
+Shuffle an iterator. This works by holding `bufsize` items back and yielding
+them sometime later. Obviously, this is not unbiased – but should be good enough
+for batching. Larger `buffsize` means less bias.
+
+> #### Example
+>
+> ```python
+> values = range(1000)
+> shuffled = itershuffle(values)
+> ```
+
+| Name       | Type     | Description            |
+| ---------- | -------- | ---------------------- |
+| `iterable` | iterable | Iterator to shuffle.   |
+| `buffsize` | int      | Items to hold back.    |
+| **YIELDS** | iterable | The shuffled iterator. |
+
+## Compatibility functions {#compat source="spacy/compaty.py"}
+
+All Python code is written in an **intersection of Python 2 and Python 3**. This
+is easy in Cython, but somewhat ugly in Python. Logic that deals with Python or
+platform compatibility only lives in `spacy.compat`. To distinguish them from
+the builtin functions, replacement functions are suffixed with an underscore,
+e.e `unicode_`.
+
+> #### Example
+>
+> ```python
+> from spacy.compat import unicode_
+>
+> compatible_unicode = unicode_("hello world")
+> ```
+
+| Name                 | Python 2                           | Python 3    |
+| -------------------- | ---------------------------------- | ----------- |
+| `compat.bytes_`      | `str`                              | `bytes`     |
+| `compat.unicode_`    | `unicode`                          | `str`       |
+| `compat.basestring_` | `basestring`                       | `str`       |
+| `compat.input_`      | `raw_input`                        | `input`     |
+| `compat.path2str`    | `str(path)` with `.decode('utf8')` | `str(path)` |
+
+### compat.is_config {#is_config tag="function"}
+
+Check if a specific configuration of Python version and operating system matches
+the user's setup. Mostly used to display targeted error messages.
+
+> #### Example
+>
+> ```python
+> from spacy.compat import is_config
+>
+> if is_config(python2=True, windows=True):
+>     print("You are using Python 2 on Windows.")
+> ```
+
+| Name        | Type | Description                                                      |
+| ----------- | ---- | ---------------------------------------------------------------- |
+| `python2`   | bool | spaCy is executed with Python 2.x.                               |
+| `python3`   | bool | spaCy is executed with Python 3.x.                               |
+| `windows`   | bool | spaCy is executed on Windows.                                    |
+| `linux`     | bool | spaCy is executed on Linux.                                      |
+| `osx`       | bool | spaCy is executed on OS X or macOS.                              |
+| **RETURNS** | bool | Whether the specified configuration matches the user's platform. |
diff --git a/website/docs/api/vectors.md b/website/docs/api/vectors.md
new file mode 100644
index 000000000..62e91edc7
--- /dev/null
+++ b/website/docs/api/vectors.md
@@ -0,0 +1,375 @@
+---
+title: Vectors
+teaser: Store, save and load word vectors
+tag: class
+source: spacy/vectors.pyx
+new: 2
+---
+
+Vectors data is kept in the `Vectors.data` attribute, which should be an
+instance of `numpy.ndarray` (for CPU vectors) or `cupy.ndarray` (for GPU
+vectors). Multiple keys can be mapped to the same vector, and not all of the
+rows in the table need to be assigned – so `vectors.n_keys` may be greater or
+smaller than `vectors.shape[0]`.
+
+## Vectors.\_\_init\_\_ {#init tag="method"}
+
+Create a new vector store. You can set the vector values and keys directly on
+initialization, or supply a `shape` keyword argument to create an empty table
+you can add vectors to later.
+
+> #### Example
+>
+> ```python
+> from spacy.vectors import Vectors
+>
+> empty_vectors = Vectors(shape=(10000, 300))
+>
+> data = numpy.zeros((3, 300), dtype='f')
+> keys = [u"cat", u"dog", u"rat"]
+> vectors = Vectors(data=data, keys=keys)
+> ```
+
+| Name        | Type                               | Description                                                                                                                                                        |
+| ----------- | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `data`      | `ndarray[ndim=1, dtype='float32']` | The vector data.                                                                                                                                                   |
+| `keys`      | iterable                           | A sequence of keys aligned with the data.                                                                                                                          |
+| `shape`     | tuple                              | Size of the table as `(n_entries, n_columns)`, the number of entries and number of columns. Not required if you're initializing the object with `data` and `keys`. |
+| **RETURNS** | `Vectors`                          | The newly created object.                                                                                                                                          |
+
+## Vectors.\_\_getitem\_\_ {#getitem tag="method"}
+
+Get a vector by key. If the key is not found in the table, a `KeyError` is
+raised.
+
+> #### Example
+>
+> ```python
+> cat_id = nlp.vocab.strings[u"cat"]
+> cat_vector = nlp.vocab.vectors[cat_id]
+> assert cat_vector == nlp.vocab[u"cat"].vector
+> ```
+
+| Name    | Type                               | Description                    |
+| ------- | ---------------------------------- | ------------------------------ |
+| `key`   | int                                | The key to get the vector for. |
+| returns | `ndarray[ndim=1, dtype='float32']` | The vector for the key.        |
+
+## Vectors.\_\_setitem\_\_ {#setitem tag="method"}
+
+Set a vector for the given key.
+
+> #### Example
+>
+> ```python
+> cat_id = nlp.vocab.strings[u"cat"]
+> vector = numpy.random.uniform(-1, 1, (300,))
+> nlp.vocab.vectors[cat_id] = vector
+> ```
+
+| Name     | Type                               | Description                    |
+| -------- | ---------------------------------- | ------------------------------ |
+| `key`    | int                                | The key to set the vector for. |
+| `vector` | `ndarray[ndim=1, dtype='float32']` | The vector to set.             |
+
+## Vectors.\_\_iter\_\_ {#iter tag="method"}
+
+Iterate over the keys in the table.
+
+> #### Example
+>
+> ```python
+> for key in nlp.vocab.vectors:
+>    print(key, nlp.vocab.strings[key])
+> ```
+
+| Name       | Type | Description         |
+| ---------- | ---- | ------------------- |
+| **YIELDS** | int  | A key in the table. |
+
+## Vectors.\_\_len\_\_ {#len tag="method"}
+
+Return the number of vectors in the table.
+
+> #### Example
+>
+> ```python
+> vectors = Vectors(shape=(3, 300))
+> assert len(vectors) == 3
+> ```
+
+| Name        | Type | Description                         |
+| ----------- | ---- | ----------------------------------- |
+| **RETURNS** | int  | The number of vectors in the table. |
+
+## Vectors.\_\_contains\_\_ {#contains tag="method"}
+
+Check whether a key has been mapped to a vector entry in the table.
+
+> #### Example
+>
+> ```python
+> cat_id = nlp.vocab.strings[u"cat"]
+> nlp.vectors.add(cat_id, numpy.random.uniform(-1, 1, (300,)))
+> assert cat_id in vectors
+> ```
+
+| Name        | Type | Description                         |
+| ----------- | ---- | ----------------------------------- |
+| `key`       | int  | The key to check.                   |
+| **RETURNS** | bool | Whether the key has a vector entry. |
+
+## Vectors.add {#add tag="method"}
+
+Add a key to the table, optionally setting a vector value as well. Keys can be
+mapped to an existing vector by setting `row`, or a new vector can be added.
+When adding unicode keys, keep in mind that the `Vectors` class itself has no
+[`StringStore`](/api/stringstore), so you have to store the hash-to-string
+mapping separately. If you need to manage the strings, you should use the
+`Vectors` via the [`Vocab`](/api/vocab) class, e.g. `vocab.vectors`.
+
+> #### Example
+>
+> ```python
+> vector = numpy.random.uniform(-1, 1, (300,))
+> cat_id = nlp.vocab.strings[u"cat"]
+> nlp.vocab.vectors.add(cat_id, vector=vector)
+> nlp.vocab.vectors.add(u"dog", row=0)
+> ```
+
+| Name        | Type                               | Description                                           |
+| ----------- | ---------------------------------- | ----------------------------------------------------- |
+| `key`       | unicode / int                      | The key to add.                                       |
+| `vector`    | `ndarray[ndim=1, dtype='float32']` | An optional vector to add for the key.                |
+| `row`       | int                                | An optional row number of a vector to map the key to. |
+| **RETURNS** | int                                | The row the vector was added to.                      |
+
+## Vectors.resize {#resize tag="method"}
+
+Resize the underlying vectors array. If `inplace=True`, the memory is
+reallocated. This may cause other references to the data to become invalid, so
+only use `inplace=True` if you're sure that's what you want. If the number of
+vectors is reduced, keys mapped to rows that have been deleted are removed.
+These removed items are returned as a list of `(key, row)` tuples.
+
+> #### Example
+>
+> ```python
+> removed = nlp.vocab.vectors.resize((10000, 300))
+> ```
+
+| Name        | Type  | Description                                                          |
+| ----------- | ----- | -------------------------------------------------------------------- |
+| `shape`     | tuple | A `(rows, dims)` tuple describing the number of rows and dimensions. |
+| `inplace`   | bool  | Reallocate the memory.                                               |
+| **RETURNS** | list  | The removed items as a list of `(key, row)` tuples.                  |
+
+## Vectors.keys {#keys tag="method"}
+
+A sequence of the keys in the table.
+
+> #### Example
+>
+> ```python
+> for key in nlp.vocab.vectors.keys():
+>     print(key, nlp.vocab.strings[key])
+> ```
+
+| Name        | Type     | Description |
+| ----------- | -------- | ----------- |
+| **RETURNS** | iterable | The keys.   |
+
+## Vectors.values {#values tag="method"}
+
+Iterate over vectors that have been assigned to at least one key. Note that some
+vectors may be unassigned, so the number of vectors returned may be less than
+the length of the vectors table.
+
+> #### Example
+>
+> ```python
+> for vector in nlp.vocab.vectors.values():
+>     print(vector)
+> ```
+
+| Name       | Type                               | Description            |
+| ---------- | ---------------------------------- | ---------------------- |
+| **YIELDS** | `ndarray[ndim=1, dtype='float32']` | A vector in the table. |
+
+## Vectors.items {#items tag="method"}
+
+Iterate over `(key, vector)` pairs, in order.
+
+> #### Example
+>
+> ```python
+> for key, vector in nlp.vocab.vectors.items():
+>    print(key, nlp.vocab.strings[key], vector)
+> ```
+
+| Name       | Type  | Description                      |
+| ---------- | ----- | -------------------------------- |
+| **YIELDS** | tuple | `(key, vector)` pairs, in order. |
+
+## Vectors.shape {#shape tag="property"}
+
+Get `(rows, dims)` tuples of number of rows and number of dimensions in the
+vector table.
+
+> #### Example
+>
+> ```python
+> vectors = Vectors(shape(1, 300))
+> vectors.add(u"cat", numpy.random.uniform(-1, 1, (300,)))
+> rows, dims = vectors.shape
+> assert rows == 1
+> assert dims == 300
+> ```
+
+| Name        | Type  | Description            |
+| ----------- | ----- | ---------------------- |
+| **RETURNS** | tuple | A `(rows, dims)` pair. |
+
+## Vectors.size {#size tag="property"}
+
+The vector size, i.e. `rows * dims`.
+
+> #### Example
+>
+> ```python
+> vectors = Vectors(shape=(500, 300))
+> assert vectors.size == 150000
+> ```
+
+| Name        | Type | Description      |
+| ----------- | ---- | ---------------- |
+| **RETURNS** | int  | The vector size. |
+
+## Vectors.is_full {#is_full tag="property"}
+
+Whether the vectors table is full and has no slots are available for new keys.
+If a table is full, it can be resized using
+[`Vectors.resize`](/api/vectors#resize).
+
+> #### Example
+>
+> ```python
+> vectors = Vectors(shape=(1, 300))
+> vectors.add(u"cat", numpy.random.uniform(-1, 1, (300,)))
+> assert vectors.is_full
+> ```
+
+| Name        | Type | Description                        |
+| ----------- | ---- | ---------------------------------- |
+| **RETURNS** | bool | Whether the vectors table is full. |
+
+## Vectors.n_keys {#n_keys tag="property"}
+
+Get the number of keys in the table. Note that this is the number of _all_ keys,
+not just unique vectors. If several keys are mapped are mapped to the same
+vectors, they will be counted individually.
+
+> #### Example
+>
+> ```python
+> vectors = Vectors(shape=(10, 300))
+> assert len(vectors) == 10
+> assert vectors.n_keys == 0
+> ```
+
+| Name        | Type | Description                          |
+| ----------- | ---- | ------------------------------------ |
+| **RETURNS** | int  | The number of all keys in the table. |
+
+## Vectors.from_glove {#from_glove tag="method"}
+
+Load [GloVe](https://nlp.stanford.edu/projects/glove/) vectors from a directory.
+Assumes binary format, that the vocab is in a `vocab.txt`, and that vectors are
+named `vectors.{size}.[fd`.bin], e.g. `vectors.128.f.bin` for 128d float32
+vectors, `vectors.300.d.bin` for 300d float64 (double) vectors, etc. By default
+GloVe outputs 64-bit vectors.
+
+> #### Example
+>
+> ```python
+> vectors = Vectors()
+> vectors.from_glove("/path/to/glove_vectors")
+> ```
+
+| Name   | Type             | Description                              |
+| ------ | ---------------- | ---------------------------------------- |
+| `path` | unicode / `Path` | The path to load the GloVe vectors from. |
+
+## Vectors.to_disk {#to_disk tag="method"}
+
+Save the current state to a directory.
+
+> #### Example
+>
+> ```python
+> vectors.to_disk("/path/to/vectors")
+>
+> ```
+
+| Name        | Type             | Description                                                                                                           |
+| ----------- | ---------------- | --------------------------------------------------------------------------------------------------------------------- |
+| `path`      | unicode / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
+| `**exclude` | -                | Named attributes to prevent from being saved.                                                                         |
+
+## Vectors.from_disk {#from_disk tag="method"}
+
+Loads state from a directory. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> vectors = Vectors(StringStore())
+> vectors.from_disk("/path/to/vectors")
+> ```
+
+| Name        | Type             | Description                                                                |
+| ----------- | ---------------- | -------------------------------------------------------------------------- |
+| `path`      | unicode / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
+| **RETURNS** | `Vectors`        | The modified `Vectors` object.                                             |
+
+## Vectors.to_bytes {#to_bytes tag="method"}
+
+Serialize the current state to a binary string.
+
+> #### Example
+>
+> ```python
+> vectors_bytes = vectors.to_bytes()
+> ```
+
+| Name        | Type  | Description                                        |
+| ----------- | ----- | -------------------------------------------------- |
+| `**exclude` | -     | Named attributes to prevent from being serialized. |
+| **RETURNS** | bytes | The serialized form of the `Vectors` object.       |
+
+## Vectors.from_bytes {#from_bytes tag="method"}
+
+Load state from a binary string.
+
+> #### Example
+>
+> ```python
+> fron spacy.vectors import Vectors
+> vectors_bytes = vectors.to_bytes()
+> new_vectors = Vectors(StringStore())
+> new_vectors.from_bytes(vectors_bytes)
+> ```
+
+| Name        | Type      | Description                                    |
+| ----------- | --------- | ---------------------------------------------- |
+| `data`      | bytes     | The data to load from.                         |
+| `**exclude` | -         | Named attributes to prevent from being loaded. |
+| **RETURNS** | `Vectors` | The `Vectors` object.                          |
+
+## Attributes {#attributes}
+
+| Name      | Type                               | Description                                                                     |
+| --------- | ---------------------------------- | ------------------------------------------------------------------------------- |
+| `data`    | `ndarray[ndim=1, dtype='float32']` | Stored vectors data. `numpy` is used for CPU vectors, `cupy` for GPU vectors.   |
+| `key2row` | dict                               | Dictionary mapping word hashes to rows in the `Vectors.data` table.             |
+| `keys`    | `ndarray[ndim=1, dtype='float32']` | Array keeping the keys in order, such that `keys[vectors.key2row[key]] == key`. |
diff --git a/website/docs/api/vocab.md b/website/docs/api/vocab.md
new file mode 100644
index 000000000..1e962ac74
--- /dev/null
+++ b/website/docs/api/vocab.md
@@ -0,0 +1,293 @@
+---
+title: Vocab
+teaser: A storage class for vocabulary and other data shared across a language
+tag: class
+source: spacy/vocab.pyx
+---
+
+The `Vocab` object provides a lookup table that allows you to access
+[`Lexeme`](/api/lexeme) objects, as well as the
+[`StringStore`](/api/stringstore). It also owns underlying C-data that is shared
+between `Doc` objects.
+
+## Vocab.\_\_init\_\_ {#init tag="method"}
+
+Create the vocabulary.
+
+> #### Example
+>
+> ```python
+> from spacy.vocab import Vocab
+> vocab = Vocab(strings=[u"hello", u"world"])
+> ```
+
+| Name               | Type                 | Description                                                                                                        |
+| ------------------ | -------------------- | ------------------------------------------------------------------------------------------------------------------ |
+| `lex_attr_getters` | dict                 | A dictionary mapping attribute IDs to functions to compute them. Defaults to `None`.                               |
+| `tag_map`          | dict                 | A dictionary mapping fine-grained tags to coarse-grained parts-of-speech, and optionally morphological attributes. |
+| `lemmatizer`       | object               | A lemmatizer. Defaults to `None`.                                                                                  |
+| `strings`          | `StringStore` / list | A [`StringStore`](/api/stringstore) that maps strings to hash values, and vice versa, or a list of strings.        |
+| **RETURNS**        | `Vocab`              | The newly constructed object.                                                                                      |
+
+## Vocab.\_\_len\_\_ {#len tag="method"}
+
+Get the current number of lexemes in the vocabulary.
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"This is a sentence.")
+> assert len(nlp.vocab) > 0
+> ```
+
+| Name        | Type | Description                              |
+| ----------- | ---- | ---------------------------------------- |
+| **RETURNS** | int  | The number of lexemes in the vocabulary. |
+
+## Vocab.\_\_getitem\_\_ {#getitem tag="method"}
+
+Retrieve a lexeme, given an int ID or a unicode string. If a previously unseen
+unicode string is given, a new lexeme is created and stored.
+
+> #### Example
+>
+> ```python
+> apple = nlp.vocab.strings[u"apple"]
+> assert nlp.vocab[apple] == nlp.vocab[u"apple"]
+> ```
+
+| Name           | Type          | Description                                      |
+| -------------- | ------------- | ------------------------------------------------ |
+| `id_or_string` | int / unicode | The hash value of a word, or its unicode string. |
+| **RETURNS**    | `Lexeme`      | The lexeme indicated by the given ID.            |
+
+## Vocab.\_\_iter\_\_ {#iter tag="method"}
+
+Iterate over the lexemes in the vocabulary.
+
+> #### Example
+>
+> ```python
+> stop_words = (lex for lex in nlp.vocab if lex.is_stop)
+> ```
+
+| Name       | Type     | Description                 |
+| ---------- | -------- | --------------------------- |
+| **YIELDS** | `Lexeme` | An entry in the vocabulary. |
+
+## Vocab.\_\_contains\_\_ {#contains tag="method"}
+
+Check whether the string has an entry in the vocabulary. To get the ID for a
+given string, you need to look it up in
+[`vocab.strings`](/api/vocab#attributes).
+
+> #### Example
+>
+> ```python
+> apple = nlp.vocab.strings[u"apple"]
+> oov = nlp.vocab.strings[u"dskfodkfos"]
+> assert apple in nlp.vocab
+> assert oov not in nlp.vocab
+> ```
+
+| Name        | Type    | Description                                        |
+| ----------- | ------- | -------------------------------------------------- |
+| `string`    | unicode | The ID string.                                     |
+| **RETURNS** | bool    | Whether the string has an entry in the vocabulary. |
+
+## Vocab.add_flag {#add_flag tag="method"}
+
+Set a new boolean flag to words in the vocabulary. The `flag_getter` function
+will be called over the words currently in the vocab, and then applied to new
+words as they occur. You'll then be able to access the flag value on each token,
+using `token.check_flag(flag_id)`.
+
+> #### Example
+>
+> ```python
+> def is_my_product(text):
+>     products = [u"spaCy", u"Thinc", u"displaCy"]
+>     return text in products
+>
+> MY_PRODUCT = nlp.vocab.add_flag(is_my_product)
+> doc = nlp(u"I like spaCy")
+> assert doc[2].check_flag(MY_PRODUCT) == True
+> ```
+
+| Name          | Type | Description                                                                                                                                     |
+| ------------- | ---- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
+| `flag_getter` | dict | A function `f(unicode) -> bool`, to get the flag value.                                                                                         |
+| `flag_id`     | int  | An integer between 1 and 63 (inclusive), specifying the bit at which the flag will be stored. If `-1`, the lowest available bit will be chosen. |
+| **RETURNS**   | int  | The integer ID by which the flag value can be checked.                                                                                          |
+
+## Vocab.reset_vectors {#reset_vectors tag="method" new="2"}
+
+Drop the current vector table. Because all vectors must be the same width, you
+have to call this to change the size of the vectors. Only one of the `width` and
+`shape` keyword arguments can be specified.
+
+> #### Example
+>
+> ```python
+> nlp.vocab.reset_vectors(width=300)
+> ```
+
+| Name    | Type | Description                            |
+| ------- | ---- | -------------------------------------- |
+| `width` | int  | The new width (keyword argument only). |
+| `shape` | int  | The new shape (keyword argument only). |
+
+## Vocab.prune_vectors {#prune_vectors tag="method" new="2"}
+
+Reduce the current vector table to `nr_row` unique entries. Words mapped to the
+discarded vectors will be remapped to the closest vector among those remaining.
+For example, suppose the original table had vectors for the words:
+`['sat', 'cat', 'feline', 'reclined']`. If we prune the vector table to, two
+rows, we would discard the vectors for "feline" and "reclined". These words
+would then be remapped to the closest remaining vector – so "feline" would have
+the same vector as "cat", and "reclined" would have the same vector as "sat".
+The similarities are judged by cosine. The original vectors may be large, so the
+cosines are calculated in minibatches, to reduce memory usage.
+
+> #### Example
+>
+> ```python
+> nlp.vocab.prune_vectors(10000)
+> assert len(nlp.vocab.vectors) <= 1000
+> ```
+
+| Name         | Type | Description                                                                                                                                                                                 |
+| ------------ | ---- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `nr_row`     | int  | The number of rows to keep in the vector table.                                                                                                                                             |
+| `batch_size` | int  | Batch of vectors for calculating the similarities. Larger batch sizes might be faster, while temporarily requiring more memory.                                                             |
+| **RETURNS**  | dict | A dictionary keyed by removed words mapped to `(string, score)` tuples, where `string` is the entry the removed word was mapped to, and `score` the similarity score between the two words. |
+
+## Vocab.get_vector {#get_vector tag="method" new="2"}
+
+Retrieve a vector for a word in the vocabulary. Words can be looked up by string
+or hash value. If no vectors data is loaded, a `ValueError` is raised.
+
+> #### Example
+>
+> ```python
+> nlp.vocab.get_vector(u"apple")
+> ```
+
+| Name        | Type                                     | Description                                                                   |
+| ----------- | ---------------------------------------- | ----------------------------------------------------------------------------- |
+| `orth`      | int / unicode                            | The hash value of a word, or its unicode string.                              |
+| **RETURNS** | `numpy.ndarray[ndim=1, dtype='float32']` | A word vector. Size and shape are determined by the `Vocab.vectors` instance. |
+
+## Vocab.set_vector {#set_vector tag="method" new="2"}
+
+Set a vector for a word in the vocabulary. Words can be referenced by by string
+or hash value.
+
+> #### Example
+>
+> ```python
+> nlp.vocab.set_vector(u"apple", array([...]))
+> ```
+
+| Name     | Type                                     | Description                                      |
+| -------- | ---------------------------------------- | ------------------------------------------------ |
+| `orth`   | int / unicode                            | The hash value of a word, or its unicode string. |
+| `vector` | `numpy.ndarray[ndim=1, dtype='float32']` | The vector to set.                               |
+
+## Vocab.has_vector {#has_vector tag="method" new="2"}
+
+Check whether a word has a vector. Returns `False` if no vectors are loaded.
+Words can be looked up by string or hash value.
+
+> #### Example
+>
+> ```python
+> if nlp.vocab.has_vector(u"apple"):
+>     vector = nlp.vocab.get_vector(u"apple")
+> ```
+
+| Name        | Type          | Description                                      |
+| ----------- | ------------- | ------------------------------------------------ |
+| `orth`      | int / unicode | The hash value of a word, or its unicode string. |
+| **RETURNS** | bool          | Whether the word has a vector.                   |
+
+## Vocab.to_disk {#to_disk tag="method" new="2"}
+
+Save the current state to a directory.
+
+> #### Example
+>
+> ```python
+> nlp.vocab.to_disk("/path/to/vocab")
+> ```
+
+| Name   | Type             | Description                                                                                                           |
+| ------ | ---------------- | --------------------------------------------------------------------------------------------------------------------- |
+| `path` | unicode / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
+
+## Vocab.from_disk {#from_disk tag="method" new="2"}
+
+Loads state from a directory. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> from spacy.vocab import Vocab
+> vocab = Vocab().from_disk("/path/to/vocab")
+> ```
+
+| Name        | Type             | Description                                                                |
+| ----------- | ---------------- | -------------------------------------------------------------------------- |
+| `path`      | unicode / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
+| **RETURNS** | `Vocab`          | The modified `Vocab` object.                                               |
+
+## Vocab.to_bytes {#to_bytes tag="method"}
+
+Serialize the current state to a binary string.
+
+> #### Example
+>
+> ```python
+> vocab_bytes = nlp.vocab.to_bytes()
+> ```
+
+| Name        | Type  | Description                                        |
+| ----------- | ----- | -------------------------------------------------- |
+| `**exclude` | -     | Named attributes to prevent from being serialized. |
+| **RETURNS** | bytes | The serialized form of the `Vocab` object.         |
+
+## Vocab.from_bytes {#from_bytes tag="method"}
+
+Load state from a binary string.
+
+> #### Example
+>
+> ```python
+> from spacy.vocab import Vocab
+> vocab_bytes = nlp.vocab.to_bytes()
+> vocab = Vocab()
+> vocab.from_bytes(vocab_bytes)
+> ```
+
+| Name         | Type    | Description                                    |
+| ------------ | ------- | ---------------------------------------------- |
+| `bytes_data` | bytes   | The data to load from.                         |
+| `**exclude`  | -       | Named attributes to prevent from being loaded. |
+| **RETURNS**  | `Vocab` | The `Vocab` object.                            |
+
+## Attributes {#attributes}
+
+> #### Example
+>
+> ```python
+> apple_id = nlp.vocab.strings[u"apple"]
+> assert type(apple_id) == int
+> PERSON = nlp.vocab.strings[u"PERSON"]
+> assert type(PERSON) == int
+> ```
+
+| Name                                 | Type          | Description                                   |
+| ------------------------------------ | ------------- | --------------------------------------------- |
+| `strings`                            | `StringStore` | A table managing the string-to-int mapping.   |
+| `vectors` <Tag variant="new">2</Tag> | `Vectors`     | A table associating word IDs to word vectors. |
+| `vectors_length`                     | int           | Number of dimensions for each word vector.    |
diff --git a/website/assets/img/architecture.svg b/website/docs/images/architecture.svg
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rename from website/assets/img/architecture.svg
rename to website/docs/images/architecture.svg
index 911aaec60..8279e6432 100644
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-    <text class="svg__architecture__text-small" dy="0.85em" transform="translate(273.5 77.5)" width="14" height="14">zh</text>
+    <text class="svg__architecture__text-small" dy="0.85em" transform="translate(273.5 77.5)" width="14" height="14">...</text>
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+    <text class="svg__architecture__text-medium" dy="1em" transform="translate(815 50)" width="58" height="18">Custom <tspan dy="1.25em" dx="-4.8em">Components</tspan></text>
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+    <text class="svg__architecture__text-medium" dy="1em" transform="translate(690 101.5)" width="84" height="18">TextCategorizer</text>
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+    <path class="displacy-arc" id="arrow-0-4" stroke-width="2px" d="M420,264.5 C420,2.0 925.0,2.0 925.0,264.5" fill="none" stroke="currentColor"/>
+    <text dy="1.25em" style="font-size: 16px; letter-spacing: 1px">
+        <textPath xlink:href="#arrow-0-4" class="displacy-label" startOffset="50%" fill="currentColor" text-anchor="middle">ATTRIBUTE</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="M925.0,266.5 L933.0,254.5 917.0,254.5" fill="currentColor"/>
+</g>
+</svg>
diff --git a/website/docs/images/displacy-ent-custom.html b/website/docs/images/displacy-ent-custom.html
new file mode 100644
index 000000000..15294db49
--- /dev/null
+++ b/website/docs/images/displacy-ent-custom.html
@@ -0,0 +1,9 @@
+<div class="entities" style="line-height: 2.5; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; font-size: 18px">But 
+<mark class="entity" style="background: linear-gradient(90deg, #AA9CFC, #FC9CE7); padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Google 
+<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">ORG</span></mark>is starting from behind. The company made a late push into hardware,
+and 
+<mark class="entity" style="background: linear-gradient(90deg, #AA9CFC, #FC9CE7); padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Apple 
+<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">ORG</span></mark>’s Siri, available on iPhones, and 
+<mark class="entity" style="background: linear-gradient(90deg, #AA9CFC, #FC9CE7); padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Amazon 
+<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">ORG</span></mark>’s Alexa software, which runs on its Echo and Dot devices, have clear
+leads in consumer adoption.</div>
diff --git a/website/docs/images/displacy-ent.html b/website/docs/images/displacy-ent.html
new file mode 100644
index 000000000..4432bfd45
--- /dev/null
+++ b/website/docs/images/displacy-ent.html
@@ -0,0 +1,18 @@
+<div class="entities" style="line-height: 2.5; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; font-size: 18px">But 
+<mark class="entity" style="background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Google 
+<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">ORG</span></mark>is starting from behind. The company made a late push into hardware,
+and 
+<mark class="entity" style="background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Apple 
+<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">ORG</span></mark>’s 
+<mark class="entity" style="background: #bfeeb7; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Siri 
+<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">PRODUCT</span></mark>, available on 
+<mark class="entity" style="background: #bfeeb7; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">iPhones 
+<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">PRODUCT</span></mark>, and 
+<mark class="entity" style="background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Amazon 
+<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">ORG</span></mark>’s 
+<mark class="entity" style="background: #bfeeb7; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Alexa 
+<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">PRODUCT</span></mark>software, which runs on its 
+<mark class="entity" style="background: #bfeeb7; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Echo 
+<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">PRODUCT</span></mark>and 
+<mark class="entity" style="background: #bfeeb7; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Dot 
+<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">PRODUCT</span></mark>devices, have clear leads in consumer adoption.</div>
diff --git a/website/docs/images/displacy-long.html b/website/docs/images/displacy-long.html
new file mode 100644
index 000000000..0e87a44a8
--- /dev/null
+++ b/website/docs/images/displacy-long.html
@@ -0,0 +1,105 @@
+<svg class="displacy" height="399.5" id="0" style="max-width: none; height: 399.5px; color: #000000; background: #ffffff; font-family: Arial" width="1800" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+  <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="50">Apple</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="50">NNP</tspan>
+  </text>
+  <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="225">is</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="225">VBZ</tspan>
+  </text>
+  <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="400">looking</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="400">VBG</tspan>
+  </text>
+  <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="575">at</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="575">IN</tspan>
+  </text>
+  <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="750">buying</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="750">VBG</tspan>
+  </text>
+  <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="925">U.K. startup</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="925">NN</tspan>
+  </text>
+  <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="1100">for</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="1100">IN</tspan>
+  </text>
+  <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="1275">$</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="1275">$</tspan>
+  </text>
+  <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="1450">1</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="1450">CD</tspan>
+  </text>
+  <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="1625">billion</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="1625">CD</tspan>
+  </text>
+  <g class="displacy-arrow">
+    <path class="displacy-arc" d="M70,264.5 C70,89.5 395.0,89.5 395.0,264.5" fill="none" id="arrow-0-0" stroke="currentColor" stroke-width="2px" />
+    <text dy="1.25em" style="font-size: 0.8em">
+      <textPath class="displacy-label" fill="currentColor" startOffset="50%" text-anchor="middle" xlink:href="#arrow-0-0">nsubj</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="M70,266.5 L62,254.5 78,254.5" fill="currentColor" />
+  </g>
+  <g class="displacy-arrow">
+    <path class="displacy-arc" d="M245,264.5 C245,177.0 390.0,177.0 390.0,264.5" fill="none" id="arrow-0-1" stroke="currentColor" stroke-width="2px" />
+    <text dy="1.25em" style="font-size: 0.8em">
+      <textPath class="displacy-label" fill="currentColor" startOffset="50%" text-anchor="middle" xlink:href="#arrow-0-1">aux</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="M245,266.5 L237,254.5 253,254.5" fill="currentColor" />
+  </g>
+  <g class="displacy-arrow">
+    <path class="displacy-arc" d="M420,264.5 C420,177.0 565.0,177.0 565.0,264.5" fill="none" id="arrow-0-2" stroke="currentColor" stroke-width="2px" />
+    <text dy="1.25em" style="font-size: 0.8em">
+      <textPath class="displacy-label" fill="currentColor" startOffset="50%" text-anchor="middle" xlink:href="#arrow-0-2">prep</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="M565.0,266.5 L573.0,254.5 557.0,254.5" fill="currentColor" />
+  </g>
+  <g class="displacy-arrow">
+    <path class="displacy-arc" d="M595,264.5 C595,177.0 740.0,177.0 740.0,264.5" fill="none" id="arrow-0-3" stroke="currentColor" stroke-width="2px" />
+    <text dy="1.25em" style="font-size: 0.8em">
+      <textPath class="displacy-label" fill="currentColor" startOffset="50%" text-anchor="middle" xlink:href="#arrow-0-3">pcomp</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="M740.0,266.5 L748.0,254.5 732.0,254.5" fill="currentColor" />
+  </g>
+  <g class="displacy-arrow">
+    <path class="displacy-arc" d="M770,264.5 C770,177.0 915.0,177.0 915.0,264.5" fill="none" id="arrow-0-4" stroke="currentColor" stroke-width="2px" />
+    <text dy="1.25em" style="font-size: 0.8em">
+      <textPath class="displacy-label" fill="currentColor" startOffset="50%" text-anchor="middle" xlink:href="#arrow-0-4">dobj</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="M915.0,266.5 L923.0,254.5 907.0,254.5" fill="currentColor" />
+  </g>
+  <g class="displacy-arrow">
+    <path class="displacy-arc" d="M770,264.5 C770,89.5 1095.0,89.5 1095.0,264.5" fill="none" id="arrow-0-5" stroke="currentColor" stroke-width="2px" />
+    <text dy="1.25em" style="font-size: 0.8em">
+      <textPath class="displacy-label" fill="currentColor" startOffset="50%" text-anchor="middle" xlink:href="#arrow-0-5">prep</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="M1095.0,266.5 L1103.0,254.5 1087.0,254.5" fill="currentColor" />
+  </g>
+  <g class="displacy-arrow">
+    <path class="displacy-arc" d="M1295,264.5 C1295,89.5 1620.0,89.5 1620.0,264.5" fill="none" id="arrow-0-6" stroke="currentColor" stroke-width="2px" />
+    <text dy="1.25em" style="font-size: 0.8em">
+      <textPath class="displacy-label" fill="currentColor" startOffset="50%" text-anchor="middle" xlink:href="#arrow-0-6">quantmod</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="M1295,266.5 L1287,254.5 1303,254.5" fill="currentColor" />
+  </g>
+  <g class="displacy-arrow">
+    <path class="displacy-arc" d="M1470,264.5 C1470,177.0 1615.0,177.0 1615.0,264.5" fill="none" id="arrow-0-7" stroke="currentColor" stroke-width="2px" />
+    <text dy="1.25em" style="font-size: 0.8em">
+      <textPath class="displacy-label" fill="currentColor" startOffset="50%" text-anchor="middle" xlink:href="#arrow-0-7">compound</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="M1470,266.5 L1462,254.5 1478,254.5" fill="currentColor" />
+  </g>
+  <g class="displacy-arrow">
+    <path class="displacy-arc" d="M1120,264.5 C1120,2.0 1625.0,2.0 1625.0,264.5" fill="none" id="arrow-0-8" stroke="currentColor" stroke-width="2px" />
+    <text dy="1.25em" style="font-size: 0.8em">
+      <textPath class="displacy-label" fill="currentColor" startOffset="50%" text-anchor="middle" xlink:href="#arrow-0-8">pobj</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="M1625.0,266.5 L1633.0,254.5 1617.0,254.5" fill="currentColor" />
+  </g>
+</svg>
diff --git a/website/docs/images/displacy-long2.html b/website/docs/images/displacy-long2.html
new file mode 100644
index 000000000..abe18c42a
--- /dev/null
+++ b/website/docs/images/displacy-long2.html
@@ -0,0 +1,84 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" id="0" class="displacy" width="1275" height="399.5" style="max-width: none; height: 399.5px; color: #000000; background: #ffffff; font-family: Arial">
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="50">Autonomous</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="50">ADJ</tspan>
+</text>
+
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="225">cars</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="225">NOUN</tspan>
+</text>
+
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="400">shift</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="400">VERB</tspan>
+</text>
+
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="575">insurance</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="575">NOUN</tspan>
+</text>
+
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="750">liability</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="750">NOUN</tspan>
+</text>
+
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="925">toward</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="925">ADP</tspan>
+</text>
+
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+    <tspan class="displacy-word" fill="currentColor" x="1100">manufacturers</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="1100">NOUN</tspan>
+</text>
+
+<g class="displacy-arrow">
+    <path class="displacy-arc" id="arrow-0-0" stroke-width="2px" d="M70,264.5 C70,177.0 215.0,177.0 215.0,264.5" fill="none" stroke="currentColor"></path>
+    <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+        <textpath xlink:href="#arrow-0-0" class="displacy-label" startOffset="50%" fill="currentColor" text-anchor="middle">amod</textpath>
+    </text>
+    <path class="displacy-arrowhead" d="M70,266.5 L62,254.5 78,254.5" fill="currentColor"></path>
+</g>
+
+<g class="displacy-arrow">
+    <path class="displacy-arc" id="arrow-0-1" stroke-width="2px" d="M245,264.5 C245,177.0 390.0,177.0 390.0,264.5" fill="none" stroke="currentColor"></path>
+    <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+        <textpath xlink:href="#arrow-0-1" class="displacy-label" startOffset="50%" fill="currentColor" text-anchor="middle">nsubj</textpath>
+    </text>
+    <path class="displacy-arrowhead" d="M245,266.5 L237,254.5 253,254.5" fill="currentColor"></path>
+</g>
+
+<g class="displacy-arrow">
+    <path class="displacy-arc" id="arrow-0-2" stroke-width="2px" d="M595,264.5 C595,177.0 740.0,177.0 740.0,264.5" fill="none" stroke="currentColor"></path>
+    <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+        <textpath xlink:href="#arrow-0-2" class="displacy-label" startOffset="50%" fill="currentColor" text-anchor="middle">compound</textpath>
+    </text>
+    <path class="displacy-arrowhead" d="M595,266.5 L587,254.5 603,254.5" fill="currentColor"></path>
+</g>
+
+<g class="displacy-arrow">
+    <path class="displacy-arc" id="arrow-0-3" stroke-width="2px" d="M420,264.5 C420,89.5 745.0,89.5 745.0,264.5" fill="none" stroke="currentColor"></path>
+    <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+        <textpath xlink:href="#arrow-0-3" class="displacy-label" startOffset="50%" fill="currentColor" text-anchor="middle">dobj</textpath>
+    </text>
+    <path class="displacy-arrowhead" d="M745.0,266.5 L753.0,254.5 737.0,254.5" fill="currentColor"></path>
+</g>
+
+<g class="displacy-arrow">
+    <path class="displacy-arc" id="arrow-0-4" stroke-width="2px" d="M420,264.5 C420,2.0 925.0,2.0 925.0,264.5" fill="none" stroke="currentColor"></path>
+    <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+        <textpath xlink:href="#arrow-0-4" class="displacy-label" startOffset="50%" fill="currentColor" text-anchor="middle">prep</textpath>
+    </text>
+    <path class="displacy-arrowhead" d="M925.0,266.5 L933.0,254.5 917.0,254.5" fill="currentColor"></path>
+</g>
+
+<g class="displacy-arrow">
+    <path class="displacy-arc" id="arrow-0-5" stroke-width="2px" d="M945,264.5 C945,177.0 1090.0,177.0 1090.0,264.5" fill="none" stroke="currentColor"></path>
+    <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+        <textpath xlink:href="#arrow-0-5" class="displacy-label" startOffset="50%" fill="currentColor" text-anchor="middle">pobj</textpath>
+    </text>
+    <path class="displacy-arrowhead" d="M1090.0,266.5 L1098.0,254.5 1082.0,254.5" fill="currentColor"></path>
+</g>
+</svg>
diff --git a/website/docs/images/displacy-model-rules.svg b/website/docs/images/displacy-model-rules.svg
new file mode 100644
index 000000000..71f7ffcad
--- /dev/null
+++ b/website/docs/images/displacy-model-rules.svg
@@ -0,0 +1,45 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" id="800-0" class="displacy" width="650" height="180" style="max-width: none; color: #000000; background: #ffffff; font-family: Arial">
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="122.0">
+    <tspan class="displacy-word" fill="currentColor" x="50">Alex Smith</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="50">NNP</tspan>
+</text>
+
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="122.0">
+    <tspan class="displacy-word" fill="currentColor" x="200">worked</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="200">VBD</tspan>
+</text>
+
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="122.0">
+    <tspan class="displacy-word" fill="currentColor" x="350">at</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="350">IN</tspan>
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diff --git a/website/docs/images/displacy-model-rules2.svg b/website/docs/images/displacy-model-rules2.svg
new file mode 100644
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+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="197.0">
+    <tspan class="displacy-word" fill="currentColor" x="50">Alex Smith</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="50">NNP</tspan>
+</text>
+
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="197.0">
+    <tspan class="displacy-word" fill="currentColor" x="200">was</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="200">VBD</tspan>
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+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="197.0">
+    <tspan class="displacy-word" fill="currentColor" x="350">working</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="350">VBG</tspan>
+</text>
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+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="197.0">
+    <tspan class="displacy-word" fill="currentColor" x="500">at</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="500">IN</tspan>
+</text>
+
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="197.0">
+    <tspan class="displacy-word" fill="currentColor" x="650">Acme Corp Inc.</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="650">NNP</tspan>
+</text>
+
+<g class="displacy-arrow">
+    <path class="displacy-arc" id="arrow-808-0-0" stroke-width="2px" d="M70,152.0 C70,2.0 350.0,2.0 350.0,152.0" fill="none" stroke="currentColor"/>
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+        <textPath xlink:href="#arrow-808-0-1" class="displacy-label" startOffset="50%" fill="currentColor" text-anchor="middle">aux</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="M220,154.0 L212,142.0 228,142.0" fill="currentColor"/>
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+<g class="displacy-arrow">
+    <path class="displacy-arc" id="arrow-808-0-2" stroke-width="2px" d="M370,152.0 C370,77.0 495.0,77.0 495.0,152.0" fill="none" stroke="currentColor"/>
+    <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+        <textPath xlink:href="#arrow-808-0-2" class="displacy-label" startOffset="50%" fill="currentColor" text-anchor="middle">prep</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="M495.0,154.0 L503.0,142.0 487.0,142.0" fill="currentColor"/>
+</g>
+
+<g class="displacy-arrow">
+    <path class="displacy-arc" id="arrow-808-0-3" stroke-width="2px" d="M520,152.0 C520,77.0 645.0,77.0 645.0,152.0" fill="none" stroke="currentColor"/>
+    <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+        <textPath xlink:href="#arrow-808-0-3" class="displacy-label" startOffset="50%" fill="currentColor" text-anchor="middle">pobj</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="M645.0,154.0 L653.0,142.0 637.0,142.0" fill="currentColor"/>
+</g>
+</svg>
diff --git a/website/docs/images/displacy-small.svg b/website/docs/images/displacy-small.svg
new file mode 100644
index 000000000..db7801992
--- /dev/null
+++ b/website/docs/images/displacy-small.svg
@@ -0,0 +1,33 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" class="o-svg" viewBox="270 35 125 240" width="400" height="150" style="max-width: none; color: #fff; background: #1a1e23; font-family: Arial; font-size: 20px">
+    <text fill="currentColor" text-anchor="middle" y="222.0">
+        <tspan style="font-weight: bold" fill="currentColor" x="50">This</tspan>
+        <tspan dy="2em" class="u-color-theme" style="font-weight: bold" fill="currentColor" x="50">DT</tspan>
+    </text>
+    <text fill="currentColor" text-anchor="middle" y="222.0">
+        <tspan style="font-weight: bold" fill="currentColor" x="225">is</tspan>
+        <tspan dy="2em" class="u-color-theme" style="font-weight: bold" fill="currentColor" x="225">VBZ</tspan>
+    </text>
+    <text fill="currentColor" text-anchor="middle" y="222.0">
+        <tspan style="font-weight: bold" fill="currentColor" x="400">a</tspan>
+        <tspan dy="2em" class="u-color-theme" style="font-weight: bold" fill="currentColor" x="400">DT</tspan>
+    </text>
+    <text fill="currentColor" text-anchor="middle" y="222.0">
+        <tspan style="font-weight: bold" fill="currentColor" x="575">sentence.</tspan>
+        <tspan dy="2em" class="u-color-theme" style="font-weight: bold" fill="currentColor" x="575">NN</tspan>
+    </text>
+    <path id="arrow-0-0" stroke-width="2px" d="M70,177.0 C70,89.5 220.0,89.5 220.0,177.0" fill="none" stroke="currentColor"/>
+    <text dy="1.25em" style="font-size: 0.9em; letter-spacing: 2px">
+        <textPath xlink:href="#arrow-0-0" startOffset="50%" fill="currentColor" text-anchor="middle">nsubj</textPath>
+    </text>
+    <path d="M70,179.0 L62,167.0 78,167.0" fill="currentColor"/>
+    <path id="arrow-0-1" stroke-width="2px" d="M420,177.0 C420,89.5 570.0,89.5 570.0,177.0" fill="none" stroke="currentColor"/>
+    <text dy="1.25em" style="font-size: 0.9em; letter-spacing: 2px">
+        <textPath xlink:href="#arrow-0-1" startOffset="50%" fill="currentColor" text-anchor="middle">det</textPath>
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+    <path d="M420,179.0 L412,167.0 428,167.0" fill="currentColor"/>
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+    <text dy="1.25em" style="font-size: 0.9em; letter-spacing: 2px">
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diff --git a/website/docs/images/displacy.svg b/website/docs/images/displacy.svg
new file mode 100644
index 000000000..0fbc7ab08
--- /dev/null
+++ b/website/docs/images/displacy.svg
@@ -0,0 +1,45 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" id="0" class="displacy" width="750" height="312.0" style="max-width: none; height: 312.0px; color: #000000; background: #ffffff; font-family: Arial">
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="222.0">
+    <tspan class="displacy-word" fill="currentColor" x="50">This</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="50">DT</tspan>
+</text>
+
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="222.0">
+    <tspan class="displacy-word" fill="currentColor" x="225">is</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="225">VBZ</tspan>
+</text>
+
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="222.0">
+    <tspan class="displacy-word" fill="currentColor" x="400">a</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="400">DT</tspan>
+</text>
+
+<text class="displacy-token" fill="currentColor" text-anchor="middle" y="222.0">
+    <tspan class="displacy-word" fill="currentColor" x="575">sentence.</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="575">NN</tspan>
+</text>
+
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+    <path class="displacy-arc" id="arrow-0-0" stroke-width="2px" d="M70,177.0 C70,89.5 220.0,89.5 220.0,177.0" fill="none" stroke="currentColor"/>
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+        <textPath xlink:href="#arrow-0-0" class="displacy-label" startOffset="50%" fill="currentColor" text-anchor="middle">nsubj</textPath>
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+    <path class="displacy-arrowhead" d="M70,179.0 L62,167.0 78,167.0" fill="currentColor"/>
+</g>
+
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+    <text dy="1.25em" style="font-size: 0.8em">
+        <textPath xlink:href="#arrow-0-2" class="displacy-label" startOffset="50%" fill="currentColor" text-anchor="middle">attr</textPath>
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+</g>
+</svg>
diff --git a/website/assets/img/displacy_jupyter.jpg b/website/docs/images/displacy_jupyter.jpg
similarity index 100%
rename from website/assets/img/displacy_jupyter.jpg
rename to website/docs/images/displacy_jupyter.jpg
diff --git a/website/assets/img/language_data.svg b/website/docs/images/language_data.svg
similarity index 97%
rename from website/assets/img/language_data.svg
rename to website/docs/images/language_data.svg
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--- a/website/assets/img/language_data.svg
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-    <text class="svg__langdata__text-small" transform="translate(375 187.5)" width="39" height="30">stop <tspan dx="-2.8em" dy="1.25em">words</tspan></text>
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-    <text class="svg__langdata__text-small" transform="translate(481 187.5)" width="85" height="30">lexical <tspan dx="-4em" dy="1.25em">attributes</tspan></text>
+    <text class="svg__langdata__text-small" transform="translate(473 187.5)" width="85" height="30">lexical <tspan dx="-4em" dy="1.25em">attributes</tspan></text>
     <path fill="none" stroke="#b85450" stroke-width="3" stroke-miterlimit="10" d="M653 225l5.6 127.8" stroke-dasharray="1 6" stroke-linecap="round"/>
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+    <text class="svg__langdata__text-small" transform="translate(582 187.5)" width="85" height="30">tokenizer <tspan dx="-5.2em" dy="1.25em">exceptions</tspan></text>
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diff --git a/website/assets/img/tokenization.svg b/website/docs/images/tokenization.svg
similarity index 100%
rename from website/assets/img/tokenization.svg
rename to website/docs/images/tokenization.svg
diff --git a/website/assets/img/training-loop.svg b/website/docs/images/training-loop.svg
similarity index 100%
rename from website/assets/img/training-loop.svg
rename to website/docs/images/training-loop.svg
diff --git a/website/assets/img/training.svg b/website/docs/images/training.svg
similarity index 98%
rename from website/assets/img/training.svg
rename to website/docs/images/training.svg
index 65f6de0b6..0a4649c48 100644
--- a/website/assets/img/training.svg
+++ b/website/docs/images/training.svg
@@ -25,7 +25,7 @@
     <path fill="none" stroke="#09a3d5" stroke-width="2" stroke-miterlimit="10" d="M121 19h61.8"/>
     <path fill="#09a3d5" stroke="#09a3d5" stroke-width="2" stroke-miterlimit="10" d="M188.8 19l-8 4 2-4-2-4z"/>
     <rect width="120" height="71" x="1" y="1" fill="#dae8fc" stroke="#09a3d5" stroke-width="2" rx="10.7" ry="10.7"/>
-    <text class="svg__training__text" dy="0.9em" transform="translate(13.5 26.5)" width="93" height="18">Training data</text>
+    <text class="svg__training__text" dy="0.9em" transform="translate(10 26.5)" width="93" height="18">Training data</text>
     <path fill="none" stroke="#87e02d" stroke-width="2" stroke-miterlimit="10" d="M311 54h51.8"/>
     <path fill="#87e02d" stroke="#87e02d" stroke-width="2" stroke-miterlimit="10" d="M368.8 54l-8 4 2-4-2-4z"/>
     <path fill="#dae8fc" stroke="#09a3d5" stroke-width="2" d="M191 39h120v30H191z"/>
diff --git a/website/assets/img/vocab_stringstore.svg b/website/docs/images/vocab_stringstore.svg
similarity index 100%
rename from website/assets/img/vocab_stringstore.svg
rename to website/docs/images/vocab_stringstore.svg
diff --git a/website/docs/index.md b/website/docs/index.md
new file mode 100644
index 000000000..48e487d08
--- /dev/null
+++ b/website/docs/index.md
@@ -0,0 +1,6 @@
+---
+---
+
+import Landing from 'widgets/landing.js'
+
+<Landing />
diff --git a/website/docs/models/index.md b/website/docs/models/index.md
new file mode 100644
index 000000000..1d2bd6d63
--- /dev/null
+++ b/website/docs/models/index.md
@@ -0,0 +1,166 @@
+---
+title: Models
+teaser: Downloadable statistical models for spaCy to predict linguistic features
+menu:
+  - ['Quickstart', 'quickstart']
+  - ['Model Architecture', 'architecture']
+  - ['Conventions', 'conventions']
+---
+
+spaCy v2.0 features new neural models for **tagging**, **parsing** and **entity
+recognition**. The models have been designed and implemented from scratch
+specifically for spaCy, to give you an unmatched balance of speed, size and
+accuracy. A novel bloom embedding strategy with subword features is used to
+support huge vocabularies in tiny tables. Convolutional layers with residual
+connections, layer normalization and maxout non-linearity are used, giving much
+better efficiency than the standard BiLSTM solution. For more details, see the
+notes on the [model architecture](#architecture).
+
+The parser and NER use an imitation learning objective to deliver **accuracy
+in-line with the latest research systems**, even when evaluated from raw text.
+With these innovations, spaCy v2.0's models are **10× smaller**, **20% more
+accurate**, and **even cheaper to run** than the previous generation.
+
+### Quickstart {hidden="true"}
+
+import QuickstartModels from 'widgets/quickstart-models.js'
+
+<QuickstartModels title="Quickstart" id="quickstart" description="Install a default model, get the code to load it from within spaCy and an example to test it. For more options, see the section on available models below." />
+
+<Infobox title="📖 Installation and usage">
+
+For more details on how to use models with spaCy, see the
+[usage guide on models](/usage/models).
+
+</Infobox>
+
+## Model architecture {#architecture}
+
+spaCy's statistical models have been custom-designed to give a high-performance
+mix of speed and accuracy. The current architecture hasn't been published yet,
+but in the meantime we prepared a video that explains how the models work, with
+particular focus on NER.
+
+<YouTube id="sqDHBH9IjRU" />
+
+The parsing model is a blend of recent results. The two recent inspirations have
+been the work of Eli Klipperwasser and Yoav Goldberg at Bar Ilan[^1], and the
+SyntaxNet team from Google. The foundation of the parser is still based on the
+work of Joakim Nivre[^2], who introduced the transition-based framework[^3], the
+arc-eager transition system, and the imitation learning objective. The model is
+implemented using [Thinc](https://github.com/explosion/thinc), spaCy's machine
+learning library. We first predict context-sensitive vectors for each word in
+the input:
+
+```python
+(embed_lower | embed_prefix | embed_suffix | embed_shape)
+    >> Maxout(token_width)
+    >> convolution ** 4
+```
+
+This convolutional layer is shared between the tagger, parser and NER, and will
+also be shared by the future neural lemmatizer. Because the parser shares these
+layers with the tagger, the parser does not require tag features. I got this
+trick from David Weiss's "Stack Combination" paper[^4].
+
+To boost the representation, the tagger actually predicts a "super tag" with
+POS, morphology and dependency label[^5]. The tagger predicts these supertags by
+adding a softmax layer onto the convolutional layer – so, we're teaching the
+convolutional layer to give us a representation that's one affine transform from
+this informative lexical information. This is obviously good for the parser
+(which backprops to the convolutions, too). The parser model makes a state
+vector by concatenating the vector representations for its context tokens. The
+current context tokens:
+
+| Context tokens                                                                     | Description                                                                 |
+| ---------------------------------------------------------------------------------- | --------------------------------------------------------------------------- |
+| `S0`, `S1`, `S2`                                                                   | Top three words on the stack.                                               |
+| `B0`, `B1`                                                                         | First two words of the buffer.                                              |
+| `S0L1`, `S1L1`, `S2L1`, `B0L1`, `B1L1`<br />`S0L2`, `S1L2`, `S2L2`, `B0L2`, `B1L2` | Leftmost and second leftmost children of `S0`, `S1`, `S2`, `B0` and `B1`.   |
+| `S0R1`, `S1R1`, `S2R1`, `B0R1`, `B1R1`<br />`S0R2`, `S1R2`, `S2R2`, `B0R2`, `B1R2` | Rightmost and second rightmost children of `S0`, `S1`, `S2`, `B0` and `B1`. |
+
+This makes the state vector quite long: `13*T`, where `T` is the token vector
+width (128 is working well). Fortunately, there's a way to structure the
+computation to save some expense (and make it more GPU-friendly).
+
+The parser typically visits `2*N` states for a sentence of length `N` (although
+it may visit more, if it back-tracks with a non-monotonic transition[^4]). A
+naive implementation would require `2*N (B, 13*T) @ (13*T, H)` matrix
+multiplications for a batch of size `B`. We can instead perform one
+`(B*N, T) @ (T, 13*H)` multiplication, to pre-compute the hidden weights for
+each positional feature with respect to the words in the batch. (Note that our
+token vectors come from the CNN — so we can't play this trick over the
+vocabulary. That's how Stanford's NN parser[^3] works — and why its model is so
+big.)
+
+This pre-computation strategy allows a nice compromise between GPU-friendliness
+and implementation simplicity. The CNN and the wide lower layer are computed on
+the GPU, and then the precomputed hidden weights are moved to the CPU, before we
+start the transition-based parsing process. This makes a lot of things much
+easier. We don't have to worry about variable-length batch sizes, and we don't
+have to implement the dynamic oracle in CUDA to train.
+
+Currently the parser's loss function is multi-label log loss[^6], as the dynamic
+oracle allows multiple states to be 0 cost. This is defined as follows, where
+`gZ` is the sum of the scores assigned to gold classes:
+
+```python
+(exp(score) / Z) - (exp(score) / gZ)
+```
+
+<Infobox title="Bibliography">
+
+1. [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations {#fn-1}](https://www.semanticscholar.org/paper/Simple-and-Accurate-Dependency-Parsing-Using-Bidir-Kiperwasser-Goldberg/3cf31ecb2724b5088783d7c96a5fc0d5604cbf41).
+   Eliyahu Kiperwasser, Yoav Goldberg. (2016)
+2. [A Dynamic Oracle for Arc-Eager Dependency Parsing {#fn-2}](https://www.semanticscholar.org/paper/A-Dynamic-Oracle-for-Arc-Eager-Dependency-Parsing-Goldberg-Nivre/22697256ec19ecc3e14fcfc63624a44cf9c22df4).
+   Yoav Goldberg, Joakim Nivre (2012)
+3. [Parsing English in 500 Lines of Python {#fn-3}](https://explosion.ai/blog/parsing-english-in-python).
+   Matthew Honnibal (2013)
+4. [Stack-propagation: Improved Representation Learning for Syntax {#fn-4}](https://www.semanticscholar.org/paper/Stack-propagation-Improved-Representation-Learning-Zhang-Weiss/0c133f79b23e8c680891d2e49a66f0e3d37f1466).
+   Yuan Zhang, David Weiss (2016)
+5. [Deep multi-task learning with low level tasks supervised at lower layers {#fn-5}](https://www.semanticscholar.org/paper/Deep-multi-task-learning-with-low-level-tasks-supe-S%C3%B8gaard-Goldberg/03ad06583c9721855ccd82c3d969a01360218d86).
+   Anders Søgaard, Yoav Goldberg (2016)
+6. [An Improved Non-monotonic Transition System for Dependency Parsing {#fn-6}](https://www.semanticscholar.org/paper/An-Improved-Non-monotonic-Transition-System-for-De-Honnibal-Johnson/4094cee47ade13b77b5ab4d2e6cb9dd2b8a2917c).
+   Matthew Honnibal, Mark Johnson (2015)
+7. [A Fast and Accurate Dependency Parser using Neural Networks {#fn-7}](http://cs.stanford.edu/people/danqi/papers/emnlp2014.pdf).
+   Danqi Cheng, Christopher D. Manning (2014)
+8. [Parsing the Wall Street Journal using a Lexical-Functional Grammar and Discriminative Estimation Techniques {#fn-8}](https://www.semanticscholar.org/paper/Parsing-the-Wall-Street-Journal-using-a-Lexical-Fu-Riezler-King/0ad07862a91cd59b7eb5de38267e47725a62b8b2).
+   Stefan Riezler et al. (2002)
+
+</Infobox>
+
+## Model naming conventions {#conventions}
+
+In general, spaCy expects all model packages to follow the naming convention of
+`[lang`\_[name]]. For spaCy's models, we also chose to divide the name into
+three components:
+
+1. **Type:** Model capabilities (e.g. `core` for general-purpose model with
+   vocabulary, syntax, entities and word vectors, or `depent` for only vocab,
+   syntax and entities).
+2. **Genre:** Type of text the model is trained on, e.g. `web` or `news`.
+3. **Size:** Model size indicator, `sm`, `md` or `lg`.
+
+For example, `en_core_web_sm` is a small English model trained on written web
+text (blogs, news, comments), that includes vocabulary, vectors, syntax and
+entities.
+
+### Model versioning {#model-versioning}
+
+Additionally, the model versioning reflects both the compatibility with spaCy,
+as well as the major and minor model version. A model version `a.b.c` translates
+to:
+
+- `a`: **spaCy major version**. For example, `2` for spaCy v2.x.
+- `b`: **Model major version**. Models with a different major version can't be
+  loaded by the same code. For example, changing the width of the model, adding
+  hidden layers or changing the activation changes the model major version.
+- `c`: **Model minor version**. Same model structure, but different parameter
+  values, e.g. from being trained on different data, for different numbers of
+  iterations, etc.
+
+For a detailed compatibility overview, see the
+[`compatibility.json`](https://github.com/explosion/spacy-models/tree/master/compatibility.json)
+in the models repository. This is also the source of spaCy's internal
+compatibility check, performed when you run the [`download`](/api/cli#download)
+command.
diff --git a/website/docs/styleguide.md b/website/docs/styleguide.md
new file mode 100644
index 000000000..009e937ca
--- /dev/null
+++ b/website/docs/styleguide.md
@@ -0,0 +1,28 @@
+---
+title: Styleguide
+section: styleguide
+menu:
+  - ['Logo', 'logo']
+  - ['Colors', 'colors']
+  - ['Typography', 'typography']
+  - ['Elements', 'elements']
+  - ['Components', 'components']
+  - ['Setup & Installation', 'setup']
+  - ['Markdown Reference', 'markdown']
+  - ['Project Structure', 'structure']
+sidebar:
+  - label: Styleguide
+    items:
+      - text: ''
+        url: '/styleguide'
+  - label: Resources
+    items:
+      - text: Website Source
+        url: https://github.com/explosion/spacy/tree/master/website
+      - text: Contributing Guide
+        url: https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md
+---
+
+import Readme from 'README.md'
+
+<Readme />
diff --git a/website/docs/usage/101/_architecture.md b/website/docs/usage/101/_architecture.md
new file mode 100644
index 000000000..d797250f9
--- /dev/null
+++ b/website/docs/usage/101/_architecture.md
@@ -0,0 +1,52 @@
+The central data structures in spaCy are the `Doc` and the `Vocab`. The `Doc`
+object owns the **sequence of tokens** and all their annotations. The `Vocab`
+object owns a set of **look-up tables** that make common information available
+across documents. By centralizing strings, word vectors and lexical attributes,
+we avoid storing multiple copies of this data. This saves memory, and ensures
+there's a **single source of truth**.
+
+Text annotations are also designed to allow a single source of truth: the `Doc`
+object owns the data, and `Span` and `Token` are **views that point into it**.
+The `Doc` object is constructed by the `Tokenizer`, and then **modified in
+place** by the components of the pipeline. The `Language` object coordinates
+these components. It takes raw text and sends it through the pipeline, returning
+an **annotated document**. It also orchestrates training and serialization.
+
+![Library architecture](../../images/architecture.svg)
+
+### Container objects {#architecture-containers}
+
+| Name                    | Description                                                                                                                                             |
+| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [`Doc`](/api/doc)       | A container for accessing linguistic annotations.                                                                                                       |
+| [`Span`](/api/span)     | A slice from a `Doc` object.                                                                                                                            |
+| [`Token`](/api/token)   | An individual token — i.e. a word, punctuation symbol, whitespace, etc.                                                                                 |
+| [`Lexeme`](/api/lexeme) | An entry in the vocabulary. It's a word type with no context, as opposed to a word token. It therefore has no part-of-speech tag, dependency parse etc. |
+
+### Processing pipeline {#architecture-pipeline}
+
+| Name                                          | Description                                                                                                                   |
+| --------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
+| [`Language`](/api/language)                   | A text-processing pipeline. Usually you'll load this once per process as `nlp` and pass the instance around your application. |
+| [`Tokenizer`](/api/tokenizer)                 | Segment text, and create `Doc` objects with the discovered segment boundaries.                                                |
+| [`Lemmatizer`](/api/lemmatizer)               | Determine the base forms of words.                                                                                            |
+| `Morphology`                                  | Assign linguistic features like lemmas, noun case, verb tense etc. based on the word and its part-of-speech tag.              |
+| [`Tagger`](/api/tagger)                       | Annotate part-of-speech tags on `Doc` objects.                                                                                |
+| [`DependencyParser`](/api/dependencyparser)   | Annotate syntactic dependencies on `Doc` objects.                                                                             |
+| [`EntityRecognizer`](/api/entityrecognizer)   | Annotate named entities, e.g. persons or products, on `Doc` objects.                                                          |
+| [`TextCategorizer`](/api/textcategorizer)     | Assign categories or labels to `Doc` objects.                                                                                 |
+| [`Matcher`](/api/matcher)                     | Match sequences of tokens, based on pattern rules, similar to regular expressions.                                            |
+| [`PhraseMatcher`](/api/phrasematcher)         | Match sequences of tokens based on phrases.                                                                                   |
+| [`EntityRuler`](/api/entityruler)             | Add entity spans to the `Doc` using token-based rules or exact phrase matches.                                                |
+| [`SentenceSegmenter`](/api/sentencesegmenter) | Implement custom sentence boundary detection logic that doesn't require the dependency parse.                                 |
+| [Other functions](/api/pipeline-functions)    | Automatically apply something to the `Doc`, e.g. to merge spans of tokens.                                                    |
+
+### Other classes {#architecture-other}
+
+| Name                              | Description                                                                                                   |
+| --------------------------------- | ------------------------------------------------------------------------------------------------------------- |
+| [`Vocab`](/api/vocab)             | A lookup table for the vocabulary that allows you to access `Lexeme` objects.                                 |
+| [`StringStore`](/api/stringstore) | Map strings to and from hash values.                                                                          |
+| [`Vectors`](/api/vectors)         | Container class for vector data keyed by string.                                                              |
+| [`GoldParse`](/api/goldparse)     | Collection for training annotations.                                                                          |
+| [`GoldCorpus`](/api/goldcorpus)   | An annotated corpus, using the JSON file format. Manages annotations for tagging, dependency parsing and NER. |
diff --git a/website/docs/usage/101/_language-data.md b/website/docs/usage/101/_language-data.md
new file mode 100644
index 000000000..782fc7e5d
--- /dev/null
+++ b/website/docs/usage/101/_language-data.md
@@ -0,0 +1,60 @@
+Every language is different – and usually full of **exceptions and special
+cases**, especially amongst the most common words. Some of these exceptions are
+shared across languages, while others are **entirely specific** – usually so
+specific that they need to be hard-coded. The
+[`lang`](https://github.com/explosion/spaCy/tree/master/spacy/lang) module
+contains all language-specific data, organized in simple Python files. This
+makes the data easy to update and extend.
+
+The **shared language data** in the directory root includes rules that can be
+generalized across languages – for example, rules for basic punctuation, emoji,
+emoticons, single-letter abbreviations and norms for equivalent tokens with
+different spellings, like `"` and `”`. This helps the models make more accurate
+predictions. The **individual language data** in a submodule contains rules that
+are only relevant to a particular language. It also takes care of putting
+together all components and creating the `Language` subclass – for example,
+`English` or `German`.
+
+> ```python
+> from spacy.lang.en import English
+> from spacy.lang.de import German
+>
+> nlp_en = English()  # Includes English data
+> nlp_de = German()  # Includes German data
+> ```
+
+![Language data architecture](../../images/language_data.svg)
+
+| Name                                                                               | Description                                                                                                                                              |
+| ---------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Stop words**<br />[`stop_words.py`][stop_words.py]                               | List of most common words of a language that are often useful to filter out, for example "and" or "I". Matching tokens will return `True` for `is_stop`. |
+| **Tokenizer exceptions**<br />[`tokenizer_exceptions.py`][tokenizer_exceptions.py] | Special-case rules for the tokenizer, for example, contractions like "can't" and abbreviations with punctuation, like "U.K.".                            |
+| **Norm exceptions**<br />[`norm_exceptions.py`][norm_exceptions.py]                | Special-case rules for normalizing tokens to improve the model's predictions, for example on American vs. British spelling.                              |
+| **Punctuation rules**<br />[`punctuation.py`][punctuation.py]                      | Regular expressions for splitting tokens, e.g. on punctuation or special characters like emoji. Includes rules for prefixes, suffixes and infixes.       |
+| **Character classes**<br />[`char_classes.py`][char_classes.py]                    | Character classes to be used in regular expressions, for example, latin characters, quotes, hyphens or icons.                                            |
+| **Lexical attributes**<br />[`lex_attrs.py`][lex_attrs.py]                         | Custom functions for setting lexical attributes on tokens, e.g. `like_num`, which includes language-specific words like "ten" or "hundred".              |
+| **Syntax iterators**<br />[`syntax_iterators.py`][syntax_iterators.py]             | Functions that compute views of a `Doc` object based on its syntax. At the moment, only used for [noun chunks](/usage/linguistic-features#noun-chunks).  |
+| **Lemmatizer**<br />[`lemmatizer.py`][lemmatizer.py]                               | Lemmatization rules or a lookup-based lemmatization table to assign base forms, for example "be" for "was".                                              |
+| **Tag map**<br />[`tag_map.py`][tag_map.py]                                        | Dictionary mapping strings in your tag set to [Universal Dependencies](http://universaldependencies.org/u/pos/all.html) tags.                            |
+| **Morph rules**<br />[`morph_rules.py`][morph_rules.py]                            | Exception rules for morphological analysis of irregular words like personal pronouns.                                                                    |
+
+[stop_words.py]:
+  https://github.com/explosion/spacy-dev-resources/tree/master/templates/new_language/stop_words.py
+[tokenizer_exceptions.py]:
+  https://github.com/explosion/spacy-dev-resources/tree/master/templates/new_language/tokenizer_exceptions.py
+[norm_exceptions.py]:
+  https://github.com/explosion/spaCy/tree/master/spacy/lang/norm_exceptions.py
+[punctuation.py]:
+  https://github.com/explosion/spaCy/tree/master/spacy/lang/punctuation.py
+[char_classes.py]:
+  https://github.com/explosion/spaCy/tree/master/spacy/lang/char_classes.py
+[lex_attrs.py]:
+  https://github.com/explosion/spacy-dev-resources/tree/master/templates/new_language/lex_attrs.py
+[syntax_iterators.py]:
+  https://github.com/explosion/spaCy/tree/master/spacy/lang/en/syntax_iterators.py
+[lemmatizer.py]:
+  https://github.com/explosion/spacy-dev-resources/tree/master/templates/new_language/lemmatizer.py
+[tag_map.py]:
+  https://github.com/explosion/spacy-dev-resources/tree/master/templates/new_language/tag_map.py
+[morph_rules.py]:
+  https://github.com/explosion/spaCy/tree/master/spacy/lang/en/morph_rules.py
diff --git a/website/docs/usage/101/_named-entities.md b/website/docs/usage/101/_named-entities.md
new file mode 100644
index 000000000..597c75e2b
--- /dev/null
+++ b/website/docs/usage/101/_named-entities.md
@@ -0,0 +1,38 @@
+A named entity is a "real-world object" that's assigned a name – for example, a
+person, a country, a product or a book title. spaCy can **recognize**
+[various types](/api/annotation#named-entities) of named entities in a document,
+by asking the model for a **prediction**. Because models are statistical and
+strongly depend on the examples they were trained on, this doesn't always work
+_perfectly_ and might need some tuning later, depending on your use case.
+
+Named entities are available as the `ents` property of a `Doc`:
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"Apple is looking at buying U.K. startup for $1 billion")
+
+for ent in doc.ents:
+    print(ent.text, ent.start_char, ent.end_char, ent.label_)
+```
+
+> - **Text:** The original entity text.
+> - **Start:** Index of start of entity in the `Doc`.
+> - **End:** Index of end of entity in the `Doc`.
+> - **LabeL:** Entity label, i.e. type.
+
+| Text        | Start | End | Label   | Description                                          |
+| ----------- | :---: | :-: | ------- | ---------------------------------------------------- |
+| Apple       |   0   |  5  | `ORG`   | Companies, agencies, institutions.                   |
+| U.K.        |  27   | 31  | `GPE`   | Geopolitical entity, i.e. countries, cities, states. |
+| \$1 billion |  44   | 54  | `MONEY` | Monetary values, including unit.                     |
+
+Using spaCy's built-in [displaCy visualizer](/usage/visualizers), here's what
+our example sentence and its named entities look like:
+
+import DisplaCyLongHtml from 'images/displacy-long.html'; import { Iframe } from
+'components/embed'
+
+<Iframe title="displaCy visualization of dependencies and entities" html={DisplaCyLongHtml} height={450} />
diff --git a/website/docs/usage/101/_pipelines.md b/website/docs/usage/101/_pipelines.md
new file mode 100644
index 000000000..c13f7f0fe
--- /dev/null
+++ b/website/docs/usage/101/_pipelines.md
@@ -0,0 +1,42 @@
+When you call `nlp` on a text, spaCy first tokenizes the text to produce a `Doc`
+object. The `Doc` is then processed in several different steps – this is also
+referred to as the **processing pipeline**. The pipeline used by the
+[default models](/models) consists of a tagger, a parser and an entity
+recognizer. Each pipeline component returns the processed `Doc`, which is then
+passed on to the next component.
+
+![The processing pipeline](../../images/pipeline.svg)
+
+> - **Name**: ID of the pipeline component.
+> - **Component:** spaCy's implementation of the component.
+> - **Creates:** Objects, attributes and properties modified and set by the
+>   component.
+
+| Name          | Component                                                          | Creates                                                     | Description                                      |
+| ------------- | ------------------------------------------------------------------ | ----------------------------------------------------------- | ------------------------------------------------ |
+| **tokenizer** | [`Tokenizer`](/api/tokenizer)                                      | `Doc`                                                       | Segment text into tokens.                        |
+| **tagger**    | [`Tagger`](/api/tagger)                                            | `Doc[i].tag`                                                | Assign part-of-speech tags.                      |
+| **parser**    | [`DependencyParser`](/api/dependencyparser)                        | `Doc[i].head`, `Doc[i].dep`, `Doc.sents`, `Doc.noun_chunks` | Assign dependency labels.                        |
+| **ner**       | [`EntityRecognizer`](/api/entityrecognizer)                        | `Doc.ents`, `Doc[i].ent_iob`, `Doc[i].ent_type`             | Detect and label named entities.                 |
+| **textcat**   | [`TextCategorizer`](/api/textcategorizer)                          | `Doc.cats`                                                  | Assign document labels.                          |
+| ...           | [custom components](/usage/processing-pipelines#custom-components) | `Doc._.xxx`, `Token._.xxx`, `Span._.xxx`                    | Assign custom attributes, methods or properties. |
+
+The processing pipeline always **depends on the statistical model** and its
+capabilities. For example, a pipeline can only include an entity recognizer
+component if the model includes data to make predictions of entity labels. This
+is why each model will specify the pipeline to use in its meta data, as a simple
+list containing the component names:
+
+```json
+"pipeline": ["tagger", "parser", "ner"]
+```
+
+import Accordion from 'components/accordion.js'
+
+<Accordion title="Does the order of pipeline components matter?">
+
+No
+
+</Accordion>
+
+---
diff --git a/website/docs/usage/101/_pos-deps.md b/website/docs/usage/101/_pos-deps.md
new file mode 100644
index 000000000..d86ee123d
--- /dev/null
+++ b/website/docs/usage/101/_pos-deps.md
@@ -0,0 +1,62 @@
+After tokenization, spaCy can **parse** and **tag** a given `Doc`. This is where
+the statistical model comes in, which enables spaCy to **make a prediction** of
+which tag or label most likely applies in this context. A model consists of
+binary data and is produced by showing a system enough examples for it to make
+predictions that generalize across the language – for example, a word following
+"the" in English is most likely a noun.
+
+Linguistic annotations are available as
+[`Token` attributes](/api/token#attributes). Like many NLP libraries, spaCy
+**encodes all strings to hash values** to reduce memory usage and improve
+efficiency. So to get the readable string representation of an attribute, we
+need to add an underscore `_` to its name:
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load('en_core_web_sm')
+doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
+
+for token in doc:
+    print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
+            token.shape_, token.is_alpha, token.is_stop)
+```
+
+> - **Text:** The original word text.
+> - **Lemma:** The base form of the word.
+> - **POS:** The simple part-of-speech tag.
+> - **Tag:** The detailed part-of-speech tag.
+> - **Dep:** Syntactic dependency, i.e. the relation between tokens.
+> - **Shape:** The word shape – capitalization, punctuation, digits.
+> - **is alpha:** Is the token an alpha character?
+> - **is stop:** Is the token part of a stop list, i.e. the most common words of
+>   the language?
+
+| Text    | Lemma   | POS     | Tag   | Dep        | Shape   | alpha   | stop    |
+| ------- | ------- | ------- | ----- | ---------- | ------- | ------- | ------- |
+| Apple   | apple   | `PROPN` | `NNP` | `nsubj`    | `Xxxxx` | `True`  | `False` |
+| is      | be      | `VERB`  | `VBZ` | `aux`      | `xx`    | `True`  | `True`  |
+| looking | look    | `VERB`  | `VBG` | `ROOT`     | `xxxx`  | `True`  | `False` |
+| at      | at      | `ADP`   | `IN`  | `prep`     | `xx`    | `True`  | `True`  |
+| buying  | buy     | `VERB`  | `VBG` | `pcomp`    | `xxxx`  | `True`  | `False` |
+| U.K.    | u.k.    | `PROPN` | `NNP` | `compound` | `X.X.`  | `False` | `False` |
+| startup | startup | `NOUN`  | `NN`  | `dobj`     | `xxxx`  | `True`  | `False` |
+| for     | for     | `ADP`   | `IN`  | `prep`     | `xxx`   | `True`  | `True`  |
+| \$      | \$      | `SYM`   | `$`   | `quantmod` | `$`     | `False` | `False` |
+| 1       | 1       | `NUM`   | `CD`  | `compound` | `d`     | `False` | `False` |
+| billion | billion | `NUM`   | `CD`  | `probj`    | `xxxx`  | `True`  | `False` |
+
+> #### Tip: Understanding tags and labels
+>
+> Most of the tags and labels look pretty abstract, and they vary between
+> languages. `spacy.explain` will show you a short description – for example,
+> `spacy.explain("VBZ")` returns "verb, 3rd person singular present".
+
+Using spaCy's built-in [displaCy visualizer](/usage/visualizers), here's what
+our example sentence and its dependencies look like:
+
+import DisplaCyLongHtml from 'images/displacy-long.html'; import { Iframe } from
+'components/embed'
+
+<Iframe title="displaCy visualization of dependencies and entities" html={DisplaCyLongHtml} height={450} />
diff --git a/website/docs/usage/101/_serialization.md b/website/docs/usage/101/_serialization.md
new file mode 100644
index 000000000..9b00ece04
--- /dev/null
+++ b/website/docs/usage/101/_serialization.md
@@ -0,0 +1,29 @@
+If you've been modifying the pipeline, vocabulary, vectors and entities, or made
+updates to the model, you'll eventually want to **save your progress** – for
+example, everything that's in your `nlp` object. This means you'll have to
+translate its contents and structure into a format that can be saved, like a
+file or a byte string. This process is called serialization. spaCy comes with
+**built-in serialization methods** and supports the
+[Pickle protocol](http://www.diveintopython3.net/serializing.html#dump).
+
+> #### What's pickle?
+>
+> Pickle is Python's built-in object persistence system. It lets you transfer
+> arbitrary Python objects between processes. This is usually used to load an
+> object to and from disk, but it's also used for distributed computing, e.g.
+> with
+> [PySpark](https://spark.apache.org/docs/0.9.0/python-programming-guide.html)
+> or [Dask](http://dask.pydata.org/en/latest/). When you unpickle an object,
+> you're agreeing to execute whatever code it contains. It's like calling
+> `eval()` on a string – so don't unpickle objects from untrusted sources.
+
+All container classes, i.e. [`Language`](/api/language) (`nlp`),
+[`Doc`](/api/doc), [`Vocab`](/api/vocab) and [`StringStore`](/api/stringstore)
+have the following methods available:
+
+| Method       | Returns | Example                  |
+| ------------ | ------- | ------------------------ |
+| `to_bytes`   | bytes   | `data = nlp.to_bytes()`  |
+| `from_bytes` | object  | `nlp.from_bytes(data)`   |
+| `to_disk`    | -       | `nlp.to_disk("/path")`   |
+| `from_disk`  | object  | `nlp.from_disk("/path")` |
diff --git a/website/docs/usage/101/_tokenization.md b/website/docs/usage/101/_tokenization.md
new file mode 100644
index 000000000..e5f3d3080
--- /dev/null
+++ b/website/docs/usage/101/_tokenization.md
@@ -0,0 +1,50 @@
+During processing, spaCy first **tokenizes** the text, i.e. segments it into
+words, punctuation and so on. This is done by applying rules specific to each
+language. For example, punctuation at the end of a sentence should be split off
+– whereas "U.K." should remain one token. Each `Doc` consists of individual
+tokens, and we can iterate over them:
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"Apple is looking at buying U.K. startup for $1 billion")
+for token in doc:
+    print(token.text)
+```
+
+|   0   |  1  |    2    |  3  |   4    |  5   |    6    |  7  |  8  |  9  |   10    |
+| :---: | :-: | :-----: | :-: | :----: | :--: | :-----: | :-: | :-: | :-: | :-----: |
+| Apple | is  | looking | at  | buying | U.K. | startup | for | \$  |  1  | billion |
+
+First, the raw text is split on whitespace characters, similar to
+`text.split(' ')`. Then, the tokenizer processes the text from left to right. On
+each substring, it performs two checks:
+
+1. **Does the substring match a tokenizer exception rule?** For example, "don't"
+   does not contain whitespace, but should be split into two tokens, "do" and
+   "n't", while "U.K." should always remain one token.
+
+2. **Can a prefix, suffix or infix be split off?** For example punctuation like
+   commas, periods, hyphens or quotes.
+
+If there's a match, the rule is applied and the tokenizer continues its loop,
+starting with the newly split substrings. This way, spaCy can split **complex,
+nested tokens** like combinations of abbreviations and multiple punctuation
+marks.
+
+> - **Tokenizer exception:** Special-case rule to split a string into several
+>   tokens or prevent a token from being split when punctuation rules are
+>   applied.
+> - **Prefix:** Character(s) at the beginning, e.g. `$`, `(`, `“`, `¿`.
+> - **Suffix:** Character(s) at the end, e.g. `km`, `)`, `”`, `!`.
+> - **Infix:** Character(s) in between, e.g. `-`, `--`, `/`, `…`.
+
+![Example of the tokenization process](../../images/tokenization.svg)
+
+While punctuation rules are usually pretty general, tokenizer exceptions
+strongly depend on the specifics of the individual language. This is why each
+[available language](/usage/models#languages) has its own subclass like
+`English` or `German`, that loads in lists of hard-coded data and exception
+rules.
diff --git a/website/docs/usage/101/_training.md b/website/docs/usage/101/_training.md
new file mode 100644
index 000000000..61e047748
--- /dev/null
+++ b/website/docs/usage/101/_training.md
@@ -0,0 +1,38 @@
+spaCy's models are **statistical** and every "decision" they make – for example,
+which part-of-speech tag to assign, or whether a word is a named entity – is a
+**prediction**. This prediction is based on the examples the model has seen
+during **training**. To train a model, you first need training data – examples
+of text, and the labels you want the model to predict. This could be a
+part-of-speech tag, a named entity or any other information.
+
+The model is then shown the unlabelled text and will make a prediction. Because
+we know the correct answer, we can give the model feedback on its prediction in
+the form of an **error gradient** of the **loss function** that calculates the
+difference between the training example and the expected output. The greater the
+difference, the more significant the gradient and the updates to our model.
+
+> - **Training data:** Examples and their annotations.
+> - **Text:** The input text the model should predict a label for.
+> - **Label:** The label the model should predict.
+> - **Gradient:** Gradient of the loss function calculating the difference
+>   between input and expected output.
+
+![The training process](../../images/training.svg)
+
+When training a model, we don't just want it to memorize our examples – we want
+it to come up with theory that can be **generalized across other examples**.
+After all, we don't just want the model to learn that this one instance of
+"Amazon" right here is a company – we want it to learn that "Amazon", in
+contexts _like this_, is most likely a company. That's why the training data
+should always be representative of the data we want to process. A model trained
+on Wikipedia, where sentences in the first person are extremely rare, will
+likely perform badly on Twitter. Similarly, a model trained on romantic novels
+will likely perform badly on legal text.
+
+This also means that in order to know how the model is performing, and whether
+it's learning the right things, you don't only need **training data** – you'll
+also need **evaluation data**. If you only test the model with the data it was
+trained on, you'll have no idea how well it's generalizing. If you want to train
+a model from scratch, you usually need at least a few hundred examples for both
+training and evaluation. To update an existing model, you can already achieve
+decent results with very few examples – as long as they're representative.
diff --git a/website/docs/usage/101/_vectors-similarity.md b/website/docs/usage/101/_vectors-similarity.md
new file mode 100644
index 000000000..2001d1481
--- /dev/null
+++ b/website/docs/usage/101/_vectors-similarity.md
@@ -0,0 +1,102 @@
+import Infobox from 'components/infobox'
+
+Similarity is determined by comparing **word vectors** or "word embeddings",
+multi-dimensional meaning representations of a word. Word vectors can be
+generated using an algorithm like
+[word2vec](https://en.wikipedia.org/wiki/Word2vec) and usually look like this:
+
+```python
+### banana.vector
+array([2.02280000e-01,  -7.66180009e-02,   3.70319992e-01,
+       3.28450017e-02,  -4.19569999e-01,   7.20689967e-02,
+      -3.74760002e-01,   5.74599989e-02,  -1.24009997e-02,
+       5.29489994e-01,  -5.23800015e-01,  -1.97710007e-01,
+      -3.41470003e-01,   5.33169985e-01,  -2.53309999e-02,
+       1.73800007e-01,   1.67720005e-01,   8.39839995e-01,
+       5.51070012e-02,   1.05470002e-01,   3.78719985e-01,
+       2.42750004e-01,   1.47449998e-02,   5.59509993e-01,
+       1.25210002e-01,  -6.75960004e-01,   3.58420014e-01,
+       # ... and so on ...
+       3.66849989e-01,   2.52470002e-03,  -6.40089989e-01,
+      -2.97650009e-01,   7.89430022e-01,   3.31680000e-01,
+      -1.19659996e+00,  -4.71559986e-02,   5.31750023e-01], dtype=float32)
+```
+
+<Infobox title="Important note" variant="warning">
+
+To make them compact and fast, spaCy's small [models](/models) (all packages
+that end in `sm`) **don't ship with word vectors**, and only include
+context-sensitive **tensors**. This means you can still use the `similarity()`
+methods to compare documents, spans and tokens – but the result won't be as
+good, and individual tokens won't have any vectors assigned. So in order to use
+_real_ word vectors, you need to download a larger model:
+
+```diff
+- python -m spacy download en_core_web_sm
++ python -m spacy download en_core_web_lg
+```
+
+</Infobox>
+
+Models that come with built-in word vectors make them available as the
+[`Token.vector`](/api/token#vector) attribute. [`Doc.vector`](/api/doc#vector)
+and [`Span.vector`](/api/span#vector) will default to an average of their token
+vectors. You can also check if a token has a vector assigned, and get the L2
+norm, which can be used to normalize vectors.
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load('en_core_web_md')
+tokens = nlp(u'dog cat banana afskfsd')
+
+for token in tokens:
+    print(token.text, token.has_vector, token.vector_norm, token.is_oov)
+```
+
+> - **Text**: The original token text.
+> - **has vector**: Does the token have a vector representation?
+> - **Vector norm**: The L2 norm of the token's vector (the square root of the
+>   sum of the values squared)
+> - **OOV**: Out-of-vocabulary
+
+The words "dog", "cat" and "banana" are all pretty common in English, so they're
+part of the model's vocabulary, and come with a vector. The word "afskfsd" on
+the other hand is a lot less common and out-of-vocabulary – so its vector
+representation consists of 300 dimensions of `0`, which means it's practically
+nonexistent. If your application will benefit from a **large vocabulary** with
+more vectors, you should consider using one of the larger models or loading in a
+full vector package, for example,
+[`en_vectors_web_lg`](/models/en#en_vectors_web_lg), which includes over **1
+million unique vectors**.
+
+spaCy is able to compare two objects, and make a prediction of **how similar
+they are**. Predicting similarity is useful for building recommendation systems
+or flagging duplicates. For example, you can suggest a user content that's
+similar to what they're currently looking at, or label a support ticket as a
+duplicate if it's very similar to an already existing one.
+
+Each `Doc`, `Span` and `Token` comes with a
+[`.similarity()`](/api/token#similarity) method that lets you compare it with
+another object, and determine the similarity. Of course similarity is always
+subjective – whether "dog" and "cat" are similar really depends on how you're
+looking at it. spaCy's similarity model usually assumes a pretty general-purpose
+definition of similarity.
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load('en_core_web_md')  # make sure to use larger model!
+tokens = nlp(u'dog cat banana')
+
+for token1 in tokens:
+    for token2 in tokens:
+        print(token1.text, token2.text, token1.similarity(token2))
+```
+
+In this case, the model's predictions are pretty on point. A dog is very similar
+to a cat, whereas a banana is not very similar to either of them. Identical
+tokens are obviously 100% similar to each other (just not always exactly `1.0`,
+because of vector math and floating point imprecisions).
diff --git a/website/docs/usage/_benchmarks-choi.md b/website/docs/usage/_benchmarks-choi.md
new file mode 100644
index 000000000..47d6f479f
--- /dev/null
+++ b/website/docs/usage/_benchmarks-choi.md
@@ -0,0 +1,10 @@
+import { Help } from 'components/typography'
+
+| System         | Year | Language        | Accuracy |                                                                                                                                                                    Speed (wps) |
+| -------------- | ---- | --------------- | -------: | -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| **spaCy v2.x** | 2017 | Python / Cython | **92.6** | _n/a_ <Help>This table shows speed as benchmarked by Choi et al. We therefore can't provide comparable figures, as we'd be running the benchmark on different hardware.</Help> |
+| **spaCy v1.x** | 2015 | Python / Cython |     91.8 |                                                                                                                                                                         13,963 |
+| ClearNLP       | 2015 | Java            |     91.7 |                                                                                                                                                                         10,271 |
+| CoreNLP        | 2015 | Java            |     89.6 |                                                                                                                                                                          8,602 |
+| MATE           | 2015 | Java            |     92.5 |                                                                                                                                                                            550 |
+| Turbo          | 2015 | C++             |     92.4 |                                                                                                                                                                            349 |
diff --git a/website/docs/usage/adding-languages.md b/website/docs/usage/adding-languages.md
new file mode 100644
index 000000000..ca7d26b74
--- /dev/null
+++ b/website/docs/usage/adding-languages.md
@@ -0,0 +1,675 @@
+---
+title: Adding Languages
+next: /usage/training
+menu:
+  - ['Language Data', 'language-data']
+  - ['Testing', 'testing']
+  - ['Training', 'training']
+---
+
+Adding full support for a language touches many different parts of the spaCy
+library. This guide explains how to fit everything together, and points you to
+the specific workflows for each component.
+
+> #### Working on spaCy's source
+>
+> To add a new language to spaCy, you'll need to **modify the library's code**.
+> The easiest way to do this is to clone the
+> [repository](https://github.com/explosion/spaCy/tree/master/) and **build
+> spaCy from source**. For more information on this, see the
+> [installation guide](/usage). Unlike spaCy's core, which is mostly written in
+> Cython, all language data is stored in regular Python files. This means that
+> you won't have to rebuild anything in between – you can simply make edits and
+> reload spaCy to test them.
+
+<Grid cols={2}>
+
+<div>
+
+Obviously, there are lots of ways you can organize your code when you implement
+your own language data. This guide will focus on how it's done within spaCy. For
+full language support, you'll need to create a `Language` subclass, define
+custom **language data**, like a stop list and tokenizer exceptions and test the
+new tokenizer. Once the language is set up, you can **build the vocabulary**,
+including word frequencies, Brown clusters and word vectors. Finally, you can
+**train the tagger and parser**, and save the model to a directory.
+
+For some languages, you may also want to develop a solution for lemmatization
+and morphological analysis.
+
+</div>
+
+<Infobox title="Table of Contents">
+
+- [Language data 101](#101)
+- [The Language subclass](#language-subclass)
+- [Stop words](#stop-words)
+- [Tokenizer exceptions](#tokenizer-exceptions)
+- [Norm exceptions](#norm-exceptions)
+- [Lexical attributes](#lex-attrs)
+- [Syntax iterators](#syntax-iterators)
+- [Lemmatizer](#lemmatizer)
+- [Tag map](#tag-map)
+- [Morph rules](#morph-rules)
+- [Testing the language](#testing)
+- [Training](#training)
+
+</Infobox>
+
+</Grid>
+
+## Language data {#language-data}
+
+import LanguageData101 from 'usage/101/\_language-data.md'
+
+<LanguageData101 />
+
+The individual components **expose variables** that can be imported within a
+language module, and added to the language's `Defaults`. Some components, like
+the punctuation rules, usually don't need much customization and can be imported
+from the global rules. Others, like the tokenizer and norm exceptions, are very
+specific and will make a big difference to spaCy's performance on the particular
+language and training a language model.
+
+| Variable                                  | Type  | Description                                                                                                |
+| ----------------------------------------- | ----- | ---------------------------------------------------------------------------------------------------------- |
+| `STOP_WORDS`                              | set   | Individual words.                                                                                          |
+| `TOKENIZER_EXCEPTIONS`                    | dict  | Keyed by strings mapped to list of one dict per token with token attributes.                               |
+| `TOKEN_MATCH`                             | regex | Regexes to match complex tokens, e.g. URLs.                                                                |
+| `NORM_EXCEPTIONS`                         | dict  | Keyed by strings, mapped to their norms.                                                                   |
+| `TOKENIZER_PREFIXES`                      | list  | Strings or regexes, usually not customized.                                                                |
+| `TOKENIZER_SUFFIXES`                      | list  | Strings or regexes, usually not customized.                                                                |
+| `TOKENIZER_INFIXES`                       | list  | Strings or regexes, usually not customized.                                                                |
+| `LEX_ATTRS`                               | dict  | Attribute ID mapped to function.                                                                           |
+| `SYNTAX_ITERATORS`                        | dict  | Iterator ID mapped to function. Currently only supports `'noun_chunks'`.                                   |
+| `LOOKUP`                                  | dict  | Keyed by strings mapping to their lemma.                                                                   |
+| `LEMMA_RULES`, `LEMMA_INDEX`, `LEMMA_EXC` | dict  | Lemmatization rules, keyed by part of speech.                                                              |
+| `TAG_MAP`                                 | dict  | Keyed by strings mapped to [Universal Dependencies](http://universaldependencies.org/u/pos/all.html) tags. |
+| `MORPH_RULES`                             | dict  | Keyed by strings mapped to a dict of their morphological features.                                         |
+
+> #### Should I ever update the global data?
+>
+> Reusable language data is collected as atomic pieces in the root of the
+> [`spacy.lang`](https://github.com/explosion/spaCy/tree/master/spacy/lang)
+> module. Often, when a new language is added, you'll find a pattern or symbol
+> that's missing. Even if it isn't common in other languages, it might be best
+> to add it to the shared language data, unless it has some conflicting
+> interpretation. For instance, we don't expect to see guillemot quotation
+> symbols (`»` and `«`) in English text. But if we do see them, we'd probably
+> prefer the tokenizer to split them off.
+
+<Infobox title="For languages with non-latin characters">
+
+In order for the tokenizer to split suffixes, prefixes and infixes, spaCy needs
+to know the language's character set. If the language you're adding uses
+non-latin characters, you might need to add the required character classes to
+the global
+[`char_classes.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/char_classes.py).
+spaCy uses the [`regex` library](https://pypi.python.org/pypi/regex/) to keep
+this simple and readable. If the language requires very specific punctuation
+rules, you should consider overwriting the default regular expressions with your
+own in the language's `Defaults`.
+
+</Infobox>
+
+### Creating a `Language` subclass {#language-subclass}
+
+Language-specific code and resources should be organized into a sub-package of
+spaCy, named according to the language's
+[ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). For instance,
+code and resources specific to Spanish are placed into a directory
+`spacy/lang/es`, which can be imported as `spacy.lang.es`.
+
+To get started, you can use our
+[templates](https://github.com/explosion/spacy-dev-resources/templates/new_language)
+for the most important files. Here's what the class template looks like:
+
+```python
+### __init__.py (excerpt)
+# import language-specific data
+from .stop_words import STOP_WORDS
+from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
+from .lex_attrs import LEX_ATTRS
+
+from ..tokenizer_exceptions import BASE_EXCEPTIONS
+from ...language import Language
+from ...attrs import LANG
+from ...util import update_exc
+
+# Create Defaults class in the module scope (necessary for pickling!)
+class XxxxxDefaults(Language.Defaults):
+    lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
+    lex_attr_getters[LANG] = lambda text: "xx" # language ISO code
+
+    # Optional: replace flags with custom functions, e.g. like_num()
+    lex_attr_getters.update(LEX_ATTRS)
+
+    # Merge base exceptions and custom tokenizer exceptions
+    tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
+    stop_words = STOP_WORDS
+
+# Create actual Language class
+class Xxxxx(Language):
+    lang = "xx" # Language ISO code
+    Defaults = XxxxxDefaults # Override defaults
+
+# Set default export – this allows the language class to be lazy-loaded
+__all__ = ["Xxxxx"]
+```
+
+<Infobox title="Why lazy-loading?">
+
+Some languages contain large volumes of custom data, like lemmatizer lookup
+tables, or complex regular expression that are expensive to compute. As of spaCy
+v2.0, `Language` classes are not imported on initialization and are only loaded
+when you import them directly, or load a model that requires a language to be
+loaded. To lazy-load languages in your application, you can use the
+[`util.get_lang_class`](/api/top-level#util.get_lang_class) helper function with
+the two-letter language code as its argument.
+
+</Infobox>
+
+### Stop words {#stop-words}
+
+A ["stop list"](https://en.wikipedia.org/wiki/Stop_words) is a classic trick
+from the early days of information retrieval when search was largely about
+keyword presence and absence. It is still sometimes useful today to filter out
+common words from a bag-of-words model. To improve readability, `STOP_WORDS` are
+separated by spaces and newlines, and added as a multiline string.
+
+> #### What does spaCy consider a stop word?
+>
+> There's no particularly principled logic behind what words should be added to
+> the stop list. Make a list that you think might be useful to people and is
+> likely to be unsurprising. As a rule of thumb, words that are very rare are
+> unlikely to be useful stop words.
+
+```python
+### Example
+STOP_WORDS = set("""
+a about above across after afterwards again against all almost alone along
+already also although always am among amongst amount an and another any anyhow
+anyone anything anyway anywhere are around as at
+
+back be became because become becomes becoming been before beforehand behind
+being below beside besides between beyond both bottom but by
+""".split())
+```
+
+<Infobox title="Important note" variant="warning">
+
+When adding stop words from an online source, always **include the link** in a
+comment. Make sure to **proofread** and double-check the words carefully. A lot
+of the lists available online have been passed around for years and often
+contain mistakes, like unicode errors or random words that have once been added
+for a specific use case, but don't actually qualify.
+
+</Infobox>
+
+### Tokenizer exceptions {#tokenizer-exceptions}
+
+spaCy's [tokenization algorithm](/usage/linguistic-features#how-tokenizer-works)
+lets you deal with whitespace-delimited chunks separately. This makes it easy to
+define special-case rules, without worrying about how they interact with the
+rest of the tokenizer. Whenever the key string is matched, the special-case rule
+is applied, giving the defined sequence of tokens. You can also attach
+attributes to the subtokens, covered by your special case, such as the subtokens
+`LEMMA` or `TAG`.
+
+Tokenizer exceptions can be added in the following format:
+
+```python
+### tokenizer_exceptions.py (excerpt)
+TOKENIZER_EXCEPTIONS = {
+    "don't": [
+        {ORTH: "do", LEMMA: "do"},
+        {ORTH: "n't", LEMMA: "not", NORM: "not", TAG: "RB"}]
+}
+```
+
+<Infobox title="Important note" variant="warning">
+
+If an exception consists of more than one token, the `ORTH` values combined
+always need to **match the original string**. The way the original string is
+split up can be pretty arbitrary sometimes – for example `"gonna"` is split into
+`"gon"` (lemma "go") and `"na"` (lemma "to"). Because of how the tokenizer
+works, it's currently not possible to split single-letter strings into multiple
+tokens.
+
+</Infobox>
+
+Unambiguous abbreviations, like month names or locations in English, should be
+added to exceptions with a lemma assigned, for example
+`{ORTH: "Jan.", LEMMA: "January"}`. Since the exceptions are added in Python,
+you can use custom logic to generate them more efficiently and make your data
+less verbose. How you do this ultimately depends on the language. Here's an
+example of how exceptions for time formats like "1a.m." and "1am" are generated
+in the English
+[`tokenizer_exceptions.py`](https://github.com/explosion/spaCy/tree/master/spacy/en/lang/tokenizer_exceptions.py):
+
+```python
+### tokenizer_exceptions.py (excerpt)
+# use short, internal variable for readability
+_exc = {}
+
+for h in range(1, 12 + 1):
+    for period in ["a.m.", "am"]:
+        # always keep an eye on string interpolation!
+        _exc["%d%s" % (h, period)] = [
+            {ORTH: "%d" % h},
+            {ORTH: period, LEMMA: "a.m."}]
+    for period in ["p.m.", "pm"]:
+        _exc["%d%s" % (h, period)] = [
+            {ORTH: "%d" % h},
+            {ORTH: period, LEMMA: "p.m."}]
+
+# only declare this at the bottom
+TOKENIZER_EXCEPTIONS = _exc
+```
+
+> #### Generating tokenizer exceptions
+>
+> Keep in mind that generating exceptions only makes sense if there's a clearly
+> defined and **finite number** of them, like common contractions in English.
+> This is not always the case – in Spanish for instance, infinitive or
+> imperative reflexive verbs and pronouns are one token (e.g. "vestirme"). In
+> cases like this, spaCy shouldn't be generating exceptions for _all verbs_.
+> Instead, this will be handled at a later stage during lemmatization.
+
+When adding the tokenizer exceptions to the `Defaults`, you can use the
+[`update_exc`](/api/top-level#util.update_exc) helper function to merge them
+with the global base exceptions (including one-letter abbreviations and
+emoticons). The function performs a basic check to make sure exceptions are
+provided in the correct format. It can take any number of exceptions dicts as
+its arguments, and will update and overwrite the exception in this order. For
+example, if your language's tokenizer exceptions include a custom tokenization
+pattern for "a.", it will overwrite the base exceptions with the language's
+custom one.
+
+```python
+### Example
+from ...util import update_exc
+
+BASE_EXCEPTIONS =  {"a.": [{ORTH: "a."}], ":)": [{ORTH: ":)"}]}
+TOKENIZER_EXCEPTIONS = {"a.": [{ORTH: "a.", LEMMA: "all"}]}
+
+tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
+# {"a.": [{ORTH: "a.", LEMMA: "all"}], ":)": [{ORTH: ":)"}]}
+```
+
+<Infobox title="About spaCy's custom pronoun lemma" variant="warning">
+
+Unlike verbs and common nouns, there's no clear base form of a personal pronoun.
+Should the lemma of "me" be "I", or should we normalize person as well, giving
+"it" — or maybe "he"? spaCy's solution is to introduce a novel symbol, `-PRON-`,
+which is used as the lemma for all personal pronouns.
+
+</Infobox>
+
+### Norm exceptions {#norm-exceptions new="2"}
+
+In addition to `ORTH` or `LEMMA`, tokenizer exceptions can also set a `NORM`
+attribute. This is useful to specify a normalized version of the token – for
+example, the norm of "n't" is "not". By default, a token's norm equals its
+lowercase text. If the lowercase spelling of a word exists, norms should always
+be in lowercase.
+
+> #### Norms vs. lemmas
+>
+> ```python
+> doc = nlp(u"I'm gonna realise")
+> norms = [token.norm_ for token in doc]
+> lemmas = [token.lemma_ for token in doc]
+> assert norms == ["i", "am", "going", "to", "realize"]
+> assert lemmas == ["i", "be", "go", "to", "realise"]
+> ```
+
+spaCy usually tries to normalize words with different spellings to a single,
+common spelling. This has no effect on any other token attributes, or
+tokenization in general, but it ensures that **equivalent tokens receive similar
+representations**. This can improve the model's predictions on words that
+weren't common in the training data, but are equivalent to other words – for
+example, "realize" and "realize", or "thx" and "thanks".
+
+Similarly, spaCy also includes
+[global base norms](https://github.com/explosion/spaCy/tree/master/spacy/lang/norm_exceptions.py)
+for normalizing different styles of quotation marks and currency symbols. Even
+though `$` and `€` are very different, spaCy normalizes them both to `$`. This
+way, they'll always be seen as similar, no matter how common they were in the
+training data.
+
+Norm exceptions can be provided as a simple dictionary. For more examples, see
+the English
+[`norm_exceptions.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/en/norm_exceptions.py).
+
+```python
+### Example
+NORM_EXCEPTIONS = {
+    "cos": "because",
+    "fav": "favorite",
+    "accessorise": "accessorize",
+    "accessorised": "accessorized"
+}
+```
+
+To add the custom norm exceptions lookup table, you can use the `add_lookups()`
+helper functions. It takes the default attribute getter function as its first
+argument, plus a variable list of dictionaries. If a string's norm is found in
+one of the dictionaries, that value is used – otherwise, the default function is
+called and the token is assigned its default norm.
+
+```python
+lex_attr_getters[NORM] = add_lookups(Language.Defaults.lex_attr_getters[NORM],
+                                     NORM_EXCEPTIONS, BASE_NORMS)
+```
+
+The order of the dictionaries is also the lookup order – so if your language's
+norm exceptions overwrite any of the global exceptions, they should be added
+first. Also note that the tokenizer exceptions will always have priority over
+the attribute getters.
+
+### Lexical attributes {#lex-attrs new="2"}
+
+spaCy provides a range of [`Token` attributes](/api/token#attributes) that
+return useful information on that token – for example, whether it's uppercase or
+lowercase, a left or right punctuation mark, or whether it resembles a number or
+email address. Most of these functions, like `is_lower` or `like_url` should be
+language-independent. Others, like `like_num` (which includes both digits and
+number words), requires some customization.
+
+> #### Best practices
+>
+> Keep in mind that those functions are only intended to be an approximation.
+> It's always better to prioritize simplicity and performance over covering very
+> specific edge cases.
+>
+> English number words are pretty simple, because even large numbers consist of
+> individual tokens, and we can get away with splitting and matching strings
+> against a list. In other languages, like German, "two hundred and thirty-four"
+> is one word, and thus one token. Here, it's best to match a string against a
+> list of number word fragments (instead of a technically almost infinite list
+> of possible number words).
+
+Here's an example from the English
+[`lex_attrs.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/en/lex_attrs.py):
+
+```python
+### lex_attrs.py
+_num_words = ["zero", "one", "two", "three", "four", "five", "six", "seven",
+              "eight", "nine", "ten", "eleven", "twelve", "thirteen", "fourteen",
+              "fifteen", "sixteen", "seventeen", "eighteen", "nineteen", "twenty",
+              "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety",
+              "hundred", "thousand", "million", "billion", "trillion", "quadrillion",
+              "gajillion", "bazillion"]
+
+def like_num(text):
+    text = text.replace(",", "").replace(".", "")
+    if text.isdigit():
+        return True
+    if text.count("/") == 1:
+        num, denom = text.split("/")
+        if num.isdigit() and denom.isdigit():
+            return True
+    if text.lower() in _num_words:
+        return True
+    return False
+
+LEX_ATTRS = {
+    LIKE_NUM: like_num
+}
+```
+
+By updating the default lexical attributes with a custom `LEX_ATTRS` dictionary
+in the language's defaults via `lex_attr_getters.update(LEX_ATTRS)`, only the
+new custom functions are overwritten.
+
+### Syntax iterators {#syntax-iterators}
+
+Syntax iterators are functions that compute views of a `Doc` object based on its
+syntax. At the moment, this data is only used for extracting
+[noun chunks](/usage/linguistic-features#noun-chunks), which are available as
+the [`Doc.noun_chunks`](/api/doc#noun_chunks) property. Because base noun
+phrases work differently across languages, the rules to compute them are part of
+the individual language's data. If a language does not include a noun chunks
+iterator, the property won't be available. For examples, see the existing syntax
+iterators:
+
+> #### Noun chunks example
+>
+> ```python
+> doc = nlp(u"A phrase with another phrase occurs.")
+> chunks = list(doc.noun_chunks)
+> assert chunks[0].text == u"A phrase"
+> assert chunks[1].text == u"another phrase"
+> ```
+
+| Language | Code | Source                                                                                                            |
+| -------- | ---- | ----------------------------------------------------------------------------------------------------------------- |
+| English  | `en` | [`lang/en/syntax_iterators.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/en/syntax_iterators.py) |
+| German   | `de` | [`lang/de/syntax_iterators.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/de/syntax_iterators.py) |
+| French   | `fr` | [`lang/fr/syntax_iterators.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/fr/syntax_iterators.py) |
+| Spanish  | `es` | [`lang/es/syntax_iterators.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/es/syntax_iterators.py) |
+
+### Lemmatizer {#lemmatizer new="2"}
+
+As of v2.0, spaCy supports simple lookup-based lemmatization. This is usually
+the quickest and easiest way to get started. The data is stored in a dictionary
+mapping a string to its lemma. To determine a token's lemma, spaCy simply looks
+it up in the table. Here's an example from the Spanish language data:
+
+```python
+### lang/es/lemmatizer.py (excerpt)
+LOOKUP = {
+    "aba": "abar",
+    "ababa": "abar",
+    "ababais": "abar",
+    "ababan": "abar",
+    "ababanes": "ababán",
+    "ababas": "abar",
+    "ababoles": "ababol",
+    "ababábites": "ababábite"
+}
+```
+
+To provide a lookup lemmatizer for your language, import the lookup table and
+add it to the `Language` class as `lemma_lookup`:
+
+```python
+lemma_lookup = LOOKUP
+```
+
+### Tag map {#tag-map}
+
+Most treebanks define a custom part-of-speech tag scheme, striking a balance
+between level of detail and ease of prediction. While it's useful to have custom
+tagging schemes, it's also useful to have a common scheme, to which the more
+specific tags can be related. The tagger can learn a tag scheme with any
+arbitrary symbols. However, you need to define how those symbols map down to the
+[Universal Dependencies tag set](http://universaldependencies.org/u/pos/all.html).
+This is done by providing a tag map.
+
+The keys of the tag map should be **strings in your tag set**. The values should
+be a dictionary. The dictionary must have an entry POS whose value is one of the
+[Universal Dependencies](http://universaldependencies.org/u/pos/all.html) tags.
+Optionally, you can also include morphological features or other token
+attributes in the tag map as well. This allows you to do simple
+[rule-based morphological analysis](/usage/linguistic-features#rule-based-morphology).
+
+```python
+### Example
+from ..symbols import POS, NOUN, VERB, DET
+
+TAG_MAP = {
+    "NNS":  {POS: NOUN, "Number": "plur"},
+    "VBG":  {POS: VERB, "VerbForm": "part", "Tense": "pres", "Aspect": "prog"},
+    "DT":   {POS: DET}
+}
+```
+
+### Morph rules {#morph-rules}
+
+The morphology rules let you set token attributes such as lemmas, keyed by the
+extended part-of-speech tag and token text. The morphological features and their
+possible values are language-specific and based on the
+[Universal Dependencies scheme](http://universaldependencies.org).
+
+```python
+### Example
+from ..symbols import LEMMA
+
+MORPH_RULES = {
+    "VBZ": {
+        "am": {LEMMA: "be", "VerbForm": "Fin", "Person": "One", "Tense": "Pres", "Mood": "Ind"},
+        "are": {LEMMA: "be", "VerbForm": "Fin", "Person": "Two", "Tense": "Pres", "Mood": "Ind"},
+        "is": {LEMMA: "be", "VerbForm": "Fin", "Person": "Three", "Tense": "Pres", "Mood": "Ind"},
+        "'re": {LEMMA: "be", "VerbForm": "Fin", "Person": "Two", "Tense": "Pres", "Mood": "Ind"},
+        "'s": {LEMMA: "be", "VerbForm": "Fin", "Person": "Three", "Tense": "Pres", "Mood": "Ind"}
+    }
+}
+```
+
+In the example of `"am"`, the attributes look like this:
+
+| Attribute           | Description                                                                                                                    |
+| ------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
+| `LEMMA: "be"`       | Base form, e.g. "to be".                                                                                                       |
+| `"VerbForm": "Fin"` | Finite verb. Finite verbs have a subject and can be the root of an independent clause – "I am." is a valid, complete sentence. |
+| `"Person": "One"`   | First person, i.e. "**I** am".                                                                                                 |
+| `"Tense": "Pres"`   | Present tense, i.e. actions that are happening right now or actions that usually happen.                                       |
+| `"Mood": "Ind"`     | Indicative, i.e. something happens, has happened or will happen (as opposed to imperative or conditional).                     |
+
+<Infobox title="Important note" variant="warning">
+
+The morphological attributes are currently **not all used by spaCy**. Full
+integration is still being developed. In the meantime, it can still be useful to
+add them, especially if the language you're adding includes important
+distinctions and special cases. This ensures that as soon as full support is
+introduced, your language will be able to assign all possible attributes.
+
+</Infobox>
+
+## Testing the new language {#testing}
+
+Before using the new language or submitting a
+[pull request](https://github.com/explosion/spaCy/pulls) to spaCy, you should
+make sure it works as expected. This is especially important if you've added
+custom regular expressions for token matching or punctuation – you don't want to
+be causing regressions.
+
+<Infobox title="spaCy's test suite">
+
+spaCy uses the [pytest framework](https://docs.pytest.org/en/latest/) for
+testing. For more details on how the tests are structured and best practices for
+writing your own tests, see our
+[tests documentation](https://github.com/explosion/spaCy/tree/master/spacy/tests).
+
+</Infobox>
+
+### Writing language-specific tests {#testing-custom}
+
+It's recommended to always add at least some tests with examples specific to the
+language. Language tests should be located in
+[`tests/lang`](https://github.com/explosion/spaCy/tree/master/spacy/tests/lang)
+in a directory named after the language ID. You'll also need to create a fixture
+for your tokenizer in the
+[`conftest.py`](https://github.com/explosion/spaCy/tree/master/spacy/tests/conftest.py).
+Always use the [`get_lang_class`](/api/top-level#util.get_lang_class) helper
+function within the fixture, instead of importing the class at the top of the
+file. This will load the language data only when it's needed. (Otherwise, _all
+data_ would be loaded every time you run a test.)
+
+```python
+@pytest.fixture
+def en_tokenizer():
+    return util.get_lang_class("en").Defaults.create_tokenizer()
+```
+
+When adding test cases, always
+[`parametrize`](https://github.com/explosion/spaCy/tree/master/spacy/tests#parameters)
+them – this will make it easier for others to add more test cases without having
+to modify the test itself. You can also add parameter tuples, for example, a
+test sentence and its expected length, or a list of expected tokens. Here's an
+example of an English tokenizer test for combinations of punctuation and
+abbreviations:
+
+```python
+### Example test
+@pytest.mark.parametrize('text,length', [
+    ("The U.S. Army likes Shock and Awe.", 8),
+    ("U.N. regulations are not a part of their concern.", 10),
+    ("“Isn't it?”", 6)])
+def test_en_tokenizer_handles_punct_abbrev(en_tokenizer, text, length):
+    tokens = en_tokenizer(text)
+    assert len(tokens) == length
+```
+
+## Training a language model {#training}
+
+Much of spaCy's functionality requires models to be trained from labeled data.
+For instance, in order to use the named entity recognizer, you need to first
+train a model on text annotated with examples of the entities you want to
+recognize. The parser, part-of-speech tagger and text categorizer all also
+require models to be trained from labeled examples. The word vectors, word
+probabilities and word clusters also require training, although these can be
+trained from unlabeled text, which tends to be much easier to collect.
+
+### Creating a vocabulary file
+
+spaCy expects that common words will be cached in a [`Vocab`](/api/vocab)
+instance. The vocabulary caches lexical features. spaCy loads the vocabulary
+from binary data, in order to keep loading efficient. The easiest way to save
+out a new binary vocabulary file is to use the `spacy init-model` command, which
+expects a JSONL file with words and their lexical attributes. See the docs on
+the [vocab JSONL format](/api/annotation#vocab-jsonl) for details.
+
+#### Training the word vectors {#word-vectors}
+
+[Word2vec](https://en.wikipedia.org/wiki/Word2vec) and related algorithms let
+you train useful word similarity models from unlabeled text. This is a key part
+of using deep learning for NLP with limited labeled data. The vectors are also
+useful by themselves – they power the `.similarity` methods in spaCy. For best
+results, you should pre-process the text with spaCy before training the Word2vec
+model. This ensures your tokenization will match. You can use our
+[word vectors training script](https://github.com/explosion/spacy-dev-resources/tree/master/training/word_vectors.py),
+which pre-processes the text with your language-specific tokenizer and trains
+the model using [Gensim](https://radimrehurek.com/gensim/). The `vectors.bin`
+file should consist of one word and vector per line.
+
+```python
+https://github.com/explosion/spacy-dev-resources/tree/master/training/word_vectors.py
+```
+
+If you don't have a large sample of text available, you can also convert word
+vectors produced by a variety of other tools into spaCy's format. See the docs
+on [converting word vectors](/usage/vectors-similarity#converting) for details.
+
+### Creating or converting a training corpus
+
+The easiest way to train spaCy's tagger, parser, entity recognizer or text
+categorizer is to use the [`spacy train`](/api/cli#train) command-line utility.
+In order to use this, you'll need training and evaluation data in the
+[JSON format](/api/annotation#json-input) spaCy expects for training.
+
+You can now train the model using a corpus for your language annotated with If
+your data is in one of the supported formats, the easiest solution might be to
+use the [`spacy convert`](/api/cli#convert) command-line utility. This supports
+several popular formats, including the IOB format for named entity recognition,
+the JSONL format produced by our annotation tool [Prodigy](https://prodi.gy),
+and the [CoNLL-U](http://universaldependencies.org/docs/format.html) format used
+by the [Universal Dependencies](http://universaldependencies.org/) corpus.
+
+One thing to keep in mind is that spaCy expects to train its models from **whole
+documents**, not just single sentences. If your corpus only contains single
+sentences, spaCy's models will never learn to expect multi-sentence documents,
+leading to low performance on real text. To mitigate this problem, you can use
+the `-N` argument to the `spacy convert` command, to merge some of the sentences
+into longer pseudo-documents.
+
+### Training the tagger and parser {#train-tagger-parser}
+
+Once you have your training and evaluation data in the format spaCy expects, you
+can train your model use the using spaCy's [`train`](/api/cli#train) command.
+Note that training statistical models still involves a degree of
+trial-and-error. You may need to tune one or more settings, also called
+"hyper-parameters", to achieve optimal performance. See the
+[usage guide on training](/usage/training#tagger-parser) for more details.
diff --git a/website/docs/usage/examples.md b/website/docs/usage/examples.md
new file mode 100644
index 000000000..ced7ae7ee
--- /dev/null
+++ b/website/docs/usage/examples.md
@@ -0,0 +1,192 @@
+---
+title: Examples
+teaser: Full code examples you can modify and run
+menu:
+  - ['Information Extraction', 'information-extraction']
+  - ['Pipeline', 'pipeline']
+  - ['Training', 'training']
+  - ['Vectors & Similarity', 'vectors']
+  - ['Deep Learning', 'deep-learning']
+---
+
+## Information Extraction {#information-extraction hidden="true"}
+
+### Using spaCy's phrase matcher {#phrase-matcher new="2"}
+
+This example shows how to use the new [`PhraseMatcher`](/api/phrasematcher) to
+efficiently find entities from a large terminology list.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/information_extraction/phrase_matcher.py
+```
+
+### Extracting entity relations {#entity-relations}
+
+A simple example of extracting relations between phrases and entities using
+spaCy's named entity recognizer and the dependency parse. Here, we extract money
+and currency values (entities labelled as `MONEY`) and then check the dependency
+tree to find the noun phrase they are referring to – for example:
+`"$9.4 million"` → `"Net income"`.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/information_extraction/entity_relations.py
+```
+
+### Navigating the parse tree and subtrees {#subtrees}
+
+This example shows how to navigate the parse tree including subtrees attached to
+a word.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/information_extraction/parse_subtrees.py
+```
+
+## Pipeline {#pipeline hidden="true"}
+
+### Custom pipeline components and attribute extensions {#custom-components-entities new="2"}
+
+This example shows the implementation of a pipeline component that sets entity
+annotations based on a list of single or multiple-word company names, merges
+entities into one token and sets custom attributes on the `Doc`, `Span` and
+`Token`.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/pipeline/custom_component_entities.py
+```
+
+### Custom pipeline components and attribute extensions via a REST API {#custom-components-api new="2"}
+
+This example shows the implementation of a pipeline component that fetches
+country meta data via the [REST Countries API](https://restcountries.eu) sets
+entity annotations for countries, merges entities into one token and sets custom
+attributes on the `Doc`, `Span` and `Token` – for example, the capital,
+latitude/longitude coordinates and the country flag.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/pipeline/custom_component_countries_api.py
+```
+
+### Custom method extensions {#custom-components-attr-methods new="2"}
+
+A collection of snippets showing examples of extensions adding custom methods to
+the `Doc`, `Token` and `Span`.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/pipeline/custom_attr_methods.py
+```
+
+### Multi-processing with Joblib {#multi-processing}
+
+This example shows how to use multiple cores to process text using spaCy and
+[Joblib](https://joblib.readthedocs.io/en/latest/). We're exporting
+part-of-speech-tagged, true-cased, (very roughly) sentence-separated text, with
+each "sentence" on a newline, and spaces between tokens. Data is loaded from the
+IMDB movie reviews dataset and will be loaded automatically via Thinc's built-in
+dataset loader.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/pipeline/multi_processing.py
+```
+
+## Training {#training hidden="true"}
+
+### Training spaCy's Named Entity Recognizer {#training-ner}
+
+This example shows how to update spaCy's entity recognizer with your own
+examples, starting off with an existing, pre-trained model, or from scratch
+using a blank `Language` class.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/training/train_ner.py
+```
+
+### Training an additional entity type {#new-entity-type}
+
+This script shows how to add a new entity type to an existing pre-trained NER
+model. To keep the example short and simple, only four sentences are provided as
+examples. In practice, you'll need many more — a few hundred would be a good
+start.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/training/train_new_entity_type.py
+```
+
+### Training spaCy's Dependency Parser {#parser}
+
+This example shows how to update spaCy's dependency parser, starting off with an
+existing, pre-trained model, or from scratch using a blank `Language` class.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/training/train_parser.py
+```
+
+### Training spaCy's Part-of-speech Tagger {#tagger}
+
+In this example, we're training spaCy's part-of-speech tagger with a custom tag
+map, mapping our own tags to the mapping those tags to the
+[Universal Dependencies scheme](http://universaldependencies.github.io/docs/u/pos/index.html).
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/training/train_tagger.py
+```
+
+### Training a custom parser for chat intent semantics {#intent-parser}
+
+spaCy's parser component can be used to trained to predict any type of tree
+structure over your input text. You can also predict trees over whole documents
+or chat logs, with connections between the sentence-roots used to annotate
+discourse structure. In this example, we'll build a message parser for a common
+"chat intent": finding local businesses. Our message semantics will have the
+following types of relations: `ROOT`, `PLACE`, `QUALITY`, `ATTRIBUTE`, `TIME`
+and `LOCATION`.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/training/train_intent_parser.py
+```
+
+### Training spaCy's text classifier {#textcat new="2"}
+
+This example shows how to train a multi-label convolutional neural network text
+classifier on IMDB movie reviews, using spaCy's new
+[`TextCategorizer`](/api/textcategorizer) component. The dataset will be loaded
+automatically via Thinc's built-in dataset loader. Predictions are available via
+[`Doc.cats`](/api/doc#attributes).
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/training/train_textcat.py
+```
+
+## Vectors {#vectors hidden="true"}
+
+### Visualizing spaCy vectors in TensorBoard {#tensorboard}
+
+These two scripts let you load any spaCy model containing word vectors into
+[TensorBoard](https://projector.tensorflow.org/) to create an
+[embedding visualization](https://www.tensorflow.org/versions/r1.1/get_started/embedding_viz).
+The first example uses TensorBoard, the second example TensorBoard's standalone
+embedding projector.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/vectors_tensorboard.py
+```
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/vectors_tensorboard_standalone.py
+```
+
+## Deep Learning {#deep-learning hidden="true"}
+
+### Text classification with Keras {#keras}
+
+This example shows how to use a [Keras](https://keras.io) LSTM sentiment
+classification model in spaCy. spaCy splits the document into sentences, and
+each sentence is classified using the LSTM. The scores for the sentences are
+then aggregated to give the document score. This kind of hierarchical model is
+quite difficult in "pure" Keras or TensorFlow, but it's very effective. The
+Keras example on this dataset performs quite poorly, because it cuts off the
+documents so that they're a fixed size. This hurts review accuracy a lot,
+because people often summarize their rating in the final sentence.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/deep_learning_keras.py
+```
diff --git a/website/docs/usage/facts-figures.md b/website/docs/usage/facts-figures.md
new file mode 100644
index 000000000..55bca0f9d
--- /dev/null
+++ b/website/docs/usage/facts-figures.md
@@ -0,0 +1,260 @@
+---
+title: Facts & Figures
+teaser: The hard numbers for spaCy and how it compares to other tools
+next: /usage/spacy-101
+menu:
+  - ['Feature Comparison', 'comparison']
+  - ['Benchmarks', 'benchmarks']
+  - ['Powered by spaCy', 'powered-by']
+  - ['Other Libraries', 'other-libraries']
+---
+
+## Feature comparison {#comparison}
+
+Here's a quick comparison of the functionalities offered by spaCy,
+[NLTK](http://www.nltk.org/py-modindex.html) and
+[CoreNLP](http://stanfordnlp.github.io/CoreNLP/).
+
+|                         | spaCy  |  NLTK  |    CoreNLP    |
+| ----------------------- | :----: | :----: | :-----------: |
+| Programming language    | Python | Python | Java / Python |
+| Neural network models   |   ✅   |   ❌   |      ✅       |
+| Integrated word vectors |   ✅   |   ❌   |      ❌       |
+| Multi-language support  |   ✅   |   ✅   |      ✅       |
+| Tokenization            |   ✅   |   ✅   |      ✅       |
+| Part-of-speech tagging  |   ✅   |   ✅   |      ✅       |
+| Sentence segmentation   |   ✅   |   ✅   |      ✅       |
+| Dependency parsing      |   ✅   |   ❌   |      ✅       |
+| Entity recognition      |   ✅   |   ✅   |      ✅       |
+| Entity linking          |   ❌   |   ❌   |      ❌       |
+| Coreference resolution  |   ❌   |   ❌   |      ✅       |
+
+### When should I use what?
+
+Natural Language Understanding is an active area of research and development, so
+there are many different tools or technologies catering to different use-cases.
+The table below summarizes a few libraries (spaCy,
+[NLTK](http://www.nltk.org/py-modindex.html), [AllenNLP](https://allennlp.org/),
+[StanfordNLP](https://stanfordnlp.github.io/stanfordnlp/) and
+[TensorFlow](https://www.tensorflow.org/)) to help you get a feel for things fit
+together.
+
+|                                                                   | spaCy | NLTK | Allen-<br />NLP | Stanford-<br />NLP | Tensor-<br />Flow |
+| ----------------------------------------------------------------- | :---: | :--: | :-------------: | :----------------: | :---------------: |
+| I'm a beginner and just getting started with NLP.                 |  ✅   |  ✅  |       ❌        |         ✅         |        ❌         |
+| I want to build an end-to-end production application.             |  ✅   |  ❌  |       ❌        |         ❌         |        ✅         |
+| I want to try out different neural network architectures for NLP. |  ❌   |  ❌  |       ✅        |         ❌         |        ✅         |
+| I want to try the latest models with state-of-the-art accuracy.   |  ❌   |  ❌  |       ✅        |         ✅         |        ✅         |
+| I want to train models from my own data.                          |  ✅   |  ✅  |       ✅        |         ✅         |        ✅         |
+| I want my application to be efficient on CPU.                     |  ✅   |  ✅  |       ❌        |         ❌         |        ❌         |
+
+## Benchmarks {#benchmarks}
+
+Two peer-reviewed papers in 2015 confirm that spaCy offers the **fastest
+syntactic parser in the world** and that **its accuracy is within 1% of the
+best** available. The few systems that are more accurate are 20× slower or more.
+
+> #### About the evaluation
+>
+> The first of the evaluations was published by **Yahoo! Labs** and **Emory
+> University**, as part of a survey of current parsing technologies
+> ([Choi et al., 2015](https://aclweb.org/anthology/P/P15/P15-1038.pdf)). Their
+> results and subsequent discussions helped us develop a novel
+> psychologically-motivated technique to improve spaCy's accuracy, which we
+> published in joint work with Macquarie University
+> ([Honnibal and Johnson, 2015](https://www.aclweb.org/anthology/D/D15/D15-1162.pdf)).
+
+import BenchmarksChoi from 'usage/\_benchmarks-choi.md'
+
+<BenchmarksChoi />
+
+### Algorithm comparison {#algorithm}
+
+In this section, we compare spaCy's algorithms to recently published systems,
+using some of the most popular benchmarks. These benchmarks are designed to help
+isolate the contributions of specific algorithmic decisions, so they promote
+slightly "idealized" conditions. Specifically, the text comes pre-processed with
+"gold standard" token and sentence boundaries. The data sets also tend to be
+fairly small, to help researchers iterate quickly. These conditions mean the
+models trained on these data sets are not always useful for practical purposes.
+
+#### Parse accuracy (Penn Treebank / Wall Street Journal) {#parse-accuracy-penn}
+
+This is the "classic" evaluation, so it's the number parsing researchers are
+most easily able to put in context. However, it's quite far removed from actual
+usage: it uses sentences with gold-standard segmentation and tokenization, from
+a pretty specific type of text (articles from a single newspaper, 1984-1989).
+
+> #### Methodology
+>
+> [Andor et al. (2016)](http://arxiv.org/abs/1603.06042) chose slightly
+> different experimental conditions from
+> [Choi et al. (2015)](https://aclweb.org/anthology/P/P15/P15-1038.pdf), so the
+> two accuracy tables here do not present directly comparable figures.
+
+| System                                                       | Year | Type   |  Accuracy |
+| ------------------------------------------------------------ | ---- | ------ | --------: |
+| spaCy v2.0.0                                                 | 2017 | neural |     94.48 |
+| spaCy v1.1.0                                                 | 2016 | linear |     92.80 |
+| [Dozat and Manning][dozat and manning]                       | 2017 | neural | **95.75** |
+| [Andor et al.][andor et al.]                                 | 2016 | neural |     94.44 |
+| [SyntaxNet Parsey McParseface][syntaxnet parsey mcparseface] | 2016 | neural |     94.15 |
+| [Weiss et al.][weiss et al.]                                 | 2015 | neural |     93.91 |
+| [Zhang and McDonald][zhang and mcdonald]                     | 2014 | linear |     93.32 |
+| [Martins et al.][martins et al.]                             | 2013 | linear |     93.10 |
+
+[dozat and manning]: https://arxiv.org/pdf/1611.01734.pdf
+[andor et al.]: http://arxiv.org/abs/1603.06042
+[syntaxnet parsey mcparseface]:
+  https://github.com/tensorflow/models/tree/master/research/syntaxnet
+[weiss et al.]:
+  http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43800.pdf
+[zhang and mcdonald]: http://research.google.com/pubs/archive/38148.pdf
+[martins et al.]: http://www.cs.cmu.edu/~ark/TurboParser/
+
+#### NER accuracy (OntoNotes 5, no pre-process) {#ner-accuracy-ontonotes5}
+
+This is the evaluation we use to tune spaCy's parameters to decide which
+algorithms are better than the others. It's reasonably close to actual usage,
+because it requires the parses to be produced from raw text, without any
+pre-processing.
+
+| System                                             | Year | Type   |  Accuracy |
+| -------------------------------------------------- | ---- | ------ | --------: |
+| spaCy [`en_core_web_lg`][en_core_web_lg] v2.0.0a3  | 2017 | neural |     85.85 |
+| [Strubell et al.][strubell et al.]                 | 2017 | neural | **86.81** |
+| [Chiu and Nichols][chiu and nichols]               | 2016 | neural |     86.19 |
+| [Durrett and Klein][durrett and klein]             | 2014 | neural |     84.04 |
+| [Ratinov and Roth][ratinov and roth]               | 2009 | linear |     83.45 |
+
+[en_core_web_lg]: /models/en#en_core_web_lg
+[strubell et al.]: https://arxiv.org/pdf/1702.02098.pdf
+[chiu and nichols]:
+  https://www.semanticscholar.org/paper/Named-Entity-Recognition-with-Bidirectional-LSTM-C-Chiu-Nichols/10a4db59e81d26b2e0e896d3186ef81b4458b93f
+[durrett and klein]:
+  https://www.semanticscholar.org/paper/A-Joint-Model-for-Entity-Analysis-Coreference-Typi-Durrett-Klein/28eb033eee5f51c5e5389cbb6b777779203a6778
+[ratinov and roth]: http://www.aclweb.org/anthology/W09-1119
+
+### Model comparison {#spacy-models}
+
+In this section, we provide benchmark accuracies for the pre-trained model
+pipelines we distribute with spaCy. Evaluations are conducted end-to-end from
+raw text, with no "gold standard" pre-processing, over text from a mix of genres
+where possible.
+
+> #### Methodology
+>
+> The evaluation was conducted on raw text with no gold standard information.
+> The parser, tagger and entity recognizer were trained on the
+> [OntoNotes 5](https://www.gabormelli.com/RKB/OntoNotes_Corpus) corpus, the
+> word vectors on [Common Crawl](http://commoncrawl.org).
+
+#### English {#benchmarks-models-english}
+
+| Model                                               | spaCy | Type   |      UAS |    NER F |      POS |       WPS |     Size |
+| --------------------------------------------------- | ----- | ------ | -------: | -------: | -------: | --------: | -------: |
+| [`en_core_web_sm`](/models/en#en_core_web_sm) 2.0.0 | 2.x   | neural |     91.7 |     85.3 |     97.0 |     10.1k | **35MB** |
+| [`en_core_web_md`](/models/en#en_core_web_md) 2.0.0 | 2.x   | neural |     91.7 | **85.9** |     97.1 |     10.0k |    115MB |
+| [`en_core_web_lg`](/models/en#en_core_web_lg) 2.0.0 | 2.x   | neural | **91.9** | **85.9** | **97.2** |     10.0k |    812MB |
+| `en_core_web_sm` 1.2.0                              | 1.x   | linear |     86.6 |     78.5 |     96.6 | **25.7k** |     50MB |
+| `en_core_web_md` 1.2.1                              | 1.x   | linear |     90.6 |     81.4 |     96.7 |     18.8k |      1GB |
+
+#### Spanish {#benchmarks-models-spanish}
+
+> #### Evaluation note
+>
+> The NER accuracy refers to the "silver standard" annotations in the WikiNER
+> corpus. Accuracy on these annotations tends to be higher than correct human
+> annotations.
+
+| Model                                                 | spaCy | Type   |      UAS |    NER F |      POS |   WPS |     Size |
+| ----------------------------------------------------- | ----- | ------ | -------: | -------: | -------: | ----: | -------: |
+| [`es_core_news_sm`](/models/es#es_core_news_sm) 2.0.0 | 2.x   | neural |     89.8 |     88.7 | **96.9** | _n/a_ | **35MB** |
+| [`es_core_news_md`](/models/es#es_core_news_md) 2.0.0 | 2.x   | neural | **90.2** |     89.0 |     97.8 | _n/a_ |     93MB |
+| `es_core_web_md` 1.1.0                                | 1.x   | linear |     87.5 | **94.2** |     96.7 | _n/a_ |    377MB |
+
+### Detailed speed comparison {#speed-comparison}
+
+Here we compare the per-document processing time of various spaCy
+functionalities against other NLP libraries. We show both absolute timings (in
+ms) and relative performance (normalized to spaCy). Lower is better.
+
+<Infobox title="Important note" variant="warning">
+
+This evaluation was conducted in 2015. We're working on benchmarks on current
+CPU and GPU hardware. In the meantime, we're grateful to the Stanford folks for
+drawing our attention to what seems to be
+[a long-standing error](https://nlp.stanford.edu/software/tokenizer.html#Speed)
+in our CoreNLP benchmarks, especially for their tokenizer. Until we run
+corrected experiments, we have updated the table using their figures.
+
+</Infobox>
+
+> #### Methodology
+>
+> - **Set up:** 100,000 plain-text documents were streamed from an SQLite3
+>   database, and processed with an NLP library, to one of three levels of
+>   detail — tokenization, tagging, or parsing. The tasks are additive: to parse
+>   the text you have to tokenize and tag it. The pre-processing was not
+>   subtracted from the times — we report the time required for the pipeline to
+>   complete. We report mean times per document, in milliseconds.
+> - **Hardware**: Intel i7-3770 (2012)
+> - **Implementation**:
+>   [`spacy-benchmarks`](https://github.com/explosion/spacy-benchmarks)
+
+<Table>
+<thead>
+    <Tr>
+        <Th></Th>
+        <Th colSpan="3">Absolute (ms per doc)</Th>
+        <Th colSpan="3">Relative (to spaCy)</Th>
+    </Tr>
+    <Tr>
+        <Th>System</Th>
+        <Th>Tokenize</Th>
+        <Th>Tag</Th>
+        <Th>Parse</Th>
+        <Th>Tokenize</Th>
+        <Th>Tag</Th>
+        <Th>Parse</Th>
+    </Tr>
+</thead>
+<tbody style="text-align: right">
+    <Tr>
+        <Td style="text-align: left"><strong>spaCy</strong></Td>
+        <Td>0.2ms</Td>
+        <Td>1ms</Td>
+        <Td>19ms</Td>
+        <Td>1x</Td>
+        <Td>1x</Td>
+        <Td>1x</Td>
+    </Tr>
+    <Tr>
+        <Td style="text-align: left">CoreNLP</Td>
+        <Td>0.18ms</Td>
+        <Td>10ms</Td>
+        <Td>49ms</Td>
+        <Td>0.9x</Td>
+        <Td>10x</Td>
+        <Td>2.6x</Td>
+    </Tr>
+    <Tr>
+        <Td style="text-align: left">ZPar</Td>
+        <Td>1ms</Td>
+        <Td>8ms</Td>
+        <Td>850ms</Td>
+        <Td>5x</Td>
+        <Td>8x</Td>
+        <Td>44.7x</Td>
+    </Tr>
+    <Tr>
+        <Td style="text-align: left">NLTK</Td>
+        <Td>4ms</Td>
+        <Td>443ms</Td>
+        <Td><em>n/a</em></Td>
+        <Td>20x</Td>
+        <Td>443x</Td>
+        <Td><em>n/a</em></Td>
+    </Tr>
+</tbody>
+</Table>
diff --git a/website/docs/usage/index.md b/website/docs/usage/index.md
new file mode 100644
index 000000000..0e0f4b6fa
--- /dev/null
+++ b/website/docs/usage/index.md
@@ -0,0 +1,425 @@
+---
+title: Install spaCy
+next: /usage/models
+menu:
+  - ['Quickstart', 'quickstart']
+  - ['Instructions', 'installation']
+  - ['Troubleshooting', 'troubleshooting']
+  - ['Changelog', 'changelog']
+---
+
+spaCy is compatible with **64-bit CPython 2.6+/3.3+** and runs on
+**Unix/Linux**, **macOS/OS X** and **Windows**. The latest spaCy releases are
+available over [pip](https://pypi.python.org/pypi/spacy) and
+[conda](https://anaconda.org/conda-forge/spacy).
+
+> #### 📖 Looking for the old docs?
+>
+> To help you make the transition from v1.x to v2.0, we've uploaded the old
+> website to [**legacy.spacy.io**](https://legacy.spacy.io/docs). Wherever
+> possible, the new docs also include notes on features that have changed in
+> v2.0, and features that were introduced in the new version.
+
+## Quickstart {hidden="true"}
+
+import QuickstartInstall from 'widgets/quickstart-install.js'
+
+<QuickstartInstall title="Quickstart" id="quickstart" />
+
+## Installation instructions {#installation}
+
+### pip
+
+Using pip, spaCy releases are available as source packages and binary wheels (as
+of v2.0.13).
+
+```bash
+$ pip install -U spacy
+```
+
+> #### Download models
+>
+> After installation you need to download a language model. For more info and
+> available models, see the [docs on models](/models).
+>
+> ```bash
+> $ python -m spacy download en_core_web_sm
+>
+> >>> import spacy
+> >>> nlp = spacy.load("en_core_web_sm")
+> ```
+
+When using pip it is generally recommended to install packages in a virtual
+environment to avoid modifying system state:
+
+```bash
+python -m venv .env
+source .env/bin/activate
+pip install spacy
+```
+
+### conda
+
+Thanks to our great community, we've been able to re-add conda support. You can
+also install spaCy via `conda-forge`:
+
+```bash
+$ conda install -c conda-forge spacy
+```
+
+For the feedstock including the build recipe and configuration, check out
+[this repository](https://github.com/conda-forge/spacy-feedstock). Improvements
+and pull requests to the recipe and setup are always appreciated.
+
+### Upgrading spaCy {#upgrading}
+
+> #### Upgrading from v1 to v2
+>
+> Although we've tried to keep breaking changes to a minimum, upgrading from
+> spaCy v1.x to v2.x may still require some changes to your code base. For
+> details see the sections on [backwards incompatibilities](/usage/v2#incompat)
+> and [migrating](/usage/v2#migrating). Also remember to download the new
+> models, and retrain your own models.
+
+When updating to a newer version of spaCy, it's generally recommended to start
+with a clean virtual environment. If you're upgrading to a new major version,
+make sure you have the latest **compatible models** installed, and that there
+are no old shortcut links or incompatible model packages left over in your
+environment, as this can often lead to unexpected results and errors. If you've
+trained your own models, keep in mind that your train and runtime inputs must
+match. This means you'll have to **retrain your models** with the new version.
+
+As of v2.0, spaCy also provides a [`validate`](/api/cli#validate) command, which
+lets you verify that all installed models are compatible with your spaCy
+version. If incompatible models are found, tips and installation instructions
+are printed. The command is also useful to detect out-of-sync model links
+resulting from links created in different virtual environments. It's recommended
+to run the command with `python -m` to make sure you're executing the correct
+version of spaCy.
+
+```bash
+pip install -U spacy
+python -m spacy validate
+```
+
+### Run spaCy with GPU {#gpu new="2.0.14"}
+
+As of v2.0, spaCy comes with neural network models that are implemented in our
+machine learning library, [Thinc](https://github.com/explosion/thinc). For GPU
+support, we've been grateful to use the work of Chainer's
+[CuPy](https://cupy.chainer.org) module, which provides a numpy-compatible
+interface for GPU arrays.
+
+spaCy can be installed on GPU by specifying `spacy[cuda]`, `spacy[cuda90]`,
+`spacy[cuda91]`, `spacy[cuda92]` or `spacy[cuda100]`. If you know your cuda
+version, using the more explicit specifier allows cupy to be installed via
+wheel, saving some compilation time. The specifiers should install two
+libraries: [`cupy`](https://cupy.chainer.org) and
+[`thinc_gpu_ops`](https://github.com/explosion/thinc_gpu_ops).
+
+```bash
+$ pip install -U spacy[cuda92]
+```
+
+Once you have a GPU-enabled installation, the best way to activate it is to call
+[`spacy.prefer_gpu`](/api/top-level#spacy.prefer_gpu) or
+[`spacy.require_gpu()`](/api/top-level#spacy.require_gpu) somewhere in your
+script before any models have been loaded. `require_gpu` will raise an error if
+no GPU is available.
+
+```python
+import spacy
+
+spacy.prefer_gpu()
+nlp = spacy.load("en_core_web_sm")
+```
+
+### Compile from source {#source}
+
+The other way to install spaCy is to clone its
+[GitHub repository](https://github.com/explosion/spaCy) and build it from
+source. That is the common way if you want to make changes to the code base.
+You'll need to make sure that you have a development environment consisting of a
+Python distribution including header files, a compiler,
+[pip](https://pip.pypa.io/en/latest/installing/),
+[virtualenv](https://virtualenv.pypa.io/) and [git](https://git-scm.com)
+installed. The compiler part is the trickiest. How to do that depends on your
+system. See notes on [Ubuntu](#source-ubuntu), [macOS / OS X](#source-osx) and
+[Windows](#source-windows) for details.
+
+```bash
+python -m pip install -U pip                   # update pip
+git clone https://github.com/explosion/spaCy   # clone spaCy
+cd spaCy                                       # navigate into directory
+
+python -m venv .env                            # create environment in .env
+source .env/bin/activate                       # activate virtual environment
+\export PYTHONPATH=`pwd`                        # set Python path to spaCy directory
+pip install -r requirements.txt                # install all requirements
+python setup.py build_ext --inplace            # compile spaCy
+```
+
+Compared to regular install via pip, the
+[`requirements.txt`](https://github.com/explosion/spaCy/tree/master/requirements.txt)
+additionally installs developer dependencies such as Cython. See the the
+[quickstart widget](#quickstart) to get the right commands for your platform and
+Python version.
+
+#### Ubuntu {#source-ubuntu}
+
+Install system-level dependencies via `apt-get`:
+
+```bash
+$ sudo apt-get install build-essential python-dev git
+```
+
+#### macOS / OS X {#source-osx}
+
+Install a recent version of [XCode](https://developer.apple.com/xcode/),
+including the so-called "Command Line Tools". macOS and OS X ship with Python
+and git preinstalled.
+
+#### Windows {#source-windows}
+
+Install a version of the
+[Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
+or
+[Visual Studio Express](https://www.visualstudio.com/vs/visual-studio-express/)
+that matches the version that was used to compile your Python interpreter. For
+official distributions these are:
+
+| Distribution | Version            |
+| ------------ | ------------------ |
+| Python 2.7   | Visual Studio 2008 |
+| Python 3.4   | Visual Studio 2010 |
+| Python 3.5+  | Visual Studio 2015 |
+
+### Run tests
+
+spaCy comes with an
+[extensive test suite](https://github.com/explosion/spaCy/tree/master/spacy/tests).
+In order to run the tests, you'll usually want to clone the
+[repository](https://github.com/explosion/spaCy/tree/master/) and
+[build spaCy from source](#source). This will also install the required
+development dependencies and test utilities defined in the `requirements.txt`.
+
+Alternatively, you can find out where spaCy is installed and run `pytest` on
+that directory. Don't forget to also install the test utilities via spaCy's
+[`requirements.txt`](https://github.com/explosion/spaCy/tree/master/requirements.txt):
+
+```bash
+python -c "import os; import spacy; print(os.path.dirname(spacy.__file__))"
+pip install -r path/to/requirements.txt
+python -m pytest [spacy directory]
+```
+
+Calling `pytest` on the spaCy directory will run only the basic tests. The flag
+`--slow` is optional and enables additional tests that take longer.
+
+```bash
+# make sure you are using recent pytest version
+python -m pip install -U pytest
+
+python -m pytest [spacy directory]                 # basic tests
+python -m pytest [spacy directory] --slow          # basic and slow tests
+```
+
+## Troubleshooting guide {#troubleshooting}
+
+This section collects some of the most common errors you may come across when
+installing, loading and using spaCy, as well as their solutions.
+
+> #### Help us improve this guide
+>
+> Did you come across a problem like the ones listed here and want to share the
+> solution? You can find the "Suggest edits" button at the bottom of this page
+> that points you to the source. We always appreciate
+> [pull requests](https://github.com/explosion/spaCy/pulls)!
+
+<Accordion title="No compatible model found" id="compatible-model">
+
+```
+No compatible model found for [lang] (spaCy vX.X.X).
+```
+
+This usually means that the model you're trying to download does not exist, or
+isn't available for your version of spaCy. Check the
+[compatibility table](https://github.com/explosion/spacy-models/tree/master/compatibility.json)
+to see which models are available for your spaCy version. If you're using an old
+version, consider upgrading to the latest release. Note that while spaCy
+supports tokenization for [a variety of languages](/usage/models#languages), not
+all of them come with statistical models. To only use the tokenizer, import the
+language's `Language` class instead, for example
+`from spacy.lang.fr import French`.
+
+</Accordion>
+
+<Accordion title="Symbolic link privilege not held" id="symlink-privilege">
+
+```
+OSError: symbolic link privilege not held
+```
+
+To create [shortcut links](/usage/models#usage) that let you load models by
+name, spaCy creates a symbolic link in the `spacy/data` directory. This means
+your user needs permission to do this. The above error mostly occurs when doing
+a system-wide installation, which will create the symlinks in a system
+directory. Run the `download` or `link` command as administrator (on Windows,
+you can either right-click on your terminal or shell and select "Run as
+Administrator"), set the `--user` flag when installing a model or use a virtual
+environment to install spaCy in a user directory, instead of doing a system-wide
+installation.
+
+</Accordion>
+
+<Accordion title="No such option: --no-cache-dir" id="no-cache-dir">
+
+```
+no such option: --no-cache-dir
+```
+
+The `download` command uses pip to install the models and sets the
+`--no-cache-dir` flag to prevent it from requiring too much memory.
+[This setting](https://pip.pypa.io/en/stable/reference/pip_install/#caching)
+requires pip v6.0 or newer. Run `pip install -U pip` to upgrade to the latest
+version of pip. To see which version you have installed, run `pip --version`.
+
+</Accordion>
+
+<Accordion title="Unknown locale: UTF-8" id="unknown-locale">
+
+```
+ValueError: unknown locale: UTF-8
+```
+
+This error can sometimes occur on OSX and is likely related to a still
+unresolved [Python bug](https://bugs.python.org/issue18378). However, it's easy
+to fix: just add the following to your `~/.bash_profile` or `~/.zshrc` and then
+run `source ~/.bash_profile` or `source ~/.zshrc`. Make sure to add **both
+lines** for `LC_ALL` and `LANG`.
+
+```bash
+\export LC_ALL=en_US.UTF-8
+\export LANG=en_US.UTF-8
+```
+
+</Accordion>
+
+<Accordion title="Import error: No module named spacy" id="import-error">
+
+```
+Import Error: No module named spacy
+```
+
+This error means that the spaCy module can't be located on your system, or in
+your environment. Make sure you have spaCy installed. If you're using a virtual
+environment, make sure it's activated and check that spaCy is installed in that
+environment – otherwise, you're trying to load a system installation. You can
+also run `which python` to find out where your Python executable is located.
+
+</Accordion>
+
+<Accordion title="Import error: No module named [model]" id="import-error-models">
+
+```
+ImportError: No module named 'en_core_web_sm'
+```
+
+As of spaCy v1.7, all models can be installed as Python packages. This means
+that they'll become importable modules of your application. When creating
+[shortcut links](/usage/models#usage), spaCy will also try to import the model
+to load its meta data. If this fails, it's usually a sign that the package is
+not installed in the current environment. Run `pip list` or `pip freeze` to
+check which model packages you have installed, and install the
+[correct models](/models) if necessary. If you're importing a model manually at
+the top of a file, make sure to use the name of the package, not the shortcut
+link you've created.
+
+</Accordion>
+
+<Accordion title="Command not found: spacy" id="command-not-found">
+
+```
+command not found: spacy
+```
+
+This error may occur when running the `spacy` command from the command line.
+spaCy does not currently add an entry to your `PATH` environment variable, as
+this can lead to unexpected results, especially when using a virtual
+environment. Instead, spaCy adds an auto-alias that maps `spacy` to
+`python -m spacy]`. If this is not working as expected, run the command with
+`python -m`, yourself – for example `python -m spacy download en`. For more info
+on this, see the [`download`](/api/cli#download) command.
+
+</Accordion>
+
+<Accordion title="'module' object has no attribute 'load'" id="module-load">
+
+```
+AttributeError: 'module' object has no attribute 'load'
+```
+
+While this could technically have many causes, including spaCy being broken, the
+most likely one is that your script's file or directory name is "shadowing" the
+module – e.g. your file is called `spacy.py`, or a directory you're importing
+from is called `spacy`. So, when using spaCy, never call anything else `spacy`.
+
+</Accordion>
+
+<Accordion title="Pronoun lemma is returned as -PRON-" id="pron-lemma">
+
+```python
+doc = nlp(u"They are")
+print(doc[0].lemma_)
+# -PRON-
+```
+
+This is in fact expected behavior and not a bug. Unlike verbs and common nouns,
+there's no clear base form of a personal pronoun. Should the lemma of "me" be
+"I", or should we normalize person as well, giving "it" — or maybe "he"? spaCy's
+solution is to introduce a novel symbol, `-PRON-`, which is used as the lemma
+for all personal pronouns. For more info on this, see the
+[lemmatization specs](/api/annotation#lemmatization).
+
+</Accordion>
+
+<Accordion title="NER model doesn't recognise other entities anymore after training" id="catastrophic-forgetting">
+
+If your training data only contained new entities and you didn't mix in any
+examples the model previously recognized, it can cause the model to "forget"
+what it had previously learned. This is also referred to as the "catastrophic
+forgetting problem". A solution is to pre-label some text, and mix it with the
+new text in your updates. You can also do this by running spaCy over some text,
+extracting a bunch of entities the model previously recognized correctly, and
+adding them to your training examples.
+
+</Accordion>
+
+<Accordion title="Unhashable type: 'list'" id="unhashable-list">
+
+```
+TypeError: unhashable type: 'list'
+```
+
+If you're training models, writing them to disk, and versioning them with git,
+you might encounter this error when trying to load them in a Windows
+environment. This happens because a default install of Git for Windows is
+configured to automatically convert Unix-style end-of-line characters (LF) to
+Windows-style ones (CRLF) during file checkout (and the reverse when
+committing). While that's mostly fine for text files, a trained model written to
+disk has some binary files that should not go through this conversion. When they
+do, you get the error above. You can fix it by either changing your
+[`core.autocrlf`](https://git-scm.com/book/en/v2/Customizing-Git-Git-Configuration)
+setting to `"false"`, or by committing a
+[`.gitattributes`](https://git-scm.com/docs/gitattributes) file] to your
+repository to tell git on which files or folders it shouldn't do LF-to-CRLF
+conversion, with an entry like `path/to/spacy/model/** -text`. After you've done
+either of these, clone your repository again.
+
+</Accordion>
+
+## Changelog
+
+import Changelog from 'widgets/changelog.js'
+
+<Changelog />
diff --git a/website/docs/usage/linguistic-features.md b/website/docs/usage/linguistic-features.md
new file mode 100644
index 000000000..1b01cb4d3
--- /dev/null
+++ b/website/docs/usage/linguistic-features.md
@@ -0,0 +1,1189 @@
+---
+title: Linguistic Features
+next: /usage/rule-based-matching
+menu:
+  - ['POS Tagging', 'pos-tagging']
+  - ['Dependency Parse', 'dependency-parse']
+  - ['Named Entities', 'named-entities']
+  - ['Tokenization', 'tokenization']
+  - ['Merging & Splitting', 'retokenization']
+  - ['Sentence Segmentation', 'sbd']
+---
+
+Processing raw text intelligently is difficult: most words are rare, and it's
+common for words that look completely different to mean almost the same thing.
+The same words in a different order can mean something completely different.
+Even splitting text into useful word-like units can be difficult in many
+languages. While it's possible to solve some problems starting from only the raw
+characters, it's usually better to use linguistic knowledge to add useful
+information. That's exactly what spaCy is designed to do: you put in raw text,
+and get back a [`Doc`](/api/doc) object, that comes with a variety of
+annotations.
+
+## Part-of-speech tagging {#pos-tagging model="tagger, parser"}
+
+import PosDeps101 from 'usage/101/\_pos-deps.md'
+
+<PosDeps101 />
+
+### Rule-based morphology {#rule-based-morphology}
+
+Inflectional morphology is the process by which a root form of a word is
+modified by adding prefixes or suffixes that specify its grammatical function
+but do not changes its part-of-speech. We say that a **lemma** (root form) is
+**inflected** (modified/combined) with one or more **morphological features** to
+create a surface form. Here are some examples:
+
+| Context                                  | Surface | Lemma | POS  |  Morphological Features                  |
+| ---------------------------------------- | ------- | ----- | ---- | ---------------------------------------- |
+| I was reading the paper                  | reading | read  | verb | `VerbForm=Ger`                           |
+| I don't watch the news, I read the paper | read    | read  | verb | `VerbForm=Fin`, `Mood=Ind`, `Tense=Pres` |
+| I read the paper yesterday               | read    | read  | verb | `VerbForm=Fin`, `Mood=Ind`, `Tense=Past` |
+
+English has a relatively simple morphological system, which spaCy handles using
+rules that can be keyed by the token, the part-of-speech tag, or the combination
+of the two. The system works as follows:
+
+1. The tokenizer consults a
+   [mapping table](/usage/adding-languages#tokenizer-exceptions)
+   `TOKENIZER_EXCEPTIONS`, which allows sequences of characters to be mapped to
+   multiple tokens. Each token may be assigned a part of speech and one or more
+   morphological features.
+2. The part-of-speech tagger then assigns each token an **extended POS tag**. In
+   the API, these tags are known as `Token.tag`. They express the part-of-speech
+   (e.g. `VERB`) and some amount of morphological information, e.g. that the
+   verb is past tense.
+3. For words whose POS is not set by a prior process, a
+   [mapping table](/usage/adding-languages#tag-map) `TAG_MAP` maps the tags to a
+   part-of-speech and a set of morphological features.
+4. Finally, a **rule-based deterministic lemmatizer** maps the surface form, to
+   a lemma in light of the previously assigned extended part-of-speech and
+   morphological information, without consulting the context of the token. The
+   lemmatizer also accepts list-based exception files, acquired from
+   [WordNet](https://wordnet.princeton.edu/).
+
+<Infobox title="📖 Part-of-speech tag scheme">
+
+For a list of the fine-grained and coarse-grained part-of-speech tags assigned
+by spaCy's models across different languages, see the
+[POS tag scheme documentation](/api/annotation#pos-tagging).
+
+</Infobox>
+
+## Dependency Parsing {#dependency-parse model="parser"}
+
+spaCy features a fast and accurate syntactic dependency parser, and has a rich
+API for navigating the tree. The parser also powers the sentence boundary
+detection, and lets you iterate over base noun phrases, or "chunks". You can
+check whether a [`Doc`](/api/doc) object has been parsed with the
+`doc.is_parsed` attribute, which returns a boolean value. If this attribute is
+`False`, the default sentence iterator will raise an exception.
+
+### Noun chunks {#noun-chunks}
+
+Noun chunks are "base noun phrases" – flat phrases that have a noun as their
+head. You can think of noun chunks as a noun plus the words describing the noun
+– for example, "the lavish green grass" or "the world’s largest tech fund". To
+get the noun chunks in a document, simply iterate over
+[`Doc.noun_chunks`](/api/doc#noun_chunks)
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"Autonomous cars shift insurance liability toward manufacturers")
+for chunk in doc.noun_chunks:
+    print(chunk.text, chunk.root.text, chunk.root.dep_,
+            chunk.root.head.text)
+```
+
+> - **Text:** The original noun chunk text.
+> - **Root text:** The original text of the word connecting the noun chunk to
+>   the rest of the parse.
+> - **Root dep:** Dependency relation connecting the root to its head.
+> - **Root head text:** The text of the root token's head.
+
+| Text                | root.text     | root.dep\_ | root.head.text |
+| ------------------- | ------------- | ---------- | -------------- |
+| Autonomous cars     | cars          | `nsubj`    | shift          |
+| insurance liability | liability     | `dobj`     | shift          |
+| manufacturers       | manufacturers | `pobj`     | toward         |
+
+### Navigating the parse tree {#navigating}
+
+spaCy uses the terms **head** and **child** to describe the words **connected by
+a single arc** in the dependency tree. The term **dep** is used for the arc
+label, which describes the type of syntactic relation that connects the child to
+the head. As with other attributes, the value of `.dep` is a hash value. You can
+get the string value with `.dep_`.
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"Autonomous cars shift insurance liability toward manufacturers")
+for token in doc:
+    print(token.text, token.dep_, token.head.text, token.head.pos_,
+            [child for child in token.children])
+```
+
+> - **Text:** The original token text.
+> - **Dep:** The syntactic relation connecting child to head.
+> - **Head text:** The original text of the token head.
+> - **Head POS:** The part-of-speech tag of the token head.
+> - **Children:** The immediate syntactic dependents of the token.
+
+| Text          | Dep        | Head text | Head POS | Children                |
+| ------------- | ---------- | --------- | -------- | ----------------------- |
+| Autonomous    | `amod`     | cars      | `NOUN`   |                         |
+| cars          | `nsubj`    | shift     | `VERB`   | Autonomous              |
+| shift         | `ROOT`     | shift     | `VERB`   | cars, liability, toward |
+| insurance     | `compound` | liability | `NOUN`   |                         |
+| liability     | `dobj`     | shift     | `VERB`   | insurance               |
+| toward        | `prep`     | shift     | `NOUN`   | manufacturers           |
+| manufacturers | `pobj`     | toward    | `ADP`    |                         |
+
+import DisplaCyLong2Html from 'images/displacy-long2.html'
+
+<Iframe title="displaCy visualization of dependencies and entities 2" html={DisplaCyLong2Html} height={450} />
+
+Because the syntactic relations form a tree, every word has **exactly one
+head**. You can therefore iterate over the arcs in the tree by iterating over
+the words in the sentence. This is usually the best way to match an arc of
+interest — from below:
+
+```python
+### {executable="true"}
+import spacy
+from spacy.symbols import nsubj, VERB
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"Autonomous cars shift insurance liability toward manufacturers")
+
+# Finding a verb with a subject from below — good
+verbs = set()
+for possible_subject in doc:
+    if possible_subject.dep == nsubj and possible_subject.head.pos == VERB:
+        verbs.add(possible_subject.head)
+print(verbs)
+```
+
+If you try to match from above, you'll have to iterate twice. Once for the head,
+and then again through the children:
+
+```python
+# Finding a verb with a subject from above — less good
+verbs = []
+for possible_verb in doc:
+    if possible_verb.pos == VERB:
+        for possible_subject in possible_verb.children:
+            if possible_subject.dep == nsubj:
+                verbs.append(possible_verb)
+                break
+```
+
+To iterate through the children, use the `token.children` attribute, which
+provides a sequence of [`Token`](/api/token) objects.
+
+#### Iterating around the local tree {#navigating-around}
+
+A few more convenience attributes are provided for iterating around the local
+tree from the token. [`Token.lefts`](/api/token#lefts) and
+[`Token.rights`](/api/token#rights) attributes provide sequences of syntactic
+children that occur before and after the token. Both sequences are in sentence
+order. There are also two integer-typed attributes,
+[`Token.n_lefts`](/api/token#n_lefts) and
+[`Token.n_rights`](/api/token#n_rights) that give the number of left and right
+children.
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"bright red apples on the tree")
+print([token.text for token in doc[2].lefts])  # ['bright', 'red']
+print([token.text for token in doc[2].rights])  # ['on']
+print(doc[2].n_lefts)  # 2
+print(doc[2].n_rights)  # 1
+```
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("de_core_news_sm")
+doc = nlp(u"schöne rote Äpfel auf dem Baum")
+print([token.text for token in doc[2].lefts])  # ['schöne', 'rote']
+print([token.text for token in doc[2].rights])  # ['auf']
+```
+
+You can get a whole phrase by its syntactic head using the
+[`Token.subtree`](/api/token#subtree) attribute. This returns an ordered
+sequence of tokens. You can walk up the tree with the
+[`Token.ancestors`](/api/token#ancestors) attribute, and check dominance with
+[`Token.is_ancestor`](/api/token#is_ancestor)
+
+> #### Projective vs. non-projective
+>
+> For the [default English model](/models/en), the parse tree is **projective**,
+> which means that there are no crossing brackets. The tokens returned by
+> `.subtree` are therefore guaranteed to be contiguous. This is not true for the
+> German model, which has many
+> [non-projective dependencies](https://explosion.ai/blog/german-model#word-order).
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"Credit and mortgage account holders must submit their requests")
+
+root = [token for token in doc if token.head == token][0]
+subject = list(root.lefts)[0]
+for descendant in subject.subtree:
+    assert subject is descendant or subject.is_ancestor(descendant)
+    print(descendant.text, descendant.dep_, descendant.n_lefts,
+            descendant.n_rights,
+            [ancestor.text for ancestor in descendant.ancestors])
+```
+
+| Text     | Dep        | n_lefts | n_rights | ancestors                        |
+| -------- | ---------- | ------- | -------- | -------------------------------- |
+| Credit   | `nmod`     | `0`     | `2`      | holders, submit                  |
+| and      | `cc`       | `0`     | `0`      | holders, submit                  |
+| mortgage | `compound` | `0`     | `0`      | account, Credit, holders, submit |
+| account  | `conj`     | `1`     | `0`      | Credit, holders, submit          |
+| holders  | `nsubj`    | `1`     | `0`      | submit                           |
+
+Finally, the `.left_edge` and `.right_edge` attributes can be especially useful,
+because they give you the first and last token of the subtree. This is the
+easiest way to create a `Span` object for a syntactic phrase. Note that
+`.right_edge` gives a token **within** the subtree — so if you use it as the
+end-point of a range, don't forget to `+1`!
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"Credit and mortgage account holders must submit their requests")
+span = doc[doc[4].left_edge.i : doc[4].right_edge.i+1]
+with doc.retokenize() as retokenizer:
+    retokenizer.merge(span)
+for token in doc:
+    print(token.text, token.pos_, token.dep_, token.head.text)
+```
+
+| Text                                |  POS   | Dep     | Head text |
+| ----------------------------------- | ------ | ------- | --------- |
+| Credit and mortgage account holders | `NOUN` | `nsubj` | submit    |
+| must                                | `VERB` | `aux`   | submit    |
+| submit                              | `VERB` | `ROOT`  | submit    |
+| their                               | `ADJ`  | `poss`  | requests  |
+| requests                            | `NOUN` | `dobj`  | submit    |
+
+<Infobox title="📖 Dependency label scheme">
+
+For a list of the syntactic dependency labels assigned by spaCy's models across
+different languages, see the
+[dependency label scheme documentation](/api/annotation#pos-tagging).
+
+</Infobox>
+
+### Visualizing dependencies {#displacy}
+
+The best way to understand spaCy's dependency parser is interactively. To make
+this easier, spaCy v2.0+ comes with a visualization module. You can pass a `Doc`
+or a list of `Doc` objects to displaCy and run
+[`displacy.serve`](top-level#displacy.serve) to run the web server, or
+[`displacy.render`](top-level#displacy.render) to generate the raw markup. If
+you want to know how to write rules that hook into some type of syntactic
+construction, just plug the sentence into the visualizer and see how spaCy
+annotates it.
+
+```python
+### {executable="true"}
+import spacy
+from spacy import displacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"Autonomous cars shift insurance liability toward manufacturers")
+# Since this is an interactive Jupyter environment, we can use displacy.render here
+displacy.render(doc, style='dep')
+```
+
+<Infobox>
+
+For more details and examples, see the
+[usage guide on visualizing spaCy](/usage/visualizers). You can also test
+displaCy in our [online demo](https://explosion.ai/demos/displacy)..
+
+</Infobox>
+
+### Disabling the parser {#disabling}
+
+In the [default models](/models), the parser is loaded and enabled as part of
+the [standard processing pipeline](/usage/processing-pipelin). If you don't need
+any of the syntactic information, you should disable the parser. Disabling the
+parser will make spaCy load and run much faster. If you want to load the parser,
+but need to disable it for specific documents, you can also control its use on
+the `nlp` object.
+
+```python
+nlp = spacy.load("en_core_web_sm", disable=["parser"])
+nlp = English().from_disk("/model", disable=["parser"])
+doc = nlp(u"I don't want parsed", disable=["parser"])
+```
+
+<Infobox title="Important note: disabling pipeline components" variant="warning">
+
+Since spaCy v2.0 comes with better support for customizing the processing
+pipeline components, the `parser` keyword argument has been replaced with
+`disable`, which takes a list of
+[pipeline component names](/usage/processing-pipelines). This lets you disable
+both default and custom components when loading a model, or initializing a
+Language class via [`from_disk`](/api/language#from_disk).
+
+```diff
++ nlp = spacy.load("en_core_web_sm", disable=["parser"])
++ doc = nlp(u"I don't want parsed", disable=["parser"])
+
+- nlp = spacy.load("en_core_web_sm", parser=False)
+- doc = nlp(u"I don't want parsed", parse=False)
+```
+
+</Infobox>
+
+## Named Entity Recognition {#named-entities}
+
+spaCy features an extremely fast statistical entity recognition system, that
+assigns labels to contiguous spans of tokens. The default model identifies a
+variety of named and numeric entities, including companies, locations,
+organizations and products. You can add arbitrary classes to the entity
+recognition system, and update the model with new examples.
+
+### Named Entity Recognition 101 {#named-entities-101}
+
+import NER101 from 'usage/101/\_named-entities.md'
+
+<NER101 />
+
+### Accessing entity annotations {#accessing}
+
+The standard way to access entity annotations is the [`doc.ents`](/api/doc#ents)
+property, which produces a sequence of [`Span`](/api/span) objects. The entity
+type is accessible either as a hash value or as a string, using the attributes
+`ent.label` and `ent.label_`. The `Span` object acts as a sequence of tokens, so
+you can iterate over the entity or index into it. You can also get the text form
+of the whole entity, as though it were a single token.
+
+You can also access token entity annotations using the
+[`token.ent_iob`](/api/token#attributes) and
+[`token.ent_type`](/api/token#attributes) attributes. `token.ent_iob` indicates
+whether an entity starts, continues or ends on the tag. If no entity type is set
+on a token, it will return an empty string.
+
+> #### IOB Scheme
+>
+> - `I` – Token is inside an entity.
+> - `O` – Token is outside an entity.
+> - `B` – Token is the beginning of an entity.
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"San Francisco considers banning sidewalk delivery robots")
+
+# document level
+ents = [(e.text, e.start_char, e.end_char, e.label_) for e in doc.ents]
+print(ents)
+
+# token level
+ent_san = [doc[0].text, doc[0].ent_iob_, doc[0].ent_type_]
+ent_francisco = [doc[1].text, doc[1].ent_iob_, doc[1].ent_type_]
+print(ent_san)  # [u'San', u'B', u'GPE']
+print(ent_francisco)  # [u'Francisco', u'I', u'GPE']
+```
+
+| Text      | ent_iob | ent_iob\_ | ent_type\_ | Description            |
+| --------- | ------- | --------- | ---------- | ---------------------- |
+| San       | `3`     | `B`       | `"GPE"`    | beginning of an entity |
+| Francisco | `1`     | `I`       | `"GPE"`    | inside an entity       |
+| considers | `2`     | `O`       | `""`       | outside an entity      |
+| banning   | `2`     | `O`       | `""`       | outside an entity      |
+| sidewalk  | `2`     | `O`       | `""`       | outside an entity      |
+| delivery  | `2`     | `O`       | `""`       | outside an entity      |
+| robots    | `2`     | `O`       | `""`       | outside an entity      |
+
+### Setting entity annotations {#setting-entities}
+
+To ensure that the sequence of token annotations remains consistent, you have to
+set entity annotations **at the document level**. However, you can't write
+directly to the `token.ent_iob` or `token.ent_type` attributes, so the easiest
+way to set entities is to assign to the [`doc.ents`](/api/doc#ents) attribute
+and create the new entity as a [`Span`](/api/span).
+
+```python
+### {executable="true"}
+import spacy
+from spacy.tokens import Span
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"FB is hiring a new Vice President of global policy")
+ents = [(e.text, e.start_char, e.end_char, e.label_) for e in doc.ents]
+print('Before', ents)
+# the model didn't recognise "FB" as an entity :(
+
+ORG = doc.vocab.strings[u"ORG"]  # get hash value of entity label
+fb_ent = Span(doc, 0, 1, label=ORG) # create a Span for the new entity
+doc.ents = list(doc.ents) + [fb_ent]
+
+ents = [(e.text, e.start_char, e.end_char, e.label_) for e in doc.ents]
+print('After', ents)
+# [(u'FB', 0, 2, 'ORG')] 🎉
+```
+
+Keep in mind that you need to create a `Span` with the start and end index of
+the **token**, not the start and end index of the entity in the document. In
+this case, "FB" is token `(0, 1)` – but at the document level, the entity will
+have the start and end indices `(0, 2)`.
+
+#### Setting entity annotations from array {#setting-from-array}
+
+You can also assign entity annotations using the
+[`doc.from_array`](/api/doc#from_array) method. To do this, you should include
+both the `ENT_TYPE` and the `ENT_IOB` attributes in the array you're importing
+from.
+
+```python
+### {executable="true"}
+import numpy
+import spacy
+from spacy.attrs import ENT_IOB, ENT_TYPE
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp.make_doc(u"London is a big city in the United Kingdom.")
+print("Before", doc.ents)  # []
+
+header = [ENT_IOB, ENT_TYPE]
+attr_array = numpy.zeros((len(doc), len(header)))
+attr_array[0, 0] = 3  # B
+attr_array[0, 1] = doc.vocab.strings[u"GPE"]
+doc.from_array(header, attr_array)
+print("After", doc.ents)  # [London]
+```
+
+#### Setting entity annotations in Cython {#setting-cython}
+
+Finally, you can always write to the underlying struct, if you compile a
+[Cython](http://cython.org/) function. This is easy to do, and allows you to
+write efficient native code.
+
+```python
+# cython: infer_types=True
+from spacy.tokens.doc cimport Doc
+
+cpdef set_entity(Doc doc, int start, int end, int ent_type):
+    for i in range(start, end):
+        doc.c[i].ent_type = ent_type
+    doc.c[start].ent_iob = 3
+    for i in range(start+1, end):
+        doc.c[i].ent_iob = 2
+```
+
+Obviously, if you write directly to the array of `TokenC*` structs, you'll have
+responsibility for ensuring that the data is left in a consistent state.
+
+### Built-in entity types {#entity-types}
+
+> #### Tip: Understanding entity types
+>
+> You can also use `spacy.explain()` to get the description for the string
+> representation of an entity label. For example, `spacy.explain("LANGUAGE")`
+> will return "any named language".
+
+<Infobox title="Annotation scheme">
+
+For details on the entity types available in spaCy's pre-trained models, see the
+[NER annotation scheme](/api/annotation#named-entities).
+
+</Infobox>
+
+### Training and updating {#updating}
+
+To provide training examples to the entity recognizer, you'll first need to
+create an instance of the [`GoldParse`](/api/goldparse) class. You can specify
+your annotations in a stand-off format or as token tags. If a character offset
+in your entity annotations doesn't fall on a token boundary, the `GoldParse`
+class will treat that annotation as a missing value. This allows for more
+realistic training, because the entity recognizer is allowed to learn from
+examples that may feature tokenizer errors.
+
+```python
+train_data = [
+    ("Who is Chaka Khan?", [(7, 17, "PERSON")]),
+    ("I like London and Berlin.", [(7, 13, "LOC"), (18, 24, "LOC")]),
+]
+```
+
+```python
+doc = Doc(nlp.vocab, [u"rats", u"make", u"good", u"pets"])
+gold = GoldParse(doc, entities=[u"U-ANIMAL", u"O", u"O", u"O"])
+```
+
+<Infobox>
+
+For more details on **training and updating** the named entity recognizer, see
+the usage guides on [training](/usage/training) or check out the runnable
+[training script](https://github.com/explosion/spaCy/tree/master/examples/training/train_ner.py)
+on GitHub.
+
+</Infobox>
+
+### Visualizing named entities {#displacy}
+
+The
+[displaCy <sup>ENT</sup> visualizer](https://explosion.ai/demos/displacy-ent)
+lets you explore an entity recognition model's behavior interactively. If you're
+training a model, it's very useful to run the visualization yourself. To help
+you do that, spaCy v2.0+ comes with a visualization module. You can pass a `Doc`
+or a list of `Doc` objects to displaCy and run
+[`displacy.serve`](/api/top-level#displacy.serve) to run the web server, or
+[`displacy.render`](/api/top-level#displacy.render) to generate the raw markup.
+
+For more details and examples, see the
+[usage guide on visualizing spaCy](/usage/visualizers).
+
+```python
+### Named Entity example
+import spacy
+from spacy import displacy
+
+text = """But Google is starting from behind. The company made a late push
+into hardware, and Apple’s Siri, available on iPhones, and Amazon’s Alexa
+software, which runs on its Echo and Dot devices, have clear leads in
+consumer adoption."""
+
+nlp = spacy.load("custom_ner_model")
+doc = nlp(text)
+displacy.serve(doc, style="ent")
+```
+
+import DisplacyEntHtml from 'images/displacy-ent.html'
+
+<Iframe title="displaCy visualizer for entities" html={DisplacyEntHtml} height={275} />
+
+## Tokenization {#tokenization}
+
+Tokenization is the task of splitting a text into meaningful segments, called
+_tokens_. The input to the tokenizer is a unicode text, and the output is a
+[`Doc`](/api/doc) object. To construct a `Doc` object, you need a
+[`Vocab`](/api/vocab) instance, a sequence of `word` strings, and optionally a
+sequence of `spaces` booleans, which allow you to maintain alignment of the
+tokens into the original string.
+
+<Infobox title="Important note" variant="warning">
+
+spaCy's tokenization is **non-destructive**, which means that you'll always be
+able to reconstruct the original input from the tokenized output. Whitespace
+information is preserved in the tokens and no information is added or removed
+during tokenization. This is kind of a core principle of spaCy's `Doc` object:
+`doc.text == input_text` should always hold true.
+
+</Infobox>
+
+import Tokenization101 from 'usage/101/\_tokenization.md'
+
+<Tokenization101 />
+
+### Tokenizer data {#101-data}
+
+**Global** and **language-specific** tokenizer data is supplied via the language
+data in
+[`spacy/lang`](https://github.com/explosion/spaCy/tree/master/spacy/lang). The
+tokenizer exceptions define special cases like "don't" in English, which needs
+to be split into two tokens: `{ORTH: "do"}` and `{ORTH: "n't", LEMMA: "not"}`.
+The prefixes, suffixes and infixes mostly define punctuation rules – for
+example, when to split off periods (at the end of a sentence), and when to leave
+tokens containing periods intact (abbreviations like "U.S.").
+
+![Language data architecture](../images/language_data.svg)
+
+<Infobox title="📖 Language data">
+
+For more details on the language-specific data, see the usage guide on
+[adding languages](/usage/adding-languages).
+
+</Infobox>
+
+<Accordion title="Should I change the language data or add custom tokenizer rules?">
+
+Tokenization rules that are specific to one language, but can be **generalized
+across that language** should ideally live in the language data in
+[`spacy/lang`](https://github.com/explosion/spaCy/tree/master/spacy/lang) – we
+always appreciate pull requests! Anything that's specific to a domain or text
+type – like financial trading abbreviations, or Bavarian youth slang – should be
+added as a special case rule to your tokenizer instance. If you're dealing with
+a lot of customizations, it might make sense to create an entirely custom
+subclass.
+
+</Accordion>
+
+---
+
+### Adding special case tokenization rules {#special-cases}
+
+Most domains have at least some idiosyncrasies that require custom tokenization
+rules. This could be very certain expressions, or abbreviations only used in
+this specific field. Here's how to add a special case rule to an existing
+[`Tokenizer`](/api/tokenizer) instance:
+
+```python
+### {executable="true"}
+import spacy
+from spacy.symbols import ORTH, LEMMA, POS, TAG
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"gimme that")  # phrase to tokenize
+print([w.text for w in doc])  # ['gimme', 'that']
+
+# add special case rule
+special_case = [{ORTH: u"gim", LEMMA: u"give", POS: u"VERB"}, {ORTH: u"me"}]
+nlp.tokenizer.add_special_case(u"gimme", special_case)
+
+# check new tokenization
+print([w.text for w in nlp(u"gimme that")])  # ['gim', 'me', 'that']
+
+# Pronoun lemma is returned as -PRON-!
+print([w.lemma_ for w in nlp(u"gimme that")])  # ['give', '-PRON-', 'that']
+```
+
+<Infobox title="Why -PRON-?" variant="warning">
+
+For details on spaCy's custom pronoun lemma `-PRON-`,
+[see here](/usage/#pron-lemma).
+
+</Infobox>
+
+The special case doesn't have to match an entire whitespace-delimited substring.
+The tokenizer will incrementally split off punctuation, and keep looking up the
+remaining substring:
+
+```python
+assert "gimme" not in [w.text for w in nlp(u"gimme!")]
+assert "gimme" not in [w.text for w in nlp(u'("...gimme...?")')]
+```
+
+The special case rules have precedence over the punctuation splitting:
+
+```python
+special_case = [{ORTH: u"...gimme...?", LEMMA: u"give", TAG: u"VB"}]
+nlp.tokenizer.add_special_case(u"...gimme...?", special_case)
+assert len(nlp(u"...gimme...?")) == 1
+```
+
+Because the special-case rules allow you to set arbitrary token attributes, such
+as the part-of-speech, lemma, etc, they make a good mechanism for arbitrary
+fix-up rules. Having this logic live in the tokenizer isn't very satisfying from
+a design perspective, however, so the API may eventually be exposed on the
+[`Language`](/api/language) class itself.
+
+### How spaCy's tokenizer works {#how-tokenizer-works}
+
+spaCy introduces a novel tokenization algorithm, that gives a better balance
+between performance, ease of definition, and ease of alignment into the original
+string.
+
+After consuming a prefix or infix, we consult the special cases again. We want
+the special cases to handle things like "don't" in English, and we want the same
+rule to work for "(don't)!". We do this by splitting off the open bracket, then
+the exclamation, then the close bracket, and finally matching the special-case.
+Here's an implementation of the algorithm in Python, optimized for readability
+rather than performance:
+
+```python
+def tokenizer_pseudo_code(text, special_cases,
+                          find_prefix, find_suffix, find_infixes):
+    tokens = []
+    for substring in text.split(' '):
+        suffixes = []
+        while substring:
+            if substring in special_cases:
+                tokens.extend(special_cases[substring])
+                substring = ''
+            elif find_prefix(substring) is not None:
+                split = find_prefix(substring)
+                tokens.append(substring[:split])
+                substring = substring[split:]
+            elif find_suffix(substring) is not None:
+                split = find_suffix(substring)
+                suffixes.append(substring[-split:])
+                substring = substring[:-split]
+            elif find_infixes(substring):
+                infixes = find_infixes(substring)
+                offset = 0
+                for match in infixes:
+                    tokens.append(substring[offset : match.start()])
+                    tokens.append(substring[match.start() : match.end()])
+                    offset = match.end()
+                substring = substring[offset:]
+            else:
+                tokens.append(substring)
+                substring = ''
+        tokens.extend(reversed(suffixes))
+    return tokens
+```
+
+The algorithm can be summarized as follows:
+
+1. Iterate over space-separated substrings
+2. Check whether we have an explicitly defined rule for this substring. If we
+   do, use it.
+3. Otherwise, try to consume a prefix.
+4. If we consumed a prefix, go back to the beginning of the loop, so that
+   special-cases always get priority.
+5. If we didn't consume a prefix, try to consume a suffix.
+6. If we can't consume a prefix or suffix, look for "infixes" — stuff like
+   hyphens etc.
+7. Once we can't consume any more of the string, handle it as a single token.
+
+### Customizing spaCy's Tokenizer class {#native-tokenizers}
+
+Let's imagine you wanted to create a tokenizer for a new language or specific
+domain. There are five things you would need to define:
+
+1. A dictionary of **special cases**. This handles things like contractions,
+   units of measurement, emoticons, certain abbreviations, etc.
+2. A function `prefix_search`, to handle **preceding punctuation**, such as open
+   quotes, open brackets, etc.
+3. A function `suffix_search`, to handle **succeeding punctuation**, such as
+   commas, periods, close quotes, etc.
+4. A function `infixes_finditer`, to handle non-whitespace separators, such as
+   hyphens etc.
+5. An optional boolean function `token_match` matching strings that should never
+   be split, overriding the previous rules. Useful for things like URLs or
+   numbers.
+
+You shouldn't usually need to create a `Tokenizer` subclass. Standard usage is
+to use `re.compile()` to build a regular expression object, and pass its
+`.search()` and `.finditer()` methods:
+
+```python
+### {executable="true"}
+import re
+import spacy
+from spacy.tokenizer import Tokenizer
+
+prefix_re = re.compile(r'''^[\[\("']''')
+suffix_re = re.compile(r'''[\]\)"']$''')
+infix_re = re.compile(r'''[-~]''')
+simple_url_re = re.compile(r'''^https?://''')
+
+def custom_tokenizer(nlp):
+    return Tokenizer(nlp.vocab, prefix_search=prefix_re.search,
+                                suffix_search=suffix_re.search,
+                                infix_finditer=infix_re.finditer,
+                                token_match=simple_url_re.match)
+
+nlp = spacy.load("en_core_web_sm")
+nlp.tokenizer = custom_tokenizer(nlp)
+doc = nlp(u"hello-world.")
+print([t.text for t in doc])
+```
+
+If you need to subclass the tokenizer instead, the relevant methods to
+specialize are `find_prefix`, `find_suffix` and `find_infix`.
+
+<Infobox title="Important note" variant="warning">
+
+When customizing the prefix, suffix and infix handling, remember that you're
+passing in **functions** for spaCy to execute, e.g. `prefix_re.search` – not
+just the regular expressions. This means that your functions also need to define
+how the rules should be applied. For example, if you're adding your own prefix
+rules, you need to make sure they're only applied to characters at the
+**beginning of a token**, e.g. by adding `^`. Similarly, suffix rules should
+only be applied at the **end of a token**, so your expression should end with a
+`$`.
+
+</Infobox>
+
+### Hooking an arbitrary tokenizer into the pipeline {#custom-tokenizer}
+
+The tokenizer is the first component of the processing pipeline and the only one
+that can't be replaced by writing to `nlp.pipeline`. This is because it has a
+different signature from all the other components: it takes a text and returns a
+`Doc`, whereas all other components expect to already receive a tokenized `Doc`.
+
+![The processing pipeline](../images/pipeline.svg)
+
+To overwrite the existing tokenizer, you need to replace `nlp.tokenizer` with a
+custom function that takes a text, and returns a `Doc`.
+
+```python
+nlp = spacy.load("en_core_web_sm")
+nlp.tokenizer = my_tokenizer
+```
+
+| Argument    | Type    | Description               |
+| ----------- | ------- | ------------------------- |
+| `text`      | unicode | The raw text to tokenize. |
+| **RETURNS** | `Doc`   | The tokenized document.   |
+
+<Infobox title="Important note: using a custom tokenizer" variant="warning">
+
+In spaCy v1.x, you had to add a custom tokenizer by passing it to the `make_doc`
+keyword argument, or by passing a tokenizer "factory" to `create_make_doc`. This
+was unnecessarily complicated. Since spaCy v2.0, you can write to
+`nlp.tokenizer` instead. If your tokenizer needs the vocab, you can write a
+function and use `nlp.vocab`.
+
+```diff
+- nlp = spacy.load("en_core_web_sm", make_doc=my_tokenizer)
+- nlp = spacy.load("en_core_web_sm", create_make_doc=my_tokenizer_factory)
+
++ nlp.tokenizer = my_tokenizer
++ nlp.tokenizer = my_tokenizer_factory(nlp.vocab)
+```
+
+</Infobox>
+
+### Example: A custom whitespace tokenizer {#custom-tokenizer-example}
+
+To construct the tokenizer, we usually want attributes of the `nlp` pipeline.
+Specifically, we want the tokenizer to hold a reference to the vocabulary
+object. Let's say we have the following class as our tokenizer:
+
+```python
+### {executable="true"}
+import spacy
+from spacy.tokens import Doc
+
+class WhitespaceTokenizer(object):
+    def __init__(self, vocab):
+        self.vocab = vocab
+
+    def __call__(self, text):
+        words = text.split(' ')
+        # All tokens 'own' a subsequent space character in this tokenizer
+        spaces = [True] * len(words)
+        return Doc(self.vocab, words=words, spaces=spaces)
+
+nlp = spacy.load("en_core_web_sm")
+nlp.tokenizer = WhitespaceTokenizer(nlp.vocab)
+doc = nlp(u"What's happened to me? he thought. It wasn't a dream.")
+print([t.text for t in doc])
+```
+
+As you can see, we need a `Vocab` instance to construct this — but we won't have
+it until we get back the loaded `nlp` object. The simplest solution is to build
+the tokenizer in two steps. This also means that you can reuse the "tokenizer
+factory" and initialize it with different instances of `Vocab`.
+
+### Bringing your own annotations {#own-annotations}
+
+spaCy generally assumes by default that your data is raw text. However,
+sometimes your data is partially annotated, e.g. with pre-existing tokenization,
+part-of-speech tags, etc. The most common situation is that you have pre-defined
+tokenization. If you have a list of strings, you can create a `Doc` object
+directly. Optionally, you can also specify a list of boolean values, indicating
+whether each word has a subsequent space.
+
+```python
+### {executable="true"}
+import spacy
+from spacy.tokens import Doc
+from spacy.lang.en import English
+
+nlp = English()
+doc = Doc(nlp.vocab, words=[u"Hello", u",", u"world", u"!"],
+          spaces=[False, True, False, False])
+print([(t.text, t.text_with_ws, t.whitespace_) for t in doc])
+```
+
+If provided, the spaces list must be the same length as the words list. The
+spaces list affects the `doc.text`, `span.text`, `token.idx`, `span.start_char`
+and `span.end_char` attributes. If you don't provide a `spaces` sequence, spaCy
+will assume that all words are whitespace delimited.
+
+```python
+### {executable="true"}
+import spacy
+from spacy.tokens import Doc
+from spacy.lang.en import English
+
+nlp = English()
+bad_spaces = Doc(nlp.vocab, words=[u"Hello", u",", u"world", u"!"])
+good_spaces = Doc(nlp.vocab, words=[u"Hello", u",", u"world", u"!"],
+                  spaces=[False, True, False, False])
+
+print(bad_spaces.text)   # 'Hello , world !'
+print(good_spaces.text)  # 'Hello, world!'
+```
+
+Once you have a [`Doc`](/api/doc) object, you can write to its attributes to set
+the part-of-speech tags, syntactic dependencies, named entities and other
+attributes. For details, see the respective usage pages.
+
+## Merging and splitting {#retokenization new="2.1"}
+
+The [`Doc.retokenize`](/api/doc#retokenize) context manager lets you merge and
+split tokens. Modifications to the tokenization are stored and performed all at
+once when the context manager exits. To merge several tokens into one single
+token, pass a `Span` to [`retokenizer.merge`](/api/doc#retokenizer.merge). An
+optional dictionary of `attrs` lets you set attributes that will be assigned to
+the merged token – for example, the lemma, part-of-speech tag or entity type. By
+default, the merged token will receive the same attributes as the merged span's
+root.
+
+> #### ✏️ Things to try
+>
+> 1. Inspect the `token.lemma_` attribute with and without setting the `attrs`.
+>    You'll see that the lemma defaults to "New", the lemma of the span's root.
+> 2. Overwrite other attributes like the `"ENT_TYPE"`. Since "New York" is also
+>    recognized as a named entity, this change will also be reflected in the
+>    `doc.ents`.
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp("I live in New York")
+print("Before:", [token.text for token in doc])
+
+with doc.retokenize() as retokenizer:
+    retokenizer.merge(doc[3:5], attrs={"LEMMA": "new york"})
+print("After:", [token.text for token in doc])
+```
+
+<Infobox title="Tip: merging entities and noun phrases">
+
+If you need to merge named entities or noun chunks, check out the built-in
+[`merge_entities`](/api/pipeline-functions#merge_entities) and
+[`merge_noun_chunks`](/api/pipeline-functions#merge_noun_chunks) pipeline
+components. When added to your pipeline using `nlp.add_pipe`, they'll take care
+of merging the spans automatically.
+
+</Infobox>
+
+The [`retokenizer.split`](/api/doc#retokenizer.split) method allows splitting
+one token into two or more tokens. This can be useful for cases where
+tokenization rules alone aren't sufficient. For example, you might want to split
+"its" into the tokens "it" and "is" — but not the possessive pronoun "its". You
+can write rule-based logic that can find only the correct "its" to split, but by
+that time, the `Doc` will already be tokenized.
+
+This process of splitting a token requires more settings, because you need to
+specify the text of the individual tokens, optional per-token attributes and how
+the should be attached to the existing syntax tree. This can be done by
+supplying a list of `heads` – either the token to attach the newly split token
+to, or a `(token, subtoken)` tuple if the newly split token should be attached
+to another subtoken. In this case, "New" should be attached to "York" (the
+second split subtoken) and "York" should be attached to "in".
+
+> #### ✏️ Things to try
+>
+> 1. Assign different attributes to the subtokens and compare the result.
+> 2. Change the heads so that "New" is attached to "in" and "York" is attached
+>    to "New".
+> 3. Split the token into three tokens instead of two – for example,
+>    `["New", "Yo", "rk"]`.
+
+```python
+### {executable="true"}
+import spacy
+from spacy import displacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp("I live in NewYork")
+print("Before:", [token.text for token in doc])
+displacy.render(doc)  # displacy.serve if you're not in a Jupyter environment
+
+with doc.retokenize() as retokenizer:
+    heads = [(doc[3], 1), doc[2]]
+    attrs = {"POS": ["PROPN", "PROPN"], "DEP": ["pobj", "compound"]}
+    retokenizer.split(doc[3], ["New", "York"], heads=heads, attrs=attrs)
+print("After:", [token.text for token in doc])
+displacy.render(doc)  # displacy.serve if you're not in a Jupyter environment
+```
+
+Specifying the heads as a list of `token` or `(token, subtoken)` tuples allows
+attaching split subtokens to other subtokens, without having to keep track of
+the token indices after splitting.
+
+| Token    | Head          | Description                                                                                         |
+| -------- | ------------- | --------------------------------------------------------------------------------------------------- |
+| `"New"`  | `(doc[3], 1)` | Attach this token to the second subtoken (index `1`) that `doc[3]` will be split into, i.e. "York". |
+| `"York"` | `doc[2]`      | Attach this token to `doc[1]` in the original `Doc`, i.e. "in".                                     |
+
+If you don't care about the heads (for example, if you're only running the
+tokenizer and not the parser), you can each subtoken to itself:
+
+```python
+### {highlight="3"}
+doc = nlp("I live in NewYorkCity")
+with doc.retokenize() as retokenizer:
+    heads = [(doc[3], 0), (doc[3], 1), (doc[3], 2)]
+    retokenizer.split(doc[3], ["New", "York", "City"], heads=heads)
+```
+
+<Infobox title="Important note" variant="warning">
+
+When splitting tokens, the subtoken texts always have to match the original
+token text – or, put differently `''.join(subtokens) == token.text` always needs
+to hold true. If this wasn't the case, splitting tokens could easily end up
+producing confusing and unexpected results that would contradict spaCy's
+non-destructive tokenization policy.
+
+```diff
+doc = nlp("I live in L.A.")
+with doc.retokenize() as retokenizer:
+-    retokenizer.split(doc[3], ["Los", "Angeles"], heads=[(doc[3], 1), doc[2]])
++    retokenizer.split(doc[3], ["L.", "A."], heads=[(doc[3], 1), doc[2]])
+```
+
+</Infobox>
+
+## Sentence Segmentation {#sbd}
+
+A [`Doc`](/api/doc) object's sentences are available via the `Doc.sents`
+property. Unlike other libraries, spaCy uses the dependency parse to determine
+sentence boundaries. This is usually more accurate than a rule-based approach,
+but it also means you'll need a **statistical model** and accurate predictions.
+If your texts are closer to general-purpose news or web text, this should work
+well out-of-the-box. For social media or conversational text that doesn't follow
+the same rules, your application may benefit from a custom rule-based
+implementation. You can either plug a rule-based component into your
+[processing pipeline](/usage/processing-pipelines) or use the
+`SentenceSegmenter` component with a custom strategy.
+
+### Default: Using the dependency parse {#sbd-parser model="parser"}
+
+To view a `Doc`'s sentences, you can iterate over the `Doc.sents`, a generator
+that yields [`Span`](/api/span) objects.
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"This is a sentence. This is another sentence.")
+for sent in doc.sents:
+    print(sent.text)
+```
+
+### Setting boundaries manually {#sbd-manual}
+
+spaCy's dependency parser respects already set boundaries, so you can preprocess
+your `Doc` using custom rules _before_ it's parsed. This can be done by adding a
+[custom pipeline component](/usage/processing-pipelines). Depending on your
+text, this may also improve accuracy, since the parser is constrained to predict
+parses consistent with the sentence boundaries.
+
+<Infobox title="Important note" variant="warning">
+
+To prevent inconsistent state, you can only set boundaries **before** a document
+is parsed (and `Doc.is_parsed` is `False`). To ensure that your component is
+added in the right place, you can set `before='parser'` or `first=True` when
+adding it to the pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
+
+</Infobox>
+
+Here's an example of a component that implements a pre-processing rule for
+splitting on `'...'` tokens. The component is added before the parser, which is
+then used to further segment the text. This approach can be useful if you want
+to implement **additional** rules specific to your data, while still being able
+to take advantage of dependency-based sentence segmentation.
+
+```python
+### {executable="true"}
+import spacy
+
+text = u"this is a sentence...hello...and another sentence."
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(text)
+print("Before:", [sent.text for sent in doc.sents])
+
+def set_custom_boundaries(doc):
+    for token in doc[:-1]:
+        if token.text == "...":
+            doc[token.i+1].is_sent_start = True
+    return doc
+
+nlp.add_pipe(set_custom_boundaries, before="parser")
+doc = nlp(text)
+print("After:", [sent.text for sent in doc.sents])
+```
+
+### Rule-based pipeline component {#sbd-component}
+
+The `sentencizer` component is a
+[pipeline component](/usage/processing-pipelines) that splits sentences on
+punctuation like `.`, `!` or `?`. You can plug it into your pipeline if you only
+need sentence boundaries without the dependency parse. Note that `Doc.sents`
+will **raise an error** if no sentence boundaries are set.
+
+```python
+### {executable="true"}
+import spacy
+from spacy.lang.en import English
+
+nlp = English()  # just the language with no model
+sentencizer = nlp.create_pipe("sentencizer")
+nlp.add_pipe(sentencizer)
+doc = nlp(u"This is a sentence. This is another sentence.")
+for sent in doc.sents:
+    print(sent.text)
+```
+
+### Custom rule-based strategy {#sbd-custom}
+
+If you want to implement your own strategy that differs from the default
+rule-based approach of splitting on sentences, you can also instantiate the
+`SentenceSegmenter` directly and pass in your own strategy. The strategy should
+be a function that takes a `Doc` object and yields a `Span` for each sentence.
+Here's an example of a custom segmentation strategy for splitting on newlines
+only:
+
+```python
+### {executable="true"}
+from spacy.lang.en import English
+from spacy.pipeline import SentenceSegmenter
+
+def split_on_newlines(doc):
+    start = 0
+    seen_newline = False
+    for word in doc:
+        if seen_newline and not word.is_space:
+            yield doc[start:word.i]
+            start = word.i
+            seen_newline = False
+        elif word.text == '\\n':
+            seen_newline = True
+    if start < len(doc):
+        yield doc[start:len(doc)]
+
+nlp = English()  # Just the language with no model
+sentencizer = SentenceSegmenter(nlp.vocab, strategy=split_on_newlines)
+nlp.add_pipe(sentencizer)
+doc = nlp(u"This is a sentence\\n\\nThis is another sentence\\nAnd more")
+for sent in doc.sents:
+    print([token.text for token in sent])
+```
+
+## Rule-based matching {#rule-based-matching hidden="true"}
+
+<div id="rule-based-matching">
+<Infobox title="📖 Rule-based matching" id="rule-based-matching">
+
+The documentation on rule-based matching
+[has moved to its own page](/usage/rule-based-matching).
+
+</Infobox>
+</div>
diff --git a/website/docs/usage/models.md b/website/docs/usage/models.md
new file mode 100644
index 000000000..bc56d64af
--- /dev/null
+++ b/website/docs/usage/models.md
@@ -0,0 +1,371 @@
+---
+title: Models & Languages
+next: usage/facts-figures
+menu:
+  - ['Quickstart', 'quickstart']
+  - ['Language Support', 'languages']
+  - ['Installation & Usage', 'download']
+  - ['Production Use', 'production']
+---
+
+spaCy's models can be installed as **Python packages**. This means that they're
+a component of your application, just like any other module. They're versioned
+and can be defined as a dependency in your `requirements.txt`. Models can be
+installed from a download URL or a local directory, manually or via
+[pip](https://pypi.python.org/pypi/pip). Their data can be located anywhere on
+your file system.
+
+> #### Important note
+>
+> If you're upgrading to spaCy v1.7.x or v2.x, you need to **download the new
+> models**. If you've trained statistical models that use spaCy's annotations,
+> you should **retrain your models** after updating spaCy. If you don't retrain,
+> you may suffer train/test skew, which might decrease your accuracy.
+
+## Quickstart {hidden="true"}
+
+import QuickstartModels from 'widgets/quickstart-models.js'
+
+<QuickstartModels title="Quickstart" id="quickstart" description="Install a default model, get the code to load it from within spaCy and an example to test it. For more options, see the section on available models below." />
+
+## Language support {#languages}
+
+spaCy currently provides support for the following languages. You can help by
+[improving the existing language data](/usage/adding-languages#language-data)
+and extending the tokenization patterns.
+[See here](https://github.com/explosion/spaCy/issues/3056) for details on how to
+contribute to model development.
+
+> #### Usage note
+>
+> If a model is available for a language, you can download it using the
+> [`spacy download`](/api/cli#download) command. In order to use languages that
+> don't yet come with a model, you have to import them directly, or use
+> [`spacy.blank`](api/top-level#spacy.blank):
+>
+> ```python
+> from spacy.lang.fi import Finnish
+> nlp = Finnish()  # use directly
+> nlp = spacy.blank("fi")  # blank instance
+> ```
+
+import Languages from 'widgets/languages.js'
+
+<Languages />
+
+### Multi-language support {#multi-language new="2"}
+
+> ```python
+> # Standard import
+> from spacy.lang.xx import MultiLanguage
+> nlp = MultiLanguage()
+>
+> # With lazy-loading
+> from spacy.util import get_lang_class
+> nlp = get_lang_class('xx')
+> ```
+
+As of v2.0, spaCy supports models trained on more than one language. This is
+especially useful for named entity recognition. The language ID used for
+multi-language or language-neutral models is `xx`. The language class, a generic
+subclass containing only the base language data, can be found in
+[`lang/xx`](https://github.com/explosion/spaCy/tree/master/spacy/lang/xx).
+
+To load your model with the neutral, multi-language class, simply set
+`"language": "xx"` in your [model package](/usage/training#models-generating)'s
+`meta.json`. You can also import the class directly, or call
+[`util.get_lang_class()`](/api/top-level#util.get_lang_class) for lazy-loading.
+
+## Installing and using models {#download}
+
+> #### Downloading models in spaCy < v1.7
+>
+> In older versions of spaCy, you can still use the old download commands. This
+> will download and install the models into the `spacy/data` directory.
+>
+> ```bash
+>  python -m spacy.en.download all
+>  python -m spacy.de.download all
+>  python -m spacy.en.download glove
+> ```
+>
+> The old models are also
+> [attached to the v1.6.0 release](https://github.com/explosion/spaCy/tree/v1.6.0).
+> To download and install them manually, unpack the archive, drop the contained
+> directory into `spacy/data`.
+
+The easiest way to download a model is via spaCy's
+[`download`](/api/cli#download) command. It takes care of finding the
+best-matching model compatible with your spaCy installation.
+
+```bash
+# out-of-the-box: download best-matching default model
+python -m spacy download en
+
+# download best-matching version of specific model for your spaCy installation
+python -m spacy download en_core_web_sm
+
+# download exact model version (doesn't create shortcut link)
+python -m spacy download en_core_web_sm-2.0.0 --direct
+```
+
+The download command will [install the model](/usage/models#download-pip) via
+pip, place the package in your `site-packages` directory and create a
+[shortcut link](/usage/models#usage) that lets you load the model by a custom
+name. The shortcut link will be the same as the model name used in
+`spacy download`.
+
+```bash
+pip install spacy
+python -m spacy download en
+```
+
+```python
+import spacy
+nlp = spacy.load("en")
+doc = nlp(u"This is a sentence.")
+```
+
+<Infobox title="Important note" variant="warning">
+
+To allow loading models via convenient shortcuts like `"en"`, spaCy will create
+a symlink within the `spacy/data` directory. This means that your user needs the
+**required permissions**. If you've installed spaCy to a system directory and
+don't have admin privileges, the model linking may fail. The easiest solution is
+to re-run the command as admin, set the `--user` flag or use a virtual
+environment. For more info on this, see the
+[troubleshooting guide](/usage/#symlink-privilege).
+
+</Infobox>
+
+### Installation via pip {#download-pip}
+
+To download a model directly using [pip](https://pypi.python.org/pypi/pip),
+point `pip install` to the URL or local path of the archive file. To find the
+direct link to a model, head over to the
+[model releases](https://github.com/explosion/spacy-models/releases), right
+click on the archive link and copy it to your clipboard.
+
+```bash
+# with external URL
+pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz
+
+# with local file
+pip install /Users/you/en_core_web_md-1.2.0.tar.gz
+```
+
+By default, this will install the model into your `site-packages` directory. You
+can then use `spacy.load()` to load it via its package name, create a
+[shortcut link](#usage-link) to assign it a custom name, or
+[import it](#usage-import) explicitly as a module. If you need to download
+models as part of an automated process, we recommend using pip with a direct
+link, instead of relying on spaCy's [`download`](/api/cli#download) command.
+
+You can also add the direct download link to your application's
+`requirements.txt`. For more details, see the section on
+[working with models in production](#production).
+
+### Manual download and installation {#download-manual}
+
+In some cases, you might prefer downloading the data manually, for example to
+place it into a custom directory. You can download the model via your browser
+from the [latest releases](https://github.com/explosion/spacy-models/releases),
+or configure your own download script using the URL of the archive file. The
+archive consists of a model directory that contains another directory with the
+model data.
+
+```yaml
+### Directory structure {highlight="7"}
+└── en_core_web_md-2.1.0.tar.gz       # downloaded archive
+    ├── meta.json                     # model meta data
+    ├── setup.py                      # setup file for pip installation
+    └── en_core_web_md                # 📦 model package
+        ├── __init__.py               # init for pip installation
+        ├── meta.json                 # model meta data
+        └── en_core_web_md-2.1.0      # model data
+```
+
+You can place the **model package directory** anywhere on your local file
+system. To use it with spaCy, assign it a name by creating a shortcut link for
+the data directory.
+
+### Using models with spaCy {#usage}
+
+To load a model, use [`spacy.load`](/api/top-level#spacy.load) with the model's
+shortcut link, package name or a path to the data directory:
+
+```python
+import spacy
+nlp = spacy.load("en")                       # load model with shortcut link "en"
+nlp = spacy.load("en_core_web_sm")           # load model package "en_core_web_sm"
+nlp = spacy.load("/path/to/en_core_web_sm")  # load package from a directory
+
+doc = nlp(u"This is a sentence.")
+```
+
+<Infobox title="Tip: Preview model info">
+
+You can use the [`info`](/api/cli#info) command or
+[`spacy.info()`](/api/top-level#spacy.info) method to print a model's meta data
+before loading it. Each `Language` object with a loaded model also exposes the
+model's meta data as the attribute `meta`. For example, `nlp.meta['version']`
+will return the model's version.
+
+</Infobox>
+
+### Using custom shortcut links {#usage-link}
+
+While previous versions of spaCy required you to maintain a data directory
+containing the models for each installation, you can now choose **how and where
+you want to keep your data**. For example, you could download all models
+manually and put them into a local directory. Whenever your spaCy projects need
+a model, you create a shortcut link to tell spaCy to load it from there. This
+means you'll never end up with duplicate data.
+
+The [`link`](/api/cli#link) command will create a symlink in the `spacy/data`
+directory.
+
+> #### Why does spaCy use symlinks?
+>
+> Symlinks were originally introduced to maintain backwards compatibility, as
+> older versions expected model data to live within `spacy/data`. However, we
+> decided to keep using them in v2.0 instead of opting for a config file.
+> There'll always be a need for assigning and saving custom model names or IDs.
+> And your system already comes with a native solution to mapping unicode
+> aliases to file paths: symbolic links.
+
+```bash
+$ python -m spacy link [package name or path] [shortcut] [--force]
+```
+
+The first argument is the **package name** (if the model was installed via pip),
+or a local path to the the **model package**. The second argument is the
+internal name you want to use for the model. Setting the `--force` flag will
+overwrite any existing links.
+
+```bash
+### Examples
+# set up shortcut link to load installed package as "en_default"
+python -m spacy link en_core_web_md en_default
+
+# set up shortcut link to load local model as "my_amazing_model"
+python -m spacy link /Users/you/model my_amazing_model
+```
+
+<Infobox title="Important note" variant="warning">
+
+In order to create a symlink, your user needs the **required permissions**. If
+you've installed spaCy to a system directory and don't have admin privileges,
+the `spacy link` command may fail. The easiest solution is to re-run the command
+as admin, set the `--user` flag or use a virtual environment. For more info on
+this, see the [troubleshooting guide](/usage/#symlink-privilege).
+
+</Infobox>
+
+### Importing models as modules {#usage-import}
+
+If you've installed a model via spaCy's downloader, or directly via pip, you can
+also `import` it and then call its `load()` method with no arguments:
+
+```python
+### {executable="true"}
+import en_core_web_sm
+
+nlp = en_core_web_sm.load()
+doc = nlp(u"This is a sentence.")
+```
+
+How you choose to load your models ultimately depends on personal preference.
+However, **for larger code bases**, we usually recommend native imports, as this
+will make it easier to integrate models with your existing build process,
+continuous integration workflow and testing framework. It'll also prevent you
+from ever trying to load a model that is not installed, as your code will raise
+an `ImportError` immediately, instead of failing somewhere down the line when
+calling `spacy.load()`.
+
+For more details, see the section on
+[working with models in production](#production).
+
+### Using your own models {#own-models}
+
+If you've trained your own model, for example for
+[additional languages](/usage/adding-languages) or
+[custom named entities](/usage/training#ner), you can save its state using the
+[`Language.to_disk()`](/api/language#to_disk) method. To make the model more
+convenient to deploy, we recommend wrapping it as a Python package.
+
+For more information and a detailed guide on how to package your model, see the
+documentation on [saving and loading models](/usage/training#saving-loading).
+
+## Using models in production {#production}
+
+If your application depends on one or more models, you'll usually want to
+integrate them into your continuous integration workflow and build process.
+While spaCy provides a range of useful helpers for downloading, linking and
+loading models, the underlying functionality is entirely based on native Python
+packages. This allows your application to handle a model like any other package
+dependency.
+
+For an example of an automated model training and build process, see
+[this overview](/usage/training#example-training-spacy) of how we're training
+and packaging our models for spaCy.
+
+### Downloading and requiring model dependencies {#models-download}
+
+spaCy's built-in [`download`](/api/cli#download) command is mostly intended as a
+convenient, interactive wrapper. It performs compatibility checks and prints
+detailed error messages and warnings. However, if you're downloading models as
+part of an automated build process, this only adds an unnecessary layer of
+complexity. If you know which models your application needs, you should be
+specifying them directly.
+
+Because all models are valid Python packages, you can add them to your
+application's `requirements.txt`. If you're running your own internal PyPi
+installation, you can upload the models there. pip's
+[requirements file format](https://pip.pypa.io/en/latest/reference/pip_install/#requirements-file-format)
+supports both package names to download via a PyPi server, as well as direct
+URLs.
+
+```text
+### requirements.txt
+spacy>=2.0.0,<3.0.0
+https://github.com/spacy-models/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz#egg=en_core_web_sm
+```
+
+Specifying `#egg=` with the package name tells pip which package to expect from
+the download URL. This way, the package won't be re-downloaded and overwritten
+if it's already installed - just like when you're downloading a package from
+PyPi.
+
+All models are versioned and specify their spaCy dependency. This ensures
+cross-compatibility and lets you specify exact version requirements for each
+model. If you've trained your own model, you can use the
+[`package`](/api/cli#package) command to generate the required meta data and
+turn it into a loadable package.
+
+### Loading and testing models {#models-loading}
+
+Downloading models directly via pip won't call spaCy's link
+[`package`](/api/cli#link) command, which creates symlinks for model shortcuts.
+This means that you'll have to run this command separately, or use the native
+`import` syntax to load the models:
+
+```python
+import en_core_web_sm
+nlp = en_core_web_sm.load()
+```
+
+In general, this approach is recommended for larger code bases, as it's more
+"native", and doesn't depend on symlinks or rely on spaCy's loader to resolve
+string names to model packages. If a model can't be imported, Python will raise
+an `ImportError` immediately. And if a model is imported but not used, any
+linter will catch that.
+
+Similarly, it'll give you more flexibility when writing tests that require
+loading models. For example, instead of writing your own `try` and `except`
+logic around spaCy's loader, you can use
+[pytest](http://pytest.readthedocs.io/en/latest/)'s
+[`importorskip()`](https://docs.pytest.org/en/latest/builtin.html#_pytest.outcomes.importorskip)
+method to only run a test if a specific model or model version is installed.
+Each model package exposes a `__version__` attribute which you can also use to
+perform your own version compatibility checks before loading a model.
diff --git a/website/docs/usage/processing-pipelines.md b/website/docs/usage/processing-pipelines.md
new file mode 100644
index 000000000..f284deada
--- /dev/null
+++ b/website/docs/usage/processing-pipelines.md
@@ -0,0 +1,836 @@
+---
+title: Language Processing Pipelines
+next: vectors-similarity
+menu:
+  - ['How Pipelines Work', 'pipelines']
+  - ['Custom Components', 'custom-components']
+  - ['Extension Attributes', 'custom-components-attributes']
+  - ['Plugins & Wrappers', 'plugins']
+---
+
+import Pipelines101 from 'usage/101/\_pipelines.md'
+
+<Pipelines101 />
+
+## How pipelines work {#pipelines}
+
+spaCy makes it very easy to create your own pipelines consisting of reusable
+components – this includes spaCy's default tagger, parser and entity recognizer,
+but also your own custom processing functions. A pipeline component can be added
+to an already existing `nlp` object, specified when initializing a `Language`
+class, or defined within a [model package](/usage/training#saving-loading).
+
+When you load a model, spaCy first consults the model's
+[`meta.json`](/usage/training#saving-loading). The meta typically includes the
+model details, the ID of a language class, and an optional list of pipeline
+components. spaCy then does the following:
+
+> #### meta.json (excerpt)
+>
+> ```json
+> {
+>   "name": "example_model",
+>   "lang": "en",
+>   "description": "Example model for spaCy",
+>   "pipeline": ["tagger", "parser"]
+> }
+> ```
+
+1. Load the **language class and data** for the given ID via
+   [`get_lang_class`](/api/top-level#util.get_lang_class) and initialize it. The
+   `Language` class contains the shared vocabulary, tokenization rules and the
+   language-specific annotation scheme.
+2. Iterate over the **pipeline names** and create each component using
+   [`create_pipe`](/api/anguage#create_pipe), which looks them up in
+   `Language.factories`.
+3. Add each pipeline component to the pipeline in order, using
+   [`add_pipe`](/api/language#add_pipe).
+4. Make the **model data** available to the `Language` class by calling
+   [`from_disk`](language#from_disk) with the path to the model data directory.
+
+So when you call this...
+
+```python
+nlp = spacy.load("en")
+```
+
+... the model tells spaCy to use the language `"en"` and the pipeline
+`["tagger", "parser", "ner"]`. spaCy will then initialize
+`spacy.lang.en.English`, and create each pipeline component and add it to the
+processing pipeline. It'll then load in the model's data from its data directory
+and return the modified `Language` class for you to use as the `nlp` object.
+
+Fundamentally, a [spaCy model](/models) consists of three components: **the
+weights**, i.e. binary data loaded in from a directory, a **pipeline** of
+functions called in order, and **language data** like the tokenization rules and
+annotation scheme. All of this is specific to each model, and defined in the
+model's `meta.json` – for example, a Spanish NER model requires different
+weights, language data and pipeline components than an English parsing and
+tagging model. This is also why the pipeline state is always held by the
+`Language` class. [`spacy.load`](/api/top-level#spacy.load) puts this all
+together and returns an instance of `Language` with a pipeline set and access to
+the binary data:
+
+```python
+### spacy.load under the hood
+lang = "en"
+pipeline = ["tagger", "parser", "ner"]
+data_path = "path/to/en_core_web_sm/en_core_web_sm-2.0.0"
+
+cls = spacy.util.get_lang_class(lang)   # 1. Get Language instance, e.g. English()
+nlp = cls()                             # 2. Initialize it
+for name in pipeline:
+    component = nlp.create_pipe(name)   # 3. Create the pipeline components
+    nlp.add_pipe(component)             # 4. Add the component to the pipeline
+nlp.from_disk(model_data_path)          # 5. Load in the binary data
+```
+
+When you call `nlp` on a text, spaCy will **tokenize** it and then **call each
+component** on the `Doc`, in order. Since the model data is loaded, the
+components can access it to assign annotations to the `Doc` object, and
+subsequently to the `Token` and `Span` which are only views of the `Doc`, and
+don't own any data themselves. All components return the modified document,
+which is then processed by the component next in the pipeline.
+
+```python
+### The pipeline under the hood
+doc = nlp.make_doc(u"This is a sentence")   # create a Doc from raw text
+for name, proc in nlp.pipeline:             # iterate over components in order
+    doc = proc(doc)                         # apply each component
+```
+
+The current processing pipeline is available as `nlp.pipeline`, which returns a
+list of `(name, component)` tuples, or `nlp.pipe_names`, which only returns a
+list of human-readable component names.
+
+```python
+print(nlp.pipeline)
+# [('tagger', <spacy.pipeline.Tagger>), ('parser', <spacy.pipeline.DependencyParser>), ('ner', <spacy.pipeline.EntityRecognizer>)]
+print(nlp.pipe_names)
+# ['tagger', 'parser', 'ner']
+```
+
+### Built-in pipeline components
+
+spaCy ships with several built-in pipeline components that are also available in
+the `Language.factories`. This means that you can initialize them by calling
+[`nlp.create_pipe`](/api/language#create_pipe) with their string names and
+require them in the pipeline settings in your model's `meta.json`.
+
+> #### Usage
+>
+> ```python
+> # Option 1: Import and initialize
+> from spacy.pipeline import EntityRuler
+> ruler = EntityRuler(nlp)
+> nlp.add_pipe(ruler)
+>
+> # Option 2: Using nlp.create_pipe
+> sentencizer = nlp.create_pipe("sentencizer")
+> nlp.add_pipe(sentencizer)
+> ```
+
+| String name         | Component                                                        | Description                                                                                   |
+| ------------------- | ---------------------------------------------------------------- | --------------------------------------------------------------------------------------------- |
+| `tagger`            | [`Tagger`](/api/tagger)                                          | Assign part-of-speech-tags.                                                                   |
+| `parser`            | [`DependencyParser`](/api/dependencyparser)                      | Assign dependency labels.                                                                     |
+| `ner`               | [`EntityRecognizer`](/api/entityrecognizer)                      | Assign named entities.                                                                        |
+| `textcat`           | [`TextCategorizer`](/api/textcategorizer)                        | Assign text categories.                                                                       |
+| `entity_ruler`      | [`EntityRuler`](/api/entityruler)                                | Assign named entities based on pattern rules.                                                 |
+| `sentencizer`       | [`SentenceSegmenter`](/api/sentencesegmenter)                    | Add rule-based sentence segmentation without the dependency parse.                            |
+| `merge_noun_chunks` | [`merge_noun_chunks`](/api/pipeline-functions#merge_noun_chunks) | Merge all noun chunks into a single token. Should be added after the tagger and parser.       |
+| `merge_entities`    | [`merge_entities`](/api/pipeline-functions#merge_entities)       | Merge all entities into a single token. Should be added after the entity recognizer.          |
+| `merge_subtokens`   | [`merge_subtokens`](/api/pipeline-functions#merge_subtokens)     | Merge subtokens predicted by the parser into single tokens. Should be added after the parser. |
+
+### Disabling and modifying pipeline components {#disabling}
+
+If you don't need a particular component of the pipeline – for example, the
+tagger or the parser, you can disable loading it. This can sometimes make a big
+difference and improve loading speed. Disabled component names can be provided
+to [`spacy.load`](/api/top-level#spacy.load),
+[`Language.from_disk`](/api/language#from_disk) or the `nlp` object itself as a
+list:
+
+```python
+nlp = spacy.load("en", disable=["parser", "tagger"])
+nlp = English().from_disk("/model", disable=["ner"])
+```
+
+You can also use the [`remove_pipe`](/api/language#remove_pipe) method to remove
+pipeline components from an existing pipeline, the
+[`rename_pipe`](/api/language#rename_pipe) method to rename them, or the
+[`replace_pipe`](/api/language#replace_pipe) method to replace them with a
+custom component entirely (more details on this in the section on
+[custom components](#custom-components).
+
+```python
+nlp.remove_pipe("parser")
+nlp.rename_pipe("ner", "entityrecognizer")
+nlp.replace_pipe("tagger", my_custom_tagger)
+```
+
+<Infobox title="Important note: disabling pipeline components" variant="warning">
+
+Since spaCy v2.0 comes with better support for customizing the processing
+pipeline components, the `parser`, `tagger` and `entity` keyword arguments have
+been replaced with `disable`, which takes a list of pipeline component names.
+This lets you disable pre-defined components when loading a model, or
+initializing a Language class via [`from_disk`](/api/language#from_disk).
+
+```diff
+- nlp = spacy.load('en', tagger=False, entity=False)
+- doc = nlp(u"I don't want parsed", parse=False)
+
++ nlp = spacy.load('en', disable=['ner'])
++ nlp.remove_pipe('parser')
++ doc = nlp(u"I don't want parsed")
+```
+
+</Infobox>
+
+## Creating custom pipeline components {#custom-components}
+
+A component receives a `Doc` object and can modify it – for example, by using
+the current weights to make a prediction and set some annotation on the
+document. By adding a component to the pipeline, you'll get access to the `Doc`
+at any point **during processing** – instead of only being able to modify it
+afterwards.
+
+> #### Example
+>
+> ```python
+> def my_component(doc):
+>    # do something to the doc here
+>    return doc
+> ```
+
+| Argument    | Type  | Description                                            |
+| ----------- | ----- | ------------------------------------------------------ |
+| `doc`       | `Doc` | The `Doc` object processed by the previous component.  |
+| **RETURNS** | `Doc` | The `Doc` object processed by this pipeline component. |
+
+Custom components can be added to the pipeline using the
+[`add_pipe`](/api/language#add_pipe) method. Optionally, you can either specify
+a component to add it **before or after**, tell spaCy to add it **first or
+last** in the pipeline, or define a **custom name**. If no name is set and no
+`name` attribute is present on your component, the function name is used.
+
+> #### Example
+>
+> ```python
+> nlp.add_pipe(my_component)
+> nlp.add_pipe(my_component, first=True)
+> nlp.add_pipe(my_component, before="parser")
+> ```
+
+| Argument | Type    | Description                                                              |
+| -------- | ------- | ------------------------------------------------------------------------ |
+| `last`   | bool    | If set to `True`, component is added **last** in the pipeline (default). |
+| `first`  | bool    | If set to `True`, component is added **first** in the pipeline.          |
+| `before` | unicode | String name of component to add the new component **before**.            |
+| `after`  | unicode | String name of component to add the new component **after**.             |
+
+### Example: A simple pipeline component {#custom-components-simple}
+
+The following component receives the `Doc` in the pipeline and prints some
+information about it: the number of tokens, the part-of-speech tags of the
+tokens and a conditional message based on the document length.
+
+> #### ✏️ Things to try
+>
+> 1. Add the component first in the pipeline by setting `first=True`. You'll see
+>    that the part-of-speech tags are empty, because the component now runs
+>    before the tagger and the tags aren't available yet.
+> 2. Change the component `name` or remove the `name` argument. You should see
+>    this change reflected in `nlp.pipe_names`.
+> 3. Print `nlp.pipeline`. You'll see a list of tuples describing the component
+>    name and the function that's called on the `Doc` object in the pipeline.
+
+```python
+### {executable="true"}
+import spacy
+
+def my_component(doc):
+    print("After tokenization, this doc has {} tokens.".format(len(doc)))
+    print("The part-of-speech tags are:", [token.pos_ for token in doc])
+    if len(doc) < 10:
+        print("This is a pretty short document.")
+    return doc
+
+nlp = spacy.load("en_core_web_sm")
+nlp.add_pipe(my_component, name="print_info", last=True)
+print(nlp.pipe_names)  # ['print_info', 'tagger', 'parser', 'ner']
+doc = nlp(u"This is a sentence.")
+
+```
+
+Of course, you can also wrap your component as a class to allow initializing it
+with custom settings and hold state within the component. This is useful for
+**stateful components**, especially ones which **depend on shared data**. In the
+following example, the custom component `EntityMatcher` can be initialized with
+`nlp` object, a terminology list and an entity label. Using the
+[`PhraseMatcher`](/api/phrasematcher), it then matches the terms in the `Doc`
+and adds them to the existing entities.
+
+<Infobox title="Important note" variant="warning">
+
+As of v2.1.0, spaCy ships with the [`EntityRuler`](/api/entityruler), a pipeline
+component for easy, rule-based named entity recognition. Its implementation is
+similar to the `EntityMatcher` code shown below, but it includes some additional
+features like support for phrase patterns and token patterns, handling overlaps
+with existing entities and pattern export as JSONL.
+
+We'll still keep the pipeline component example below, as it works well to
+illustrate complex components. But if you're planning on using this type of
+component in your application, you might find the `EntityRuler` more convenient.
+[See here](/usage/rule-based-matching#entityruler) for more details and
+examples.
+
+</Infobox>
+
+```python
+### {executable="true"}
+import spacy
+from spacy.matcher import PhraseMatcher
+from spacy.tokens import Span
+
+class EntityMatcher(object):
+    name = "entity_matcher"
+
+    def __init__(self, nlp, terms, label):
+        patterns = [nlp.make_doc(text) for text in terms]
+        self.matcher = PhraseMatcher(nlp.vocab)
+        self.matcher.add(label, None, *patterns)
+
+    def __call__(self, doc):
+        matches = self.matcher(doc)
+        for match_id, start, end in matches:
+            span = Span(doc, start, end, label=match_id)
+            doc.ents = list(doc.ents) + [span]
+        return doc
+
+nlp = spacy.load("en_core_web_sm")
+terms = (u"cat", u"dog", u"tree kangaroo", u"giant sea spider")
+entity_matcher = EntityMatcher(nlp, terms, "ANIMAL")
+
+nlp.add_pipe(entity_matcher, after="ner")
+
+print(nlp.pipe_names)  # The components in the pipeline
+
+doc = nlp(u"This is a text about Barack Obama and a tree kangaroo")
+print([(ent.text, ent.label_) for ent in doc.ents])
+```
+
+### Example: Custom sentence segmentation logic {#component-example1}
+
+Let's say you want to implement custom logic to improve spaCy's sentence
+boundary detection. Currently, sentence segmentation is based on the dependency
+parse, which doesn't always produce ideal results. The custom logic should
+therefore be applied **after** tokenization, but _before_ the dependency parsing
+– this way, the parser can also take advantage of the sentence boundaries.
+
+> #### ✏️ Things to try
+>
+> 1. Print `[token.dep_ for token in doc]` with and without the custom pipeline
+>    component. You'll see that the predicted dependency parse changes to match
+>    the sentence boundaries.
+> 2. Remove the `else` block. All other tokens will now have `is_sent_start` set
+>    to `None` (missing value), the parser will assign sentence boundaries in
+>    between.
+
+```python
+### {executable="true"}
+import spacy
+
+def custom_sentencizer(doc):
+    for i, token in enumerate(doc[:-2]):
+        # Define sentence start if pipe + titlecase token
+        if token.text == "|" and doc[i+1].is_title:
+            doc[i+1].is_sent_start = True
+        else:
+            # Explicitly set sentence start to False otherwise, to tell
+            # the parser to leave those tokens alone
+            doc[i+1].is_sent_start = False
+    return doc
+
+nlp = spacy.load("en_core_web_sm")
+nlp.add_pipe(custom_sentencizer, before="parser")  # Insert before the parser
+doc = nlp(u"This is. A sentence. | This is. Another sentence.")
+for sent in doc.sents:
+    print(sent.text)
+```
+
+### Example: Pipeline component for entity matching and tagging with custom attributes {#component-example2}
+
+This example shows how to create a spaCy extension that takes a terminology list
+(in this case, single- and multi-word company names), matches the occurrences in
+a document, labels them as `ORG` entities, merges the tokens and sets custom
+`is_tech_org` and `has_tech_org` attributes. For efficient matching, the example
+uses the [`PhraseMatcher`](/api/phrasematcher) which accepts `Doc` objects as
+match patterns and works well for large terminology lists. It also ensures your
+patterns will always match, even when you customize spaCy's tokenization rules.
+When you call `nlp` on a text, the custom pipeline component is applied to the
+`Doc`.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/pipeline/custom_component_entities.py
+```
+
+Wrapping this functionality in a pipeline component allows you to reuse the
+module with different settings, and have all pre-processing taken care of when
+you call `nlp` on your text and receive a `Doc` object.
+
+### Adding factories {#custom-components-factories}
+
+When spaCy loads a model via its `meta.json`, it will iterate over the
+`"pipeline"` setting, look up every component name in the internal factories and
+call [`nlp.create_pipe`](/api/language#create_pipe) to initialize the individual
+components, like the tagger, parser or entity recognizer. If your model uses
+custom components, this won't work – so you'll have to tell spaCy **where to
+find your component**. You can do this by writing to the `Language.factories`:
+
+```python
+from spacy.language import Language
+Language.factories["entity_matcher"] = lambda nlp, **cfg: EntityMatcher(nlp, **cfg)
+```
+
+You can also ship the above code and your custom component in your packaged
+model's `__init__.py`, so it's executed when you load your model. The `**cfg`
+config parameters are passed all the way down from
+[`spacy.load`](/api/top-level#spacy.load), so you can load the model and its
+components with custom settings:
+
+```python
+nlp = spacy.load("your_custom_model", terms=(u"tree kangaroo"), label="ANIMAL")
+```
+
+<Infobox title="Important note" variant="warning">
+
+When you load a model via its shortcut or package name, like `en_core_web_sm`,
+spaCy will import the package and then call its `load()` method. This means that
+custom code in the model's `__init__.py` will be executed, too. This is **not
+the case** if you're loading a model from a path containing the model data.
+Here, spaCy will only read in the `meta.json`. If you want to use custom
+factories with a model loaded from a path, you need to add them to
+`Language.factories` _before_ you load the model.
+
+</Infobox>
+
+## Extension attributes {#custom-components-attributes new="2"}
+
+As of v2.0, spaCy allows you to set any custom attributes and methods on the
+`Doc`, `Span` and `Token`, which become available as `Doc._`, `Span._` and
+`Token._` – for example, `Token._.my_attr`. This lets you store additional
+information relevant to your application, add new features and functionality to
+spaCy, and implement your own models trained with other machine learning
+libraries. It also lets you take advantage of spaCy's data structures and the
+`Doc` object as the "single source of truth".
+
+<Accordion title="Why ._ and not just a top-level attribute?">
+
+Writing to a `._` attribute instead of to the `Doc` directly keeps a clearer
+separation and makes it easier to ensure backwards compatibility. For example,
+if you've implemented your own `.coref` property and spaCy claims it one day,
+it'll break your code. Similarly, just by looking at the code, you'll
+immediately know what's built-in and what's custom – for example,
+`doc.sentiment` is spaCy, while `doc._.sent_score` isn't.
+
+</Accordion>
+
+<Accordion title="How is the ._ implemented?">
+
+Extension definitions – the defaults, methods, getters and setters you pass in
+to `set_extension` – are stored in class attributes on the `Underscore` class.
+If you write to an extension attribute, e.g. `doc._.hello = True`, the data is
+stored within the [`Doc.user_data`](/api/doc#attributes) dictionary. To keep the
+underscore data separate from your other dictionary entries, the string `"._."`
+is placed before the name, in a tuple.
+
+</Accordion>
+
+---
+
+There are three main types of extensions, which can be defined using the
+[`Doc.set_extension`](/api/doc#set_extension),
+[`Span.set_extension`](/api/span#set_extension) and
+[`Token.set_extension`](/api/token#set_extension) methods.
+
+1. **Attribute extensions.** Set a default value for an attribute, which can be
+   overwritten manually at any time. Attribute extensions work like "normal"
+   variables and are the quickest way to store arbitrary information on a `Doc`,
+   `Span` or `Token`. Attribute defaults behaves just like argument defaults
+   [in Python functions](http://docs.python-guide.org/en/latest/writing/gotchas/#mutable-default-arguments),
+   and should not be used for mutable values like dictionaries or lists.
+
+   ```python
+    Doc.set_extension("hello", default=True)
+    assert doc._.hello
+    doc._.hello = False
+   ```
+
+2. **Property extensions.** Define a getter and an optional setter function. If
+   no setter is provided, the extension is immutable. Since the getter and
+   setter functions are only called when you _retrieve_ the attribute, you can
+   also access values of previously added attribute extensions. For example, a
+   `Doc` getter can average over `Token` attributes. For `Span` extensions,
+   you'll almost always want to use a property – otherwise, you'd have to write
+   to _every possible_ `Span` in the `Doc` to set up the values correctly.
+
+   ```python
+   Doc.set_extension("hello", getter=get_hello_value, setter=set_hello_value)
+   assert doc._.hello
+   doc._.hello = "Hi!"
+   ```
+
+3. **Method extensions.** Assign a function that becomes available as an object
+   method. Method extensions are always immutable. For more details and
+   implementation ideas, see
+   [these examples](/usage/examples#custom-components-attr-methods).
+
+   ```python
+   Doc.set_extension("hello", method=lambda doc, name: "Hi {}!".format(name))
+   assert doc._.hello("Bob") == "Hi Bob!"
+   ```
+
+Before you can access a custom extension, you need to register it using the
+`set_extension` method on the object you want to add it to, e.g. the `Doc`. Keep
+in mind that extensions are always **added globally** and not just on a
+particular instance. If an attribute of the same name already exists, or if
+you're trying to access an attribute that hasn't been registered, spaCy will
+raise an `AttributeError`.
+
+```python
+### Example
+from spacy.tokens import Doc, Span, Token
+
+fruits = [u"apple", u"pear", u"banana", u"orange", u"strawberry"]
+is_fruit_getter = lambda token: token.text in fruits
+has_fruit_getter = lambda obj: any([t.text in fruits for t in obj])
+
+Token.set_extension("is_fruit", getter=is_fruit_getter)
+Doc.set_extension("has_fruit", getter=has_fruit_getter)
+Span.set_extension("has_fruit", getter=has_fruit_getter)
+```
+
+> #### Usage example
+>
+> ```python
+> doc = nlp(u"I have an apple and a melon")
+> assert doc[3]._.is_fruit      # get Token attributes
+> assert not doc[0]._.is_fruit
+> assert doc._.has_fruit        # get Doc attributes
+> assert doc[1:4]._.has_fruit   # get Span attributes
+> ```
+
+Once you've registered your custom attribute, you can also use the built-in
+`set`, `get` and `has` methods to modify and retrieve the attributes. This is
+especially useful it you want to pass in a string instead of calling
+`doc._.my_attr`.
+
+<Infobox title="Using mutable default values" variant="danger">
+
+When using **mutable values** like dictionaries or lists as the `default`
+argument, keep in mind that they behave just like mutable default arguments
+[in Python functions](http://docs.python-guide.org/en/latest/writing/gotchas/#mutable-default-arguments).
+This can easily cause unintended results, like the same value being set on _all_
+objects instead of only one particular instance. In most cases, it's better to
+use **getters and setters**, and only set the `default` for boolean or string
+values.
+
+```diff
++ Doc.set_extension('fruits', getter=get_fruits, setter=set_fruits)
+
+- Doc.set_extension('fruits', default={})
+- doc._.fruits['apple'] = u'🍎'  # all docs now have {'apple': u'🍎'}
+```
+
+</Infobox>
+
+### Example: Pipeline component for GPE entities and country meta data via a REST API {#component-example3}
+
+This example shows the implementation of a pipeline component that fetches
+country meta data via the [REST Countries API](https://restcountries.eu), sets
+entity annotations for countries, merges entities into one token and sets custom
+attributes on the `Doc`, `Span` and `Token` – for example, the capital,
+latitude/longitude coordinates and even the country flag.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/pipeline/custom_component_countries_api.py
+```
+
+In this case, all data can be fetched on initialization in one request. However,
+if you're working with text that contains incomplete country names, spelling
+mistakes or foreign-language versions, you could also implement a
+`like_country`-style getter function that makes a request to the search API
+endpoint and returns the best-matching result.
+
+### User hooks {#custom-components-user-hooks}
+
+While it's generally recommended to use the `Doc._`, `Span._` and `Token._`
+proxies to add your own custom attributes, spaCy offers a few exceptions to
+allow **customizing the built-in methods** like
+[`Doc.similarity`](/api/doc#similarity) or [`Doc.vector`](/api/doc#vector) with
+your own hooks, which can rely on statistical models you train yourself. For
+instance, you can provide your own on-the-fly sentence segmentation algorithm or
+document similarity method.
+
+Hooks let you customize some of the behaviors of the `Doc`, `Span` or `Token`
+objects by adding a component to the pipeline. For instance, to customize the
+[`Doc.similarity`](/api/doc#similarity) method, you can add a component that
+sets a custom function to `doc.user_hooks['similarity']`. The built-in
+`Doc.similarity` method will check the `user_hooks` dict, and delegate to your
+function if you've set one. Similar results can be achieved by setting functions
+to `Doc.user_span_hooks` and `Doc.user_token_hooks`.
+
+> #### Implementation note
+>
+> The hooks live on the `Doc` object because the `Span` and `Token` objects are
+> created lazily, and don't own any data. They just proxy to their parent `Doc`.
+> This turns out to be convenient here — we only have to worry about installing
+> hooks in one place.
+
+| Name               | Customizes                                                                                                                                                                                                              |
+| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `user_hooks`       | [`Doc.vector`](/api/doc#vector), [`Doc.has_vector`](/api/doc#has_vector), [`Doc.vector_norm`](/api/doc#vector_norm), [`Doc.sents`](/api/doc#sents)                                                                      |
+| `user_token_hooks` | [`Token.similarity`](/api/token#similarity), [`Token.vector`](/api/token#vector), [`Token.has_vector`](/api/token#has_vector), [`Token.vector_norm`](/api/token#vector_norm), [`Token.conjuncts`](/api/token#conjuncts) |
+| `user_span_hooks`  | [`Span.similarity`](/api/span#similarity), [`Span.vector`](/api/span#vector), [`Span.has_vector`](/api/span#has_vector), [`Span.vector_norm`](/api/span#vector_norm), [`Span.root`](/api/span#root)                     |
+
+```python
+### Add custom similarity hooks
+class SimilarityModel(object):
+    def __init__(self, model):
+        self._model = model
+
+    def __call__(self, doc):
+        doc.user_hooks["similarity"] = self.similarity
+        doc.user_span_hooks["similarity"] = self.similarity
+        doc.user_token_hooks["similarity"] = self.similarity
+
+    def similarity(self, obj1, obj2):
+        y = self._model([obj1.vector, obj2.vector])
+        return float(y[0])
+```
+
+## Developing plugins and wrappers {#plugins}
+
+We're very excited about all the new possibilities for community extensions and
+plugins in spaCy v2.0, and we can't wait to see what you build with it! To get
+you started, here are a few tips, tricks and best
+practices. [See here](/universe/?category=pipeline) for examples of other spaCy
+extensions.
+
+### Usage ideas {#custom-components-usage-ideas}
+
+- **Adding new features and hooking in models.** For example, a sentiment
+  analysis model, or your preferred solution for lemmatization or sentiment
+  analysis. spaCy's built-in tagger, parser and entity recognizer respect
+  annotations that were already set on the `Doc` in a previous step of the
+  pipeline.
+- **Integrating other libraries and APIs.** For example, your pipeline component
+  can write additional information and data directly to the `Doc` or `Token` as
+  custom attributes, while making sure no information is lost in the process.
+  This can be output generated by other libraries and models, or an external
+  service with a REST API.
+- **Debugging and logging.** For example, a component which stores and/or
+  exports relevant information about the current state of the processed
+  document, and insert it at any point of your pipeline.
+
+### Best practices {#custom-components-best-practices}
+
+Extensions can claim their own `._` namespace and exist as standalone packages.
+If you're developing a tool or library and want to make it easy for others to
+use it with spaCy and add it to their pipeline, all you have to do is expose a
+function that takes a `Doc`, modifies it and returns it.
+
+- Make sure to choose a **descriptive and specific name** for your pipeline
+  component class, and set it as its `name` attribute. Avoid names that are too
+  common or likely to clash with built-in or a user's other custom components.
+  While it's fine to call your package `"spacy_my_extension"`, avoid component
+  names including `"spacy"`, since this can easily lead to confusion.
+
+  ```diff
+  + name = "myapp_lemmatizer"
+  - name = "lemmatizer"
+  ```
+
+- When writing to `Doc`, `Token` or `Span` objects, **use getter functions**
+  wherever possible, and avoid setting values explicitly. Tokens and spans don't
+  own any data themselves, and they're implemented as C extension classes – so
+  you can't usually add new attributes to them like you could with most pure
+  Python objects.
+
+  ```diff
+  + is_fruit = lambda token: token.text in ("apple", "orange")
+  + Token.set_extension("is_fruit", getter=is_fruit)
+
+  - token._.set_extension("is_fruit", default=False)
+  - if token.text in ('"apple", "orange"):
+  -     token._.set("is_fruit", True)
+  ```
+
+- Always add your custom attributes to the **global** `Doc`, `Token` or `Span`
+  objects, not a particular instance of them. Add the attributes **as early as
+  possible**, e.g. in your extension's `__init__` method or in the global scope
+  of your module. This means that in the case of namespace collisions, the user
+  will see an error immediately, not just when they run their pipeline.
+
+  ```diff
+  + from spacy.tokens import Doc
+  + def __init__(attr="my_attr"):
+  +     Doc.set_extension(attr, getter=self.get_doc_attr)
+
+  - def __call__(doc):
+  -     doc.set_extension("my_attr", getter=self.get_doc_attr)
+  ```
+
+- If your extension is setting properties on the `Doc`, `Token` or `Span`,
+  include an option to **let the user to change those attribute names**. This
+  makes it easier to avoid namespace collisions and accommodate users with
+  different naming preferences. We recommend adding an `attrs` argument to the
+  `__init__` method of your class so you can write the names to class attributes
+  and reuse them across your component.
+
+  ```diff
+  + Doc.set_extension(self.doc_attr, default="some value")
+  - Doc.set_extension("my_doc_attr", default="some value")
+  ```
+
+- Ideally, extensions should be **standalone packages** with spaCy and
+  optionally, other packages specified as a dependency. They can freely assign
+  to their own `._` namespace, but should stick to that. If your extension's
+  only job is to provide a better `.similarity` implementation, and your docs
+  state this explicitly, there's no problem with writing to the
+  [`user_hooks`](#custom-components-user-hooks) and overwriting spaCy's built-in
+  method. However, a third-party extension should **never silently overwrite
+  built-ins**, or attributes set by other extensions.
+
+- If you're looking to publish a model that depends on a custom pipeline
+  component, you can either **require it** in the model package's dependencies,
+  or – if the component is specific and lightweight – choose to **ship it with
+  your model package** and add it to the `Language` instance returned by the
+  model's `load()` method. For examples of this, check out the implementations
+  of spaCy's
+  [`load_model_from_init_py`](/api/top-level#util.load_model_from_init_py)
+  [`load_model_from_path`](/api/top-level#util.load_model_from_path) utility
+  functions.
+
+  ```diff
+  + nlp.add_pipe(my_custom_component)
+  +     return nlp.from_disk(model_path)
+  ```
+
+- Once you're ready to share your extension with others, make sure to **add docs
+  and installation instructions** (you can always link to this page for more
+  info). Make it easy for others to install and use your extension, for example
+  by uploading it to [PyPi](https://pypi.python.org). If you're sharing your
+  code on GitHub, don't forget to tag it with
+  [`spacy`](https://github.com/topics/spacy?o=desc&s=stars) and
+  [`spacy-extension`](https://github.com/topics/spacy-extension?o=desc&s=stars)
+  to help people find it. If you post it on Twitter, feel free to tag
+  [@spacy_io](https://twitter.com/spacy_io) so we can check it out.
+
+### Wrapping other models and libraries {#wrapping-models-libraries}
+
+Let's say you have a custom entity recognizer that takes a list of strings and
+returns their [BILUO tags](/api/annotation#biluo). Given an input like
+`["A", "text", "about", "Facebook"]`, it will predict and return
+`["O", "O", "O", "U-ORG"]`. To integrate it into your spaCy pipeline and make it
+add those entities to the `doc.ents`, you can wrap it in a custom pipeline
+component function and pass it the token texts from the `Doc` object received by
+the component.
+
+The [`gold.spans_from_biluo_tags`](/api/goldparse#spans_from_biluo_tags) is very
+helpful here, because it takes a `Doc` object and token-based BILUO tags and
+returns a sequence of `Span` objects in the `Doc` with added labels. So all your
+wrapper has to do is compute the entity spans and overwrite the `doc.ents`.
+
+> #### How the doc.ents work
+>
+> When you add spans to the `doc.ents`, spaCy will automatically resolve them
+> back to the underlying tokens and set the `Token.ent_type` and `Token.ent_iob`
+> attributes. By definition, each token can only be part of one entity, so
+> overlapping entity spans are not allowed.
+
+```python
+### {highlight="1,6-7"}
+import your_custom_entity_recognizer
+from spacy.gold import offsets_from_biluo_tags
+
+def custom_ner_wrapper(doc):
+    words = [token.text for token in doc]
+    custom_entities = your_custom_entity_recognizer(words)
+    doc.ents = spans_from_biluo_tags(doc, custom_entities)
+    return doc
+```
+
+The `custom_ner_wrapper` can then be added to the pipeline of a blank model
+using [`nlp.add_pipe`](/api/language#add_pipe). You can also replace the
+existing entity recognizer of a pre-trained model with
+[`nlp.replace_pipe`](/api/language#replace_pipe).
+
+Here's another example of a custom model, `your_custom_model`, that takes a list
+of tokens and returns lists of fine-grained part-of-speech tags, coarse-grained
+part-of-speech tags, dependency labels and head token indices. Here, we can use
+the [`Doc.from_array`](/api/doc#from_array) to create a new `Doc` object using
+those values. To create a numpy array we need integers, so we can look up the
+string labels in the [`StringStore`](/api/stringstore). The
+[`doc.vocab.strings.add`](/api/stringstore#add) method comes in handy here,
+because it returns the integer ID of the string _and_ makes sure it's added to
+the vocab. This is especially important if the custom model uses a different
+label scheme than spaCy's default models.
+
+> #### Example: spacy-stanfordnlp
+>
+> For an example of an end-to-end wrapper for statistical tokenization, tagging
+> and parsing, check out
+> [`spacy-stanfordnlp`](https://github.com/explosion/spacy-stanfordnlp). It uses
+> a very similar approach to the example in this section – the only difference
+> is that it fully replaces the `nlp` object instead of providing a pipeline
+> component, since it also needs to handle tokenization.
+
+```python
+### {highlight="1,9,15-17"}
+import your_custom_model
+from spacy.symbols import POS, TAG, DEP, HEAD
+from spacy.tokens import Doc
+import numpy
+
+def custom_model_wrapper(doc):
+    words = [token.text for token in doc]
+    spaces = [token.whitespace for token in doc]
+    pos, tags, deps, heads = your_custom_model(words)
+    # Convert the strings to integers and add them to the string store
+    pos = [doc.vocab.strings.add(label) for label in pos]
+    tags = [doc.vocab.strings.add(label) for label in tags]
+    deps = [doc.vocab.strings.add(label) for label in deps]
+    # Create a new Doc from a numpy array
+    attrs = [POS, TAG, DEP, HEAD]
+    arr = numpy.array(list(zip(pos, tags, deps, heads)), dtype="uint64")
+    new_doc = Doc(doc.vocab, words=words, spaces=spaces).from_array(attrs, arr)
+    return new_doc
+```
+
+<Infobox title="Sentence boundaries and heads" variant="warning">
+
+If you create a `Doc` object with dependencies and heads, spaCy is able to
+resolve the sentence boundaries automatically. However, note that the `HEAD`
+value used to construct a `Doc` is the token index **relative** to the current
+token – e.g. `-1` for the previous token. The CoNLL format typically annotates
+heads as `1`-indexed absolute indices with `0` indicating the root. If that's
+the case in your annotations, you need to convert them first:
+
+```python
+heads = [2, 0, 4, 2, 2]
+new_heads = [head - i - 1 if head != 0 else 0 for i, head in enumerate(heads)]
+```
+
+</Infobox>
+
+<Infobox title="📖 Advanced usage, serialization and entry points">
+
+For more details on how to write and package custom components, make them
+available to spaCy via entry points and implement your own serialization
+methods, check out the usage guide on
+[saving and loading](/usage/saving-loading).
+
+</Infobox>
diff --git a/website/docs/usage/rule-based-matching.md b/website/docs/usage/rule-based-matching.md
new file mode 100644
index 000000000..43ff50b86
--- /dev/null
+++ b/website/docs/usage/rule-based-matching.md
@@ -0,0 +1,1213 @@
+---
+title: Rule-based matching
+teaser: Find phrases and tokens, and match entities
+menu:
+  - ['Token Matcher', 'matcher']
+  - ['Phrase Matcher', 'phrasematcher']
+  - ['Entity Ruler', 'entityruler']
+  - ['Models & Rules', 'models-rules']
+---
+
+Compared to using regular expressions on raw text, spaCy's rule-based matcher
+engines and components not only let you find you the words and phrases you're
+looking for – they also give you access to the tokens within the document and
+their relationships. This means you can easily access and analyze the
+surrounding tokens, merge spans into single tokens or add entries to the named
+entities in `doc.ents`.
+
+<Accordion title="Should I use rules or train a model?">
+
+For complex tasks, it's usually better to train a statistical entity recognition
+model. However, statistical models require training data, so for many
+situations, rule-based approaches are more practical. This is especially true at
+the start of a project: you can use a rule-based approach as part of a data
+collection process, to help you "bootstrap" a statistical model.
+
+Training a model is useful if you have some examples and you want your system to
+be able to **generalize** based on those examples. It works especially well if
+there are clues in the _local context_. For instance, if you're trying to detect
+person or company names, your application may benefit from a statistical named
+entity recognition model.
+
+Rule-based systems are a good choice if there's a more or less **finite number**
+of examples that you want to find in the data, or if there's a very **clear,
+structured pattern** you can express with token rules or regular expressions.
+For instance, country names, IP addresses or URLs are things you might be able
+to handle well with a purely rule-based approach.
+
+You can also combine both approaches and improve a statistical model with rules
+to handle very specific cases and boost accuracy. For details, see the section
+on [rule-based entity recognition](#entityruler).
+
+</Accordion>
+
+<Accordion title="When should I use the token matcher vs. the phrase matcher?">
+
+The `PhraseMatcher` is useful if you already have a large terminology list or
+gazetteer consisting of single or multi-token phrases that you want to find
+exact instances of in your data. As of spaCy v2.1.0, you can also match on the
+`LOWER` attribute for fast and case-insensitive matching.
+
+The `Matcher` isn't as blazing fast as the `PhraseMatcher`, since it compares
+across individual token attributes. However, it allows you to write very
+abstract representations of the tokens you're looking for, using lexical
+attributes, linguistic features predicted by the model, operators, set
+membership and rich comparison. For example, you can find a noun, followed by a
+verb with the lemma "love" or "like", followed by an optional determiner and
+another token that's at least ten characters long.
+
+</Accordion>
+
+## Token-based matching {#matcher}
+
+spaCy features a rule-matching engine, the [`Matcher`](/api/matcher), that
+operates over tokens, similar to regular expressions. The rules can refer to
+token annotations (e.g. the token `text` or `tag_`, and flags (e.g. `IS_PUNCT`).
+The rule matcher also lets you pass in a custom callback to act on matches – for
+example, to merge entities and apply custom labels. You can also associate
+patterns with entity IDs, to allow some basic entity linking or disambiguation.
+To match large terminology lists, you can use the
+[`PhraseMatcher`](/api/phrasematcher), which accepts `Doc` objects as match
+patterns.
+
+### Adding patterns {#adding-patterns}
+
+Let's say we want to enable spaCy to find a combination of three tokens:
+
+1. A token whose **lowercase form matches "hello"**, e.g. "Hello" or "HELLO".
+2. A token whose **`is_punct` flag is set to `True`**, i.e. any punctuation.
+3. A token whose **lowercase form matches "world"**, e.g. "World" or "WORLD".
+
+```python
+[{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}]
+```
+
+<Infobox title="Important note" variant="danger">
+
+When writing patterns, keep in mind that **each dictionary** represents **one
+token**. If spaCy's tokenization doesn't match the tokens defined in a pattern,
+the pattern is not going to produce any results. When developing complex
+patterns, make sure to check examples against spaCy's tokenization:
+
+```python
+doc = nlp(u"A complex-example,!")
+print([token.text for token in doc])
+```
+
+</Infobox>
+
+First, we initialize the `Matcher` with a vocab. The matcher must always share
+the same vocab with the documents it will operate on. We can now call
+[`matcher.add()`](/api/matcher#add) with an ID and our custom pattern. The
+second argument lets you pass in an optional callback function to invoke on a
+successful match. For now, we set it to `None`.
+
+```python
+### {executable="true"}
+import spacy
+from spacy.matcher import Matcher
+
+nlp = spacy.load("en_core_web_sm")
+matcher = Matcher(nlp.vocab)
+# Add match ID "HelloWorld" with no callback and one pattern
+pattern = [{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}]
+matcher.add("HelloWorld", None, pattern)
+
+doc = nlp(u"Hello, world! Hello world!")
+matches = matcher(doc)
+for match_id, start, end in matches:
+    string_id = nlp.vocab.strings[match_id]  # Get string representation
+    span = doc[start:end]  # The matched span
+    print(match_id, string_id, start, end, span.text)
+```
+
+The matcher returns a list of `(match_id, start, end)` tuples – in this case,
+`[('15578876784678163569', 0, 2)]`, which maps to the span `doc[0:2]` of our
+original document. The `match_id` is the [hash value](/usage/spacy-101#vocab) of
+the string ID "HelloWorld". To get the string value, you can look up the ID in
+the [`StringStore`](/api/stringstore).
+
+```python
+for match_id, start, end in matches:
+    string_id = nlp.vocab.strings[match_id]  # 'HelloWorld'
+    span = doc[start:end]                    # The matched span
+```
+
+Optionally, we could also choose to add more than one pattern, for example to
+also match sequences without punctuation between "hello" and "world":
+
+```python
+matcher.add("HelloWorld", None,
+            [{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}],
+            [{"LOWER": "hello"}, {"LOWER": "world"}])
+```
+
+By default, the matcher will only return the matches and **not do anything
+else**, like merge entities or assign labels. This is all up to you and can be
+defined individually for each pattern, by passing in a callback function as the
+`on_match` argument on `add()`. This is useful, because it lets you write
+entirely custom and **pattern-specific logic**. For example, you might want to
+merge _some_ patterns into one token, while adding entity labels for other
+pattern types. You shouldn't have to create different matchers for each of those
+processes.
+
+#### Available token attributes {#adding-patterns-attributes}
+
+The available token pattern keys are uppercase versions of the
+[`Token` attributes](/api/token#attributes). The most relevant ones for
+rule-based matching are:
+
+| Attribute                              | Type    |  Description                                                                                     |
+| -------------------------------------- | ------- | ------------------------------------------------------------------------------------------------ |
+| `ORTH`                                 | unicode | The exact verbatim text of a token.                                                              |
+| `TEXT` <Tag variant="new">2.1</Tag>    | unicode | The exact verbatim text of a token.                                                              |
+| `LOWER`                                | unicode | The lowercase form of the token text.                                                            |
+|  `LENGTH`                              | int     | The length of the token text.                                                                    |
+|  `IS_ALPHA`, `IS_ASCII`, `IS_DIGIT`    | bool    | Token text consists of alphanumeric characters, ASCII characters, digits.                        |
+|  `IS_LOWER`, `IS_UPPER`, `IS_TITLE`    | bool    | Token text is in lowercase, uppercase, titlecase.                                                |
+|  `IS_PUNCT`, `IS_SPACE`, `IS_STOP`     | bool    | Token is punctuation, whitespace, stop word.                                                     |
+|  `LIKE_NUM`, `LIKE_URL`, `LIKE_EMAIL`  | bool    | Token text resembles a number, URL, email.                                                       |
+|  `POS`, `TAG`, `DEP`, `LEMMA`, `SHAPE` | unicode | The token's simple and extended part-of-speech tag, dependency label, lemma, shape.              |
+| `ENT_TYPE`                             | unicode | The token's entity label.                                                                        |
+| `_` <Tag variant="new">2.1</Tag>       | dict    | Properties in [custom extension attributes](/processing-pipelines#custom-components-attributes). |
+
+<Infobox title="Tip: Try the interactive matcher explorer">
+
+[![Matcher demo](../images/matcher-demo.jpg)](https://explosion.ai/demos/matcher)
+
+The [Matcher Explorer](https://explosion.ai/demos/matcher) lets you test the
+rule-based `Matcher` by creating token patterns interactively and running them
+over your text. Each token can set multiple attributes like text value,
+part-of-speech tag or boolean flags. The token-based view lets you explore how
+spaCy processes your text – and why your pattern matches, or why it doesn't.
+
+</Infobox>
+
+#### Extended pattern syntax and attributes {#adding-patterns-attributes-extended new="2.1"}
+
+Instead of mapping to a single value, token patterns can also map to a
+**dictionary of properties**. For example, to specify that the value of a lemma
+should be part of a list of values, or to set a minimum character length. The
+following rich comparison attributes are available:
+
+> #### Example
+>
+> ```python
+> # Matches "love cats" or "likes flowers"
+> pattern1 = [{"LEMMA": {"IN": ["like", "love"]}},
+>             {"POS": "NOUN"}]
+>
+> # Matches tokens of length >= 10
+> pattern2 = [{"LENGTH": {">=": 10}}]
+> ```
+
+| Attribute                  | Value Type | Description                                                                       |
+| -------------------------- | ---------- | --------------------------------------------------------------------------------- |
+| `IN`                       | any        | Attribute value is member of a list.                                              |
+| `NOT_IN`                   | any        | Attribute value is _not_ member of a list.                                        |
+| `==`, `>=`, `<=`, `>`, `<` | int, float | Attribute value is equal, greater or equal, smaller or equal, greater or smaller. |
+
+#### Regular expressions {#regex new="2.1"}
+
+In some cases, only matching tokens and token attributes isn't enough – for
+example, you might want to match different spellings of a word, without having
+to add a new pattern for each spelling.
+
+```python
+pattern = [{"TEXT": {"REGEX": "^([Uu](\\.?|nited) ?[Ss](\\.?|tates)"}},
+           {"LOWER": "president"}]
+```
+
+`'REGEX'` as an operator (instead of a top-level property that only matches on
+the token's text) allows defining rules for any string value, including custom
+attributes:
+
+```python
+# Match tokens with fine-grained POS tags starting with 'V'
+pattern = [{"TAG": {"REGEX": "^V"}}]
+
+# Match custom attribute values with regular expressions
+pattern = [{"_": {"country": {"REGEX": "^([Uu](\\.?|nited) ?[Ss](\\.?|tates)"}}}]
+```
+
+<Infobox title="Regular expressions in older versions" variant="warning">
+
+Versions before v2.1.0 don't yet support the `REGEX` operator. A simple solution
+is to match a regular expression on the `Doc.text` with `re.finditer` and use
+the [`Doc.char_span`](/api/doc#char_span) method to create a `Span` from the
+character indices of the match.
+
+You can also use the regular expression by converting it to a **binary token
+flag**. [`Vocab.add_flag`](/api/vocab#add_flag) returns a flag ID which you can
+use as a key of a token match pattern.
+
+```python
+definitely_flag = lambda text: bool(re.compile(r"deff?in[ia]tely").match(text))
+IS_DEFINITELY = nlp.vocab.add_flag(definitely_flag)
+pattern = [{IS_DEFINITELY: True}]
+```
+
+</Infobox>
+
+#### Operators and quantifiers {#quantifiers}
+
+The matcher also lets you use quantifiers, specified as the `'OP'` key.
+Quantifiers let you define sequences of tokens to be matched, e.g. one or more
+punctuation marks, or specify optional tokens. Note that there are no nested or
+scoped quantifiers – instead, you can build those behaviors with `on_match`
+callbacks.
+
+| OP  | Description                                                      |
+| --- | ---------------------------------------------------------------- |
+| `!` | Negate the pattern, by requiring it to match exactly 0 times.    |
+| `?` | Make the pattern optional, by allowing it to match 0 or 1 times. |
+| `+` | Require the pattern to match 1 or more times.                    |
+| `*` | Allow the pattern to match zero or more times.                   |
+
+> #### Example
+>
+> ```python
+> pattern = [{"LOWER": "hello"},
+>            {"IS_PUNCT": True, "OP": "?"}]
+> ```
+
+<Infobox title="Note on operator behaviour" variant="warning">
+
+In versions before v2.1.0, the semantics of the `+` and `*` operators behave
+inconsistently. They were usually interpreted "greedily", i.e. longer matches
+are returned where possible. However, if you specify two `+` and `*` patterns in
+a row and their matches overlap, the first operator will behave non-greedily.
+This quirk in the semantics is corrected in spaCy v2.1.0.
+
+</Infobox>
+
+#### Using wildcard token patterns {#adding-patterns-wildcard new="2"}
+
+While the token attributes offer many options to write highly specific patterns,
+you can also use an empty dictionary, `{}` as a wildcard representing **any
+token**. This is useful if you know the context of what you're trying to match,
+but very little about the specific token and its characters. For example, let's
+say you're trying to extract people's user names from your data. All you know is
+that they are listed as "User name: {username}". The name itself may contain any
+character, but no whitespace – so you'll know it will be handled as one token.
+
+```python
+[{'ORTH': 'User'}, {'ORTH': 'name'}, {'ORTH': ':'}, {}]
+```
+
+### Adding on_match rules {#on_match}
+
+To move on to a more realistic example, let's say you're working with a large
+corpus of blog articles, and you want to match all mentions of "Google I/O"
+(which spaCy tokenizes as `['Google', 'I', '/', 'O'`]). To be safe, you only
+match on the uppercase versions, in case someone has written it as "Google i/o".
+You also add a second pattern with an added `{IS_DIGIT: True}` token – this will
+make sure you also match on "Google I/O 2017". If your pattern matches, spaCy
+should execute your custom callback function `add_event_ent`.
+
+```python
+### {executable="true"}
+import spacy
+from spacy.matcher import Matcher
+
+nlp = spacy.load("en_core_web_sm")
+matcher = Matcher(nlp.vocab)
+
+# Get the ID of the 'EVENT' entity type. This is required to set an entity.
+EVENT = nlp.vocab.strings["EVENT"]
+
+def add_event_ent(matcher, doc, i, matches):
+    # Get the current match and create tuple of entity label, start and end.
+    # Append entity to the doc's entity. (Don't overwrite doc.ents!)
+    match_id, start, end = matches[i]
+    entity = (EVENT, start, end)
+    doc.ents += (entity,)
+    print(doc[start:end].text, entity)
+
+matcher.add("GoogleIO", add_event_ent,
+            [{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}],
+            [{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}, {"IS_DIGIT": True}],)
+doc = nlp(u"This is a text about Google I/O 2015.")
+matches = matcher(doc)
+```
+
+> #### Tip: Visualizing matches
+>
+> When working with entities, you can use [displaCy](/api/top-level#displacy) to
+> quickly generate a NER visualization from your updated `Doc`, which can be
+> exported as an HTML file:
+>
+> ```python
+> from spacy import displacy
+> html = displacy.render(doc, style="ent", page=True,
+>                        options={"ents": ["EVENT"]})
+> ```
+>
+> For more info and examples, see the usage guide on
+> [visualizing spaCy](/usage/visualizers).
+
+We can now call the matcher on our documents. The patterns will be matched in
+the order they occur in the text. The matcher will then iterate over the
+matches, look up the callback for the match ID that was matched, and invoke it.
+
+```python
+doc = nlp(YOUR_TEXT_HERE)
+matcher(doc)
+```
+
+When the callback is invoked, it is passed four arguments: the matcher itself,
+the document, the position of the current match, and the total list of matches.
+This allows you to write callbacks that consider the entire set of matched
+phrases, so that you can resolve overlaps and other conflicts in whatever way
+you prefer.
+
+| Argument  | Type      | Description                                                                                                          |
+| --------- | --------- | -------------------------------------------------------------------------------------------------------------------- |
+| `matcher` | `Matcher` | The matcher instance.                                                                                                |
+| `doc`     | `Doc`     | The document the matcher was used on.                                                                                |
+| `i`       | int       | Index of the current match (`matches[i`]).                                                                           |
+| `matches` | list      |  A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. |
+
+### Using custom pipeline components {#matcher-pipeline}
+
+Let's say your data also contains some annoying pre-processing artifacts, like
+leftover HTML line breaks (e.g. `<br>` or `<BR/>`). To make your text easier to
+analyze, you want to merge those into one token and flag them, to make sure you
+can ignore them later. Ideally, this should all be done automatically as you
+process the text. You can achieve this by adding a
+[custom pipeline component](/usage/processing-pipelines#custom-components)
+that's called on each `Doc` object, merges the leftover HTML spans and sets an
+attribute `bad_html` on the token.
+
+```python
+### {executable="true"}
+import spacy
+from spacy.matcher import Matcher
+from spacy.tokens import Token
+
+# We're using a class because the component needs to be initialised with
+# the shared vocab via the nlp object
+class BadHTMLMerger(object):
+    def __init__(self, nlp):
+        # Register a new token extension to flag bad HTML
+        Token.set_extension("bad_html", default=False)
+        self.matcher = Matcher(nlp.vocab)
+        self.matcher.add(
+            "BAD_HTML",
+            None,
+            [{"ORTH": "<"}, {"LOWER": "br"}, {"ORTH": ">"}],
+            [{"ORTH": "<"}, {"LOWER": "br/"}, {"ORTH": ">"}],
+        )
+
+    def __call__(self, doc):
+        # This method is invoked when the component is called on a Doc
+        matches = self.matcher(doc)
+        spans = []  # Collect the matched spans here
+        for match_id, start, end in matches:
+            spans.append(doc[start:end])
+        with doc.retokenize() as retokenizer:
+            for span in spans:
+                retokenizer.merge(span)
+                for token in span:
+                    token._.bad_html = True  # Mark token as bad HTML
+        return doc
+
+nlp = spacy.load("en_core_web_sm")
+html_merger = BadHTMLMerger(nlp)
+nlp.add_pipe(html_merger, last=True)  # Add component to the pipeline
+doc = nlp(u"Hello<br>world! <br/> This is a test.")
+for token in doc:
+    print(token.text, token._.bad_html)
+
+```
+
+Instead of hard-coding the patterns into the component, you could also make it
+take a path to a JSON file containing the patterns. This lets you reuse the
+component with different patterns, depending on your application:
+
+```python
+html_merger = BadHTMLMerger(nlp, path='/path/to/patterns.json')
+```
+
+<Infobox title="📖 Processing pipelines">
+
+For more details and examples of how to **create custom pipeline components**
+and **extension attributes**, see the
+[usage guide](/usage/processing-pipelines).
+
+</Infobox>
+
+### Example: Using linguistic annotations {#example1}
+
+Let's say you're analyzing user comments and you want to find out what people
+are saying about Facebook. You want to start off by finding adjectives following
+"Facebook is" or "Facebook was". This is obviously a very rudimentary solution,
+but it'll be fast, and a great way to get an idea for what's in your data. Your
+pattern could look like this:
+
+```python
+[{"LOWER": "facebook"}, {"LEMMA": "be"}, {"POS": "ADV", "OP": "*"}, {"POS": "ADJ"}]
+```
+
+This translates to a token whose lowercase form matches "facebook" (like
+Facebook, facebook or FACEBOOK), followed by a token with the lemma "be" (for
+example, is, was, or 's), followed by an **optional** adverb, followed by an
+adjective. Using the linguistic annotations here is especially useful, because
+you can tell spaCy to match "Facebook's annoying", but **not** "Facebook's
+annoying ads". The optional adverb makes sure you won't miss adjectives with
+intensifiers, like "pretty awful" or "very nice".
+
+To get a quick overview of the results, you could collect all sentences
+containing a match and render them with the
+[displaCy visualizer](/usage/visualizers). In the callback function, you'll have
+access to the `start` and `end` of each match, as well as the parent `Doc`. This
+lets you determine the sentence containing the match, `doc[start : end`.sent],
+and calculate the start and end of the matched span within the sentence. Using
+displaCy in ["manual" mode](/usage/visualizers#manual-usage) lets you pass in a
+list of dictionaries containing the text and entities to render.
+
+```python
+### {executable="true"}
+import spacy
+from spacy import displacy
+from spacy.matcher import Matcher
+
+nlp = spacy.load("en_core_web_sm")
+matcher = Matcher(nlp.vocab)
+matched_sents = []  # Collect data of matched sentences to be visualized
+
+def collect_sents(matcher, doc, i, matches):
+    match_id, start, end = matches[i]
+    span = doc[start:end]  # Matched span
+    sent = span.sent  # Sentence containing matched span
+    # Append mock entity for match in displaCy style to matched_sents
+    # get the match span by ofsetting the start and end of the span with the
+    # start and end of the sentence in the doc
+    match_ents = [{
+        "start": span.start_char - sent.start_char,
+        "end": span.end_char - sent.start_char,
+        "label": "MATCH",
+    }]
+    matched_sents.append({"text": sent.text, "ents": match_ents})
+
+pattern = [{"LOWER": "facebook"}, {"LEMMA": "be"}, {"POS": "ADV", "OP": "*"},
+           {"POS": "ADJ"}]
+matcher.add("FacebookIs", collect_sents, pattern)  # add pattern
+doc = nlp(u"I'd say that Facebook is evil. – Facebook is pretty cool, right?")
+matches = matcher(doc)
+
+# Serve visualization of sentences containing match with displaCy
+# set manual=True to make displaCy render straight from a dictionary
+# (if you're not running the code within a Jupyer environment, you can
+# use displacy.serve instead)
+displacy.render(matched_sents, style="ent", manual=True)
+```
+
+### Example: Phone numbers {#example2}
+
+Phone numbers can have many different formats and matching them is often tricky.
+During tokenization, spaCy will leave sequences of numbers intact and only split
+on whitespace and punctuation. This means that your match pattern will have to
+look out for number sequences of a certain length, surrounded by specific
+punctuation – depending on the
+[national conventions](https://en.wikipedia.org/wiki/National_conventions_for_writing_telephone_numbers).
+
+The `IS_DIGIT` flag is not very helpful here, because it doesn't tell us
+anything about the length. However, you can use the `SHAPE` flag, with each `d`
+representing a digit:
+
+```python
+[{"ORTH": "("}, {"SHAPE": "ddd"}, {"ORTH": ")"}, {"SHAPE": "dddd"},
+ {"ORTH": "-", "OP": "?"}, {"SHAPE": "dddd"}]
+```
+
+This will match phone numbers of the format **(123) 4567 8901** or **(123)
+4567-8901**. To also match formats like **(123) 456 789**, you can add a second
+pattern using `'ddd'` in place of `'dddd'`. By hard-coding some values, you can
+match only certain, country-specific numbers. For example, here's a pattern to
+match the most common formats of
+[international German numbers](https://en.wikipedia.org/wiki/National_conventions_for_writing_telephone_numbers#Germany):
+
+```python
+[{"ORTH": "+"}, {"ORTH": "49"}, {"ORTH": "(", "OP": "?"}, {"SHAPE": "dddd"},
+ {"ORTH": ")", "OP": "?"}, {"SHAPE": "dddddd"}]
+```
+
+Depending on the formats your application needs to match, creating an extensive
+set of rules like this is often better than training a model. It'll produce more
+predictable results, is much easier to modify and extend, and doesn't require
+any training data – only a set of test cases.
+
+```python
+### {executable="true"}
+import spacy
+from spacy.matcher import Matcher
+
+nlp = spacy.load("en_core_web_sm")
+matcher = Matcher(nlp.vocab)
+pattern = [{"ORTH": "("}, {"SHAPE": "ddd"}, {"ORTH": ")"}, {"SHAPE": "ddd"},
+           {"ORTH": "-", "OP": "?"}, {"SHAPE": "ddd"}]
+matcher.add("PHONE_NUMBER", None, pattern)
+
+doc = nlp(u"Call me at (123) 456 789 or (123) 456 789!")
+print([t.text for t in doc])
+matches = matcher(doc)
+for match_id, start, end in matches:
+    span = doc[start:end]
+    print(span.text)
+```
+
+### Example: Hashtags and emoji on social media {#example3}
+
+Social media posts, especially tweets, can be difficult to work with. They're
+very short and often contain various emoji and hashtags. By only looking at the
+plain text, you'll lose a lot of valuable semantic information.
+
+Let's say you've extracted a large sample of social media posts on a specific
+topic, for example posts mentioning a brand name or product. As the first step
+of your data exploration, you want to filter out posts containing certain emoji
+and use them to assign a general sentiment score, based on whether the expressed
+emotion is positive or negative, e.g. 😀 or 😞. You also want to find, merge and
+label hashtags like `#MondayMotivation`, to be able to ignore or analyze them
+later.
+
+> #### Note on sentiment analysis
+>
+> Ultimately, sentiment analysis is not always _that_ easy. In addition to the
+> emoji, you'll also want to take specific words into account and check the
+> `subtree` for intensifiers like "very", to increase the sentiment score. At
+> some point, you might also want to train a sentiment model. However, the
+> approach described in this example is very useful for **bootstrapping rules to
+> collect training data**. It's also an incredibly fast way to gather first
+> insights into your data – with about 1 million tweets, you'd be looking at a
+> processing time of **under 1 minute**.
+
+By default, spaCy's tokenizer will split emoji into separate tokens. This means
+that you can create a pattern for one or more emoji tokens. Valid hashtags
+usually consist of a `#`, plus a sequence of ASCII characters with no
+whitespace, making them easy to match as well.
+
+```python
+### {executable="true"}
+from spacy.lang.en import English
+from spacy.matcher import Matcher
+
+nlp = English()  # We only want the tokenizer, so no need to load a model
+matcher = Matcher(nlp.vocab)
+
+pos_emoji = [u"😀", u"😃", u"😂", u"🤣", u"😊", u"😍"]  # Positive emoji
+neg_emoji = [u"😞", u"😠", u"😩", u"😢", u"😭", u"😒"]  # Negative emoji
+
+# Add patterns to match one or more emoji tokens
+pos_patterns = [[{"ORTH": emoji}] for emoji in pos_emoji]
+neg_patterns = [[{"ORTH": emoji}] for emoji in neg_emoji]
+
+# Function to label the sentiment
+def label_sentiment(matcher, doc, i, matches):
+    match_id, start, end = matches[i]
+    if doc.vocab.strings[match_id] == "HAPPY":  # Don't forget to get string!
+        doc.sentiment += 0.1  # Add 0.1 for positive sentiment
+    elif doc.vocab.strings[match_id] == "SAD":
+        doc.sentiment -= 0.1  # Subtract 0.1 for negative sentiment
+
+matcher.add("HAPPY", label_sentiment, *pos_patterns)  # Add positive pattern
+matcher.add("SAD", label_sentiment, *neg_patterns)  # Add negative pattern
+
+# Add pattern for valid hashtag, i.e. '#' plus any ASCII token
+matcher.add("HASHTAG", None, [{"ORTH": "#"}, {"IS_ASCII": True}])
+
+doc = nlp(u"Hello world 😀 #MondayMotivation")
+matches = matcher(doc)
+for match_id, start, end in matches:
+    string_id = doc.vocab.strings[match_id]  # Look up string ID
+    span = doc[start:end]
+    print(string_id, span.text)
+```
+
+Because the `on_match` callback receives the ID of each match, you can use the
+same function to handle the sentiment assignment for both the positive and
+negative pattern. To keep it simple, we'll either add or subtract `0.1` points –
+this way, the score will also reflect combinations of emoji, even positive _and_
+negative ones.
+
+With a library like [Emojipedia](https://github.com/bcongdon/python-emojipedia),
+we can also retrieve a short description for each emoji – for example, 😍's
+official title is "Smiling Face With Heart-Eyes". Assigning it to a
+[custom attribute](/usage/processing-pipelines#custom-components-attributes) on
+the emoji span will make it available as `span._.emoji_desc`.
+
+```python
+from emojipedia import Emojipedia  # Installation: pip install emojipedia
+from spacy.tokens import Span  # Get the global Span object
+
+Span.set_extension("emoji_desc", default=None)  # Register the custom attribute
+
+def label_sentiment(matcher, doc, i, matches):
+    match_id, start, end = matches[i]
+    if doc.vocab.strings[match_id] == "HAPPY":  # Don't forget to get string!
+        doc.sentiment += 0.1  # Add 0.1 for positive sentiment
+    elif doc.vocab.strings[match_id] == "SAD":
+        doc.sentiment -= 0.1  # Subtract 0.1 for negative sentiment
+    span = doc[start:end]
+    emoji = Emojipedia.search(span[0].text)  # Get data for emoji
+    span._.emoji_desc = emoji.title  # Assign emoji description
+
+```
+
+To label the hashtags, we can use a
+[custom attribute](/usage/processing-pipelines#custom-components-attributes) set
+on the respective token:
+
+```python
+### {executable="true"}
+import spacy
+from spacy.matcher import Matcher
+from spacy.tokens import Token
+
+nlp = spacy.load("en_core_web_sm")
+matcher = Matcher(nlp.vocab)
+
+# Add pattern for valid hashtag, i.e. '#' plus any ASCII token
+matcher.add("HASHTAG", None, [{"ORTH": "#"}, {"IS_ASCII": True}])
+
+# Register token extension
+Token.set_extension("is_hashtag", default=False)
+
+doc = nlp(u"Hello world 😀 #MondayMotivation")
+matches = matcher(doc)
+hashtags = []
+for match_id, start, end in matches:
+    if doc.vocab.strings[match_id] == "HASHTAG":
+        hashtags.append(doc[start:end])
+with doc.retokenize() as retokenizer:
+    for span in spans:
+        retokenizer.merge(span)
+        for token in span:
+            token._.is_hashtag = True
+
+for token in doc:
+    print(token.text, token._.is_hashtag)
+```
+
+To process a stream of social media posts, we can use
+[`Language.pipe`](/api/language#pipe), which will return a stream of `Doc`
+objects that we can pass to [`Matcher.pipe`](/api/matcher#pipe).
+
+```python
+docs = nlp.pipe(LOTS_OF_TWEETS)
+matches = matcher.pipe(docs)
+```
+
+## Efficient phrase matching {#phrasematcher}
+
+If you need to match large terminology lists, you can also use the
+[`PhraseMatcher`](/api/phrasematcher) and create [`Doc`](/api/doc) objects
+instead of token patterns, which is much more efficient overall. The `Doc`
+patterns can contain single or multiple tokens.
+
+### Adding phrase patterns {#adding-phrase-patterns}
+
+```python
+### {executable="true"}
+import spacy
+from spacy.matcher import PhraseMatcher
+
+nlp = spacy.load('en_core_web_sm')
+matcher = PhraseMatcher(nlp.vocab)
+terminology_list = [u"Barack Obama", u"Angela Merkel", u"Washington, D.C."]
+# Only run nlp.make_doc to speed things up
+patterns = [nlp.make_doc(text) for text in terminology_list]
+matcher.add("TerminologyList", None, *patterns)
+
+doc = nlp(u"German Chancellor Angela Merkel and US President Barack Obama "
+          u"converse in the Oval Office inside the White House in Washington, D.C.")
+matches = matcher(doc)
+for match_id, start, end in matches:
+    span = doc[start:end]
+    print(span.text)
+```
+
+Since spaCy is used for processing both the patterns and the text to be matched,
+you won't have to worry about specific tokenization – for example, you can
+simply pass in `nlp(u"Washington, D.C.")` and won't have to write a complex
+token pattern covering the exact tokenization of the term.
+
+<Infobox title="Important note on creating patterns" variant="warning">
+
+To create the patterns, each phrase has to be processed with the `nlp` object.
+If you have a mode loaded, doing this in a loop or list comprehension can easily
+become inefficient and slow. If you only need the tokenization and lexical
+attributes, you can run [`nlp.make_doc`](/api/language#make_doc) instead, which
+will only run the tokenizer. For an additional speed boost, you can also use the
+[`nlp.tokenizer.pipe`](/api/tokenizer#pipe) method, which will process the texts
+as a stream.
+
+```diff
+- patterns = [nlp(term) for term in LOTS_OF_TERMS]
++ patterns = [nlp.make_doc(term) for term in LOTS_OF_TERMS]
++ patterns = list(nlp.tokenizer.pipe(LOTS_OF_TERMS))
+```
+
+</Infobox>
+
+### Matching on other token attributes {#phrasematcher-attrs new="2.1"}
+
+By default, the `PhraseMatcher` will match on the verbatim token text, e.g.
+`Token.text`. By setting the `attr` argument on initialization, you can change
+**which token attribute the matcher should use** when comparing the phrase
+pattern to the matched `Doc`. For example, using the attribute `LOWER` lets you
+match on `Token.lower` and create case-insensitive match patterns:
+
+```python
+### {executable="true"}
+from spacy.lang.en import English
+from spacy.matcher import PhraseMatcher
+
+nlp = English()
+matcher = PhraseMatcher(nlp.vocab, attr="LOWER")
+patterns = [nlp.make_doc(name) for name in [u"Angela Merkel", u"Barack Obama"]]
+matcher.add("Names", None, *patterns)
+
+doc = nlp(u"angela merkel and us president barack Obama")
+for match_id, start, end in matcher(doc):
+    print("Matched based on lowercase token text:", doc[start:end])
+```
+
+Another possible use case is matching number tokens like IP addresses based on
+their shape. This means that you won't have to worry about how those string will
+be tokenized and you'll be able to find tokens and combinations of tokens based
+on a few examples. Here, we're matching on the shapes `ddd.d.d.d` and
+`ddd.ddd.d.d`:
+
+```python
+### {executable="true"}
+from spacy.lang.en import English
+from spacy.matcher import PhraseMatcher
+
+nlp = English()
+matcher = PhraseMatcher(nlp.vocab, attr="SHAPE")
+matcher.add("IP", None, nlp(u"127.0.0.1"), nlp(u"127.127.0.0"))
+
+doc = nlp(u"Often the router will have an IP address such as 192.168.1.1 or 192.168.2.1.")
+for match_id, start, end in matcher(doc):
+    print("Matched based on token shape:", doc[start:end])
+```
+
+In theory, the same also works for attributes like `POS`. For example, a pattern
+`nlp("I like cats")` matched based on its part-of-speech tag would return a
+match for "I love dogs". You could also match on boolean flags like `IS_PUNCT`
+to match phrases with the same sequence of punctuation and non-punctuation
+tokens as the pattern. But this can easily get confusing and doesn't have much
+of an advantage over writing one or two token patterns.
+
+## Rule-based entity recognition {#entityruler new="2.1"}
+
+The [`EntityRuler`](/api/entityruler) is an exciting new component that lets you
+add named entities based on pattern dictionaries, and makes it easy to combine
+rule-based and statistical named entity recognition for even more powerful
+models.
+
+### Entity Patterns {#entityruler-patterns}
+
+Entity patterns are dictionaries with two keys: `"label"`, specifying the label
+to assign to the entity if the pattern is matched, and `"pattern"`, the match
+pattern. The entity ruler accepts two types of patterns:
+
+1. **Phrase patterns** for exact string matches (string).
+
+   ```python
+   {"label": "ORG", "pattern": "Apple"}
+   ```
+
+2. **Token patterns** with one dictionary describing one token (list).
+
+   ```python
+   {"label": "GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]}
+   ```
+
+### Using the entity ruler {#entityruler-usage}
+
+The [`EntityRuler`](/api/entityruler) is a pipeline component that's typically
+added via [`nlp.add_pipe`](/api/language#add_pipe). When the `nlp` object is
+called on a text, it will find matches in the `doc` and add them as entities to
+the `doc.ents`, using the specified pattern label as the entity label.
+
+```python
+### {executable="true"}
+from spacy.lang.en import English
+from spacy.pipeline import EntityRuler
+
+nlp = English()
+ruler = EntityRuler(nlp)
+patterns = [{"label": "ORG", "pattern": "Apple"},
+            {"label": "GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]}]
+ruler.add_patterns(patterns)
+nlp.add_pipe(ruler)
+
+doc = nlp(u"Apple is opening its first big office in San Francisco.")
+print([(ent.text, ent.label_) for ent in doc.ents])
+```
+
+The entity ruler is designed to integrate with spaCy's existing statistical
+models and enhance the named entity recognizer. If it's added **before the
+`"ner"` component**, the entity recognizer will respect the existing entity
+spans and adjust its predictions around it. This can significantly improve
+accuracy in some cases. If it's added **after the `"ner"` component**, the
+entity ruler will only add spans to the `doc.ents` if they don't overlap with
+existing entities predicted by the model. To overwrite overlapping entities, you
+can set `overwrite_ents=True` on initialization.
+
+```python
+### {executable="true"}
+import spacy
+from spacy.pipeline import EntityRuler
+
+nlp = spacy.load("en_core_web_sm")
+ruler = EntityRuler(nlp)
+patterns = [{"label": "ORG", "pattern": "MyCorp Inc."}]
+ruler.add_patterns(patterns)
+nlp.add_pipe(ruler)
+
+doc = nlp(u"MyCorp Inc. is a company in the U.S.")
+print([(ent.text, ent.label_) for ent in doc.ents])
+```
+
+### Using pattern files {#entityruler-files}
+
+The [`to_disk`](/api/entityruler#to_disk) and
+[`from_disk`](/api/entityruler#from_disk) let you save and load patterns to and
+from JSONL (newline-delimited JSON) files, containing one pattern object per
+line.
+
+```json
+### patterns.jsonl
+{"label": "ORG", "pattern": "Apple"}
+{"label": "GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]}
+```
+
+```python
+ruler.to_disk("./patterns.jsonl")
+new_ruler = EntityRuler(nlp).from_disk("./patterns.jsonl")
+```
+
+<Infobox title="Integration with Prodigy">
+
+If you're using the [Prodigy](https://prodi.gy) annotation tool, you might
+recognize these pattern files from bootstrapping your named entity and text
+classification labelling. The patterns for the `EntityRuler` follow the same
+syntax, so you can use your existing Prodigy pattern files in spaCy, and vice
+versa.
+
+</Infobox>
+
+When you save out an `nlp` object that has an `EntityRuler` added to its
+pipeline, its patterns are automatically exported to the model directory:
+
+```python
+nlp = spacy.load("en_core_web_sm")
+ruler = EntityRuler(nlp)
+ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
+nlp.add_pipe(ruler)
+nlp.to_disk("/path/to/model")
+```
+
+The saved model now includes the `"entity_ruler"` in its `"pipeline"` setting in
+the `meta.json`, and the model directory contains a file `entityruler.jsonl`
+with the patterns. When you load the model back in, all pipeline components will
+be restored and deserialized – including the entity ruler. This lets you ship
+powerful model packages with binary weights _and_ rules included!
+
+## Combining models and rules {#models-rules}
+
+You can combine statistical and rule-based components in a variety of ways.
+Rule-based components can be used to improve the accuracy of statistical models,
+by presetting tags, entities or sentence boundaries for specific tokens. The
+statistical models will usually respect these preset annotations, which
+sometimes improves the accuracy of other decisions. You can also use rule-based
+components after a statistical model to correct common errors. Finally,
+rule-based components can reference the attributes set by statistical models, in
+order to implement more abstract logic.
+
+### Example: Expanding named entities {#models-rules-ner}
+
+When using the a pre-trained
+[named entity recognition](/usage/linguistic-features/#named-entities) model to
+extract information from your texts, you may find that the predicted span only
+includes parts of the entity you're looking for. Sometimes, this happens if
+statistical model predicts entities incorrectly. Other times, it happens if the
+way the entity type way defined in the original training corpus doesn't match
+what you need for your application.
+
+> #### Where corpora come from
+>
+> Corpora used to train models from scratch are often produced in academia. They
+> contain text from various sources with linguistic features labeled manually by
+> human annotators (following a set of specific guidelines). The corpora are
+> then distributed with evaluation data, so other researchers can benchmark
+> their algorithms and everyone can report numbers on the same data. However,
+> most applications need to learn information that isn't contained in any
+> available corpus.
+
+For example, the corpus spaCy's [English models](/models/en) were trained on
+defines a `PERSON` entity as just the **person name**, without titles like "Mr"
+or "Dr". This makes sense, because it makes it easier to resolve the entity type
+back to a knowledge base. But what if your application needs the full names,
+_including_ the titles?
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp("Dr Alex Smith chaired first board meeting of Acme Corp Inc.")
+print([(ent.text, ent.label_) for ent in doc.ents])
+```
+
+While you could try and teach the model a new definition of the `PERSON` entity
+by [updating it](/usage/training/#example-train-ner) with more examples of spans
+that include the title, this might not be the most efficient approach. The
+existing model was trained on over 2 million words, so in order to completely
+change the definition of an entity type, you might need a lot of training
+examples. However, if you already have the predicted `PERSON` entities, you can
+use a rule-based approach that checks whether they come with a title and if so,
+expands the entity span by one token. After all, what all titles in this example
+have in common is that _if_ they occur, they occur in the **previous token**
+right before the person entity.
+
+```python
+### {highlight="7-11"}
+from spacy.tokens import Span
+
+def expand_person_entities(doc):
+    new_ents = []
+    for ent in doc.ents:
+        # Only check for title if it's a person and not the first token
+        if ent.label_ == "PERSON" and ent.start != 0:
+            prev_token = doc[ent.start - 1]
+            if prev_token.text in ("Dr", "Dr.", "Mr", "Mr.", "Ms", "Ms."):
+                new_ent = Span(doc, ent.start - 1, ent.end, label=ent.label)
+                new_ents.append(new_ent)
+        else:
+            new_ents.append(ent)
+    doc.ents = new_ents
+    return doc
+```
+
+The above function takes a `Doc` object, modifies its `doc.ents` and returns it.
+This is exactly what a [pipeline component](/usage/processing-pipelines) does,
+so in order to let it run automatically when processing a text with the `nlp`
+object, we can use [`nlp.add_pipe`](/api/language#add_pipe) to add it to the
+current pipeline.
+
+```python
+### {executable="true"}
+import spacy
+from spacy.tokens import Span
+
+nlp = spacy.load("en_core_web_sm")
+
+def expand_person_entities(doc):
+    new_ents = []
+    for ent in doc.ents:
+        if ent.label_ == "PERSON" and ent.start != 0:
+            prev_token = doc[ent.start - 1]
+            if prev_token.text in ("Dr", "Dr.", "Mr", "Mr.", "Ms", "Ms."):
+                new_ent = Span(doc, ent.start - 1, ent.end, label=ent.label)
+                new_ents.append(new_ent)
+        else:
+            new_ents.append(ent)
+    doc.ents = new_ents
+    return doc
+
+# Add the component after the named entity recognizer
+nlp.add_pipe(expand_person_entities, after='ner')
+
+doc = nlp("Dr Alex Smith chaired first board meeting of Acme Corp Inc.")
+print([(ent.text, ent.label_) for ent in doc.ents])
+```
+
+An alternative approach would be to an
+[extension attribute](/usage/processing-pipelines/#custom-components-attributes)
+like `._.person_title` and add it to `Span` objects (which includes entity spans
+in `doc.ents`). The advantage here is that the entity text stays intact and can
+still be used to look up the name in a knowledge base. The following function
+takes a `Span` object, checks the previous token if it's a `PERSON` entity and
+returns the title if one is found. The `Span.doc` attribute gives us easy access
+to the span's parent document.
+
+```python
+def get_person_title(span):
+    if span.label_ == "PERSON" and span.start != 0:
+        prev_token = span.doc[span.start - 1]
+        if prev_token.text in ("Dr", "Dr.", "Mr", "Mr.", "Ms", "Ms."):
+            return prev_token.text
+```
+
+We can now use the [`Span.set_extension`](/api/span#set_extension) method to add
+the custom extension attribute `"person_title"`, using `get_person_title` as the
+getter function.
+
+```python
+### {executable="true"}
+import spacy
+from spacy.tokens import Span
+
+nlp = spacy.load("en_core_web_sm")
+
+def get_person_title(span):
+    if span.label_ == "PERSON" and span.start != 0:
+        prev_token = span.doc[span.start - 1]
+        if prev_token.text in ("Dr", "Dr.", "Mr", "Mr.", "Ms", "Ms."):
+            return prev_token.text
+
+# Register the Span extension as 'person_title'
+Span.set_extension("person_title", getter=get_person_title)
+
+doc = nlp("Dr Alex Smith chaired first board meeting of Acme Corp Inc.")
+print([(ent.text, ent.label_, ent._.person_title) for ent in doc.ents])
+```
+
+### Example: Using entities, part-of-speech tags and the dependency parse {#models-rules-pos-dep}
+
+> #### Linguistic features
+>
+> This example makes extensive use of part-of-speech tag and dependency
+> attributes and related `Doc`, `Token` and `Span` methods. For an introduction
+> on this, see the guide on
+> [linguistic features](http://localhost:8000/usage/linguistic-features/). Also
+> see the [annotation specs](/api/annotation#pos-tagging) for details on the
+> label schemes.
+
+Let's say you want to parse professional biographies and extract the person
+names and company names, and whether it's a company they're _currently_ working
+at, or a _previous_ company. One approach could be to try and train a named
+entity recognizer to predict `CURRENT_ORG` and `PREVIOUS_ORG` – but this
+distinction is very subtle and something the entity recognizer may struggle to
+learn. Nothing about "Acme Corp Inc." is inherently "current" or "previous".
+
+However, the syntax of the sentence holds some very important clues: we can
+check for trigger words like "work", whether they're **past tense** or **present
+tense**, whether company names are attached to it and whether the person is the
+subject. All of this information is available in the part-of-speech tags and the
+dependency parse.
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp("Alex Smith worked at Acme Corp Inc.")
+print([(ent.text, ent.label_) for ent in doc.ents])
+```
+
+> - `nsubj`: Nominal subject.
+> - `prep`: Preposition.
+> - `pobj`: Object of preposition.
+> - `NNP`: Proper noun, singular.
+> - `VBD`: Verb, past tense.
+> - `IN`: Conjunction, subordinating or preposition.
+
+![Visualization of dependency parse](../images/displacy-model-rules.svg "[`spacy.displacy`](/api/top-level#displacy) visualization with `options={'fine_grained': True}` to output the fine-grained part-of-speech tags, i.e. `Token.tag_`")
+
+In this example, "worked" is the root of the sentence and is a past tense verb.
+Its subject is "Alex Smith", the person who worked. "at Acme Corp Inc." is a
+prepositional phrase attached to the verb "worked". To extract this
+relationship, we can start by looking at the predicted `PERSON` entities, find
+their heads and check whether they're attached to a trigger word like "work".
+Next, we can check for prepositional phrases attached to the head and whether
+they contain an `ORG` entity. Finally, to determine whether the company
+affiliation is current, we can check the head's part-of-speech tag.
+
+```python
+person_entities = [ent for ent in doc.ents if ent.label_ == "PERSON"]
+for ent in person_entities:
+    # Because the entity is a spans, we need to use its root token. The head
+    # is the syntactic governor of the person, e.g. the verb
+    head = ent.root.head
+    if head.lemma_ == "work":
+        # Check if the children contain a preposition
+        preps = [token for token in head.children if token.dep_ == "prep"]
+        for prep in preps:
+            # Check if tokens part of ORG entities are in the preposition's
+            # children, e.g. at -> Acme Corp Inc.
+            orgs = [token for token in prep.children if token.ent_type_ == "ORG"]
+            # If the verb is in past tense, the company was a previous company
+            print({'person': ent, 'orgs': orgs, 'past': head.tag_ == "VBD"})
+```
+
+To apply this logic automatically when we process a text, we can add it to the
+`nlp` object as a
+[custom pipeline component](/usage/processing-pipelines/#custom-components). The
+above logic also expects that entities are merged into single tokens. spaCy
+ships with a handy built-in `merge_entities` that takes care of that. Instead of
+just printing the result, you could also write it to
+[custom attributes](/processing-pipelines#custom-components-attributes) on the
+entity `Span` – for example `._.orgs` or `._.prev_orgs` and `._.current_orgs`.
+
+> #### Merging entities
+>
+> Under the hood, entities are merged using the
+> [`Doc.retokenize`](/api/doc#retokenize) context manager:
+>
+> ```python
+> with doc.retokenize() as retokenize:
+>   for ent in doc.ents:
+>       retokenizer.merge(ent)
+> ```
+
+```python
+### {executable="true"}
+import spacy
+from spacy.pipeline import merge_entities
+from spacy import displacy
+
+nlp = spacy.load("en_core_web_sm")
+
+def extract_person_orgs(doc):
+    person_entities = [ent for ent in doc.ents if ent.label_ == "PERSON"]
+    for ent in person_entities:
+        head = ent.root.head
+        if head.lemma_ == "work":
+            preps = [token for token in head.children if token.dep_ == "prep"]
+            for prep in preps:
+                orgs = [token for token in prep.children if token.ent_type_ == "ORG"]
+                print({'person': ent, 'orgs': orgs, 'past': head.tag_ == "VBD"})
+    return doc
+
+# To make the entities easier to work with, we'll merge them into single tokens
+nlp.add_pipe(merge_entities)
+nlp.add_pipe(extract_person_orgs)
+
+doc = nlp("Alex Smith worked at Acme Corp Inc.")
+# If you're not in a Jupyter / IPython environment, use displacy.serve
+displacy.render(doc, options={'fine_grained': True})
+```
+
+If you change the sentence structure above, for example to "was working", you'll
+notice that our current logic fails and doesn't correctly detect the company as
+a past organization. That's because the root is a participle and the tense
+information is in the attached auxiliary "was":
+
+![Visualization of dependency parse](../images/displacy-model-rules2.svg)
+
+To solve this, we can adjust the rules to also check for the above construction:
+
+```python
+### {highlight="9-11"}
+def extract_person_orgs(doc):
+    person_entities = [ent for ent in doc.ents if ent.label_ == "PERSON"]
+    for ent in person_entities:
+        head = ent.root.head
+        if head.lemma_ == "work":
+            preps = [token for token in head.children if token.dep_ == "prep"]
+            for prep in preps:
+                orgs = [t for t in prep.children if t.ent_type_ == "ORG"]
+                aux = [token for token in head.children if token.dep_ == "aux"]
+                past_aux = any(t.tag_ == "VBD" for t in aux)
+                past = head.tag_ == "VBD" or head.tag_ == "VBG" and past_aux
+                print({'person': ent, 'orgs': orgs, 'past': past})
+    return doc
+```
+
+In your final rule-based system, you may end up with **several different code
+paths** to cover the types of constructions that occur in your data.
diff --git a/website/docs/usage/saving-loading.md b/website/docs/usage/saving-loading.md
new file mode 100644
index 000000000..b7a8476a8
--- /dev/null
+++ b/website/docs/usage/saving-loading.md
@@ -0,0 +1,642 @@
+---
+title: Saving and Loading
+menu:
+  - ['Basics', 'basics']
+  - ['Serialization Methods', 'serialization-methods']
+  - ['Entry Points', 'entry-points']
+  - ['Models', 'models']
+---
+
+## Basics {#basics hidden="true"}
+
+import Serialization101 from 'usage/101/\_serialization.md'
+
+<Serialization101 />
+
+<Infobox title="Important note" variant="warning">
+
+In spaCy v2.0, the API for saving and loading has changed to only use the four
+methods listed above consistently across objects and classes. For an overview of
+the changes, see [this table](/usage/v2#incompat) and the notes on
+[migrating](/usage/v2#migrating-saving-loading).
+
+</Infobox>
+
+### Using Pickle {#pickle}
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"This is a text.")
+> data = pickle.dumps(doc)
+> ```
+
+When pickling spaCy's objects like the [`Doc`](/api/doc) or the
+[`EntityRecognizer`](/api/entityrecognizer), keep in mind that they all require
+the shared [`Vocab`](/api/vocab) (which includes the string to hash mappings,
+label schemes and optional vectors). This means that their pickled
+representations can become very large, especially if you have word vectors
+loaded, because it won't only include the object itself, but also the entire
+shared vocab it depends on.
+
+If you need to pickle multiple objects, try to pickle them **together** instead
+of separately. For instance, instead of pickling all pipeline components, pickle
+the entire pipeline once. And instead of pickling several `Doc` objects
+separately, pickle a list of `Doc` objects. Since the all share a reference to
+the _same_ `Vocab` object, it will only be included once.
+
+```python
+### Pickling objects with shared data {highlight="8-9"}
+doc1 = nlp(u"Hello world")
+doc2 = nlp(u"This is a test")
+
+doc1_data = pickle.dumps(doc1)
+doc2_data = pickle.dumps(doc2)
+print(len(doc1_data) + len(doc2_data))  # 6636116 😞
+
+doc_data = pickle.dumps([doc1, doc2])
+print(len(doc_data))  # 3319761 😃
+```
+
+<Infobox title="Pickling spans and tokens" variant="warning">
+
+Pickling `Token` and `Span` objects isn't supported. They're only views of the
+`Doc` and can't exist on their own. Pickling them would always mean pulling in
+the parent document and its vocabulary, which has practically no advantage over
+pickling the parent `Doc`.
+
+```diff
+- data = pickle.dumps(doc[10:20])
++ data = pickle.dumps(doc)
+```
+
+If you really only need a span – for example, a particular sentence – you can
+use [`Span.as_doc`](/api/span#as_doc) to make a copy of it and convert it to a
+`Doc` object. However, note that this will not let you recover contextual
+information from _outside_ the span.
+
+```diff
++ span_doc = doc[10:20].as_doc()
+data = pickle.dumps(span_doc)
+```
+
+</Infobox>
+
+## Implementing serialization methods {#serialization-methods}
+
+When you call [`nlp.to_disk`](/api/language#to_disk),
+[`nlp.from_disk`](/api/language#from_disk) or load a model package, spaCy will
+iterate over the components in the pipeline, check if they expose a `to_disk` or
+`from_disk` method and if so, call it with the path to the model directory plus
+the string name of the component. For example, if you're calling
+`nlp.to_disk("/path")`, the data for the named entity recognizer will be saved
+in `/path/ner`.
+
+If you're using custom pipeline components that depend on external data – for
+example, model weights or terminology lists – you can take advantage of spaCy's
+built-in component serialization by making your custom component expose its own
+`to_disk` and `from_disk` or `to_bytes` and `from_bytes` methods. When an `nlp`
+object with the component in its pipeline is saved or loaded, the component will
+then be able to serialize and deserialize itself. The following example shows a
+custom component that keeps arbitrary JSON-serializable data, allows the user to
+add to that data and saves and loads the data to and from a JSON file.
+
+> #### Real-world example
+>
+> To see custom serialization methods in action, check out the new
+> [`EntityRuler`](/api/entityruler) component and its
+> [source](https://github.com/explosion/spaCy/tree/master/spacy/pipeline/entityruler.py).
+> Patterns added to the component will be saved to a `.jsonl` file if the
+> pipeline is serialized to disk, and to a bytestring if the pipeline is
+> serialized to bytes. This allows saving out a model with a rule-based entity
+> recognizer and including all rules _with_ the model data.
+
+```python
+### {highlight="15-19,21-26"}
+class CustomComponent(object):
+    name = "my_component"
+
+    def __init__(self):
+        self.data = []
+
+    def __call__(self, doc):
+        # Do something to the doc here
+        return doc
+
+    def add(self, data):
+        # Add something to the component's data
+        self.data.append(data)
+
+    def to_disk(self, path):
+        # This will receive the directory path + /my_component
+        data_path = path / "data.json"
+        with data_path.open("w", encoding="utf8") as f:
+            f.write(json.dumps(self.data))
+
+    def from_disk(self, path, **cfg):
+        # This will receive the directory path + /my_component
+        data_path = path / "data.json"
+        with data_path.open("r", encoding="utf8") as f:
+            self.data = json.loads(f)
+        return self
+```
+
+After adding the component to the pipeline and adding some data to it, we can
+serialize the `nlp` object to a directory, which will call the custom
+component's `to_disk` method.
+
+```python
+### {highlight="2-4"}
+nlp = spacy.load("en_core_web_sm")
+my_component = CustomComponent()
+my_component.add({"hello": "world"})
+nlp.add_pipe(my_component)
+nlp.to_disk("/path/to/model")
+```
+
+The contents of the directory would then look like this.
+`CustomComponent.to_disk` converted the data to a JSON string and saved it to a
+file `data.json` in its subdirectory:
+
+```yaml
+### Directory structure {highlight="2-3"}
+└── /path/to/model
+    ├── my_component     # data serialized by "my_component"
+    |   └── data.json
+    ├── ner              # data for "ner" component
+    ├── parser           # data for "parser" component
+    ├── tagger           # data for "tagger" component
+    ├── vocab            # model vocabulary
+    ├── meta.json        # model meta.json with name, language and pipeline
+    └── tokenizer        # tokenization rules
+```
+
+When you load the data back in, spaCy will call the custom component's
+`from_disk` method with the given file path, and the component can then load the
+contents of `data.json`, convert them to a Python object and restore the
+component state. The same works for other types of data, of course – for
+instance, you could add a
+[wrapper for a model](/usage/processing-pipelines#wrapping-models-libraries)
+trained with a different library like TensorFlow or PyTorch and make spaCy load
+its weights automatically when you load the model package.
+
+<Infobox title="Important note on loading components" variant="warning">
+
+When you load a model from disk, spaCy will check the `"pipeline"` in the
+model's `meta.json` and look up the component name in the internal factories. To
+make sure spaCy knows how to initialize `"my_component"`, you'll need to add it
+to the factories:
+
+```python
+from spacy.language import Language
+Language.factories["my_component"] = lambda nlp, **cfg: CustomComponent()
+```
+
+For more details, see the documentation on
+[adding factories](/usage/processing-pipelines#custom-components-factories) or
+use [entry points](#entry-points) to make your extension package expose your
+custom components to spaCy automatically.
+
+</Infobox>
+
+## Using entry points {#entry-points new="2.1"}
+
+When you load a model, spaCy will generally use the model's `meta.json` to set
+up the language class and construct the pipeline. The pipeline is specified as a
+list of strings, e.g. `"pipeline": ["tagger", "paser", "ner"]`. For each of
+those strings, spaCy will call `nlp.create_pipe` and look up the name in the
+[built-in factories](#custom-components-factories). If your model wanted to
+specify its own custom components, you usually have to write to
+`Language.factories` _before_ loading the model.
+
+```python
+pipe = nlp.create_pipe("custom_component")  # fails 👎
+
+Language.factories["custom_component"] = CustomComponentFactory
+pipe = nlp.create_pipe("custom_component")  # works 👍
+```
+
+This is inconvenient and usually required shipping a bunch of component
+initialization code with the model. Using entry points, model packages and
+extension packages can now define their own `"spacy_factories"`, which will be
+added to the built-in factories when the `Language` class is initialized. If a
+package in the same environment exposes spaCy entry points, all of this happens
+automatically and no further user action is required.
+
+#### Custom components via entry points {#entry-points-components}
+
+For a quick and fun intro to entry points in Python, I recommend
+[this excellent blog post](https://amir.rachum.com/blog/2017/07/28/python-entry-points/).
+To stick with the theme of the post, consider the following custom spaCy
+extension which is initialized with the shared `nlp` object and will print a
+snake when it's called as a pipeline component.
+
+> #### Package directory structure
+>
+> ```yaml
+> ├── snek.py   # the extension code
+> └── setup.py  # setup file for pip installation
+> ```
+
+```python
+### snek.py
+snek = """
+    --..,_                     _,.--.
+       `'.'.                .'`__ o  `;__.
+          '.'.            .'.'`  '---'`  `
+            '.`'--....--'`.'
+              `'--....--'`
+"""
+
+class SnekFactory(object):
+    def __init__(self, nlp, **cfg):
+        self.nlp = nlp
+
+    def __call__(self, doc):
+        print(snek)
+        return doc
+```
+
+Since it's a very complex and sophisticated module, you want to split it off
+into its own package so you can version it and upload it to PyPi. You also want
+your custom model to be able to define `"pipeline": ["snek"]` in its
+`meta.json`. For that, you need to be able to tell spaCy where to find the
+factory for `"snek"`. If you don't do this, spaCy will raise an error when you
+try to load the model because there's no built-in `"snek"` factory. To add an
+entry to the factories, you can now expose it in your `setup.py` via the
+`entry_points` dictionary:
+
+```python
+### setup.py {highlight="5-8"}
+from setuptools import setup
+
+setup(
+    name="snek",
+    entry_points={
+        "spacy_factories": [
+            "snek = snek:SnekFactory"
+         ]
+    }
+)
+```
+
+The entry point definition tells spaCy that the name `snek` can be found in the
+module `snek` (i.e. `snek.py`) as `SnekFactory`. The same package can expose
+multiple entry points. To make them available to spaCy, all you need to do is
+install the package:
+
+```bash
+$ python setup.py develop
+```
+
+spaCy is now able to create the pipeline component `'snek'`:
+
+```
+>>> from spacy.lang.en import English
+>>> nlp = English()
+>>> snek = nlp.create_pipe("snek")  # this now works! 🐍🎉
+>>> nlp.add_pipe(snek)
+>>> doc = nlp(u"I am snek")
+    --..,_                     _,.--.
+       `'.'.                .'`__ o  `;__.
+          '.'.            .'.'`  '---'`  `
+            '.`'--....--'`.'
+              `'--....--'`
+```
+
+Arguably, this gets even more exciting when you train your `en_core_snek_sm`
+model. To make sure `snek` is installed with the model, you can add it to the
+model's `setup.py`. You can then tell spaCy to construct the model pipeline with
+the `snek` component by setting `"pipeline": ["snek"]` in the `meta.json`.
+
+> #### meta.json
+>
+> ```diff
+> {
+>     "lang": "en",
+>     "name": "core_snek_sm",
+>     "version": "1.0.0",
+> +   "pipeline": ["snek"]
+> }
+> ```
+
+In theory, the entry point mechanism also lets you overwrite built-in factories
+– including the tokenizer. By default, spaCy will output a warning in these
+cases, to prevent accidental overwrites and unintended results.
+
+#### Advanced components with settings
+
+The `**cfg` keyword arguments that the factory receives are passed down all the
+way from `spacy.load`. This means that the factory can respond to custom
+settings defined when loading the model – for example, the style of the snake to
+load:
+
+```python
+nlp = spacy.load("en_core_snek_sm", snek_style="cute")
+```
+
+```python
+SNEKS = {"basic": snek, "cute": cute_snek}  # collection of sneks
+
+class SnekFactory(object):
+    def __init__(self, nlp, **cfg):
+        self.nlp = nlp
+        self.snek_style = cfg.get("snek_style", "basic")
+        self.snek = SNEKS[self.snek_style]
+
+    def __call__(self, doc):
+        print(self.snek)
+        return doc
+```
+
+The factory can also implement other pipeline component like `to_disk` and
+`from_disk` for serialization, or even `update` to make the component trainable.
+If a component exposes a `from_disk` method and is included in a model's
+pipeline, spaCy will call it on load. This lets you ship custom data with your
+model. When you save out a model using `nlp.to_disk` and the component exposes a
+`to_disk` method, it will be called with the disk path.
+
+```python
+def to_disk(self, path):
+    snek_path = path / "snek.txt"
+    with snek_path.open("w", encoding="utf8") as snek_file:
+        snek_file.write(self.snek)
+
+def from_disk(self, path, **cfg):
+    snek_path = path / "snek.txt"
+    with snek_path.open("r", encoding="utf8") as snek_file:
+        self.snek = snek_file.read()
+    return self
+```
+
+The above example will serialize the current snake in a `snek.txt` in the model
+data directory. When a model using the `snek` component is loaded, it will open
+the `snek.txt` and make it available to the component.
+
+#### Custom language classes via entry points {#entry-points-components}
+
+To stay with the theme of the previous example and
+[this blog post on entry points](https://amir.rachum.com/blog/2017/07/28/python-entry-points/),
+let's imagine you wanted to implement your own `SnekLanguage` class for your
+custom model – but you don't necessarily want to modify spaCy's code to
+[add a language](/usage/adding-languages). In your package, you could then
+implement the following:
+
+```python
+### snek.py
+from spacy.language import Language
+from spacy.attrs import LANG
+
+class SnekDefaults(Language.Defaults):
+    lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
+    lex_attr_getters[LANG] = lambda text: "snk"
+
+
+class SnekLanguage(Language):
+    lang = "snk"
+    Defaults = SnekDefaults
+    # Some custom snek language stuff here
+```
+
+Alongside the `spacy_factories`, there's also an entry point option for
+`spacy_languages`, which maps language codes to language-specific `Language`
+subclasses:
+
+```diff
+### setup.py
+from setuptools import setup
+
+setup(
+    name="snek",
+    entry_points={
+        "spacy_factories": [
+            "snek = snek:SnekFactory"
+         ]
++       "spacy_languages": [
++           "sk = snek:SnekLanguage"
++       ]
+    }
+)
+```
+
+In spaCy, you can then load the custom `sk` language and it will be resolved to
+`SnekLanguage` via the custom entry point. This is especially relevant for model
+packages, which could then specify `"lang": "snk"` in their `meta.json` without
+spaCy raising an error because the language is not available in the core
+library.
+
+> #### meta.json
+>
+> ```diff
+> {
+> -   "lang": "en",
+> +   "lang": "snk",
+>     "name": "core_snek_sm",
+>     "version": "1.0.0",
+>     "pipeline": ["snek"]
+> }
+> ```
+
+```python
+from spacy.util import get_lang_class
+
+SnekLanguage = get_lang_class("snk")
+nlp = SnekLanguage()
+```
+
+## Saving, loading and distributing models {#models}
+
+After training your model, you'll usually want to save its state, and load it
+back later. You can do this with the
+[`Language.to_disk()`](/api/language#to_disk) method:
+
+```python
+nlp.to_disk('/home/me/data/en_example_model')
+```
+
+The directory will be created if it doesn't exist, and the whole pipeline will
+be written out. To make the model more convenient to deploy, we recommend
+wrapping it as a Python package.
+
+### Generating a model package {#models-generating}
+
+<Infobox title="Important note" variant="warning">
+
+The model packages are **not suitable** for the public
+[pypi.python.org](https://pypi.python.org) directory, which is not designed for
+binary data and files over 50 MB. However, if your company is running an
+**internal installation** of PyPi, publishing your models on there can be a
+convenient way to share them with your team.
+
+</Infobox>
+
+spaCy comes with a handy CLI command that will create all required files, and
+walk you through generating the meta data. You can also create the meta.json
+manually and place it in the model data directory, or supply a path to it using
+the `--meta` flag. For more info on this, see the [`package`](/api/cli#package)
+docs.
+
+> #### meta.json
+>
+> ```json
+> {
+>   "name": "example_model",
+>   "lang": "en",
+>   "version": "1.0.0",
+>   "spacy_version": ">=2.0.0,<3.0.0",
+>   "description": "Example model for spaCy",
+>   "author": "You",
+>   "email": "you@example.com",
+>   "license": "CC BY-SA 3.0",
+>   "pipeline": ["tagger", "parser", "ner"]
+> }
+> ```
+
+```bash
+$ python -m spacy package /home/me/data/en_example_model /home/me/my_models
+```
+
+This command will create a model package directory that should look like this:
+
+```yaml
+### Directory structure
+└── /
+    ├── MANIFEST.in                   # to include meta.json
+    ├── meta.json                     # model meta data
+    ├── setup.py                      # setup file for pip installation
+    └── en_example_model              # model directory
+        ├── __init__.py               # init for pip installation
+        └── en_example_model-1.0.0    # model data
+```
+
+You can also find templates for all files on
+[GitHub](https://github.com/explosion/spacy-models/tree/master/template). If
+you're creating the package manually, keep in mind that the directories need to
+be named according to the naming conventions of `lang_name` and
+`lang_name-version`.
+
+### Customizing the model setup {#models-custom}
+
+The meta.json includes the model details, like name, requirements and license,
+and lets you customize how the model should be initialized and loaded. You can
+define the language data to be loaded and the
+[processing pipeline](/usage/processing-pipelines) to execute.
+
+| Setting    | Type    | Description                                                                                                                                                          |
+| ---------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `lang`     | unicode | ID of the language class to initialize.                                                                                                                              |
+| `pipeline` | list    | A list of strings mapping to the IDs of pipeline factories to apply in that order. If not set, spaCy's [default pipeline](/usage/processing-pipelines) will be used. |
+
+The `load()` method that comes with our model package templates will take care
+of putting all this together and returning a `Language` object with the loaded
+pipeline and data. If your model requires custom
+[pipeline components](/usage/processing-pipelines) or a custom language class,
+you can also **ship the code with your model**. For examples of this, check out
+the implementations of spaCy's
+[`load_model_from_init_py`](/api/top-level#util.load_model_from_init_py) and
+[`load_model_from_path`](/api/top-level#util.load_model_from_path) utility
+functions.
+
+### Building the model package {#models-building}
+
+To build the package, run the following command from within the directory. For
+more information on building Python packages, see the docs on Python's
+[setuptools](https://setuptools.readthedocs.io/en/latest/).
+
+```bash
+$ python setup.py sdist
+```
+
+This will create a `.tar.gz` archive in a directory `/dist`. The model can be
+installed by pointing pip to the path of the archive:
+
+```bash
+$ pip install /path/to/en_example_model-1.0.0.tar.gz
+```
+
+You can then load the model via its name, `en_example_model`, or import it
+directly as a module and then call its `load()` method.
+
+### Loading a custom model package {#loading}
+
+To load a model from a data directory, you can use
+[`spacy.load()`](/api/top-level#spacy.load) with the local path. This will look
+for a meta.json in the directory and use the `lang` and `pipeline` settings to
+initialize a `Language` class with a processing pipeline and load in the model
+data.
+
+```python
+nlp = spacy.load("/path/to/model")
+```
+
+If you want to **load only the binary data**, you'll have to create a `Language`
+class and call [`from_disk`](/api/language#from_disk) instead.
+
+```python
+nlp = spacy.blank("en").from_disk("/path/to/data")
+```
+
+<Infobox title="Important note: Loading data in v2.x" variant="warning">
+
+In spaCy 1.x, the distinction between `spacy.load()` and the `Language` class
+constructor was quite unclear. You could call `spacy.load()` when no model was
+present, and it would silently return an empty object. Likewise, you could pass
+a path to `English`, even if the mode required a different language. spaCy v2.0
+solves this with a clear distinction between setting up the instance and loading
+the data.
+
+```diff
+- nlp = spacy.load("en", path="/path/to/data")
++ nlp = spacy.blank("en").from_disk("/path/to/data")
+```
+
+</Infobox>
+
+### How we're training and packaging models for spaCy {#example-training-spacy}
+
+Publishing a new version of spaCy often means re-training all available models,
+which is [quite a lot](/usage/models#languages). To make this run smoothly,
+we're using an automated build process and a [`spacy train`](/api/cli#train)
+template that looks like this:
+
+```bash
+$ python -m spacy train {lang} {models_dir}/{name} {train_data} {dev_data} -m meta/{name}.json -V {version} -g {gpu_id} -n {n_epoch} -ns {n_sents}
+```
+
+> #### meta.json template
+>
+> ```json
+> {
+>   "lang": "en",
+>   "name": "core_web_sm",
+>   "license": "CC BY-SA 3.0",
+>   "author": "Explosion AI",
+>   "url": "https://explosion.ai",
+>   "email": "contact@explosion.ai",
+>   "sources": ["OntoNotes 5", "Common Crawl"],
+>   "description": "English multi-task CNN trained on OntoNotes, with GloVe vectors trained on common crawl. Assigns word vectors, context-specific token vectors, POS tags, dependency parse and named entities."
+> }
+> ```
+
+In a directory `meta`, we keep `meta.json` templates for the individual models,
+containing all relevant information that doesn't change across versions, like
+the name, description, author info and training data sources. When we train the
+model, we pass in the file to the meta template as the `--meta` argument, and
+specify the current model version as the `--version` argument.
+
+On each epoch, the model is saved out with a `meta.json` using our template and
+added properties, like the `pipeline`, `accuracy` scores and the `spacy_version`
+used to train the model. After training completion, the best model is selected
+automatically and packaged using the [`package`](/api/cli#package) command.
+Since a full meta file is already present on the trained model, no further setup
+is required to build a valid model package.
+
+```bash
+python -m spacy package -f {best_model} dist/
+cd dist/{model_name}
+python setup.py sdist
+```
+
+This process allows us to quickly trigger the model training and build process
+for all available models and languages, and generate the correct meta data
+automatically.
diff --git a/website/docs/usage/spacy-101.md b/website/docs/usage/spacy-101.md
new file mode 100644
index 000000000..f3fea3322
--- /dev/null
+++ b/website/docs/usage/spacy-101.md
@@ -0,0 +1,911 @@
+---
+title: 'spaCy 101: Everything you need to know'
+teaser: The most important concepts, explained in simple terms
+menu:
+  - ["What's spaCy?", 'whats-spacy']
+  - ['Features', 'features']
+  - ['Linguistic Annotations', 'annotations']
+  - ['Pipelines', 'pipelines']
+  - ['Vocab', 'vocab']
+  - ['Serialization', 'serialization']
+  - ['Training', 'training']
+  - ['Language Data', 'language-data']
+  - ['Lightning Tour', 'lightning-tour']
+  - ['Architecture', 'architecture']
+  - ['Community & FAQ', 'community-faq']
+---
+
+Whether you're new to spaCy, or just want to brush up on some NLP basics and
+implementation details – this page should have you covered. Each section will
+explain one of spaCy's features in simple terms and with examples or
+illustrations. Some sections will also reappear across the usage guides as a
+quick introduction.
+
+> #### Help us improve the docs
+>
+> Did you spot a mistake or come across explanations that are unclear? We always
+> appreciate improvement
+> [suggestions](https://github.com/explosion/spaCy/issues) or
+> [pull requests](https://github.com/explosion/spaCy/pulls). You can find a
+> "Suggest edits" link at the bottom of each page that points you to the source.
+
+## What's spaCy? {#whats-spacy}
+
+<Grid cols={2}>
+
+<div>
+
+spaCy is a **free, open-source library** for advanced **Natural Language
+Processing** (NLP) in Python.
+
+If you're working with a lot of text, you'll eventually want to know more about
+it. For example, what's it about? What do the words mean in context? Who is
+doing what to whom? What companies and products are mentioned? Which texts are
+similar to each other?
+
+spaCy is designed specifically for **production use** and helps you build
+applications that process and "understand" large volumes of text. It can be used
+to build **information extraction** or **natural language understanding**
+systems, or to pre-process text for **deep learning**.
+
+</div>
+
+<Infobox title="Table of contents">
+
+- [Features](#features)
+- [Linguistic annotations](#annotations)
+- [Tokenization](#annotations-token)
+- [POS tags and dependencies](#annotations-pos-deps)
+- [Named entities](#annotations-ner)
+- [Word vectors and similarity](#vectors-similarity)
+- [Pipelines](#pipelines)
+- [Vocab, hashes and lexemes](#vocab)
+- [Serialization](#serialization)
+- [Training](#training)
+- [Language data](#language-data)
+- [Lightning tour](#lightning-tour)
+- [Architecture](#architecture)
+- [Community & FAQ](#community)
+
+</Infobox>
+
+</Grid>
+
+### What spaCy isn't {#what-spacy-isnt}
+
+- **spaCy is not a platform or "an API"**. Unlike a platform, spaCy does not
+  provide a software as a service, or a web application. It's an open-source
+  library designed to help you build NLP applications, not a consumable service.
+
+- **spaCy is not an out-of-the-box chat bot engine**. While spaCy can be used to
+  power conversational applications, it's not designed specifically for chat
+  bots, and only provides the underlying text processing capabilities.
+
+- **spaCy is not research software**. It's built on the latest research, but
+  it's designed to get things done. This leads to fairly different design
+  decisions than [NLTK](https://github.com/nltk/nltk) or
+  [CoreNLP](https://stanfordnlp.github.io/CoreNLP/), which were created as
+  platforms for teaching and research. The main difference is that spaCy is
+  integrated and opinionated. spaCy tries to avoid asking the user to choose
+  between multiple algorithms that deliver equivalent functionality. Keeping the
+  menu small lets spaCy deliver generally better performance and developer
+  experience.M
+
+- **spaCy is not a company**. It's an open-source library. Our company
+  publishing spaCy and other software is called
+  [Explosion AI](https://explosion.ai).
+
+## Features {#features}
+
+In the documentation, you'll come across mentions of spaCy's features and
+capabilities. Some of them refer to linguistic concepts, while others are
+related to more general machine learning functionality.
+
+| Name                                  | Description                                                                                                        |
+| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------ |
+| **Tokenization**                      | Segmenting text into words, punctuations marks etc.                                                                |
+| **Part-of-speech** (POS) **Tagging**  | Assigning word types to tokens, like verb or noun.                                                                 |
+| **Dependency Parsing**                | Assigning syntactic dependency labels, describing the relations between individual tokens, like subject or object. |
+| **Lemmatization**                     | Assigning the base forms of words. For example, the lemma of "was" is "be", and the lemma of "rats" is "rat".      |
+| **Sentence Boundary Detection** (SBD) | Finding and segmenting individual sentences.                                                                       |
+| **Named Entity Recognition** (NER)    | Labelling named "real-world" objects, like persons, companies or locations.                                        |
+| **Similarity**                        | Comparing words, text spans and documents and how similar they are to each other.                                  |
+| **Text Classification**               | Assigning categories or labels to a whole document, or parts of a document.                                        |
+| **Rule-based Matching**               | Finding sequences of tokens based on their texts and linguistic annotations, similar to regular expressions.       |
+| **Training**                          | Updating and improving a statistical model's predictions.                                                          |
+| **Serialization**                     | Saving objects to files or byte strings.                                                                           |
+
+### Statistical models {#statistical-models}
+
+While some of spaCy's features work independently, others require
+[ statistical models](/models) to be loaded, which enable spaCy to **predict**
+linguistic annotations – for example, whether a word is a verb or a noun. spaCy
+currently offers statistical models for a variety of languages, which can be
+installed as individual Python modules. Models can differ in size, speed, memory
+usage, accuracy and the data they include. The model you choose always depends
+on your use case and the texts you're working with. For a general-purpose use
+case, the small, default models are always a good start. They typically include
+the following components:
+
+- **Binary weights** for the part-of-speech tagger, dependency parser and named
+  entity recognizer to predict those annotations in context.
+- **Lexical entries** in the vocabulary, i.e. words and their
+  context-independent attributes like the shape or spelling.
+- **Word vectors**, i.e. multi-dimensional meaning representations of words that
+  let you determine how similar they are to each other.
+- **Configuration** options, like the language and processing pipeline settings,
+  to put spaCy in the correct state when you load in the model.
+
+## Linguistic annotations {#annotations}
+
+spaCy provides a variety of linguistic annotations to give you **insights into a
+text's grammatical structure**. This includes the word types, like the parts of
+speech, and how the words are related to each other. For example, if you're
+analyzing text, it makes a huge difference whether a noun is the subject of a
+sentence, or the object – or whether "google" is used as a verb, or refers to
+the website or company in a specific context.
+
+> #### Loading models
+>
+> ```bash
+> $ python -m spacy download en_core_web_sm
+>
+> >>> import spacy
+> >>> nlp = spacy.load("en_core_web_sm")
+> ```
+
+Once you've [downloaded and installed](/usage/models) a model, you can load it
+via [`spacy.load()`](/api/top-level#spacy.load). This will return a `Language`
+object containing all components and data needed to process text. We usually
+call it `nlp`. Calling the `nlp` object on a string of text will return a
+processed `Doc`:
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"Apple is looking at buying U.K. startup for $1 billion")
+for token in doc:
+    print(token.text, token.pos_, token.dep_)
+```
+
+Even though a `Doc` is processed – e.g. split into individual words and
+annotated – it still holds **all information of the original text**, like
+whitespace characters. You can always get the offset of a token into the
+original string, or reconstruct the original by joining the tokens and their
+trailing whitespace. This way, you'll never lose any information when processing
+text with spaCy.
+
+### Tokenization {#annotations-token}
+
+import Tokenization101 from 'usage/101/\_tokenization.md'
+
+<Tokenization101 />
+
+<Infobox title="📖 Tokenization rules">
+
+To learn more about how spaCy's tokenization rules work in detail, how to
+**customize and replace** the default tokenizer and how to **add
+language-specific data**, see the usage guides on
+[adding languages](/usage/adding-languages) and
+[customizing the tokenizer](/usage/linguistic-features#tokenization).
+
+</Infobox>
+
+### Part-of-speech tags and dependencies {#annotations-pos-deps model="parser"}
+
+import PosDeps101 from 'usage/101/\_pos-deps.md'
+
+<PosDeps101 />
+
+<Infobox title="📖 Part-of-speech tagging and morphology">
+
+To learn more about **part-of-speech tagging** and rule-based morphology, and
+how to **navigate and use the parse tree** effectively, see the usage guides on
+[part-of-speech tagging](/usage/linguistic-features#pos-tagging) and
+[using the dependency parse](/usage/linguistic-features#dependency-parse).
+
+</Infobox>
+
+### Named Entities {#annotations-ner model="ner"}
+
+import NER101 from 'usage/101/\_named-entities.md'
+
+<NER101 />
+
+<Infobox title="📖 Named Entity Recognition">
+
+To learn more about entity recognition in spaCy, how to **add your own
+entities** to a document and how to **train and update** the entity predictions
+of a model, see the usage guides on
+[named entity recognition](/usage/linguistic-features#named-entities) and
+[training the named entity recognizer](/usage/training#ner).
+
+</Infobox>
+
+### Word vectors and similarity {#vectors-similarity model="vectors"}
+
+import Vectors101 from 'usage/101/\_vectors-similarity.md'
+
+<Vectors101 />
+
+<Infobox title="📖 Word vectors">
+
+To learn more about word vectors, how to **customize them** and how to load
+**your own vectors** into spaCy, see the usage guide on
+[using word vectors and semantic similarities](/usage/vectors-similarity).
+
+</Infobox>
+
+## Pipelines {#pipelines}
+
+import Pipelines101 from 'usage/101/\_pipelines.md'
+
+<Pipelines101 />
+
+<Infobox title="📖 Processing pipelines">
+
+To learn more about **how processing pipelines work** in detail, how to enable
+and disable their components, and how to **create your own**, see the usage
+guide on [language processing pipelines](/usage/processing-pipelines).
+
+</Infobox>
+
+## Vocab, hashes and lexemes {#vocab}
+
+Whenever possible, spaCy tries to store data in a vocabulary, the
+[`Vocab`](/api/vocab), that will be **shared by multiple documents**. To save
+memory, spaCy also encodes all strings to **hash values** – in this case for
+example, "coffee" has the hash `3197928453018144401`. Entity labels like "ORG"
+and part-of-speech tags like "VERB" are also encoded. Internally, spaCy only
+"speaks" in hash values.
+
+> - **Token**: A word, punctuation mark etc. _in context_, including its
+>   attributes, tags and dependencies.
+> - **Lexeme**: A "word type" with no context. Includes the word shape and
+>   flags, e.g. if it's lowercase, a digit or punctuation.
+> - **Doc**: A processed container of tokens in context.
+> - **Vocab**: The collection of lexemes.
+> - **StringStore**: The dictionary mapping hash values to strings, for example
+>   `3197928453018144401` → "coffee".
+
+![Doc, Vocab, Lexeme and StringStore](../images/vocab_stringstore.svg)
+
+If you process lots of documents containing the word "coffee" in all kinds of
+different contexts, storing the exact string "coffee" every time would take up
+way too much space. So instead, spaCy hashes the string and stores it in the
+[`StringStore`](/api/stringstore). You can think of the `StringStore` as a
+**lookup table that works in both directions** – you can look up a string to get
+its hash, or a hash to get its string:
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"I love coffee")
+print(doc.vocab.strings[u"coffee"])  # 3197928453018144401
+print(doc.vocab.strings[3197928453018144401])  # 'coffee'
+```
+
+> #### What does 'L' at the end of a hash mean?
+>
+> If you return a hash value in the **Python 2 interpreter**, it'll show up as
+> `3197928453018144401L`. The `L` just means "long integer" – it's **not**
+> actually a part of the hash value.
+
+Now that all strings are encoded, the entries in the vocabulary **don't need to
+include the word text** themselves. Instead, they can look it up in the
+`StringStore` via its hash value. Each entry in the vocabulary, also called
+[`Lexeme`](/api/lexeme), contains the **context-independent** information about
+a word. For example, no matter if "love" is used as a verb or a noun in some
+context, its spelling and whether it consists of alphabetic characters won't
+ever change. Its hash value will also always be the same.
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"I love coffee")
+for word in doc:
+    lexeme = doc.vocab[word.text]
+    print(lexeme.text, lexeme.orth, lexeme.shape_, lexeme.prefix_, lexeme.suffix_,
+            lexeme.is_alpha, lexeme.is_digit, lexeme.is_title, lexeme.lang_)
+```
+
+> - **Text**: The original text of the lexeme.
+> - **Orth**: The hash value of the lexeme.
+> - **Shape**: The abstract word shape of the lexeme.
+> - **Prefix**: By default, the first letter of the word string.
+> - **Suffix**: By default, the last three letters of the word string.
+> - **is alpha**: Does the lexeme consist of alphabetic characters?
+> - **is digit**: Does the lexeme consist of digits?
+
+| Text   | Orth                  | Shape  | Prefix | Suffix | is_alpha | is_digit |
+| ------ | --------------------- | ------ | ------ | ------ | -------- | -------- |
+| I      | `4690420944186131903` | `X`    | I      | I      | `True`   | `False`  |
+| love   | `3702023516439754181` | `xxxx` | l      | ove    | `True`   | `False`  |
+| coffee | `3197928453018144401` | `xxxx` | c      | fee    | `True`   | `False`  |
+
+The mapping of words to hashes doesn't depend on any state. To make sure each
+value is unique, spaCy uses a
+[hash function](https://en.wikipedia.org/wiki/Hash_function) to calculate the
+hash **based on the word string**. This also means that the hash for "coffee"
+will always be the same, no matter which model you're using or how you've
+configured spaCy.
+
+However, hashes **cannot be reversed** and there's no way to resolve
+`3197928453018144401` back to "coffee". All spaCy can do is look it up in the
+vocabulary. That's why you always need to make sure all objects you create have
+access to the same vocabulary. If they don't, spaCy might not be able to find
+the strings it needs.
+
+```python
+### {executable="true"}
+import spacy
+from spacy.tokens import Doc
+from spacy.vocab import Vocab
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"I love coffee")  # Original Doc
+print(doc.vocab.strings[u"coffee"])  # 3197928453018144401
+print(doc.vocab.strings[3197928453018144401])  # 'coffee' 👍
+
+empty_doc = Doc(Vocab())  # New Doc with empty Vocab
+# empty_doc.vocab.strings[3197928453018144401] will raise an error :(
+
+empty_doc.vocab.strings.add(u"coffee")  # Add "coffee" and generate hash
+print(empty_doc.vocab.strings[3197928453018144401])  # 'coffee' 👍
+
+new_doc = Doc(doc.vocab)  # Create new doc with first doc's vocab
+print(new_doc.vocab.strings[3197928453018144401])  # 'coffee' 👍
+```
+
+If the vocabulary doesn't contain a string for `3197928453018144401`, spaCy will
+raise an error. You can re-add "coffee" manually, but this only works if you
+actually _know_ that the document contains that word. To prevent this problem,
+spaCy will also export the `Vocab` when you save a `Doc` or `nlp` object. This
+will give you the object and its encoded annotations, plus the "key" to decode
+it.
+
+## Serialization {#serialization}
+
+import Serialization101 from 'usage/101/\_serialization.md'
+
+<Serialization101 />
+
+<Infobox title="📖 Saving and loading">
+
+To learn more about how to **save and load your own models**, see the usage
+guide on [saving and loading](/usage/training#saving-loading).
+
+</Infobox>
+
+## Training {#training}
+
+import Training101 from 'usage/101/\_training.md'
+
+<Training101 />
+
+<Infobox title="📖 Training statistical models">
+
+To learn more about **training and updating** models, how to create training
+data and how to improve spaCy's named entity recognition models, see the usage
+guides on [training](/usage/training).
+
+</Infobox>
+
+## Language data {#language-data}
+
+import LanguageData101 from 'usage/101/\_language-data.md'
+
+<LanguageData101 />
+
+<Infobox title="📖 Language data">
+
+To learn more about the individual components of the language data and how to
+**add a new language** to spaCy in preparation for training a language model,
+see the usage guide on [adding languages](/usage/adding-languages).
+
+</Infobox>
+
+## Lightning tour {#lightning-tour}
+
+The following examples and code snippets give you an overview of spaCy's
+functionality and its usage.
+
+### Install models and process text {#lightning-tour-models}
+
+```bash
+python -m spacy download en_core_web_sm
+python -m spacy download de_core_news_sm
+```
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"Hello, world. Here are two sentences.")
+print([t.text for t in doc])
+
+nlp_de = spacy.load("de_core_news_sm")
+doc_de = nlp_de(u"Ich bin ein Berliner.")
+print([t.text for t in doc_de])
+
+```
+
+<Infobox>
+
+**API:** [`spacy.load()`](/api/top-level#spacy.load) **Usage:**
+[Models](/usage/models), [spaCy 101](/usage/spacy-101)
+
+</Infobox>
+
+### Get tokens, noun chunks & sentences {#lightning-tour-tokens-sentences model="parser"}
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"Peach emoji is where it has always been. Peach is the superior "
+          u"emoji. It's outranking eggplant 🍑 ")
+print(doc[0].text)          # 'Peach'
+print(doc[1].text)          # 'emoji'
+print(doc[-1].text)         # '🍑'
+print(doc[17:19].text)      # 'outranking eggplant'
+
+noun_chunks = list(doc.noun_chunks)
+print(noun_chunks[0].text)  # 'Peach emoji'
+
+sentences = list(doc.sents)
+assert len(sentences) == 3
+print(sentences[1].text)    # 'Peach is the superior emoji.'
+```
+
+<Infobox>
+
+**API:** [`Doc`](/api/doc), [`Token`](/api/token) **Usage:**
+[spaCy 101](/usage/spacy-101)
+
+</Infobox>
+
+### Get part-of-speech tags and flags {#lightning-tour-pos-tags model="tagger"}
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"Apple is looking at buying U.K. startup for $1 billion")
+apple = doc[0]
+print("Fine-grained POS tag", apple.pos_, apple.pos)
+print("Coarse-grained POS tag", apple.tag_, apple.tag)
+print("Word shape", apple.shape_, apple.shape)
+print("Alphanumeric characters?", apple.is_alpha)
+print("Punctuation mark?", apple.is_punct)
+
+billion = doc[10]
+print("Digit?", billion.is_digit)
+print("Like a number?", billion.like_num)
+print("Like an email address?", billion.like_email)
+```
+
+<Infobox>
+
+**API:** [`Token`](/api/token) **Usage:**
+[Part-of-speech tagging](/usage/linguistic-features#pos-tagging)
+
+</Infobox>
+
+### Use hash values for any string {#lightning-tour-hashes}
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"I love coffee")
+
+coffee_hash = nlp.vocab.strings[u"coffee"]  # 3197928453018144401
+coffee_text = nlp.vocab.strings[coffee_hash]  # 'coffee'
+print(coffee_hash, coffee_text)
+print(doc[2].orth, coffee_hash)  # 3197928453018144401
+print(doc[2].text, coffee_text)  # 'coffee'
+
+beer_hash = doc.vocab.strings.add(u"beer")  # 3073001599257881079
+beer_text = doc.vocab.strings[beer_hash]  # 'beer'
+print(beer_hash, beer_text)
+
+unicorn_hash = doc.vocab.strings.add(u"🦄 ")  # 18234233413267120783
+unicorn_text = doc.vocab.strings[unicorn_hash]  # '🦄 '
+print(unicorn_hash, unicorn_text)
+```
+
+<Infobox>
+
+**API:** [`StringStore`](/api/stringstore) **Usage:**
+[Vocab, hashes and lexemes 101](/usage/spacy-101#vocab)
+
+</Infobox>
+
+### Recognize and update named entities {#lightning-tour-entities model="ner"}
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"San Francisco considers banning sidewalk delivery robots")
+for ent in doc.ents:
+    print(ent.text, ent.start_char, ent.end_char, ent.label_)
+
+from spacy.tokens import Span
+
+doc = nlp(u"FB is hiring a new VP of global policy")
+doc.ents = [Span(doc, 0, 1, label=doc.vocab.strings[u"ORG"])]
+for ent in doc.ents:
+    print(ent.text, ent.start_char, ent.end_char, ent.label_)
+
+```
+
+<Infobox>
+
+**Usage:** [Named entity recognition](/usage/linguistic-features#named-entities)
+
+</Infobox>
+
+### Train and update neural network models {#lightning-tour-training"}
+
+```python
+import spacy
+import random
+
+nlp = spacy.load("en_core_web_sm")
+train_data = [(u"Uber blew through $1 million", {"entities": [(0, 4, "ORG")]})]
+
+other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
+with nlp.disable_pipes(*other_pipes):
+    optimizer = nlp.begin_training()
+    for i in range(10):
+        random.shuffle(train_data)
+        for text, annotations in train_data:
+            nlp.update([text], [annotations], sgd=optimizer)
+nlp.to_disk("/model")
+```
+
+<Infobox>
+
+**API:** [`Language.update`](/api/language#update) **Usage:**
+[Training spaCy's statistical models](/usage/training)
+
+</Infobox>
+
+### Visualize a dependency parse and named entities in your browser {#lightning-tour-displacy model="parser, ner" new="2"}
+
+> #### Output
+>
+> ![displaCy visualization](../images/displacy-small.svg)
+
+```python
+from spacy import displacy
+
+doc_dep = nlp(u"This is a sentence.")
+displacy.serve(doc_dep, style="dep")
+
+doc_ent = nlp(u"When Sebastian Thrun started working on self-driving cars at Google "
+              u"in 2007, few people outside of the company took him seriously.")
+displacy.serve(doc_ent, style="ent")
+```
+
+<Infobox>
+
+**API:** [`displacy`](/api/top-level#displacy) **Usage:**
+[Visualizers](/usage/visualizers)
+
+</Infobox>
+
+### Get word vectors and similarity {#lightning-tour-word-vectors model="vectors"}
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_md")
+doc = nlp(u"Apple and banana are similar. Pasta and hippo aren't.")
+
+apple = doc[0]
+banana = doc[2]
+pasta = doc[6]
+hippo = doc[8]
+
+print("apple <-> banana", apple.similarity(banana))
+print("pasta <-> hippo", pasta.similarity(hippo))
+print(apple.has_vector, banana.has_vector, pasta.has_vector, hippo.has_vector)
+```
+
+For the best results, you should run this example using the
+[`en_vectors_web_lg`](/models/en#en_vectors_web_lg) model (currently not
+available in the live demo).
+
+<Infobox>
+
+**Usage:** [Word vectors and similarity](/usage/vectors-similarity)
+
+</Infobox>
+
+### Simple and efficient serialization {#lightning-tour-serialization}
+
+```python
+import spacy
+from spacy.tokens import Doc
+from spacy.vocab import Vocab
+
+nlp = spacy.load("en_core_web_sm")
+customer_feedback = open("customer_feedback_627.txt").read()
+doc = nlp(customer_feedback)
+doc.to_disk("/tmp/customer_feedback_627.bin")
+
+new_doc = Doc(Vocab()).from_disk("/tmp/customer_feedback_627.bin")
+```
+
+<Infobox>
+
+**API:** [`Language`](/api/language), [`Doc`](/api/doc) **Usage:**
+[Saving and loading models](/usage/models#saving-loading)
+
+</Infobox>
+
+### Match text with token rules {#lightning-tour-rule-matcher}
+
+```python
+### {executable="true"}
+import spacy
+from spacy.matcher import Matcher
+
+nlp = spacy.load("en_core_web_sm")
+matcher = Matcher(nlp.vocab)
+
+def set_sentiment(matcher, doc, i, matches):
+    doc.sentiment += 0.1
+
+pattern1 = [{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}]
+pattern2 = [[{"ORTH": emoji, "OP": "+"}] for emoji in ["😀", "😂", "🤣", "😍"]]
+matcher.add("GoogleIO", None, pattern1)  # Match "Google I/O" or "Google i/o"
+matcher.add("HAPPY", set_sentiment, *pattern2)  # Match one or more happy emoji
+
+doc = nlp(u"A text about Google I/O 😀😀")
+matches = matcher(doc)
+
+for match_id, start, end in matches:
+    string_id = nlp.vocab.strings[match_id]
+    span = doc[start:end]
+    print(string_id, span.text)
+print("Sentiment", doc.sentiment)
+```
+
+<Infobox>
+
+**API:** [`Matcher`](/api/matcher) **Usage:**
+[Rule-based matching](/usage/linguistic-features#rule-based-matching)
+
+</Infobox>
+
+### Minibatched stream processing {#lightning-tour-minibatched}
+
+```python
+texts = [u"One document.", u"...", u"Lots of documents"]
+# .pipe streams input, and produces streaming output
+iter_texts = (texts[i % 3] for i in range(100000000))
+for i, doc in enumerate(nlp.pipe(iter_texts, batch_size=50)):
+    assert doc.is_parsed
+    if i == 100:
+        break
+```
+
+### Get syntactic dependencies {#lightning-tour-dependencies model="parser"}
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"When Sebastian Thrun started working on self-driving cars at Google "
+          u"in 2007, few people outside of the company took him seriously.")
+
+dep_labels = []
+for token in doc:
+    while token.head != token:
+        dep_labels.append(token.dep_)
+        token = token.head
+print(dep_labels)
+```
+
+<Infobox>
+
+**API:** [`Token`](/api/token) **Usage:**
+[Using the dependency parse](/usage/linguistic-features#dependency-parse)
+
+</Infobox>
+
+### Export to numpy arrays {#lightning-tour-numpy-arrays}
+
+```python
+### {executable="true"}
+import spacy
+from spacy.attrs import ORTH, LIKE_URL
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"Check out https://spacy.io")
+for token in doc:
+    print(token.text, token.orth, token.like_url)
+
+attr_ids = [ORTH, LIKE_URL]
+doc_array = doc.to_array(attr_ids)
+print(doc_array.shape)
+print(len(doc), len(attr_ids))
+
+assert doc[0].orth == doc_array[0, 0]
+assert doc[1].orth == doc_array[1, 0]
+assert doc[0].like_url == doc_array[0, 1]
+
+assert list(doc_array[:, 1]) == [t.like_url for t in doc]
+print(list(doc_array[:, 1]))
+```
+
+### Calculate inline markup on original string {#lightning-tour-inline}
+
+```python
+### {executable="true"}
+import spacy
+
+def put_spans_around_tokens(doc):
+    """Here, we're building a custom "syntax highlighter" for
+    part-of-speech tags and dependencies. We put each token in a
+    span element, with the appropriate classes computed. All whitespace is
+    preserved, outside of the spans. (Of course, HTML will only display
+    multiple whitespace if enabled – but the point is, no information is lost
+    and you can calculate what you need, e.g. <br />, <p> etc.)
+    """
+    output = []
+    html = '<span class="{classes}">{word}</span>{space}'
+    for token in doc:
+        if token.is_space:
+            output.append(token.text)
+        else:
+            classes = "pos-{} dep-{}".format(token.pos_, token.dep_)
+            output.append(html.format(classes=classes, word=token.text, space=token.whitespace_))
+    string = "".join(output)
+    string = string.replace("\\n", "")
+    string = string.replace("\\t", "    ")
+    return "<pre>{}</pre>".format(string)
+
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"This is a test.\\n\\nHello   world.")
+html = put_spans_around_tokens(doc)
+print(html)
+```
+
+## Architecture {#architecture}
+
+import Architecture101 from 'usage/101/\_architecture.md'
+
+<Architecture101 />
+
+## Community & FAQ {#community-faq}
+
+We're very happy to see the spaCy community grow and include a mix of people
+from all kinds of different backgrounds – computational linguistics, data
+science, deep learning, research and more. If you'd like to get involved, below
+are some answers to the most important questions and resources for further
+reading.
+
+### Help, my code isn't working! {#faq-help-code}
+
+Bugs suck, and we're doing our best to continuously improve the tests and fix
+bugs as soon as possible. Before you submit an issue, do a quick search and
+check if the problem has already been reported. If you're having installation or
+loading problems, make sure to also check out the
+[troubleshooting guide](/usage/#troubleshooting). Help with spaCy is available
+via the following platforms:
+
+> #### How do I know if something is a bug?
+>
+> Of course, it's always hard to know for sure, so don't worry – we're not going
+> to be mad if a bug report turns out to be a typo in your code. As a simple
+> rule, any C-level error without a Python traceback, like a **segmentation
+> fault** or **memory error**, is **always** a spaCy bug.
+>
+> Because models are statistical, their performance will never be _perfect_.
+> However, if you come across **patterns that might indicate an underlying
+> issue**, please do file a report. Similarly, we also care about behaviors that
+> **contradict our docs**.
+
+- [Stack Overflow](https://stackoverflow.com/questions/tagged/spacy): **Usage
+  questions** and everything related to problems with your specific code. The
+  Stack Overflow community is much larger than ours, so if your problem can be
+  solved by others, you'll receive help much quicker.
+- [Gitter chat](https://gitter.im/explosion/spaCy): **General discussion** about
+  spaCy, meeting other community members and exchanging **tips, tricks and best
+  practices**.
+- [GitHub issue tracker](https://github.com/explosion/spaCy/issues): **Bug
+  reports** and **improvement suggestions**, i.e. everything that's likely
+  spaCy's fault. This also includes problems with the models beyond statistical
+  imprecisions, like patterns that point to a bug.
+
+<Infobox title="Important note" variant="warning">
+
+Please understand that we won't be able to provide individual support via email.
+We also believe that help is much more valuable if it's shared publicly, so that
+**more people can benefit from it**. If you come across an issue and you think
+you might be able to help, consider posting a quick update with your solution.
+No matter how simple, it can easily save someone a lot of time and headache –
+and the next time you need help, they might repay the favor.
+
+</Infobox>
+
+### How can I contribute to spaCy? {#faq-contributing}
+
+You don't have to be an NLP expert or Python pro to contribute, and we're happy
+to help you get started. If you're new to spaCy, a good place to start is the
+[`help wanted (easy)` label](https://github.com/explosion/spaCy/issues?q=is%3Aissue+is%3Aopen+label%3A"help+wanted+%28easy%29")
+on GitHub, which we use to tag bugs and feature requests that are easy and
+self-contained. We also appreciate contributions to the docs – whether it's
+fixing a typo, improving an example or adding additional explanations. You'll
+find a "Suggest edits" link at the bottom of each page that points you to the
+source.
+
+Another way of getting involved is to help us improve the
+[language data](/usage/adding-languages#language-data) – especially if you
+happen to speak one of the languages currently in
+[alpha support](/usage/models#languages). Even adding simple tokenizer
+exceptions, stop words or lemmatizer data can make a big difference. It will
+also make it easier for us to provide a statistical model for the language in
+the future. Submitting a test that documents a bug or performance issue, or
+covers functionality that's especially important for your application is also
+very helpful. This way, you'll also make sure we never accidentally introduce
+regressions to the parts of the library that you care about the most.
+
+**For more details on the types of contributions we're looking for, the code
+conventions and other useful tips, make sure to check out the
+[contributing guidelines](https://github.com/explosion/spaCy/tree/master/CONTRIBUTING.md).**
+
+<Infobox title="Code of Conduct" variant="warning">
+
+spaCy adheres to the
+[Contributor Covenant Code of Conduct](http://contributor-covenant.org/version/1/4/).
+By participating, you are expected to uphold this code.
+
+</Infobox>
+
+### I've built something cool with spaCy – how can I get the word out? {#faq-project-with-spacy}
+
+First, congrats – we'd love to check it out! When you share your project on
+Twitter, don't forget to tag [@spacy_io](https://twitter.com/spacy_io) so we
+don't miss it. If you think your project would be a good fit for the
+[spaCy Universe](/universe), **feel free to submit it!** Tutorials are also
+incredibly valuable to other users and a great way to get exposure. So we
+strongly encourage **writing up your experiences**, or sharing your code and
+some tips and tricks on your blog. Since our website is open-source, you can add
+your project or tutorial by making a pull request on GitHub.
+
+If you would like to use the spaCy logo on your site, please get in touch and
+ask us first. However, if you want to show support and tell others that your
+project is using spaCy, you can grab one of our **spaCy badges** here:
+
+<img src={`https://img.shields.io/badge/built%20with-spaCy-09a3d5.svg`} />
+
+```markdown
+[![Built with spaCy](https://img.shields.io/badge/built%20with-spaCy-09a3d5.svg)](https://spacy.io)
+```
+
+<img src={`https://img.shields.io/badge/made%20with%20❤%20and-spaCy-09a3d5.svg`}
+/>
+
+```markdown
+[![Built with spaCy](https://img.shields.io/badge/made%20with%20❤%20and-spaCy-09a3d5.svg)](https://spacy.io)
+```
diff --git a/website/docs/usage/training.md b/website/docs/usage/training.md
new file mode 100644
index 000000000..d7a5c6d18
--- /dev/null
+++ b/website/docs/usage/training.md
@@ -0,0 +1,675 @@
+---
+title: Training spaCy's Statistical Models
+next: /usage/adding-languages
+menu:
+  - ['Basics', 'basics']
+  - ['NER', 'ner']
+  - ['Tagger & Parser', 'tagger-parser']
+  - ['Text Classification', 'textcat']
+  - ['Tips and Advice', 'tips']
+  - ['Saving & Loading', 'saving-loading']
+---
+
+This guide describes how to train new statistical models for spaCy's
+part-of-speech tagger, named entity recognizer and dependency parser. Once the
+model is trained, you can then [save and load](/usage/models#saving-loading) it.
+
+## Training basics {#basics}
+
+import Training101 from 'usage/101/\_training.md'
+
+<Training101 />
+
+### Training via the command-line interface {#spacy-train-cli}
+
+For most purposes, the best way to train spaCy is via the command-line
+interface. The [`spacy train`](/api/cli#train) command takes care of many
+details for you, including making sure that the data is minibatched and shuffled
+correctly, progress is printed, and models are saved after each epoch. You can
+prepare your data for use in [`spacy train`](/api/cli#train) using the
+[`spacy convert`](/api/cli#convert) command, which accepts many common NLP data
+formats, including `.iob` for named entities, and the CoNLL format for
+dependencies:
+
+```bash
+git clone https://github.com/UniversalDependencies/UD_Spanish-AnCora
+mkdir ancora-json
+python -m spacy convert UD_Spanish-AnCora/es_ancora-ud-train.conllu ancora-json
+python -m spacy convert UD_Spanish-AnCora/es_ancora-ud-dev.conllu ancora-json
+mkdir models
+python -m spacy train es models ancora-json/es_ancora-ud-train.json ancora-json/es_ancora-ud-dev.json
+```
+
+### Improving accuracy with transfer learning {#transfer-learning new="2.1"}
+
+In most projects, you'll usually have a small amount of labelled data, and
+access to a much bigger sample of raw text. The raw text contains a lot of
+information about the language in general. Learning this general information
+from the raw text can help your model use the smaller labelled data more
+efficiently.
+
+The two main ways to use raw text in your spaCy models are **word vectors** and
+**language model pretraining**. Word vectors provide information about the
+definitions of words. The vectors are a look-up table, so each word only has one
+representation, regardless of its context. Language model pretraining lets you
+learn contextualized word representations. Instead of initializing spaCy's
+convolutional neural network layers with random weights, the `spacy pretrain`
+command trains a language model to predict each word's word vector based on the
+surrounding words. The information used to predict this task is a good starting
+point for other tasks such as named entity recognition, text classification or
+dependency parsing.
+
+<Infobox title="📖 Vectors and pretraining">
+
+For more details, see the documentation on
+[vectors and similarity](/usage/vectors-similarity) and the
+[`spacy pretrain`](/api/cli#pretrain) command.
+
+</Infobox>
+
+### How do I get training data? {#training-data}
+
+Collecting training data may sound incredibly painful – and it can be, if you're
+planning a large-scale annotation project. However, if your main goal is to
+update an existing model's predictions – for example, spaCy's named entity
+recognition – the hard part is usually not creating the actual annotations. It's
+finding representative examples and **extracting potential candidates**. The
+good news is, if you've been noticing bad performance on your data, you likely
+already have some relevant text, and you can use spaCy to **bootstrap a first
+set of training examples**. For example, after processing a few sentences, you
+may end up with the following entities, some correct, some incorrect.
+
+> #### How many examples do I need?
+>
+> As a rule of thumb, you should allocate at least 10% of your project resources
+> to creating training and evaluation data. If you're looking to improve an
+> existing model, you might be able to start off with only a handful of
+> examples. Keep in mind that you'll always want a lot more than that for
+> **evaluation** – especially previous errors the model has made. Otherwise, you
+> won't be able to sufficiently verify that the model has actually made the
+> **correct generalizations** required for your use case.
+
+| Text                               |  Entity | Start | End  | Label    |     |
+| ---------------------------------- | ------- | ----- | ---- | -------- | --- |
+| Uber blew through 1 million a week | Uber    | `0`   | `4`  | `ORG`    | ✅  |
+| Android Pay expands to Canada      | Android | `0`   | `7`  | `PERSON` | ❌  |
+| Android Pay expands to Canada      | Canada  | `23`  | `30` | `GPE`    | ✅  |
+| Spotify steps up Asia expansion    | Spotify | `0`   | `8`  | `ORG`    | ✅  |
+| Spotify steps up Asia expansion    | Asia    | `17`  | `21` | `NORP`   | ❌  |
+
+Alternatively, the
+[rule-based matcher](/usage/linguistic-features#rule-based-matching) can be a
+useful tool to extract tokens or combinations of tokens, as well as their start
+and end index in a document. In this case, we'll extract mentions of Google and
+assume they're an `ORG`.
+
+| Text                                  |  Entity | Start | End  | Label |     |
+| ------------------------------------- | ------- | ----- | ---- | ----- | --- |
+| let me google this for you            | google  | `7`   | `13` | `ORG` | ❌  |
+| Google Maps launches location sharing | Google  | `0`   | `6`  | `ORG` | ❌  |
+| Google rebrands its business apps     | Google  | `0`   | `6`  | `ORG` | ✅  |
+| look what i found on google! 😂       | google  | `21`  | `27` | `ORG` | ✅  |
+
+Based on the few examples above, you can already create six training sentences
+with eight entities in total. Of course, what you consider a "correct
+annotation" will always depend on **what you want the model to learn**. While
+there are some entity annotations that are more or less universally correct –
+like Canada being a geopolitical entity – your application may have its very own
+definition of the [NER annotation scheme](/api/annotation#named-entities).
+
+```python
+train_data = [
+    ("Uber blew through $1 million a week", [(0, 4, 'ORG')]),
+    ("Android Pay expands to Canada", [(0, 11, 'PRODUCT'), (23, 30, 'GPE')]),
+    ("Spotify steps up Asia expansion", [(0, 8, "ORG"), (17, 21, "LOC")]),
+    ("Google Maps launches location sharing", [(0, 11, "PRODUCT")]),
+    ("Google rebrands its business apps", [(0, 6, "ORG")]),
+    ("look what i found on google! 😂", [(21, 27, "PRODUCT")])]
+```
+
+<Infobox title="Tip: Try the Prodigy annotation tool">
+
+[![Prodigy: Radically efficient machine teaching](../images/prodigy.jpg)](https://prodi.gy)
+
+If you need to label a lot of data, check out [Prodigy](https://prodi.gy), a
+new, active learning-powered annotation tool we've developed. Prodigy is fast
+and extensible, and comes with a modern **web application** that helps you
+collect training data faster. It integrates seamlessly with spaCy, pre-selects
+the **most relevant examples** for annotation, and lets you train and evaluate
+ready-to-use spaCy models.
+
+</Infobox>
+
+### Training with annotations {#annotations}
+
+The [`GoldParse`](/api/goldparse) object collects the annotated training
+examples, also called the **gold standard**. It's initialized with the
+[`Doc`](/api/doc) object it refers to, and keyword arguments specifying the
+annotations, like `tags` or `entities`. Its job is to encode the annotations,
+keep them aligned and create the C-level data structures required for efficient
+access. Here's an example of a simple `GoldParse` for part-of-speech tags:
+
+```python
+vocab = Vocab(tag_map={"N": {"pos": "NOUN"}, "V": {"pos": "VERB"}})
+doc = Doc(vocab, words=["I", "like", "stuff"])
+gold = GoldParse(doc, tags=["N", "V", "N"])
+```
+
+Using the `Doc` and its gold-standard annotations, the model can be updated to
+learn a sentence of three words with their assigned part-of-speech tags. The
+[tag map](/usage/adding-languages#tag-map) is part of the vocabulary and defines
+the annotation scheme. If you're training a new language model, this will let
+you map the tags present in the treebank you train on to spaCy's tag scheme.
+
+```python
+doc = Doc(Vocab(), words=["Facebook", "released", "React", "in", "2014"])
+gold = GoldParse(doc, entities=["U-ORG", "O", "U-TECHNOLOGY", "O", "U-DATE"])
+```
+
+The same goes for named entities. The letters added before the labels refer to
+the tags of the [BILUO scheme](/usage/linguistic-features#updating-biluo) – `O`
+is a token outside an entity, `U` an single entity unit, `B` the beginning of an
+entity, `I` a token inside an entity and `L` the last token of an entity.
+
+> - **Training data**: The training examples.
+> - **Text and label**: The current example.
+> - **Doc**: A `Doc` object created from the example text.
+> - **GoldParse**: A `GoldParse` object of the `Doc` and label.
+> - **nlp**: The `nlp` object with the model.
+> - **Optimizer**: A function that holds state between updates.
+> - **Update**: Update the model's weights.
+
+![The training loop](../images/training-loop.svg)
+
+Of course, it's not enough to only show a model a single example once.
+Especially if you only have few examples, you'll want to train for a **number of
+iterations**. At each iteration, the training data is **shuffled** to ensure the
+model doesn't make any generalizations based on the order of examples. Another
+technique to improve the learning results is to set a **dropout rate**, a rate
+at which to randomly "drop" individual features and representations. This makes
+it harder for the model to memorize the training data. For example, a `0.25`
+dropout means that each feature or internal representation has a 1/4 likelihood
+of being dropped.
+
+> - [`begin_training()`](/api/language#begin_training): Start the training and
+>   return an optimizer function to update the model's weights. Can take an
+>   optional function converting the training data to spaCy's training
+>   format. -[`update()`](/api/language#update): Update the model with the
+>   training example and gold data. -[`to_disk()`](/api/language#to_disk): Save
+>   the updated model to a directory.
+
+```python
+### Example training loop
+optimizer = nlp.begin_training(get_data)
+for itn in range(100):
+    random.shuffle(train_data)
+    for raw_text, entity_offsets in train_data:
+        doc = nlp.make_doc(raw_text)
+        gold = GoldParse(doc, entities=entity_offsets)
+        nlp.update([doc], [gold], drop=0.5, sgd=optimizer)
+nlp.to_disk("/model")
+```
+
+The [`nlp.update`](/api/language#update) method takes the following arguments:
+
+| Name    | Description                                                                                                                                                                                                   |
+| ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `docs`  | [`Doc`](/api/doc) objects. The `update` method takes a sequence of them, so you can batch up your training examples. Alternatively, you can also pass in a sequence of raw texts.                             |
+| `golds` | [`GoldParse`](/api/goldparse) objects. The `update` method takes a sequence of them, so you can batch up your training examples. Alternatively, you can also pass in a dictionary containing the annotations. |
+| `drop`  | Dropout rate. Makes it harder for the model to just memorize the data.                                                                                                                                        |
+| `sgd`   | An optimizer, i.e. a callable to update the model's weights. If not set, spaCy will create a new one and save it for further use.                                                                             |
+
+Instead of writing your own training loop, you can also use the built-in
+[`train`](/api/cli#train) command, which expects data in spaCy's
+[JSON format](/api/annotation#json-input). On each epoch, a model will be saved
+out to the directory. After training, you can use the
+[`package`](/api/cli#package) command to generate an installable Python package
+from your model.
+
+```bash
+python -m spacy convert /tmp/train.conllu /tmp/data
+python -m spacy train en /tmp/model /tmp/data/train.json -n 5
+```
+
+### Simple training style {#training-simple-style new="2"}
+
+Instead of sequences of `Doc` and `GoldParse` objects, you can also use the
+"simple training style" and pass **raw texts** and **dictionaries of
+annotations** to [`nlp.update`](/api/language#update). The dictionaries can have
+the keys `entities`, `heads`, `deps`, `tags` and `cats`. This is generally
+recommended, as it removes one layer of abstraction, and avoids unnecessary
+imports. It also makes it easier to structure and load your training data.
+
+> #### Example Annotations
+>
+> ```python
+> {
+>    "entities": [(0, 4, "ORG")],
+>    "heads": [1, 1, 1, 5, 5, 2, 7, 5],
+>    "deps": ["nsubj", "ROOT", "prt", "quantmod", "compound", "pobj", "det", "npadvmod"],
+>    "tags": ["PROPN", "VERB", "ADP", "SYM", "NUM", "NUM", "DET", "NOUN"],
+>    "cats": {"BUSINESS": 1.0},
+> }
+> ```
+
+```python
+### Simple training loop
+TRAIN_DATA = [
+        (u"Uber blew through $1 million a week", {"entities": [(0, 4, "ORG")]}),
+        (u"Google rebrands its business apps", {"entities": [(0, 6, "ORG")]})]
+
+nlp = spacy.blank('en')
+optimizer = nlp.begin_training()
+for i in range(20):
+    random.shuffle(TRAIN_DATA)
+    for text, annotations in TRAIN_DATA:
+        nlp.update([text], [annotations], sgd=optimizer)
+nlp.to_disk("/model")
+```
+
+The above training loop leaves out a few details that can really improve
+accuracy – but the principle really is _that_ simple. Once you've got your
+pipeline together and you want to tune the accuracy, you usually want to process
+your training examples in batches, and experiment with
+[`minibatch`](/api/top-level#util.minibatch) sizes and dropout rates, set via
+the `drop` keyword argument. See the [`Language`](/api/language) and
+[`Pipe`](/api/pipe) API docs for available options.
+
+## Training the named entity recognizer {#ner}
+
+All [spaCy models](/models) support online learning, so you can update a
+pre-trained model with new examples. You'll usually need to provide many
+**examples** to meaningfully improve the system — a few hundred is a good start,
+although more is better.
+
+You should avoid iterating over the same few examples multiple times, or the
+model is likely to "forget" how to annotate other examples. If you iterate over
+the same few examples, you're effectively changing the loss function. The
+optimizer will find a way to minimize the loss on your examples, without regard
+for the consequences on the examples it's no longer paying attention to. One way
+to avoid this
+["catastrophic forgetting" problem](https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting)
+is to "remind" the model of other examples by augmenting your annotations with
+sentences annotated with entities automatically recognized by the original
+model. Ultimately, this is an empirical process: you'll need to **experiment on
+your data** to find a solution that works best for you.
+
+> #### Tip: Converting entity annotations
+>
+> You can train the entity recognizer with entity offsets or annotations in the
+> [BILUO scheme](/api/annotation#biluo). The `spacy.gold` module also exposes
+> [two helper functions](/api/goldparse#util) to convert offsets to BILUO tags,
+> and BILUO tags to entity offsets.
+
+### Updating the Named Entity Recognizer {#example-train-ner}
+
+This example shows how to update spaCy's entity recognizer with your own
+examples, starting off with an existing, pre-trained model, or from scratch
+using a blank `Language` class. To do this, you'll need **example texts** and
+the **character offsets** and **labels** of each entity contained in the texts.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/training/train_ner.py
+```
+
+#### Step by step guide {#step-by-step-ner}
+
+1. **Load the model** you want to start with, or create an **empty model** using
+   [`spacy.blank`](/api/top-level#spacy.blank) with the ID of your language. If
+   you're using a blank model, don't forget to add the entity recognizer to the
+   pipeline. If you're using an existing model, make sure to disable all other
+   pipeline components during training using
+   [`nlp.disable_pipes`](/api/language#disable_pipes). This way, you'll only be
+   training the entity recognizer.
+2. **Shuffle and loop over** the examples. For each example, **update the
+   model** by calling [`nlp.update`](/api/language#update), which steps through
+   the words of the input. At each word, it makes a **prediction**. It then
+   consults the annotations to see whether it was right. If it was wrong, it
+   adjusts its weights so that the correct action will score higher next time.
+3. **Save** the trained model using [`nlp.to_disk`](/api/language#to_disk).
+4. **Test** the model to make sure the entities in the training data are
+   recognized correctly.
+
+### Training an additional entity type {#example-new-entity-type}
+
+This script shows how to add a new entity type `ANIMAL` to an existing
+pre-trained NER model, or an empty `Language` class. To keep the example short
+and simple, only a few sentences are provided as examples. In practice, you'll
+need many more — a few hundred would be a good start. You will also likely need
+to mix in examples of other entity types, which might be obtained by running the
+entity recognizer over unlabelled sentences, and adding their annotations to the
+training set.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/training/train_new_entity_type.py
+```
+
+<Infobox title="Important note" variant="warning">
+
+If you're using an existing model, make sure to mix in examples of **other
+entity types** that spaCy correctly recognized before. Otherwise, your model
+might learn the new type, but "forget" what it previously knew. This is also
+referred to as the "catastrophic forgetting" problem.
+
+</Infobox>
+
+#### Step by step guide {#step-by-step-ner-new}
+
+1. **Load the model** you want to start with, or create an **empty model** using
+   [`spacy.blank`](/api/top-level#spacy.blank) with the ID of your language. If
+   you're using a blank model, don't forget to add the entity recognizer to the
+   pipeline. If you're using an existing model, make sure to disable all other
+   pipeline components during training using
+   [`nlp.disable_pipes`](/api/language#disable_pipes). This way, you'll only be
+   training the entity recognizer.
+2. **Add the new entity label** to the entity recognizer using the
+   [`add_label`](/api/entityrecognizer#add_label) method. You can access the
+   entity recognizer in the pipeline via `nlp.get_pipe('ner')`.
+3. **Loop over** the examples and call [`nlp.update`](/api/language#update),
+   which steps through the words of the input. At each word, it makes a
+   **prediction**. It then consults the annotations, to see whether it was
+   right. If it was wrong, it adjusts its weights so that the correct action
+   will score higher next time.
+4. **Save** the trained model using [`nlp.to_disk`](/api/language#to_disk).
+5. **Test** the model to make sure the new entity is recognized correctly.
+
+## Training the tagger and parser {#tagger-parser}
+
+### Updating the Dependency Parser {#example-train-parser}
+
+This example shows how to train spaCy's dependency parser, starting off with an
+existing model or a blank model. You'll need a set of **training examples** and
+the respective **heads** and **dependency label** for each token of the example
+texts.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/training/train_parser.py
+```
+
+#### Step by step guide {#step-by-step-parser}
+
+1. **Load the model** you want to start with, or create an **empty model** using
+   [`spacy.blank`](/api/top-level#spacy.blank) with the ID of your language. If
+   you're using a blank model, don't forget to add the parser to the pipeline.
+   If you're using an existing model, make sure to disable all other pipeline
+   components during training using
+   [`nlp.disable_pipes`](/api/language#disable_pipes). This way, you'll only be
+   training the parser.
+2. **Add the dependency labels** to the parser using the
+   [`add_label`](/api/dependencyparser#add_label) method. If you're starting off
+   with a pre-trained spaCy model, this is usually not necessary – but it
+   doesn't hurt either, just to be safe.
+3. **Shuffle and loop over** the examples. For each example, **update the
+   model** by calling [`nlp.update`](/api/language#update), which steps through
+   the words of the input. At each word, it makes a **prediction**. It then
+   consults the annotations to see whether it was right. If it was wrong, it
+   adjusts its weights so that the correct action will score higher next time.
+4. **Save** the trained model using [`nlp.to_disk`](/api/language#to_disk).
+5. **Test** the model to make sure the parser works as expected.
+
+### Updating the Part-of-speech Tagger {#example-train-tagger}
+
+In this example, we're training spaCy's part-of-speech tagger with a custom tag
+map. We start off with a blank `Language` class, update its defaults with our
+custom tags and then train the tagger. You'll need a set of **training
+examples** and the respective **custom tags**, as well as a dictionary mapping
+those tags to the
+[Universal Dependencies scheme](http://universaldependencies.github.io/docs/u/pos/index.html).
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/training/train_tagger.py
+```
+
+#### Step by step guide {#step-by-step-tagger}
+
+1. **Load the model** you want to start with, or create an **empty model** using
+   [`spacy.blank`](/api/top-level#spacy.blank) with the ID of your language. If
+   you're using a blank model, don't forget to add the tagger to the pipeline.
+   If you're using an existing model, make sure to disable all other pipeline
+   components during training using
+   [`nlp.disable_pipes`](/api/language#disable_pipes). This way, you'll only be
+   training the tagger.
+2. **Add the tag map** to the tagger using the
+   [`add_label`](/api/tagger#add_label) method. The first argument is the new
+   tag name, the second the mapping to spaCy's coarse-grained tags, e.g.
+   `{'pos': 'NOUN'}`.
+3. **Shuffle and loop over** the examples. For each example, **update the
+   model** by calling [`nlp.update`](/api/language#update), which steps through
+   the words of the input. At each word, it makes a **prediction**. It then
+   consults the annotations to see whether it was right. If it was wrong, it
+   adjusts its weights so that the correct action will score higher next time.
+4. **Save** the trained model using [`nlp.to_disk`](/api/language#to_disk).
+5. **Test** the model to make sure the parser works as expected.
+
+### Training a parser for custom semantics {#intent-parser}
+
+spaCy's parser component can be used to be trained to predict any type of tree
+structure over your input text – including **semantic relations** that are not
+syntactic dependencies. This can be useful to for **conversational
+applications**, which need to predict trees over whole documents or chat logs,
+with connections between the sentence roots used to annotate discourse
+structure. For example, you can train spaCy's parser to label intents and their
+targets, like attributes, quality, time and locations. The result could look
+like this:
+
+![Custom dependencies](../images/displacy-custom-parser.svg)
+
+```python
+doc = nlp(u"find a hotel with good wifi")
+print([(t.text, t.dep_, t.head.text) for t in doc if t.dep_ != '-'])
+# [('find', 'ROOT', 'find'), ('hotel', 'PLACE', 'find'),
+#  ('good', 'QUALITY', 'wifi'), ('wifi', 'ATTRIBUTE', 'hotel')]
+```
+
+The above tree attaches "wifi" to "hotel" and assigns the dependency label
+`ATTRIBUTE`. This may not be a correct syntactic dependency – but in this case,
+it expresses exactly what we need: the user is looking for a hotel with the
+attribute "wifi" of the quality "good". This query can then be processed by your
+application and used to trigger the respective action – e.g. search the database
+for hotels with high ratings for their wifi offerings.
+
+> #### Tip: merge phrases and entities
+>
+> To achieve even better accuracy, try merging multi-word tokens and entities
+> specific to your domain into one token before parsing your text. You can do
+> this by running the entity recognizer or
+> [rule-based matcher](/usage/linguistic-features#rule-based-matching) to find
+> relevant spans, and merging them using
+> [`Doc.retokenize`](/api/doc#retokenize). You could even add your own custom
+> [pipeline component](/usage/processing-pipelines#custom-components) to do this
+> automatically – just make sure to add it `before='parser'`.
+
+The following example shows a full implementation of a training loop for a
+custom message parser for a common "chat intent": finding local businesses. Our
+message semantics will have the following types of relations: `ROOT`, `PLACE`,
+`QUALITY`, `ATTRIBUTE`, `TIME` and `LOCATION`.
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/training/train_intent_parser.py
+```
+
+#### Step by step guide {#step-by-step-parser-custom}
+
+1. **Create the training data** consisting of words, their heads and their
+   dependency labels in order. A token's head is the index of the token it is
+   attached to. The heads don't need to be syntactically correct – they should
+   express the **semantic relations** you want the parser to learn. For words
+   that shouldn't receive a label, you can choose an arbitrary placeholder, for
+   example `-`.
+2. **Load the model** you want to start with, or create an **empty model** using
+   [`spacy.blank`](/api/top-level#spacy.blank) with the ID of your language. If
+   you're using a blank model, don't forget to add the custom parser to the
+   pipeline. If you're using an existing model, make sure to **remove the old
+   parser** from the pipeline, and disable all other pipeline components during
+   training using [`nlp.disable_pipes`](/api/language#disable_pipes). This way,
+   you'll only be training the parser.
+3. **Add the dependency labels** to the parser using the
+   [`add_label`](/api/dependencyparser#add_label) method.
+4. **Shuffle and loop over** the examples. For each example, **update the
+   model** by calling [`nlp.update`](/api/language#update), which steps through
+   the words of the input. At each word, it makes a **prediction**. It then
+   consults the annotations to see whether it was right. If it was wrong, it
+   adjusts its weights so that the correct action will score higher next time.
+5. **Save** the trained model using [`nlp.to_disk`](/api/language#to_disk).
+6. **Test** the model to make sure the parser works as expected.
+
+## Training a text classification model {#textcat}
+
+### Adding a text classifier to a spaCy model {#example-textcat new="2"}
+
+This example shows how to train a convolutional neural network text classifier
+on IMDB movie reviews, using spaCy's new
+[`TextCategorizer`](/api/textcategorizer) component. The dataset will be loaded
+automatically via Thinc's built-in dataset loader. Predictions are available via
+[`Doc.cats`](/api/doc#attributes).
+
+```python
+https://github.com/explosion/spaCy/tree/master/examples/training/train_textcat.py
+```
+
+#### Step by step guide {#step-by-step-textcat}
+
+1. **Load the model** you want to start with, or create an **empty model** using
+   [`spacy.blank`](/api/top-level#spacy.blank) with the ID of your language. If
+   you're using an existing model, make sure to disable all other pipeline
+   components during training using
+   [`nlp.disable_pipes`](/api/language#disable_pipes). This way, you'll only be
+   training the text classifier.
+2. **Add the text classifier** to the pipeline, and add the labels you want to
+   train – for example, `POSITIVE`.
+3. **Load and pre-process the dataset**, shuffle the data and split off a part
+   of it to hold back for evaluation. This way, you'll be able to see results on
+   each training iteration.
+4. **Loop over** the training examples and partition them into batches using
+   spaCy's [`minibatch`](/api/top-level#util.minibatch) and
+   [`compounding`](/api/top-level#util.compounding) helpers.
+5. **Update the model** by calling [`nlp.update`](/api/language#update), which
+   steps through the examples and makes a **prediction**. It then consults the
+   annotations to see whether it was right. If it was wrong, it adjusts its
+   weights so that the correct prediction will score higher next time.
+6. Optionally, you can also **evaluate the text classifier** on each iteration,
+   by checking how it performs on the development data held back from the
+   dataset. This lets you print the **precision**, **recall** and **F-score**.
+7. **Save** the trained model using [`nlp.to_disk`](/api/language#to_disk).
+8. **Test** the model to make sure the text classifier works as expected.
+
+## Optimization tips and advice {#tips}
+
+There are lots of conflicting "recipes" for training deep neural networks at the
+moment. The cutting-edge models take a very long time to train, so most
+researchers can't run enough experiments to figure out what's _really_ going on.
+For what it's worth, here's a recipe that seems to work well on a lot of NLP
+problems:
+
+1. Initialize with batch size 1, and compound to a maximum determined by your
+   data size and problem type.
+2. Use Adam solver with fixed learning rate.
+3. Use averaged parameters
+4. Use L2 regularization.
+5. Clip gradients by L2 norm to 1.
+6. On small data sizes, start at a high dropout rate, with linear decay.
+
+This recipe has been cobbled together experimentally. Here's why the various
+elements of the recipe made enough sense to try initially, and what you might
+try changing, depending on your problem.
+
+### Compounding batch size {#tips-batch-size}
+
+The trick of increasing the batch size is starting to become quite popular (see
+[Smith et al., 2017](https://arxiv.org/abs/1711.00489)). Their recipe is quite
+different from how spaCy's models are being trained, but there are some
+similarities. In training the various spaCy models, we haven't found much
+advantage from decaying the learning rate – but starting with a low batch size
+has definitely helped. You should try it out on your data, and see how you go.
+Here's our current strategy:
+
+```python
+### Batch heuristic
+def get_batches(train_data, model_type):
+    max_batch_sizes = {"tagger": 32, "parser": 16, "ner": 16, "textcat": 64}
+    max_batch_size = max_batch_sizes[model_type]
+    if len(train_data) < 1000:
+        max_batch_size /= 2
+    if len(train_data) < 500:
+        max_batch_size /= 2
+    batch_size = compounding(1, max_batch_size, 1.001)
+    batches = minibatch(train_data, size=batch_size)
+    return batches
+```
+
+This will set the batch size to start at `1`, and increase each batch until it
+reaches a maximum size. The tagger, parser and entity recognizer all take whole
+sentences as input, so they're learning a lot of labels in a single example. You
+therefore need smaller batches for them. The batch size for the text categorizer
+should be somewhat larger, especially if your documents are long.
+
+### Learning rate, regularization and gradient clipping {#tips-hyperparams}
+
+By default spaCy uses the Adam solver, with default settings (learning rate
+`0.001`, `beta1=0.9`, `beta2=0.999`). Some researchers have said they found
+these settings terrible on their problems – but they've always performed very
+well in training spaCy's models, in combination with the rest of our recipe. You
+can change these settings directly, by modifying the corresponding attributes on
+the `optimizer` object. You can also set environment variables, to adjust the
+defaults.
+
+There are two other key hyper-parameters of the solver: `L2` **regularization**,
+and **gradient clipping** (`max_grad_norm`). Gradient clipping is a hack that's
+not discussed often, but everybody seems to be using. It's quite important in
+helping to ensure the network doesn't diverge, which is a fancy way of saying
+"fall over during training". The effect is sort of similar to setting the
+learning rate low. It can also compensate for a large batch size (this is a good
+example of how the choices of all these hyper-parameters intersect).
+
+### Dropout rate {#tips-dropout}
+
+For small datasets, it's useful to set a **high dropout rate at first**, and
+**decay** it down towards a more reasonable value. This helps avoid the network
+immediately overfitting, while still encouraging it to learn some of the more
+interesting things in your data. spaCy comes with a
+[`decaying`](/api/top-level#util.decaying) utility function to facilitate this.
+You might try setting:
+
+```python
+from spacy.util import decaying
+dropout = decaying(0.6, 0.2, 1e-4)
+```
+
+You can then draw values from the iterator with `next(dropout)`, which you would
+pass to the `drop` keyword argument of [`nlp.update`](/api/language#update).
+It's pretty much always a good idea to use at least **some dropout**. All of the
+models currently use Bernoulli dropout, for no particularly principled reason –
+we just haven't experimented with another scheme like Gaussian dropout yet.
+
+### Parameter averaging {#tips-param-avg}
+
+The last part of our optimization recipe is **parameter averaging**, an old
+trick introduced by
+[Freund and Schapire (1999)](https://cseweb.ucsd.edu/~yfreund/papers/LargeMarginsUsingPerceptron.pdf),
+popularized in the NLP community by
+[Collins (2002)](http://www.aclweb.org/anthology/P04-1015), and explained in
+more detail by [Leon Bottou](http://leon.bottou.org/projects/sgd). Just about
+the only other people who seem to be using this for neural network training are
+the SyntaxNet team (one of whom is Michael Collins) – but it really seems to
+work great on every problem.
+
+The trick is to store the moving average of the weights during training. We
+don't optimize this average – we just track it. Then when we want to actually
+use the model, we use the averages, not the most recent value. In spaCy (and
+[Thinc](https://github.com/explosion/thinc)) this is done by using a context
+manager, [`use_params`](/api/language#use_params), to temporarily replace the
+weights:
+
+```python
+with nlp.use_params(optimizer.averages):
+    nlp.to_disk("/model")
+```
+
+The context manager is handy because you naturally want to evaluate and save the
+model at various points during training (e.g. after each epoch). After
+evaluating and saving, the context manager will exit and the weights will be
+restored, so you resume training from the most recent value, rather than the
+average. By evaluating the model after each epoch, you can remove one
+hyper-parameter from consideration (the number of epochs). Having one less magic
+number to guess is extremely nice – so having the averaging under a context
+manager is very convenient.
diff --git a/website/docs/usage/v2-1.md b/website/docs/usage/v2-1.md
new file mode 100644
index 000000000..e000d73d7
--- /dev/null
+++ b/website/docs/usage/v2-1.md
@@ -0,0 +1,260 @@
+---
+title: What's New in v2.1
+teaser: New features, backwards incompatibilities and migration guide
+menu:
+  - ['New Features', 'features']
+  - ['Backwards Incompatibilities', 'incompat']
+---
+
+## New Features {#features hidden="true"}
+
+spaCy v2.1 has focussed primarily on stability and performance, solidifying the
+design changes introduced in [v2.0](/usage/v2). As well as smaller models,
+faster runtime, and many bug-fixes, v2.1 also introduces experimental support
+for some exciting new NLP innovations. For the full changelog, see the
+[release notes on GitHub](https://github.com/explosion/spaCy/releases/tag/v2.1.0).
+
+### BERT/ULMFit/Elmo-style pre-training
+
+> #### Example
+>
+> ```bash
+> $ python -m spacy pretrain ./raw_text.jsonl
+> en_vectors_web_lg ./pretrained-model
+> ```
+
+spaCy v2.1 introduces a new CLI command, `spacy pretrain`, that can make your
+models much more accurate. It's especially useful when you have **limited
+training data**. The `spacy pretrain` command lets you use transfer learning to
+initialize your models with information from raw text, using a language model
+objective similar to the one used in Google's BERT system. We've taken
+particular care to ensure that pretraining works well even with spaCy's small
+default architecture sizes, so you don't have to compromise on efficiency to use
+it.
+
+<Infobox>
+
+**API:** [`spacy pretrain`](/api/cli#pretrain) **Usage: **
+[Improving accuracy with transfer learning](/usage/training#transfer-learning)
+
+</Infobox>
+
+### Extended match pattern API
+
+> #### Example
+>
+> ```python
+> # Matches "love cats" or "likes flowers"
+> pattern1 = [{"LEMMA": {"IN": ["like", "love"]}}, {"POS": "NOUN"}]
+> # Matches tokens of length >= 10
+> pattern2 = [{"LENGTH": {">=": 10}}]
+> # Matches custom attribute with regex
+> pattern3 = [{"_": {"country": {"REGEX": "^([Uu](\\.?|nited) ?[Ss](\\.?|tates)"}}}]
+> ```
+
+Instead of mapping to a single value, token patterns can now also map to a
+**dictionary of properties**. For example, to specify that the value of a lemma
+should be part of a list of values, or to set a minimum character length. It now
+also supports a `REGEX` property, as well as set membership via `IN` and
+`NOT_IN`, custom extension attributes via `_` and rich comparison for numeric
+values.
+
+<Infobox>
+
+**API:** [`Matcher`](/api/matcher) **Usage: **
+[Extended pattern syntax and attributes](/usage/rule-based-matching#adding-patterns-attributes-extended),
+[Regular expressions](/usage/rule-based-matching#regex)
+
+</Infobox>
+
+### Easy rule-based entity recognition
+
+> #### Example
+>
+> ```python
+> from spacy.pipeline import EntityRuler
+> ruler = EntityRuler(nlp)
+> ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
+> nlp.add_pipe(ruler, before="ner")
+> ```
+
+The `EntityRuler` is an exciting new component that lets you add named entities
+based on pattern dictionaries, and makes it easy to combine rule-based and
+statistical named entity recognition for even more powerful models. Entity rules
+can be phrase patterns for exact string matches, or token patterns for full
+flexibility.
+
+<Infobox>
+
+**API:** [`EntityRuler`](/api/entityruler) **Usage: **
+[Rule-based entity recognition](/usage/rule-based-matching#entityruler)
+
+</Infobox>
+
+### Phrase matching with other attributes
+
+> #### Example
+>
+> ```python
+> matcher = PhraseMatcher(nlp.vocab, attr="POS")
+> matcher.add("PATTERN", None, nlp(u"I love cats"))
+> doc = nlp(u"You like dogs")
+> matches = matcher(doc)
+> ```
+
+By default, the `PhraseMatcher` will match on the verbatim token text, e.g.
+`Token.text`. By setting the `attr` argument on initialization, you can change
+**which token attribute the matcher should use** when comparing the phrase
+pattern to the matched `Doc`. For example, `LOWER` for case-insensitive matches
+or `POS` for finding sequences of the same part-of-speech tags.
+
+<Infobox>
+
+**API:** [`PhraseMatcher`](/api/phrasematcher) **Usage: **
+[Matching on other token attributes](/usage/rule-based-matching#phrasematcher-attrs)
+
+</Infobox>
+
+### Components and languages via entry points
+
+> #### Example
+>
+> ```python
+> from setuptools import setup
+> setup(
+>     name="custom_extension_package",
+>     entry_points={
+>         "spacy_factories": ["your_component = component:ComponentFactory"]
+>         "spacy_languages": ["xyz = language:XYZLanguage"]
+>    }
+> )
+> ```
+
+Using entry points, model packages and extension packages can now define their
+own `"spacy_factories"` and `"spacy_languages"`, which will be added to the
+built-in factories and languages. If a package in the same environment exposes
+spaCy entry points, all of this happens automatically and no further user action
+is required.
+
+<Infobox>
+
+**Usage:** [Using entry points](/usage/saving-loading#entry-points)
+
+</Infobox>
+
+### Retokenizer for merging and splitting
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I like David Bowie")
+> with doc.retokenize() as retokenizer:
+>     attrs = {"LEMMA": u"David Bowie"}
+>     retokenizer.merge(doc[2:4], attrs=attrs)
+> ```
+
+The new `Doc.retokenize` context manager allows merging spans of multiple tokens
+into one single token, and splitting single tokens into multiple tokens.
+Modifications to the `Doc`'s tokenization are stored, and then made all at once
+when the context manager exits. This is much more efficient, and less
+error-prone. `Doc.merge` and `Span.merge` still work, but they're considered
+deprecated.
+
+<Infobox>
+
+**API:** [`Doc.retokenize`](/api/doc#retokenize),
+[`Retokenizer.merge`](/api/doc#retokenizer.merge),
+[`Retokenizer.split`](/api/doc#retokenizer.split)<br />**Usage:
+**[Merging and splitting](/usage/linguistic-features#retokenization)
+
+</Infobox>
+
+### Improved documentation
+
+Although it looks pretty much the same, we've rebuilt the entire documentation
+using [Gatsby](https://www.gatsbyjs.org/) and [MDX](https://mdxjs.com/). It's
+now an even faster progressive web app and allows us to write all content
+entirely **in Markdown**, without having to compromise on easy-to-use custom UI
+components. We're hoping that the Markdown source will make it even easier to
+contribute to the documentation. For more details, check out the
+[styleguide](/styleguide) and
+[source](https://github.com/explosion/spaCy/tree/master/website). While
+converting the pages to Markdown, we've also fixed a bunch of typos, improved
+the existing pages and added some new content:
+
+- **Usage Guide:** [Rule-based Matching](/usage/rule-based-matching)<br/>How to
+  use the `Matcher`, `PhraseMatcher` and the new `EntityRuler`, and write
+  powerful components to combine statistical models and rules.
+- **Usage Guide:** [Saving and Loading](/usage/saving-loading)<br/>Everything
+  you need to know about serialization, and how to save and load pipeline
+  components, package your spaCy models as Python modules and use entry points.
+- **Usage Guide: **
+  [Merging and Splitting](/usage/linguistic-features#retokenization)<br />How to
+  retokenize a `Doc` using the new `retokenize` context manager and merge spans
+  into single tokens and split single tokens into multiple.
+- **Universe:** [Videos](/universe/category/videos) and
+  [Podcasts](/universe/category/podcasts)
+- **API:** [`EntityRuler`](/api/entityruler)
+- **API:** [`SentenceSegmenter`](/api/sentencesegmenter)
+- **API:** [Pipeline functions](/api/pipeline-functions)
+
+## Backwards incompatibilities {#incompat}
+
+<Infobox title="Important note on models" variant="warning">
+
+If you've been training **your own models**, you'll need to **retrain** them
+with the new version. Also don't forget to upgrade all models to the latest
+versions. Models for v2.0.x aren't compatible with models for v2.1.x. To check
+if all of your models are up to date, you can run the
+[`spacy validate`](/api/cli#validate) command.
+
+</Infobox>
+
+- While the [`Matcher`](/api/matcher) API is fully backwards compatible, its
+  algorithm has changed to fix a number of bugs and performance issues. This
+  means that the `Matcher` in v2.1.x may produce different results compared to
+  the `Matcher` in v2.0.x.
+
+- For better compatibility with the Universal Dependencies data, the lemmatizer
+  now preserves capitalization, e.g. for proper nouns. See
+  [this issue](https://github.com/explosion/spaCy/issues/3256) for details.
+
+- The built-in rule-based sentence boundary detector is now only called
+  `"sentencizer"` – the name `"sbd"` is deprecated.
+
+  ```diff
+  - sentence_splitter = nlp.create_pipe("sbd")
+  + sentence_splitter = nlp.create_pipe("sentencizer")
+  ```
+
+- The `Doc.print_tree` method is now deprecated. If you need a custom nested
+  JSON representation of a `Doc` object, you might want to write your own helper
+  function. For a simple and consistent JSON representation of the `Doc` object
+  and its annotations, you can now use the [`Doc.to_json`](/api/doc#to_json)
+  method. Going forward, this method will output the same format as the JSON
+  training data expected by [`spacy train`](/api/cli#train).
+
+- The [`spacy train`](/api/cli#train) command now lets you specify a
+  comma-separated list of pipeline component names, instead of separate flags
+  like `--no-parser` to disable components. This is more flexible and also
+  handles custom components out-of-the-box.
+
+  ```diff
+  - $ spacy train en /output train_data.json dev_data.json --no-parser
+  + $ spacy train en /output train_data.json dev_data.json --pipeline tagger,ner
+  ```
+
+- The [`spacy init-model`](/api/cli#init-model) command now uses a `--jsonl-loc`
+  argument to pass in a a newline-delimited JSON (JSONL) file containing one
+  lexical entry per line instead of a separate `--freqs-loc` and
+  `--clusters-loc`.
+
+  ```diff
+  - $ spacy init-model en ./model --freqs-loc ./freqs.txt --clusters-loc ./clusters.txt
+  + $ spacy init-model en ./model --jsonl-loc ./vocab.jsonl
+  ```
+
+- Also note that some of the model licenses have changed:
+  [`it_core_news_sm`](/models/it#it_core_news_sm) is now correctly licensed
+  under CC BY-NC-SA 3.0, and all [English](/models/en) and [German](/models/de)
+  models are now published under the MIT license.
diff --git a/website/docs/usage/v2.md b/website/docs/usage/v2.md
new file mode 100644
index 000000000..a08d68fc7
--- /dev/null
+++ b/website/docs/usage/v2.md
@@ -0,0 +1,616 @@
+---
+title: What's New in v2.0
+teaser: New features, backwards incompatibilities and migration guide
+menu:
+  - ['Summary', 'summary']
+  - ['New Features', 'features']
+  - ['Backwards Incompatibilities', 'incompat']
+  - ['Migrating from v1.x', 'migrating']
+---
+
+We're very excited to finally introduce spaCy v2.0! On this page, you'll find a
+summary of the new features, information on the backwards incompatibilities,
+including a handy overview of what's been renamed or deprecated. To help you
+make the most of v2.0, we also **re-wrote almost all of the usage guides and API
+docs**, and added more [real-world examples](/usage/examples). If you're new to
+spaCy, or just want to brush up on some NLP basics and the details of the
+library, check out the [spaCy 101 guide](/usage/spacy-101) that explains the
+most important concepts with examples and illustrations.
+
+## Summary {#summary}
+
+<Grid cols={2}>
+
+<div>
+
+This release features entirely new **deep learning-powered models** for spaCy's
+tagger, parser and entity recognizer. The new models are **10× smaller**, **20%
+more accurate** and **even cheaper to run** than the previous generation.
+
+We've also made several usability improvements that are particularly helpful for
+**production deployments**. spaCy v2 now fully supports the Pickle protocol,
+making it easy to use spaCy with [Apache Spark](https://spark.apache.org/). The
+string-to-integer mapping is **no longer stateful**, making it easy to reconcile
+annotations made in different processes. Models are smaller and use less memory,
+and the APIs for serialization are now much more consistent. Custom pipeline
+components let you modify the `Doc` at any stage in the pipeline. You can now
+also add your own custom attributes, properties and methods to the `Doc`,
+`Token` and `Span`.
+
+</div>
+
+<Infobox title="Table of Contents">
+
+- [Summary](#summary)
+- [New features](#features)
+- [Neural network models](#features-models)
+- [Improved processing pipelines](#features-pipelines)
+- [Text classification](#features-text-classification)
+- [Hash values as IDs](#features-hash-ids)
+- [Improved word vectors support](#features-vectors)
+- [Saving, loading and serialization](#features-serializer)
+- [displaCy visualizer](#features-displacy)
+- [Language data and lazy loading](#features-language)
+- [Revised matcher API and phrase matcher](#features-matcher)
+- [Backwards incompatibilities](#incompat)
+- [Migrating from spaCy v1.x](#migrating)
+
+</Infobox>
+
+</Grid>
+
+The main usability improvements you'll notice in spaCy v2.0 are around
+**defining, training and loading your own models** and components. The new
+neural network models make it much easier to train a model from scratch, or
+update an existing model with a few examples. In v1.x, the statistical models
+depended on the state of the `Vocab`. If you taught the model a new word, you
+would have to save and load a lot of data — otherwise the model wouldn't
+correctly recall the features of your new example. That's no longer the case.
+
+Due to some clever use of hashing, the statistical models **never change size**,
+even as they learn new vocabulary items. The whole pipeline is also now fully
+differentiable. Even if you don't have explicitly annotated data, you can update
+spaCy using all the **latest deep learning tricks** like adversarial training,
+noise contrastive estimation or reinforcement learning.
+
+## New features {#features}
+
+This section contains an overview of the most important **new features and
+improvements**. The [API docs](/api) include additional deprecation notes. New
+methods and functions that were introduced in this version are marked with the
+tag <Tag variant="new">2</Tag>.
+
+### Convolutional neural network models {#features-models}
+
+> #### Example
+>
+> ```bash
+> python -m spacy download en_core_web_sm
+> python -m spacy download de_core_news_sm
+> python -m spacy download xx_ent_wiki_sm
+> ```
+
+spaCy v2.0 features new neural models for tagging, parsing and entity
+recognition. The models have been designed and implemented from scratch
+specifically for spaCy, to give you an unmatched balance of speed, size and
+accuracy. The new models are **10× smaller**, **20% more accurate**, and **even
+cheaper to run** than the previous generation.
+
+spaCy v2.0's new neural network models bring significant improvements in
+accuracy, especially for English Named Entity Recognition. The new
+[`en_core_web_lg`](/models/en#en_core_web_lg) model makes about **25% fewer
+mistakes** than the corresponding v1.x model and is within **1% of the current
+state-of-the-art**
+([Strubell et al., 2017](https://arxiv.org/pdf/1702.02098.pdf)). The v2.0 models
+are also cheaper to run at scale, as they require **under 1 GB of memory** per
+process.
+
+<Infobox>
+
+**Usage:** [Models directory](/models) [Benchmarks](#benchmarks)
+
+</Infobox>
+
+### Improved processing pipelines {#features-pipelines}
+
+> #### Example
+>
+> ```python
+> # Set custom attributes
+> Doc.set_extension("my_attr", default=False)
+> Token.set_extension("my_attr", getter=my_token_getter)
+> assert doc._.my_attr, token._.my_attr
+>
+> # Add components to the pipeline
+> my_component = lambda doc: doc
+> nlp.add_pipe(my_component)
+> ```
+
+It's now much easier to **customize the pipeline** with your own components:
+functions that receive a `Doc` object, modify and return it. Extensions let you
+write any **attributes, properties and methods** to the `Doc`, `Token` and
+`Span`. You can add data, implement new features, integrate other libraries with
+spaCy or plug in your own machine learning models.
+
+![The processing pipeline](../images/pipeline.svg)
+
+<Infobox>
+
+**API:** [`Language`](/api/language),
+[`Doc.set_extension`](/api/doc#set_extension),
+[`Span.set_extension`](/api/span#set_extension),
+[`Token.set_extension`](/api/token#set_extension) **Usage:**
+[Processing pipelines](/usage/processing-pipelines) **Code:**
+[Pipeline examples](/usage/examples#section-pipeline)
+
+</Infobox>
+
+### Text classification {#features-text-classification}
+
+> #### Example
+>
+> ```python
+> textcat = nlp.create_pipe("textcat")
+> nlp.add_pipe(textcat, last=True)
+> nlp.begin_training()
+> for itn in range(100):
+>    for doc, gold in train_data:
+>        nlp.update([doc], [gold])
+> doc = nlp(u"This is a text.")
+> print(doc.cats)
+> ```
+
+spaCy v2.0 lets you add text categorization models to spaCy pipelines. The model
+supports classification with multiple, non-mutually exclusive labels – so
+multiple labels can apply at once. You can change the model architecture rather
+easily, but by default, the `TextCategorizer` class uses a convolutional neural
+network to assign position-sensitive vectors to each word in the document.
+
+<Infobox>
+
+**API:** [`TextCategorizer`](/api/textcategorizer),
+[`Doc.cats`](/api/doc#attributes), [`GoldParse.cats`](/api/goldparse#attributes)
+**Usage:** [Training a text classification model](/usage/training#textcat)
+
+</Infobox>
+
+### Hash values instead of integer IDs {#features-hash-ids}
+
+> #### Example
+>
+> ```python
+> doc = nlp(u"I love coffee")
+> assert doc.vocab.strings[u"coffee"] == 3197928453018144401
+> assert doc.vocab.strings[3197928453018144401] == u"coffee"
+>
+> beer_hash = doc.vocab.strings.add(u"beer")
+> assert doc.vocab.strings[u"beer"] == beer_hash
+> assert doc.vocab.strings[beer_hash] == u"beer"
+> ```
+
+The [`StringStore`](/api/stringstore) now resolves all strings to hash values
+instead of integer IDs. This means that the string-to-int mapping **no longer
+depends on the vocabulary state**, making a lot of workflows much simpler,
+especially during training. Unlike integer IDs in spaCy v1.x, hash values will
+**always match** – even across models. Strings can now be added explicitly using
+the new [`Stringstore.add`](/api/stringstore#add) method. A token's hash is
+available via `token.orth`.
+
+<Infobox>
+
+**API:** [`StringStore`](/api/stringstore) **Usage:**
+[Vocab, hashes and lexemes 101](/usage/spacy-101#vocab)
+
+</Infobox>
+
+### Improved word vectors support {#features-vectors}
+
+> #### Example
+>
+> ```python
+> for word, vector in vector_data:
+>     nlp.vocab.set_vector(word, vector)
+> nlp.vocab.vectors.from_glove("/path/to/vectors")
+> # Keep 10000 unique vectors and remap the rest
+> nlp.vocab.prune_vectors(10000)
+> nlp.to_disk("/model")
+> ```
+
+The new [`Vectors`](/api/vectors) class helps the `Vocab` manage the vectors
+assigned to strings, and lets you assign vectors individually, or
+[load in GloVe vectors](/usage/vectors-similarity#custom-loading-glove) from a
+directory. To help you strike a good balance between coverage and memory usage,
+the `Vectors` class lets you map **multiple keys** to the **same row** of the
+table. If you're using the [`spacy init-model`](/api/cli#init-model) command to
+create a vocabulary, pruning the vectors will be taken care of automatically if
+you set the `--prune-vectors` flag. Otherwise, you can use the new
+[`Vocab.prune_vectors`](/api/vocab#prune_vectors).
+
+<Infobox>
+
+**API:** [`Vectors`](/api/vectors), [`Vocab`](/api/vocab) **Usage:**
+[Word vectors and semantic similarity](/usage/vectors-similarity)
+
+</Infobox>
+
+### Saving, loading and serialization {#features-serializer}
+
+> #### Example
+>
+> ```python
+> nlp = spacy.load("en") # shortcut link
+> nlp = spacy.load("en_core_web_sm") # package
+> nlp = spacy.load("/path/to/en") # unicode path
+> nlp = spacy.load(Path("/path/to/en")) # pathlib Path
+>
+> nlp.to_disk("/path/to/nlp")
+> nlp = English().from_disk("/path/to/nlp")
+> ```
+
+spaCy's serialization API has been made consistent across classes and objects.
+All container classes, i.e. `Language`, `Doc`, `Vocab` and `StringStore` now
+have a `to_bytes()`, `from_bytes()`, `to_disk()` and `from_disk()` method that
+supports the Pickle protocol.
+
+The improved `spacy.load` makes loading models easier and more transparent. You
+can load a model by supplying its [shortcut link](/usage/models#usage), the name
+of an installed [model package](/models) or a path. The `Language` class to
+initialize will be determined based on the model's settings. For a blank
+language, you can import the class directly, e.g.
+`from spacy.lang.en import English` or use
+[`spacy.blank()`](/api/top-level#spacy.blank).
+
+<Infobox>
+
+**API:** [`spacy.load`](/api/top-level#spacy.load),
+[`Language.to_disk`](/api/language#to_disk) **Usage:**
+[Models](/usage/models#usage),
+[Saving and loading](/usage/training#saving-loading)
+
+</Infobox>
+
+### displaCy visualizer with Jupyter support {#features-displacy}
+
+> #### Example
+>
+> ```python
+> from spacy import displacy
+> doc = nlp(u"This is a sentence about Facebook.")
+> displacy.serve(doc, style="dep") # run the web server
+> html = displacy.render(doc, style="ent") # generate HTML
+> ```
+
+Our popular dependency and named entity visualizers are now an official part of
+the spaCy library. displaCy can run a simple web server, or generate raw HTML
+markup or SVG files to be exported. You can pass in one or more docs, and
+customize the style. displaCy also auto-detects whether you're running
+[Jupyter](https://jupyter.org) and will render the visualizations in your
+notebook.
+
+<Infobox>
+
+**API:** [`displacy`](/api/top-level#displacy) **Usage:**
+[Visualizing spaCy](/usage/visualizers)
+
+</Infobox>
+
+### Improved language data and lazy loading {#features-language}
+
+Language-specific data now lives in its own submodule, `spacy.lang`. Languages
+are lazy-loaded, i.e. only loaded when you import a `Language` class, or load a
+model that initializes one. This allows languages to contain more custom data,
+e.g. lemmatizer lookup tables, or complex regular expressions. The language data
+has also been tidied up and simplified. spaCy now also supports simple
+lookup-based lemmatization – and **many new languages**!
+
+<Infobox>
+
+**API:** [`Language`](/api/language) **Code:**
+[`spacy/lang`](https://github.com/explosion/spaCy/tree/master/spacy/lang)
+**Usage:** [Adding languages](/usage/adding-languages)
+
+</Infobox>
+
+### Revised matcher API and phrase matcher {#features-matcher}
+
+> #### Example
+>
+> ```python
+> from spacy.matcher import Matcher, PhraseMatcher
+>
+> matcher = Matcher(nlp.vocab)
+> matcher.add('HEARTS', None, [{"ORTH": "❤️", "OP": '+'}])
+>
+> phrasematcher = PhraseMatcher(nlp.vocab)
+> phrasematcher.add("OBAMA", None, nlp(u"Barack Obama"))
+> ```
+
+Patterns can now be added to the matcher by calling
+[`matcher.add()`](/api/matcher#add) with a match ID, an optional callback
+function to be invoked on each match, and one or more patterns. This allows you
+to write powerful, pattern-specific logic using only one matcher. For example,
+you might only want to merge some entity types, and set custom flags for other
+matched patterns. The new [`PhraseMatcher`](/api/phrasematcher) lets you
+efficiently match very large terminology lists using `Doc` objects as match
+patterns.
+
+<Infobox>
+
+**API:** [`Matcher`](/api/matcher), [`PhraseMatcher`](/api/phrasematcher)
+**Usage:** [Rule-based matching](/usage/linguistic-features#rule-based-matching)
+
+</Infobox>
+
+## Backwards incompatibilities {#incompat}
+
+The following modules, classes and methods have changed between v1.x and v2.0.
+
+| Old                                                    | New                                                                                                                                                 |
+| ------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `spacy.download.en`, `spacy.download.de`               | [`cli.download`](/api/cli#download)                                                                                                                 |
+| `spacy.en` etc.                                        | `spacy.lang.en` etc.                                                                                                                                |
+| `spacy.en.word_sets`                                   | `spacy.lang.en.stop_words`                                                                                                                          |
+| `spacy.orth`                                           | `spacy.lang.xx.lex_attrs`                                                                                                                           |
+| `spacy.syntax.iterators`                               | `spacy.lang.xx.syntax_iterators`                                                                                                                    |
+| `spacy.tagger.Tagger`                                  | `spacy.pipeline.Tagger`                                                                                                                             |
+| `spacy.cli.model`                                      | [`spacy.cli.vocab`](/api/cli#vocab)                                                                                                                 |
+| `Language.save_to_directory`                           | [`Language.to_disk`](/api/language#to_disk)                                                                                                         |
+| `Language.end_training`                                | [`Language.begin_training`](/api/language#begin_training)                                                                                           |
+| `Language.create_make_doc`                             | [`Language.tokenizer`](/api/language#attributes)                                                                                                    |
+| `Vocab.resize_vectors`                                 | [`Vectors.resize`](/api/vectors#resize)                                                                                                             |
+| `Vocab.load` `Vocab.load_lexemes`                      | [`Vocab.from_disk`](/api/vocab#from_disk) [`Vocab.from_bytes`](/api/vocab#from_bytes)                                                               |
+| `Vocab.dump`                                           | [`Vocab.to_disk`](/api/vocab#to_disk) [`Vocab.to_bytes`](/api/vocab#to_bytes)                                                                       |
+| `Vocab.load_vectors` `Vocab.load_vectors_from_bin_loc` | [`Vectors.from_disk`](/api/vectors#from_disk) [`Vectors.from_bytes`](/api/vectors#from_bytes) [`Vectors.from_glove`](/api/vectors#from_glove)       |
+| `Vocab.dump_vectors`                                   | [`Vectors.to_disk`](/api/vectors#to_disk) [`Vectors.to_bytes`](/api/vectors#to_bytes)                                                               |
+| `StringStore.load`                                     | [`StringStore.from_disk`](/api/stringstore#from_disk) [`StringStore.from_bytes`](/api/stringstore#from_bytes)                                       |
+| `StringStore.dump`                                     | [`StringStore.to_disk`](/api/stringstore#to_disk) [`StringStore.to_bytes`](/api/stringstore#to_bytes)                                               |
+| `Tokenizer.load`                                       | [`Tokenizer.from_disk`](/api/tokenizer#from_disk) [`Tokenizer.from_bytes`](/api/tokenizer#from_bytes)                                               |
+| `Tagger.load`                                          | [`Tagger.from_disk`](/api/tagger#from_disk) [`Tagger.from_bytes`](/api/tagger#from_bytes)                                                           |
+| `Tagger.tag_names`                                     | `Tagger.labels`                                                                                                                                     |
+| `DependencyParser.load`                                | [`DependencyParser.from_disk`](/api/dependencyparser#from_disk) [`DependencyParser.from_bytes`](/api/dependencyparser#from_bytes)                   |
+| `EntityRecognizer.load`                                | [`EntityRecognizer.from_disk`](/api/entityrecognizer#from_disk) [`EntityRecognizer.from_bytes`](/api/entityrecognizer#from_bytes)                   |
+| `Matcher.load`                                         | -                                                                                                                                                   |
+| `Matcher.add_pattern` `Matcher.add_entity`             | [`Matcher.add`](/api/matcher#add) [`PhraseMatcher.add`](/api/phrasematcher#add)                                                                     |
+| `Matcher.get_entity`                                   | [`Matcher.get`](/api/matcher#get)                                                                                                                   |
+| `Matcher.has_entity`                                   | [`Matcher.has_key`](/api/matcher#has_key)                                                                                                           |
+| `Doc.read_bytes`                                       | [`Doc.to_bytes`](/api/doc#to_bytes) [`Doc.from_bytes`](/api/doc#from_bytes) [`Doc.to_disk`](/api/doc#to_disk) [`Doc.from_disk`](/api/doc#from_disk) |
+| `Token.is_ancestor_of`                                 | [`Token.is_ancestor`](/api/token#is_ancestor)                                                                                                       |
+
+### Deprecated {#deprecated}
+
+The following methods are deprecated. They can still be used, but should be
+replaced.
+
+| Old                          | New                                             |
+| ---------------------------- | ----------------------------------------------- |
+| `Tokenizer.tokens_from_list` | [`Doc`](/api/doc)                               |
+| `Span.sent_start`            | [`Span.is_sent_start`](/api/span#is_sent_start) |
+
+## Migrating from spaCy 1.x {#migrating}
+
+Because we'e made so many architectural changes to the library, we've tried to
+**keep breaking changes to a minimum**. A lot of projects follow the philosophy
+that if you're going to break anything, you may as well break everything. We
+think migration is easier if there's a logic to what has changed. We've
+therefore followed a policy of avoiding breaking changes to the `Doc`, `Span`
+and `Token` objects. This way, you can focus on only migrating the code that
+does training, loading and serialization — in other words, code that works with
+the `nlp` object directly. Code that uses the annotations should continue to
+work.
+
+<Infobox title="Important note" variant="warning">
+
+If you've trained your own models, keep in mind that your train and runtime
+inputs must match. This means you'll have to **retrain your models** with spaCy
+v2.0.
+
+</Infobox>
+
+### Document processing {#migrating-document-processing}
+
+The [`Language.pipe`](/api/language#pipe) method allows spaCy to batch
+documents, which brings a **significant performance advantage** in v2.0. The new
+neural networks introduce some overhead per batch, so if you're processing a
+number of documents in a row, you should use `nlp.pipe` and process the texts as
+a stream.
+
+```diff
+- docs = (nlp(text) for text in texts)
+
++ docs = nlp.pipe(texts)
+```
+
+To make usage easier, there's now a boolean `as_tuples` keyword argument, that
+lets you pass in an iterator of `(text, context)` pairs, so you can get back an
+iterator of `(doc, context)` tuples.
+
+### Saving, loading and serialization {#migrating-saving-loading}
+
+Double-check all calls to `spacy.load()` and make sure they don't use the `path`
+keyword argument. If you're only loading in binary data and not a model package
+that can construct its own `Language` class and pipeline, you should now use the
+[`Language.from_disk`](/api/language#from_disk) method.
+
+```diff
+- nlp = spacy.load("en", path="/model")
+
++ nlp = spacy.load("/model")
++ nlp = spacy.blank("en").from_disk("/model/data")
+```
+
+Review all other code that writes state to disk or bytes. All containers, now
+share the same, consistent API for saving and loading. Replace saving with
+`to_disk()` or `to_bytes()`, and loading with `from_disk()` and `from_bytes()`.
+
+```diff
+- nlp.save_to_directory("/model")
+- nlp.vocab.dump("/vocab")
+
++ nlp.to_disk("/model")
++ nlp.vocab.to_disk("/vocab")
+```
+
+If you've trained models with input from v1.x, you'll need to **retrain them**
+with spaCy v2.0. All previous models will not be compatible with the new
+version.
+
+### Processing pipelines and language data {#migrating-languages}
+
+If you're importing language data or `Language` classes, make sure to change
+your import statements to import from `spacy.lang`. If you've added your own
+custom language, it needs to be moved to `spacy/lang/xx` and adjusted
+accordingly.
+
+```diff
+- from spacy.en import English
+
++ from spacy.lang.en import English
+```
+
+If you've been using custom pipeline components, check out the new guide on
+[processing pipelines](/usage/processing-pipelines). Pipeline components are now
+`(name, func)` tuples. Appending them to the pipeline still works – but the
+[`add_pipe`](/api/language#add_pipe) method now makes this much more convenient.
+Methods for removing, renaming, replacing and retrieving components have been
+added as well. Components can now be disabled by passing a list of their names
+to the `disable` keyword argument on load, or by using
+[`disable_pipes`](/api/language#disable_pipes) as a method or context manager:
+
+```diff
+- nlp = spacy.load("en", tagger=False, entity=False)
+- doc = nlp(u"I don't want parsed", parse=False)
+
++ nlp = spacy.load("en", disable=["tagger", "ner"])
++ with nlp.disable_pipes("parser"):
++    doc = nlp(u"I don't want parsed")
+```
+
+To add spaCy's built-in pipeline components to your pipeline, you can still
+import and instantiate them directly – but it's more convenient to use the new
+[`create_pipe`](/api/language#create_pipe) method with the component name, i.e.
+`'tagger'`, `'parser'`, `'ner'` or `'textcat'`.
+
+```diff
+- from spacy.pipeline import Tagger
+- tagger = Tagger(nlp.vocab)
+- nlp.pipeline.insert(0, tagger)
+
++ tagger = nlp.create_pipe("tagger")
++ nlp.add_pipe(tagger, first=True)
+```
+
+### Training {#migrating-training}
+
+All built-in pipeline components are now subclasses of [`Pipe`](/api/pipe),
+fully trainable and serializable, and follow the same API. Instead of updating
+the model and telling spaCy when to _stop_, you can now explicitly call
+[`begin_training`](/api/language#begin_training), which returns an optimizer you
+can pass into the [`update`](/api/language#update) function. While `update`
+still accepts sequences of `Doc` and `GoldParse` objects, you can now also pass
+in a list of strings and dictionaries describing the annotations. We call this
+the ["simple training style"](/usage/training#training-simple-style). This is
+also the recommended usage, as it removes one layer of abstraction from the
+training.
+
+```diff
+- for itn in range(1000):
+-     for text, entities in train_data:
+-         doc = Doc(text)
+-         gold = GoldParse(doc, entities=entities)
+-         nlp.update(doc, gold)
+- nlp.end_training()
+- nlp.save_to_directory("/model")
+
++ nlp.begin_training()
++ for itn in range(1000):
++     for texts, annotations in train_data:
++         nlp.update(texts, annotations)
++ nlp.to_disk("/model")
+```
+
+### Attaching custom data to the Doc {#migrating-doc}
+
+Previously, you had to create a new container in order to attach custom data to
+a `Doc` object. This often required converting the `Doc` objects to and from
+arrays. In spaCy v2.0, you can set your own attributes, properties and methods
+on the `Doc`, `Token` and `Span` via
+[custom extensions](/usage/processing-pipelines#custom-components-attributes).
+This means that your application can – and should – only pass around `Doc`
+objects and refer to them as the single source of truth.
+
+```diff
+- doc = nlp(u"This is a regular doc")
+- doc_array = doc.to_array(["ORTH", "POS"])
+- doc_with_meta = {"doc_array": doc_array, "meta": get_doc_meta(doc_array)}
+
++ Doc.set_extension("meta", getter=get_doc_meta)
++ doc_with_meta = nlp(u'This is a doc with meta data')
++ meta = doc._.meta
+```
+
+If you wrap your extension attributes in a
+[custom pipeline component](/usage/processing-pipelines#custom-components), they
+will be assigned automatically when you call `nlp` on a text. If your
+application assigns custom data to spaCy's container objects, or includes other
+utilities that interact with the pipeline, consider moving this logic into its
+own extension module.
+
+```diff
+- doc = nlp(u"Doc with a standard pipeline")
+- meta = get_meta(doc)
+
++ nlp.add_pipe(meta_component)
++ doc = nlp(u"Doc with a custom pipeline that assigns meta")
++ meta = doc._.meta
+```
+
+### Strings and hash values {#migrating-strings}
+
+The change from integer IDs to hash values may not actually affect your code
+very much. However, if you're adding strings to the vocab manually, you now need
+to call [`StringStore.add`](/api/stringstore#add) explicitly. You can also now
+be sure that the string-to-hash mapping will always match across vocabularies.
+
+```diff
+- nlp.vocab.strings[u"coffee"]       # 3672
+- other_nlp.vocab.strings[u"coffee"] # 40259
+
++ nlp.vocab.strings.add(u"coffee")
++ nlp.vocab.strings[u"coffee"]       # 3197928453018144401
++ other_nlp.vocab.strings[u"coffee"] # 3197928453018144401
+```
+
+### Adding patterns and callbacks to the matcher {#migrating-matcher}
+
+If you're using the matcher, you can now add patterns in one step. This should
+be easy to update – simply merge the ID, callback and patterns into one call to
+[`Matcher.add()`](/api/matcher#add). The matcher now also supports string keys,
+which saves you an extra import. If you've been using **acceptor functions**,
+you'll need to move this logic into the
+[`on_match` callbacks](/usage/linguistic-features#on_match). The callback
+function is invoked on every match and will give you access to the doc, the
+index of the current match and all total matches. This lets you both accept or
+reject the match, and define the actions to be triggered.
+
+```diff
+- matcher.add_entity("GoogleNow", on_match=merge_phrases)
+- matcher.add_pattern("GoogleNow", [{ORTH: "Google"}, {ORTH: "Now"}])
+
++ matcher.add("GoogleNow", merge_phrases, [{"ORTH": "Google"}, {"ORTH": "Now"}])
+```
+
+If you need to match large terminology lists, you can now also use the
+[`PhraseMatcher`](/api/phrasematcher), which accepts `Doc` objects as match
+patterns and is more efficient than the regular, rule-based matcher.
+
+```diff
+- matcher = Matcher(nlp.vocab)
+- matcher.add_entity("PRODUCT")
+- for text in large_terminology_list
+-     matcher.add_pattern("PRODUCT", [{ORTH: text}])
+
++ from spacy.matcher import PhraseMatcher
++ matcher = PhraseMatcher(nlp.vocab)
++ patterns = [nlp.make_doc(text) for text in large_terminology_list]
++ matcher.add("PRODUCT", None, *patterns)
+```
diff --git a/website/docs/usage/vectors-similarity.md b/website/docs/usage/vectors-similarity.md
new file mode 100644
index 000000000..f7c9d1cd9
--- /dev/null
+++ b/website/docs/usage/vectors-similarity.md
@@ -0,0 +1,257 @@
+---
+title: Word Vectors and Semantic Similarity
+menu:
+  - ['Basics', 'basics']
+  - ['Custom Vectors', 'custom']
+  - ['GPU Usage', 'gpu']
+---
+
+## Basics {#basics hidden="true"}
+
+> #### Training word vectors
+>
+> Dense, real valued vectors representing distributional similarity information
+> are now a cornerstone of practical NLP. The most common way to train these
+> vectors is the [Word2vec](https://en.wikipedia.org/wiki/Word2vec) family of
+> algorithms. If you need to train a word2vec model, we recommend the
+> implementation in the Python library
+> [Gensim](https://radimrehurek.com/gensim/).
+
+import Vectors101 from 'usage/101/\_vectors-similarity.md'
+
+<Vectors101 />
+
+## Customizing word vectors {#custom}
+
+Word vectors let you import knowledge from raw text into your model. The
+knowledge is represented as a table of numbers, with one row per term in your
+vocabulary. If two terms are used in similar contexts, the algorithm that learns
+the vectors should assign them **rows that are quite similar**, while words that
+are used in different contexts will have quite different values. This lets you
+use the row-values assigned to the words as a kind of dictionary, to tell you
+some things about what the words in your text mean.
+
+Word vectors are particularly useful for terms which **aren't well represented
+in your labelled training data**. For instance, if you're doing named entity
+recognition, there will always be lots of names that you don't have examples of.
+For instance, imagine your training data happens to contain some examples of the
+term "Microsoft", but it doesn't contain any examples of the term "Symantec". In
+your raw text sample, there are plenty of examples of both terms, and they're
+used in similar contexts. The word vectors make that fact available to the
+entity recognition model. It still won't see examples of "Symantec" labelled as
+a company. However, it'll see that "Symantec" has a word vector that usually
+corresponds to company terms, so it can **make the inference**.
+
+In order to make best use of the word vectors, you want the word vectors table
+to cover a **very large vocabulary**. However, most words are rare, so most of
+the rows in a large word vectors table will be accessed very rarely, or never at
+all. You can usually cover more than **95% of the tokens** in your corpus with
+just **a few thousand rows** in the vector table. However, it's those **5% of
+rare terms** where the word vectors are **most useful**. The problem is that
+increasing the size of the vector table produces rapidly diminishing returns in
+coverage over these rare terms.
+
+### Converting word vectors for use in spaCy {#converting new="2.0.10"}
+
+Custom word vectors can be trained using a number of open-source libraries, such
+as [Gensim](https://radimrehurek.com/gensim), [Fast Text](https://fasttext.cc),
+or Tomas Mikolov's original
+[word2vec implementation](https://code.google.com/archive/p/word2vec/). Most
+word vector libraries output an easy-to-read text-based format, where each line
+consists of the word followed by its vector. For everyday use, we want to
+convert the vectors model into a binary format that loads faster and takes up
+less space on disk. The easiest way to do this is the
+[`init-model`](/api/cli#init-model) command-line utility:
+
+```bash
+wget https://s3-us-west-1.amazonaws.com/fasttext-vectors/word-vectors-v2/cc.la.300.vec.gz
+python -m spacy init-model en /tmp/la_vectors_wiki_lg --vectors-loc cc.la.300.vec.gz
+```
+
+This will output a spaCy model in the directory `/tmp/la_vectors_wiki_lg`,
+giving you access to some nice Latin vectors 😉 You can then pass the directory
+path to [`spacy.load()`](/api/top-level#spacy.load).
+
+```python
+nlp_latin = spacy.load("/tmp/la_vectors_wiki_lg")
+doc1 = nlp_latin(u"Caecilius est in horto")
+doc2 = nlp_latin(u"servus est in atrio")
+doc1.similarity(doc2)
+```
+
+The model directory will have a `/vocab` directory with the strings, lexical
+entries and word vectors from the input vectors model. The
+[`init-model`](/api/cli#init-model) command supports a number of archive formats
+for the word vectors: the vectors can be in plain text (`.txt`), zipped
+(`.zip`), or tarred and zipped (`.tgz`).
+
+### Optimizing vector coverage {#custom-vectors-coverage new="2"}
+
+To help you strike a good balance between coverage and memory usage, spaCy's
+[`Vectors`](/api/vectors) class lets you map **multiple keys** to the **same
+row** of the table. If you're using the
+[`spacy init-model`](/api/cli#init-model) command to create a vocabulary,
+pruning the vectors will be taken care of automatically if you set the
+`--prune-vectors` flag. You can also do it manually in the following steps:
+
+1. Start with a **word vectors model** that covers a huge vocabulary. For
+   instance, the [`en_vectors_web_lg`](/models/en#en_vectors_web_lg) model
+   provides 300-dimensional GloVe vectors for over 1 million terms of English.
+2. If your vocabulary has values set for the `Lexeme.prob` attribute, the
+   lexemes will be sorted by descending probability to determine which vectors
+   to prune. Otherwise, lexemes will be sorted by their order in the `Vocab`.
+3. Call [`Vocab.prune_vectors`](/api/vocab#prune_vectors) with the number of
+   vectors you want to keep.
+
+```python
+nlp = spacy.load('en_vectors_web_lg')
+n_vectors = 105000  # number of vectors to keep
+removed_words = nlp.vocab.prune_vectors(n_vectors)
+
+assert len(nlp.vocab.vectors) <= n_vectors  # unique vectors have been pruned
+assert nlp.vocab.vectors.n_keys > n_vectors  # but not the total entries
+```
+
+[`Vocab.prune_vectors`](/api/vocab#prune_vectors) reduces the current vector
+table to a given number of unique entries, and returns a dictionary containing
+the removed words, mapped to `(string, score)` tuples, where `string` is the
+entry the removed word was mapped to, and `score` the similarity score between
+the two words.
+
+```python
+### Removed words
+{
+    "Shore": ("coast", 0.732257),
+    "Precautionary": ("caution", 0.490973),
+    "hopelessness": ("sadness", 0.742366),
+    "Continous": ("continuous", 0.732549),
+    "Disemboweled": ("corpse", 0.499432),
+    "biostatistician": ("scientist", 0.339724),
+    "somewheres": ("somewheres", 0.402736),
+    "observing": ("observe", 0.823096),
+    "Leaving": ("leaving", 1.0),
+}
+```
+
+In the example above, the vector for "Shore" was removed and remapped to the
+vector of "coast", which is deemed about 73% similar. "Leaving" was remapped to
+the vector of "leaving", which is identical.
+
+If you're using the [`init-model`](/api/cli#init-model) command, you can set the
+`--prune-vectors` option to easily reduce the size of the vectors as you add
+them to a spaCy model:
+
+```bash
+$ python -m spacy init-model /tmp/la_vectors_web_md --vectors-loc la.300d.vec.tgz --prune-vectors 10000
+```
+
+This will create a spaCy model with vectors for the first 10,000 words in the
+vectors model. All other words in the vectors model are mapped to the closest
+vector among those retained.
+
+### Adding vectors {#custom-vectors-add new="2"}
+
+spaCy's new [`Vectors`](/api/vectors) class greatly improves the way word
+vectors are stored, accessed and used. The data is stored in two structures:
+
+- An array, which can be either on CPU or [GPU](#gpu).
+- A dictionary mapping string-hashes to rows in the table.
+
+Keep in mind that the `Vectors` class itself has no
+[`StringStore`](/api/stringstore), so you have to store the hash-to-string
+mapping separately. If you need to manage the strings, you should use the
+`Vectors` via the [`Vocab`](/api/vocab) class, e.g. `vocab.vectors`. To add
+vectors to the vocabulary, you can use the
+[`Vocab.set_vector`](/api/vocab#set_vector) method.
+
+```python
+### Adding vectors
+from spacy.vocab import Vocab
+
+vector_data = {u"dog": numpy.random.uniform(-1, 1, (300,)),
+               u"cat": numpy.random.uniform(-1, 1, (300,)),
+               u"orange": numpy.random.uniform(-1, 1, (300,))}
+
+vocab = Vocab()
+for word, vector in vector_data.items():
+    vocab.set_vector(word, vector)
+```
+
+### Loading GloVe vectors {#custom-loading-glove new="2"}
+
+spaCy comes with built-in support for loading
+[GloVe](https://nlp.stanford.edu/projects/glove/) vectors from a directory. The
+[`Vectors.from_glove`](/api/vectors#from_glove) method assumes a binary format,
+the vocab provided in a `vocab.txt`, and the naming scheme of
+`vectors.{size}.[fd`.bin]. For example:
+
+```yaml
+### Directory structure
+└── vectors
+    ├── vectors.128.f.bin  # vectors file
+    └── vocab.txt          # vocabulary
+```
+
+| File name           | Dimensions | Data type        |
+| ------------------- | ---------- | ---------------- |
+| `vectors.128.f.bin` | 128        | float32          |
+| `vectors.300.d.bin` | 300        | float64 (double) |
+
+```python
+nlp = spacy.load("en_core_web_sm")
+nlp.vocab.vectors.from_glove("/path/to/vectors")
+```
+
+If your instance of `Language` already contains vectors, they will be
+overwritten. To create your own GloVe vectors model package like spaCy's
+[`en_vectors_web_lg`](/models/en#en_vectors_web_lg), you can call
+[`nlp.to_disk`](/api/language#to_disk), and then package the model using the
+[`package`](/api/cli#package) command.
+
+### Using custom similarity methods {#custom-similarity}
+
+By default, [`Token.vector`](/api/token#vector) returns the vector for its
+underlying [`Lexeme`](/api/lexeme), while [`Doc.vector`](/api/doc#vector) and
+[`Span.vector`](/api/span#vector) return an average of the vectors of their
+tokens. You can customize these behaviors by modifying the `doc.user_hooks`,
+`doc.user_span_hooks` and `doc.user_token_hooks` dictionaries.
+
+<Infobox title="📖 Custom user hooks">
+
+For more details on **adding hooks** and **overwriting** the built-in `Doc`,
+`Span` and `Token` methods, see the usage guide on
+[user hooks](/usage/processing-pipelines#user-hooks).
+
+</Infobox>
+
+## Storing vectors on a GPU {#gpu}
+
+If you're using a GPU, it's much more efficient to keep the word vectors on the
+device. You can do that by setting the [`Vectors.data`](/api/vectors#attributes)
+attribute to a `cupy.ndarray` object if you're using spaCy or
+[Chainer]("https://chainer.org"), or a `torch.Tensor` object if you're using
+[PyTorch]("http://pytorch.org"). The `data` object just needs to support
+`__iter__` and `__getitem__`, so if you're using another library such as
+[TensorFlow]("https://www.tensorflow.org"), you could also create a wrapper for
+your vectors data.
+
+```python
+### spaCy, Thinc or Chainer
+import cupy.cuda
+from spacy.vectors import Vectors
+
+vector_table = numpy.zeros((3, 300), dtype="f")
+vectors = Vectors([u"dog", u"cat", u"orange"], vector_table)
+with cupy.cuda.Device(0):
+    vectors.data = cupy.asarray(vectors.data)
+```
+
+```python
+### PyTorch
+import torch
+from spacy.vectors import Vectors
+
+vector_table = numpy.zeros((3, 300), dtype="f")
+vectors = Vectors([u"dog", u"cat", u"orange"], vector_table)
+vectors.data = torch.Tensor(vectors.data).cuda(0)
+```
diff --git a/website/docs/usage/visualizers.md b/website/docs/usage/visualizers.md
new file mode 100644
index 000000000..b1a6b8c7c
--- /dev/null
+++ b/website/docs/usage/visualizers.md
@@ -0,0 +1,399 @@
+---
+title: Visualizers
+teaser: Visualize dependencies and entities in your browser or in a notebook
+new: 2
+menu:
+  - ['Dependencies', 'dep']
+  - ['Entities', 'ent']
+  - ['Jupyter Notebooks', 'jupyter']
+  - ['Rendering HTML', 'html']
+---
+
+As of v2.0, our popular visualizers,
+[displaCy](https://explosion.ai/demos/displacy) and
+[displaCy <sup>ENT</sup>](https://explosion.ai/demos/displacy-ent) are finally
+an official part of the library. Visualizing a dependency parse or named
+entities in a text is not only a fun NLP demo – it can also be incredibly
+helpful in speeding up development and debugging your code and training process.
+If you're running a [Jupyter](https://jupyter.org) notebook, displaCy will
+detect this and return the markup in a format
+[ready to be rendered and exported](#jupyter).
+
+> #### What about the old visualizers?
+>
+> Our JavaScript-based visualizers
+> [`displacy.js`](https://github.com/explosion/displacy) and
+> [`displacy-ent.js`](https://github.com/explosion/displacy-ent) will still be
+> available on GitHub. If you're looking to implement web-based visualizations,
+> we generally recommend using those instead of spaCy's built-in `displacy`
+> module. It'll allow your application to perform all rendering on the client
+> and only rely on the server for the text processing. The generated markup is
+> also more compatible with modern web standards.
+
+The quickest way to visualize `Doc` is to use
+[`displacy.serve`](/api/top-level#displacy.serve). This will spin up a simple
+web server and let you view the result straight from your browser. displaCy can
+either take a single `Doc` or a list of `Doc` objects as its first argument.
+This lets you construct them however you like – using any model or modifications
+you like.
+
+## Visualizing the dependency parse {#dep}
+
+The dependency visualizer, `dep`, shows part-of-speech tags and syntactic
+dependencies.
+
+```python
+### Dependency example
+import spacy
+from spacy import displacy
+
+nlp = spacy.load("en_core_web_sm")
+doc = nlp(u"This is a sentence.")
+displacy.serve(doc, style="dep")
+```
+
+![displaCy visualizer](../images/displacy.svg)
+
+The argument `options` lets you specify a dictionary of settings to customize
+the layout, for example:
+
+<Infobox title="Important note" variant="warning">
+
+There's currently a known issue with the `compact` mode for sentences with short
+arrows and long dependency labels, that causes labels longer than the arrow to
+wrap. So if you come across this problem, especially when using custom labels,
+you'll have to increase the `distance` setting in the `options` to allow longer
+arcs.
+
+</Infobox>
+
+| Argument  | Type    | Description                                                 | Default     |
+| --------- | ------- | ----------------------------------------------------------- | ----------- |
+| `compact` | bool    | "Compact mode" with square arrows that takes up less space. | `False`     |
+| `color`   | unicode | Text color (HEX, RGB or color names).                       | `"#000000"` |
+| `bg`      | unicode | Background color (HEX, RGB or color names).                 | `"#ffffff"` |
+| `font`    | unicode | Font name or font family for all text.                      | `"Arial"`   |
+
+For a list of all available options, see the
+[`displacy` API documentation](top-level#displacy_options).
+
+> #### Options example
+>
+> ```python
+> options = {"compact": True, "bg": "#09a3d5",
+>            "color": "white", "font": "Source Sans Pro"}
+> displacy.serve(doc, style="dep", options=options)
+> ```
+
+![displaCy visualizer (compact mode)](../images/displacy-compact.svg)
+
+### Visualizing long texts {#dep-long-text new="2.0.12"}
+
+Long texts can become difficult to read when displayed in one row, so it's often
+better to visualize them sentence-by-sentence instead. As of v2.0.12, `displacy`
+supports rendering both [`Doc`](/api/doc) and [`Span`](/api/span) objects, as
+well as lists of `Doc`s or `Span`s. Instead of passing the full `Doc` to
+`displacy.serve`, you can also pass in a list `doc.sents`. This will create one
+visualization for each sentence.
+
+```python
+import spacy
+from spacy import displacy
+
+nlp = spacy.load("en_core_web_sm")
+text = u"""In ancient Rome, some neighbors live in three adjacent houses. In the center is the house of Senex, who lives there with wife Domina, son Hero, and several slaves, including head slave Hysterium and the musical's main character Pseudolus. A slave belonging to Hero, Pseudolus wishes to buy, win, or steal his freedom. One of the neighboring houses is owned by Marcus Lycus, who is a buyer and seller of beautiful women; the other belongs to the ancient Erronius, who is abroad searching for his long-lost children (stolen in infancy by pirates). One day, Senex and Domina go on a trip and leave Pseudolus in charge of Hero. Hero confides in Pseudolus that he is in love with the lovely Philia, one of the courtesans in the House of Lycus (albeit still a virgin)."""
+doc = nlp(text)
+sentence_spans = list(doc.sents)
+displacy.serve(sentence_spans, style="dep")
+```
+
+## Visualizing the entity recognizer {#ent}
+
+The entity visualizer, `ent`, highlights named entities and their labels in a
+text.
+
+```python
+### Named Entity example
+import spacy
+from spacy import displacy
+
+text = """But Google is starting from behind. The company made a late push
+into hardware, and Apple’s Siri, available on iPhones, and Amazon’s Alexa
+software, which runs on its Echo and Dot devices, have clear leads in
+consumer adoption."""
+
+nlp = spacy.load("custom_ner_model")
+doc = nlp(text)
+displacy.serve(doc, style="ent")
+```
+
+import DisplacyEntHtml from 'images/displacy-ent.html'
+
+<Iframe title="displaCy visualizer for entities" html={DisplacyEntHtml} height={275} />
+
+The entity visualizer lets you customize the following `options`:
+
+| Argument | Type | Description                                                                           | Default |
+| -------- | ---- | ------------------------------------------------------------------------------------- | ------- |
+| `ents`   | list |  Entity types to highlight (`None` for all types).                                    | `None`  |
+| `colors` | dict | Color overrides. Entity types in lowercase should be mapped to color names or values. | `{}`    |
+
+If you specify a list of `ents`, only those entity types will be rendered – for
+example, you can choose to display `PERSON` entities. Internally, the visualizer
+knows nothing about available entity types and will render whichever spans and
+labels it receives. This makes it especially easy to work with custom entity
+types. By default, displaCy comes with colors for all
+[entity types supported by spaCy](/api/annotation#named-entities). If you're
+using custom entity types, you can use the `colors` setting to add your own
+colors for them.
+
+> #### Options example
+>
+> ```python
+> colors = {"ORG": "linear-gradient(90deg, #aa9cfc, #fc9ce7)"}
+> options = {"ents": ["ORG"], "colors": colors}
+> displacy.serve(doc, style="ent", options=options)
+> ```
+
+import DisplacyEntCustomHtml from 'images/displacy-ent-custom.html'
+
+<Iframe title="displaCy visualizer for entities (custom styling)" html={DisplacyEntCustomHtml} height={225} />
+
+The above example uses a little trick: Since the background color values are
+added as the `background` style attribute, you can use any
+[valid background value](https://tympanus.net/codrops/css_reference/background/)
+or shorthand — including gradients and even images!
+
+### Adding titles to documents {#ent-titles}
+
+Rendering several large documents on one page can easily become confusing. To
+add a headline to each visualization, you can add a `title` to its `user_data`.
+User data is never touched or modified by spaCy.
+
+```python
+doc = nlp(u"This is a sentence about Google.")
+doc.user_data["title"] = "This is a title"
+displacy.serve(doc, style="ent")
+```
+
+This feature is especially handy if you're using displaCy to compare performance
+at different stages of a process, e.g. during training. Here you could use the
+title for a brief description of the text example and the number of iterations.
+
+## Using displaCy in Jupyter notebooks {#jupyter}
+
+displaCy is able to detect whether you're working in a
+[Jupyter](https://jupyter.org) notebook, and will return markup that can be
+rendered in a cell straight away. When you export your notebook, the
+visualizations will be included as HTML.
+
+```python
+### Jupyter example
+# Don't forget to install a model, e.g.: python -m spacy download en
+
+# In[1]:
+import spacy
+from spacy import displacy
+
+# In[2]:
+doc = nlp(u"Rats are various medium-sized, long-tailed rodents.")
+displacy.render(doc, style="dep")
+
+# In[3]:
+doc2 = nlp(LONG_NEWS_ARTICLE)
+displacy.render(doc2, style="ent")
+```
+
+> #### Enabling or disabling Jupyter mode
+>
+> To explicitly enable or disable "Jupyter mode", you can use the jupyter`
+> keyword argument – e.g. to return raw HTML in a notebook, or to force Jupyter
+> rendering if auto-detection fails.
+
+![displaCy visualizer in a Jupyter notebook](../images/displacy_jupyter.jpg)
+
+Internally, displaCy imports `display` and `HTML` from `IPython.core.display`
+and returns a Jupyter HTML object. If you were doing it manually, it'd look like
+this:
+
+```python
+from IPython.core.display import display, HTML
+
+html = displacy.render(doc, style="dep")
+display(HTML(html))
+```
+
+## Rendering HTML {#html}
+
+If you don't need the web server and just want to generate the markup – for
+example, to export it to a file or serve it in a custom way – you can use
+[`displacy.render`](/api/top-level#displacy.render). It works the same way, but
+returns a string containing the markup.
+
+```python
+### Example
+import spacy
+from spacy import displacy
+
+nlp = spacy.load("en_core_web_sm")
+doc1 = nlp(u"This is a sentence.")
+doc2 = nlp(u"This is another sentence.")
+html = displacy.render([doc1, doc2], style="dep", page=True)
+```
+
+`page=True` renders the markup wrapped as a full HTML page. For minified and
+more compact HTML markup, you can set `minify=True`. If you're rendering a
+dependency parse, you can also export it as an `.svg` file.
+
+> #### What's SVG?
+>
+> Unlike other image formats, the SVG (Scalable Vector Graphics) uses XML markup
+> that's easy to manipulate
+> [using CSS](https://www.smashingmagazine.com/2014/11/styling-and-animating-svgs-with-css/)
+> or
+> [JavaScript](https://css-tricks.com/smil-is-dead-long-live-smil-a-guide-to-alternatives-to-smil-features/).
+> Essentially, SVG lets you design with code, which makes it a perfect fit for
+> visualizing dependency trees. SVGs can be embedded online in an `<img>` tag,
+> or inlined in an HTML document. They're also pretty easy to
+> [convert](https://convertio.co/image-converter/).
+
+```python
+svg = displacy.render(doc, style="dep")
+output_path = Path("/images/sentence.svg")
+output_path.open("w", encoding="utf-8").write(svg)
+```
+
+<Infobox title="Important note" variant="warning">
+
+Since each visualization is generated as a separate SVG, exporting `.svg` files
+only works if you're rendering **one single doc** at a time. (This makes sense –
+after all, each visualization should be a standalone graphic.) So instead of
+rendering all `Doc`s at one, loop over them and export them separately.
+
+</Infobox>
+
+### Example: Export SVG graphics of dependency parses {#examples-export-svg}
+
+```python
+### Example
+import spacy
+from spacy import displacy
+from pathlib import Path
+
+nlp = spacy.load("en_core_web_sm")
+sentences = [u"This is an example.", u"This is another one."]
+for sent in sentences:
+    doc = nlp(sentence)
+    svg = displacy.render(doc, style="dep")
+    file_name = '-'.join([w.text for w in doc if not w.is_punct]) + ".svg"
+    output_path = Path("/images/" + file_name)
+    output_path.open("w", encoding="utf-8").write(svg)
+```
+
+The above code will generate the dependency visualizations as two files,
+`This-is-an-example.svg` and `This-is-another-one.svg`.
+
+### Rendering data manually {#manual-usage}
+
+You can also use displaCy to manually render data. This can be useful if you
+want to visualize output from other libraries, like [NLTK](http://www.nltk.org)
+or
+[SyntaxNet](https://github.com/tensorflow/models/tree/master/research/syntaxnet).
+If you set `manual=True` on either `render()` or `serve()`, you can pass in data
+in displaCy's format (instead of `Doc` objects). When setting `ents` manually,
+make sure to supply them in the right order, i.e. starting with the lowest start
+position.
+
+> #### Example
+>
+> ```python
+> ex = [{"text": "But Google is starting from behind.",
+>        "ents": [{"start": 4, "end": 10, "label": "ORG"}],
+>        "title": None}]
+> html = displacy.render(ex, style="ent", manual=True)
+> ```
+
+```python
+### DEP input
+{
+    "words": [
+        {"text": "This", "tag": "DT"},
+        {"text": "is", "tag": "VBZ"},
+        {"text": "a", "tag": "DT"},
+        {"text": "sentence", "tag": "NN"}
+    ],
+    "arcs": [
+        {"start": 0, "end": 1, "label": "nsubj", "dir": "left"},
+        {"start": 2, "end": 3, "label": "det", "dir": "left"},
+        {"start": 1, "end": 3, "label": "attr", "dir": "right"}
+    ]
+}
+```
+
+```python
+### ENT input
+{
+    "text": "But Google is starting from behind.",
+    "ents": [{"start": 4, "end": 10, "label": "ORG"}],
+    "title": None
+}
+```
+
+### Using displaCy in a web application {#webapp}
+
+If you want to use the visualizers as part of a web application, for example to
+create something like our [online demo](https://explosion.ai/demos/displacy),
+it's not recommended to only wrap and serve the displaCy renderer. Instead, you
+should only rely on the server to perform spaCy's processing capabilities, and
+use a client-side implementation like
+[`displaCy.js`](https://github.com/explosion/displacy) to render the
+JSON-formatted output.
+
+> #### Why not return the HTML by the server?
+>
+> It's certainly possible to just have your server return the markup. But
+> outputting raw, unsanitized HTML is risky and makes your app vulnerable to
+> [cross-site scripting](https://en.wikipedia.org/wiki/Cross-site_scripting)
+> (XSS). All your user needs to do is find a way to make spaCy return text like
+> `<script src="malicious-code.js"></script>`, which is pretty easy in NER mode.
+> Instead of relying on the server to render and sanitize HTML, you can do this
+> on the client in JavaScript. displaCy.js creates the markup as DOM nodes and
+> will never insert raw HTML.
+
+The `parse_deps` function takes a `Doc` object and returns a dictionary in a
+format that can be rendered by displaCy.
+
+```python
+### Example
+import spacy
+from spacy import displacy
+
+nlp = spacy.load("en_core_web_sm")
+
+def displacy_service(text):
+    doc = nlp(text)
+    return displacy.parse_deps(doc)
+```
+
+Using a library like [Flask](http://flask.pocoo.org/) or
+[Hug](http://www.hug.rest/), you can easily turn the above code into a simple
+REST API that receives a text and returns a JSON-formatted parse. In your
+front-end, include [`displacy.js`](https://github.com/explosion/displacy) and
+initialize it with the API URL and the ID or query selector of the container to
+render the visualization in, e.g. `'#displacy'` for `<div id="displacy">`.
+
+```javascript
+/// script.js
+var displacy = new displaCy('http://localhost:8080', {
+  container: '#displacy',
+})
+
+function parse(text) {
+  displacy.parse(text)
+}
+```
+
+When you call `parse`, it will make a request to your API, receive the
+JSON-formatted parse and render it in your container. To create an interactive
+experience, you could trigger this function by a button and read the text from
+an `<input>` field.
diff --git a/website/gatsby-browser.js b/website/gatsby-browser.js
new file mode 100644
index 000000000..25fedd4b4
--- /dev/null
+++ b/website/gatsby-browser.js
@@ -0,0 +1,50 @@
+import { navigate } from 'gatsby'
+
+import './src/styles/layout.sass'
+import Juniper from './src/components/juniper'
+
+// Workaround to rewrite anchor links
+const clientSideRewrites = {
+    '/usage/linguistic-features/#rule-based-matching': '/usage/rule-based-matching',
+}
+
+/* eslint-disable */
+import HKGroteskSemiBoldWOFF from './src/fonts/hkgrotesk-semibold.woff'
+import HKGroteskSemiBoldWOFF2 from './src/fonts/hkgrotesk-semibold.woff2'
+import HKGroteskSemiBoldItalicWOFF from './src/fonts/hkgrotesk-semibolditalic.woff'
+import HKGroteskSemiBoldItalicWOFF2 from './src/fonts/hkgrotesk-semibolditalic.woff2'
+import HKGroteskBoldWOFF from './src/fonts/hkgrotesk-bold.woff'
+import HKGroteskBoldWOFF2 from './src/fonts/hkgrotesk-bold.woff2'
+import HKGroteskBoldItalicWOFF from './src/fonts/hkgrotesk-bolditalic.woff'
+import HKGroteskBoldItalicWOFF2 from './src/fonts/hkgrotesk-bolditalic.woff2'
+/* eslint-enable */
+
+export const onInitialClientRender = () => {
+    // Importing Juniper in the component currently causes various problems
+    // because of the global window reference in its dependencies. So this
+    // is kinda hacky at the moment.
+    window.Juniper = Juniper
+}
+
+export const onRouteUpdate = ({ location }) => {
+    window.dispatchEvent(new Event('resize')) // for progress
+    window.dispatchEvent(new Event('scroll')) // for progress
+    if (location.hash) {
+        // Client-side rewrites
+        const rewrite = clientSideRewrites[location.pathname + location.hash]
+        if (rewrite) {
+            navigate(rewrite)
+            return
+        }
+        setTimeout(() => {
+            const el = document.querySelector(`${location.hash}`)
+            if (el) {
+                // Navigate to targeted element
+                el.scrollIntoView()
+                // Force recomputing :target pseudo class with pushState/popState
+                window.location.hash = ''
+                window.location.hash = location.hash
+            }
+        }, 0)
+    }
+}
diff --git a/website/gatsby-config.js b/website/gatsby-config.js
new file mode 100644
index 000000000..aacc25545
--- /dev/null
+++ b/website/gatsby-config.js
@@ -0,0 +1,145 @@
+const autoprefixer = require('autoprefixer')
+const path = require('path')
+
+// Markdown plugins
+const wrapSectionPlugin = require('./src/plugins/remark-wrap-section.js')
+const customAttrsPlugin = require('./src/plugins/remark-custom-attrs.js')
+const codeBlocksPlugin = require('./src/plugins/remark-code-blocks.js')
+
+// Import metadata
+const site = require('./meta/site.json')
+const logos = require('./meta/logos.json')
+const sidebars = require('./meta/sidebars.json')
+const models = require('./meta/languages.json')
+const universe = require('./meta/universe.json')
+
+const DEFAULT_TEMPLATE = path.resolve('./src/templates/index.js')
+
+module.exports = {
+    siteMetadata: {
+        ...site,
+        ...logos,
+        sidebars,
+        ...models,
+        universe,
+    },
+
+    plugins: [
+        {
+            resolve: `gatsby-plugin-svgr`,
+            options: {
+                svgo: false,
+                svgoConfig: {
+                    removeViewBox: false,
+                },
+            },
+        },
+        {
+            resolve: `gatsby-plugin-sass`,
+            options: {
+                indentedSyntax: true,
+                postCssPlugins: [autoprefixer()],
+                cssLoaderOptions: {
+                    localIdentName:
+                        process.env.NODE_ENV == 'development'
+                            ? '[name]-[local]-[hash:8]'
+                            : '[hash:8]',
+                },
+            },
+        },
+        `gatsby-plugin-react-helmet`,
+        {
+            resolve: `gatsby-source-filesystem`,
+            options: {
+                name: `docs`,
+                path: `${__dirname}/docs`,
+            },
+        },
+        {
+            resolve: `gatsby-source-filesystem`,
+            options: {
+                name: `pages`,
+                path: `${__dirname}/src/pages`,
+            },
+        },
+        {
+            resolve: `gatsby-source-filesystem`,
+            options: {
+                name: `images`,
+                path: `${__dirname}/src/images`,
+            },
+        },
+        {
+            resolve: `gatsby-source-filesystem`,
+            options: {
+                name: `docsImages`,
+                path: `${__dirname}/docs/images`,
+            },
+        },
+        {
+            resolve: `gatsby-mdx`,
+            options: {
+                root: __dirname,
+                extensions: ['.md', '.mdx'],
+                defaultLayouts: {
+                    pages: DEFAULT_TEMPLATE,
+                },
+                mdPlugins: [customAttrsPlugin, wrapSectionPlugin, codeBlocksPlugin],
+                gatsbyRemarkPlugins: [
+                    {
+                        resolve: `gatsby-remark-smartypants`,
+                        options: {
+                            backticks: false,
+                            dashes: 'oldschool',
+                        },
+                    },
+                    {
+                        resolve: `gatsby-remark-images`,
+                        options: {
+                            maxWidth: 650,
+                            linkImagesToOriginal: true,
+                            sizeByPixelDensity: false,
+                            showCaptions: true,
+                            quality: 80,
+                            withWebp: { quality: 80 },
+                            wrapperStyle: { marginBottom: '20px' },
+                        },
+                    },
+                    {
+                        // NB: This need to run after gatsby-remark-images!
+                        resolve: `gatsby-remark-unwrap-images`,
+                    },
+                    {
+                        resolve: `gatsby-remark-copy-linked-files`,
+                    },
+                ],
+            },
+        },
+        `gatsby-transformer-sharp`,
+        `gatsby-plugin-sharp`,
+        `gatsby-plugin-catch-links`,
+        `gatsby-plugin-sitemap`,
+        {
+            resolve: `gatsby-plugin-manifest`,
+            options: {
+                name: site.title,
+                short_name: site.title,
+                start_url: `/`,
+                background_color: site.theme,
+                theme_color: site.theme,
+                display: `minimal-ui`,
+                icon: `src/images/icon.png`,
+            },
+        },
+        {
+            resolve: `gatsby-plugin-google-analytics`,
+            options: {
+                trackingId: site.analytics,
+                head: false,
+                anonymize: true,
+                respectDNT: true,
+            },
+        },
+        `gatsby-plugin-offline`,
+    ],
+}
diff --git a/website/gatsby-node.js b/website/gatsby-node.js
new file mode 100644
index 000000000..716444382
--- /dev/null
+++ b/website/gatsby-node.js
@@ -0,0 +1,270 @@
+const path = require('path')
+const { createFilePath } = require('gatsby-source-filesystem')
+
+const DEFAULT_TEMPLATE = path.resolve('./src/templates/index.js')
+const BASE_PATH = 'docs'
+const PAGE_EXTENSIONS = ['.md', '.mdx']
+
+function replacePath(pagePath) {
+    return pagePath === `/` ? pagePath : pagePath.replace(/\/$/, ``)
+}
+
+function getNodeTitle({ childMdx }) {
+    const frontmatter = (childMdx || {}).frontmatter || {}
+    return (frontmatter.title || '').replace("'", '’')
+}
+
+exports.createPages = ({ graphql, actions }) => {
+    const { createPage } = actions
+
+    return new Promise((resolve, reject) => {
+        resolve(
+            graphql(
+                `
+                    {
+                        site {
+                            siteMetadata {
+                                sections {
+                                    id
+                                    title
+                                    theme
+                                }
+                                languages {
+                                    code
+                                    name
+                                    models
+                                    example
+                                    has_examples
+                                }
+                                universe {
+                                    resources {
+                                        id
+                                        title
+                                    }
+                                    categories {
+                                        label
+                                        items {
+                                            id
+                                            title
+                                            description
+                                        }
+                                    }
+                                }
+                            }
+                        }
+                        allFile(filter: { ext: { in: [".md", ".mdx"] } }) {
+                            edges {
+                                node {
+                                    base
+                                    ext
+                                    name
+                                    relativeDirectory
+                                    absolutePath
+                                    childMdx {
+                                        code {
+                                            scope
+                                        }
+                                        frontmatter {
+                                            title
+                                            teaser
+                                            source
+                                            tag
+                                            new
+                                            next
+                                            menu
+                                            sidebar {
+                                                label
+                                                items {
+                                                    text
+                                                    url
+                                                }
+                                            }
+                                            section
+                                        }
+                                    }
+                                    fields {
+                                        id
+                                        slug
+                                        fileName
+                                    }
+                                }
+                            }
+                        }
+                    }
+                `
+            ).then(result => {
+                if (result.errors) {
+                    console.log(result.errors)
+                    reject(result.errors)
+                }
+
+                const sectionData = result.data.site.siteMetadata.sections
+                const sections = Object.assign({}, ...sectionData.map(s => ({ [s.id]: s })))
+
+                /* Regular pages */
+
+                const pages = result.data.allFile.edges
+                pages.forEach(page => {
+                    const { name } = path.parse(page.node.absolutePath)
+                    if (!name.startsWith('_')) {
+                        const mdx = page.node.childMdx || {}
+                        const frontmatter = mdx.frontmatter || {}
+                        const section = frontmatter.section || page.node.relativeDirectory
+                        const sectionMeta = sections[section] || {}
+                        const title = getNodeTitle(page.node)
+                        const next = frontmatter.next
+                            ? pages.find(({ node }) => node.fields.slug === frontmatter.next)
+                            : null
+
+                        createPage({
+                            path: replacePath(page.node.fields.slug),
+                            component: DEFAULT_TEMPLATE,
+                            context: {
+                                id: page.node.id,
+                                slug: page.node.fields.slug,
+                                isIndex: page.node.fields.fileName === 'index',
+                                title,
+                                section,
+                                sectionTitle: sectionMeta.title,
+                                menu: frontmatter.menu || [],
+                                teaser: frontmatter.teaser,
+                                source: frontmatter.source,
+                                sidebar: frontmatter.sidebar,
+                                tag: frontmatter.tag,
+                                version: frontmatter.new,
+                                theme: sectionMeta.theme,
+                                relativePath: page.node.relativePath,
+                                next: next
+                                    ? {
+                                          title: getNodeTitle(next.node),
+                                          slug: next.node.fields.slug,
+                                      }
+                                    : null,
+                            },
+                        })
+                    }
+                })
+
+                /* Universe */
+
+                const universeContext = {
+                    section: 'universe',
+                    sectionTitle: sections.universe.title,
+                    theme: sections.universe.theme,
+                }
+
+                createPage({
+                    path: '/universe',
+                    component: DEFAULT_TEMPLATE,
+                    context: {
+                        slug: '/universe',
+                        isIndex: true,
+                        title: 'Overview',
+                        ...universeContext,
+                    },
+                })
+
+                const universe = result.data.site.siteMetadata.universe.resources
+                universe.forEach(page => {
+                    const slug = `/universe/project/${page.id}`
+
+                    createPage({
+                        path: slug,
+                        component: DEFAULT_TEMPLATE,
+                        context: {
+                            id: page.id,
+                            slug: slug,
+                            isIndex: false,
+                            title: page.title,
+                            data: { ...page, isProject: true },
+                            ...universeContext,
+                        },
+                    })
+                })
+
+                const universeCategories = result.data.site.siteMetadata.universe.categories
+                const categories = [].concat.apply([], universeCategories.map(cat => cat.items))
+                categories.forEach(page => {
+                    const slug = `/universe/category/${page.id}`
+
+                    createPage({
+                        path: slug,
+                        component: DEFAULT_TEMPLATE,
+                        context: {
+                            id: page.id,
+                            slug: slug,
+                            isIndex: false,
+                            title: page.title,
+                            teaser: page.description,
+                            data: { ...page, isCategory: true },
+                            ...universeContext,
+                        },
+                    })
+                })
+
+                /* Models */
+
+                const langs = result.data.site.siteMetadata.languages
+                const modelLangs = langs.filter(({ models }) => models && models.length)
+                modelLangs.forEach(({ code, name, models, example, has_examples }, i) => {
+                    const slug = `/models/${code}`
+                    const next = i < modelLangs.length - 1 ? modelLangs[i + 1] : null
+                    createPage({
+                        path: slug,
+                        component: DEFAULT_TEMPLATE,
+                        context: {
+                            id: code,
+                            slug: slug,
+                            isIndex: false,
+                            title: name,
+                            section: 'models',
+                            sectionTitle: sections.models.title,
+                            theme: sections.models.theme,
+                            next: next ? { title: next.name, slug: `/models/${next.code}` } : null,
+                            meta: { models, example, hasExamples: has_examples },
+                        },
+                    })
+                })
+            })
+        )
+    })
+}
+
+exports.onCreateNode = ({ node, actions, getNode }) => {
+    const { createNodeField } = actions
+    if (PAGE_EXTENSIONS.includes(node.ext)) {
+        const slug = createFilePath({ node, getNode, basePath: BASE_PATH, trailingSlash: false })
+        const { name } = path.parse(node.absolutePath)
+        createNodeField({ name: 'fileName', node, value: name })
+        createNodeField({ name: 'slug', node, value: slug })
+        createNodeField({ name: 'id', node, value: node.id })
+    }
+}
+
+exports.onCreateWebpackConfig = ({ stage, loaders, actions }) => {
+    // Support relative paths in MDX components
+    actions.setWebpackConfig({
+        resolve: {
+            modules: [
+                path.resolve(__dirname),
+                path.resolve(__dirname, 'docs'),
+                path.resolve(__dirname, 'src'),
+                'node_modules',
+            ],
+        },
+        module: {
+            rules: [
+                {
+                    test: /\.(html|svg)$/,
+                    use: 'raw-loader',
+                },
+            ],
+        },
+    })
+    if (stage === 'build-javascript') {
+        // Turn off source maps
+        actions.setWebpackConfig({
+            devtool: false,
+        })
+    }
+}
diff --git a/website/index.jade b/website/index.jade
deleted file mode 100644
index 41f724a1e..000000000
--- a/website/index.jade
+++ /dev/null
@@ -1,162 +0,0 @@
-//- 💫 LANDING PAGE
-
-include _includes/_mixins
-
-+landing-header
-    h1.c-landing__title.u-heading-0
-        | Industrial-Strength#[br]
-        | Natural Language#[br]
-        | Processing
-
-    h2.c-landing__title.o-block.u-heading-3
-        span.u-text-label.u-text-label--light in Python
-
-+grid.o-content.c-landing__blocks
-    +grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
-        +h(3) Fastest in the world
-        p
-            |  spaCy excels at large-scale information extraction tasks.
-            |  It's written from the ground up in carefully memory-managed
-            |  Cython. Independent research has confirmed that spaCy is
-            |  the fastest in the world.  If your application needs to
-            |  process entire web dumps, spaCy is the library you want to
-            |  be using.
-
-        +button("/usage/facts-figures", true, "primary")
-            |  Facts & figures
-
-    +grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
-        +h(3) Get things done
-        p
-            |  spaCy is designed to help you do real work — to build real
-            |  products, or gather real insights. The library respects
-            |  your time, and tries to avoid wasting it. It's easy to
-            |  install, and its API is simple and productive. We like to
-            |  think of spaCy as the Ruby on Rails of Natural Language
-            |  Processing.
-
-        +button("/usage", true, "primary")
-            |  Get started
-
-    +grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
-        +h(3) Deep learning
-        p
-            |  spaCy is the best way to prepare text for deep learning.
-            |  It interoperates seamlessly with TensorFlow, PyTorch,
-            |  scikit-learn, Gensim and the rest of Python's awesome AI
-            |  ecosystem. With spaCy, you can easily construct linguistically
-            |  sophisticated statistical models for a variety of NLP problems.
-
-        +button("/usage/training", true, "primary")
-            |  Read more
-
-.o-content
-    +grid
-        +grid-col("two-thirds")
-            +code-exec("Edit the code & try spaCy", true).
-                # pip install spacy
-                # python -m spacy download en_core_web_sm
-
-                import spacy
-
-                # Load English tokenizer, tagger, parser, NER and word vectors
-                nlp = spacy.load('en_core_web_sm')
-
-                # Process whole documents
-                text = (u"When Sebastian Thrun started working on self-driving cars at "
-                        u"Google in 2007, few people outside of the company took him "
-                        u"seriously. “I can tell you very senior CEOs of major American "
-                        u"car companies would shake my hand and turn away because I wasn’t "
-                        u"worth talking to,” said Thrun, now the co-founder and CEO of "
-                        u"online higher education startup Udacity, in an interview with "
-                        u"Recode earlier this week.")
-                doc = nlp(text)
-
-                # Find named entities, phrases and concepts
-                for entity in doc.ents:
-                    print(entity.text, entity.label_)
-
-                # Determine semantic similarities
-                doc1 = nlp(u"my fries were super gross")
-                doc2 = nlp(u"such disgusting fries")
-                similarity = doc1.similarity(doc2)
-                print(doc1.text, doc2.text, similarity)
-
-        +grid-col("third")
-            +h(2) Features
-            +list
-                +item Non-destructive #[strong tokenization]
-                +item #[strong Named entity] recognition
-                +item Support for #[strong #{LANG_COUNT}+ languages]
-                +item #[strong #{MODEL_COUNT} statistical models] for #{MODEL_LANG_COUNT} languages
-                +item Pre-trained #[strong word vectors]
-                +item Easy #[strong deep learning] integration
-                +item Part-of-speech tagging
-                +item Labelled dependency parsing
-                +item Syntax-driven sentence segmentation
-                +item Built in #[strong visualizers] for syntax and NER
-                +item Convenient string-to-hash mapping
-                +item Export to numpy data arrays
-                +item Efficient binary serialization
-                +item Easy #[strong model packaging] and deployment
-                +item State-of-the-art speed
-                +item Robust, rigorously evaluated accuracy
-
-+landing-banner("Convolutional neural network models", "New in v2.0")
-    p
-        |  spaCy v2.0 features new neural models for #[strong tagging],
-        |  #[strong parsing] and #[strong entity recognition]. The models have
-        |  been designed and implemented from scratch specifically for spaCy, to
-        |  give you an unmatched balance of speed, size and accuracy. A novel
-        |  bloom embedding strategy with subword features is used to support
-        |  huge vocabularies in tiny tables. Convolutional layers with residual
-        |  connections, layer normalization and maxout non-linearity are used,
-        |  giving much better efficiency than the standard BiLSTM solution.
-        |  Finally, the parser and NER use an imitation learning objective to
-        |  deliver accuracy in-line with the latest research systems,
-        |  even when  evaluated from raw text. With these innovations, spaCy
-        |  v2.0's models are #[strong 10&times; smaller],
-        |  #[strong 20% more accurate], and #[strong even cheaper to run] than
-        |  the previous generation.
-
-    .o-block-small.u-text-right
-        +button("/models", true, "secondary-light") Download models
-
-+landing-logos("spaCy is trusted by", logos)
-    +button(gh("spacy") + "/stargazers", false, "secondary", "small")
-        |  and many more
-
-+landing-logos("Featured on", features).o-block-small
-
-+landing-banner("Prodigy: Radically efficient machine teaching", "From the makers of spaCy")
-    p
-        |  Prodigy is an #[strong annotation tool] so efficient that data scientists can
-        |  do the annotation themselves, enabling a new level of rapid
-        |  iteration. Whether you're working on entity recognition, intent
-        |  detection or image classification, Prodigy can help you
-        |  #[strong train and evaluate] your models faster. Stream in your own examples or
-        |  real-world data from live APIs, update your model in real-time and
-        |  chain models together to build more complex systems.
-
-    .o-block-small.u-text-right
-        +button("https://prodi.gy", true, "secondary-light") Try it out
-
-.o-content
-    +grid
-        +grid-col("half")
-            +h(2) Benchmarks
-
-            p
-                |  In 2015, independent researchers from Emory University and
-                |  Yahoo! Labs showed that spaCy offered the
-                |  #[strong fastest syntactic parser in the world] and that its
-                |  accuracy was #[strong within 1% of the best] available
-                |  (#[+a("https://aclweb.org/anthology/P/P15/P15-1038.pdf") Choi et al., 2015]).
-                |  spaCy v2.0, released in 2017, is more accurate than any of
-                |  the systems Choi et al. evaluated.
-
-            .o-inline-list
-                +button("/usage/facts-figures#benchmarks", true, "secondary") See details
-
-        +grid-col("half")
-            include usage/_facts-figures/_benchmarks-choi-2015
diff --git a/website/meta/languages.json b/website/meta/languages.json
new file mode 100644
index 000000000..ed3f087df
--- /dev/null
+++ b/website/meta/languages.json
@@ -0,0 +1,165 @@
+{
+    "languages": [
+        {
+            "code": "en",
+            "name": "English",
+            "models": ["en_core_web_sm", "en_core_web_md", "en_core_web_lg", "en_vectors_web_lg"],
+            "example": "This is a sentence.",
+            "has_examples": true
+        },
+        {
+            "code": "de",
+            "name": "German",
+            "models": ["de_core_news_sm", "de_core_news_md"],
+            "example": "Dies ist ein Satz.",
+            "has_examples": true
+        },
+        {
+            "code": "fr",
+            "name": "French",
+            "models": ["fr_core_news_sm", "fr_core_news_md"],
+            "example": "C'est une phrase.",
+            "has_examples": true
+        },
+        {
+            "code": "es",
+            "name": "Spanish",
+            "models": ["es_core_news_sm", "es_core_news_md"],
+            "example": "Esto es una frase.",
+            "has_examples": true
+        },
+        {
+            "code": "pt",
+            "name": "Portuguese",
+            "models": ["pt_core_news_sm"],
+            "example": "Esta é uma frase.",
+            "has_examples": true
+        },
+        {
+            "code": "it",
+            "name": "Italian",
+            "models": ["it_core_news_sm"],
+            "example": "Questa è una frase.",
+            "has_examples": true
+        },
+        {
+            "code": "nl",
+            "name": "Dutch",
+            "models": ["nl_core_news_sm"],
+            "example": "Dit is een zin.",
+            "has_examples": true
+        },
+        {
+            "code": "el",
+            "name": "Greek",
+            "models": ["el_core_news_sm", "el_core_news_md"],
+            "example": "Αυτή είναι μια πρόταση.",
+            "has_examples": true
+        },
+        {
+            "code": "xx",
+            "name": "Multi-language",
+            "models": ["xx_ent_wiki_sm"],
+            "example": "This is a sentence about Facebook."
+        },
+        { "code": "sv", "name": "Swedish", "has_examples": true },
+        { "code": "fi", "name": "Finnish", "has_examples": true },
+        {
+            "code": "nb",
+            "name": "Norwegian Bokmål",
+            "example": "Dette er en setning.",
+            "has_examples": true
+        },
+        { "code": "da", "name": "Danish", "example": "Dette er en sætning.", "has_examples": true },
+        { "code": "hu", "name": "Hungarian", "example": "Ez egy mondat.", "has_examples": true },
+        { "code": "pl", "name": "Polish", "example": "To jest zdanie.", "has_examples": true },
+        {
+            "code": "ru",
+            "name": "Russian",
+            "has_examples": true,
+            "dependencies": [{ "name": "pymorphy2", "url": "https://github.com/kmike/pymorphy2" }]
+        },
+        {
+            "code": "uk",
+            "name": "Ukrainian",
+            "has_examples": true,
+            "dependencies": [{ "name": "pymorphy2", "url": "https://github.com/kmike/pymorphy2" }]
+        },
+        {
+            "code": "ro",
+            "name": "Romanian",
+            "example": "Aceasta este o propoziție.",
+            "has_examples": true
+        },
+        { "code": "hr", "name": "Croatian" },
+        { "code": "tr", "name": "Turkish", "example": "Bu bir cümledir.", "has_examples": true },
+        { "code": "ca", "name": "Catalan", "example": "Això és una frase.", "has_examples": true },
+        { "code": "he", "name": "Hebrew", "example": "זהו משפט.", "has_examples": true },
+        { "code": "ar", "name": "Arabic", "has_examples": true },
+        { "code": "fa", "name": "Persian", "has_examples": true },
+        { "code": "ur", "name": "Urdu", "example": "یہ ایک جملہ ہے", "has_examples": true },
+        { "code": "tt", "name": "Tatar", "has_examples": true },
+        { "code": "te", "name": "Telugu", "example": "ఇది ఒక వాక్యం.", "has_examples": true },
+        { "code": "si", "name": "Sinhala", "example": "මෙය වාක්‍යයකි.", "has_examples": true },
+        { "code": "ga", "name": "Irish" },
+        { "code": "bn", "name": "Bengali" },
+        { "code": "hi", "name": "Hindi", "example": "यह एक वाक्य है।", "has_examples": true },
+        { "code": "kn", "name": "Kannada" },
+        { "code": "ta", "name": "Tamil", "has_examples": true },
+        { "code": "id", "name": "Indonesian", "has_examples": true },
+        { "code": "tl", "name": "Tagalog" },
+        { "code": "af", "name": "Afrikaans" },
+        { "code": "bg", "name": "Bulgarian" },
+        { "code": "cs", "name": "Czech" },
+        { "code": "is", "name": "Icelandic" },
+        { "code": "lt", "name": "Lithuanian" },
+        { "code": "lv", "name": "Latvian" },
+        { "code": "sk", "name": "Slovak" },
+        { "code": "sl", "name": "Slovenian" },
+        { "code": "sq", "name": "Albanian" },
+        {
+            "code": "th",
+            "name": "Thai",
+            "dependencies": [
+                { "name": "pythainlp", "url": "https://github.com/wannaphongcom/pythainlp" }
+            ],
+            "example": "นี่คือประโยค",
+            "has_examples": true
+        },
+        {
+            "code": "zh",
+            "name": "Chinese",
+            "dependencies": [{ "name": "Jieba", "url": "https://github.com/fxsjy/jieba" }],
+            "has_examples": true
+        },
+        {
+            "code": "ja",
+            "name": "Japanese",
+            "dependencies": [
+                { "name": "Unidic", "url": "http://unidic.ninjal.ac.jp/back_number#unidic_cwj" },
+                { "name": "Mecab", "url": "https://github.com/taku910/mecab" }
+            ],
+            "example": "これは文章です。",
+            "has_examples": true
+        },
+        {
+            "code": "vi",
+            "name": "Vietnamese",
+            "dependencies": [{ "name": "Pyvi", "url": "https://github.com/trungtv/pyvi" }]
+        }
+    ],
+    "licenses": [
+        { "id": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/" },
+        { "id": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/" },
+        { "id": "CC BY-SA", "url": "https://creativecommons.org/licenses/by-sa/3.0/" },
+        { "id": "CC BY-SA 3.0", "url": "https://creativecommons.org/licenses/by-sa/3.0/" },
+        { "id": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/" },
+        { "id": "CC BY-NC", "url": "https://creativecommons.org/licenses/by-nc/3.0/" },
+        { "id": "CC BY-NC 3.0", "url": "https://creativecommons.org/licenses/by-nc/3.0/" },
+        { "id": "CC BY-NC 4.0", "url": "https://creativecommons.org/licenses/by-nc/4.0/" },
+        { "id": "CC-BY-NC-SA 3.0", "url": "https://creativecommons.org/licenses/by-nc-sa/3.0/" },
+        { "id": "GPL", "url": "https://www.gnu.org/licenses/gpl.html" },
+        { "id": "LGPL", "url": "https://www.gnu.org/licenses/lgpl.html" },
+        { "id": "MIT", "url": "https://opensource.org/licenses/MIT" }
+    ]
+}
diff --git a/website/meta/logos.json b/website/meta/logos.json
new file mode 100644
index 000000000..acfe5a260
--- /dev/null
+++ b/website/meta/logos.json
@@ -0,0 +1,36 @@
+{
+    "logosUsers": [
+        { "id": "airbnb", "url": "https://www.airbnb.com" },
+        { "id": "quora", "url": "https://www.quora.com" },
+        { "id": "retriever", "url": "https://www.retriever.no" },
+        { "id": "stitchfix", "url": "https://www.stitchfix.com" },
+        { "id": "chartbeat", "url": "https://chartbeat.com" },
+        { "id": "allenai", "url": "https://appenai.org" }
+    ],
+    "logosPublications": [
+        {
+            "id": "recode",
+            "url": "https://www.recode.net/2017/6/22/15855492/ai-artificial-intelligence-nonprofit-good-human-chatbots-machine-learning"
+        },
+        {
+            "id": "wapo",
+            "url": "https://www.washingtonpost.com/news/wonk/wp/2016/05/18/googles-new-artificial-intelligence-cant-understand-these-sentences-can-you/"
+        },
+        {
+            "id": "bbc",
+            "url": "http://www.bbc.co.uk/rd/blog/2017-08-irfs-weeknotes-number-250"
+        },
+        {
+            "id": "microsoft",
+            "url": "https://www.microsoft.com/developerblog/2016/09/13/training-a-classifier-for-relation-extraction-from-medical-literature/"
+        },
+        {
+            "id": "venturebeat",
+            "url": "https://venturebeat.com/2017/01/27/4-ai-startups-that-analyze-customer-reviews/"
+        },
+        {
+            "id": "thoughtworks",
+            "url": "https://www.thoughtworks.com/radar/tools"
+        }
+    ]
+}
diff --git a/website/meta/sidebars.json b/website/meta/sidebars.json
new file mode 100644
index 000000000..fb4075ee5
--- /dev/null
+++ b/website/meta/sidebars.json
@@ -0,0 +1,106 @@
+[
+    {
+        "section": "usage",
+        "items": [
+            {
+                "label": "Get started",
+                "items": [
+                    { "text": "Installation", "url": "/usage" },
+                    { "text": "Models & Languages", "url": "/usage/models" },
+                    { "text": "Facts & Figures", "url": "/usage/facts-figures" },
+                    { "text": "spaCy 101", "url": "/usage/spacy-101" },
+                    { "text": "New in v2.1", "url": "/usage/v2-1" },
+                    { "text": "New in v2.0", "url": "/usage/v2" }
+                ]
+            },
+            {
+                "label": "Guides",
+                "items": [
+                    { "text": "Linguistic Features", "url": "/usage/linguistic-features" },
+                    { "text": "Rule-based Matching", "url": "/usage/rule-based-matching" },
+                    { "text": "Processing Pipelines", "url": "/usage/processing-pipelines" },
+                    { "text": "Vectors & Similarity", "url": "/usage/vectors-similarity" },
+                    { "text": "Training Models", "url": "/usage/training" },
+                    { "text": "Saving & Loading", "url": "/usage/saving-loading" },
+                    { "text": "Adding Languages", "url": "/usage/adding-languages" },
+                    { "text": "Visualizers", "url": "/usage/visualizers" }
+                ]
+            },
+            {
+                "label": "In-depth",
+                "items": [{ "text": "Code Examples", "url": "/usage/examples" }]
+            }
+        ]
+    },
+    {
+        "section": "models",
+        "items": [
+            {
+                "label": "Models",
+                "items": [{ "text": "Overview", "url": "/models" }]
+            },
+            {
+                "label": "Language Models",
+                "items": []
+            }
+        ]
+    },
+    {
+        "section": "api",
+        "items": [
+            {
+                "label": "Overview",
+                "items": [
+                    { "text": "Architecture", "url": "/api" },
+                    { "text": "Annotation Specs", "url": "/api/annotation" },
+                    { "text": "Command Line", "url": "/api/cli" },
+                    { "text": "Functions", "url": "/api/top-level" }
+                ]
+            },
+            {
+                "label": "Containers",
+                "items": [
+                    { "text": "Doc", "url": "/api/doc" },
+                    { "text": "Token", "url": "/api/token" },
+                    { "text": "Span", "url": "/api/span" },
+                    { "text": "Lexeme", "url": "/api/lexeme" }
+                ]
+            },
+            {
+                "label": "Pipeline",
+                "items": [
+                    { "text": "Language", "url": "/api/language" },
+                    { "text": "Tokenizer", "url": "/api/tokenizer" },
+                    { "text": "Lemmatizer", "url": "/api/lemmatizer" },
+                    { "text": "Tagger", "url": "/api/tagger" },
+                    { "text": "DependencyParser", "url": "/api/dependencyparser" },
+                    { "text": "EntityRecognizer", "url": "/api/entityrecognizer" },
+                    { "text": "TextCategorizer", "url": "/api/textcategorizer" },
+                    { "text": "Matcher", "url": "/api/matcher" },
+                    { "text": "PhraseMatcher", "url": "/api/phrasematcher" },
+                    { "text": "EntityRuler", "url": "/api/entityruler" },
+                    { "text": "SentenceSegmenter", "url": "/api/sentencesegmenter" },
+                    { "text": "Other Functions", "url": "/api/pipeline-functions" }
+                ]
+            },
+            {
+                "label": "Other",
+                "items": [
+                    { "text": "Vocab", "url": "/api/vocab" },
+                    { "text": "StringStore", "url": "/api/stringstore" },
+                    { "text": "Vectors", "url": "/api/vectors" },
+                    { "text": "GoldParse", "url": "/api/goldparse" },
+                    { "text": "GoldCorpus", "url": "/api/goldcorpus" }
+                ]
+            },
+            {
+                "label": "Cython",
+                "items": [
+                    { "text": "Architecture", "url": "/api/cython" },
+                    { "text": "Structs", "url": "/api/cython-structs" },
+                    { "text": "Classes", "url": "/api/cython-classes" }
+                ]
+            }
+        ]
+    }
+]
diff --git a/website/meta/site.json b/website/meta/site.json
new file mode 100644
index 000000000..a77d73dfe
--- /dev/null
+++ b/website/meta/site.json
@@ -0,0 +1,72 @@
+{
+    "title": "spaCy",
+    "description": "spaCy is a free open-source library for Natural Language Processing in Python. It features NER, POS tagging, dependency parsing, word vectors and more.",
+    "slogan": "Industrial-strength Natural Language Processing in Python",
+    "siteUrl": "https://spacy.io",
+    "email": "contact@explosion.ai",
+    "company": "Explosion AI",
+    "companyUrl": "https://explosion.ai",
+    "repo": "explosion/spaCy",
+    "modelsRepo": "explosion/spacy-models",
+    "social": {
+        "twitter": "spacy_io",
+        "github": "explosion",
+        "reddit": "spacynlp",
+        "codepen": "explosion",
+        "gitter": "explosion/spaCy"
+    },
+    "theme": "#09a3d5",
+    "analytics": "UA-58931649-1",
+    "newsletter": {
+        "user": "spacy.us12",
+        "id": "83b0498b1e7fa3c91ce68c3f1",
+        "list": "89ad33e698"
+    },
+    "spacyVersion": "2.1",
+    "binderUrl": "ines/spacy-io-binder",
+    "binderBranch": "nightly",
+    "binderVersion": "2.1.0a5",
+    "sections": [
+        { "id": "usage", "title": "Usage Documentation", "theme": "blue" },
+        { "id": "models", "title": "Models Documentation", "theme": "blue" },
+        { "id": "api", "title": "API Documentation", "theme": "green" },
+        { "id": "universe", "title": "Universe", "theme": "purple" }
+    ],
+    "navigation": [
+        { "text": "Usage", "url": "/usage" },
+        { "text": "Models", "url": "/models" },
+        { "text": "API", "url": "/api" },
+        { "text": "Universe", "url": "/universe" }
+    ],
+    "footer": [
+        {
+            "label": "spaCy",
+            "items": [
+                { "text": "Usage", "url": "/usage" },
+                { "text": "Models", "url": "/models" },
+                { "text": "API", "url": "/api" },
+                { "text": "Universe", "url": "/universe" }
+            ]
+        },
+        {
+            "label": "Support",
+            "items": [
+                { "text": "Issue Tracker", "url": "https://github.com/explosion/spaCy/issues" },
+                {
+                    "text": "Stack Overflow",
+                    "url": "http://stackoverflow.com/questions/tagged/spacy"
+                },
+                { "text": "Reddit User Group", "url": "https://www.reddit.com/r/spacynlp/" },
+                { "text": "Gitter Chat", "url": "https://gitter.im/explosion/spaCy" }
+            ]
+        },
+        {
+            "label": "Connect",
+            "items": [
+                { "text": "Twitter", "url": "https://twitter.com/spacy_io" },
+                { "text": "GitHub", "url": "https://github.com/explosion/spaCy" },
+                { "text": "Blog", "url": "https://explosion.ai/blog" }
+            ]
+        }
+    ]
+}
diff --git a/website/universe/universe.json b/website/meta/universe.json
similarity index 87%
rename from website/universe/universe.json
rename to website/meta/universe.json
index 414df4a74..c1635a875 100644
--- a/website/universe/universe.json
+++ b/website/meta/universe.json
@@ -678,7 +678,6 @@
                 "twitter": "jekbradbury"
             },
             "category": ["nonpython"]
-
         },
         {
             "id": "spacy_api",
@@ -827,6 +826,17 @@
             "author": "Dipanjan Sarkar, Raghav Bali, Tushar Sharma",
             "category": ["books"]
         },
+        {
+            "type": "education",
+            "id": "packt-nlp-computational-linguistics",
+            "title": "Natural Language Processing and Computational Linguistics",
+            "slogan": "Packt, 2018",
+            "description": "This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.",
+            "cover": "https://i.imgur.com/aleMf1Y.jpg",
+            "url": "https://www.amazon.com/Natural-Language-Processing-Computational-Linguistics-ebook/dp/B07BWH779J",
+            "author": "Bhargav Srinivasa-Desikan",
+            "category": ["books"]
+        },
         {
             "type": "education",
             "id": "datacamp-nlp-fundamentals",
@@ -852,6 +862,75 @@
             "author": "Aaron Kramer",
             "category": ["courses"]
         },
+        {
+            "type": "education",
+            "id": "video-spacys-ner-model",
+            "title": "spaCy's NER model",
+            "slogan": "Incremental parsing with bloom embeddings and residual CNNs",
+            "description": "spaCy v2.0's Named Entity Recognition system features a sophisticated word embedding strategy using subword features and \"Bloom\" embeddings, a deep convolutional neural network with residual connections, and a novel transition-based approach to named entity parsing. The system is designed to give a good balance of efficiency, accuracy and adaptability. In this talk, I sketch out the components of the system, explaining the intuition behind the various choices. I also give a brief introduction to the named entity recognition problem, with an overview of what else Explosion AI is working on, and why.",
+            "youtube": "sqDHBH9IjRU",
+            "author": "Matthew Honnibal",
+            "author_links": {
+                "twitter": "honnibal",
+                "github": "honnibal",
+                "website": "https://explosion.ai"
+            },
+            "category": ["videos"]
+        },
+        {
+            "type": "education",
+            "id": "video-new-nlp-solutions",
+            "title": "Building new NLP solutions with spaCy and Prodigy",
+            "slogan": "PyData Berlin 2018",
+            "description": "In this talk, I will discuss how to address some of the most likely causes of failure for new Natural Language Processing (NLP) projects. My main recommendation is to take an iterative approach: don't assume you know what your pipeline should look like, let alone your annotation schemes or model architectures.",
+            "author": "Matthew Honnibal",
+            "author_links": {
+                "twitter": "honnibal",
+                "github": "honnibal",
+                "website": "https://explosion.ai"
+            },
+            "youtube": "jpWqz85F_4Y",
+            "category": ["videos"]
+        },
+        {
+            "type": "education",
+            "id": "video-modern-nlp-in-python",
+            "title": "Modern NLP in Python",
+            "slogan": "PyData DC 2016",
+            "description": "Academic and industry research in Natural Language Processing (NLP) has progressed at an accelerating pace over the last several years. Members of the Python community have been hard at work moving cutting-edge research out of papers and into open source, \"batteries included\" software libraries that can be applied to practical problems. We'll explore some of these tools for modern NLP in Python.",
+            "author": "Patrick Harrison",
+            "youtube": "6zm9NC9uRkk",
+            "category": ["videos"]
+        },
+        {
+            "type": "education",
+            "id": "podcast-nlp-highlights",
+            "title": "NLP Highlights 78: Where do corpora come from?",
+            "slogan": "January 2019",
+            "description": "Most NLP projects rely crucially on the quality of annotations used for training and evaluating models. In this episode, Matt and Ines of Explosion AI tell us how Prodigy can improve data annotation and model development workflows. Prodigy is an annotation tool implemented as a python library, and it comes with a web application and a command line interface. A developer can define input data streams and design simple annotation interfaces. Prodigy can help break down complex annotation decisions into a series of binary decisions, and it provides easy integration with spaCy models. Developers can specify how models should be modified as new annotations come in in an active learning framework.",
+            "soundcloud": "559200912",
+            "thumb": "https://i.imgur.com/hOBQEzc.jpg",
+            "author": "Matt Gardner, Waleed Ammar (Allen AI)",
+            "author_links": {
+                "website": "https://soundcloud.com/nlp-highlights"
+            },
+            "category": ["podcasts"]
+        },
+        {
+            "type": "education",
+            "id": "podcast-init",
+            "title": "Podcast.__init__ 87: spaCy with Matthew Honnibal",
+            "slogan": "December 2017",
+            "description": "As the amount of text available on the internet and in businesses continues to increase, the need for fast and accurate language analysis becomes more prominent. This week Matthew Honnibal, the creator of SpaCy, talks about his experiences researching natural language processing and creating a library to make his findings accessible to industry.",
+            "iframe": "https://www.pythonpodcast.com/wp-content/plugins/podlove-podcasting-plugin-for-wordpress/lib/modules/podlove_web_player/player_v4/dist/share.html?episode=https://www.pythonpodcast.com/?podlove_player4=176",
+            "iframe_height": 200,
+            "thumb": "https://i.imgur.com/rpo6BuY.png",
+            "author": "Tobias Macey",
+            "author_links": {
+                "website": "https://www.podcastinit.com"
+            },
+            "category": ["podcasts"]
+        },
         {
             "id": "adam_qas",
             "title": "ADAM: Question Answering System",
@@ -873,12 +952,12 @@
                 "website": "https://shirishkadam.com/"
             },
             "category": ["standalone"],
-            "tags": [ "question-answering", "elasticsearch"]
+            "tags": ["question-answering", "elasticsearch"]
         },
         {
             "id": "epitator",
             "title": "EpiTator",
-            "thumb": "http://apps.eha.io/images/eha-logo.jpg",
+            "thumb": "https://i.imgur.com/NYFY1Km.jpg",
             "slogan": "Extracts case counts, resolved location/species/disease names, date ranges and more",
             "description": "EcoHealth Alliance uses EpiTator to catalog the what, where and when of infectious disease case counts reported in online news. Each of these aspects is extracted using independent annotators than can be applied to other domains. EpiTator organizes annotations by creating \"AnnoTiers\" for each type. AnnoTiers have methods for manipulating, combining and searching annotations. For instance, the `with_following_spans_from()` method can be used to create a new tier that combines a tier of one type (such as numbers), with another (say, kitchenware). The resulting tier will contain all the phrases in the document that match that pattern, like \"5 plates\" or \"2 cups.\"\n\nAnother commonly used method is `group_spans_by_containing_span()` which can be used to do things like find all the spaCy tokens in all the GeoNames a document mentions. spaCy tokens, named entities, sentences and noun chunks are exposed through the spaCy annotator which will create a AnnoTier for each. These are basis of many of the other annotators. EpiTator also includes an annotator for extracting tables embedded in free text articles. Another neat feature is that the lexicons used for entity resolution are all stored in an embedded sqlite database so there is no need to run any external services in order to use EpiTator.",
             "url": "https://github.com/ecohealthalliance/EpiTator",
@@ -1104,14 +1183,13 @@
             "author_links": {
                 "github": "BramVanroy",
                 "website": "https://bramvanroy.be"
-
             },
             "github": "BramVanroy/spacy_conll",
             "category": ["standalone"]
         },
         {
             "id": "spacy-langdetect",
-            "title": "spacy-langdetect" ,
+            "title": "spacy-langdetect",
             "slogan": "A fully customizable language detection pipeline for spaCy",
             "description": "This module allows you to add language detection capabilites to your spaCy pipeline. Also supports custom language detectors!",
             "pip": "spacy-langdetect",
@@ -1139,48 +1217,76 @@
             "tags": ["language-detection"]
         }
     ],
-    "projectCats": {
-        "pipeline": {
-            "title": "Pipeline",
-            "description": "Custom pipeline components and extensions"
+    "categories": [
+        {
+            "label": "Projects",
+            "items": [
+                {
+                    "id": "pipeline",
+                    "title": "Pipeline",
+                    "description": "Custom pipeline components and extensions"
+                },
+                {
+                    "id": "training",
+                    "title": "Training",
+                    "description": "Helpers and toolkits for training spaCy models"
+                },
+                {
+                    "id": "conversational",
+                    "title": "Conversational",
+                    "description": "Frameworks and utilities for working with conversational text, e.g. for chat bots"
+                },
+                {
+                    "id": "research",
+                    "title": "Research",
+                    "description": "Frameworks and utilities for developing better NLP models, especially using neural networks"
+                },
+                {
+                    "id": "visualizers",
+                    "title": "Visualizers",
+                    "description": "Demos and tools to visualize NLP annotations or systems"
+                },
+                {
+                    "id": "apis",
+                    "title": "Containers & APIs",
+                    "description": "Infrastructure tools for managing or deploying spaCy"
+                },
+                {
+                    "id": "nonpython",
+                    "title": "Non-Python",
+                    "description": "Wrappers, bindings and implementations in other programming languages"
+                },
+                {
+                    "id": "standalone",
+                    "title": "Standalone",
+                    "description": "Self-contained libraries or tools that use spaCy under the hood"
+                }
+            ]
         },
-        "training": {
-            "title": "Training",
-            "description": "Helpers and toolkits for training spaCy models"
-        },
-        "conversational": {
-            "title": "Conversational",
-            "description": "Frameworks and utilities for working with conversational text, e.g. for chat bots"
-        },
-        "research": {
-            "title": "Research",
-            "description": "Frameworks and utilities for developing better NLP models, especially using neural networks"
-        },
-        "visualizers": {
-            "title": "Visualizers",
-            "description": "Demos and tools to visualize NLP annotations or systems"
-        },
-        "apis": {
-            "title": "Containers & APIs",
-            "description": "Infrastructure tools for managing or deploying spaCy"
-        },
-        "nonpython": {
-            "title": "Non-Python",
-            "description": "Wrappers, bindings and implementations in other programming languages"
-        },
-        "standalone": {
-            "title": "Standalone",
-            "description": "Self-contained libraries or tools that use spaCy under the hood"
+        {
+            "label": "Education",
+            "items": [
+                {
+                    "id": "books",
+                    "title": "Books",
+                    "description": "Books about or featuring spaCy"
+                },
+                {
+                    "id": "courses",
+                    "title": "Courses",
+                    "description": "Online courses and interactive tutorials"
+                },
+                {
+                    "id": "videos",
+                    "title": "Videos",
+                    "description": "Talks and tutorials in video format"
+                },
+                {
+                    "id": "podcasts",
+                    "title": "Podcasts",
+                    "description": "Episodes about spaCy or interviews with the spaCy team"
+                }
+            ]
         }
-    },
-    "educationCats": {
-        "books": {
-            "title": "Books",
-            "description": "Books about or featuring spaCy"
-        },
-        "courses": {
-            "title": "Courses",
-            "description": "Online courses and interactive tutorials"
-        }
-    }
+    ]
 }
diff --git a/website/models/_data.json b/website/models/_data.json
deleted file mode 100644
index 7e3fa3715..000000000
--- a/website/models/_data.json
+++ /dev/null
@@ -1,141 +0,0 @@
-{
-    "sidebar": {
-        "Models": {
-            "Overview": "./"
-        },
-
-        "Language models": {
-            "English": "en",
-            "German": "de",
-            "Spanish": "es",
-            "Portuguese": "pt",
-            "French": "fr",
-            "Italian": "it",
-            "Dutch": "nl",
-            "Greek": "el",
-            "Multi-Language": "xx"
-        }
-    },
-
-    "index": {
-        "title": "Models Overview",
-        "teaser": "Downloadable statistical models for spaCy to predict and assign linguistic features.",
-        "quickstart": true,
-        "menu": {
-            "Quickstart": "quickstart",
-            "Installation": "install",
-            "Naming Conventions": "conventions"
-        }
-    },
-
-    "MODELS": {
-        "en": ["en_core_web_sm", "en_core_web_md", "en_core_web_lg", "en_vectors_web_lg"],
-        "de": ["de_core_news_sm", "de_core_news_md"],
-        "es": ["es_core_news_sm", "es_core_news_md"],
-        "pt": ["pt_core_news_sm"],
-        "fr": ["fr_core_news_sm", "fr_core_news_md"],
-        "it": ["it_core_news_sm"],
-        "nl": ["nl_core_news_sm"],
-        "el": ["el_core_news_sm", "el_core_news_md"],
-        "xx": ["xx_ent_wiki_sm"]
-    },
-
-    "MODEL_META": {
-        "core": "Vocabulary, syntax, entities, vectors",
-        "core_sm": "Vocabulary, syntax, entities",
-        "dep": "Vocabulary, syntax",
-        "ent": "Named entities",
-        "vectors": "Word vectors",
-        "web": "written text (blogs, news, comments)",
-        "news": "written text (news, media)",
-        "wiki": "Wikipedia",
-        "uas": "Unlabelled dependencies",
-        "las": "Labelled dependencies",
-        "tags_acc": "Part-of-speech tags (fine grained tags, Token.tag)",
-        "ents_f": "Entities (F-score)",
-        "ents_p": "Entities (precision)",
-        "ents_r": "Entities (recall)",
-        "cpu": "words per second on CPU",
-        "gpu": "words per second on GPU",
-        "pipeline": "Processing pipeline components in order",
-        "sources": "Sources of training data",
-        "vecs": "Word vectors included in the model. Models that only support context vectors compute similarity via the tensors shared with the pipeline.",
-        "benchmark_parser": "Syntax accuracy",
-        "benchmark_ner": "NER accuracy",
-        "benchmark_speed": "Speed",
-        "compat": "Latest compatible model version for your spaCy installation"
-    },
-
-    "MODEL_LICENSES": {
-        "MIT":             "https://opensource.org/licenses/MIT",
-        "CC BY 4.0":       "https://creativecommons.org/licenses/by/4.0/",
-        "CC BY-SA":        "https://creativecommons.org/licenses/by-sa/3.0/",
-        "CC BY-SA 3.0":    "https://creativecommons.org/licenses/by-sa/3.0/",
-        "CC BY-SA 4.0":    "https://creativecommons.org/licenses/by-sa/4.0/",
-        "CC BY-NC":        "https://creativecommons.org/licenses/by-nc/3.0/",
-        "CC BY-NC 3.0":    "https://creativecommons.org/licenses/by-nc/3.0/",
-        "CC BY-NC 4.0":    "https://creativecommons.org/licenses/by-nc/4.0/",
-        "CC-BY-NC-SA 3.0": "https://creativecommons.org/licenses/by-nc-sa/3.0/",
-        "GPL":             "https://www.gnu.org/licenses/gpl.html",
-        "LGPL":            "https://www.gnu.org/licenses/lgpl.html"
-    },
-
-    "MODEL_BENCHMARKS": {
-        "parser": { "uas": "UAS", "las": "LAS", "tags_acc": "POS" },
-        "ner":    { "ents_f": "NER F", "ents_p": "NER P", "ents_r": "NER R" }
-    },
-
-    "EXAMPLE_SENT_LANGS": [
-        "da", "de", "en", "es", "fa", "fr", "he", "hi", "hu", "id", "it", "ja",
-        "nb", "pl", "pt", "ru", "sv", "tr", "zh"
-    ],
-
-    "LANGUAGES": {
-        "en": "English",
-        "de": "German",
-        "fr": "French",
-        "es": "Spanish",
-        "it": "Italian",
-        "pt": "Portuguese",
-        "nl": "Dutch",
-        "sv": "Swedish",
-        "fi": "Finnish",
-        "nb": "Norwegian Bokmål",
-        "da": "Danish",
-        "hu": "Hungarian",
-        "pl": "Polish",
-        "ru": "Russian",
-        "ro": "Romanian",
-        "hr": "Croatian",
-        "tr": "Turkish",
-        "el": "Greek",
-        "he": "Hebrew",
-        "ar": "Arabic",
-        "fa": "Persian",
-        "ur": "Urdu",
-        "tt": "Tatar",
-        "te": "Telugu",
-        "si": "Sinhala",
-        "ga": "Irish",
-        "bn": "Bengali",
-        "hi": "Hindi",
-        "id": "Indonesian",
-        "th": "Thai",
-        "zh": "Chinese",
-        "ja": "Japanese",
-        "vi": "Vietnamese",
-        "ca": "Catalan",
-        "xx": "Multi-language"
-    },
-
-    "EXAMPLE_SENTENCES": {
-        "en": "This is a sentence.",
-        "de": "Dies ist ein Satz.",
-        "fr": "C'est une phrase.",
-        "es": "Esto es una frase.",
-        "pt": "Esta é uma frase.",
-        "it": "Questa è una frase.",
-        "nl": "Dit is een zin.",
-        "xx": "This is a sentence about Facebook."
-    }
-}
diff --git a/website/models/de.jade b/website/models/de.jade
deleted file mode 100644
index 113290b7a..000000000
--- a/website/models/de.jade
+++ /dev/null
@@ -1,6 +0,0 @@
-//- 💫 DOCS > MODELS > DE
-
-include ../_includes/_mixins
-
-//- This is a placeholder. The page is rendered via the template at
-//- /_includes/_page-model.jade.
diff --git a/website/models/el.jade b/website/models/el.jade
deleted file mode 100644
index 901ce80e1..000000000
--- a/website/models/el.jade
+++ /dev/null
@@ -1,6 +0,0 @@
-//- 💫 DOCS > MODELS > EL
-
-include ../_includes/_mixins
-
-//- This is a placeholder. The page is rendered via the template at
-//- /_includes/_page-model.jade.
diff --git a/website/models/en.jade b/website/models/en.jade
deleted file mode 100644
index 4f400662b..000000000
--- a/website/models/en.jade
+++ /dev/null
@@ -1,6 +0,0 @@
-//- 💫 DOCS > MODELS > EN
-
-include ../_includes/_mixins
-
-//- This is a placeholder. The page is rendered via the template at
-//- /_includes/_page-model.jade.
diff --git a/website/models/es.jade b/website/models/es.jade
deleted file mode 100644
index 7aad72e81..000000000
--- a/website/models/es.jade
+++ /dev/null
@@ -1,6 +0,0 @@
-//- 💫 DOCS > MODELS > ES
-
-include ../_includes/_mixins
-
-//- This is a placeholder. The page is rendered via the template at
-//- /_includes/_page-model.jade.
diff --git a/website/models/fr.jade b/website/models/fr.jade
deleted file mode 100644
index 1b3cc3fde..000000000
--- a/website/models/fr.jade
+++ /dev/null
@@ -1,6 +0,0 @@
-//- 💫 DOCS > MODELS > FR
-
-include ../_includes/_mixins
-
-//- This is a placeholder. The page is rendered via the template at
-//- /_includes/_page-model.jade.
diff --git a/website/models/index.jade b/website/models/index.jade
deleted file mode 100644
index 5298da3df..000000000
--- a/website/models/index.jade
+++ /dev/null
@@ -1,102 +0,0 @@
-//- 💫 DOCS > MODELS
-
-include ../_includes/_mixins
-
-+section("quickstart")
-    p
-        |  spaCy v2.0 features new neural models for #[strong tagging],
-        |  #[strong parsing] and #[strong entity recognition]. The models have
-        |  been designed and implemented from scratch specifically for spaCy, to
-        |  give you an unmatched balance of speed, size and accuracy. A novel
-        |  bloom embedding strategy with subword features is used to support
-        |  huge vocabularies in tiny tables. Convolutional layers with residual
-        |  connections, layer normalization and maxout non-linearity are used,
-        |  giving much better efficiency than the standard BiLSTM solution. For
-        |  more details, see the notes on the
-        |  #[+a("/api/#nn-models") model architecture].
-
-    p
-        |  The parser and NER use an imitation learning objective to
-        |  deliver #[strong accuracy in-line with the latest research systems],
-        |  even when  evaluated from raw text. With these innovations, spaCy
-        |  v2.0's models are #[strong 10&times; smaller],
-        |  #[strong 20% more accurate], and #[strong even cheaper to run] than
-        |  the previous generation.
-
-    include ../usage/_models/_quickstart
-
-+section("install")
-    +h(2, "install") Installation & Usage
-
-    include ../usage/_models/_install-basics
-
-    +infobox
-        |  For more details on how to use models with spaCy, see the
-        |  #[+a("/usage/models") usage guide on models].
-
-    +h(3, "available-models") Available models
-
-    include ../usage/_models/_available-models
-
-+section("conventions")
-    +h(2, "model-naming") Model naming conventions
-
-    p
-        |  In general, spaCy expects all model packages to follow the naming
-        |  convention of #[code [lang]_[name]]. For spaCy's models, we also
-        |  chose to divide the name into three components:
-
-    +table
-        +row
-            +cell #[+label Type]
-            +cell
-                |  Model capabilities (e.g. #[code core] for general-purpose
-                |  model with vocabulary, syntax, entities and word vectors, or
-                |  #[code depent] for only vocab, syntax and entities).
-        +row
-            +cell #[+label Genre]
-            +cell
-                |  Type of text the model is trained on, e.g. #[code web] or
-                |  #[code news].
-        +row
-            +cell #[+label Size]
-            +cell Model size indicator, #[code sm], #[code md] or #[code lg].
-
-    p
-        |  For example, #[code en_core_web_sm] is a small English model trained
-        |  on written web text (blogs, news, comments), that includes
-        |  vocabulary, vectors, syntax and entities.
-
-    +h(3, "model-versioning") Model versioning
-
-    p
-        |  Additionally, the model versioning reflects both the compatibility
-        |  with spaCy, as well as the major and minor model version. A model
-        |  version #[code a.b.c] translates to:
-
-    +table
-        +row
-            +cell #[code a]
-            +cell
-                |  #[strong spaCy major version]. For example, #[code 2] for
-                |  spaCy v2.x.
-        +row
-            +cell #[code b]
-            +cell
-                |  #[strong Model major version]. Models with a different major
-                |  version can't be loaded by the same code. For example,
-                |  changing the width of the model, adding hidden layers or
-                |  changing the activation changes the model major version.
-        +row
-            +cell #[code c]
-            +cell
-                |  #[strong Model minor version]. Same model structure, but
-                |  different parameter values, e.g. from being trained on
-                |  different data, for different numbers of iterations, etc.
-
-    p
-        |  For a detailed compatibility overview, see the
-        |  #[+src(gh("spacy-models", "compatibility.json")) #[code compatibility.json]]
-        |  in the models repository. This is also the source of spaCy's internal
-        |  compatibility check, performed when you run the
-        |  #[+api("cli#download") #[code download]] command.
diff --git a/website/models/it.jade b/website/models/it.jade
deleted file mode 100644
index f0a797c43..000000000
--- a/website/models/it.jade
+++ /dev/null
@@ -1,6 +0,0 @@
-//- 💫 DOCS > MODELS > IT
-
-include ../_includes/_mixins
-
-//- This is a placeholder. The page is rendered via the template at
-//- /_includes/_page-model.jade.
diff --git a/website/models/nl.jade b/website/models/nl.jade
deleted file mode 100644
index 081b4a712..000000000
--- a/website/models/nl.jade
+++ /dev/null
@@ -1,6 +0,0 @@
-//- 💫 DOCS > MODELS > NL
-
-include ../_includes/_mixins
-
-//- This is a placeholder. The page is rendered via the template at
-//- /_includes/_page-model.jade.
diff --git a/website/models/pt.jade b/website/models/pt.jade
deleted file mode 100644
index 0836dca6b..000000000
--- a/website/models/pt.jade
+++ /dev/null
@@ -1,6 +0,0 @@
-//- 💫 DOCS > MODELS > PT
-
-include ../_includes/_mixins
-
-//- This is a placeholder. The page is rendered via the template at
-//- /_includes/_page-model.jade.
diff --git a/website/models/xx.jade b/website/models/xx.jade
deleted file mode 100644
index 8967f38fa..000000000
--- a/website/models/xx.jade
+++ /dev/null
@@ -1,6 +0,0 @@
-//- 💫 DOCS > MODELS > XX
-
-include ../_includes/_mixins
-
-//- This is a placeholder. The page is rendered via the template at
-//- /_includes/_page-model.jade.
diff --git a/website/netlify.toml b/website/netlify.toml
new file mode 100644
index 000000000..7774e67cf
--- /dev/null
+++ b/website/netlify.toml
@@ -0,0 +1,43 @@
+redirects = [
+    # Old demos
+    {from = "/demos/*", to = "https://explosion.ai/demos/:splat"},
+    # Old blog
+    {from = "/blog/*", to = "https://explosion.ai/blog/:splat"},
+    {from = "/feed", to = "https://explosion.ai/feed"},
+    {from = "/feed.xml", to = "https://explosion.ai/feed"},
+    # Old documentation pages (1.x)
+    {from = "/docs/usage/processing-text", to = "/usage/linguistic-features"},
+    {from = "/docs/usage/deep-learning", to = "/usage/training"},
+    {from = "/docs/usage/pos-tagging", to = "/usage/linguistic-features#pos-tagging"},
+    {from = "/docs/usage/dependency-parse", to = "/usage/linguistic-features#dependency-parse"},
+    {from = "/docs/usage/entity-recognition", to = "/usage/linguistic-features#named-entities"},
+    {from = "/docs/usage/word-vectors-similarities", to = "/usage/vectors-similarity"},
+    {from = "/docs/usage/customizing-tokenizer", to = "/usage/linguistic-features#tokenization"},
+    {from = "/docs/usage/language-processing-pipeline", to = "/usage/processing-pipelines"},
+    {from = "/docs/usage/customizing-pipeline", to = "/usage/processing-pipelines"},
+    {from = "/docs/usage/training-ner", to = "/usage/training#ner"},
+    {from = "/docs/usage/tutorials", to = "/usage/examples"},
+    {from = "/docs/usage/data-model", to = "/api"},
+    {from = "/docs/usage/cli", to = "/api/cli"},
+    {from = "/docs/usage/lightning-tour", to = "/usage/spacy-101#lightning-tour"},
+    {from = "/docs/api/language-models", to = "/usage/models#languages"},
+    {from = "/docs/api/spacy", to = "/docs/api/top-level"},
+    {from = "/docs/api/displacy", to = "/api/top-level#displacy"},
+    {from = "/docs/api/util", to = "/api/top-level#util"},
+    {from = "/docs/api/features", to = "/models/#architecture"},
+    {from = "/docs/api/philosophy", to = "/usage/spacy-101"},
+    {from = "/docs/usage/showcase", to = "/universe"},
+    {from = "/tutorials/load-new-word-vectors", to = "/usage/vectors-similarity#custom"},
+    {from = "/tutorials", to = "/usage/examples"},
+    # Rewrite all other docs pages to /
+    {from = "/docs/*", to = "/:splat"},
+    # Updated documentation pages
+    {from = "/usage/resources", to = "/universe"},
+    {from = "/usage/lightning-tour", to = "/usage/spacy-101#lightning-tour"},
+    {from = "/usage/linguistic-features#rule-based-matching", to = "/usage/rule-based-matching"},
+    {from = "/models/comparison", to = "/models"},
+    {from = "/api/#section-cython", to = "/api/cython"},
+    {from = "/api/#cython", to = "/api/cython"},
+    {from = "/universe", to = "/universe/project/:id", query = {id = ":id"}},
+    {from = "/universe", to = "/universe/category/:category", query = {category = ":category"}},
+]
diff --git a/website/package-lock.json b/website/package-lock.json
new file mode 100644
index 000000000..abbfac96e
--- /dev/null
+++ b/website/package-lock.json
@@ -0,0 +1,22880 @@
+{
+  "name": "spacy-io",
+  "version": "3.0.0",
+  "lockfileVersion": 1,
+  "requires": true,
+  "dependencies": {
+    "@babel/code-frame": {
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.0.0.tgz",
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+      "requires": {
+        "@babel/highlight": "^7.0.0"
+      }
+    },
+    "@babel/core": {
+      "version": "7.2.2",
+      "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.2.2.tgz",
+      "integrity": "sha512-59vB0RWt09cAct5EIe58+NzGP4TFSD3Bz//2/ELy3ZeTeKF6VTD1AXlH8BGGbCX0PuobZBsIzO7IAI9PH67eKw==",
+      "requires": {
+        "@babel/code-frame": "^7.0.0",
+        "@babel/generator": "^7.2.2",
+        "@babel/helpers": "^7.2.0",
+        "@babel/parser": "^7.2.2",
+        "@babel/template": "^7.2.2",
+        "@babel/traverse": "^7.2.2",
+        "@babel/types": "^7.2.2",
+        "convert-source-map": "^1.1.0",
+        "debug": "^4.1.0",
+        "json5": "^2.1.0",
+        "lodash": "^4.17.10",
+        "resolve": "^1.3.2",
+        "semver": "^5.4.1",
+        "source-map": "^0.5.0"
+      },
+      "dependencies": {
+        "debug": {
+          "version": "4.1.1",
+          "resolved": "https://registry.npmjs.org/debug/-/debug-4.1.1.tgz",
+          "integrity": "sha512-pYAIzeRo8J6KPEaJ0VWOh5Pzkbw/RetuzehGM7QRRX5he4fPHx2rdKMB256ehJCkX+XRQm16eZLqLNS8RSZXZw==",
+          "requires": {
+            "ms": "^2.1.1"
+          }
+        }
+      }
+    },
+    "@babel/generator": {
+      "version": "7.2.2",
+      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.2.2.tgz",
+      "integrity": "sha512-I4o675J/iS8k+P38dvJ3IBGqObLXyQLTxtrR4u9cSUJOURvafeEWb/pFMOTwtNrmq73mJzyF6ueTbO1BtN0Zeg==",
+      "requires": {
+        "@babel/types": "^7.2.2",
+        "jsesc": "^2.5.1",
+        "lodash": "^4.17.10",
+        "source-map": "^0.5.0",
+        "trim-right": "^1.0.1"
+      }
+    },
+    "@babel/helper-annotate-as-pure": {
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-annotate-as-pure/-/helper-annotate-as-pure-7.0.0.tgz",
+      "integrity": "sha512-3UYcJUj9kvSLbLbUIfQTqzcy5VX7GRZ/CCDrnOaZorFFM01aXp1+GJwuFGV4NDDoAS+mOUyHcO6UD/RfqOks3Q==",
+      "requires": {
+        "@babel/types": "^7.0.0"
+      }
+    },
+    "@babel/helper-builder-binary-assignment-operator-visitor": {
+      "version": "7.1.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-builder-binary-assignment-operator-visitor/-/helper-builder-binary-assignment-operator-visitor-7.1.0.tgz",
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+      "requires": {
+        "@babel/helper-explode-assignable-expression": "^7.1.0",
+        "@babel/types": "^7.0.0"
+      }
+    },
+    "@babel/helper-builder-react-jsx": {
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-builder-react-jsx/-/helper-builder-react-jsx-7.0.0.tgz",
+      "integrity": "sha512-ebJ2JM6NAKW0fQEqN8hOLxK84RbRz9OkUhGS/Xd5u56ejMfVbayJ4+LykERZCOUM6faa6Fp3SZNX3fcT16MKHw==",
+      "requires": {
+        "@babel/types": "^7.0.0",
+        "esutils": "^2.0.0"
+      }
+    },
+    "@babel/helper-call-delegate": {
+      "version": "7.1.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-call-delegate/-/helper-call-delegate-7.1.0.tgz",
+      "integrity": "sha512-YEtYZrw3GUK6emQHKthltKNZwszBcHK58Ygcis+gVUrF4/FmTVr5CCqQNSfmvg2y+YDEANyYoaLz/SHsnusCwQ==",
+      "requires": {
+        "@babel/helper-hoist-variables": "^7.0.0",
+        "@babel/traverse": "^7.1.0",
+        "@babel/types": "^7.0.0"
+      }
+    },
+    "@babel/helper-create-class-features-plugin": {
+      "version": "7.3.2",
+      "resolved": "https://registry.npmjs.org/@babel/helper-create-class-features-plugin/-/helper-create-class-features-plugin-7.3.2.tgz",
+      "integrity": "sha512-tdW8+V8ceh2US4GsYdNVNoohq5uVwOf9k6krjwW4E1lINcHgttnWcNqgdoessn12dAy8QkbezlbQh2nXISNY+A==",
+      "requires": {
+        "@babel/helper-function-name": "^7.1.0",
+        "@babel/helper-member-expression-to-functions": "^7.0.0",
+        "@babel/helper-optimise-call-expression": "^7.0.0",
+        "@babel/helper-plugin-utils": "^7.0.0",
+        "@babel/helper-replace-supers": "^7.2.3"
+      }
+    },
+    "@babel/helper-define-map": {
+      "version": "7.1.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-define-map/-/helper-define-map-7.1.0.tgz",
+      "integrity": "sha512-yPPcW8dc3gZLN+U1mhYV91QU3n5uTbx7DUdf8NnPbjS0RMwBuHi9Xt2MUgppmNz7CJxTBWsGczTiEp1CSOTPRg==",
+      "requires": {
+        "@babel/helper-function-name": "^7.1.0",
+        "@babel/types": "^7.0.0",
+        "lodash": "^4.17.10"
+      }
+    },
+    "@babel/helper-explode-assignable-expression": {
+      "version": "7.1.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-explode-assignable-expression/-/helper-explode-assignable-expression-7.1.0.tgz",
+      "integrity": "sha512-NRQpfHrJ1msCHtKjbzs9YcMmJZOg6mQMmGRB+hbamEdG5PNpaSm95275VD92DvJKuyl0s2sFiDmMZ+EnnvufqA==",
+      "requires": {
+        "@babel/traverse": "^7.1.0",
+        "@babel/types": "^7.0.0"
+      }
+    },
+    "@babel/helper-function-name": {
+      "version": "7.1.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.1.0.tgz",
+      "integrity": "sha512-A95XEoCpb3TO+KZzJ4S/5uW5fNe26DjBGqf1o9ucyLyCmi1dXq/B3c8iaWTfBk3VvetUxl16e8tIrd5teOCfGw==",
+      "requires": {
+        "@babel/helper-get-function-arity": "^7.0.0",
+        "@babel/template": "^7.1.0",
+        "@babel/types": "^7.0.0"
+      }
+    },
+    "@babel/helper-get-function-arity": {
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-get-function-arity/-/helper-get-function-arity-7.0.0.tgz",
+      "integrity": "sha512-r2DbJeg4svYvt3HOS74U4eWKsUAMRH01Z1ds1zx8KNTPtpTL5JAsdFv8BNyOpVqdFhHkkRDIg5B4AsxmkjAlmQ==",
+      "requires": {
+        "@babel/types": "^7.0.0"
+      }
+    },
+    "@babel/helper-hoist-variables": {
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-hoist-variables/-/helper-hoist-variables-7.0.0.tgz",
+      "integrity": "sha512-Ggv5sldXUeSKsuzLkddtyhyHe2YantsxWKNi7A+7LeD12ExRDWTRk29JCXpaHPAbMaIPZSil7n+lq78WY2VY7w==",
+      "requires": {
+        "@babel/types": "^7.0.0"
+      }
+    },
+    "@babel/helper-member-expression-to-functions": {
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-member-expression-to-functions/-/helper-member-expression-to-functions-7.0.0.tgz",
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+      "requires": {
+        "@babel/types": "^7.0.0"
+      }
+    },
+    "@babel/helper-module-imports": {
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.0.0.tgz",
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+      "requires": {
+        "@babel/types": "^7.0.0"
+      }
+    },
+    "@babel/helper-module-transforms": {
+      "version": "7.2.2",
+      "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.2.2.tgz",
+      "integrity": "sha512-YRD7I6Wsv+IHuTPkAmAS4HhY0dkPobgLftHp0cRGZSdrRvmZY8rFvae/GVu3bD00qscuvK3WPHB3YdNpBXUqrA==",
+      "requires": {
+        "@babel/helper-module-imports": "^7.0.0",
+        "@babel/helper-simple-access": "^7.1.0",
+        "@babel/helper-split-export-declaration": "^7.0.0",
+        "@babel/template": "^7.2.2",
+        "@babel/types": "^7.2.2",
+        "lodash": "^4.17.10"
+      }
+    },
+    "@babel/helper-optimise-call-expression": {
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-optimise-call-expression/-/helper-optimise-call-expression-7.0.0.tgz",
+      "integrity": "sha512-u8nd9NQePYNQV8iPWu/pLLYBqZBa4ZaY1YWRFMuxrid94wKI1QNt67NEZ7GAe5Kc/0LLScbim05xZFWkAdrj9g==",
+      "requires": {
+        "@babel/types": "^7.0.0"
+      }
+    },
+    "@babel/helper-plugin-utils": {
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.0.0.tgz",
+      "integrity": "sha512-CYAOUCARwExnEixLdB6sDm2dIJ/YgEAKDM1MOeMeZu9Ld/bDgVo8aiWrXwcY7OBh+1Ea2uUcVRcxKk0GJvW7QA=="
+    },
+    "@babel/helper-regex": {
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-regex/-/helper-regex-7.0.0.tgz",
+      "integrity": "sha512-TR0/N0NDCcUIUEbqV6dCO+LptmmSQFQ7q70lfcEB4URsjD0E1HzicrwUH+ap6BAQ2jhCX9Q4UqZy4wilujWlkg==",
+      "requires": {
+        "lodash": "^4.17.10"
+      }
+    },
+    "@babel/helper-remap-async-to-generator": {
+      "version": "7.1.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-remap-async-to-generator/-/helper-remap-async-to-generator-7.1.0.tgz",
+      "integrity": "sha512-3fOK0L+Fdlg8S5al8u/hWE6vhufGSn0bN09xm2LXMy//REAF8kDCrYoOBKYmA8m5Nom+sV9LyLCwrFynA8/slg==",
+      "requires": {
+        "@babel/helper-annotate-as-pure": "^7.0.0",
+        "@babel/helper-wrap-function": "^7.1.0",
+        "@babel/template": "^7.1.0",
+        "@babel/traverse": "^7.1.0",
+        "@babel/types": "^7.0.0"
+      }
+    },
+    "@babel/helper-replace-supers": {
+      "version": "7.2.3",
+      "resolved": "https://registry.npmjs.org/@babel/helper-replace-supers/-/helper-replace-supers-7.2.3.tgz",
+      "integrity": "sha512-GyieIznGUfPXPWu0yLS6U55Mz67AZD9cUk0BfirOWlPrXlBcan9Gz+vHGz+cPfuoweZSnPzPIm67VtQM0OWZbA==",
+      "requires": {
+        "@babel/helper-member-expression-to-functions": "^7.0.0",
+        "@babel/helper-optimise-call-expression": "^7.0.0",
+        "@babel/traverse": "^7.2.3",
+        "@babel/types": "^7.0.0"
+      }
+    },
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+    }
+  }
+}
diff --git a/website/package.json b/website/package.json
index 44297e93d..7538c72de 100644
--- a/website/package.json
+++ b/website/package.json
@@ -1,29 +1,75 @@
 {
-  "name": "spacy.io",
-  "private": true,
-  "version": "2.0.0",
-  "description": "spacy.io website",
-  "author": "Explosion AI",
-  "license": "MIT",
-  "devDependencies": {
-    "babel-cli": "^6.14.0",
-    "harp": "^0.24.0",
-    "rollup": "^0.50.0",
-    "uglify-js": "^2.7.3",
-    "broken-link-checker": "^0.7.6",
-    "valid-json-cli": "^1.3.0"
-  },
-  "dependencies": {},
-  "scripts": {
-    "check_links": "blc https://spacy.io -ro",
-
-    "rollup": "rollup www/assets/js/main.js --output.format iife --output.file www/assets/js/main.js",
-    "babel": "babel www/assets/js/main.js --out-file www/assets/js/main.js --presets=es2015",
-    "uglify": "uglifyjs www/assets/js/main.js --output www/assets/js/main.js",
-
-    "validate": "validjson universe/universe.json",
-    "compile": "NODE_ENV=deploy harp compile",
-    "bundle": "npm run rollup && npm run babel && npm run uglify",
-    "deploy": "rsync -P --compress --recursive --checksum --delete --exclude=\".*\" --exclude=\"*.vue.js\" --exclude=\"README.html\" --exclude \"package.json\" --exclude \"www\" www/ $1"
-  }
+    "name": "spacy-io",
+    "private": true,
+    "description": "spaCy website",
+    "version": "3.0.0",
+    "author": "Explosion AI <contact@explosion.ai>",
+    "license": "MIT",
+    "dependencies": {
+        "@jupyterlab/outputarea": "^0.19.1",
+        "@jupyterlab/rendermime": "^0.19.1",
+        "@mdx-js/mdx": "^0.17.5",
+        "@mdx-js/tag": "^0.17.5",
+        "@phosphor/widgets": "^1.6.0",
+        "@rehooks/online-status": "^1.0.0",
+        "@sindresorhus/slugify": "^0.8.0",
+        "@svgr/webpack": "^4.1.0",
+        "autoprefixer": "^9.4.7",
+        "classnames": "^2.2.6",
+        "codemirror": "^5.43.0",
+        "gatsby": "^2.1.4",
+        "gatsby-image": "^2.0.29",
+        "gatsby-mdx": "^0.3.6",
+        "gatsby-plugin-catch-links": "^2.0.11",
+        "gatsby-plugin-google-analytics": "^2.0.14",
+        "gatsby-plugin-manifest": "^2.0.17",
+        "gatsby-plugin-offline": "^2.0.23",
+        "gatsby-plugin-react-helmet": "^3.0.6",
+        "gatsby-plugin-react-svg": "^2.0.0",
+        "gatsby-plugin-sass": "^2.0.10",
+        "gatsby-plugin-sharp": "^2.0.20",
+        "gatsby-plugin-sitemap": "^2.0.5",
+        "gatsby-plugin-svgr": "^2.0.1",
+        "gatsby-remark-copy-linked-files": "^2.0.9",
+        "gatsby-remark-images": "^3.0.4",
+        "gatsby-remark-prismjs": "^3.2.4",
+        "gatsby-remark-smartypants": "^2.0.8",
+        "gatsby-remark-unwrap-images": "^1.0.1",
+        "gatsby-source-filesystem": "^2.0.20",
+        "gatsby-transformer-remark": "^2.2.5",
+        "gatsby-transformer-sharp": "^2.1.13",
+        "html-to-react": "^1.3.4",
+        "node-sass": "^4.11.0",
+        "npm": "^6.8.0",
+        "parse-numeric-range": "0.0.2",
+        "prismjs": "^1.15.0",
+        "prop-types": "^15.7.2",
+        "react": "^16.8.2",
+        "react-dom": "^16.8.2",
+        "react-helmet": "^5.2.0",
+        "react-intersection-observer": "^8.0.1",
+        "remark-react": "^5.0.1"
+    },
+    "scripts": {
+        "build": "gatsby build",
+        "dev": "gatsby develop",
+        "lint": "eslint **",
+        "clear": "rm -rf .cache",
+        "test": "echo \"Write tests! -> https://gatsby.app/unit-testing\""
+    },
+    "devDependencies": {
+        "browser-monads": "^1.0.0",
+        "gatsby-plugin-remove-trailing-slashes": "^2.0.7",
+        "md-attr-parser": "^1.2.1",
+        "prettier": "^1.16.4",
+        "raw-loader": "^1.0.0",
+        "unist-util-visit": "^1.4.0"
+    },
+    "repository": {
+        "type": "git",
+        "url": "https://github.com/explosion/spaCy"
+    },
+    "bugs": {
+        "url": "https://github.com/explosion/spaCy/issues"
+    }
 }
diff --git a/website/robots.txt.jade b/website/robots.txt.jade
deleted file mode 100644
index 437b98c30..000000000
--- a/website/robots.txt.jade
+++ /dev/null
@@ -1,9 +0,0 @@
-//- 💫 ROBOTS.TXT
-
-if environment != "deploy"
-    | User-agent: *
-    | Disallow: /
-
-if ALPHA
-    | User-agent: Googlebot
-    | Disallow: /
diff --git a/website/src/components/accordion.js b/website/src/components/accordion.js
new file mode 100644
index 000000000..7653c93ed
--- /dev/null
+++ b/website/src/components/accordion.js
@@ -0,0 +1,64 @@
+import React, { useState } from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+import slugify from '@sindresorhus/slugify'
+
+import Link from './link'
+import classes from '../styles/accordion.module.sass'
+
+const Accordion = ({ title, id, expanded, children }) => {
+    const anchorId = id ? id : slugify(title)
+    const [isExpanded, setIsExpanded] = useState(expanded)
+    const contentClassNames = classNames(classes.content, {
+        [classes.hidden]: !isExpanded,
+    })
+    const iconClassNames = classNames({
+        [classes.hidden]: isExpanded,
+    })
+    return (
+        <section id={anchorId}>
+            <div className={classes.root}>
+                <h3>
+                    <button
+                        className={classes.button}
+                        aria-expanded={String(isExpanded)}
+                        onClick={() => setIsExpanded(!isExpanded)}
+                    >
+                        <span>
+                            {title}
+                            {isExpanded && (
+                                <Link to={`#${anchorId}`} className={classes.anchor} hidden>
+                                    &para;
+                                </Link>
+                            )}
+                        </span>
+                        <svg
+                            className={classes.icon}
+                            width={20}
+                            height={20}
+                            viewBox="0 0 10 10"
+                            aria-hidden="true"
+                            focusable="false"
+                        >
+                            <rect className={iconClassNames} height={8} width={2} x={4} y={1} />
+                            <rect height={2} width={8} x={1} y={4} />
+                        </svg>
+                    </button>
+                </h3>
+                <div className={contentClassNames}>{children}</div>
+            </div>
+        </section>
+    )
+}
+
+Accordion.defaultProps = {
+    expanded: false,
+}
+
+Accordion.propTypes = {
+    title: PropTypes.string,
+    id: PropTypes.string,
+    children: PropTypes.node.isRequired,
+}
+
+export default Accordion
diff --git a/website/src/components/alert.js b/website/src/components/alert.js
new file mode 100644
index 000000000..271ea18db
--- /dev/null
+++ b/website/src/components/alert.js
@@ -0,0 +1,35 @@
+import React, { useState } from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+
+import Icon from './icon'
+import classes from '../styles/alert.module.sass'
+
+const Alert = ({ title, icon, variant, closeOnClick, children }) => {
+    const [visible, setVisible] = useState(true)
+    const alertClassNames = classNames(classes.root, {
+        [classes.warning]: variant === 'warning',
+        [classes.clickable]: !!closeOnClick,
+    })
+    const handleClick = () => !!closeOnClick && setVisible(false)
+    return !visible ? null : (
+        <aside className={alertClassNames} role="alert" onClick={handleClick}>
+            {icon && <Icon name={icon} width={18} inline />}
+            {title && <strong>{title}</strong>} {children}
+        </aside>
+    )
+}
+
+Alert.defaultProps = {
+    closeOnClick: true,
+}
+
+Alert.propTypes = {
+    title: PropTypes.string,
+    icon: PropTypes.string,
+    variant: PropTypes.oneOf(['warning']),
+    closeOnClick: PropTypes.bool,
+    children: PropTypes.node,
+}
+
+export default Alert
diff --git a/website/src/components/aside.js b/website/src/components/aside.js
new file mode 100644
index 000000000..a53262dbb
--- /dev/null
+++ b/website/src/components/aside.js
@@ -0,0 +1,22 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+
+import classes from '../styles/aside.module.sass'
+
+const Aside = ({ title, children }) => (
+    <aside className={classes.root}>
+        <div className={classes.content} role="complementary">
+            <div className={classes.text}>
+                {title && <h4 className={classes.title}>{title}</h4>}
+                {children}
+            </div>
+        </div>
+    </aside>
+)
+
+Aside.propTypes = {
+    title: PropTypes.string,
+    children: PropTypes.node.isRequired,
+}
+
+export default Aside
diff --git a/website/src/components/button.js b/website/src/components/button.js
new file mode 100644
index 000000000..5e03ca312
--- /dev/null
+++ b/website/src/components/button.js
@@ -0,0 +1,37 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+
+import Link from './link'
+import Icon from './icon'
+import classes from '../styles/button.module.sass'
+
+const Button = ({ to, variant, large, icon, className, children, ...props }) => {
+    const buttonClassNames = classNames(classes.root, className, {
+        [classes.large]: large,
+        [classes.primary]: variant === 'primary',
+        [classes.secondary]: variant === 'secondary',
+        [classes.tertiary]: variant === 'tertiary',
+    })
+    return (
+        <Link to={to} className={buttonClassNames} hideIcon={true} {...props}>
+            {icon && <Icon name={icon} width={large ? 16 : 14} inline />}
+            {children}
+        </Link>
+    )
+}
+
+Button.defaultProps = {
+    variant: 'secondary',
+    large: false,
+}
+
+Button.propTypes = {
+    to: PropTypes.string.isRequired,
+    variant: PropTypes.oneOf(['primary', 'secondary', 'tertiary']),
+    large: PropTypes.bool,
+    icon: PropTypes.string,
+    className: PropTypes.string,
+}
+
+export default Button
diff --git a/website/src/components/card.js b/website/src/components/card.js
new file mode 100644
index 000000000..ca4619b06
--- /dev/null
+++ b/website/src/components/card.js
@@ -0,0 +1,42 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+
+import Link from './link'
+import { H5 } from './typography'
+import classes from '../styles/card.module.sass'
+
+const Card = ({ title, to, image, header, onClick, children }) => (
+    <div className={classes.root}>
+        {header && (
+            <Link to={to} onClick={onClick} hidden>
+                {header}
+            </Link>
+        )}
+        <H5>
+            {image && (
+                <div className={classes.image}>
+                    <img src={image} width={35} alt="" />
+                </div>
+            )}
+            {title && (
+                <Link to={to} onClick={onClick} hidden>
+                    {title}
+                </Link>
+            )}
+        </H5>
+        <Link to={to} onClick={onClick} hidden>
+            {children}
+        </Link>
+    </div>
+)
+
+Card.propTypes = {
+    title: PropTypes.string,
+    to: PropTypes.string,
+    image: PropTypes.string,
+    card: PropTypes.node,
+    onClick: PropTypes.func,
+    children: PropTypes.node,
+}
+
+export default Card
diff --git a/website/src/components/code.js b/website/src/components/code.js
new file mode 100644
index 000000000..5184da833
--- /dev/null
+++ b/website/src/components/code.js
@@ -0,0 +1,169 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+import highlightCode from 'gatsby-remark-prismjs/highlight-code.js'
+import rangeParser from 'parse-numeric-range'
+import { StaticQuery, graphql } from 'gatsby'
+import { window } from 'browser-monads'
+
+import { isString, htmlToReact } from './util'
+import Link from './link'
+import GitHubCode from './github'
+import classes from '../styles/code.module.sass'
+
+export default props => (
+    <Pre>
+        <Code {...props} />
+    </Pre>
+)
+
+export const Pre = props => {
+    return <pre className={classes.pre}>{props.children}</pre>
+}
+
+export const InlineCode = ({ wrap, className, children, ...props }) => {
+    const codeClassNames = classNames(classes.inlineCode, className, {
+        [classes.wrap]: wrap || (isString(children) && children.length >= 20),
+    })
+    return (
+        <code className={codeClassNames} {...props}>
+            {children}
+        </code>
+    )
+}
+
+InlineCode.defaultProps = {
+    wrap: false,
+}
+
+InlineCode.propTypes = {
+    wrap: PropTypes.bool,
+    className: PropTypes.string,
+    children: PropTypes.node,
+}
+
+export class Code extends React.Component {
+    state = { Juniper: null }
+
+    static defaultProps = {
+        lang: 'none',
+        executable: null,
+        github: false,
+    }
+
+    static propTypes = {
+        lang: PropTypes.string,
+        title: PropTypes.string,
+        executable: PropTypes.oneOf(['true', 'false', true, false, null]),
+        github: PropTypes.oneOf(['true', 'false', true, false, null]),
+        prompt: PropTypes.string,
+        highlight: PropTypes.string,
+        className: PropTypes.string,
+        children: PropTypes.node,
+    }
+
+    updateJuniper() {
+        if (this.state.Juniper == null && window.Juniper !== null) {
+            this.setState({ Juniper: window.Juniper })
+        }
+    }
+
+    componentDidMount() {
+        this.updateJuniper()
+    }
+
+    componentDidUpdate() {
+        this.updateJuniper()
+    }
+
+    render() {
+        const {
+            lang,
+            title,
+            executable,
+            github,
+            prompt,
+            highlight,
+            className,
+            children,
+        } = this.props
+        const codeClassNames = classNames(classes.code, className, `language-${lang}`, {
+            [classes.wrap]: !!highlight,
+        })
+        const ghClassNames = classNames(codeClassNames, classes.maxHeight)
+        const { Juniper } = this.state
+
+        if (github) {
+            return <GitHubCode url={children} className={ghClassNames} lang={lang} />
+        }
+        if (!!executable && Juniper) {
+            return (
+                <JuniperWrapper Juniper={Juniper} title={title} lang={lang}>
+                    {children}
+                </JuniperWrapper>
+            )
+        }
+
+        const codeText = Array.isArray(children) ? children.join('') : children || ''
+        const highlightRange = highlight ? rangeParser.parse(highlight).filter(n => n > 0) : []
+        const html = lang === 'none' ? codeText : highlightCode(lang, codeText, highlightRange)
+
+        return (
+            <>
+                {title && <h4 className={classes.title}>{title}</h4>}
+                <code className={codeClassNames} data-prompt={prompt}>
+                    {htmlToReact(html)}
+                </code>
+            </>
+        )
+    }
+}
+
+const JuniperWrapper = ({ Juniper, title, lang, children }) => (
+    <StaticQuery
+        query={query}
+        render={data => {
+            const { binderUrl, binderBranch, binderVersion } = data.site.siteMetadata
+            const juniperTitle = title || 'Editable Code'
+            return (
+                <div className={classes.juniperWrapper}>
+                    <h4 className={classes.juniperTitle}>
+                        {juniperTitle}
+                        <span className={classes.juniperMeta}>
+                            spaCy v{binderVersion} &middot; Python 3 &middot; via{' '}
+                            <Link to="https://mybinder.org/" hidden>
+                                Binder
+                            </Link>
+                        </span>
+                    </h4>
+
+                    <Juniper
+                        repo={binderUrl}
+                        branch={binderBranch}
+                        lang={lang}
+                        classNames={{
+                            cell: classes.juniperCell,
+                            input: classes.juniperInput,
+                            button: classes.juniperButton,
+                            output: classes.juniperOutput,
+                        }}
+                    >
+                        {children}
+                    </Juniper>
+                </div>
+            )
+        }}
+    />
+)
+
+const query = graphql`
+    query JuniperQuery {
+        site {
+            siteMetadata {
+                binderUrl
+                binderBranch
+                binderVersion
+            }
+        }
+    }
+`
diff --git a/website/src/components/embed.js b/website/src/components/embed.js
new file mode 100644
index 000000000..56c0e95ca
--- /dev/null
+++ b/website/src/components/embed.js
@@ -0,0 +1,120 @@
+import React, { Fragment } from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+
+import Link from './link'
+import { InlineCode } from './code'
+import { markdownToReact } from './util'
+
+import classes from '../styles/embed.module.sass'
+
+const YouTube = ({ id, ratio }) => {
+    const embedClassNames = classNames(classes.root, classes.responsive, {
+        [classes.ratio16x9]: ratio === '16x9',
+        [classes.ratio4x3]: ratio === '4x3',
+    })
+    const url = `https://www.youtube.com/embed/${id}`
+    return (
+        <figure className={embedClassNames}>
+            <iframe
+                className={classes.iframe}
+                title={id}
+                src={url}
+                frameBorder={0}
+                height={500}
+                allowFullScreen
+            />
+        </figure>
+    )
+}
+
+YouTube.defaultProps = {
+    ratio: '16x9',
+}
+
+YouTube.propTypes = {
+    id: PropTypes.string.isRequired,
+    ratio: PropTypes.oneOf(['16x9', '4x3']),
+}
+
+const SoundCloud = ({ id, color, title }) => {
+    const url = `https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/${id}&color=%23${color}&auto_play=false&hide_related=false&show_comments=true&show_user=true&show_reposts=false&show_teaser=true`
+    return (
+        <figure className={classes.root}>
+            <iframe
+                title={title}
+                width="100%"
+                height={166}
+                scrolling="no"
+                frameborder="no"
+                allow="autoplay"
+                src={url}
+            />
+        </figure>
+    )
+}
+
+SoundCloud.defaultProps = {
+    color: '09a3d5',
+}
+
+SoundCloud.propTypes = {
+    id: PropTypes.string.isRequired,
+    title: PropTypes.string.isRequired,
+    color: PropTypes.string,
+}
+
+function formatHTML(html) {
+    const encoded = encodeURIComponent(html)
+    return `<html><head><meta charset="UTF-8"></head><body>${encoded}</body></html>`
+}
+
+const Iframe = ({ title, src, html, width, height }) => {
+    const source = html ? `data:text/html,${formatHTML(html)}` : src
+    return (
+        <iframe
+            className={classes.standalone}
+            title={title}
+            src={source}
+            width={width}
+            height={height}
+            allowFullScreen
+            frameBorder="0"
+        />
+    )
+}
+
+Iframe.defaultProps = {
+    width: 800,
+    height: 300,
+}
+
+Iframe.propTypes = {
+    title: PropTypes.string.isRequired,
+    src: PropTypes.string,
+    html: PropTypes.string,
+    width: PropTypes.number,
+    height: PropTypes.number,
+}
+
+const Image = ({ src, alt, title, ...props }) => {
+    // This is only needed for image types that are NOT handled by
+    // gatsby-remark-images, i.e. mostly SVGs. The plugin adds formatting
+    // and support for captions, so this normalises that behaviour.
+    const linkClassNames = classNames('gatsby-resp-image-link', classes.imageLink)
+    const markdownComponents = { code: InlineCode, p: Fragment, a: Link }
+    return (
+        <figure className="gatsby-resp-image-figure">
+            <Link className={linkClassNames} href={src} hidden forceExternal>
+                <img className={classes.image} src={src} alt={alt} width={650} height="auto" />
+            </Link>
+            {title && (
+                <figcaption className="gatsby-resp-image-figcaption">
+                    {markdownToReact(title, markdownComponents)}
+                </figcaption>
+            )}
+        </figure>
+    )
+}
+
+export { YouTube, SoundCloud, Iframe, Image }
diff --git a/website/src/components/footer.js b/website/src/components/footer.js
new file mode 100644
index 000000000..8e66666f6
--- /dev/null
+++ b/website/src/components/footer.js
@@ -0,0 +1,95 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import { StaticQuery, graphql } from 'gatsby'
+import classNames from 'classnames'
+
+import Link from './link'
+import Grid from './grid'
+import Newsletter from './newsletter'
+import { ReactComponent as ExplosionLogo } from '../images/explosion.svg'
+import classes from '../styles/footer.module.sass'
+
+const Footer = ({ wide }) => (
+    <StaticQuery
+        query={query}
+        render={data => {
+            const { companyUrl, company, footer, newsletter } = data.site.siteMetadata
+            return (
+                <footer className={classes.root}>
+                    <Grid cols={wide ? 4 : 3} narrow className={classes.content}>
+                        {footer.map(({ label, items }, i) => (
+                            <section key={i}>
+                                <ul className={classes.column}>
+                                    <li className={classes.label}>{label}</li>
+                                    {items.map(({ text, url }, j) => (
+                                        <li key={j}>
+                                            <Link to={url} hidden>
+                                                {text}
+                                            </Link>
+                                        </li>
+                                    ))}
+                                </ul>
+                            </section>
+                        ))}
+                        <section className={wide ? null : classes.full}>
+                            <ul className={classes.column}>
+                                <li className={classes.label}>Stay in the loop!</li>
+                                <li>Receive updates about new releases, tutorials and more.</li>
+                                <li>
+                                    <Newsletter {...newsletter} />
+                                </li>
+                            </ul>
+                        </section>
+                    </Grid>
+                    <div className={classNames(classes.content, classes.copy)}>
+                        <span>
+                            &copy; 2016-{new Date().getFullYear()}{' '}
+                            <Link to={companyUrl} hidden>
+                                {company}
+                            </Link>
+                        </span>
+                        <Link to={companyUrl} aria-label={company} hidden className={classes.logo}>
+                            <ExplosionLogo width={45} height={45} />
+                        </Link>
+                        <Link to={`${companyUrl}/legal`} hidden>
+                            Legal / Imprint
+                        </Link>
+                    </div>
+                </footer>
+            )
+        }}
+    />
+)
+
+Footer.defaultProps = {
+    wide: false,
+}
+
+Footer.propTypes = {
+    wide: PropTypes.bool,
+}
+
+export default Footer
+
+const query = graphql`
+    query FooterQuery {
+        site {
+            siteMetadata {
+                company
+                companyUrl
+                footer {
+                    label
+                    items {
+                        text
+                        url
+                    }
+                }
+                newsletter {
+                    user
+                    id
+                    list
+                }
+            }
+        }
+    }
+`
diff --git a/website/src/components/github.js b/website/src/components/github.js
new file mode 100644
index 000000000..73555da65
--- /dev/null
+++ b/website/src/components/github.js
@@ -0,0 +1,69 @@
+import React, { useState, useEffect } from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+import highlightCode from 'gatsby-remark-prismjs/highlight-code.js'
+
+import Icon from './icon'
+import Link from './link'
+import classes from '../styles/code.module.sass'
+
+const defaultErrorMsg = `Can't fetch code example from GitHub :(
+
+Please use the link below to view the example. If you've come across
+a broken link, we always appreciate a pull request to the repository,
+or a report on the issue tracker. Thanks!`
+
+const GitHubCode = ({ url, lang, errorMsg, className }) => {
+    const [initialized, setInitialized] = useState(false)
+    const [code, setCode] = useState(errorMsg)
+    const codeClassNames = classNames(classes.code, classes.maxHeight, className)
+
+    const rawUrl = url
+        .replace('github.com', 'raw.githubusercontent.com')
+        .replace('/blob', '')
+        .replace('/tree', '')
+
+    useEffect(() => {
+        if (!initialized) {
+            setCode(null)
+            fetch(rawUrl)
+                .then(res => res.text().then(text => ({ text, ok: res.ok })))
+                .then(({ text, ok }) => {
+                    setCode(ok ? text : errorMsg)
+                })
+                .catch(err => {
+                    setCode(errorMsg)
+                })
+            setInitialized(true)
+        }
+    }, [])
+
+    const highlighted = lang === 'none' || !code ? code : highlightCode(lang, code)
+
+    return (
+        <>
+            <header className={classes.header}>
+                <Link to={url} hidden>
+                    <Icon name="github" width={16} inline />
+                    <code className={classNames(classes.inlineCode, classes.inlineCodeDark)}>
+                        {rawUrl.split('.com/')[1]}
+                    </code>
+                </Link>
+            </header>
+            <code className={codeClassNames} dangerouslySetInnerHTML={{ __html: highlighted }} />
+        </>
+    )
+}
+
+GitHubCode.defaultProps = {
+    errorMsg: defaultErrorMsg,
+}
+
+GitHubCode.propTypes = {
+    url: PropTypes.string.isRequired,
+    lang: PropTypes.string,
+    errorMsg: PropTypes.string,
+    className: PropTypes.string,
+}
+
+export default React.memo(GitHubCode)
diff --git a/website/src/components/grid.js b/website/src/components/grid.js
new file mode 100644
index 000000000..75fbd85e6
--- /dev/null
+++ b/website/src/components/grid.js
@@ -0,0 +1,30 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+
+import classes from '../styles/grid.module.sass'
+
+const Grid = ({ cols, narrow, gutterBottom, className, children }) => {
+    const gridClassNames = classNames(classes.root, className, {
+        [classes.narrow]: narrow,
+        [classes.spacing]: gutterBottom,
+        [classes.half]: cols === 2,
+        [classes.third]: cols === 3,
+        [classes.quarter]: cols === 4,
+    })
+    return <div className={gridClassNames}>{children}</div>
+}
+
+Grid.defaultProps = {
+    cols: 1,
+    narrow: false,
+    gutterBottom: true,
+}
+
+Grid.propTypes = {
+    cols: PropTypes.oneOf([1, 2, 3, 4]),
+    narrow: PropTypes.bool,
+    gutterBottom: PropTypes.bool,
+}
+
+export default Grid
diff --git a/website/src/components/icon.js b/website/src/components/icon.js
new file mode 100644
index 000000000..246820a2f
--- /dev/null
+++ b/website/src/components/icon.js
@@ -0,0 +1,76 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+
+import { ReactComponent as GitHubIcon } from '../images/icons/github.svg'
+import { ReactComponent as TwitterIcon } from '../images/icons/twitter.svg'
+import { ReactComponent as WebsiteIcon } from '../images/icons/website.svg'
+import { ReactComponent as WarningIcon } from '../images/icons/warning.svg'
+import { ReactComponent as InfoIcon } from '../images/icons/info.svg'
+import { ReactComponent as AcceptIcon } from '../images/icons/accept.svg'
+import { ReactComponent as RejectIcon } from '../images/icons/reject.svg'
+import { ReactComponent as DocsIcon } from '../images/icons/docs.svg'
+import { ReactComponent as CodeIcon } from '../images/icons/code.svg'
+import { ReactComponent as HelpIcon } from '../images/icons/help.svg'
+import { ReactComponent as HelpOutlineIcon } from '../images/icons/help-outline.svg'
+import { ReactComponent as ArrowRightIcon } from '../images/icons/arrow-right.svg'
+import { ReactComponent as YesIcon } from '../images/icons/yes.svg'
+import { ReactComponent as NoIcon } from '../images/icons/no.svg'
+import { ReactComponent as NeutralIcon } from '../images/icons/neutral.svg'
+import { ReactComponent as OfflineIcon } from '../images/icons/offline.svg'
+
+import classes from '../styles/icon.module.sass'
+
+const icons = {
+    github: GitHubIcon,
+    twitter: TwitterIcon,
+    website: WebsiteIcon,
+    warning: WarningIcon,
+    danger: InfoIcon,
+    info: InfoIcon,
+    accept: AcceptIcon,
+    reject: RejectIcon,
+    docs: DocsIcon,
+    code: CodeIcon,
+    help: HelpIcon,
+    help2: HelpOutlineIcon,
+    arrowright: ArrowRightIcon,
+    yes: YesIcon,
+    no: NoIcon,
+    neutral: NeutralIcon,
+    offline: OfflineIcon,
+}
+
+const Icon = ({ name, width, height, inline, variant, className }) => {
+    const IconComponent = icons[name]
+    const iconClassNames = classNames(classes.root, className, {
+        [classes.inline]: inline,
+        [classes.success]: variant === 'success',
+        [classes.error]: variant === 'error',
+        [classes.subtle]: variant === 'subtle',
+    })
+    return !IconComponent ? null : (
+        <IconComponent
+            className={iconClassNames}
+            aria-hidden="true"
+            width={width}
+            height={height || width}
+        />
+    )
+}
+
+Icon.defaultProps = {
+    width: 20,
+    inline: false,
+}
+
+Icon.propTypes = {
+    name: PropTypes.oneOf(Object.keys(icons)),
+    width: PropTypes.number,
+    height: PropTypes.number,
+    inline: PropTypes.bool,
+    variant: PropTypes.oneOf(['success', 'error', 'subtle']),
+    className: PropTypes.string,
+}
+
+export default Icon
diff --git a/website/src/components/infobox.js b/website/src/components/infobox.js
new file mode 100644
index 000000000..fb23ecedc
--- /dev/null
+++ b/website/src/components/infobox.js
@@ -0,0 +1,39 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+
+import Icon from './icon'
+import classes from '../styles/infobox.module.sass'
+
+const Infobox = ({ title, variant, className, children }) => {
+    const infoboxClassNames = classNames(classes.root, className, {
+        [classes.warning]: variant === 'warning',
+        [classes.danger]: variant === 'danger',
+    })
+    return (
+        <aside className={infoboxClassNames}>
+            {title && (
+                <h4 className={classes.title}>
+                    {variant !== 'default' && (
+                        <Icon width={18} name={variant} inline className={classes.icon} />
+                    )}
+                    <span className={classes.titleText}>{title}</span>
+                </h4>
+            )}
+            {children}
+        </aside>
+    )
+}
+
+Infobox.defaultProps = {
+    variant: 'default',
+}
+
+Infobox.propTypes = {
+    title: PropTypes.string,
+    variant: PropTypes.oneOf(['default', 'warning', 'danger']),
+    className: PropTypes.string,
+    children: PropTypes.node.isRequired,
+}
+
+export default Infobox
diff --git a/website/src/components/juniper.js b/website/src/components/juniper.js
new file mode 100644
index 000000000..e157a9734
--- /dev/null
+++ b/website/src/components/juniper.js
@@ -0,0 +1,273 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import CodeMirror from 'codemirror'
+import python from 'codemirror/mode/python/python' // eslint-disable-line no-unused-vars
+import { Widget } from '@phosphor/widgets'
+import { Kernel, ServerConnection } from '@jupyterlab/services'
+import { OutputArea, OutputAreaModel } from '@jupyterlab/outputarea'
+import { RenderMimeRegistry, standardRendererFactories } from '@jupyterlab/rendermime'
+import { window } from 'browser-monads'
+
+class Juniper extends React.Component {
+    outputRef = null
+    inputRef = null
+    state = { kernel: null, renderers: null, fromStorage: null }
+
+    componentDidMount() {
+        const renderers = standardRendererFactories.filter(factory =>
+            factory.mimeTypes.includes('text/latex') ? window.MathJax : true
+        )
+
+        const outputArea = new OutputArea({
+            model: new OutputAreaModel({ trusted: true }),
+            rendermime: new RenderMimeRegistry({ initialFactories: renderers }),
+        })
+
+        const cm = new CodeMirror(this.inputRef, {
+            value: this.props.children.trim(),
+            mode: this.props.lang,
+            theme: this.props.theme,
+        })
+
+        const runCode = () => this.execute(outputArea, cm.getValue())
+        cm.setOption('extraKeys', { 'Shift-Enter': runCode })
+        Widget.attach(outputArea, this.outputRef)
+        this.setState({ runCode })
+    }
+
+    log(logFunction) {
+        if (this.props.debug) {
+            logFunction()
+        }
+    }
+
+    /**
+     * Request a binder, e.g. from mybinder.org
+     * @param {string} repo - Repository name in the format 'user/repo'.
+     * @param {string} branch - The repository branch, e.g. 'master'.
+     * @param {string} url - The binder reployment URL, including 'http(s)'.
+     * @returns {Promise} - Resolved with Binder settings, rejected with Error.
+     */
+    requestBinder(repo, branch, url) {
+        const binderUrl = `${url}/build/gh/${repo}/${branch}`
+        this.log(() => console.info('building', { binderUrl }))
+        return new Promise((resolve, reject) => {
+            const es = new EventSource(binderUrl)
+            es.onerror = err => {
+                es.close()
+                this.log(() => console.error('failed'))
+                reject(new Error(err))
+            }
+            let phase = null
+            es.onmessage = ({ data }) => {
+                const msg = JSON.parse(data)
+                if (msg.phase && msg.phase !== phase) {
+                    phase = msg.phase.toLowerCase()
+                    this.log(() => console.info(phase === 'ready' ? 'server-ready' : phase))
+                }
+                if (msg.phase === 'failed') {
+                    es.close()
+                    reject(new Error(msg))
+                } else if (msg.phase === 'ready') {
+                    es.close()
+                    const settings = {
+                        baseUrl: msg.url,
+                        wsUrl: `ws${msg.url.slice(4)}`,
+                        token: msg.token,
+                    }
+                    resolve(settings)
+                }
+            }
+        })
+    }
+
+    /**
+     * Request kernel and estabish a server connection via the JupyerLab service
+     * @param {object} settings - The server settings.
+     * @returns {Promise} - A promise that's resolved with the kernel.
+     */
+    requestKernel(settings) {
+        if (this.props.useStorage) {
+            const timestamp = new Date().getTime() + this.props.storageExpire * 60 * 1000
+            const json = JSON.stringify({ settings, timestamp })
+            window.localStorage.setItem(this.props.storageKey, json)
+        }
+        const serverSettings = ServerConnection.makeSettings(settings)
+        return Kernel.startNew({ type: this.props.kernelType, serverSettings }).then(kernel => {
+            this.log(() => console.info('ready'))
+            return kernel
+        })
+    }
+
+    /**
+     * Get a kernel by requesting a binder or from localStorage / user settings
+     * @returns {Promise}
+     */
+    getKernel() {
+        if (this.props.useStorage) {
+            const stored = window.localStorage.getItem(this.props.storageKey)
+            if (stored) {
+                this.setState({ fromStorage: true })
+                const { settings, timestamp } = JSON.parse(stored)
+                if (timestamp && new Date().getTime() < timestamp) {
+                    return this.requestKernel(settings)
+                }
+                window.localStorage.removeItem(this.props.storageKey)
+            }
+        }
+        if (this.props.useBinder) {
+            return this.requestBinder(this.props.repo, this.props.branch, this.props.url).then(
+                settings => this.requestKernel(settings)
+            )
+        }
+        return this.requestKernel(this.props.serverSettings)
+    }
+
+    /**
+     * Render the kernel response in a JupyterLab output area
+     * @param {OutputArea} outputArea - The cell's output area.
+     * @param {string} code - The code to execute.
+     */
+    renderResponse(outputArea, code) {
+        outputArea.future = this.state.kernel.requestExecute({ code })
+        outputArea.model.add({
+            output_type: 'stream',
+            name: 'loading',
+            text: this.props.msgLoading,
+        })
+        outputArea.model.clear(true)
+    }
+
+    /**
+     * Process request to execute the code
+     * @param {OutputArea} - outputArea - The cell's output area.
+     * @param {string} code - The code to execute.
+     */
+    execute(outputArea, code) {
+        this.log(() => console.info('executing'))
+        if (this.state.kernel) {
+            if (this.props.isolateCells) {
+                this.state.kernel
+                    .restart()
+                    .then(() => this.renderResponse(outputArea, code))
+                    .catch(() => {
+                        this.log(() => console.error('failed'))
+                        this.setState({ kernel: null })
+                        outputArea.model.clear()
+                        outputArea.model.add({
+                            output_type: 'stream',
+                            name: 'failure',
+                            text: this.props.msgError,
+                        })
+                    })
+                return
+            }
+            this.renderResponse(outputArea, code)
+            return
+        }
+        this.log(() => console.info('requesting kernel'))
+        const url = this.props.url.split('//')[1]
+        const action = !this.state.fromStorage ? 'Launching' : 'Reconnecting to'
+        outputArea.model.clear()
+        outputArea.model.add({
+            output_type: 'stream',
+            name: 'stdout',
+            text: `${action} Docker container on ${url}...`,
+        })
+        new Promise((resolve, reject) =>
+            this.getKernel()
+                .then(resolve)
+                .catch(reject)
+        )
+            .then(kernel => {
+                this.setState({ kernel })
+                this.renderResponse(outputArea, code)
+            })
+            .catch(() => {
+                this.log(() => console.error('failed'))
+                this.setState({ kernel: null })
+                if (this.props.useStorage) {
+                    this.setState({ fromStorage: false })
+                    window.localStorage.removeItem(this.props.storageKey)
+                }
+                outputArea.model.clear()
+                outputArea.model.add({
+                    output_type: 'stream',
+                    name: 'failure',
+                    text: this.props.msgError,
+                })
+            })
+    }
+
+    render() {
+        return (
+            <div className={this.props.classNames.cell}>
+                <div
+                    className={this.props.classNames.input}
+                    ref={x => {
+                        this.inputRef = x
+                    }}
+                />
+                <button className={this.props.classNames.button} onClick={this.state.runCode}>
+                    {this.props.msgButton}
+                </button>
+                <div
+                    ref={x => {
+                        this.outputRef = x
+                    }}
+                    className={this.props.classNames.output}
+                />
+            </div>
+        )
+    }
+}
+
+Juniper.defaultProps = {
+    children: '',
+    branch: 'master',
+    url: 'https://mybinder.org',
+    serverSettings: {},
+    kernelType: 'python3',
+    lang: 'python',
+    theme: 'default',
+    isolateCells: true,
+    useBinder: true,
+    useStorage: true,
+    storageExpire: 60,
+    debug: false,
+    msgButton: 'run',
+    msgLoading: 'Loading...',
+    msgError: 'Connecting failed. Please reload and try again.',
+    classNames: {
+        cell: 'juniper-cell',
+        input: 'juniper-input',
+        button: 'juniper-button',
+        output: 'juniper-output',
+    },
+}
+
+Juniper.propTypes = {
+    children: PropTypes.string,
+    repo: PropTypes.string.isRequired,
+    branch: PropTypes.string,
+    url: PropTypes.string,
+    serverSettings: PropTypes.object,
+    kernelType: PropTypes.string,
+    lang: PropTypes.string,
+    theme: PropTypes.string,
+    isolateCells: PropTypes.bool,
+    useBinder: PropTypes.bool,
+    useStorage: PropTypes.bool,
+    storageExpire: PropTypes.number,
+    msgButton: PropTypes.string,
+    msgLoading: PropTypes.string,
+    msgError: PropTypes.string,
+    classNames: PropTypes.shape({
+        cell: PropTypes.string,
+        input: PropTypes.string,
+        button: PropTypes.string,
+        output: PropTypes.string,
+    }),
+}
+
+export default Juniper
diff --git a/website/src/components/landing.js b/website/src/components/landing.js
new file mode 100644
index 000000000..5f4fb44bc
--- /dev/null
+++ b/website/src/components/landing.js
@@ -0,0 +1,148 @@
+import React, { Fragment } from 'react'
+import classNames from 'classnames'
+
+import pattern from '../images/pattern_blue.jpg'
+import patternOverlay from '../images/pattern_landing.jpg'
+import logoSvgs from '../images/logos'
+
+import Grid from './grid'
+import { Content } from './main'
+import Button from './button'
+import CodeBlock from './code'
+import { H1, H2, H3, Label, InlineList } from './typography'
+import Link from './link'
+import { chunkArray } from './util'
+import classes from '../styles/landing.module.sass'
+
+export const LandingHeader = ({ style = {}, children }) => {
+    const wrapperStyle = { backgroundImage: `url(${pattern})` }
+    const contentStyle = { backgroundImage: `url(${patternOverlay})`, ...style }
+    return (
+        <header className={classes.header}>
+            <div className={classes.headerWrapper} style={wrapperStyle}>
+                <div className={classes.headerContent} style={contentStyle}>
+                    {children}
+                </div>
+            </div>
+        </header>
+    )
+}
+
+export const LandingTitle = ({ children }) => <h1 className={classes.title}>{children}</h1>
+
+export const LandingSubtitle = ({ children }) => (
+    <h2>
+        <span className={classNames(classes.label, classes.subtitle)}>{children}</span>
+    </h2>
+)
+
+export const LandingGrid = ({ cols = 3, blocks = false, children }) => (
+    <Content className={classNames(classes.grid, { [classes.blocks]: blocks })}>
+        <Grid cols={cols} narrow={blocks}>
+            {children}
+        </Grid>
+    </Content>
+)
+
+export const LandingCard = ({ title, children }) => (
+    <div className={classes.card}>
+        {title && <H3>{title}</H3>}
+        {children}
+    </div>
+)
+
+export const LandingButton = ({ to, children }) => (
+    <Button to={to} variant="primary" large className={classes.button}>
+        {children}
+    </Button>
+)
+
+export const LandingDemo = ({ title, children }) => {
+    return (
+        <div className={classes.demo}>
+            <CodeBlock executable lang="python" title={title}>
+                {children}
+            </CodeBlock>
+        </div>
+    )
+}
+
+export const LandingBannerGrid = ({ children }) => (
+    <Grid cols={2} className={classes.bannerGrid}>
+        {children}
+    </Grid>
+)
+
+export const LandingBanner = ({ title, label, to, button, small, background, color, children }) => {
+    const contentClassNames = classNames(classes.bannerContent, {
+        [classes.bannerContentSmall]: small,
+    })
+    const textClassNames = classNames(classes.bannerText, {
+        [classes.bannerTextSmall]: small,
+    })
+    const style = { '--color-theme': background, '--color-back': color }
+    const Heading = small ? H2 : H1
+    return (
+        <div className={classes.banner} style={style}>
+            <Grid cols={small ? null : 3} narrow className={contentClassNames}>
+                <Heading Component="h3" className={classes.bannerTitle}>
+                    {label && (
+                        <div className={classes.bannerLabel}>
+                            <span className={classes.label}>{label}</span>
+                        </div>
+                    )}
+                    <Link to={to} hidden>
+                        {title}
+                    </Link>
+                </Heading>
+                <div className={textClassNames}>
+                    <p>{children}</p>
+
+                    {button && (
+                        <LandingBannerButton to={to} small={small}>
+                            {button}
+                        </LandingBannerButton>
+                    )}
+                </div>
+            </Grid>
+        </div>
+    )
+}
+
+export const LandingBannerButton = ({ to, small, children }) => (
+    <div className={classes.bannerButton}>
+        <Button to={to} variant="tertiary" large={!small}>
+            {children}
+        </Button>
+    </div>
+)
+
+export const LandingLogos = ({ logos = [], title, maxRow = 4, children }) => {
+    const rows = chunkArray(logos, maxRow)
+    return (
+        <Content className={classes.logos}>
+            {title && <Label>{title}</Label>}
+            {rows.map((logos, i) => (
+                <Fragment key={i}>
+                    <InlineList className={classes.logosContent}>
+                        {logos.map(({ id, url }, j) => {
+                            const Component = logoSvgs[id]
+                            return !Component ? null : (
+                                <Link
+                                    to={url}
+                                    key={j}
+                                    aria-label={id}
+                                    hidden
+                                    className={classes.logo}
+                                >
+                                    <Component />
+                                </Link>
+                            )
+                        })}
+                        {i === rows.length - 1 && children}
+                    </InlineList>
+                </Fragment>
+            ))}
+        </Content>
+    )
+}
diff --git a/website/src/components/link.js b/website/src/components/link.js
new file mode 100644
index 000000000..35b3c23cf
--- /dev/null
+++ b/website/src/components/link.js
@@ -0,0 +1,105 @@
+import React, { Fragment } from 'react'
+import PropTypes from 'prop-types'
+import { Link as GatsbyLink } from 'gatsby'
+import { OutboundLink } from 'gatsby-plugin-google-analytics'
+import classNames from 'classnames'
+
+import Icon from './icon'
+import classes from '../styles/link.module.sass'
+import { isString } from './util'
+
+const Whitespace = ({ children }) => (
+    // Ensure that links are always wrapped in spaces
+    <> {children} </>
+)
+
+const Link = ({
+    children,
+    to,
+    href,
+    onClick,
+    activeClassName,
+    hidden,
+    hideIcon,
+    ws,
+    forceExternal,
+    className,
+    ...other
+}) => {
+    const dest = to || href
+    const external = forceExternal || /(http(s?)):\/\//gi.test(dest)
+    const isApi = !external && !hidden && !hideIcon && /^\/?api/.test(dest)
+    const isSource = external && !hidden && !hideIcon && /(github.com)/.test(dest)
+    const sourceWithText = (isSource || isApi) && isString(children)
+    const linkClassNames = classNames(classes.root, className, {
+        [classes.hidden]: hidden,
+        [classes.nowrap]: (isApi || isSource) && !sourceWithText,
+        [classes.withIcon]: isApi || isSource,
+    })
+    const Wrapper = ws ? Whitespace : Fragment
+    const content = (
+        <>
+            {sourceWithText ? <span className={classes.sourceText}>{children}</span> : children}
+            {isApi && <Icon name="docs" width={16} inline className={classes.icon} />}
+            {isSource && <Icon name="code" width={16} inline className={classes.icon} />}
+        </>
+    )
+
+    if (!external) {
+        if ((dest && /^#/.test(dest)) || onClick) {
+            return (
+                <Wrapper>
+                    <a href={dest} onClick={onClick} className={linkClassNames}>
+                        {children}
+                    </a>
+                </Wrapper>
+            )
+        }
+        return (
+            <Wrapper>
+                <GatsbyLink
+                    to={dest}
+                    className={linkClassNames}
+                    activeClassName={activeClassName}
+                    {...other}
+                >
+                    {content}
+                </GatsbyLink>
+            </Wrapper>
+        )
+    }
+    return (
+        <Wrapper>
+            <OutboundLink
+                href={dest}
+                className={linkClassNames}
+                target="_blank"
+                rel="noopener nofollow noreferrer"
+                {...other}
+            >
+                {content}
+            </OutboundLink>
+        </Wrapper>
+    )
+}
+
+Link.defaultProps = {
+    hidden: false,
+    hideIcon: false,
+    ws: false,
+    forceExternal: false,
+}
+
+Link.propTypes = {
+    children: PropTypes.node.isRequired,
+    to: PropTypes.string,
+    href: PropTypes.string,
+    onClick: PropTypes.func,
+    activeClassName: PropTypes.string,
+    hidden: PropTypes.bool,
+    hideIcon: PropTypes.bool,
+    ws: PropTypes.bool,
+    className: PropTypes.string,
+}
+
+export default Link
diff --git a/website/src/components/list.js b/website/src/components/list.js
new file mode 100644
index 000000000..863eaddca
--- /dev/null
+++ b/website/src/components/list.js
@@ -0,0 +1,7 @@
+import React from 'react'
+
+import classes from '../styles/list.module.sass'
+
+export const Ol = props => <ol className={classes.ol} {...props} />
+export const Ul = props => <ul className={classes.ul} {...props} />
+export const Li = props => <li className={classes.li} {...props} />
diff --git a/website/src/components/main.js b/website/src/components/main.js
new file mode 100644
index 000000000..4131de570
--- /dev/null
+++ b/website/src/components/main.js
@@ -0,0 +1,51 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+
+import patternBlue from '../images/pattern_blue.jpg'
+import patternGreen from '../images/pattern_green.jpg'
+import patternPurple from '../images/pattern_purple.jpg'
+import classes from '../styles/main.module.sass'
+
+const patterns = { blue: patternBlue, green: patternGreen, purple: patternPurple }
+
+export const Content = ({ Component = 'div', className, children }) => (
+    <Component className={classNames(classes.content, className)}>{children}</Component>
+)
+
+const Main = ({ sidebar, asides, wrapContent, theme, footer, children }) => {
+    const mainClassNames = classNames(classes.root, {
+        [classes.withSidebar]: sidebar,
+        [classes.withAsides]: asides,
+    })
+
+    return (
+        <main className={mainClassNames}>
+            {wrapContent ? <Content Component="article">{children}</Content> : children}
+            {asides && (
+                <div
+                    className={classes.asides}
+                    style={{ backgroundImage: `url(${patterns[theme]}` }}
+                />
+            )}
+            {footer}
+        </main>
+    )
+}
+
+Main.defaultProps = {
+    sidebar: false,
+    asides: false,
+    wrapContent: false,
+    theme: 'blue',
+}
+
+Main.propTypes = {
+    sidebar: PropTypes.bool,
+    asides: PropTypes.bool,
+    wrapContent: PropTypes.bool,
+    theme: PropTypes.string.isRequired,
+    footer: PropTypes.node,
+}
+
+export default Main
diff --git a/website/src/components/navigation.js b/website/src/components/navigation.js
new file mode 100644
index 000000000..abf8a913b
--- /dev/null
+++ b/website/src/components/navigation.js
@@ -0,0 +1,57 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+
+import Link from './link'
+import Icon from './icon'
+import { github } from './util'
+import { ReactComponent as Logo } from '../images/logo.svg'
+import classes from '../styles/navigation.module.sass'
+
+const Navigation = ({ title, items, section, children }) => (
+    <nav className={classes.root}>
+        <Link to="/" aria-label={title} hidden>
+            <h1 className={classes.title}>{title}</h1>
+            <Logo className={classes.logo} width={300} height={96} />
+        </Link>
+
+        <ul className={classes.menu}>
+            {items.map(({ text, url }, i) => {
+                const isActive = section && text.toLowerCase() === section
+                const itemClassNames = classNames(classes.item, {
+                    [classes.isActive]: isActive,
+                })
+                return (
+                    <li key={i} className={itemClassNames}>
+                        <Link to={url} tabIndex={isActive ? '-1' : null} hidden>
+                            {text}
+                        </Link>
+                    </li>
+                )
+            })}
+            <li className={classes.item}>
+                <Link to={github()} aria-label="GitHub" hidden>
+                    <Icon name="github" />
+                </Link>
+            </li>
+        </ul>
+        {children}
+    </nav>
+)
+
+Navigation.defaultProps = {
+    items: [],
+}
+
+Navigation.propTypes = {
+    title: PropTypes.string.isRequired,
+    items: PropTypes.arrayOf(
+        PropTypes.shape({
+            text: PropTypes.string.isRequired,
+            url: PropTypes.string.isRequired,
+        })
+    ),
+    section: PropTypes.string,
+}
+
+export default Navigation
diff --git a/website/src/components/newsletter.js b/website/src/components/newsletter.js
new file mode 100644
index 000000000..34f1f834a
--- /dev/null
+++ b/website/src/components/newsletter.js
@@ -0,0 +1,50 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+
+import classes from '../styles/newsletter.module.sass'
+
+const Newsletter = ({ user, id, list }) => {
+    const action = `//${user}.list-manage.com/subscribe/post?u=${id}&amp;id=${list}`
+    return (
+        <form
+            id="mc-embedded-subscribe-form"
+            name="mc-embedded-subscribe-form"
+            action={action}
+            method="post"
+            target="_blank"
+            noValidate
+        >
+            {/* MailChimp spam protection */}
+            <div style={{ position: 'absolute', left: '-5000px' }} aria-hidden="true">
+                <input type="text" name={`b_${id}_${list}`} tabIndex="-1" defaultValue="" />
+            </div>
+
+            <div className={classes.root}>
+                <input
+                    className={classes.input}
+                    id="mce-EMAIL"
+                    type="email"
+                    name="EMAIL"
+                    placeholder="Your email"
+                    aria-label="Your email"
+                />
+                <button
+                    className={classes.button}
+                    id="mc-embedded-subscribe"
+                    type="submit"
+                    name="subscribe"
+                >
+                    Sign up
+                </button>
+            </div>
+        </form>
+    )
+}
+
+Newsletter.propTypes = {
+    user: PropTypes.string.isRequired,
+    id: PropTypes.string.isRequired,
+    list: PropTypes.string.isRequired,
+}
+
+export default Newsletter
diff --git a/website/src/components/progress.js b/website/src/components/progress.js
new file mode 100644
index 000000000..0a71f1cf5
--- /dev/null
+++ b/website/src/components/progress.js
@@ -0,0 +1,60 @@
+import React, { useState, useEffect, useRef } from 'react'
+import { document, window } from 'browser-monads'
+
+import classes from '../styles/progress.module.sass'
+
+function getOffset() {
+    const height = Math.max(
+        document.body.scrollHeight,
+        document.body.offsetHeight,
+        document.documentElement.clientHeight,
+        document.documentElement.scrollHeight,
+        document.documentElement.offsetHeight
+    )
+    const vh = Math.max(document.documentElement.clientHeight, window.innerHeight || 0)
+    return { height, vh }
+}
+
+function getScrollY() {
+    const pos = (window.pageYOffset || document.scrollTop) - (document.clientTop || 0)
+    return isNaN(pos) ? 0 : pos
+}
+
+const Progress = () => {
+    const progressRef = useRef()
+    const [initialized, setInitialized] = useState(false)
+    const [offset, setOffset] = useState(getOffset())
+    const [scrollY, setScrollY] = useState(getScrollY())
+
+    function handleScroll() {
+        setScrollY(getScrollY())
+    }
+
+    function handleResize() {
+        setOffset(getOffset())
+    }
+
+    useEffect(
+        () => {
+            if (!initialized && progressRef.current) {
+                handleResize()
+                setInitialized(true)
+            }
+            window.addEventListener('scroll', handleScroll)
+            window.addEventListener('resize', handleResize)
+
+            return () => {
+                window.removeEventListener('scroll', handleScroll)
+                window.removeEventListener('resize', handleResize)
+            }
+        },
+        [progressRef]
+    )
+
+    const { height, vh } = offset
+    const total = 100 - ((height - scrollY - vh) / height) * 100
+    const value = scrollY === 0 ? 0 : total || 0
+    return <progress ref={progressRef} className={classes.root} value={value} max="100" />
+}
+
+export default Progress
diff --git a/website/src/components/quickstart.js b/website/src/components/quickstart.js
new file mode 100644
index 000000000..eae3a1a1e
--- /dev/null
+++ b/website/src/components/quickstart.js
@@ -0,0 +1,183 @@
+import React, { Fragment, useState, useEffect } from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+import { window } from 'browser-monads'
+
+import Section from './section'
+import Icon from './icon'
+import { H2 } from './typography'
+import classes from '../styles/quickstart.module.sass'
+
+const Quickstart = ({ data, title, description, id, children }) => {
+    const [styles, setStyles] = useState({})
+    const [checked, setChecked] = useState({})
+    const [initialized, setInitialized] = useState(false)
+
+    const getCss = (id, checkedOptions) => {
+        const checkedForId = checkedOptions[id] || []
+        const exclude = checkedForId
+            .map(value => `:not([data-quickstart-${id}="${value}"])`)
+            .join('')
+        return `[data-quickstart-results]>[data-quickstart-${id}]${exclude} {display: none}`
+    }
+
+    useEffect(() => {
+        window.dispatchEvent(new Event('resize')) // scroll position for progress
+        if (!initialized) {
+            const initialChecked = Object.assign(
+                {},
+                ...data.map(({ id, options }) => ({
+                    [id]: options.filter(option => option.checked).map(({ id }) => id),
+                }))
+            )
+            const initialStyles = Object.assign(
+                {},
+                ...data.map(({ id }) => ({ [id]: getCss(id, initialChecked) }))
+            )
+            setChecked(initialChecked)
+            setStyles(initialStyles)
+            setInitialized(true)
+        }
+    })
+
+    return !data.length ? null : (
+        <Section id={id}>
+            <div className={classes.root}>
+                {title && (
+                    <H2 className={classes.title}>
+                        <a href={`#${id}`}>{title}</a>
+                    </H2>
+                )}
+
+                {description && <p className={classes.description}>{description}</p>}
+
+                {data.map(({ id, title, options = [], multiple, help }) => (
+                    <div key={id} data-quickstart-group={id} className={classes.group}>
+                        <style data-quickstart-style={id}>
+                            {styles[id] ||
+                                `[data-quickstart-results]>[data-quickstart-${id}] { display: none }`}
+                        </style>
+                        <div className={classes.legend}>
+                            {title}
+                            {help && (
+                                <span data-tooltip={help} className={classes.help}>
+                                    {' '}
+                                    <Icon name="help" width={16} spaced />
+                                </span>
+                            )}
+                        </div>
+                        <div className={classes.fields}>
+                            {options.map(option => {
+                                const optionType = multiple ? 'checkbox' : 'radio'
+                                const checkedForId = checked[id] || []
+                                return (
+                                    <Fragment key={option.id}>
+                                        <input
+                                            onChange={() => {
+                                                const newChecked = {
+                                                    ...checked,
+                                                    [id]: !multiple
+                                                        ? [option.id]
+                                                        : checkedForId.includes(option.id)
+                                                        ? checkedForId.filter(
+                                                              opt => opt !== option.id
+                                                          )
+                                                        : [...checkedForId, option.id],
+                                                }
+                                                setChecked(newChecked)
+                                                setStyles({
+                                                    ...styles,
+                                                    [id]: getCss(id, newChecked),
+                                                })
+                                            }}
+                                            type={optionType}
+                                            className={classNames(
+                                                classes.input,
+                                                classes[optionType]
+                                            )}
+                                            name={id}
+                                            id={`quickstart-${option.id}`}
+                                            value={option.id}
+                                            checked={checkedForId.includes(option.id)}
+                                        />
+                                        <label
+                                            className={classes.label}
+                                            htmlFor={`quickstart-${option.id}`}
+                                        >
+                                            {option.title}
+                                            {option.meta && (
+                                                <span className={classes.meta}>{option.meta}</span>
+                                            )}
+                                            {option.help && (
+                                                <span
+                                                    data-tooltip={option.help}
+                                                    className={classes.help}
+                                                >
+                                                    {' '}
+                                                    <Icon name="help" width={16} spaced />
+                                                </span>
+                                            )}
+                                        </label>
+                                    </Fragment>
+                                )
+                            })}
+                        </div>
+                    </div>
+                ))}
+                <pre className={classes.code}>
+                    <code className={classes.results} data-quickstart-results="">
+                        {children}
+                    </code>
+                </pre>
+            </div>
+        </Section>
+    )
+}
+
+Quickstart.defaultProps = {
+    data: [],
+    id: 'quickstart',
+}
+
+Quickstart.propTypes = {
+    title: PropTypes.oneOfType([PropTypes.string, PropTypes.node]),
+    description: PropTypes.oneOfType([PropTypes.string, PropTypes.node]),
+    data: PropTypes.arrayOf(
+        PropTypes.shape({
+            id: PropTypes.string.isRequired,
+            title: PropTypes.string.isRequired,
+            multiple: PropTypes.bool,
+            options: PropTypes.arrayOf(
+                PropTypes.shape({
+                    id: PropTypes.string.isRequired,
+                    title: PropTypes.string.isRequired,
+                    checked: PropTypes.bool,
+                    help: PropTypes.string,
+                })
+            ),
+            help: PropTypes.string,
+        })
+    ),
+}
+
+const QS = ({ children, prompt = 'bash', divider = false, ...props }) => {
+    const qsClassNames = classNames({
+        [classes.prompt]: !!prompt && !divider,
+        [classes.bash]: prompt === 'bash' && !divider,
+        [classes.python]: prompt === 'python' && !divider,
+        [classes.divider]: !!divider,
+    })
+    const attrs = Object.assign(
+        {},
+        ...Object.keys(props).map(key => ({
+            [`data-quickstart-${key}`]: props[key],
+        }))
+    )
+    return (
+        <span className={qsClassNames} {...attrs}>
+            {children}
+        </span>
+    )
+}
+
+export { Quickstart, QS }
diff --git a/website/src/components/readnext.js b/website/src/components/readnext.js
new file mode 100644
index 000000000..b1939e042
--- /dev/null
+++ b/website/src/components/readnext.js
@@ -0,0 +1,27 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+
+import Icon from './icon'
+import Link from './link'
+import { Label } from './typography'
+
+import classes from '../styles/readnext.module.sass'
+
+const ReadNext = ({ title, to }) => (
+    <div className={classes.root}>
+        <Link to={to} hidden>
+            <Label>Read next</Label>
+            {title}
+        </Link>
+        <Link to={to} hidden className={classes.icon} aria-hidden="true">
+            <Icon name="arrowright" />
+        </Link>
+    </div>
+)
+
+ReadNext.propTypes = {
+    title: PropTypes.string.isRequired,
+    to: PropTypes.string.isRequired,
+}
+
+export default ReadNext
diff --git a/website/src/components/section.js b/website/src/components/section.js
new file mode 100644
index 000000000..c422877b1
--- /dev/null
+++ b/website/src/components/section.js
@@ -0,0 +1,29 @@
+import React, { useEffect } from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+import { useInView } from 'react-intersection-observer'
+import { window } from 'browser-monads'
+
+import classes from '../styles/section.module.sass'
+
+const Section = ({ id, className, ...props }) => {
+    const sectionClassNames = classNames(classes.root, className)
+    const relId = id && id.startsWith('section-') ? id.slice(8) : id
+    const [ref, inView] = useInView({ threshold: 0 })
+
+    useEffect(() => {
+        if (inView && relId) {
+            window.dispatchEvent(new CustomEvent('inview', { detail: relId }))
+        }
+    })
+    return <section ref={ref} id={id} className={sectionClassNames} {...props} />
+}
+
+Section.propTypes = {
+    id: PropTypes.string,
+    className: PropTypes.string,
+}
+
+export default Section
+
+export const Hr = () => <hr className={classes.hr} />
diff --git a/website/src/components/seo.js b/website/src/components/seo.js
new file mode 100644
index 000000000..4da6ab63a
--- /dev/null
+++ b/website/src/components/seo.js
@@ -0,0 +1,137 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import Helmet from 'react-helmet'
+import { StaticQuery, graphql } from 'gatsby'
+
+import socialImageDefault from '../images/social_default.jpg'
+import socialImageApi from '../images/social_api.jpg'
+import socialImageUniverse from '../images/social_universe.jpg'
+
+function getPageTitle(title, sitename, slogan, sectionTitle) {
+    if (sectionTitle) {
+        return `${title} · ${sitename} ${sectionTitle}`
+    }
+    if (title) {
+        return `${title} · ${sitename}`
+    }
+    return `${sitename} · ${slogan}`
+}
+
+function getImage(section) {
+    if (section === 'api') return socialImageApi
+    if (section === 'universe') return socialImageUniverse
+    return socialImageDefault
+}
+
+const SEO = ({ description, lang, title, section, sectionTitle, bodyClass }) => (
+    <StaticQuery
+        query={query}
+        render={data => {
+            const siteMetadata = data.site.siteMetadata
+            const metaDescription = description || siteMetadata.description
+            const pageTitle = getPageTitle(
+                title,
+                siteMetadata.title,
+                siteMetadata.slogan,
+                sectionTitle
+            )
+            const socialImage = getImage(section)
+            const meta = [
+                {
+                    name: 'description',
+                    content: metaDescription,
+                },
+                {
+                    property: 'og:title',
+                    content: pageTitle,
+                },
+                {
+                    property: 'og:description',
+                    content: metaDescription,
+                },
+                {
+                    property: 'og:type',
+                    content: `website`,
+                },
+                {
+                    property: 'og:site_name',
+                    content: title,
+                },
+                {
+                    property: 'og:image',
+                    content: socialImage,
+                },
+                {
+                    name: 'twitter:card',
+                    content: 'summary_large_image',
+                },
+                {
+                    name: 'twitter:image',
+                    content: socialImage,
+                },
+                {
+                    name: 'twitter:creator',
+                    content: `@${data.site.siteMetadata.social.twitter}`,
+                },
+                {
+                    name: 'twitter:site',
+                    content: `@${data.site.siteMetadata.social.twitter}`,
+                },
+                {
+                    name: 'twitter:title',
+                    content: pageTitle,
+                },
+                {
+                    name: 'twitter:description',
+                    content: metaDescription,
+                },
+                {
+                    name: 'docsearch:language',
+                    content: lang,
+                },
+            ]
+
+            return (
+                <Helmet
+                    htmlAttributes={{ lang }}
+                    bodyAttributes={{ class: bodyClass }}
+                    title={pageTitle}
+                    meta={meta}
+                />
+            )
+        }}
+    />
+)
+
+SEO.defaultProps = {
+    lang: 'en',
+}
+
+SEO.propTypes = {
+    description: PropTypes.string,
+    lang: PropTypes.string,
+    meta: PropTypes.array,
+    keywords: PropTypes.arrayOf(PropTypes.string),
+    title: PropTypes.string,
+    section: PropTypes.string,
+    bodyClass: PropTypes.string,
+}
+
+export default SEO
+
+const query = graphql`
+    query DefaultSEOQuery {
+        site {
+            siteMetadata {
+                title
+                description
+                slogan
+                siteUrl
+                email
+                social {
+                    twitter
+                }
+            }
+        }
+    }
+`
diff --git a/website/src/components/sidebar.js b/website/src/components/sidebar.js
new file mode 100644
index 000000000..cadb85fe4
--- /dev/null
+++ b/website/src/components/sidebar.js
@@ -0,0 +1,107 @@
+import React, { useState, useEffect, useRef } from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+import { window } from 'browser-monads'
+
+import Link from './link'
+import classes from '../styles/sidebar.module.sass'
+
+const Sidebar = ({ items, pageMenu, slug }) => {
+    const [initialized, setInitialized] = useState(false)
+    const [activeSection, setActiveSection] = useState(null)
+    const activeRef = useRef()
+    const handleInView = ({ detail }) => setActiveSection(detail)
+
+    useEffect(() => {
+        window.addEventListener('inview', handleInView, { passive: true })
+        if (!initialized) {
+            if (activeRef && activeRef.current) {
+                activeRef.current.scrollIntoView({ block: 'center' })
+            }
+            setInitialized(true)
+        }
+        return () => {
+            window.removeEventListener('inview', handleInView)
+        }
+    }, [])
+
+    return (
+        <menu className={classes.root}>
+            {items.map((section, i) => (
+                <ul className={classes.section} key={i}>
+                    <li className={classes.label}>{section.label}</li>
+                    {section.items.map(({ text, url, onClick, menu, isActive }, j) => {
+                        const currentMenu = menu || pageMenu || []
+                        const active = isActive || slug === url
+                        const itemClassNames = classNames(classes.link, {
+                            [classes.isActive]: active,
+                        })
+
+                        return (
+                            <li key={j} ref={active ? activeRef : null}>
+                                <Link
+                                    to={url}
+                                    onClick={onClick}
+                                    className={itemClassNames}
+                                    activeClassName={classes.isActive}
+                                    hideIcon
+                                >
+                                    {text}
+                                </Link>
+                                {active && !!currentMenu.length && (
+                                    <ul className={classes.crumbs}>
+                                        {currentMenu.map(crumb => {
+                                            const currentActive = activeSection || currentMenu[0].id
+                                            const crumbClassNames = classNames(classes.crumb, {
+                                                [classes.crumbActive]: currentActive === crumb.id,
+                                            })
+                                            return (
+                                                <li className={crumbClassNames} key={crumb.id}>
+                                                    <a href={`#${crumb.id}`}>{crumb.text}</a>
+                                                </li>
+                                            )
+                                        })}
+                                    </ul>
+                                )}
+                            </li>
+                        )
+                    })}
+                </ul>
+            ))}
+        </menu>
+    )
+}
+
+Sidebar.defaultProps = {
+    items: [],
+    pageMenu: [],
+}
+
+Sidebar.propTypes = {
+    items: PropTypes.arrayOf(
+        PropTypes.shape({
+            label: PropTypes.string.isRequired,
+            items: PropTypes.arrayOf(
+                PropTypes.shape({
+                    text: PropTypes.string.isRequired,
+                    url: PropTypes.string,
+                    onClick: PropTypes.func,
+                    menu: PropTypes.arrayOf(
+                        PropTypes.shape({
+                            text: PropTypes.string.isRequired,
+                            id: PropTypes.string.isRequired,
+                        })
+                    ),
+                })
+            ).isRequired,
+        })
+    ),
+    pageMenu: PropTypes.arrayOf(
+        PropTypes.shape({
+            text: PropTypes.string.isRequired,
+            id: PropTypes.string.isRequired,
+        })
+    ),
+}
+
+export default Sidebar
diff --git a/website/src/components/table.js b/website/src/components/table.js
new file mode 100644
index 000000000..2dd98a353
--- /dev/null
+++ b/website/src/components/table.js
@@ -0,0 +1,71 @@
+import React from 'react'
+import classNames from 'classnames'
+
+import Icon from './icon'
+import { isString } from './util'
+import classes from '../styles/table.module.sass'
+
+function isNum(children) {
+    return isString(children) && /^\d+[.,]?[\dx]+?(|x|ms|mb|gb|k|m)?$/i.test(children)
+}
+
+function getCellContent(children) {
+    const icons = {
+        '✅': { name: 'yes', variant: 'success' },
+        '❌': { name: 'no', variant: 'error' },
+    }
+
+    if (isString(children) && icons[children.trim()]) {
+        const iconProps = icons[children.trim()]
+        return <Icon {...iconProps} />
+    }
+    // Work around prettier auto-escape
+    if (isString(children) && children.startsWith('\\')) {
+        return children.slice(1)
+    }
+    return children
+}
+
+function isFootRow(children) {
+    const rowRegex = /^(RETURNS|YIELDS|CREATES|PRINTS)/
+    if (children.length && children[0].props.name === 'td') {
+        const cellChildren = children[0].props.children
+        if (
+            cellChildren &&
+            cellChildren.props &&
+            cellChildren.props.children &&
+            isString(cellChildren.props.children)
+        ) {
+            return rowRegex.test(cellChildren.props.children)
+        }
+    }
+    return false
+}
+
+export const Table = props => <table className={classes.root} {...props} />
+export const Th = props => <th className={classes.th} {...props} />
+
+export const Tr = ({ children, ...props }) => {
+    const trClasssNames = classNames(classes.tr, {
+        [classes.footer]: isFootRow(children),
+    })
+
+    return (
+        <tr className={trClasssNames} {...props}>
+            {children}
+        </tr>
+    )
+}
+
+export const Td = ({ num, nowrap, className, children, ...props }) => {
+    const content = getCellContent(children)
+    const tdClassNames = classNames(classes.td, className, {
+        [classes.num]: num || isNum(children),
+        [classes.nowrap]: nowrap,
+    })
+    return (
+        <td className={tdClassNames} {...props}>
+            {content}
+        </td>
+    )
+}
diff --git a/website/src/components/tag.js b/website/src/components/tag.js
new file mode 100644
index 000000000..090cc9f81
--- /dev/null
+++ b/website/src/components/tag.js
@@ -0,0 +1,57 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+
+import { isString } from './util'
+import Icon from './icon'
+import classes from '../styles/tag.module.sass'
+
+const Tag = ({ spaced, variant, tooltip, children }) => {
+    if (variant === 'new') {
+        const isValid = isString(children) && !isNaN(children)
+        const version = isValid ? Number(children).toFixed(1) : children
+        const tooltipText = `This feature is new and was introduced in spaCy v${version}`
+        return (
+            <TagTemplate spaced={spaced} tooltip={tooltipText}>
+                v{version}
+            </TagTemplate>
+        )
+    }
+    if (variant === 'model') {
+        const tooltipText = `To use this functionality, spaCy needs a model to be installed that supports the following capabilities: ${children}`
+        return (
+            <TagTemplate spaced={spaced} tooltip={tooltipText}>
+                Needs model
+            </TagTemplate>
+        )
+    }
+    return (
+        <TagTemplate spaced={spaced} tooltip={tooltip}>
+            {children}
+        </TagTemplate>
+    )
+}
+
+const TagTemplate = ({ spaced, tooltip, children }) => {
+    const tagClassNames = classNames(classes.root, {
+        [classes.spaced]: spaced,
+    })
+    return (
+        <span className={tagClassNames} data-tooltip={tooltip}>
+            {children}
+            {tooltip && <Icon name="help" width={12} className={classes.icon} />}
+        </span>
+    )
+}
+
+Tag.defaultProps = {
+    spaced: false,
+}
+
+Tag.propTypes = {
+    spaced: PropTypes.bool,
+    tooltip: PropTypes.string,
+    children: PropTypes.node.isRequired,
+}
+
+export default Tag
diff --git a/website/src/components/title.js b/website/src/components/title.js
new file mode 100644
index 000000000..285ad9192
--- /dev/null
+++ b/website/src/components/title.js
@@ -0,0 +1,52 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+
+import Button from './button'
+import Tag from './tag'
+import { H1 } from './typography'
+
+import classes from '../styles/title.module.sass'
+
+const Title = ({ title, tag, version, teaser, source, image, children, ...props }) => (
+    <header className={classes.root}>
+        {(image || source) && (
+            <div className={classes.corner}>
+                {source && (
+                    <Button to={source} icon="code">
+                        Source
+                    </Button>
+                )}
+
+                {image && (
+                    <div className={classes.image}>
+                        <img src={image} width={100} height={100} alt="" />
+                    </div>
+                )}
+            </div>
+        )}
+        <H1 className={classes.h1} {...props}>
+            {title}
+        </H1>
+        {tag && <Tag spaced>{tag}</Tag>}
+        {version && (
+            <Tag variant="new" spaced>
+                {version}
+            </Tag>
+        )}
+
+        {teaser && <div className={classes.teaser}>{teaser}</div>}
+
+        {children}
+    </header>
+)
+
+Title.propTypes = {
+    title: PropTypes.string,
+    tag: PropTypes.string,
+    teaser: PropTypes.node,
+    source: PropTypes.string,
+    image: PropTypes.string,
+    children: PropTypes.node,
+}
+
+export default Title
diff --git a/website/src/components/typography.js b/website/src/components/typography.js
new file mode 100644
index 000000000..721521939
--- /dev/null
+++ b/website/src/components/typography.js
@@ -0,0 +1,141 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import classNames from 'classnames'
+
+import Tag from './tag'
+import Button from './button'
+import Icon from './icon'
+import { isString, github, headingTextClassName } from './util'
+import classes from '../styles/typography.module.sass'
+
+export const H1 = ({ Component = 'h1', className, ...props }) => (
+    <Headline Component={Component} className={classNames(classes.h1, className)} {...props} />
+)
+export const H2 = ({ className, ...props }) => (
+    <Headline Component="h2" className={classNames(classes.h2, className)} {...props} />
+)
+export const H3 = ({ className, ...props }) => (
+    <Headline Component="h3" className={classNames(classes.h3, className)} {...props} />
+)
+export const H4 = ({ className, ...props }) => (
+    <Headline Component="h4" className={classNames(classes.h4, className)} {...props} />
+)
+export const H5 = ({ className, ...props }) => (
+    <Headline Component="h5" className={classNames(classes.h5, className)} {...props} />
+)
+
+export const P = ({ children, ...props }) => {
+    const dontWrap = ['figure']
+    if (
+        !Array.isArray(children) &&
+        !isString(children) &&
+        children.hasOwnProperty('type') &&
+        dontWrap.includes(children.type)
+    ) {
+        return children
+    }
+    return <p {...props}>{children}</p>
+}
+
+export const Abbr = ({ title, children }) => (
+    <abbr
+        className={classes.abbr}
+        data-tooltip={title}
+        data-tooltip-style="code"
+        aria-label={title}
+    >
+        {children}
+    </abbr>
+)
+
+export const Label = ({ className, ...props }) => (
+    <span className={classNames(classes.label, className)} {...props} />
+)
+
+export const InlineList = ({ Component = 'p', gutterBottom = true, className, children }) => {
+    const listClassNames = classNames(classes.inlineList, className, {
+        [classes.noGutter]: !gutterBottom,
+    })
+    return <Component className={listClassNames}>{children}</Component>
+}
+
+export const Help = ({ children }) => (
+    <span className={classes.help} data-tooltip={children}>
+        <Icon name="help2" width={16} />
+    </span>
+)
+
+/**
+ * Allows inserting comments that will appear in .md preview on GitHub, but
+ * won't be included in the final build of the site.
+ */
+export const Comment = () => null
+
+const Permalink = ({ id, children }) =>
+    !id ? (
+        <span className={headingTextClassName}>{children}</span>
+    ) : (
+        <a href={`#${id}`} className={classNames(headingTextClassName, classes.permalink)}>
+            {children}
+        </a>
+    )
+
+const Headline = ({
+    Component,
+    id,
+    new: version,
+    model,
+    tag,
+    source,
+    hidden,
+    action,
+    className,
+    children,
+}) => {
+    // This can be set via hidden="true" and as a prop, so we need to accept both
+    if (hidden === true || hidden === 'true') return null
+    const tags = tag ? tag.split(',').map(t => t.trim()) : []
+    return (
+        <Component id={id} className={classNames(classes.heading, className)}>
+            <Permalink id={id}>{children} </Permalink>
+            {tags.map((tag, i) => (
+                <Tag spaced key={i}>
+                    {tag}
+                </Tag>
+            ))}
+            {version && (
+                <Tag spaced variant="new">
+                    {version}
+                </Tag>
+            )}
+            {model && (
+                <Tag spaced variant="model">
+                    {model}
+                </Tag>
+            )}
+
+            {(source || action) && (
+                <div className={classes.action}>
+                    {source && (
+                        <Button icon="code" to={github(source)}>
+                            Source
+                        </Button>
+                    )}
+                    {action}
+                </div>
+            )}
+        </Component>
+    )
+}
+
+Headline.propTypes = {
+    Component: PropTypes.oneOfType([PropTypes.element, PropTypes.string]).isRequired,
+    id: PropTypes.oneOfType([PropTypes.string, PropTypes.oneOf([false])]),
+    new: PropTypes.string,
+    model: PropTypes.string,
+    source: PropTypes.string,
+    tag: PropTypes.string,
+    hidden: PropTypes.oneOfType([PropTypes.bool, PropTypes.oneOf(['true', 'false'])]),
+    action: PropTypes.node,
+    className: PropTypes.string,
+}
diff --git a/website/src/components/util.js b/website/src/components/util.js
new file mode 100644
index 000000000..0d3f7bda3
--- /dev/null
+++ b/website/src/components/util.js
@@ -0,0 +1,125 @@
+import React, { Fragment } from 'react'
+import { Parser as HtmlToReactParser } from 'html-to-react'
+import remark from 'remark'
+import remark2react from 'remark-react'
+import siteMetadata from '../../meta/site.json'
+
+const htmlToReactParser = new HtmlToReactParser()
+
+export const repo = siteMetadata.repo
+export const modelsRepo = siteMetadata.modelsRepo
+
+/**
+ * This is used to provide selectors for headings so they can be crawled by
+ * Algolia's DocSearch
+ */
+export const headingTextClassName = 'heading-text'
+
+/**
+ * Create a link to the spaCy repository on GitHub
+ * @param {string} filepath - The file path relative to the root of the repo.
+ * @param {string} [branch] - Optional branch. Defaults to master.
+ * @returns {string} - URL to the file on GitHub.
+ */
+export function github(filepath, branch = 'master') {
+    const path = filepath ? '/tree/' + (branch || 'master') + '/' + filepath : ''
+    return `https://github.com/${repo}${path}`
+}
+
+/**
+ * Get the source of a file in the documentation based on its slug
+ * @param {string} slug - The slug, e.g. /api/doc.
+ * @param {boolean} [isIndex] - Whether the page is an index, e.g. /api/index.md
+ * @param {string} [branch] - Optional branch on GitHub. Defaults to master.
+ */
+export function getCurrentSource(slug, isIndex = false, branch = 'master') {
+    const ext = isIndex ? '/index.md' : '.md'
+    return github(`website/docs${slug}${ext}`, branch)
+}
+
+/**
+ * @param obj – The object to check.
+ * @returns {boolean} - Whether the object is a string.
+ */
+export function isString(obj) {
+    return typeof obj === 'string' || obj instanceof String
+}
+
+/**
+ * Convert raw HTML to React elements
+ * @param {string} html - The HTML markup to convert.
+ * @returns {Node} - The converted React elements.
+ */
+export function htmlToReact(html) {
+    return htmlToReactParser.parse(html)
+}
+
+/**
+ * Convert raw Markdown to React
+ * @param {String} markdown - The Markdown markup to convert.
+ * @param {Object} [remarkReactComponents] - Optional React components to use
+ *  for HTML elements.
+ * @returns {Node} - The converted React elements.
+ */
+export function markdownToReact(markdown, remarkReactComponents = {}) {
+    return remark()
+        .use(remark2react, { remarkReactComponents })
+        .processSync(markdown).contents
+}
+
+/**
+ * Join an array of nodes with a given string delimiter, like Array.join for React
+ * @param {Array} arr - The elements to join.
+ * @param {string} delimiter - String placed between elements.
+ * @returns {Node} - The joined array.
+ */
+export function join(arr, delimiter = ', ') {
+    return arr.map((obj, i) => (
+        <Fragment key={i}>
+            {obj}
+            {i < arr.length - 1 && delimiter}
+        </Fragment>
+    ))
+}
+
+/**
+ * Convert an array of objects to an object, using a key with a unique value (ID).
+ * e.g. [{id: 'foo', bar: 'baz'}] => {foo: { id: 'foo', bar: 'baz'}}
+ * @param {Array} arr - The array to convert.
+ * @param {string} key - The key value to use to key the dictionary by.
+ * @return {Object} - The converted object.
+ */
+export function arrayToObj(arr, key) {
+    return Object.assign({}, ...arr.map(item => ({ [item[key]]: item })))
+}
+
+/**
+ * Abbreviate a number, e.g. 14249930 --> 14.25m.
+ * @param {number|string} num - The number to convert.
+ * @param {number} fixed - Number of decimals.
+ * @returns {string} - The abbreviated number.
+ */
+export function abbrNum(num = 0, fixed = 1) {
+    const suffixes = ['', 'k', 'm', 'b', 't']
+    if (num === null || num === 0) return 0
+    const b = num.toPrecision(2).split('e')
+    const k = b.length === 1 ? 0 : Math.floor(Math.min(b[1].slice(1), 14) / 3)
+    const n = k < 1 ? num : num / Math.pow(10, k * 3)
+    const c = k >= 1 && n >= 100 ? Math.round(n) : n.toFixed(fixed)
+    return (c < 0 ? c : Math.abs(c)) + suffixes[k]
+}
+
+/**
+ * Divide an array into chunks
+ * @param {Array} arr - The array to divide.
+ * @param {number} chunkSize - Size of the individual chunks.
+ * @returns {Array} - The divided array.
+ */
+export function chunkArray(arr, chunkSize) {
+    const base = [...arr]
+    const result = []
+    while (base.length) {
+        result.push(base.splice(0, chunkSize))
+    }
+    return result
+}
diff --git a/website/assets/fonts/hkgrotesk-bold.woff b/website/src/fonts/hkgrotesk-bold.woff
similarity index 100%
rename from website/assets/fonts/hkgrotesk-bold.woff
rename to website/src/fonts/hkgrotesk-bold.woff
diff --git a/website/assets/fonts/hkgrotesk-bold.woff2 b/website/src/fonts/hkgrotesk-bold.woff2
similarity index 100%
rename from website/assets/fonts/hkgrotesk-bold.woff2
rename to website/src/fonts/hkgrotesk-bold.woff2
diff --git a/website/assets/fonts/hkgrotesk-bolditalic.woff b/website/src/fonts/hkgrotesk-bolditalic.woff
similarity index 100%
rename from website/assets/fonts/hkgrotesk-bolditalic.woff
rename to website/src/fonts/hkgrotesk-bolditalic.woff
diff --git a/website/assets/fonts/hkgrotesk-bolditalic.woff2 b/website/src/fonts/hkgrotesk-bolditalic.woff2
similarity index 100%
rename from website/assets/fonts/hkgrotesk-bolditalic.woff2
rename to website/src/fonts/hkgrotesk-bolditalic.woff2
diff --git a/website/assets/fonts/hkgrotesk-semibold.woff b/website/src/fonts/hkgrotesk-semibold.woff
similarity index 100%
rename from website/assets/fonts/hkgrotesk-semibold.woff
rename to website/src/fonts/hkgrotesk-semibold.woff
diff --git a/website/assets/fonts/hkgrotesk-semibold.woff2 b/website/src/fonts/hkgrotesk-semibold.woff2
similarity index 100%
rename from website/assets/fonts/hkgrotesk-semibold.woff2
rename to website/src/fonts/hkgrotesk-semibold.woff2
diff --git a/website/assets/fonts/hkgrotesk-semibolditalic.woff b/website/src/fonts/hkgrotesk-semibolditalic.woff
similarity index 100%
rename from website/assets/fonts/hkgrotesk-semibolditalic.woff
rename to website/src/fonts/hkgrotesk-semibolditalic.woff
diff --git a/website/assets/fonts/hkgrotesk-semibolditalic.woff2 b/website/src/fonts/hkgrotesk-semibolditalic.woff2
similarity index 100%
rename from website/assets/fonts/hkgrotesk-semibolditalic.woff2
rename to website/src/fonts/hkgrotesk-semibolditalic.woff2
diff --git a/website/src/images/explosion.svg b/website/src/images/explosion.svg
new file mode 100644
index 000000000..65abb8736
--- /dev/null
+++ b/website/src/images/explosion.svg
@@ -0,0 +1,3 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 500 500" width="200" height="200">
+    <path fill="currentColor" d="M111.7 74.9L91.2 93.1l9.1 10.2 17.8-15.8 7.4 8.4-17.8 15.8 10.1 11.4 20.6-18.2 7.7 8.7-30.4 26.9-41.9-47.3 30.3-26.9 7.6 8.6zM190.8 59.6L219 84.3l-14.4 4.5-20.4-18.2-6.4 26.6-14.4 4.5 8.9-36.4-26.9-24.1 14.3-4.5L179 54.2l5.7-25.2 14.3-4.5-8.2 35.1zM250.1 21.2l27.1 3.4c6.1.8 10.8 3.1 14 7.2 3.2 4.1 4.5 9.2 3.7 15.5-.8 6.3-3.2 11-7.4 14.1-4.1 3.1-9.2 4.3-15.3 3.5L258 63.2l-2.8 22.3-13-1.6 7.9-62.7zm11.5 13l-2.2 17.5 12.6 1.6c5.1.6 9.1-2 9.8-7.6.7-5.6-2.5-9.2-7.6-9.9l-12.6-1.6zM329.1 95.4l23.8 13.8-5.8 10L312 98.8l31.8-54.6 11.3 6.6-26 44.6zM440.5 145c-1.3 8.4-5.9 15.4-13.9 21.1s-16.2 7.7-24.6 6.1c-8.4-1.6-15.3-6.3-20.8-14.1-5.5-7.9-7.6-16-6.4-24.4 1.3-8.5 6-15.5 14-21.1 8-5.6 16.2-7.7 24.5-6 8.4 1.6 15.4 6.3 20.9 14.2 5.5 7.6 7.6 15.7 6.3 24.2zM412 119c-5.1-.8-10.3.6-15.6 4.4-5.2 3.7-8.4 8.1-9.4 13.2-1 5.2.2 10.1 3.5 14.8 3.4 4.8 7.5 7.5 12.7 8.2 5.2.8 10.4-.7 15.6-4.4 5.3-3.7 8.4-8.1 9.4-13.2 1.1-5.1-.1-9.9-3.4-14.7-3.4-4.8-7.6-7.6-12.8-8.3zM471.5 237.9c-2.8 4.8-7.1 7.6-13 8.7l-2.6-13.1c5.3-.9 8.1-5 7.2-11-.9-5.8-4.3-8.8-8.9-8.2-2.3.3-3.7 1.4-4.5 3.3-.7 1.9-1.4 5.2-1.7 10.1-.8 7.5-2.2 13.1-4.3 16.9-2.1 3.9-5.7 6.2-10.9 7-6.3.9-11.3-.5-15.2-4.4-3.9-3.8-6.3-9-7.3-15.7-1.1-7.4-.2-13.7 2.6-18.8 2.8-5.1 7.4-8.2 13.7-9.2l2.6 13c-5.6 1.1-8.7 6.6-7.7 13.4 1 6.6 3.9 9.5 8.6 8.8 4.4-.7 5.7-4.5 6.7-14.1.3-3.5.7-6.2 1.1-8.4.4-2.2 1.2-4.4 2.2-6.8 2.1-4.7 6-7.2 11.8-8.1 5.4-.8 10.3.4 14.5 3.7 4.2 3.3 6.9 8.5 8 15.6.9 6.9-.1 12.6-2.9 17.3zM408.6 293.5l2.4-12.9 62 11.7-2.4 12.9-62-11.7zM419.6 396.9c-8.3 2-16.5.3-24.8-5-8.2-5.3-13.2-12.1-14.9-20.5-1.6-8.4.1-16.6 5.3-24.6 5.2-8.1 11.9-13.1 20.2-15.1 8.4-1.9 16.6-.3 24.9 5 8.2 5.3 13.2 12.1 14.8 20.5 1.7 8.4 0 16.6-5.2 24.7-5.2 8-12 13-20.3 15zm13.4-36.3c-1.2-5.1-4.5-9.3-9.9-12.8s-10.6-4.7-15.8-3.7-9.3 4-12.4 8.9-4.1 9.8-2.8 14.8c1.2 5.1 4.5 9.3 9.9 12.8 5.5 3.5 10.7 4.8 15.8 3.7 5.1-.9 9.2-3.8 12.3-8.7s4.1-9.9 2.9-15zM303.6 416.5l9.6-5.4 43.3 20.4-19.2-34 11.4-6.4 31 55-9.6 5.4-43.4-20.5 19.2 34.1-11.3 6.4-31-55zM238.2 468.8c-49 0-96.9-17.4-134.8-49-38.3-32-64-76.7-72.5-125.9-2-11.9-3.1-24-3.1-35.9 0-36.5 9.6-72.6 27.9-104.4 2.1-3.6 6.7-4.9 10.3-2.8 3.6 2.1 4.9 6.7 2.8 10.3-16.9 29.5-25.9 63.1-25.9 96.9 0 11.1 1 22.3 2.9 33.4 7.9 45.7 31.8 87.2 67.3 116.9 35.2 29.3 79.6 45.5 125.1 45.5 11.1 0 22.3-1 33.4-2.9 4.1-.7 8 2 8.7 6.1.7 4.1-2 8-6.1 8.7-11.9 2-24 3.1-36 3.1z" />
+</svg>
diff --git a/website/src/images/icon.png b/website/src/images/icon.png
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new file mode 100644
index 000000000..2a68ebe92
--- /dev/null
+++ b/website/src/images/icons/accept.svg
@@ -0,0 +1,3 @@
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+<path d="M9 16.172l10.594-10.594 1.406 1.406-12 12-5.578-5.578 1.406-1.406z"></path>
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diff --git a/website/src/images/icons/arrow-right.svg b/website/src/images/icons/arrow-right.svg
new file mode 100644
index 000000000..2e51e2555
--- /dev/null
+++ b/website/src/images/icons/arrow-right.svg
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diff --git a/website/src/images/icons/code.svg b/website/src/images/icons/code.svg
new file mode 100644
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diff --git a/website/src/images/icons/docs.svg b/website/src/images/icons/docs.svg
new file mode 100644
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--- /dev/null
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new file mode 100644
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diff --git a/website/src/images/icons/help.svg b/website/src/images/icons/help.svg
new file mode 100644
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diff --git a/website/src/images/icons/info.svg b/website/src/images/icons/info.svg
new file mode 100644
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new file mode 100644
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new file mode 100644
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diff --git a/website/src/images/icons/offline.svg b/website/src/images/icons/offline.svg
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diff --git a/website/src/images/icons/reject.svg b/website/src/images/icons/reject.svg
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diff --git a/website/src/images/icons/twitter.svg b/website/src/images/icons/twitter.svg
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diff --git a/website/src/images/icons/warning.svg b/website/src/images/icons/warning.svg
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diff --git a/website/src/images/logo.svg b/website/src/images/logo.svg
new file mode 100644
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+<path fill="#111" d="M542.1 184.3c0 30.97-38.56 29.14-38.56 29.14H465v-56h38.54c39.7-.23 38.56 26.85 38.56 26.85M465 72.05h29.36c30.53 1.6 29.38 24.33 29.38 24.33 0 28.22-33.74 28.68-33.74 28.68h-25V72.06zm70 64.03s26.38-11.25 26.15-41.1c0 0 4-48.87-60.76-54.84h-71.9V245.1h82.4s68.84.24 68.84-57.83c0 0 1.6-39.47-44.75-51.18M348.95 0h302.6v285.17h-302.6V0zM193.14 184.3c0 30.97-38.56 29.14-38.56 29.14h-38.56v-56h38.56c39.7-.23 38.56 26.85 38.56 26.85M116.02 72.05h29.38c30.52 1.6 29.38 24.33 29.38 24.33 0 28.22-33.74 28.68-33.74 28.68h-25.02V72.06zm70 64.03s26.4-11.25 26.17-41.1c0 0 4-48.87-60.78-54.84h-71.9V245.1h82.4s68.86.24 68.86-57.83c0 0 1.6-39.47-44.76-51.18M0 0h302.6v285.17H0V0zM938.8 54.85v37.87S901.85 70 861 69.54c0 0-76.2-1.5-79.64 73.04 0 0-2.75 68.57 78.72 72.47 0 0 34.2 4.13 80.56-25.48v39.25s-62.2 36.95-134.26 8.5c0 0-60.6-22.15-62.9-94.74 0 0-2.52-74.65 78.27-99.43 0 0 21.58-8.26 60.36-4.6 0 0 23.2 2.3 56.7 16.3M697.93 285.17h302.6V0h-302.6v285.17z" />
+</svg>
diff --git a/website/src/images/logos/chartbeat.svg b/website/src/images/logos/chartbeat.svg
new file mode 100644
index 000000000..75c19d661
--- /dev/null
+++ b/website/src/images/logos/chartbeat.svg
@@ -0,0 +1,6 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="180" height="25" viewBox="0 0 915.7 130.1">
+<path fill="#51a4da" d="M157.8 8.6c-8.1 6.8-15.5 13-23 19.1-.8.7-2.2.7-3.4.7H23.3c-.8 0-1.6-.3-2.6-.4V8.6h137.1z" />
+<path fill="#b3e4fa" d="M0 33.9c2-.1 4.1-.3 6.1-.3H129c-8.7 7.2-16.2 13.5-23.8 19.6-.8.6-2.3.7-3.4.7H2.4c-.8 0-1.7-.3-2.5-.5.1-6.5.1-13 .1-19.5z" />
+<path fill="#5bc4bf" d="M36 79.2V59.4h63.2C91 66.3 83.6 72.5 76.1 78.7c-.5.4-1.3.5-1.9.5H36z" />
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+</svg>
diff --git a/website/src/images/logos/index.js b/website/src/images/logos/index.js
new file mode 100644
index 000000000..3a61a58a7
--- /dev/null
+++ b/website/src/images/logos/index.js
@@ -0,0 +1,29 @@
+import { ReactComponent as AirbnbLogo } from './airbnb.svg'
+import { ReactComponent as QuoraLogo } from './quora.svg'
+import { ReactComponent as RetrieverLogo } from './retriever.svg'
+import { ReactComponent as StitchfixLogo } from './stitchfix.svg'
+import { ReactComponent as ChartbeatLogo } from './chartbeat.svg'
+import { ReactComponent as AllenAILogo } from './allenai.svg'
+
+import { ReactComponent as RecodeLogo } from './recode.svg'
+import { ReactComponent as WapoLogo } from './wapo.svg'
+import { ReactComponent as BBCLogo } from './bbc.svg'
+import { ReactComponent as MicrosoftLogo } from './microsoft.svg'
+import { ReactComponent as VenturebeatLogo } from './venturebeat.svg'
+import { ReactComponent as ThoughtworksLogo } from './thoughtworks.svg'
+
+export default {
+    airbnb: AirbnbLogo,
+    quora: QuoraLogo,
+    retriever: RetrieverLogo,
+    stitchfix: StitchfixLogo,
+    chartbeat: ChartbeatLogo,
+    allenai: AllenAILogo,
+
+    recode: RecodeLogo,
+    wapo: WapoLogo,
+    bbc: BBCLogo,
+    microsoft: MicrosoftLogo,
+    venturebeat: VenturebeatLogo,
+    thoughtworks: ThoughtworksLogo,
+}
diff --git a/website/src/images/logos/microsoft.svg b/website/src/images/logos/microsoft.svg
new file mode 100644
index 000000000..419f240d4
--- /dev/null
+++ b/website/src/images/logos/microsoft.svg
@@ -0,0 +1,7 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="130" height="28" viewBox="0 0 609 130">
+<path fill="#737373" d="M213.2 74.3l-3.6 10.2h-.3c-.6-2.3-1.7-5.8-3.5-10L186.5 26h-18.9v77.3h12.5V55.6c0-3 0-6.4-.1-10.6-.1-2.1-.3-3.7-.4-4.9h.3c.6 3 1.3 5.2 1.8 6.6l23.2 56.4h8.8l23-56.9c.5-1.3 1-3.9 1.5-6.1h.3c-.3 5.7-.5 10.8-.6 13.9v49h13.3V25.8H233l-19.8 48.5zm50.6-26.7h13V103h-13zm6.6-23.4c-2.2 0-4 .8-5.5 2.2-1.5 1.4-2.3 3.2-2.3 5.4 0 2.1.8 3.9 2.3 5.3 1.5 1.4 3.3 2.1 5.5 2.1s4.1-.8 5.5-2.1c1.5-1.4 2.3-3.2 2.3-5.3s-.8-3.9-2.3-5.4c-1.3-1.4-3.2-2.2-5.5-2.2m52.5 22.9c-2.4-.5-4.9-.8-7.3-.8-5.9 0-11.3 1.3-15.8 3.9-4.5 2.6-8.1 6.2-10.4 10.7-2.4 4.6-3.6 9.9-3.6 16 0 5.3 1.2 10 3.5 14.3 2.3 4.2 5.5 7.6 9.8 9.9 4.1 2.3 8.9 3.5 14.3 3.5 6.2 0 11.5-1.3 15.7-3.7l.1-.1v-12l-.5.4c-1.9 1.4-4.1 2.6-6.3 3.3-2.3.8-4.4 1.2-6.2 1.2-5.2 0-9.3-1.5-12.2-4.8-3-3.2-4.5-7.6-4.5-13.1 0-5.7 1.5-10.2 4.6-13.5 3.1-3.3 7.2-5 12.2-5 4.2 0 8.5 1.4 12.4 4.2l.5.4V49.2l-.1-.1c-1.7-.7-3.6-1.5-6.2-2m42.9-.4c-3.2 0-6.2 1-8.8 3.1-2.2 1.8-3.7 4.4-5 7.5h-.1v-9.7h-13V103h13V74.7c0-4.8 1-8.8 3.2-11.7 2.2-3 5-4.5 8.4-4.5 1.2 0 2.4.3 3.9.5 1.4.4 2.4.8 3.1 1.3l.5.4v-13l-.3-.1c-.9-.6-2.7-.9-4.9-.9m35.4-.3c-9.1 0-16.4 2.7-21.5 8-5.2 5.3-7.7 12.6-7.7 21.8 0 8.6 2.6 15.6 7.6 20.7 5 5 11.8 7.6 20.3 7.6 8.9 0 16-2.7 21.1-8.1 5.2-5.4 7.7-12.6 7.7-21.5 0-8.8-2.4-15.8-7.3-20.9-4.7-5.1-11.6-7.6-20.2-7.6M411.6 89c-2.4 3.1-6.2 4.6-10.9 4.6s-8.5-1.5-11.2-4.8c-2.7-3.1-4-7.6-4-13.3 0-5.9 1.4-10.4 4-13.6 2.7-3.2 6.4-4.8 11.1-4.8 4.6 0 8.2 1.5 10.8 4.6 2.6 3.1 4 7.6 4 13.5-.2 6-1.3 10.7-3.8 13.8m46.1-18.4c-4.1-1.7-6.7-3-7.9-4.1-1-1-1.5-2.4-1.5-4.2 0-1.5.6-3 2.1-4s3.2-1.5 5.7-1.5c2.2 0 4.5.4 6.7 1s4.2 1.5 5.8 2.7l.5.4V48.7l-.3-.1c-1.5-.6-3.5-1.2-5.9-1.7-2.4-.4-4.6-.6-6.4-.6-6.2 0-11.3 1.5-15.3 4.8-4 3.1-5.9 7.3-5.9 12.2 0 2.6.4 4.9 1.3 6.8.9 1.9 2.2 3.7 4 5.2 1.8 1.4 4.4 3 8 4.5 3 1.3 5.3 2.3 6.7 3.1 1.4.8 2.3 1.7 3 2.4.5.8.8 1.8.8 3.1 0 3.7-2.8 5.5-8.5 5.5-2.2 0-4.5-.4-7.2-1.3s-5.2-2.2-7.3-3.7l-.5-.4v12.7l.3.1c1.9.9 4.2 1.5 7 2.2 2.8.5 5.3.9 7.5.9 6.7 0 12.2-1.5 16.1-4.8 4-3.2 6.1-7.3 6.1-12.6 0-3.7-1-7-3.2-9.5-2.9-2.4-6.5-4.9-11.7-6.9m49.2-24.2c-9.1 0-16.4 2.7-21.5 8s-7.7 12.6-7.7 21.8c0 8.6 2.6 15.6 7.6 20.7 5 5 11.8 7.6 20.3 7.6 8.9 0 16-2.7 21.1-8.1 5.2-5.4 7.7-12.6 7.7-21.5 0-8.8-2.4-15.8-7.3-20.9-4.7-5.1-11.6-7.6-20.2-7.6M517.2 89c-2.4 3.1-6.2 4.6-10.9 4.6-4.8 0-8.5-1.5-11.2-4.8-2.7-3.1-4-7.6-4-13.3 0-5.9 1.4-10.4 4-13.6 2.7-3.2 6.4-4.8 11.1-4.8 4.5 0 8.2 1.5 10.8 4.6 2.6 3.1 4 7.6 4 13.5 0 6-1.3 10.7-3.8 13.8M603.9 58.3V47.6h-13.1V31.2l-.4.1L578 35l-.3.1v12.5h-19.6v-7c0-3.2.8-5.7 2.2-7.3s3.5-2.4 6.1-2.4c1.8 0 3.7.4 5.8 1.3l.5.3V21.2l-.3-.1c-1.8-.6-4.2-1-7.3-1-3.9 0-7.3.9-10.4 2.4-3.1 1.7-5.4 4-7.1 7.1-1.7 3-2.6 6.4-2.6 10.3v7.7h-9.1v10.6h9.1V103h13.1V58.3h19.6v28.5c0 11.7 5.5 17.6 16.5 17.6 1.8 0 3.7-.3 5.5-.6 1.9-.4 3.3-.9 4.1-1.3l.1-.1V91.7l-.5.4c-.8.5-1.5.9-2.7 1.2-1 .3-1.9.4-2.6.4-2.6 0-4.4-.6-5.7-2.1-1.2-1.4-1.8-3.7-1.8-7.1V58.3h13.3z" />
+<path fill="#F25022" d="M0 0h61.3v61.3H0z" />
+<path fill="#7FBA00" d="M67.7 0H129v61.3H67.7z" />
+<path fill="#00A4EF" d="M0 67.7h61.3V129H0z" />
+<path fill="#FFB900" d="M67.7 67.7H129V129H67.7z" />
+</svg>
diff --git a/website/src/images/logos/quora.svg b/website/src/images/logos/quora.svg
new file mode 100644
index 000000000..edb12a23a
--- /dev/null
+++ b/website/src/images/logos/quora.svg
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HcmV?d00001

diff --git a/website/assets/img/social/preview_api.jpg b/website/src/images/social_api.jpg
similarity index 100%
rename from website/assets/img/social/preview_api.jpg
rename to website/src/images/social_api.jpg
diff --git a/website/assets/img/social/preview_default.jpg b/website/src/images/social_default.jpg
similarity index 100%
rename from website/assets/img/social/preview_default.jpg
rename to website/src/images/social_default.jpg
diff --git a/website/assets/img/social/preview_universe.jpg b/website/src/images/social_universe.jpg
similarity index 100%
rename from website/assets/img/social/preview_universe.jpg
rename to website/src/images/social_universe.jpg
diff --git a/website/src/pages/404.md b/website/src/pages/404.md
new file mode 100644
index 000000000..910b552b3
--- /dev/null
+++ b/website/src/pages/404.md
@@ -0,0 +1,7 @@
+---
+title: 404 Error
+---
+
+import Error from 'widgets/404.js'
+
+<Error />
diff --git a/website/src/plugins/remark-code-blocks.js b/website/src/plugins/remark-code-blocks.js
new file mode 100644
index 000000000..4cbb08b62
--- /dev/null
+++ b/website/src/plugins/remark-code-blocks.js
@@ -0,0 +1,71 @@
+/**
+ * Support titles, line highlights and more for code blocks
+ */
+
+const visit = require('unist-util-visit')
+const parseAttr = require('md-attr-parser')
+
+const defaultOptions = {
+    defaultPrefix: '###',
+    prefixMap: {
+        javascript: '///',
+        jsx: '///',
+    },
+    languageAliases: {},
+    prompts: ['$'],
+}
+
+function remarkCodeBlocks(userOptions = {}) {
+    const options = Object.assign({}, defaultOptions, userOptions)
+
+    function transformer(tree) {
+        visit(tree, 'code', node => {
+            if (node.value) {
+                const langName = node.lang || 'none'
+                const lang = options.languageAliases[langName] || langName
+                const prefix = options.prefixMap[lang] || options.defaultPrefix
+                const lines = node.value.split('\n')
+                let attrs = { lang }
+                const firstLine = lines[0].trim()
+                if (firstLine && firstLine.startsWith(prefix)) {
+                    const title = firstLine.slice(prefix.length).trim()
+                    attrs.title = title
+                    // Check for attributes in title, e.g. {executable="true"}
+                    const parsed = title.split(/\{(.*?)\}/)
+                    if (parsed.length >= 2 && parsed[1]) {
+                        const { prop } = parseAttr(parsed[1])
+                        attrs = { ...attrs, ...prop, title: parsed[0].trim(), lang }
+                    }
+                    // Overwrite the code text with the rest of the lines
+                    node.value = lines.slice(1).join('\n')
+                } else if (firstLine && /^https:\/\/github.com/.test(firstLine)) {
+                    // GitHub URL
+                    attrs.github = 'true'
+                }
+                // If it's a bash code block and single line, check for prompts
+                if (lang === 'bash') {
+                    const [trueFirstLine, ...trueLines] = node.value.split('\n')
+                    for (let prompt of options.prompts) {
+                        if (trueFirstLine.startsWith(prompt)) {
+                            const content = [
+                                trueFirstLine.slice(prompt.length).trim(),
+                                ...trueLines,
+                            ]
+                            attrs.prompt = prompt
+                            node.value = content.join('\n')
+                            break
+                        }
+                    }
+                }
+
+                const data = node.data || (node.data = {})
+                const hProps = data.hProperties || (data.hProperties = {})
+                node.data.hProperties = Object.assign({}, hProps, attrs)
+            }
+        })
+        return tree
+    }
+    return transformer
+}
+
+module.exports = remarkCodeBlocks
diff --git a/website/src/plugins/remark-custom-attrs.js b/website/src/plugins/remark-custom-attrs.js
new file mode 100644
index 000000000..3a2dcb3f5
--- /dev/null
+++ b/website/src/plugins/remark-custom-attrs.js
@@ -0,0 +1,52 @@
+/**
+ * Simplified implementation of remark-attr that allows custom attributes on
+ * nodes *inline* via the following syntax: {#some-id key="value"}. Extracting
+ * them inline is slightly hackier (at least in this implementation), but it
+ * makes the resulting markup valid and compatible with formatters like
+ * Prettier, which do not allow additional content right below headlines.
+ * Based on: https://github.com/arobase-che/remark-attr
+ */
+
+const visit = require('unist-util-visit')
+const parseAttr = require('md-attr-parser')
+
+const defaultOptions = {
+    elements: ['heading', 'link'],
+}
+
+function remarkCustomAttrs(userOptions = {}) {
+    const options = Object.assign({}, defaultOptions, userOptions)
+    function transformer(tree) {
+        visit(tree, null, node => {
+            if (options.elements.includes(node.type)) {
+                if (
+                    node.children &&
+                    node.children.length &&
+                    node.children[0].type === 'text' &&
+                    node.children[0].value
+                ) {
+                    if (node.children.length > 1 && node.children.every(el => el.type === 'text')) {
+                        // If headlines contain escaped characters, e.g.
+                        // Doc.\_\_init\_\_, it will be split into several nodes
+                        const mergedText = node.children.map(el => el.value).join('')
+                        node.children[0].value = mergedText
+                        node.children = [node.children[0]]
+                    }
+                    const parsed = node.children[0].value.split(/\{(.*?)\}/)
+                    if (parsed.length >= 2 && parsed[1]) {
+                        const text = parsed[0].trim()
+                        const { prop } = parseAttr(parsed[1])
+                        const data = node.data || (node.data = {})
+                        const hProps = data.hProperties || (data.hProperties = {})
+                        node.data.hProperties = Object.assign({}, hProps, prop)
+                        node.children[0].value = text
+                    }
+                }
+            }
+        })
+        return tree
+    }
+    return transformer
+}
+
+module.exports = remarkCustomAttrs
diff --git a/website/src/plugins/remark-wrap-section.js b/website/src/plugins/remark-wrap-section.js
new file mode 100644
index 000000000..907ecb636
--- /dev/null
+++ b/website/src/plugins/remark-wrap-section.js
@@ -0,0 +1,75 @@
+/**
+ * Check the tree for headlines of a certain depth and wrap the headline and
+ * all content up to the next headline in a section.
+ * Based on: https://github.com/luhmann/tufte-markdown
+ */
+
+const visit = require('unist-util-visit')
+const slugify = require('@sindresorhus/slugify')
+
+const defaultOptions = {
+    element: 'section',
+    prefix: 'section-',
+    depth: 2,
+    slugify: true,
+}
+
+function slugifyNode(node) {
+    if (node.children.length && node.children[0].type === 'text') {
+        return slugify(node.children[0].value)
+    }
+    return null
+}
+
+function remarkWrapSection(userOptions = {}) {
+    const options = Object.assign({}, defaultOptions, userOptions)
+
+    function transformer(tree) {
+        const headingsMap = []
+        const newTree = []
+
+        visit(tree, 'import', node => {
+            // For compatibility with MDX / gatsby-mdx, make sure import nodes
+            // are not moved further down into children (which means they're not
+            // recognized and interpreted anymore). Add them to the very start
+            // of the tree.
+            newTree.push(node)
+        })
+
+        visit(tree, 'heading', (node, index) => {
+            if (node.depth === options.depth) {
+                const data = node.data || (node.data = {})
+                const hProps = data.hProperties || (data.hProperties = {})
+                hProps.id = hProps.id ? hProps.id : slugifyNode(node)
+                headingsMap.push({ index, id: hProps.id })
+            }
+        })
+
+        if (headingsMap.length) {
+            for (let index = 0; index <= headingsMap.length; index++) {
+                const sectionStartIndex = index === 0 ? 0 : headingsMap[index - 1].index
+                const sectionEndIndex =
+                    index === headingsMap.length ? tree.children.length : headingsMap[index].index
+                const children = tree.children
+                    .slice(sectionStartIndex, sectionEndIndex)
+                    .filter(node => node.type !== 'import')
+
+                if (children.length) {
+                    const headingId = index === 0 ? 0 : headingsMap[index - 1].id
+                    const sectionId = headingId ? options.prefix + headingId : undefined
+                    const wrapperNode = {
+                        type: 'paragraph',
+                        children,
+                        data: { hName: options.element, hProperties: { id: sectionId } },
+                    }
+                    newTree.push(wrapperNode)
+                }
+            }
+            tree.children = newTree
+        }
+        return tree
+    }
+    return transformer
+}
+
+module.exports = remarkWrapSection
diff --git a/website/src/styles/accordion.module.sass b/website/src/styles/accordion.module.sass
new file mode 100644
index 000000000..707e29aef
--- /dev/null
+++ b/website/src/styles/accordion.module.sass
@@ -0,0 +1,48 @@
+.root
+    --color-inline-code-bg: var(--color-theme-opaque)
+    background: var(--color-theme-light)
+    margin-bottom: var(--spacing-xs)
+    border-radius: var(--border-radius)
+
+.button
+    font: bold var(--font-size-lg)/var(--line-height-md) var(--font-primary)
+    color: var(--color-theme-dark)
+    display: flex
+    align-items: center
+    justify-content: space-between
+    width: 100%
+    padding: 1rem 1.5rem
+    border-radius: var(--border-radius)
+
+    &:focus
+        background: var(--color-theme-opaque)
+
+    &[aria-expanded="true"]:focus
+        border-bottom-left-radius: 0
+        border-bottom-right-radius: 0
+
+.icon
+    width: 2rem
+    height: 2rem
+    background: var(--color-theme)
+    color: var(--color-back)
+    border-radius: 50%
+    padding: 0.35rem
+    pointer-events: none
+
+.content
+    padding: 2rem 1.5rem 3rem
+
+    & > *:last-child
+        margin-bottom: 0
+
+    p
+        margin-bottom: var(--spacing-sm)
+
+.hidden
+    display: none
+
+.anchor
+    color: var(--color-subtle-dark)
+    padding-left: 0.5em
+    opacity: 0.75
diff --git a/website/src/styles/alert.module.sass b/website/src/styles/alert.module.sass
new file mode 100644
index 000000000..d64c3287d
--- /dev/null
+++ b/website/src/styles/alert.module.sass
@@ -0,0 +1,21 @@
+.root
+    position: fixed
+    bottom: 0
+    left: 0
+    width: 100%
+    background: var(--color-subtle-light)
+    z-index: 100
+    font: var(--font-size-sm)/var(--line-height-md) var(--font-primary)
+    text-align: center
+    padding: 1rem
+
+.warning
+    --alert-bg: var(--color-yellow-light)
+    --color-theme: var(--color-yellow-dark)
+    --color-theme-dark: var(--color-yellow-dark)
+    --color-inline-code-bg: var(--color-yellow-opaque)
+    background: var(--color-yellow-light)
+    color: var(--color-yellow-dark)
+
+.clickable
+    cursor: pointer
diff --git a/website/src/styles/aside.module.sass b/website/src/styles/aside.module.sass
new file mode 100644
index 000000000..7746451b4
--- /dev/null
+++ b/website/src/styles/aside.module.sass
@@ -0,0 +1,90 @@
+@import base
+
+$triangle-size: 2rem
+$border-radius: 6px
+
+.root
+    position: relative
+
+.content
+    --font-size-sm: 1.2rem
+    --color-inline-code-text: var(--color-back)
+    --color-inline-code-bg: var(--color-dark-secondary)
+
+    background: var(--color-front)
+    border-top-left-radius: $border-radius
+    border-bottom-left-radius: $border-radius
+    font-size: var(--font-size-sm)
+    z-index: 10
+
+    pre
+        padding: 0
+        margin: 0 0 var(--spacing-sm) 0
+
+        &:last-child
+            margin: 0
+
+        code
+            padding: 0
+            margin: 0
+
+    p, ul, ol
+        font: inherit
+        margin-bottom: var(--spacing-sm)
+
+    ul li
+        padding: 0
+        margin: 0
+
+        &:before
+            content: initial
+
+@include breakpoint(min, md)
+    .content
+        position: absolute
+        top: -3rem
+        left: calc(100% + 5.5rem)
+        width: calc(var(--width-aside) + 2rem)
+
+        // Banner effect
+        &:after
+            position: absolute
+            bottom: -$triangle-size / 2
+            left: $border-radius / 2
+            width: 0
+            height: 0
+            border-color: transparent
+            border-style: solid
+            border-top-color: var(--color-dark)
+            border-width: $triangle-size / 2 0 0 calc(#{$triangle-size} - #{$border-radius / 2})
+            content: ""
+
+@include breakpoint(max, sm)
+    .content
+        display: block
+        margin: 2rem 0
+
+.text
+    font: var(--font-size-sm)/var(--line-height-sm) var(--font-primary)
+    color: var(--color-back)
+    padding: 1.5rem 2.5rem 2.5rem 2rem
+
+    a, a:hover
+        color: var(--color-subtle)
+
+    & > *:last-child
+        margin-bottom: 0
+
+.title,
+.text h4
+    font: bold var(--font-size-lg)/var(--line-height-md) var(--font-secondary)
+    text-transform: uppercase
+    display: inline-block
+    border-radius: 1em
+    padding: 0 1rem 0.15rem !important
+    background: var(--color-dark)
+    box-shadow: inset 1px 1px 1px rgba(0, 0, 0, 0.25)
+    color: var(--color-back)
+    margin-bottom: 1.25rem
+    margin-left: -0.5rem
+    pointer-events: none
diff --git a/website/src/styles/base.sass b/website/src/styles/base.sass
new file mode 100644
index 000000000..ab05a3e7d
--- /dev/null
+++ b/website/src/styles/base.sass
@@ -0,0 +1,57 @@
+/* Mixins */
+
+//- Responsive Breakpoint utility
+
+$breakpoints: ( sm: 768px, md: 992px, lg: 1200px )
+
+@mixin breakpoint($limit, $size)
+    $breakpoints-max: ( xs: map-get($breakpoints, sm) - 1, sm: map-get($breakpoints, md) - 1, md: map-get($breakpoints, lg) - 1 )
+
+    @if $limit == "min"
+        @media(min-width: #{map-get($breakpoints, $size)})
+            @content
+
+    @else if $limit == "max"
+        @media(max-width: #{map-get($breakpoints-max, $size)})
+            @content
+
+// Scroll shadows for reponsive tables
+// adapted from David Bushell, http://codepen.io/dbushell/pen/wGaamR
+// $scroll-shadow-color      - color of shadow
+// $scroll-shadow-side       - side to cover shadow (left or right)
+// $scroll-shadow-background - original background color to match
+
+@function scroll-shadow-gradient($scroll-gradient-direction, $scroll-shadow-background)
+    @return linear-gradient(to #{$scroll-gradient-direction}, $scroll-shadow-background 50%, rgba(255, 255, 255, 0) 100%)
+
+@mixin scroll-shadow-base($scroll-shadow-color)
+    background: radial-gradient(ellipse at 0 50%, $scroll-shadow-color 0%, rgba(0,0,0,0) 75%) 0 center, radial-gradient(ellipse at 100% 50%, $scroll-shadow-color 0%, transparent 75%) 100% center
+    background-attachment: scroll, scroll
+    background-repeat: no-repeat
+    background-size: 10px 100%, 10px 100%
+
+@mixin scroll-shadow-cover($scroll-shadow-side, $scroll-shadow-background)
+    $scroll-gradient-direction: right !default
+    background-repeat: no-repeat
+
+    @if $scroll-shadow-side == right
+        $scroll-gradient-direction: left
+        background-position: 100% 0
+
+    @if $scroll-shadow-side == both
+        background-image: scroll-shadow-gradient(left, $scroll-shadow-background), scroll-shadow-gradient(right, $scroll-shadow-background)
+        background-position: 100% 0, 0 0
+        background-size: 20px 100%, 20px 100%
+    @else
+        background-image: scroll-shadow-gradient($scroll-gradient-direction, $scroll-shadow-background)
+        background-size: 20px 100%
+
+// Full vertical scroll shadows
+// adapted from: https://codepen.io/laustdeleuran/pen/DBaAu
+
+@mixin scroll-shadow($background-color, $shadow-color, $shadow-offset: 0, $cover-size: 40px, $shadow-size: 15px)
+    background: linear-gradient($background-color 30%, rgba(255, 255, 255,0)) 0 $shadow-offset, linear-gradient(rgba(255,255,255,0), $background-color 70%) 0 100%, radial-gradient(farthest-side at 50% 0, $shadow-color, rgba(255,255,255,0)) -20px $shadow-offset, radial-gradient(farthest-side at 50% 100%, $shadow-color, rgba(255,255,255,0)) 0 100%
+    background-repeat: no-repeat
+    background-color: $background-color
+    background-size: 100% $cover-size, 100% $cover-size, 100% $shadow-size, 100% $shadow-size
+    background-attachment: local, local, scroll, scroll
diff --git a/website/src/styles/button.module.sass b/website/src/styles/button.module.sass
new file mode 100644
index 000000000..2b824311b
--- /dev/null
+++ b/website/src/styles/button.module.sass
@@ -0,0 +1,41 @@
+.root
+    display: inline-block
+    padding: 0.65rem 1.1rem 0.825rem
+    margin-bottom: 1px
+    border: 2px solid var(--color-theme)
+    border-radius: 2em
+    text-align: center
+    transition: background-color, color 0.25s ease
+    font: bold var(--font-size-xs)/var(--line-height-md) var(--font-secondary)
+    text-transform: uppercase
+
+    &:hover
+        border-color: var(--color-theme-dark)
+
+.large
+    font-size: var(--font-size-lg)
+    padding: 0.8em 1.1em 1em
+
+.primary
+    background: var(--color-theme)
+    color: var(--color-back)
+
+    &:hover
+        background: var(--color-theme-dark)
+        color: var(--color-back)
+
+.secondary
+    background: var(--color-back)
+    color: var(--color-theme)
+
+    &:hover
+        color: var(--color-theme-dark)
+
+.tertiary
+    background: transparent
+    color: var(--color-back)
+    border-color: currentColor
+
+    &:hover
+        color: var(--color-back)
+        border-color: currentColor
diff --git a/website/src/styles/card.module.sass b/website/src/styles/card.module.sass
new file mode 100644
index 000000000..d9e0633cf
--- /dev/null
+++ b/website/src/styles/card.module.sass
@@ -0,0 +1,14 @@
+.root
+    background: var(--color-subtle-light)
+    border-radius: var(--border-radius)
+    padding: 2rem
+    font: var(--font-size-md)/var(--line-height-md) var(--font-primary)
+    margin-bottom: var(--spacing-sm)
+
+.image
+    $image-size: 35px
+    width: $image-size
+    height: $image-size
+    overflow: hidden
+    float: right
+    border-radius: 50%
diff --git a/website/src/styles/code.module.sass b/website/src/styles/code.module.sass
new file mode 100644
index 000000000..f72f1ffe6
--- /dev/null
+++ b/website/src/styles/code.module.sass
@@ -0,0 +1,126 @@
+.pre
+    position: relative
+    background: var(--color-front)
+    color: var(--color-subtle)
+    border-radius: var(--border-radius)
+    overflow: auto
+    width: 100%
+    max-width: 100%
+    white-space: pre
+    direction: ltr
+    margin-bottom: var(--spacing-md)
+
+.max-height
+    height: 450px
+
+.code,
+.juniper-input pre,
+.juniper-output
+    font: var(--font-size-xs)/var(--line-height-lg) var(--font-code) !important
+    -webkit-font-smoothing: subpixel-antialiased
+    -moz-osx-font-smoothing: auto
+
+.code,
+.juniper-input pre
+    display: block
+    padding: 1.75em 2em
+
+.code
+    &[data-prompt]:before,
+        content: attr(data-prompt)
+        margin-right: 0.65em
+        display: inline-block
+        vertical-align: middle
+        opacity: 0.5
+
+.inline-code
+    color: var(--color-inline-code-text)
+    font-size: 90%
+    background-color: var(--color-inline-code-bg)
+    padding: 0.2rem 0.4rem
+    border-radius: 0.25rem
+    font-family: var(--font-code)
+    margin: 0
+    box-decoration-break: clone
+    white-space: nowrap
+    text-shadow: none
+    -webkit-font-smoothing: subpixel-antialiased
+    -moz-osx-font-smoothing: auto
+
+    a &
+        color: inherit
+
+.inline-code-dark
+    --color-inline-code-text: var(--color-back)
+    --color-inline-code-bg: var(--color-dark-secondary)
+
+.wrap
+    white-space: pre-wrap
+
+.title,
+.juniper-button
+    font: bold var(--font-size-lg)/var(--line-height-md) var(--font-secondary)
+    text-transform: uppercase
+    display: inline-block
+    border-radius: 1em
+    padding: 0 1rem 0.15rem
+    color: var(--color-back)
+
+.title
+    box-shadow: inset 1px 1px 1px rgba(0, 0, 0, 0.25)
+    background: var(--color-dark)
+    margin: 1.5rem 0 0 2rem
+
+.header
+    width: 100%
+    background: var(--color-front)
+    position: sticky
+    top: -0.75rem
+    margin-top: -0.75rem
+    border-top-left-radius: var(--border-radius)
+    border-top-right-radius: var(--border-radius)
+    padding: 1.25rem 1rem 1rem
+    font-size: var(--font-size-sm)
+
+.juniper-wrapper
+    background: var(--color-dark-secondary)
+    padding: 5px
+
+.juniper-title
+    font: bold var(--font-size-sm)/var(--line-height-sm) var(--font-primary)
+    text-align: center
+    padding: 0.25rem 1rem 0.5rem
+    display: flex
+    justify-content: space-between
+
+.juniper-cell
+    border: 0
+    background: var(--color-front)
+    padding: 1rem 0
+
+.juniper-input
+    padding-top: 1rem
+    padding-bottom: 2rem
+    padding-left: 2rem
+    background: var(--color-front)
+
+    & > div
+        padding: 0
+        height: auto
+
+.juniper-output
+    font: var(--font-size-xs) var(--font-code)
+
+.juniper-button
+    transition: background-color 0.15s ease
+    background: var(--color-theme)
+    margin: 0.5rem 0 1rem 2rem
+
+    &:hover
+        background: var(--color-theme-dark)
+
+.juniper-meta
+    font-size: var(--font-size-xs)
+    font-weight: normal
+    padding-top: 0.1rem
+    color: var(--color-subtle-dark)
diff --git a/website/src/styles/embed.module.sass b/website/src/styles/embed.module.sass
new file mode 100644
index 000000000..ba8a896c8
--- /dev/null
+++ b/website/src/styles/embed.module.sass
@@ -0,0 +1,34 @@
+.root
+    margin-bottom: var(--spacing-md)
+
+.responsive
+    position: relative
+    height: 0
+
+@each $ratio1, $ratio2 in (16, 9), (4, 3)
+    &.ratio#{$ratio1}x#{$ratio2}
+        padding-bottom: (100% * $ratio2 / $ratio1)
+
+.iframe
+    position: absolute
+    top: 0
+    left: 0
+    width: 100%
+    height: 100%
+    border-radius: var(--border-radius)
+
+.standalone
+    max-width: 100%
+    border: 1px solid var(--color-subtle)
+    border-radius: var(--border-radius)
+    padding: var(--spacing-xs)
+    margin-bottom: var(--spacing-md)
+
+.image
+    position: relative
+    display: block
+    max-width: 100%
+    margin: 0 auto
+
+.image-link
+    display: block
diff --git a/website/src/styles/footer.module.sass b/website/src/styles/footer.module.sass
new file mode 100644
index 000000000..40d84e3ad
--- /dev/null
+++ b/website/src/styles/footer.module.sass
@@ -0,0 +1,45 @@
+@import base
+
+.root
+    position: relative
+    padding: 2.5rem 0
+    overflow: auto
+    background: var(--color-subtle-light)
+    font: var(--font-size-md)/var(--line-height-md) var(--font-primary)
+
+.content
+    padding: 2rem 7.5rem 3rem
+    margin: 0 auto
+    width: var(--width-content)
+    max-width: 100%
+
+@include breakpoint(max, sm)
+    .content
+        padding: 3rem
+
+.label
+    color: var(--color-subtle-dark)
+    font: bold var(--font-size-lg)/var(--line-height-md) var(--font-secondary)
+    text-transform: uppercase
+
+.full
+    flex: 100%
+    grid-column: 1 / span 3
+
+.column
+    margin-bottom: var(--spacing-md)
+
+.copy
+    border-top: 1px dotted var(--color-subtle)
+    font-size: var(--font-size-xs)
+    color: var(--color-subtle-dark)
+    text-align: center
+    width: 100%
+
+.logo
+    margin: 0 3rem
+    display: inline-block
+    vertical-align: middle
+
+    &:hover
+        color: var(--color-theme)
diff --git a/website/src/styles/grid.module.sass b/website/src/styles/grid.module.sass
new file mode 100644
index 000000000..c264d7930
--- /dev/null
+++ b/website/src/styles/grid.module.sass
@@ -0,0 +1,39 @@
+@import base
+
+$grid-gap-wide: 5rem
+$grid-gap-narrow: 2rem
+$flex-gap: 2rem
+
+@mixin flex-fallback($cols, $gap: 0)
+    & > *
+        @if $gap == 0
+            flex: 0 0 100% / $cols
+        @else
+            flex: 0 0 calc(#{100% / $cols} - #{$gap * ($cols - 1)})
+
+.root
+    display: flex
+    flex-direction: row
+    flex-wrap: wrap
+    justify-content: space-between
+    grid-column-gap: $grid-gap-wide
+    grid-template-columns: 1fr
+
+    & > *
+        min-width: 0
+
+@supports(display: grid)
+    .root
+        display: grid
+
+@include breakpoint(min, lg)
+    @each $name, $cols in (half: 2, third: 3, quarter: 4)
+        .#{$name}
+            @include flex-fallback($cols, $flex-gap)
+            grid-template-columns: repeat($cols, 1fr)
+
+.narrow
+    grid-column-gap: $grid-gap-narrow
+
+.spacing
+    margin-bottom: var(--spacing-md)
diff --git a/website/src/styles/icon.module.sass b/website/src/styles/icon.module.sass
new file mode 100644
index 000000000..acbaccbeb
--- /dev/null
+++ b/website/src/styles/icon.module.sass
@@ -0,0 +1,20 @@
+.root
+    vertical-align: middle
+
+.inline
+    margin: 0 0.55em 0 0.1em
+
+.tag
+    vertical-align: bottom
+    height: 100%
+    position: relative
+    top: 1px
+
+.success
+    color: var(--color-green-medium)
+
+.error
+    color: var(--color-red-medium)
+
+.subtle
+    color: var(--color-subtle-dark)
diff --git a/website/src/styles/infobox.module.sass b/website/src/styles/infobox.module.sass
new file mode 100644
index 000000000..5fd0ff0d3
--- /dev/null
+++ b/website/src/styles/infobox.module.sass
@@ -0,0 +1,42 @@
+.root
+    margin-bottom: var(--spacing-md)
+    font: var(--font-size-sm)/var(--line-height-md) var(--font-primary)
+    background: var(--color-subtle-light)
+    padding: 1.5rem 2rem 0.75rem 1.5rem
+    border-radius: var(--border-radius)
+    color: var(--color-dark)
+    border-left: 0.75rem solid var(--color-theme)
+
+    p, pre, ul, ol
+        margin-bottom: var(--spacing-xs)
+
+    p, li
+        font-size: inherit
+        line-height: inherit
+
+.title
+    font-weight: bold
+    color: var(--color-theme)
+    display: block
+    margin-bottom: var(--spacing-xs)
+    font-size: var(--font-size-md)
+
+.icon
+    color: var(--color-theme)
+    vertical-align: baseline
+    position: relative
+    bottom: -2px
+
+.warning
+    --color-theme: var(--color-yellow-dark)
+    --color-theme-dark: var(--color-yellow-dark)
+    --color-inline-code-bg: var(--color-yellow-opaque)
+    border-color: var(--color-yellow-medium)
+    background: var(--color-yellow-light)
+
+.danger
+    --color-theme: var(--color-red-dark)
+    --color-theme-dark: var(--color-red-dark)
+    --color-inline-code-bg: var(--color-red-opaque)
+    border-color: var(--color-red-medium)
+    background: var(--color-red-light)
diff --git a/website/src/styles/landing.module.sass b/website/src/styles/landing.module.sass
new file mode 100644
index 000000000..86283090a
--- /dev/null
+++ b/website/src/styles/landing.module.sass
@@ -0,0 +1,150 @@
+@import base
+
+.header
+    background: var(--color-theme)
+    padding-top: calc(var(--height-nav) * 1.5)
+    width: 100%
+    text-align: center
+
+.header-wrapper
+    background: var(--color-theme)
+    background-position: top center
+    background-repeat: repeat
+    width: 100%
+
+.header-content
+    background: transparent
+    background-position: center -138px
+    background-repeat: no-repeat
+    width: 100%
+    min-height: 573px
+
+.title
+    font: normal 600 7rem/#{1} var(--font-secondary)
+    color: var(--color-back)
+    text-align: center
+    margin-bottom: 0.75rem
+    padding: 0 1rem
+
+@include breakpoint(max, sm)
+    .title
+        font-size: 4rem
+        padding-top: 1.5rem
+
+.label
+    font: bold var(--font-size-lg)/var(--line-height-md) var(--font-secondary)
+    text-transform: uppercase
+    display: inline-block
+    border-radius: 1em
+    padding: 0 1rem 0.15rem
+    background: var(--color-back)
+    color: var(--color-theme)
+
+.subtitle
+    margin-top: 1rem
+
+.blocks
+    position: relative
+    top: -25rem
+    margin-bottom: -25rem
+
+.card
+    padding: 3rem 2.5rem
+    background: var(--color-back)
+    border-radius: var(--border-radius)
+    box-shadow: var(--box-shadow)
+    margin-bottom: 3rem
+
+.button
+    width: 100%
+
+.demo
+    flex: 0 0 66%
+    grid-column: 1 / span 2
+
+.banner-grid
+    grid-gap: 0
+
+    & > *
+        flex-basis: 50%
+
+.banner
+    background: var(--color-theme)
+    color: var(--color-back)
+    padding: 5rem
+    margin-bottom: var(--spacing-md)
+
+.banner-content
+    margin-bottom: 0
+    height: 100%
+
+.banner-content-small
+    display: block
+
+.banner-title
+    display: block
+    margin-top: 1.5rem
+    margin-bottom: 0.5em
+
+.banner-label
+    margin-bottom: 1rem
+
+.banner-text
+    font-weight: 500
+
+    strong
+        font-weight: 800
+
+    p
+        font-size: 1.5rem
+
+.banner-text-small p
+    font-size: 1.35rem
+    margin-bottom: 1rem
+
+@include breakpoint(min, md)
+    .banner-content
+        padding: 0 6.5rem 0 4rem
+
+    .banner-content-small
+        padding: 0 2.25rem 1rem
+
+    .banner-text
+        padding-top: 7rem
+        grid-column: 2 / span 2
+        flex: 0 0 66%
+
+    .banner-text-small
+        padding-top: 1rem
+        grid-column: unset
+        flex: 0 0 100%
+        padding-right: 2rem
+
+@include breakpoint(max, md)
+    .banner-content
+        display: block
+
+    .banner-text
+        padding-top: 0
+
+.banner-button
+    margin-bottom: var(--spacing-sm)
+    text-align: right
+
+.logos
+    text-align: center
+    padding-bottom: 1rem
+
+    & + &
+        padding-bottom: 7.5rem
+
+.logos-content
+    display: flex
+    justify-content: center
+    align-items: center
+    flex-flow: row wrap
+    margin-bottom: 0
+
+.logo
+    display: inline-block
+    padding: 1.8rem
diff --git a/website/src/styles/layout.sass b/website/src/styles/layout.sass
new file mode 100644
index 000000000..0fe6d6d6e
--- /dev/null
+++ b/website/src/styles/layout.sass
@@ -0,0 +1,485 @@
+@import base
+
+/* Theme */
+
+\:root
+    // Sizes
+    --height-nav: 55px
+    --width-content: 1250px
+    --width-sidebar: 235px
+    --width-aside: 27.5vw
+
+    // Fonts
+    --font-primary: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'
+    --font-secondary: 'HK Grotesk', Roboto, Helvetica, Arial, sans-serif
+    --font-code: Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace
+
+    // Font Sizes
+    --font-size-xs: 1.1rem
+    --font-size-sm: 1.3rem
+    --font-size-md: 1.35rem
+    --font-size-lg: 1.4rem
+
+    --line-height-xs: 1.25
+    --line-height-sm: 1.375
+    --line-height-md: 1.5
+    --line-height-lg: 1.9
+
+    // Spacing
+    --spacing-xs: 1rem
+    --spacing-sm: 2rem
+    --spacing-md: 4rem
+    --spacing-lg: 6rem
+
+    // Theme Colors
+    --color-theme-blue: hsl(195, 92%, 44%)
+    --color-theme-blue-dark: hsl(195, 92%, 34%)
+    --color-theme-blue-light: hsla(195, 92%, 44%, 0.06)
+    --color-theme-blue-opaque: hsla(195, 92%, 44%, 0.11)
+
+    --color-theme-green: hsl(164, 94%, 35%)
+    --color-theme-green-dark: hsl(164, 94%, 25%)
+    --color-theme-green-light: hsla(164, 94%, 35%, 0.06)
+    --color-theme-green-opaque: hsla(164, 94%, 35%, 0.11)
+
+    --color-theme-purple: hsl(255, 61%, 54%)
+    --color-theme-purple-dark: hsl(255, 61%, 44%)
+    --color-theme-purple-light: hsla(255, 61%, 54%, 0.06)
+    --color-theme-purple-opaque: hsla(255, 61%, 54%, 0.11)
+
+    // Regular colors
+    --color-back: hsl(0, 0%, 100%)
+    --color-front: hsl(213, 15%, 12%)
+    --color-dark: hsl(214, 15%, 32%)
+    --color-dark-secondary: hsl(214, 14%, 22%)
+    --color-subtle-opaque: hsla(0, 0%, 96%, 0.56)
+    --color-subtle: hsl(0, 0%, 87%)
+    --color-subtle-light: hsl(0, 0%, 96%)
+    --color-subtle-dark: hsl(162, 5%, 60%)
+
+    --color-green-medium: hsl(108, 66%, 63%)
+    --color-green-transparent: hsla(108, 66%, 63%, 0.11)
+    --color-red-light: hsl(355, 100%, 96%)
+    --color-red-medium: hsl(346, 84%, 61%)
+    --color-red-dark: hsl(332, 64%, 34%)
+    --color-red-opaque: hsl(346, 96%, 89%)
+    --color-red-transparent: hsla(346, 84%, 61%, 0.11)
+    --color-yellow-light: hsl(46, 100%, 95%)
+    --color-yellow-medium: hsl(45, 90%, 55%)
+    --color-yellow-dark: hsl(44, 94%, 27%)
+    --color-yellow-opaque: hsl(45, 90%, 87%)
+
+    // Syntax Highlighting
+    --syntax-comment: hsl(162, 5%, 60%)
+    --syntax-tag: hsl(266, 72%, 72%)
+    --syntax-number: hsl(266, 72%, 72%)
+    --syntax-selector: hsl(31, 100%, 71%)
+    --syntax-operator: hsl(342, 100%, 59%)
+    --syntax-function: hsl(195, 70%, 54%)
+    --syntax-keyword: hsl(342, 100%, 59%)
+    --syntax-regex: hsl(45, 90%, 55%)
+
+    // Other
+    --color-inline-code-text: var(--color-dark)
+    --color-inline-code-bg: var(--color-subtle-light)
+
+    --color-shadow: hsla(0, 0%, 0%, 0.2)
+    --box-shadow: 0 1px 5px var(--color-shadow)
+    --border-radius: 6px
+
+/* Theming */
+.theme-blue
+    --color-theme: var(--color-theme-blue)
+    --color-theme-dark: var(--color-theme-blue-dark)
+    --color-theme-light: var(--color-theme-blue-light)
+    --color-theme-opaque: var(--color-theme-blue-opaque)
+
+.theme-green
+    --color-theme: var(--color-theme-green)
+    --color-theme-dark: var(--color-theme-green-dark)
+    --color-theme-light: var(--color-theme-green-light)
+    --color-theme-opaque: var(--color-theme-green-opaque)
+
+.theme-purple
+    --color-theme: var(--color-theme-purple)
+    --color-theme-dark: var(--color-theme-purple-dark)
+    --color-theme-light: var(--color-theme-purple-light)
+    --color-theme-opaque: var(--color-theme-purple-opaque)
+
+
+/* Fonts */
+
+@font-face
+    font-family: "HK Grotesk"
+    font-style: normal
+    font-weight: 500
+    src: url("../fonts/hkgrotesk-semibold.woff2") format("woff2"), url("../fonts/hkgrotesk-semibold.woff") format("woff")
+    font-display: swap
+
+@font-face
+    font-family: "HK Grotesk"
+    font-style: italic
+    font-weight: 500
+    src: url("../fonts/hkgrotesk-semibolditalic.woff2") format("woff2"), url("../fonts/hkgrotesk-semibolditalic.woff") format("woff")
+    font-display: swap
+
+@font-face
+    font-family: "HK Grotesk"
+    font-style: normal
+    font-weight: 600
+    src: url("../fonts/hkgrotesk-bold.woff2") format("woff2"), url("../fonts/hkgrotesk-bold.woff") format("woff")
+    font-display: swap
+
+@font-face
+    font-family: "HK Grotesk"
+    font-style: italic
+    font-weight: 600
+    src: url("../fonts/hkgrotesk-bolditalic.woff2") format("woff2"), url("../fonts/hkgrotesk-bolditalic.woff") format("woff")
+    font-display: swap
+
+/* Reset */
+
+*, *:before, *:after
+    box-sizing: border-box
+    padding: 0
+    margin: 0
+    border: 0
+    outline: 0
+
+html
+    font-family: sans-serif
+    text-rendering: optimizeSpeed
+    -ms-text-size-adjust: 100%
+    -webkit-text-size-adjust: 100%
+    -webkit-font-smoothing: antialiased
+    -moz-osx-font-smoothing: grayscale
+
+body
+    margin: 0
+
+article, aside, details, figcaption, figure, footer, header, main, menu, nav,
+section, summary, progress
+    display: block
+
+a
+    background-color: transparent
+    color: inherit
+    text-decoration: none
+
+    &:active,
+    &:hover
+        outline: 0
+
+abbr[title]
+    border-bottom: none
+    text-decoration: underline
+    text-decoration: underline dotted
+
+b, strong
+    font-weight: inherit
+    font-weight: bolder
+
+small
+    font-size: 80%
+
+sub, sup
+    position: relative
+    font-size: 65%
+    line-height: 0
+    vertical-align: baseline
+
+sup
+    top: -0.5em
+
+sub
+    bottom: -0.15em
+
+img
+    border: 0
+    height: auto
+    max-width: 100%
+
+svg
+    max-width: 100%
+    color-interpolation-filters: sRGB
+    fill: currentColor
+
+    &:not(:root)
+        overflow: hidden
+
+hr
+    box-sizing: content-box
+    overflow: visible
+    height: 0
+
+pre
+    overflow: auto
+
+code, pre
+    font-family: monospace, monospace
+    font-size: 1em
+
+table
+    text-align: left
+    width: 100%
+    max-width: 100%
+    border-collapse: collapse
+
+    td, th
+        vertical-align: top
+
+ul, ol
+    list-style: none
+
+input, button
+    appearance: none
+    background: transparent
+
+button
+    cursor: pointer
+
+progress
+    appearance: none
+
+/* Layout */
+
+html
+    font-size: 11px
+
+body
+    background: var(--color-back)
+    color: var(--color-front)
+
+p
+    margin-bottom: var(--spacing-md)
+    font-family: var(--font-primary)
+    font-size: var(--font-size-md)
+    line-height: var(--line-height-md)
+
+    &:empty
+        margin-bottom: 0
+
+a:focus
+    outline: 1px dotted var(--color-theme)
+
+[id]:target
+    padding-top: calc(var(--height-nav) * 1.25) !important
+
+::selection
+    background: var(--color-theme)
+    color: var(--color-back)
+    text-shadow: none
+
+// Footnotes
+
+.footnotes
+    border-top: 1px dotted var(--color-subtle)
+    color: var(--color-dark)
+
+    li
+        font-size: var(--font-size-xs)
+
+    .footnote-backref
+        border: none
+        margin-left: 1rem
+        display: inline-block
+        width: 1em
+        line-height: 1
+        overflow: hidden
+
+        &:before
+            content: "\21A9\FE0E"
+            width: 1em
+
+    hr
+        display: none
+
+
+// Prism
+
+[class*="language-"] .token
+    &.comment, &.prolog, &.doctype, &.cdata, &.punctuation
+        color: var(--syntax-comment)
+
+    &.property, &.tag, &.constant, &.symbol
+        color: var(--syntax-tag)
+
+    &.boolean, &.number
+        color: var(--syntax-number)
+
+    &.selector, &.attr-name, &.string, &.char, &.builtin
+        color: var(--syntax-selector)
+
+    @at-root .language-css .token.string,
+    &.operator, &.entity, &.url, &.variable
+        color: var(--syntax-operator)
+
+    &.atrule, &.attr-value, &.function
+        color: var(--syntax-function)
+
+    &.regex, &.important
+        color: var(--syntax-regex)
+
+    &.keyword
+        color: var(--syntax-keyword)
+
+    &.inserted, &.deleted
+        padding: 2px 0
+        border-radius: 2px
+
+    &.inserted
+        color: var(--color-green-medium)
+        background: var(--color-green-transparent)
+
+    &.deleted
+        color: var(--color-red-medium)
+        background: var(--color-red-transparent)
+
+    &.italic
+        font-style: italic
+
+.gatsby-highlight-code-line
+    background-color: var(--color-dark-secondary)
+    border-left: 0.35em solid var(--color-theme)
+    display: block
+    margin-right: -2em
+    margin-left: -2em
+    padding-right: 2em
+    padding-left: 1.65em
+
+    &:empty:before
+        // Fix issue where empty lines would disappear
+        content: " "
+
+
+// CodeMirror
+
+.CodeMirror.cm-s-default
+    background: var(--color-front)
+    color: var(--color-subtle)
+
+    .CodeMirror-selected
+        background: var(--color-theme)
+        color: var(--color-back)
+
+    .CodeMirror-cursor
+        border-left-color: currentColor
+
+    .cm-variable-2
+        color: inherit
+        font-style: italic
+
+    .cm-comment
+        color: var(--syntax-comment)
+
+    .cm-keyword, .cm-builtin
+        color: var(--syntax-keyword)
+
+    .cm-operator
+        color: var(--syntax-operator)
+
+    .cm-string
+        color: var(--syntax-selector)
+
+    .cm-number
+        color: var(--syntax-number)
+
+    .cm-def
+        color: var(--syntax-function)
+
+// Jupyter
+
+.jp-RenderedText pre
+    .ansi-cyan-fg.ansi-cyan-fg
+        color: var(--syntax-function)
+
+    .ansi-green-fg.ansi-green-fg
+        color: var(--color-green-medium)
+
+    .ansi-red-fg.ansi-red-fg
+        color: var(--syntax-operator)
+
+.jp-OutputArea
+    &:not(:empty)
+        padding: 2rem 2rem 1rem
+        border-top: 1px solid var(--color-dark)
+        margin-top: 2rem
+
+    .entities, svg
+        white-space: initial
+        font-family: inherit
+
+    .entities
+        font-size: 1.35rem
+
+    .displacy
+        margin-top: 1rem
+        font-family: var(--font-primary) !important
+        color: var(--color-subtle) !important
+        background: var(--color-front) !important
+
+.jp-OutputArea pre
+    font: inherit
+
+.jp-OutputPrompt.jp-OutputArea-prompt
+    padding-top: 0.5em
+    margin-right: 1rem
+    font-family: inherit
+    font-weight: bold
+
+/* Tooltips */
+
+[data-tooltip]
+    position: relative
+
+@include breakpoint(min, sm)
+    [data-tooltip][data-tooltip-style="code"]:before
+        -webkit-font-smoothing: subpixel-antialiased
+        -moz-osx-font-smoothing: auto
+        padding: 0.35em 0.85em 0.45em
+        font-family: var(--font-code)
+        white-space: nowrap
+        min-width: auto
+
+    [data-tooltip]:before
+        font: normal var(--font-size-xs)/var(--line-height-xs) var(--font-primary)
+        position: absolute
+        top: 125%
+        left: 50%
+        display: inline-block
+        content: attr(data-tooltip)
+        background: var(--color-front)
+        border-radius: var(--border-radius)
+        box-shadow: var(--box-shadow)
+        border: 1px solid var(--color-dark)
+        color: var(--color-back)
+        text-transform: none
+        text-align: left
+        opacity: 0
+        transform: translateX(-50%) translateY(-2px)
+        transition: opacity 0.1s ease-out, transform 0.1s ease-out
+        visibility: hidden
+        max-width: 300px
+        min-width: 200px
+        padding: 0.75em 1em 1em
+        z-index: 200
+        white-space: pre-wrap
+
+    [data-tooltip]:hover:before
+        opacity: 1
+        transform: translateX(-50%) translateY(0)
+        visibility: visible
+
+/* Gatsby Images */
+
+.gatsby-resp-image-figure
+    margin-bottom: var(--spacing-md)
+
+.gatsby-resp-image-figcaption
+    font: var(--font-size-xs)/var(--line-height-md) var(--font-primary)
+    color: var(--color-subtle-dark)
+    padding-top: 0.75rem
+    padding-left: 3rem
+
+    code
+        color: inherit
diff --git a/website/src/styles/link.module.sass b/website/src/styles/link.module.sass
new file mode 100644
index 000000000..15ad6adb9
--- /dev/null
+++ b/website/src/styles/link.module.sass
@@ -0,0 +1,30 @@
+.root
+    color: var(--color-theme)
+    border-bottom: 1px solid
+    transition: color 0.2s ease
+    cursor: pointer
+
+    &:hover
+        color: var(--color-theme-dark)
+
+.hidden
+    border: none
+    color: inherit
+
+    &:hover
+        color: inherit
+
+.icon
+    margin-left: 0.5em
+    width: 1.1em
+    height: 1.1em
+
+.nowrap
+    white-space: nowrap
+    display: inline-block
+
+.with-icon
+    border: none
+
+.source-text
+    border-bottom: 1px solid
diff --git a/website/src/styles/list.module.sass b/website/src/styles/list.module.sass
new file mode 100644
index 000000000..f0fb0dd5f
--- /dev/null
+++ b/website/src/styles/list.module.sass
@@ -0,0 +1,37 @@
+.ol, .ul
+    margin-bottom: var(--spacing-md)
+    font: var(--font-size-md)/var(--line-height-md) var(--font-primary)
+
+.ol
+    counter-reset: li
+
+    li:before
+        content: counter(li, decimal) '.'
+        font-size: 1em
+        padding-right: 1rem
+        padding-top: 0
+
+.li
+    padding-left: 2rem
+    margin-bottom: 0.5em
+    margin-left: 1.25rem
+
+    p
+        display: inline-block
+        margin-bottom: var(--spacing-sm)
+
+    &:before
+        content: '\25CF'
+        position: relative
+        bottom: 1px
+        display: inline-block
+        font-size: 0.6em
+        font-weight: bold
+        padding-right: 1.5em
+        padding-top: 0.5em
+        margin-left: -3.75rem
+        text-align: right
+        width: 2.5rem
+        counter-increment: li
+        box-sizing: content-box
+        vertical-align: top
diff --git a/website/src/styles/main.module.sass b/website/src/styles/main.module.sass
new file mode 100644
index 000000000..d36c04efb
--- /dev/null
+++ b/website/src/styles/main.module.sass
@@ -0,0 +1,40 @@
+@import base
+
+.root
+    max-width: 100%
+    min-height: 100vh
+
+@include breakpoint(min, md)
+    .root
+        padding: var(--height-nav) 0 0 0
+
+    .with-sidebar
+        margin-left: var(--width-sidebar)
+
+    .with-asides
+        margin-right: var(--width-aside)
+        position: relative
+
+    .asides
+        position: absolute
+        top: 0
+        left: 100%
+        width: var(--width-aside)
+        height: 100%
+        content: ""
+        display: block
+        background-color: var(--color-theme)
+        background-repeat: repeat
+        background-position: center top
+        z-index: -1
+        min-height: 100vh
+
+.content
+    padding: 3rem 7.5rem
+    margin: 0 auto
+    width: var(--width-content)
+    max-width: 100%
+
+@include breakpoint(max, sm)
+    .content
+        padding: 3rem
diff --git a/website/src/styles/navigation.module.sass b/website/src/styles/navigation.module.sass
new file mode 100644
index 000000000..2f2b7abd1
--- /dev/null
+++ b/website/src/styles/navigation.module.sass
@@ -0,0 +1,70 @@
+@import base
+
+.root
+    position: fixed
+    top: 0
+    left: 0
+    width: 100%
+    height: var(--height-nav)
+    background: var(--color-back)
+    color: var(--color-front)
+    align-items: center
+    display: flex
+    justify-content: space-between
+    flex-flow: row nowrap
+    padding: 0 0 0 1rem
+    z-index: 30
+    width: 100%
+    box-shadow: var(--box-shadow)
+
+.logo
+    width: 95px
+    height: 30px
+    color: var(--color-theme) !important
+    vertical-align: middle
+
+.title
+    display: none
+
+.menu
+    display: flex
+    height: 100%
+    flex-flow: row nowrap
+    border-color: inherit
+    overflow-x: auto
+    overflow-y: hidden
+    -webkit-overflow-scrolling: touch
+
+@include breakpoint(max, sm)
+    .menu
+        @include scroll-shadow-base(var(--color-shadow))
+        margin-left: 1rem
+
+@include breakpoint(min, md)
+    .menu
+        justify-content: flex-end
+
+.item
+    display: inline-flex
+    align-items: center
+    height: 100%
+    text-transform: uppercase
+    font-family: var(--font-secondary)
+    font-size: 1.6rem
+    font-weight: bold
+    color: var(--color-theme)
+
+    &:not(:first-child)
+        margin-left: 2em
+
+    &:last-child
+        @include scroll-shadow-cover(right, var(--color-back))
+        padding-right: 2rem
+
+    &:first-child
+        @include scroll-shadow-cover(left, var(--color-back))
+        padding-left: 2rem
+
+.is-active
+    color: var(--color-dark)
+    pointer-events: none
diff --git a/website/src/styles/newsletter.module.sass b/website/src/styles/newsletter.module.sass
new file mode 100644
index 000000000..6dc4f22e6
--- /dev/null
+++ b/website/src/styles/newsletter.module.sass
@@ -0,0 +1,22 @@
+.root
+    font: var(--font-size-sm)/var(--line-height-sm) var(--font-primary)
+    margin: var(--spacing-xs) 0
+    background: var(--color-back)
+    border-radius: 2em
+    border: 1px solid var(--color-subtle)
+    padding-right: 1em
+    display: inline-flex
+    max-width: 300px
+
+.input
+    font: inherit
+    background: transparent
+    padding: 0.5em 1em
+    margin: 0 0 0.25rem 0.25rem
+    flex: 100%
+
+.button
+    font: bold var(--font-size-lg)/var(--line-height-md) var(--font-secondary)
+    text-transform: uppercase
+    color: var(--color-theme)
+    white-space: nowrap
diff --git a/website/assets/css/_components/_progress.sass b/website/src/styles/progress.module.sass
similarity index 64%
rename from website/assets/css/_components/_progress.sass
rename to website/src/styles/progress.module.sass
index bbab0fddd..9755f7a9b 100644
--- a/website/assets/css/_components/_progress.sass
+++ b/website/src/styles/progress.module.sass
@@ -1,11 +1,9 @@
-//- 💫 CSS > COMPONENTS > PROGRESS
-
-.c-progress
+.root
     display: block
     flex: 105%
     width: 105%
     height: 3px
-    color: $color-theme
+    color: var(--color-theme)
     background: transparent
     border: none
     position: absolute
@@ -13,12 +11,12 @@
     left: -2.5%
 
     &::-webkit-progress-bar
-        background: $color-back
+        background: var(--color-back)
         border-radius: none
 
     &::-webkit-progress-value
-        background: $color-theme
+        background: var(--color-theme)
         border-radius: none
 
     &::-moz-progress-bar
-        background: $color-theme
+        background: var(--color-theme)
diff --git a/website/src/styles/quickstart.module.sass b/website/src/styles/quickstart.module.sass
new file mode 100644
index 000000000..51e94fa4d
--- /dev/null
+++ b/website/src/styles/quickstart.module.sass
@@ -0,0 +1,125 @@
+@import base
+
+.root
+    font: var(--font-size-sm)/var(--line-height-sm) var(--font-primary)
+    margin-bottom: var(--spacing-md)
+    border-radius: var(--border-radius)
+    background: var(--color-subtle-light)
+    padding-top: 1rem
+
+.title
+    padding: 0 2rem
+    margin-bottom: 2rem
+
+    a:before
+        content: ""
+
+.description
+    padding: 0 2rem
+
+.group
+    padding: 0.75rem 2rem 0
+    border: none
+
+@include breakpoint(min, md)
+    .group
+        display: flex
+        flex-flow: row nowrap
+
+.input
+    // Hide inputs while keeping them focussable
+    opacity: 0
+    width: 0
+    height: 0
+    position: absolute
+    left: -9999px
+
+.label
+    cursor: pointer
+    border: 1px solid var(--color-subtle)
+    border-radius: var(--border-radius)
+    display: inline-block
+    padding: 0.65rem 1.25rem
+    margin: 0 0.5rem 0.75rem 0
+    font-size: var(--font-size-xs)
+    font-weight: bold
+    background: var(--color-back)
+
+    &:hover
+        background: var(--color-subtle-light)
+
+    .input:focus + &
+        border: 1px solid var(--color-theme)
+        outline: none
+
+    .radio:checked + &
+        color: var(--color-back)
+        border-color: var(--color-theme)
+        background: var(--color-theme)
+
+    .checkbox + &:before
+        content: ""
+        display: inline-block
+        width: 20px
+        height: 20px
+        border: 1px solid var(--color-subtle)
+        vertical-align: middle
+        margin-right: 1rem
+        cursor: pointer
+        border-radius: var(--border-radius)
+
+    .checkbox:checked + &:before
+        // Embed "check" icon here for simplicity
+        background: var(--color-theme) url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0Ij4gICAgPHBhdGggZmlsbD0iI2ZmZiIgZD0iTTkgMTYuMTcybDEwLjU5NC0xMC41OTQgMS40MDYgMS40MDYtMTIgMTItNS41NzgtNS41NzggMS40MDYtMS40MDZ6Ii8+PC9zdmc+);
+        background-size: contain
+        border-color: var(--color-theme)
+
+.legend
+    color: var(--color-dark)
+    padding: 0.75rem 0
+    flex: 1 1 35%
+    font-weight: bold
+
+.fields
+    flex: 100%
+
+.code
+    background: var(--color-front)
+    color: var(--color-back)
+    padding: 0.75em 0
+    overflow: auto
+    width: 100%
+    max-width: 100%
+    margin: 1rem 0 0
+    border-bottom-left-radius: var(--border-radius)
+    border-bottom-right-radius: var(--border-radius)
+    -webkit-font-smoothing: subpixel-antialiased
+    -moz-osx-font-smoothing: auto
+
+.results
+    display: block
+    font: normal normal var(--font-size-lg)/var(--line-height-lg) var(--font-code)
+    line-height: 2
+    padding: 1em 2em
+
+    & > span
+        display: block
+
+.prompt:before
+    color: var(--color-theme)
+    margin-right: 1em
+
+.bash:before
+    content: "$"
+
+.python:before
+    content: ">>>"
+
+.divider
+    padding: 1.5rem 0
+
+.help
+    color: var(--color-subtle-dark)
+
+    .input:checked + .label &
+        color: inherit
diff --git a/website/src/styles/readnext.module.sass b/website/src/styles/readnext.module.sass
new file mode 100644
index 000000000..23aa7f016
--- /dev/null
+++ b/website/src/styles/readnext.module.sass
@@ -0,0 +1,21 @@
+.root
+    display: flex
+    justify-content: flex-end
+    align-items: center
+    text-align: right
+    font: var(--font-size-sm)/var(--line-height-md) var(--font-primary)
+
+.icon
+    $icon-size: 35px
+    width: $icon-size
+    height: $icon-size
+    background: var(--color-subtle-light)
+    color: var(--color-subtle-dark)
+    border-radius: 50%
+    padding: 0.5rem
+    transition: color 0.2s ease
+    float: right
+    margin-left: 3rem
+
+    &:hover
+        color: var(--color-theme)
diff --git a/website/src/styles/section.module.sass b/website/src/styles/section.module.sass
new file mode 100644
index 000000000..4893e88b7
--- /dev/null
+++ b/website/src/styles/section.module.sass
@@ -0,0 +1,9 @@
+.root
+    &:not(:last-child):not(:last-of-type)
+        margin-bottom: var(--spacing-md)
+        padding-bottom: var(--spacing-md)
+        border-bottom: 1px dotted var(--color-subtle)
+
+.hr
+    border: 0
+    padding: var(--spacing-sm) 0
diff --git a/website/src/styles/sidebar.module.sass b/website/src/styles/sidebar.module.sass
new file mode 100644
index 000000000..c225ac634
--- /dev/null
+++ b/website/src/styles/sidebar.module.sass
@@ -0,0 +1,118 @@
+@import base
+
+$crumb-bullet: 14px
+$crumb-bar: 2px
+
+.root
+    overflow-y: auto
+    font: var(--font-size-md)/var(--line-height-md) var(--font-primary)
+
+@include breakpoint(min, md)
+    .root
+        @include scroll-shadow(var(--color-back), var(--color-shadow), var(--height-nav))
+        position: fixed
+        top: 0
+        left: 0
+        width: var(--width-sidebar)
+        height: calc(100vh - 3px)
+        flex: 0 0 var(--width-sidebar)
+        padding-top: calc(var(--height-nav) + 1.5rem)
+        z-index: 10
+        padding-bottom: 5rem
+
+@include breakpoint(max, sm)
+    .root
+        flex: 100%
+        width: 100%
+        margin-top: var(--height-nav)
+        display: flex
+        flex-flow: row wrap
+        width: 100%
+
+.section
+    margin-bottom: var(--spacing-sm)
+
+    & > *
+        padding: 0 2rem 0.35rem
+
+@include breakpoint(max, sm)
+    .section
+        flex: 1 1 0
+        padding: 1.25rem 0
+        border-bottom: 1px solid var(--color-subtle)
+        margin: 0
+
+        &:not(:last-child)
+            border-right: 1px solid var(--color-subtle)
+
+.label
+    color: var(--color-dark)
+    font: bold var(--font-size-lg)/var(--line-height-md) var(--font-secondary)
+    text-transform: uppercase
+
+.item
+    color: var(--color-theme)
+
+    &:hover
+        color: var(--color-theme-dark)
+
+.link
+    border: none
+
+.is-active
+    font-weight: bold
+    color: var(--color-dark)
+    margin-top: 1rem
+
+    &:hover
+        color: var(--color-dark)
+
+.crumbs
+    display: block
+    padding-top: 1rem
+    padding-left: 1rem
+    position: relative
+
+.crumb
+    margin-bottom: $crumb-bullet / 2
+    position: relative
+    padding-left: 2rem
+    color: var(--color-theme)
+    font-size: 1.2rem
+
+    &:hover
+        color: var(--color-theme-dark)
+
+    &:after
+        width: $crumb-bullet
+        height: $crumb-bullet
+        position: absolute
+        top: $crumb-bullet / 4
+        left: 0
+        content: ""
+        border-radius: 50%
+        background: var(--color-theme)
+        z-index: 10
+
+    &:not(:last-child):before
+        width: $crumb-bar
+        height: 100%
+        position: absolute
+        top: $crumb-bullet
+        left: ($crumb-bullet - $crumb-bar) / 2
+        content: ""
+        background: var(--color-subtle)
+
+    &:first-child:before
+        height: calc(100% + #{$crumb-bullet * 2})
+        top: -$crumb-bullet / 2
+
+.crumb-active
+    color: var(--color-dark)
+
+    &:after
+        background: var(--color-dark)
+
+@include breakpoint(max, sm)
+    .crumbs
+        display: none
diff --git a/website/src/styles/table.module.sass b/website/src/styles/table.module.sass
new file mode 100644
index 000000000..3e73ffb7f
--- /dev/null
+++ b/website/src/styles/table.module.sass
@@ -0,0 +1,80 @@
+@import base
+
+.root
+    vertical-align: top
+    font: var(--font-size-md)/var(--line-height-md) var(--font-primary)
+    margin-bottom: var(--spacing-md)
+    max-width: 100%
+
+.tr
+    thead &:nth-child(odd)
+        background: transparent
+
+    &:nth-child(odd)
+        background: var(--color-subtle-opaque)
+
+.footer
+    background: var(--color-theme-light)
+    border-top: 2px solid var(--color-theme)
+
+    & > td:first-child
+        font-family: var(--font-secondary)
+        color: var(--color-theme)
+
+.td
+    padding: 1rem
+
+    &:not(:last-child)
+        border-right: 1px solid var(--color-subtle)
+
+.num
+    font-feature-settings: "tnum"
+    font-variant-numeric: tabular-nums
+
+    & > strong
+        font-feature-settings: initial
+        font-variant-numeric: initial
+
+.nowrap
+    white-space: nowrap
+
+.th
+    font: bold var(--font-size-md)/var(--line-height-md) var(--font-secondary)
+    text-transform: uppercase
+    color: var(--color-theme)
+    padding: 1rem 0.5rem
+    border-bottom: 2px solid var(--color-theme)
+    vertical-align: bottom
+
+// Responsive table
+// Shadows adapted from "CSS only Responsive Tables" by David Bushell
+// http://codepen.io/dbushell/pen/wGaamR
+
+@include breakpoint(max, md)
+    .root
+        @include scroll-shadow-base(var(--color-shadow))
+        display: inline-block
+        overflow-x: auto
+        overflow-y: hidden
+        width: auto
+        -webkit-overflow-scrolling: touch
+
+    .th, .td
+        &:first-child
+            @include scroll-shadow-cover(left, var(--color-back))
+
+        &:last-child
+            @include scroll-shadow-cover(right, var(--color-back))
+
+        &:first-child:last-child
+            @include scroll-shadow-cover(both, var(--color-back))
+
+    .footer .td
+        &:first-child
+            @include scroll-shadow-cover(left, var(--color-subtle-light))
+
+        &:last-child
+            @include scroll-shadow-cover(right, var(--color-subtle-light))
+
+        &:first-child:last-child
+            @include scroll-shadow-cover(both, var(--color-subtle-light))
diff --git a/website/src/styles/tag.module.sass b/website/src/styles/tag.module.sass
new file mode 100644
index 000000000..fc6b426f4
--- /dev/null
+++ b/website/src/styles/tag.module.sass
@@ -0,0 +1,16 @@
+.root
+    display: inline-block
+    font: bold var(--font-size-xs)/#{1} var(--font-secondary)
+    background: var(--color-theme)
+    color: var(--color-back)
+    padding: 2px 6px 4px
+    border-radius: 1em
+    text-transform: uppercase
+    vertical-align: middle
+
+.spaced
+    margin-left: 0.75em
+    margin-right: 0.5em
+
+.icon
+    margin-left: 0.5em
diff --git a/website/src/styles/title.module.sass b/website/src/styles/title.module.sass
new file mode 100644
index 000000000..0dea2aa95
--- /dev/null
+++ b/website/src/styles/title.module.sass
@@ -0,0 +1,33 @@
+.root
+    margin-bottom: var(--spacing-md)
+
+    &:after
+        content: ""
+        display: block
+        width: 10%
+        min-width: 6rem
+        height: 6px
+        background: var(--color-theme)
+        margin-top: 3rem
+
+.h1
+    padding-top: 0 !important
+    display: inline
+    margin-bottom: 0.5em
+    vertical-align: middle
+
+.teaser
+    font: normal var(--font-size-sm)/var(--line-height-md) var(--font-primary)
+    color: var(--color-dark)
+    margin-bottom: var(--spacing-sm)
+    margin-top: var(--spacing-sm)
+
+.image
+    $image-size: 100px
+    width: $image-size
+    height: $image-size
+    overflow: hidden
+    border-radius: 50%
+
+.corner
+    float: right
diff --git a/website/src/styles/typography.module.sass b/website/src/styles/typography.module.sass
new file mode 100644
index 000000000..586c8df01
--- /dev/null
+++ b/website/src/styles/typography.module.sass
@@ -0,0 +1,84 @@
+@import base
+
+.heading
+    margin-bottom: 1em
+
+    &:not(:first-child)
+        padding-top: 0.5em
+
+@include breakpoint(max, md)
+    .heading
+        word-wrap: break-word
+
+@each $level, $size in (1: 4.4, 2: 3.4, 3: 2.6, 4: 2.2, 5: 1.8)
+    .h#{$level}
+        font: normal 500 #{$size}rem/#{1.1} var(--font-secondary)
+
+.permalink
+    position: relative
+
+    &:before
+        content: "\00b6"
+        font-size: 0.9em
+        font-weight: normal
+        color: var(--color-subtle)
+        position: absolute
+        top: 0.15em
+        left: -2.85rem
+        opacity: 0
+        transition: opacity 0.2s ease
+
+    &:hover:before
+        opacity: 1
+
+    &:active:before
+        color: var(--color-theme)
+
+    &:target
+        display: inline-block
+
+        &:before
+            bottom: 0.15em
+            top: initial
+
+@include breakpoint(min, md)
+    .abbr
+        cursor: help
+        border-bottom: 1px dotted var(--color-subtle-dark)
+
+@supports(text-decoration: underline dotted)
+    .abbr
+        text-decoration: underline dotted var(--color-subtle-dark)
+        border-bottom: none
+
+@include breakpoint(max, sm)
+    .abbr:after
+        content: " (" attr(aria-label) ")"
+        color: var(--color-subtle-dark)
+        text-decoration: none
+
+.label
+    display: block
+    font: bold var(--font-size-lg)/var(--line-height-md) var(--font-secondary)
+    text-transform: uppercase
+    color: var(--color-dark)
+
+.inline-list
+    & > *
+        display: inline
+
+        &:not(:last-child)
+            margin-right: 2rem
+
+.no-gutter
+    margin-bottom: 0 !important
+
+.action
+    font: var(--font-size-xs)/var(--line-height-sm) var(--font-primary)
+    color: var(--color-subtle-dark)
+    float: right
+    max-width: 33%
+    text-align: right
+
+.help
+    color: var(--color-dark)
diff --git a/website/src/templates/docs.js b/website/src/templates/docs.js
new file mode 100644
index 000000000..03328b2f5
--- /dev/null
+++ b/website/src/templates/docs.js
@@ -0,0 +1,149 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import { StaticQuery, graphql } from 'gatsby'
+
+import Models from './models'
+
+import ReadNext from '../components/readnext'
+import Button from '../components/button'
+import Grid from '../components/grid'
+import Title from '../components/title'
+import Footer from '../components/footer'
+import Sidebar from '../components/sidebar'
+import Main from '../components/main'
+import { getCurrentSource, github } from '../components/util'
+
+const Docs = ({ pageContext, children }) => (
+    <StaticQuery
+        query={query}
+        render={({ site }) => {
+            const {
+                id,
+                slug,
+                title,
+                section,
+                teaser,
+                source,
+                tag,
+                isIndex,
+                next,
+                menu,
+                theme,
+                version,
+            } = pageContext
+            const { sidebars = [], modelsRepo, languages } = site.siteMetadata
+            const isModels = section === 'models'
+            const sidebar = pageContext.sidebar
+                ? { items: pageContext.sidebar }
+                : sidebars.find(bar => bar.section === section)
+            let pageMenu = menu ? menu.map(([text, id]) => ({ text, id })) : []
+
+            if (isModels) {
+                sidebar.items[1].items = languages
+                    .filter(({ models }) => models && models.length)
+                    .map(lang => ({
+                        text: lang.name,
+                        url: `/models/${lang.code}`,
+                        isActive: id === lang.code,
+                        menu: lang.models.map(model => ({
+                            text: model,
+                            id: model,
+                        })),
+                    }))
+            }
+            const sourcePath = source ? github(source) : null
+            const currentSource = getCurrentSource(slug, isIndex)
+
+            const subFooter = (
+                <Grid cols={2}>
+                    <div style={{ marginTop: 'var(--spacing-lg)' }}>
+                        {(!isModels || (isModels && isIndex)) && (
+                            <Button to={currentSource} icon="code">
+                                Suggest edits
+                            </Button>
+                        )}
+                    </div>
+                    {next && <ReadNext title={next.title} to={next.slug} />}
+                </Grid>
+            )
+
+            return (
+                <>
+                    {sidebar && <Sidebar items={sidebar.items} pageMenu={pageMenu} slug={slug} />}
+                    <Main
+                        section={section}
+                        theme={theme}
+                        sidebar
+                        asides
+                        wrapContent
+                        footer={<Footer />}
+                    >
+                        {isModels && !isIndex ? (
+                            <Models pageContext={pageContext} repo={modelsRepo}>
+                                {subFooter}
+                            </Models>
+                        ) : (
+                            <>
+                                <Title
+                                    title={title}
+                                    teaser={teaser}
+                                    source={sourcePath}
+                                    tag={tag}
+                                    version={version}
+                                />
+                                {children}
+                                {subFooter}
+                            </>
+                        )}
+                    </Main>
+                </>
+            )
+        }}
+    />
+)
+
+Docs.propTypes = {
+    pageContext: PropTypes.shape({
+        slug: PropTypes.string.isRequired,
+        title: PropTypes.string.isRequired,
+        section: PropTypes.string,
+        teaser: PropTypes.string,
+        source: PropTypes.string,
+        tag: PropTypes.string,
+        isIndex: PropTypes.bool,
+        next: PropTypes.shape({
+            title: PropTypes.string.isRequired,
+            slug: PropTypes.string.isRequired,
+        }),
+        menu: PropTypes.arrayOf(PropTypes.arrayOf(PropTypes.string)),
+        theme: PropTypes.string,
+    }),
+}
+
+export default Docs
+
+const query = graphql`
+    query DocsQuery {
+        site {
+            siteMetadata {
+                repo
+                modelsRepo
+                languages {
+                    code
+                    name
+                    models
+                }
+                sidebars {
+                    section
+                    items {
+                        label
+                        items {
+                            text
+                            url
+                        }
+                    }
+                }
+            }
+        }
+    }
+`
diff --git a/website/src/templates/index.js b/website/src/templates/index.js
new file mode 100644
index 000000000..f443f07c8
--- /dev/null
+++ b/website/src/templates/index.js
@@ -0,0 +1,198 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import { graphql } from 'gatsby'
+import { MDXProvider } from '@mdx-js/tag'
+import { withMDXScope } from 'gatsby-mdx/context'
+import useOnlineStatus from '@rehooks/online-status'
+
+import MDXRenderer from './mdx-renderer'
+
+// Templates
+import Docs from './docs'
+import Universe from './universe'
+
+// Components
+import Navigation from '../components/navigation'
+import Progress from '../components/progress'
+import Footer from '../components/footer'
+import SEO from '../components/seo'
+import Link from '../components/link'
+import Section, { Hr } from '../components/section'
+import { Table, Tr, Th, Td } from '../components/table'
+import { Pre, Code, InlineCode } from '../components/code'
+import { Ol, Ul, Li } from '../components/list'
+import { H2, H3, H4, H5, P, Abbr, Help } from '../components/typography'
+import Accordion from '../components/accordion'
+import Infobox from '../components/infobox'
+import Aside from '../components/aside'
+import Button from '../components/button'
+import Tag from '../components/tag'
+import Grid from '../components/grid'
+import { YouTube, SoundCloud, Iframe, Image } from '../components/embed'
+import Alert from '../components/alert'
+
+const mdxComponents = {
+    a: Link,
+    p: P,
+    pre: Pre,
+    code: Code,
+    inlineCode: InlineCode,
+    table: Table,
+    img: Image,
+    tr: Tr,
+    th: Th,
+    td: Td,
+    ol: Ol,
+    ul: Ul,
+    li: Li,
+    h2: H2,
+    h3: H3,
+    h4: H4,
+    h5: H5,
+    blockquote: Aside,
+    section: Section,
+    wrapper: ({ children }) => children,
+    hr: Hr,
+}
+
+const scopeComponents = {
+    Infobox,
+    Table,
+    Tr,
+    Th,
+    Td,
+    Help,
+    Button,
+    YouTube,
+    SoundCloud,
+    Iframe,
+    Abbr,
+    Tag,
+    Accordion,
+    Grid,
+    InlineCode,
+}
+
+const AlertSpace = () => {
+    const isOnline = useOnlineStatus()
+    return (
+        <>
+            {!isOnline && (
+                <Alert title="Looks like you're offline." icon="offline" variant="warning">
+                    But don't worry, your visited pages should be saved for you.
+                </Alert>
+            )}
+        </>
+    )
+}
+
+class Layout extends React.Component {
+    constructor(props) {
+        super(props)
+        // NB: Compiling the scope here instead of in render() is super
+        // important! Otherwise, it triggers unnecessary rerenders of ALL
+        // consumers (e.g. mdx elements), even on anchor navigation!
+        this.state = { scope: { ...scopeComponents, ...props.scope } }
+    }
+
+    render() {
+        const { data, pageContext, location, children } = this.props
+        const { file, site = {} } = data || {}
+        const mdx = file ? file.childMdx : null
+        const { title, section, sectionTitle, teaser, theme = 'blue' } = pageContext
+        const bodyClass = `theme-${theme}`
+        const meta = site.siteMetadata || {}
+        const isDocs = ['usage', 'models', 'api', 'styleguide'].includes(section)
+        const content = !mdx ? null : (
+            <MDXProvider components={mdxComponents}>
+                <MDXRenderer scope={this.state.scope}>{mdx.code.body}</MDXRenderer>
+            </MDXProvider>
+        )
+
+        return (
+            <>
+                <SEO
+                    title={title}
+                    description={teaser || meta.description}
+                    section={section}
+                    sectionTitle={sectionTitle}
+                    bodyClass={bodyClass}
+                />
+                <AlertSpace />
+                <Navigation title={meta.title} items={meta.navigation} section={section}>
+                    <Progress key={location.href} />
+                </Navigation>
+                {isDocs ? (
+                    <Docs pageContext={pageContext}>{content}</Docs>
+                ) : section === 'universe' ? (
+                    <Universe
+                        pageContext={pageContext}
+                        location={location}
+                        mdxComponents={mdxComponents}
+                    />
+                ) : (
+                    <>
+                        {children}
+                        {content}
+                        <Footer wide />
+                    </>
+                )}
+            </>
+        )
+    }
+}
+
+Layout.defaultProps = {
+    scope: {},
+}
+
+Layout.propTypes = {
+    data: PropTypes.shape({
+        mdx: PropTypes.shape({
+            code: PropTypes.shape({
+                body: PropTypes.string.isRequired,
+            }).isRequired,
+        }),
+    }).isRequired,
+    scope: PropTypes.object.isRequired,
+    pageContext: PropTypes.shape({
+        title: PropTypes.string,
+        section: PropTypes.string,
+        teaser: PropTypes.string,
+        source: PropTypes.string,
+        isIndex: PropTypes.bool.isRequired,
+        theme: PropTypes.string,
+        next: PropTypes.shape({
+            title: PropTypes.string.isRequired,
+            slug: PropTypes.string.isRequired,
+        }),
+    }),
+    children: PropTypes.node,
+}
+
+Layout = withMDXScope(Layout)
+
+export default Layout
+
+export const pageQuery = graphql`
+    query($slug: String!) {
+        site {
+            siteMetadata {
+                title
+                description
+                navigation {
+                    text
+                    url
+                }
+            }
+        }
+        file(fields: { slug: { eq: $slug } }) {
+            childMdx {
+                code {
+                    scope
+                    body
+                }
+            }
+        }
+    }
+`
diff --git a/website/src/templates/mdx-renderer.js b/website/src/templates/mdx-renderer.js
new file mode 100644
index 000000000..fac806ac1
--- /dev/null
+++ b/website/src/templates/mdx-renderer.js
@@ -0,0 +1,21 @@
+/**
+ * Temporary hack to prevent this issue:
+ * https://github.com/ChristopherBiscardi/gatsby-mdx/issues/244
+ */
+
+import React from 'react'
+import { MDXTag } from '@mdx-js/tag'
+import { withMDXComponents } from '@mdx-js/tag/dist/mdx-provider'
+import { withMDXScope } from 'gatsby-mdx/context'
+
+const WrappedComponent = React.memo(({ scope = {}, components = {}, children, ...props }) => {
+    if (!children) return null
+    const fullScope = { React, MDXTag, ...scope }
+    const keys = Object.keys(fullScope)
+    const values = keys.map(key => fullScope[key])
+    const fn = new Function('_fn', ...keys, `${children}`) // eslint-disable-line no-new-func
+    const End = fn({}, ...values)
+    return React.createElement(End, { components, ...props })
+})
+
+export default withMDXScope(withMDXComponents(WrappedComponent))
diff --git a/website/src/templates/models.js b/website/src/templates/models.js
new file mode 100644
index 000000000..8a8e3253c
--- /dev/null
+++ b/website/src/templates/models.js
@@ -0,0 +1,320 @@
+import React, { useEffect, useState, useMemo } from 'react'
+import { StaticQuery, graphql } from 'gatsby'
+import { window } from 'browser-monads'
+
+import Title from '../components/title'
+import Section from '../components/section'
+import Button from '../components/button'
+import Aside from '../components/aside'
+import CodeBlock, { InlineCode } from '../components/code'
+import { Table, Tr, Td, Th } from '../components/table'
+import Tag from '../components/tag'
+import { H2, Label } from '../components/typography'
+import Icon from '../components/icon'
+import Link from '../components/link'
+import Grid from '../components/grid'
+import Infobox from '../components/infobox'
+import { join, arrayToObj, abbrNum } from '../components/util'
+
+const MODEL_META = {
+    core: 'Vocabulary, syntax, entities, vectors',
+    core_sm: 'Vocabulary, syntax, entities',
+    dep: 'Vocabulary, syntax',
+    ent: 'Named entities',
+    vectors: 'Word vectors',
+    web: 'written text (blogs, news, comments)',
+    news: 'written text (news, media)',
+    wiki: 'Wikipedia',
+    uas: 'Unlabelled dependencies',
+    las: 'Labelled dependencies',
+    tags_acc: 'Part-of-speech tags (fine grained tags, Token.tag)',
+    ents_f: 'Entities (F-score)',
+    ents_p: 'Entities (precision)',
+    ents_r: 'Entities (recall)',
+    cpu: 'words per second on CPU',
+    gpu: 'words per second on GPU',
+    pipeline: 'Processing pipeline components in order',
+    sources: 'Sources of training data',
+    vecs:
+        'Word vectors included in the model. Models that only support context vectors compute similarity via the tensors shared with the pipeline.',
+    benchmark_parser: 'Syntax accuracy',
+    benchmark_ner: 'NER accuracy',
+    benchmark_speed: 'Speed',
+    compat: 'Latest compatible model version for your spaCy installation',
+}
+
+function getModelComponents(name) {
+    const [lang, type, genre, size] = name.split('_')
+    return { lang, type, genre, size }
+}
+
+function isStableVersion(v) {
+    return !v.includes('a') && !v.includes('b') && !v.includes('dev') && !v.includes('rc')
+}
+
+function getLatestVersion(modelId, compatibility) {
+    for (let [version, models] of Object.entries(compatibility)) {
+        if (isStableVersion(version) && models[modelId]) {
+            return models[modelId][0]
+        }
+    }
+}
+
+function formatVectors(data) {
+    if (!data) return 'n/a'
+    if (Object.values(data).every(n => n === 0)) return 'context vectors only'
+    const { keys, vectors, width } = data
+    return `${abbrNum(keys)} keys, ${abbrNum(vectors)} unique vectors (${width} dimensions)`
+}
+
+function formatAccuracy(data) {
+    if (!data) return null
+    const labels = { tags_acc: 'POS', ents_f: 'NER F', ents_p: 'NER P', ents_r: 'NER R' }
+    const isSyntax = key => ['tags_acc', 'las', 'uas'].includes(key)
+    const isNer = key => key.startsWith('ents_')
+    return Object.keys(data).map(key => ({
+        label: labels[key] || key.toUpperCase(),
+        value: data[key].toFixed(2),
+        help: MODEL_META[key],
+        type: isNer(key) ? 'ner' : isSyntax(key) ? 'syntax' : null,
+    }))
+}
+
+function formatModelMeta(data) {
+    return {
+        fullName: `${data.lang}_${data.name}-${data.version}`,
+        version: data.version,
+        sizeFull: data.size,
+        pipeline: data.pipeline,
+        notes: data.notes,
+        description: data.description,
+        sources: data.sources,
+        author: data.author,
+        url: data.url,
+        license: data.license,
+        vectors: formatVectors(data.vectors),
+        accuracy: formatAccuracy(data.accuracy),
+    }
+}
+
+const Help = ({ children }) => (
+    <span data-tooltip={children}>
+        <Icon name="help2" width={16} variant="subtle" inline />
+    </span>
+)
+
+const Model = ({ name, langId, langName, baseUrl, repo, compatibility, hasExamples, licenses }) => {
+    const [initialized, setInitialized] = useState(false)
+    const [isError, setIsError] = useState(false)
+    const [meta, setMeta] = useState({})
+    const { type, genre, size } = getModelComponents(name)
+    const version = useMemo(() => getLatestVersion(name, compatibility), [name, compatibility])
+
+    useEffect(() => {
+        window.dispatchEvent(new Event('resize')) // scroll position for progress
+        if (!initialized && version) {
+            fetch(`${baseUrl}/meta/${name}-${version}.json`)
+                .then(res => res.json())
+                .then(json => {
+                    setMeta(formatModelMeta(json))
+                })
+                .catch(err => {
+                    setIsError(true)
+                })
+            setInitialized(true)
+        }
+    })
+
+    const releaseTag = meta.fullName ? `/tag/${meta.fullName}` : ''
+    const releaseUrl = `https://github.com/${repo}/releases/${releaseTag}`
+    const pipeline =
+        meta.pipeline && join(meta.pipeline.map(p => <InlineCode key={p}>{p}</InlineCode>))
+    const sources = meta.sources && join(meta.sources)
+    const author = !meta.url ? meta.author : <Link to={meta.url}>{meta.author}</Link>
+    const licenseUrl = licenses[meta.license] ? licenses[meta.license].url : null
+    const license = licenseUrl ? <Link to={licenseUrl}>{meta.license}</Link> : meta.license
+
+    const rows = [
+        { label: 'Language', tag: langId, content: langName },
+        { label: 'Type', tag: type, content: MODEL_META[type] },
+        { label: 'Genre', tag: genre, content: MODEL_META[genre] },
+        { label: 'Size', tag: size, content: meta.sizeFull },
+        { label: 'Pipeline', content: pipeline, help: MODEL_META.pipeline },
+        { label: 'Vectors', content: meta.vectors, help: MODEL_META.vecs },
+        { label: 'Sources', content: sources, help: MODEL_META.sources },
+        { label: 'Author', content: author },
+        { label: 'License', content: license },
+    ]
+    const accuracy = [
+        {
+            label: 'Syntax Accuracy',
+            items: meta.accuracy ? meta.accuracy.filter(a => a.type === 'syntax') : null,
+        },
+        {
+            label: 'NER Accuracy',
+            items: meta.accuracy ? meta.accuracy.filter(a => a.type === 'ner') : null,
+        },
+    ]
+
+    const error = (
+        <Infobox title="Unable to load model details from GitHub" variant="danger">
+            <p>
+                To find out more about this model, see the overview of the{' '}
+                <Link to={`https://github.com/${repo}/releases`} ws hideIcon>
+                    latest model releases.
+                </Link>
+            </p>
+        </Infobox>
+    )
+
+    return (
+        <Section id={name}>
+            <H2
+                id={name}
+                action={
+                    <>
+                        <Button to={releaseUrl}>Release Details</Button>
+                        {version && (
+                            <div>
+                                Latest: <InlineCode>{version}</InlineCode>
+                            </div>
+                        )}
+                    </>
+                }
+            >
+                {name}
+            </H2>
+            <Aside title="Installation">
+                <CodeBlock lang="bash" prompt="$">
+                    python -m spacy download {name}
+                </CodeBlock>
+            </Aside>
+            {meta.description && <p>{meta.description}</p>}
+
+            {isError && error}
+
+            <Table>
+                <tbody>
+                    {rows.map(({ label, tag, help, content }, i) =>
+                        !tag && !content ? null : (
+                            <Tr key={i}>
+                                <Td nowrap>
+                                    <Label>
+                                        {`${label} `}
+                                        {help && <Help>{help}</Help>}
+                                    </Label>
+                                </Td>
+                                <Td>
+                                    {tag && <Tag spaced>{tag}</Tag>}
+                                    {content}
+                                </Td>
+                            </Tr>
+                        )
+                    )}
+                </tbody>
+            </Table>
+            <Grid cols={2} gutterBottom={false}>
+                {accuracy &&
+                    accuracy.map(({ label, items }, i) =>
+                        !items ? null : (
+                            <Table key={i}>
+                                <thead>
+                                    <Tr>
+                                        <Th colSpan={2}>{label}</Th>
+                                    </Tr>
+                                </thead>
+                                <tbody>
+                                    {items.map((item, i) => (
+                                        <Tr key={i}>
+                                            <Td>
+                                                <Label>
+                                                    {item.label}{' '}
+                                                    {item.help && <Help>{item.help}</Help>}
+                                                </Label>
+                                            </Td>
+                                            <Td num>{item.value}</Td>
+                                        </Tr>
+                                    ))}
+                                </tbody>
+                            </Table>
+                        )
+                    )}
+            </Grid>
+            {meta.notes && <p>{meta.notes}</p>}
+            {size === 'sm' && hasExamples && (
+                <CodeBlock title="Try out the model" lang="python" executable={true}>
+                    {[
+                        `import spacy`,
+                        `from spacy.lang.${langId}.examples import sentences `,
+                        ``,
+                        `nlp = spacy.load('${name}')`,
+                        `doc = nlp(sentences[0])`,
+                        `print(doc.text)`,
+                        `for token in doc:`,
+                        `    print(token.text, token.pos_, token.dep_)`,
+                    ].join('\n')}
+                </CodeBlock>
+            )}
+        </Section>
+    )
+}
+
+const Models = ({ pageContext, repo, children }) => {
+    const [initialized, setInitialized] = useState(false)
+    const [compatibility, setCompatibility] = useState({})
+    const { id, title, meta } = pageContext
+    const { models } = meta
+    const baseUrl = `https://raw.githubusercontent.com/${repo}/master`
+
+    useEffect(() => {
+        window.dispatchEvent(new Event('resize')) // scroll position for progress
+        if (!initialized) {
+            fetch(`${baseUrl}/compatibility.json`)
+                .then(res => res.json())
+                .then(({ spacy }) => setCompatibility(spacy))
+                .catch(err => console.error(err))
+            setInitialized(true)
+        }
+    })
+
+    return (
+        <>
+            <Title title={title} teaser={`Available pre-trained statistical models for ${title}`} />
+            <StaticQuery
+                query={query}
+                render={({ site }) =>
+                    models.map(modelName => (
+                        <Model
+                            key={modelName}
+                            name={modelName}
+                            langId={id}
+                            langName={title}
+                            compatibility={compatibility}
+                            baseUrl={baseUrl}
+                            repo={repo}
+                            hasExamples={meta.hasExamples}
+                            licenses={arrayToObj(site.siteMetadata.licenses, 'id')}
+                        />
+                    ))
+                }
+            />
+
+            {children}
+        </>
+    )
+}
+
+export default Models
+
+const query = graphql`
+    query ModelsQuery {
+        site {
+            siteMetadata {
+                licenses {
+                    id
+                    url
+                }
+            }
+        }
+    }
+`
diff --git a/website/src/templates/universe.js b/website/src/templates/universe.js
new file mode 100644
index 000000000..4f861cdbb
--- /dev/null
+++ b/website/src/templates/universe.js
@@ -0,0 +1,352 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import { StaticQuery, graphql } from 'gatsby'
+
+import Card from '../components/card'
+import Link from '../components/link'
+import Title from '../components/title'
+import Grid from '../components/grid'
+import Button from '../components/button'
+import Icon from '../components/icon'
+import CodeBlock, { InlineCode } from '../components/code'
+import Aside from '../components/aside'
+import Sidebar from '../components/sidebar'
+import Section from '../components/section'
+import Main from '../components/main'
+import Footer from '../components/footer'
+import { H3, Label, InlineList } from '../components/typography'
+import { YouTube, SoundCloud, Iframe } from '../components/embed'
+import { github, markdownToReact } from '../components/util'
+
+function getSlug(data) {
+    if (data.isCategory) return `/universe/category/${data.id}`
+    if (data.isProject) return `/universe/project/${data.id}`
+    return `/universe`
+}
+
+function filterResources(resources, data) {
+    const sorted = resources.sort((a, b) => a.id.localeCompare(b.id))
+    if (!data || !data.isCategory) return sorted
+    return sorted.filter(res => (res.category || []).includes(data.id))
+}
+
+const UniverseContent = ({ content = [], categories, pageContext, location, mdxComponents }) => {
+    const { theme, data = {} } = pageContext
+    const filteredResources = filterResources(content, data)
+    const activeData = data ? content.find(({ id }) => id === data.id) : null
+    const markdownComponents = { ...mdxComponents, code: InlineCode }
+    const slug = getSlug(data)
+    const isHome = !data.isCategory && !data.isProject
+
+    const sidebar = [
+        {
+            label: 'Overview',
+            items: [{ text: 'All Projects', url: '/universe' }],
+        },
+        ...categories.map(({ label, items }) => ({
+            label,
+            items: items.map(({ id, title }) => ({
+                text: title,
+                url: `/universe/category/${id}`,
+            })),
+        })),
+    ]
+
+    return (
+        <>
+            <Sidebar items={sidebar} slug={slug} />
+            <Main section="universe" theme={theme} sidebar asides wrapContent footer={<Footer />}>
+                {activeData ? (
+                    <Project data={activeData} components={markdownComponents} />
+                ) : (
+                    <Section>
+                        {isHome ? (
+                            <Title
+                                title="Universe"
+                                teaser="This section collects the many great resources developed with or for spaCy. It includes standalone packages, plugins, extensions, educational materials, operational utilities and bindings for other languages."
+                            />
+                        ) : (
+                            <Title
+                                title={data.title}
+                                teaser={data.description}
+                                tag={String(filteredResources.length)}
+                            />
+                        )}
+                        <Grid cols={data && data.isCategory && data.id === 'books' ? 3 : 2}>
+                            {filteredResources.map(
+                                ({ id, type, title, slogan, thumb, cover, youtube }) => {
+                                    if (isHome && type === 'education') {
+                                        return null
+                                    }
+                                    const url = `/universe/project/${id}`
+                                    const header = youtube && (
+                                        <img
+                                            src={`https://img.youtube.com/vi/${youtube}/0.jpg`}
+                                            alt=""
+                                        />
+                                    )
+                                    return cover ? (
+                                        <p key={id}>
+                                            <Link key={id} to={url} hidden>
+                                                <img src={cover} alt={title || id} />
+                                            </Link>
+                                        </p>
+                                    ) : (
+                                        <Card
+                                            key={id}
+                                            title={title || id}
+                                            image={thumb}
+                                            to={url}
+                                            header={header}
+                                        >
+                                            {slogan}
+                                        </Card>
+                                    )
+                                }
+                            )}
+                        </Grid>
+                    </Section>
+                )}
+                <section>
+                    <H3>Submit your project</H3>
+                    <p>
+                        If you have a project that you want the spaCy community to make use of, you
+                        can suggest it by submitting a pull request to the spaCy website repository.
+                        The Universe database is open-source and collected in a simple JSON file.
+                        For more details on the formats and available fields, see the documentation.
+                        Looking for inspiration your own spaCy plugin or extension? Check out the
+                        <Link to={github() + '/labels/project%20idea'} hideIcon ws>
+                            <InlineCode>project idea</InlineCode>
+                        </Link>
+                        label on the issue tracker.
+                    </p>
+
+                    <InlineList>
+                        <Button variant="primary" to={github('website/universe/README.md')}>
+                            Read the docs
+                        </Button>
+                        <Button icon="code" to={github('website/meta/universe.json')}>
+                            JSON source
+                        </Button>
+                    </InlineList>
+                </section>
+            </Main>
+        </>
+    )
+}
+
+UniverseContent.propTypes = {
+    content: PropTypes.arrayOf(PropTypes.object),
+    categories: PropTypes.arrayOf(
+        PropTypes.shape({
+            label: PropTypes.string.isRequired,
+            items: PropTypes.arrayOf(
+                PropTypes.shape({
+                    id: PropTypes.string.isRequired,
+                    title: PropTypes.string.isRequired,
+                    description: PropTypes.string.isRequired,
+                })
+            ).isRequired,
+        })
+    ).isRequired,
+    theme: PropTypes.string,
+    location: PropTypes.object.isRequired,
+    mdxComponents: PropTypes.object,
+}
+
+const Project = ({ data, components }) => (
+    <>
+        <Title title={data.title || data.id} teaser={data.slogan} image={data.thumb}>
+            {data.github && (
+                <p>
+                    <Link to={`https://github.com/${data.github}`} hidden>
+                        {[
+                            `release/${data.github}/all.svg?style=flat-square`,
+                            `license/${data.github}.svg?style=flat-square`,
+                            `stars/${data.github}.svg?style=social&label=Stars`,
+                        ].map((url, i) => (
+                            <img
+                                style={{ borderRadius: '1em', marginRight: '0.5rem' }}
+                                key={i}
+                                src={`https://img.shields.io/github/${url}`}
+                                alt=""
+                            />
+                        ))}
+                    </Link>
+                </p>
+            )}
+        </Title>
+        {data.pip && (
+            <Aside title="Installation">
+                <CodeBlock lang="bash" prompt="$">
+                    pip install {data.pip}
+                </CodeBlock>
+            </Aside>
+        )}
+        {data.cran && (
+            <Aside title="Installation">
+                <CodeBlock lang="r">install.packages("{data.cran}")</CodeBlock>
+            </Aside>
+        )}
+
+        {data.cover && (
+            <p>
+                <img src={data.cover} alt={data.title} width={250} style={{ maxWidth: '50%' }} />
+            </p>
+        )}
+
+        <Section>
+            {data.youtube && <YouTube id={data.youtube} />}
+            {data.soundcloud && <SoundCloud id={data.soundcloud} title={data.title} />}
+            {data.iframe && (
+                <Iframe
+                    src={data.iframe}
+                    title={data.title}
+                    width="100%"
+                    height={data.iframe_height}
+                />
+            )}
+
+            {data.description && <section>{markdownToReact(data.description, components)}</section>}
+
+            {data.code_example && (
+                <CodeBlock title="Example" lang={data.code_language || 'python'}>
+                    {data.code_example.join('\n')}
+                </CodeBlock>
+            )}
+
+            {data.image && (
+                <p>
+                    <img src={data.image} style={{ maxWidth: '100%' }} alt="" />
+                </p>
+            )}
+
+            {data.url && (
+                <Button variant="primary" to={data.url}>
+                    View more
+                </Button>
+            )}
+        </Section>
+
+        <Section>
+            <Grid cols={2}>
+                <div>
+                    <Label>Author info</Label>
+                    <InlineList gutterBottom={false}>
+                        <span>{data.author}</span>
+                        <span>
+                            {data.author_links && data.author_links.twitter && (
+                                <Link
+                                    to={`https://twitter.com/${data.author_links.twitter}`}
+                                    hidden
+                                    ws
+                                >
+                                    <Icon width={18} name="twitter" inline />
+                                </Link>
+                            )}
+                            {data.author_links && data.author_links.github && (
+                                <Link
+                                    to={`https://github.com/${data.author_links.github}`}
+                                    hidden
+                                    ws
+                                >
+                                    <Icon width={18} name="github" inline />
+                                </Link>
+                            )}
+                            {data.author_links && data.author_links.website && (
+                                <Link to={data.author_links.website} hidden ws>
+                                    <Icon width={18} name="website" inline />
+                                </Link>
+                            )}
+                        </span>
+                    </InlineList>
+                </div>
+                {data.github && (
+                    <p style={{ marginBottom: 0 }}>
+                        <Label>GitHub</Label>
+                        <Link to={`https://github.com/${data.github}`}>
+                            <InlineCode wrap>{data.github}</InlineCode>
+                        </Link>
+                    </p>
+                )}
+            </Grid>
+            {data.category && (
+                <p style={{ marginBottom: 0 }}>
+                    <Label>Categories</Label>
+                    {data.category.map(cat => (
+                        <Link to={`/universe/category/${cat}`} key={cat} ws>
+                            <InlineCode>{cat}</InlineCode>
+                        </Link>
+                    ))}
+                </p>
+            )}
+        </Section>
+    </>
+)
+
+const Universe = ({ pageContext, location, mdxComponents }) => (
+    <StaticQuery
+        query={query}
+        render={data => {
+            const content = data.site.siteMetadata.universe.resources
+            const categories = data.site.siteMetadata.universe.categories
+            return (
+                <UniverseContent
+                    content={content}
+                    categories={categories}
+                    pageContext={pageContext}
+                    location={location}
+                    mdxComponents={mdxComponents}
+                />
+            )
+        }}
+    />
+)
+
+export default Universe
+
+const query = graphql`
+    query UniverseQuery {
+        site {
+            siteMetadata {
+                universe {
+                    resources {
+                        type
+                        id
+                        title
+                        slogan
+                        url
+                        github
+                        description
+                        pip
+                        cran
+                        category
+                        thumb
+                        image
+                        cover
+                        code_example
+                        code_language
+                        youtube
+                        soundcloud
+                        iframe
+                        iframe_height
+                        author
+                        author_links {
+                            twitter
+                            github
+                            website
+                        }
+                    }
+                    categories {
+                        label
+                        items {
+                            id
+                            title
+                            description
+                        }
+                    }
+                }
+            }
+        }
+    }
+`
diff --git a/website/src/widgets/404.js b/website/src/widgets/404.js
new file mode 100644
index 000000000..bc65e9f0d
--- /dev/null
+++ b/website/src/widgets/404.js
@@ -0,0 +1,19 @@
+import React from 'react'
+import { window } from 'browser-monads'
+
+import { LandingHeader, LandingTitle } from '../components/landing'
+import Button from '../components/button'
+
+export default () => (
+    <LandingHeader style={{ minHeight: 400 }}>
+        <LandingTitle>
+            Ooops, this page
+            <br />
+            does not exist!
+        </LandingTitle>
+        <br />
+        <Button onClick={() => window.history.go(-1)} variant="tertiary">
+            Click here to go back
+        </Button>
+    </LandingHeader>
+)
diff --git a/website/src/widgets/changelog.js b/website/src/widgets/changelog.js
new file mode 100644
index 000000000..c722731ee
--- /dev/null
+++ b/website/src/widgets/changelog.js
@@ -0,0 +1,113 @@
+import React, { useState, useEffect } from 'react'
+import { window } from 'browser-monads'
+
+import Link from '../components/link'
+import { InlineCode } from '../components/code'
+import { Label, H3 } from '../components/typography'
+import { Table, Tr, Th, Td } from '../components/table'
+import Infobox from '../components/infobox'
+import { repo } from '../components/util'
+
+function formatReleases(json) {
+    return Object.values(json)
+        .filter(release => release.name)
+        .map(release => ({
+            title:
+                release.name.split(': ').length === 2 ? release.name.split(': ')[1] : release.name,
+            url: release.html_url,
+            date: release.published_at.split('T')[0],
+            tag: release.tag_name,
+            pre: release.prerelease,
+        }))
+}
+
+const ChangelogTable = ({ data = [] }) => {
+    return (
+        <Table>
+            <thead>
+                <Tr>
+                    <Th>Date</Th>
+                    <Th>Version</Th>
+                    <Th>Title</Th>
+                </Tr>
+            </thead>
+            <tbody>
+                {data.map(({ title, url, date, tag }) => (
+                    <Tr key={tag}>
+                        <Td nowrap>
+                            <Label>{date}</Label>
+                        </Td>
+                        <Td>
+                            <Link to={url} hideIcon>
+                                <InlineCode>{tag}</InlineCode>
+                            </Link>
+                        </Td>
+                        <Td>{title}</Td>
+                    </Tr>
+                ))}
+            </tbody>
+        </Table>
+    )
+}
+
+const Changelog = () => {
+    const [initialized, setInitialized] = useState(false)
+    const [isLoading, setIsLoading] = useState(false)
+    const [isError, setIsError] = useState(true)
+    const [releases, setReleases] = useState([])
+    const [prereleases, setPrereleases] = useState([])
+
+    useEffect(() => {
+        window.dispatchEvent(new Event('resize')) // scroll position for progress
+        if (!initialized && repo) {
+            setIsError(false)
+            setIsLoading(true)
+            fetch(`https://api.github.com/repos/${repo}/releases`)
+                .then(res => res.json())
+                .then(json => {
+                    const releases = formatReleases(json)
+                    setReleases(releases.filter(release => !release.pre))
+                    setPrereleases(releases.filter(release => release.pre))
+                    setIsLoading(false)
+                })
+                .catch(err => {
+                    setIsLoading(false)
+                    setIsError(true)
+                })
+            setInitialized(true)
+        }
+    }, [])
+
+    const error = (
+        <Infobox title="Unable to load changelog from GitHub" variant="danger">
+            <p>
+                Please see the
+                <Link to={`https://github.com/${repo}/releases`} ws hideIcon>
+                    releases page
+                </Link>
+                instead.
+            </p>
+        </Infobox>
+    )
+
+    return isError ? (
+        error
+    ) : isLoading ? null : (
+        <>
+            <H3>Stable Releases</H3>
+            <ChangelogTable data={releases} />
+
+            <H3>Pre-Releases</H3>
+
+            <p>
+                Pre-releases include alpha and beta versions, as well as release candidates. They
+                are not intended for production use. You can download spaCy pre-releases via the{' '}
+                <InlineCode>spacy-nightly</InlineCode> package on pip.
+            </p>
+
+            <ChangelogTable data={prereleases} />
+        </>
+    )
+}
+
+export default Changelog
diff --git a/website/src/widgets/landing.js b/website/src/widgets/landing.js
new file mode 100644
index 000000000..73d59406c
--- /dev/null
+++ b/website/src/widgets/landing.js
@@ -0,0 +1,281 @@
+import React from 'react'
+import PropTypes from 'prop-types'
+import { StaticQuery, graphql } from 'gatsby'
+
+import { LandingHeader, LandingTitle, LandingSubtitle, LandingGrid } from '../components/landing'
+import { LandingCard, LandingButton, LandingDemo } from '../components/landing'
+import { LandingBannerGrid, LandingBanner, LandingLogos } from '../components/landing'
+import { H2 } from '../components/typography'
+import { Ul, Li } from '../components/list'
+import Button from '../components/button'
+import Link from '../components/link'
+
+import BenchmarksChoi from 'usage/_benchmarks-choi.md'
+
+const CODE_EXAMPLE = `# pip install spacy
+# python -m spacy download en_core_web_sm
+
+import spacy
+
+# Load English tokenizer, tagger, parser, NER and word vectors
+nlp = spacy.load("en_core_web_sm")
+
+# Process whole documents
+text = (u"When Sebastian Thrun started working on self-driving cars at "
+        u"Google in 2007, few people outside of the company took him "
+        u"seriously. “I can tell you very senior CEOs of major American "
+        u"car companies would shake my hand and turn away because I wasn’t "
+        u"worth talking to,” said Thrun, now the co-founder and CEO of "
+        u"online higher education startup Udacity, in an interview with "
+        u"Recode earlier this week.")
+doc = nlp(text)
+
+# Find named entities, phrases and concepts
+for entity in doc.ents:
+    print(entity.text, entity.label_)
+
+# Determine semantic similarities
+doc1 = nlp(u"my fries were super gross")
+doc2 = nlp(u"such disgusting fries")
+similarity = doc1.similarity(doc2)
+print(doc1.text, doc2.text, similarity)
+`
+
+/**
+ * Compute the overall total counts of models and languages
+ */
+function getCounts(langs = []) {
+    return {
+        langs: langs.length,
+        modelLangs: langs.filter(({ models }) => models && !!models.length).length,
+        models: langs.map(({ models }) => (models ? models.length : 0)).reduce((a, b) => a + b, 0),
+    }
+}
+
+const Landing = ({ data }) => {
+    const counts = getCounts(data.languages)
+    return (
+        <>
+            <LandingHeader>
+                <LandingTitle>
+                    Industrial-Strength
+                    <br />
+                    Natural Language
+                    <br />
+                    Processing
+                </LandingTitle>
+                <LandingSubtitle>in Python</LandingSubtitle>
+            </LandingHeader>
+            <LandingGrid blocks>
+                <LandingCard title="Fastest in the world">
+                    <p>
+                        spaCy excels at large-scale information extraction tasks. It's written from
+                        the ground up in carefully memory-managed Cython. Independent research has
+                        confirmed that spaCy is the fastest in the world. If your application needs
+                        to process entire web dumps, spaCy is the library you want to be using.
+                    </p>
+                    <LandingButton to="/usage/facts-figures">Facts & Figures</LandingButton>
+                </LandingCard>
+
+                <LandingCard title="Get things done">
+                    <p>
+                        spaCy is designed to help you do real work — to build real products, or
+                        gather real insights. The library respects your time, and tries to avoid
+                        wasting it. It's easy to install, and its API is simple and productive. We
+                        like to think of spaCy as the Ruby on Rails of Natural Language Processing.
+                    </p>
+                    <LandingButton to="/usage">Get started</LandingButton>
+                </LandingCard>
+
+                <LandingCard title="Deep learning">
+                    <p>
+                        spaCy is the best way to prepare text for deep learning. It interoperates
+                        seamlessly with TensorFlow, PyTorch, scikit-learn, Gensim and the rest of
+                        Python's awesome AI ecosystem. With spaCy, you can easily construct
+                        linguistically sophisticated statistical models for a variety of NLP
+                        problems.
+                    </p>
+                    <LandingButton to="/usage/training">Read more</LandingButton>
+                </LandingCard>
+            </LandingGrid>
+
+            <LandingGrid>
+                <LandingDemo title="Edit the code & try spaCy">{CODE_EXAMPLE}</LandingDemo>
+
+                <div>
+                    <H2>Features</H2>
+                    <Ul>
+                        <Li>
+                            Non-destructive <strong>tokenization</strong>
+                        </Li>
+                        <Li>
+                            <strong>Named entity</strong> recognition
+                        </Li>
+                        <Li>
+                            Support for <strong>{counts.langs}+ languages</strong>
+                        </Li>
+                        <Li>
+                            <strong>{counts.models} statistical models</strong> for{' '}
+                            {counts.modelLangs} languages
+                        </Li>
+                        <Li>
+                            Pre-trained <strong>word vectors</strong>
+                        </Li>
+                        <Li>
+                            Easy <strong>deep learning</strong> integration
+                        </Li>
+                        <Li>Part-of-speech tagging</Li>
+                        <Li>Labelled dependency parsing</Li>
+                        <Li>Syntax-driven sentence segmentation</Li>
+                        <Li>
+                            Built in <strong>visualizers</strong> for syntax and NER
+                        </Li>
+                        <Li>Convenient string-to-hash mapping</Li>
+                        <Li>Export to numpy data arrays</Li>
+                        <Li>Efficient binary serialization</Li>
+                        <Li>
+                            Easy <strong>model packaging</strong> and deployment
+                        </Li>
+                        <Li>State-of-the-art speed</Li>
+                        <Li>Robust, rigorously evaluated accuracy</Li>
+                    </Ul>
+                </div>
+            </LandingGrid>
+
+            <LandingBannerGrid>
+                <LandingBanner
+                    title="BERT-style language model pretraining and more"
+                    label="New in v2.1"
+                    to="/usage/v2-1"
+                    button="Read more"
+                    small
+                >
+                    Learn more from small training corpora by initializing your models with{' '}
+                    <strong>knowledge from raw text</strong>. The new pretrain command teaches
+                    spaCy's CNN model to predict words based on their context, producing
+                    representations of words in contexts. If you've seen Google's BERT system or
+                    fast.ai's ULMFiT, spaCy's pretraining is similar – but much more efficient. It's
+                    still experimental, but users are already reporting good results, so give it a
+                    try!
+                </LandingBanner>
+
+                <LandingBanner
+                    title="Prodigy: Radically efficient machine teaching"
+                    label="From the makers of spaCy"
+                    to="https://prodi.gy"
+                    button="Try it out"
+                    background="#eee"
+                    color="#252a33"
+                    small
+                >
+                    Prodigy is an <strong>annotation tool</strong> so efficient that data scientists
+                    can do the annotation themselves, enabling a new level of rapid iteration.
+                    Whether you're working on entity recognition, intent detection or image
+                    classification, Prodigy can help you <strong>train and evaluate</strong> your
+                    models faster. Stream in your own examples or real-world data from live APIs,
+                    update your model in real-time and chain models together to build more complex
+                    systems.
+                </LandingBanner>
+            </LandingBannerGrid>
+
+            <LandingLogos title="spaCy is trusted by" logos={data.logosUsers}>
+                <Button to={`https://github.com/${data.repo}/stargazers`}>and many more</Button>
+            </LandingLogos>
+            <LandingLogos title="Featured on" logos={data.logosPublications} />
+
+            <LandingBanner
+                title="Convolutional neural network models"
+                label="New in v2.0"
+                button="Download models"
+                to="/models"
+            >
+                spaCy v2.0 features new neural models for <strong>tagging</strong>,{' '}
+                <strong>parsing</strong> and <strong>entity recognition</strong>. The models have
+                been designed and implemented from scratch specifically for spaCy, to give you an
+                unmatched balance of speed, size and accuracy. A novel bloom embedding strategy with
+                subword features is used to support huge vocabularies in tiny tables. Convolutional
+                layers with residual connections, layer normalization and maxout non-linearity are
+                used, giving much better efficiency than the standard BiLSTM solution. Finally, the
+                parser and NER use an imitation learning objective to deliver accuracy in-line with
+                the latest research systems, even when evaluated from raw text. With these
+                innovations, spaCy v2.0's models are <strong>10× smaller</strong>,{' '}
+                <strong>20% more accurate</strong>, and
+                <strong>even cheaper to run</strong> than the previous generation.
+            </LandingBanner>
+
+            <LandingGrid cols={2}>
+                <div>
+                    <H2>Benchmarks</H2>
+                    <p>
+                        In 2015, independent researchers from Emory University and Yahoo! Labs
+                        showed that spaCy offered the
+                        <strong>fastest syntactic parser in the world</strong> and that its accuracy
+                        was <strong>within 1% of the best</strong> available (
+                        <Link to="https://aclweb.org/anthology/P/P15/P15-1038.pdf">
+                            Choi et al., 2015
+                        </Link>
+                        ). spaCy v2.0, released in 2017, is more accurate than any of the systems
+                        Choi et al. evaluated.
+                    </p>
+                    <p>
+                        <Button to="/usage/facts-figures#benchmarks" large>
+                            See details
+                        </Button>
+                    </p>
+                </div>
+
+                <div>
+                    <BenchmarksChoi />
+                </div>
+            </LandingGrid>
+        </>
+    )
+}
+
+Landing.propTypes = {
+    data: PropTypes.shape({
+        repo: PropTypes.string,
+        languages: PropTypes.arrayOf(
+            PropTypes.shape({
+                models: PropTypes.arrayOf(PropTypes.string),
+            })
+        ),
+        logosUsers: PropTypes.arrayOf(
+            PropTypes.shape({
+                id: PropTypes.string.isRequired,
+                url: PropTypes.string.isRequired,
+            })
+        ),
+        logosPublications: PropTypes.arrayOf(
+            PropTypes.shape({
+                id: PropTypes.string.isRequired,
+                url: PropTypes.string.isRequired,
+            })
+        ),
+    }),
+}
+
+export default () => (
+    <StaticQuery query={landingQuery} render={({ site }) => <Landing data={site.siteMetadata} />} />
+)
+
+const landingQuery = graphql`
+    query LandingQuery {
+        site {
+            siteMetadata {
+                repo
+                languages {
+                    models
+                }
+                logosUsers {
+                    id
+                    url
+                }
+                logosPublications {
+                    id
+                    url
+                }
+            }
+        }
+    }
+`
diff --git a/website/src/widgets/languages.js b/website/src/widgets/languages.js
new file mode 100644
index 000000000..55645f951
--- /dev/null
+++ b/website/src/widgets/languages.js
@@ -0,0 +1,105 @@
+import React from 'react'
+import { StaticQuery, graphql } from 'gatsby'
+
+import Link from '../components/link'
+import { InlineCode } from '../components/code'
+import { Table, Tr, Th, Td } from '../components/table'
+import { Ul, Li } from '../components/list'
+import Infobox from '../components/infobox'
+import { github, join } from '../components/util'
+
+const Language = ({ name, code, models }) => (
+    <Tr>
+        <Td>{name}</Td>
+        <Td>
+            <InlineCode>{code}</InlineCode>
+        </Td>
+        <Td>
+            <Link to={github(`spacy/lang/${code}`)}>
+                <InlineCode>lang/{code}</InlineCode>
+            </Link>
+        </Td>
+        <Td>
+            {models && models.length ? (
+                <Link to={`/models/${code}`}>
+                    {models.length} {models.length === 1 ? 'model' : 'models'}
+                </Link>
+            ) : (
+                <em>none yet</em>
+            )}
+        </Td>
+    </Tr>
+)
+
+const Languages = () => (
+    <StaticQuery
+        query={query}
+        render={({ site }) => {
+            const langs = site.siteMetadata.languages
+            const withModels = langs
+                .filter(({ models }) => models && !!models.length)
+                .sort((a, b) => a.code.localeCompare(b.code))
+            const withoutModels = langs
+                .filter(({ models }) => !models || !models.length)
+                .sort((a, b) => a.code.localeCompare(b.code))
+            const withDeps = langs.filter(({ dependencies }) => dependencies && dependencies.length)
+            return (
+                <>
+                    <Table>
+                        <thead>
+                            <Tr>
+                                <Th>Language</Th>
+                                <Th>Code</Th>
+                                <Th>Language Data</Th>
+                                <Th>Models</Th>
+                            </Tr>
+                        </thead>
+                        <tbody>
+                            {withModels.map(model => (
+                                <Language {...model} key={model.code} />
+                            ))}
+                            {withoutModels.map(model => (
+                                <Language {...model} key={model.code} />
+                            ))}
+                        </tbody>
+                    </Table>
+                    <Infobox title="Dependencies">
+                        <p>Some language tokenizers require external dependencies.</p>
+                        <Ul>
+                            {withDeps.map(({ code, name, dependencies }) => (
+                                <Li key={code}>
+                                    <strong>{name}:</strong>{' '}
+                                    {join(
+                                        dependencies.map((dep, i) => (
+                                            <Link to={dep.url}>{dep.name}</Link>
+                                        ))
+                                    )}
+                                </Li>
+                            ))}
+                        </Ul>
+                    </Infobox>
+                </>
+            )
+        }}
+    />
+)
+
+export default Languages
+
+const query = graphql`
+    query LanguagesQuery {
+        site {
+            siteMetadata {
+                languages {
+                    code
+                    name
+                    models
+                    dependencies {
+                        name
+                        url
+                    }
+                }
+            }
+        }
+    }
+`
diff --git a/website/src/widgets/quickstart-install.js b/website/src/widgets/quickstart-install.js
new file mode 100644
index 000000000..81e28871b
--- /dev/null
+++ b/website/src/widgets/quickstart-install.js
@@ -0,0 +1,116 @@
+import React from 'react'
+import { StaticQuery, graphql } from 'gatsby'
+
+import { Quickstart, QS } from '../components/quickstart'
+import { repo } from '../components/util'
+
+const DATA = [
+    {
+        id: 'os',
+        title: 'Operating system',
+        options: [
+            { id: 'mac', title: 'macOS / OSX', checked: true },
+            { id: 'windows', title: 'Windows' },
+            { id: 'linux', title: 'Linux' },
+        ],
+    },
+    {
+        id: 'package',
+        title: 'Package manager',
+        options: [
+            { id: 'pip', title: 'pip', checked: true },
+            { id: 'conda', title: 'conda' },
+            { id: 'source', title: 'from source' },
+        ],
+    },
+    {
+        id: 'python',
+        title: 'Python version',
+        options: [{ id: '2', title: '2.x' }, { id: '3', title: '3.x', checked: true }],
+    },
+    {
+        id: 'config',
+        title: 'Configuration',
+        multiple: true,
+        options: [
+            {
+                id: 'venv',
+                title: 'virtualenv',
+                help: 'Use a virtual environment and install spaCy into a user directory',
+            },
+        ],
+    },
+]
+
+const QuickstartInstall = ({ id, title }) => (
+    <StaticQuery
+        query={query}
+        render={({ site }) => {
+            const models = site.siteMetadata.languages.filter(({ models }) => models !== null)
+            const data = [
+                ...DATA,
+                {
+                    id: 'models',
+                    title: 'Models',
+                    multiple: true,
+                    options: models.map(({ code, name }) => ({ id: code, title: name })),
+                },
+            ]
+            return (
+                <Quickstart data={data} title={title} id={id}>
+                    <QS config="venv" python="2">
+                        python -m pip install -U virtualenv
+                    </QS>
+                    <QS config="venv" python="2">
+                        virtualenv .env
+                    </QS>
+                    <QS config="venv" python="3">
+                        python -m venv .env
+                    </QS>
+                    <QS config="venv" os="mac">
+                        source .env/bin/activate
+                    </QS>
+                    <QS config="venv" os="linux">
+                        source .env/bin/activate
+                    </QS>
+                    <QS config="venv" os="windows">
+                        .env\Scripts\activate
+                    </QS>
+                    <QS package="pip">pip install -U spacy</QS>
+                    <QS package="conda">conda install -c conda-forge spacy</QS>
+                    <QS package="source">git clone https://github.com/{repo}</QS>
+                    <QS package="source">cd spaCy</QS>
+                    <QS package="source" os="linux">
+                        export PYTHONPATH=`pwd`
+                    </QS>
+                    <QS package="source" os="windows">
+                        set PYTHONPATH=/path/to/spaCy
+                    </QS>
+                    <QS package="source">pip install -r requirements.txt</QS>
+                    <QS package="source">python setup.py build_ext --inplace</QS>
+                    {models.map(({ code }) => (
+                        <QS models={code} key={code}>
+                            python -m spacy download {code}
+                        </QS>
+                    ))}
+                </Quickstart>
+            )
+        }}
+    />
+)
+
+export default QuickstartInstall
+
+const query = graphql`
+    query QuickstartInstallQuery {
+        site {
+            siteMetadata {
+                languages {
+                    code
+                    name
+                    models
+                }
+            }
+        }
+    }
+`
diff --git a/website/src/widgets/quickstart-models.js b/website/src/widgets/quickstart-models.js
new file mode 100644
index 000000000..83bb4527b
--- /dev/null
+++ b/website/src/widgets/quickstart-models.js
@@ -0,0 +1,107 @@
+import React, { Fragment } from 'react'
+import { StaticQuery, graphql } from 'gatsby'
+
+import { Quickstart, QS } from '../components/quickstart'
+
+const data = [
+    {
+        id: 'lang',
+        title: 'Language',
+    },
+    {
+        id: 'load',
+        title: 'Loading style',
+        options: [
+            {
+                id: 'spacy',
+                title: 'Use spacy.load()',
+                help: "Use spaCy's built-in loader to load the model by name.",
+                checked: true,
+            },
+            {
+                id: 'module',
+                title: 'Import as module',
+                help: 'Import the model explicitly as a Python module.',
+            },
+        ],
+    },
+    {
+        id: 'config',
+        title: 'Options',
+        multiple: true,
+        options: [{ id: 'example', title: 'Show usage example' }],
+    },
+]
+
+const QuickstartInstall = ({ id, title, description, defaultLang, children }) => (
+    <StaticQuery
+        query={query}
+        render={({ site }) => {
+            const models = site.siteMetadata.languages.filter(({ models }) => models !== null)
+            data[0].options = models.map(({ code, name }) => ({
+                id: code,
+                title: name,
+                checked: code === defaultLang,
+            }))
+            return (
+                <Quickstart data={data} title={title} id={id} description={description}>
+                    {models.map(({ code, models, example }) => {
+                        const pkg = models[0]
+                        const exampleText = example || 'No text available yet'
+                        return (
+                            <Fragment key={code}>
+                                <QS lang={code}>python -m spacy download {pkg}</QS>
+                                <QS lang={code} divider />
+                                <QS lang={code} load="spacy" prompt="python">
+                                    import spacy
+                                </QS>
+                                <QS lang={code} load="spacy" prompt="python">
+                                    nlp = spacy.load("{pkg}")
+                                </QS>
+                                <QS lang={code} load="module" prompt="python">
+                                    import {pkg}
+                                </QS>
+                                <QS lang={code} load="module" prompt="python">
+                                    nlp = {pkg}.load()
+                                </QS>
+                                <QS lang={code} config="example" prompt="python">
+                                    doc = nlp(u"{exampleText}")
+                                </QS>
+                                <QS lang={code} config="example" prompt="python">
+                                    print([
+                                    {code === 'xx'
+                                        ? '(ent.text, ent.label) for ent in doc.ents'
+                                        : '(w.text, w.pos_) for w in doc'}
+                                    ])
+                                </QS>
+                            </Fragment>
+                        )
+                    })}
+
+                    {children}
+                </Quickstart>
+            )
+        }}
+    />
+)
+
+QuickstartInstall.defaultProps = {
+    defaultLang: 'en',
+}
+
+export default QuickstartInstall
+
+const query = graphql`
+    query QuickstartModelsQuery {
+        site {
+            siteMetadata {
+                languages {
+                    code
+                    name
+                    models
+                    example
+                }
+            }
+        }
+    }
+`
diff --git a/website/src/widgets/styleguide.js b/website/src/widgets/styleguide.js
new file mode 100644
index 000000000..2470795e9
--- /dev/null
+++ b/website/src/widgets/styleguide.js
@@ -0,0 +1,111 @@
+import React from 'react'
+
+import Grid from '../components/grid'
+import { Label } from '../components/typography'
+import Link from '../components/link'
+
+import { ReactComponent as Logo } from '../images/logo.svg'
+import patternBlue from '../images/pattern_blue.jpg'
+import patternGreen from '../images/pattern_green.jpg'
+import patternPurple from '../images/pattern_purple.jpg'
+
+const colors = {
+    dark: 'var(--color-front)',
+    medium: 'var(--color-dark)',
+    light: 'var(--color-subtle)',
+    faint: 'var(--color-subtle-light)',
+    blue: 'var(--color-theme-blue)',
+    'dark blue': 'var(--color-theme-blue-dark)',
+    green: 'var(--color-theme-green)',
+    'dark green': 'var(--color-theme-green-dark)',
+    purple: 'var(--color-theme-purple)',
+    'dark purple': 'var(--color-theme-purple-dark)',
+    red: 'var(--color-red-medium)',
+    'light red': 'var(--color-red-light)',
+    yellow: 'var(--color-yellow-medium)',
+    'light yellow': 'var(--color-yellow-light)',
+}
+
+const patterns = {
+    'blue pattern': patternBlue,
+    'green pattern': patternGreen,
+    'purple pattern': patternPurple,
+}
+
+const Card = ({ style = {}, children }) => (
+    <div
+        style={{
+            borderRadius: 'var(--border-radius)',
+            boxShadow: 'var(--box-shadow)',
+            marginBottom: 'var(--spacing-sm)',
+        }}
+    >
+        <div
+            style={{
+                borderTopLeftRadius: 'var(--border-radius)',
+                borderTopRightRadius: 'var(--border-radius)',
+                ...style,
+            }}
+        />
+        <div style={{ textAlign: 'center', padding: '1.5rem 0' }}>{children}</div>
+    </div>
+)
+
+export const Colors = () => (
+    <Grid cols={4} narrow>
+        {Object.keys(colors).map(name => (
+            <Card key={name} style={{ height: 80, background: colors[name] }}>
+                <Label>{name}</Label>
+            </Card>
+        ))}
+    </Grid>
+)
+
+export const Patterns = () => {
+    const imgStyle = name => ({
+        height: 125,
+        background: `url(${patterns[name]}) center/150% repeat`,
+    })
+    const textStyle = { fontSize: 'var(--font-size-xs)', color: 'var(--color-subtle-dark)' }
+    const linkStyle = { color: 'var(--color-dark)' }
+    return (
+        <Grid cols={3} narrow>
+            {Object.keys(patterns).map(name => (
+                <Card key={name} style={imgStyle(name)}>
+                    <Label>{name}</Label>
+                    <span style={textStyle}>
+                        by
+                        <Link to="https://dribbble.com/kemal" hidden style={linkStyle} ws>
+                            Kemal Şanlı
+                        </Link>
+                    </span>
+                </Card>
+            ))}
+        </Grid>
+    )
+}
+
+export const Logos = () => {
+    const style = {
+        padding: '0 3rem',
+        border: '1px solid var(--color-subtle)',
+        borderRadius: 'var(--border-radius)',
+        boxShadow: 'var(--box-shadow)',
+    }
+    return (
+        <Grid cols={2} narrow>
+            <div style={style}>
+                <Logo />
+            </div>
+            <div
+                style={{
+                    ...style,
+                    background: 'var(--color-theme-blue)',
+                    borderColor: 'var(--color-theme-blue)',
+                }}
+            >
+                <Logo style={{ color: 'var(--color-back)' }} />
+            </div>
+        </Grid>
+    )
+}
diff --git a/website/styleguide.jade b/website/styleguide.jade
deleted file mode 100644
index 9ef468954..000000000
--- a/website/styleguide.jade
+++ /dev/null
@@ -1,659 +0,0 @@
-//- 💫 STYLEGUIDE
-
-include _includes/_mixins
-
-+section("intro")
-    p
-        |  This styleguide is loosely based on the concept and principles of
-        |  #[+a("http://bradfrost.com/blog/post/atomic-web-design/") Atomic Design].
-        |  The templates consist of small elements (atoms) which are combined
-        |  and connected to form larger molecules and full components. The site
-        |  is compiled using #[+a("http://harpjs.com/") Harp], a static web
-        |  server with built-in preprocessing. Templates are written entirely in
-        |  #[+a("http://jade-lang.com") Jade] (aka. Pug), a clean,
-        |  whitespace-sensitive templating  language that compiles to HTML.
-        |  CSS is written in #[+a("http://sass-lang.com") Sass] and preprocessed
-        |  via Harp, JavaScript is written in ES6 syntax and compiled using
-        |  #[+a("https://babeljs.io") Babel].
-
-+section("logo")
-    +h(2, "logo", "website/assets/img/logo.svg") Logo
-
-    p
-        |  If you would like to use the spaCy logo on your site, please get in
-        |  touch and ask us first. However, if you want to show support and tell
-        |  others that your project is using spaCy, you can grab one of our
-        |  #[+a("/usage/spacy-101#faq-project-with-spacy") spaCy badges].
-
-    +grid
-        each color in [["#09a3d5", "#fff"], ["#fff", "#09a3d5"]]
-            +grid-col("half").o-box.u-text-center.u-padding-medium(style="background: #{color[1]}; color: #{color[0]}")
-                +icon("spacy", 338, 108)(style="max-width: 100%")
-
-+section("colors")
-    +h(2, "colors", "website/assets/css/_variables.sass") Colors
-
-    +grid
-        each color, label in {"dark": "#1a1e23", "medium": "#45505e", "light": "#dddddd", "faint": "#f6f6f6", "blue": "#09a3d5", "dark blue": "#077ea4", "green": "#05b083", "dark green": "#047e5e"}
-            +grid-col("quarter").u-text-small.o-card
-                div(style="height: 75px; background: #{color}; border-top-left-radius: 6px; border-top-right-radius: 6px")
-                .u-text-center.u-padding-medium
-                    +label=label
-                    code=color
-
-        each pattern in ["blue", "green"]
-            +grid-col("half").u-text-small.o-card
-                div(style="background: url('/assets/img/pattern_#{pattern}.jpg') center/100% repeat; height: 125px; border-top-left-radius: 6px; border-top-right-radius: 6px")
-                .u-text-center.u-padding-medium
-                    +label #{pattern} pattern
-                    .u-text-tiny.u-color-subtle by #[+a("https://dribbble.com/kemal").u-color-dark Kemal Şanlı]
-
-+section("typography")
-    +h(2, "typography") Typography
-
-    +aside-code("Usage", "jade").
-        +h(2) Headline two
-        +h(3, "some-id") Headline three
-
-    p
-        |  Headlines are set in
-        |  #[+a("http://cargocollective.com/hanken/HK-Grotesk-Open-Source-Font") HK Grotesk]
-        |  by Hanken Design. All other body text and code uses the best-matching
-        |  default system font to provide a "native" reading experience.
-
-    each heading in [0, 1, 2, 3, 4, 5]
-        .o-block-small(class="u-heading-" + heading) Heading #{heading}
-    +label Label
-
-+section("elements")
-    +h(2, "elements", "website/_includes/_mixins.jade") Elements
-
-    p
-        |  The site comes with a collection of simple content elements,
-        |  implemented as mixins. These elements can be used individually, or as
-        |  part of larger components.
-
-    +h(3, "text-links") Special text & links
-
-    +aside-code("Usage", "jade").
-        +api("token") &#35;[code Token]
-        +src("https://github.com") GitHub source
-        +help("Help text here")
-        +fn(1, "bibliography")
-
-    p
-        |  Special link styles are implemented as mixins and can be used to
-        |  mark links to the API documentation, or links to source code.
-        |  Additionally a "help" icon can be added to provide more information
-        |  via a tooltip.
-
-    p.o-inline-list
-        +a("#") Link
-        code Inline Code
-        +api("token") #[code Token]
-        +src(gh("spacy")) Source
-        span.u-color-dark.u-nowrap Help #[+help("Help text here")]
-        span Footnote#[+fn(1, "", "This marks a footnote and can link to a section")]
-
-    +h(3, "buttons") Buttons
-
-    +aside-code("Usage", "jade").
-        +button("https://spacy.io", true, "secondary")
-        +button("https://spacy.io", true, "primary", "small")
-
-    p
-        |  Link buttons come in two variants, #[code primary] and
-        |  #[code secondary] and two sizes, with an optional #[code small] size
-        |  modifier.Since they're mostly used as enhanced links, the buttons are
-        |  implemented as styled links instead of native button elements.
-
-    p.o-inline-list
-        +button("#", false, "primary") Primary
-        +button("#", false, "secondary") Secondary
-        +button("#", false, "primary", "small") Primary small
-        +button("#", false, "secondary", "small") Secondary small
-
-    +h(3, "tags") Tags
-
-    +aside-code("Usage", "jade").
-        +tag I'm a tag
-        +tag-new(2)
-        +tag-model("Named entities")
-
-    p
-        |  Tags can be used together with headlines, or next to properties
-        |  across the documentation, and combined with tooltips to provide
-        |  additional information. The #[code +tag-new] mixin takes a version
-        |  number and can mark new features. Using the mixin, visibility of this
-        |  tag can be toggled once the feature isn't considered new anymore.
-        |  The #[code +tag-model] mixin takes a description of model
-        |  capabilities and can be used to mark features that require a
-        |  respective model to be installed.
-
-    .o-block
-        p.o-inline-list
-            +tag I'm a tag
-            +tag-new(2)
-            +tag-model("Named entities")
-
-    +h(3, "icons", "website/_includes/_svg.jade") Icons
-
-    +aside-code("Usage", "jade").
-        +icon("github", 18)
-
-    p
-        |  Icons are implemented via an SVG sprite and can be included as a
-        |  mixin, using their name and an optional size value in #[code px].
-
-    +infobox.u-text-center
-        each icon in ["code", "arrow-right", "book", "chat", "star", "help_o", "help", "yes", "no", "neutral", "accept", "reject", "markdown", "course", "github", "jupyter"]
-            .u-inline-block.u-padding-small.u-color-dark(data-tooltip=icon data-tooltip-style="code" aria-label=icon)
-                +icon(icon, 20)
-
-+section("components")
-    +h(2, "components", "website/_includes/_mixins.jade") Components
-
-    p
-        |  The site uses a collection of Jade mixins to make it easy to use
-        |  complex content elements across templates and blog posts. To read
-        |  more about the concept of modular markup components, check out our
-        |  #[+a("https://explosion.ai/blog/modular-markup", true) blog post] on
-        |  the subject.
-
-    +h(3, "grid") Grid
-
-    +aside-code("Usage", "jade").
-        +grid
-            +grid-col("half") Half
-            +grid-col("half") Half
-
-    p
-        |  For now, the grid is still implemented as a standard #[code flexbox]
-        |  grid, although it may be refactored to use CSS #[code grid] going
-        |  forward. The grid supports up to four columns and collapses on
-        |  small screens.
-
-    +grid
-        each count, label in {"full": 1, "half": 2, "third": 3, "quarter": 4}
-            each _ in Array(count)
-                +grid-col(label).o-box.u-text-center.u-text-label.u-color-dark=label
-
-    +h(3, "table") Table
-
-    +aside-code("Usage", "jade").
-        +table(["Header 1", "Header 2"])
-            +row
-                +cell Cell
-                +cell Cell
-
-    p
-        |  Tables are used to present data and API documentation. If a list of
-        |  headings is specified, those will be rendered as the table header.
-        |  An optional #[code +row("foot")] can be used to mark a footer row
-        |  with a distinct style, for example to visualise the return values
-        |  of a documented function.
-
-    - var table_cols = ["Header 1", "Header 2", "Header 3"]
-    +table(table_cols)
-        each row, i in Array(4)
-            +row((i == 3) ? "foot" : null)
-                each col, j in table_cols
-                    +cell
-                        if i == 3 && j == 0
-                            |  Footer
-
-                        else
-                            |  Row #{i + 1}, cell #{j + 1}
-
-    +h(3, "list") List
-
-    +aside-code("Usage", "jade").
-        +list("numbers", 3)
-            +item List item
-            +item List item
-
-    p
-        |  Lists are available as bulleted, numbered, lettered and lower roman.
-        |  Optionally, a start index can be defined as the second argument
-        |  on ordered lists.
-
-    +grid
-        +list
-            +item I am a bulleted list
-            +item I have nice bullets
-            +item Lorem ipsum dolor
-            +item consectetur adipiscing elit
-
-        +list("numbers")
-            +item I am an ordered list
-            +item I have nice numbers
-            +item Lorem ipsum dolor
-            +item consectetur adipiscing elit
-
-        +list("numbers", 10)
-            +item I am an numbered list
-            +item with a custom start number
-            +item Lorem ipsum dolor
-            +item consectetur adipiscing elit
-
-        +list("letters")
-            +item I am an ordered list
-            +item I have uppercase letters
-            +item Lorem ipsum dolor
-            +item consectetur adipiscing elit
-
-        +list("letters", 18)
-            +item I am an ordered list
-            +item with a custom start letter
-            +item Lorem ipsum dolor
-            +item consectetur adipiscing elit
-
-        +list("roman")
-            +item I am an ordered list
-            +item I have roman numerals
-            +item Lorem ipsum dolor
-            +item consectetur adipiscing elit
-
-    +h(3, "code") Code
-
-    +aside-code("Usage", "jade").
-        +code("Label", "python").
-            import spacy
-            nlp = spacy.load('en')
-            doc = nlp(u"This is a sentence.")
-
-    p
-        |  Code blocks use the #[+a("http://prismjs.com/") Prism] syntax
-        |  highlighter with a custom theme. The language can be set individually
-        |  on each block, and defaults to Python. An optional label can be
-        |  added as the first argument, which is displayed above the block.
-        |  When using the #[code +code] mixin, don't forget to append a period
-        |  #[code &period;] to the mixin call. This tells Jade to interpret the
-        |  indented block as plain text and preserve whitespace.
-
-    +code("Using spaCy").
-        import spacy
-        nlp = spacy.load('en')
-        doc = nlp(u"This is a sentence.")
-
-    +aside-code("Usage", "jade").
-        +code-new nlp.to_disk('/model')
-        +code-old nlp.save_to_directory('/model')
-
-    p
-        |  Code blocks can also be displayed with a coloured icon to visualise
-        |  correct and incorrect examples in comparison. This is useful to
-        |  show best practices or backwards incompatibilities in the API.
-
-    .o-block
-        +code-new nlp.to_disk('/model')
-        +code-old nlp.save_to_directory('/model')
-
-    +h(3, "aside") Aside
-
-    +aside-code("Usage", "jade").
-        +aside("Title") This is an aside
-        +aside-code("Title", "python").
-            nlp = spacy.load('en')
-
-    p
-        |  Asides can be used to display additional notes and content in the
-        |  right-hand column. Two mixins are available: #[code +aside] for
-        |  regular text with an optional title, #[code +aside-code], which
-        |  roughly mimicks the #[code +code] component. Visually, asides are
-        |  moved to the side on the X-axis, and displayed at the same level
-        |  they were inserted. On small screens, they collapse and are rendered
-        |  in their original position, in between the text.
-
-    +h(3, "infobox") Infobox
-
-    +aside-code("Usage", "jade").
-        +infobox("Label") This is text.
-        +infobox("Label", "⚠️") This is text.
-
-    p
-        |  Infoboxes can be used to add notes, updates, warnings or additional
-        |  information to a page or section. Semantically, they're implemented
-        |  and interpreted as an #[code aside] element. Since infobox titles
-        |  are especially nice with emoji, an emoji can be specified as the
-        |  second argument for optimal rendering and spacing.
-
-    +infobox("Infobox label") Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque enim ante, pretium a orci eget, varius dignissim augue. Nam eu dictum mauris, id tincidunt nisi. Integer commodo pellentesque tincidunt.
-
-    +infobox("Infobox label with emoji", "⚠️") Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque enim ante, pretium a orci eget, varius dignissim augue. Nam eu dictum mauris, id tincidunt nisi. Integer commodo pellentesque tincidunt.
-
-    +h(3, "card") Card
-
-    +aside-code("Usage", "jade").
-        +grid
-            +card("Title", "https://", "Author", "github")
-                |  Card content goes here
-    p
-        |  Cards can be used to present external content and links, like GitHub
-        |  projects, websites, books or articles. They can take an optional
-        |  value for the content author and icon, which is displayed in the
-        |  corner. The content supplied via an indented block can also include
-        |  formatting or other elements like images. Under the hood, cards are
-        |  styled grid columns and should therefore always be used as children
-        |  of #[code +grid].
-
-    +grid
-        +card("spaCy", "https://github.com/explosion/spaCy", "Explosion AI", "github")
-            |  An open-source library for industrial-strength Natural Language
-            |  Processing in Python.
-
-        +card("Prodigy", "https://prodi.gy", "Explosion AI", "star")
-            |  A new annotation tool for radically efficient machine teaching,
-            |  powered by active learning.
-
-    +h(3, "accordion") Accordion
-
-    +aside-code("Usage", "jade").
-        +accordion("Section 1")
-            p This text is shown on click.
-
-    p
-        |  Accordions are collapsible sections that are mostly used for lengthy
-        |  tables, like the tag and label annotation schemes for different
-        |  languages. They all need to be presented – but chances
-        |  are the user doesn't actually care about #[em all] of them,
-        |  especially not at the same time. So it's fairly reasonable to hide
-        |  them begin a click. This particular implementation was inspired by
-        |  the amazing
-        |  #[+a("https://inclusive-components.design/collapsible-sections/") Inclusive Components blog].
-
-
-    .o-block
-        for i in [1, 2, 3]
-            +accordion("Section " + i)
-                p Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque enim ante, pretium a orci eget, varius dignissim augue. Nam eu dictum mauris, id tincidunt nisi. Integer commodo pellentesque tincidunt. Nam at turpis finibus tortor gravida sodales tincidunt sit amet est. Nullam euismod arcu in tortor auctor, sit amet dignissim justo congue. Sed varius vel dolor id dapibus. Curabitur pharetra fermentum arcu nec suscipit. Duis aliquet eros lorem, vitae interdum magna tempus quis.
-
-+section("embeds")
-    +h(2, "embeds") Embeds
-
-    p
-        |  The framework also allows embedding content from selected sites via
-        |  mixins, usually styled wrappers for the respective embed codes.
-
-    +h(3, "codepen") CodePen
-
-    p
-        |  #[+a("https://codepen.io") CodePen] is a platform to share and
-        |  collaborate on front-end code. It comes with a powerful live editor,
-        |  and is mostly used on this site to present visualizations created by
-        |  spaCy's built-in visualizers. Embeds use a
-        |  #[+a("https://blog.codepen.io/documentation/pro-features/unlimited-embed-themes/") custom theme]
-        |  and are included using a mixin that takes the pen ID, and an optional
-        |  height to prevent content reflow on load.
-
-    +aside-code("Usage", "jade").
-        +codepen("2f2ad1408ff79fc6a326ea3aedbb353b", 160)
-
-    +codepen("2f2ad1408ff79fc6a326ea3aedbb353b", 160)
-
-    +h(3, "github") GitHub
-
-    p
-        |  GitHub only allows native embedding of Gists, but Gists are only
-        |  available for users, not organisations. So in order to be able to
-        |  embed examples from spaCy's #[+src(gh("spacy", "examples")) examples],
-        |  we ended up developing our own micro library. A #[code data-gh-embed]
-        |  attribute on the code block, set via the mixin, specifies the file
-        |  to load. The script then fetches the raw text via the GitHub API and
-        |  renders it in the container. This way, the example previews on the
-        |  site are always in sync with the examples in the repository.
-
-    +aside-code("Usage", "jade").
-        +github("spacy", "examples/training/train_textcat.py")
-
-    +github("spacy", "examples/training/train_textcat.py")
-
-+section("markup")
-    +h(2, "markup") Markup reference
-
-    p
-        |  The spaCy website is implemented
-        |  in #[+a("https://www.jade-lang.org") Jade (aka Pug)], and is built or
-        |  served by #[+a("(https://harpjs.com") Harp]. Jade is an extensible
-        |  templating language with a readable syntax, that compiles to HTML.
-        |  The website source makes extensive use of Jade mixins, so that the
-        |  design system is abstracted away from the content you're writing. You
-        |  can read more about our approach in our blog post,
-        |  #[+a("https://explosion.ai/blog/modular-markup", true) "Rebuilding a Website with Modular Markup"].
-
-    +code("Viewing the site locally", "bash").
-        sudo npm install --global harp
-        git clone #{gh("spacy")}
-        cd spacy/website
-        harp server --port 9000
-
-    +h(3, "jade") Jade conventions
-
-    p
-        |  Jade/Pug is a whitespace-sensitive markup language that compiles to
-        |  HTML. Indentation is used to nest elements, and for template logic,
-        |  like #[code if], #[code else] or #[code for], mainly used to iterate
-        |  over objects and arrays in the meta data. It also allows inline
-        |  JavaScript expressions.
-
-    +grid.o-no-block
-        +grid-col("half")
-            +code("Input", "jade").
-                ul#some-id
-                    for item in ['a', 'b', 'c']
-                        li.test=item.toUpperCase()
-                            if item == 'a'
-                                |  🎉
-
-        +grid-col("half")
-            +code("Output", "markup").
-                &lt;ul id="some-id"&gt;
-                    &lt;li class="test"&gt;A 🎉&lt;li&gt;
-                    &lt;li class="test"&gt;B&lt;li&gt;
-                    &lt;li class="test"&gt;C&lt;li&gt;
-                &lt;/ul&gt;
-
-    p
-        |  For an overview of Harp and Jade, see
-        |  #[+a("https://ines.io/blog/the-ultimate-guide-static-websites-harp-jade") this blog post].
-        |  For more info on the Jade/Pug syntax, check out their
-        |  #[+a("https://pugjs.org") documentation]. In the spacy.io source, we
-        |  use 4 spaces to indent and hard-wrap at 80 characters.
-
-    +code(false, "jade").
-        p This is a very short paragraph. It stays inline.
-
-        p
-            |  This is a much longer paragraph. It's hard-wrapped at 80 characters to
-            |  make it easier to read on GitHub and in editors that do not have soft
-            |  wrapping enabled. To prevent Jade from interpreting each line as a new
-            |  element, it's prefixed with a pipe and two spaces. This ensures that no
-            |  spaces are dropped – for example, if your editor strips out trailing
-            |  whitespace by default. Inline links are added using the inline syntax,
-            |  like this: &#35;[+a("https://google.com") Google].
-
-    +aside("Plain HTML elements used")
-        +list.o-no-block
-            +item #[code p]: Regular paragraph.
-            +item #[code code]: Inline #[code code].
-            +item #[code em]: #[em Italicized] text.
-            +item #[code strong]: #[strong Bold] text.
-
-    p
-        |  Note that for external links, #[code &plus;a("...")] is used instead
-        |  of #[code a(href="...")] – it's a mixin that takes care of adding all
-        |  required attributes. If possible, always use a mixin instead of
-        |  regular HTML elements. With a few exceptions for practical reasons,
-        |  class names and other HTML attributes should
-        |  #[strong only live in mixins] and not in the site content.
-
-    +infobox("Mixins documentation")
-        |  For a more detailed overview and API documentation of the available
-        |  mixins and their arguments, see the source of the
-        |  #[+src(gh("spacy", "website/_includes/_mixins.jade")) #[code _includes/_mixins.jade]]
-        |  file.
-
-    +h(3, "directory-structure") Directory structure
-
-    p
-        |  Each section is represented by its own subdirectory, containing a
-        |  #[code _data.json] to store its meta information. All #[code .jade]
-        |  files that are not prefixed with an underscore are later converted to
-        |  #[code .html]. Site assets like images, styles, fonts and scripts are
-        |  loaded from a directory #[code assets]. Global variables like titles,
-        |  navigations, URLs and other settings are defined in the global
-        |  #[code _harp.json].
-
-    +code("website", "yaml").
-        ├── _includes         # layout partials, shared mixins, functions
-        ├── api
-        |   ├── _data.json    # meta data for API section
-        |   └── ...           # other pages and partials
-        ├── assets
-        |   ├── css           # Sass styles, will be converted to CSS
-        |   ├── fonts         # web fonts
-        |   ├── img           # images and icons
-        |   └── js            # scripts, custom and third-party
-        ├── models
-        |   ├── _data.json    # model meta data and meta for models section
-        |   └── ...           # other pages and partials
-        ├── usage
-        |   ├── _data.json    # meta data for usage section
-        |   └── ...           # other pages and partials
-        ├── _data.json        # meta data for pages in the root
-        ├── _harp.json        # global site configuration and variables
-        ├── _layout.jade      # global layout
-        ├── 404.jade          # 404 page
-        └── index.jade        # landing page
-
-    +h(3, "data-structure") Data structure
-
-    p
-        |  While all page content lives in the #[code .jade] files, article meta
-        |  (page titles, sidebars etc.) is stored as JSON. Each folder contains
-        |  a #[code _data.json] with all required meta for its files. Meta
-        |  information is keyed by the page's filename or slug, and becomes
-        |  available to the templates as variables. The #[code menu] specifies
-        |  the sub-navigation in the sidebar and maps titles to section IDs.
-
-    +code(false, "json").
-        "v2": {
-            "title": "What's New in v2.0",
-            "teaser": "New features, backwards incompatibilities and migration guide.",
-            "menu": {
-                "Summary": "summary",
-                "New features": "features",
-                "Backwards Incompatibilities": "incompat",
-                "Migrating from v1.x": "migrating",
-                "Benchmarks": "benchmarks"
-            }
-        }
-
-    p
-        |  Long pages with multiple sections are often split into separate
-        |  partials that live in their own subdirectory. Those partials can be
-        |  included on the page, and if needed, across the site to avoid content
-        |  duplication. Partials and partial directories are prefixed with an
-        |  underscore #[code _] to prevent Harp from building them as separate
-        |  files.
-
-    +code("spacy-101.jade", "jade").
-        +section("architecture")
-            +h(2, "architecture") Architecture
-            include _spacy-101/_architecture
-
-    +h(3, "model-data", "website/models/_data.json") Model data
-
-    p
-        |  The new #[+a("/models") models directory] uses the GitHub API to
-        |  fetch meta information from the latest
-        |  #[+a(gh("spacy-models") + "/releases") model releases]. This ensures
-        |  that the website is always up to date. However, some details, like
-        |  human-readable descriptions and the list of available models and
-        |  languages, is stored in the static CMS and used across the site.
-        |  This info only lives in one place, #[code models/_data.json].
-        |  Wherever possible, the model info is generated dynamically – for
-        |  example, in installation examples, quickstart widgets and even in the
-        |  total model and language count on the landing page.
-
-    p
-        |  The following data is stored and made available in the global scope:
-
-    +table(["Variable", "Description", "Example"])
-        +row
-            +cell #[code LANGUAGES]
-            +cell All languages supported by spaCy, code mapped to name.
-            +cell
-                +code(false, "json").o-no-block "en": "English"
-
-        +row
-            +cell #[code MODELS]
-            +cell Model names (without version). Language codes mapped to list of names.
-            +cell
-                +code(false, "json").o-no-block "xx": ["xx_ent_wiki_sm"]
-
-        +row
-            +cell #[code MODEL_META]
-            +cell Description for model name components and meta data, ID mapped to string.
-            +cell
-                +code(false, "json").o-no-block "vectors": "Word vectors"
-
-        +row
-            +cell #[code MODEL_LICENSES]
-            +cell License types mapped to license URL.
-            +cell
-                +code(false, "json").o-no-block "CC BY-SA 3.0": "http://..."
-
-        +row
-            +cell #[code MODEL_BENCHMARKS]
-            +cell Display labels for accuracy and speed.
-            +cell
-                +code(false, "json").o-no-block "ents_f": "NER F"
-
-        +row
-            +cell #[code EXAMPLE_SENTENCES]
-            +cell Example sentences for different languages.
-            +cell
-                +code(false, "json").o-no-block "es": "Esto es una frase."
-
-    +h(3, "functions", "website/_includes/_functions.jade") Template functions
-
-    p
-        |  Jade allows you to implement any custom logic as inline JavaScript
-        |  expressions. Reusable functions are organised in a
-        |  #[code _functions.jade], which is included via the mixins file and
-        |  makes them accessible on each page. However, most functions deal
-        |  with internals only, e.g. prefixing class names in mixins or
-        |  converting paths and links.
-
-    +h(4, "gh") gh
-        +tag function
-
-    p
-        |  Since GitHub links can be long and tricky, this function takes care
-        |  generating them automatically for spaCy and all repositories owned
-        |  by the #[+a(gh())=SOCIAL.github] organisation.
-
-    +aside-code("Example", "jade").
-        +a(gh("spacy", "spacy/language.py")) This is a link
-
-    +table(["Name", "Type", "Description"])
-        +row
-            +cell #[code repo]
-            +cell String
-            +cell Name of the repository, e.g. #[code "spacy"].
-
-        +row
-            +cell #[code filepath]
-            +cell String
-            +cell Logical path to the file, relative to the repository root.
-
-        +row
-            +cell #[code branch]
-            +cell String
-            +cell Optional branch. Defaults to #[code "master"].
-
-        +row("foot")
-            +cell returns
-            +cell String
-            +cell The full GitHub link to the file.
diff --git a/website/universe/README.md b/website/universe/README.md
deleted file mode 100644
index e87563752..000000000
--- a/website/universe/README.md
+++ /dev/null
@@ -1,95 +0,0 @@
-<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
-
-# spaCy Universe
-
-The [spaCy Universe](https://spacy.io/universe) collects the many great resources developed with or for spaCy. It
-includes standalone packages, plugins, extensions, educational materials,
-operational utilities and bindings for other languages.
-
-If you have a project that you want the spaCy community to make use of, you can
-suggest it by submitting a pull request to this repository. The Universe
-database is open-source and collected in a simple JSON file.
-
-Looking for inspiration for your own spaCy plugin or extension? Check out the
-[`project idea`](https://github.com/explosion/spaCy/labels/project%20idea) label
-on the issue tracker.
-
-## Checklist
-
-### Projects
-
-✅ Libraries and packages should be **open-source** (with a user-friendly license) and at least somewhat **documented** (e.g. a simple `README` with usage instructions).
-
-✅ We're happy to include work in progress and prereleases, but we'd like to keep the emphasis on projects that should be useful to the community **right away**.
-
-✅ Demos and visualizers should be available via a **public URL**.
-
-### Educational Materials
-
-✅ Books should be **available for purchase or download** (not just pre-order). Ebooks and self-published books are fine, too, if they include enough substantial content.
-
-✅ The `"url"` of book entries should either point to the publisher's website or a reseller of your choice (ideally one that ships worldwide or as close as possible).
-
-✅ If an online course is only available behind a paywall, it should at least have a **free excerpt** or chapter available, so users know what to expect.
-
-## JSON format
-
-To add a project, fork this repository, edit the [`universe.json`](universe.json)
-and add an object of the following format to the list of `"resources"`. Before
-you submit your pull request, make sure to use a linter to verify that your
-markup is correct. We'll also be adding linting for the `universe.json` to our
-automated GitHub checks soon.
-
-```json
-{
-    "id": "unique-project-id",
-    "title": "Project title",
-    "slogan": "A short summary",
-    "description": "A longer description – *Mardown allowed!*",
-    "github": "user/repo",
-    "pip": "package-name",
-    "code_example": [
-        "import spacy",
-        "import package_name",
-        "",
-        "nlp = spacy.load('en')",
-        "nlp.add_pipe(package_name)"
-    ],
-    "code_language": "python",
-    "url": "https://example.com",
-    "thumb": "https://example.com/thumb.jpg",
-    "image": "https://example.com/image.jpg",
-    "author": "Your Name",
-    "author_links": {
-        "twitter": "username",
-        "github": "username",
-        "website": "https://example.com"
-    },
-    "category": ["pipeline", "standalone"],
-    "tags": ["some-tag", "etc"]
-}
-```
-
-|  Field | Type | Description |
-| --- | --- | --- |
-| `id` | string | Unique ID of the project. |
-| `title` | string | Project title. If not set, the `id` will be used as the display title. |
-| `slogan` | string | A short description of the project. Displayed in the overview and under the title. |
-| `description` | string | A longer description of the project. Markdown is allowed, but should be limited to basic formatting like bold, italics, code or links. |
-| `github` | string | Associated GitHub repo in the format `user/repo`. Will be displayed as a link and used for release, license and star badges. |
-| `pip` | string | Package name on pip. If available, the installation command will be displayed. |
-| `cran` | string | For R packages: package name on CRAN. If available, the installation command will be displayed. |
-| `code_example` | array | Short example that shows how to use the project. Formatted as an array with one string per line. |
-| `code_language` | string | Defaults to `'python'`. Optional code language used for syntax highlighting with [Prism](http://prismjs.com/). |
-| `url` | string | Optional project link to display as button. |
-| `thumb` | string | Optional URL to project thumbnail to display in overview and project header. Recommended size is 100x100px. |
-| `image` | string | Optional URL to project image to display with description. |
-| `author` | string | Name(s) of project author(s). |
-| `author_links` | object | Usernames and links to display as icons to author info. Currently supports `twitter` and `github` usernames, as well as `website` link. |
-| `category` | list | One or more categories to assign to project. Must be one of the available options. |
-| `tags` | list | Still experimental and not used for filtering: one or more tags to assign to project. |
-
-To separate them from the projects, educational materials also specify
-`"type": "education`. Books can also set a `"cover"` field containing a URL
-to a cover image. If available, it's used in the overview and displayed on
-the individual book page.
diff --git a/website/universe/_data.json b/website/universe/_data.json
deleted file mode 100644
index 39ae36984..000000000
--- a/website/universe/_data.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
-    "index": {
-        "title": "Universe",
-        "teaser": "This section collects the many great resources developed with or for spaCy. It includes standalone packages, plugins, extensions, educational materials, operational utilities and bindings for other languages.",
-        "description": "This section collects the many great resources developed with or for spaCy. It includes standalone packages, plugins, extensions, educational materials, operational utilities and bindings for other languages.",
-        "landing": true,
-        "preview": "universe"
-    }
-}
diff --git a/website/universe/index.jade b/website/universe/index.jade
deleted file mode 100644
index de87fe0d4..000000000
--- a/website/universe/index.jade
+++ /dev/null
@@ -1,146 +0,0 @@
-//- 💫 DOCS > UNIVERSE
-
-include ../_includes/_mixins
-
-mixin sidebar-section(title)
-    ul.c-sidebar__section.o-block-small
-        if title
-            li.u-text-label.u-color-dark=title
-        block
-
-section(data-vue="universe")
-    menu.c-sidebar.js-sidebar.u-text
-        +sidebar-section("Overview")
-            li.c-sidebar__item
-                a.u-hand(v-on:click="filterBy('all')" v-bind:class="{'is-active': activeMenu == 'all'}") All Projects
-
-        +sidebar-section("Projects")
-            li.c-sidebar__item(v-for="(data, id) in projectCats")
-                a.u-hand(v-on:click="filterBy(id, 'category')" v-text="data.title" v-bind:class="{ 'is-active': activeMenu == id }")
-
-        +sidebar-section("Education")
-            li.c-sidebar__item(v-for="(data, id) in educationCats")
-                a.u-hand(v-on:click="filterBy(id, 'category')" v-text="data.title" v-bind:class="{ 'is-active': activeMenu == id }")
-
-    main.o-main.o-main--sidebar.o-main--aside
-        article.o-content
-            transition-group(name="u-fade")
-                section(v-if="selected" key="selected" v-cloak="")
-                    +h(1).u-heading--title
-                        .u-float-right.o-thumb(v-if="selected.thumb")
-                            img(v-bind:src="selected.thumb" width="100" role="presentation")
-
-                        |  {{ selected.title || selected.id }}
-
-                        .u-heading__teaser.u-text-small.u-color-dark.o-block-small(v-if="selected.slogan") {{ selected.slogan }}
-
-                        p(v-if="selected.github")
-                            a.u-hide-link(v-bind:href="`https://github.com/${selected.github}`")
-                                |  #[img.o-badge(v-bind:src="`https://img.shields.io/github/release/${selected.github}/all.svg?style=flat-square`")]
-                                |  #[img.o-badge(v-bind:src="`https://img.shields.io/github/license/${selected.github}.svg?style=flat-square`")]
-                                |  #[img(v-bind:src="`https://img.shields.io/github/stars/${selected.github}.svg?style=social&label=Stars`")]
-
-                    div(v-if="selected.pip")
-                        +aside-code("Installation", "bash", "$").
-                            pip install {{ selected.pip }}
-
-                    div(v-else-if="selected.cran")
-                        +aside-code("Installation", "r").
-                            install.packages("{{ selected.cran }}")
-
-                    +section.o-section--small
-                        img.o-block-small.u-padding-medium.u-float-right(v-if="selected.cover" v-bind:src="selected.cover" v-bind:alt="selected.title" width="250" style="max-width: 50%")
-
-                        .x-markdown.o-block(v-if="selected.description")
-                            vue-markdown(v-bind:source="selected.description")
-
-                        .o-block(v-if="selected.code_example")
-                            +code("Example", "none")(v-bind:class="`lang-${selected.code_language||'#{DEFAULT_SYNTAX}'}`")
-                                | {{ selected.code_example.join('\n') }}
-
-                        figure.o-block.u-text(v-if="selected.image")
-                            img(v-bind:src="selected.image" width="800" v-bind:alt="selected.slogan || selected.title || selected.id")
-
-                        p(v-if="selected.url")
-                            +button("", false, "primary", "small")(target="_blank" v-bind:href="selected.url") View more
-
-                    +grid
-                        +grid-col("half")(v-if="selected.author")
-                            +label Author info
-                            p.o-inline-list
-                                span {{ selected.author }}
-
-                                span.u-color-subtle-dark(v-if="selected.author_links") &nbsp;
-                                    span(v-for="id in ['github', 'twitter', 'website']" v-if="selected.author_links[id]")
-                                        a.u-hide-link(rel="noopener nofollow" v-bind:href="getAuthorLink(id, selected.author_links[id])" v-bind:aria-label="id")
-                                            svg.o-icon(aria-hidden="true" viewBox="0 0 18 18" width="18" height="18")
-                                                use(v-bind:xlink:href="`#svg_${id}`")
-                                        |  &nbsp;
-
-                        +grid-col("half")(v-if="selected.github")
-                            +label GitHub
-                            p.o-no-block
-                                span.u-inline-block.u-nowrap
-                                    +a("", false)(target="_blank" v-bind:href="`https://github.com/${selected.github}`")
-                                        code.u-break.u-break--all(v-text="selected.github")
-                                    |   #[+icon("code", 16).o-icon--inline.u-color-theme]
-
-                        +grid-col("full")(v-if="selected.category")
-                            +label Categories
-                            p.o-no-block
-                                span(v-for="cat in selected.category" v-if="categories[cat]")
-                                    a.u-text.u-hand(v-on:click="filterBy(cat, 'category')")
-                                        code(v-text="cat")
-                                    |  &nbsp;
-
-                section(v-else="" key="overview")
-                    +h(1).u-heading--title
-                        span(v-if="activeMenu && categories[activeMenu]" v-cloak="")
-                            |  {{ categories[activeMenu].title }}
-                            +tag {{ resources.length }}
-                            .u-heading__teaser.u-text-small.u-color-dark(v-if="categories[activeMenu].description" v-text="categories[activeMenu].description")
-
-                        span(v-else)=title
-                            .u-heading__teaser.u-text-small.u-color-dark=teaser
-
-                    +section().o-section--small
-                        +infobox()(v-if="false")
-                            |  Couldn't load the projects overview. This may
-                            |  happen if there's a bug in our code, or if you
-                            |  have JavaScript disabled. The resources list
-                            |  displayed on this page is open-source and
-                            |  available on GitHub – see
-                            |  #[+src(gh("spacy", "website/universe/universe.json")) #[code universe.json]]
-                            |  for the full data.
-
-                        +grid()(v-cloak="" v-bind:data-loading="loading")
-                            +grid-col().u-text(v-for="resource in resources" v-bind:key="resource.id" v-bind:class="{'o-box': !resource.cover, 'o-grid__col--third': resource.cover, 'o-grid__col--half': !resource.cover}" v-if="(activeMenu && activeMenu != 'all') || resource.type != 'education'")
-                                a.u-hand(v-on:click="viewResource(resource.id)")
-                                    img(v-bind:src="resource.cover" v-bind:alt="resource.title" v-if="resource.cover")
-                                    div(v-else)
-                                        +h(5).o-block-small
-                                            .o-thumb.o-thumb--small.u-float-right(v-if="resource.thumb")
-                                                img(v-bind:src="resource.thumb" width="35" role="presentation")
-                                            span {{ resource.title || resource.id }}
-                                        .u-text-small.o-no-block(v-if="resource.slogan" v-text="resource.slogan")
-
-                    +section().o-section--small
-                        +h(3) Submit your project
-
-                        p
-                            |  If you have a project that you want the spaCy
-                            |  community to make use of, you can suggest it by
-                            |  submitting a pull request to the spaCy website
-                            |  repository. The Universe database is open-source
-                            |  and collected in a simple JSON file. For more
-                            |  details on the formats and available fields, see
-                            |  the documentation. Looking for inspiration your
-                            |  own spaCy plugin or extension? Check out the
-                            |  #[+a(gh("spacy") + "/labels/project%20idea") #[code project idea]]
-                            |  label on the issue tracker.
-
-                        p.o-inline-list
-                            +button(gh("spacy", "website/universe/README.md"), false, "small", "primary") Read the docs
-                            +button(gh("spacy", "website/universe/universe.json"), false, "small", "secondary") JSON source #[+icon("code", 16)]
-
-        include ../_includes/_footer
diff --git a/website/usage/_adding-languages/_language-data.jade b/website/usage/_adding-languages/_language-data.jade
deleted file mode 100644
index bcb2f555e..000000000
--- a/website/usage/_adding-languages/_language-data.jade
+++ /dev/null
@@ -1,558 +0,0 @@
-//- 💫 DOCS > USAGE > ADDING LANGUAGES > LANGUAGE DATA
-
-p
-    |  The individual components #[strong expose variables] that can be imported
-    |  within a language module, and added to the language's #[code Defaults].
-    |  Some components, like the punctuation rules, usually don't need much
-    |  customisation and can be imported from the global rules. Others,
-    |  like the tokenizer and norm exceptions, are very specific and will make
-    |  a big difference to spaCy's performance on the particular language and
-    |  training a language model.
-
-
-+table(["Variable", "Type", "Description"])
-    +row
-        +cell #[code STOP_WORDS]
-        +cell set
-        +cell Individual words.
-
-    +row
-        +cell #[code TOKENIZER_EXCEPTIONS]
-        +cell dict
-        +cell Keyed by strings mapped to list of one dict per token with token attributes.
-
-    +row
-        +cell #[code TOKEN_MATCH]
-        +cell regex
-        +cell Regexes to match complex tokens, e.g. URLs.
-
-    +row
-        +cell #[code NORM_EXCEPTIONS]
-        +cell dict
-        +cell Keyed by strings, mapped to their norms.
-
-    +row
-        +cell #[code TOKENIZER_PREFIXES]
-        +cell list
-        +cell Strings or regexes, usually not customised.
-
-    +row
-        +cell #[code TOKENIZER_SUFFIXES]
-        +cell list
-        +cell Strings or regexes, usually not customised.
-
-    +row
-        +cell #[code TOKENIZER_INFIXES]
-        +cell list
-        +cell Strings or regexes, usually not customised.
-
-    +row
-        +cell #[code LEX_ATTRS]
-        +cell dict
-        +cell Attribute ID mapped to function.
-
-    +row
-        +cell #[code SYNTAX_ITERATORS]
-        +cell dict
-        +cell
-            |  Iterator ID mapped to function. Currently only supports
-            |  #[code 'noun_chunks'].
-
-    +row
-        +cell #[code LOOKUP]
-        +cell dict
-        +cell Keyed by strings mapping to their lemma.
-
-    +row
-        +cell #[code LEMMA_RULES], #[code LEMMA_INDEX], #[code LEMMA_EXC]
-        +cell dict
-        +cell Lemmatization rules, keyed by part of speech.
-
-    +row
-        +cell #[code TAG_MAP]
-        +cell dict
-        +cell
-            |  Keyed by strings mapped to
-            |  #[+a("http://universaldependencies.org/u/pos/all.html") Universal Dependencies]
-            |  tags.
-
-    +row
-        +cell #[code MORPH_RULES]
-        +cell dict
-        +cell Keyed by strings mapped to a dict of their morphological features.
-
-+aside("Should I ever update the global data?")
-    |  Reuseable language data is collected as atomic pieces in the root of the
-    |  #[+src(gh("spaCy", "lang")) #[code spacy.lang]] package. Often, when a new
-    |  language is added, you'll find a pattern or symbol that's missing. Even
-    |  if it isn't common in other languages, it might be best to add it to the
-    |  shared language data, unless it has some conflicting interpretation. For
-    |  instance, we don't expect to see guillemot quotation symbols
-    |  (#[code &raquo;] and #[code &laquo;]) in English text. But if we do see
-    |  them, we'd probably prefer the tokenizer to split them off.
-
-+infobox("For languages with non-latin characters")
-    |  In order for the tokenizer to split suffixes, prefixes and infixes, spaCy
-    |  needs to know the language's character set. If the language you're adding
-    |  uses non-latin characters, you might need to add the required character
-    |  classes to the global
-    |  #[+src(gh("spacy", "spacy/lang/char_classes.py")) #[code char_classes.py]].
-    |  spaCy uses the #[+a("https://pypi.python.org/pypi/regex/") #[code regex] library]
-    |  to keep this simple and readable. If the language requires very specific
-    |  punctuation rules, you should consider overwriting the default regular
-    |  expressions with your own in the language's #[code Defaults].
-
-
-+h(3, "language-subclass") Creating a #[code Language] subclass
-
-p
-    |  Language-specific code and resources should be organised into a
-    |  subpackage of spaCy, named according to the language's
-    |  #[+a("https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes") ISO code].
-    |  For instance, code and resources specific to Spanish are placed into a
-    |  directory #[code spacy/lang/es], which can be imported as
-    |  #[code spacy.lang.es].
-
-p
-    |  To get started, you can use our
-    |  #[+src(gh("spacy-dev-resources", "templates/new_language")) templates]
-    |  for the most important files. Here's what the class template looks like:
-
-+code("__init__.py (excerpt)").
-    # import language-specific data
-    from .stop_words import STOP_WORDS
-    from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
-    from .lex_attrs import LEX_ATTRS
-
-    from ..tokenizer_exceptions import BASE_EXCEPTIONS
-    from ...language import Language
-    from ...attrs import LANG
-    from ...util import update_exc
-
-    # create Defaults class in the module scope (necessary for pickling!)
-    class XxxxxDefaults(Language.Defaults):
-        lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
-        lex_attr_getters[LANG] = lambda text: 'xx' # language ISO code
-
-        # optional: replace flags with custom functions, e.g. like_num()
-        lex_attr_getters.update(LEX_ATTRS)
-
-        # merge base exceptions and custom tokenizer exceptions
-        tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
-        stop_words = STOP_WORDS
-
-    # create actual Language class
-    class Xxxxx(Language):
-        lang = 'xx' # language ISO code
-        Defaults = XxxxxDefaults # override defaults
-
-    # set default export – this allows the language class to be lazy-loaded
-    __all__ = ['Xxxxx']
-
-+infobox("Why lazy-loading?")
-    |  Some languages contain large volumes of custom data, like lemmatizer
-    |  lookup tables, or complex regular expression that are expensive to
-    |  compute. As of spaCy v2.0, #[code Language] classes are not imported on
-    |  initialisation and are only loaded when you import them directly, or load
-    |  a model that requires a language to be loaded. To lazy-load languages in
-    |  your application, you can use the
-    |  #[+api("util#get_lang_class") #[code util.get_lang_class()]] helper
-    |  function with the two-letter language code as its argument.
-
-+h(3, "stop-words") Stop words
-
-p
-    |  A #[+a("https://en.wikipedia.org/wiki/Stop_words") "stop list"] is a
-    |  classic trick from the early days of information retrieval when search
-    |  was largely about keyword presence and absence. It is still sometimes
-    |  useful today to filter out common words from a bag-of-words model. To
-    |  improve readability, #[code STOP_WORDS] are separated by spaces and
-    |  newlines, and added as a multiline string.
-
-+aside("What does spaCy consider a stop word?")
-    |  There's no particularly principled logic behind what words should be
-    |  added to the stop list. Make a list that you think might be useful
-    |  to people and is likely to be unsurprising. As a rule of thumb, words
-    |  that are very rare are unlikely to be useful stop words.
-
-+code("Example").
-    STOP_WORDS = set(&quot;&quot;&quot;
-    a about above across after afterwards again against all almost alone along
-    already also although always am among amongst amount an and another any anyhow
-    anyone anything anyway anywhere are around as at
-
-    back be became because become becomes becoming been before beforehand behind
-    being below beside besides between beyond both bottom but by
-    &quot;&quot;&quot;.split())
-
-+infobox("Important note")
-    |  When adding stop words from an online source, always #[strong include the link]
-    |  in a comment. Make sure to #[strong proofread] and double-check the words
-    |  carefully. A lot of the lists available online have been passed around
-    |  for years and often contain mistakes, like unicode errors or random words
-    |  that have once been added for a specific use case, but don't actually
-    |  qualify.
-
-+h(3, "tokenizer-exceptions") Tokenizer exceptions
-
-p
-    |  spaCy's #[+a("/usage/linguistic-features#how-tokenizer-works") tokenization algorithm]
-    |  lets you deal with whitespace-delimited chunks separately. This makes it
-    |  easy to define special-case rules, without worrying about how they
-    |  interact with the rest of the tokenizer. Whenever the key string is
-    |  matched, the special-case rule is applied, giving the defined sequence of
-    |  tokens. You can also attach attributes to the subtokens, covered by your
-    |  special case, such as the subtokens #[code LEMMA] or #[code TAG].
-
-p
-    |  Tokenizer exceptions can be added in the following format:
-
-+code("tokenizer_exceptions.py (excerpt)").
-    TOKENIZER_EXCEPTIONS = {
-        "don't": [
-            {ORTH: "do", LEMMA: "do"},
-            {ORTH: "n't", LEMMA: "not", NORM: "not", TAG: "RB"}]
-    }
-
-+infobox("Important note")
-    |  If an exception consists of more than one token, the #[code ORTH] values
-    |  combined always need to #[strong match the original string]. The way the
-    |  original string is split up can be pretty arbitrary sometimes – for
-    |  example "gonna" is split into "gon" (lemma "go") and "na" (lemma "to").
-    |  Because of how the tokenizer works, it's currently not possible to split
-    |  single-letter strings into multiple tokens.
-
-p
-    |  Unambiguous abbreviations, like month names or locations in English,
-    |  should be added to exceptions with a lemma assigned, for example
-    |  #[code {ORTH: "Jan.", LEMMA: "January"}]. Since the exceptions are
-    |  added in Python, you can use custom logic to generate them more
-    |  efficiently and make your data less verbose. How you do this ultimately
-    |  depends on the language. Here's an example of how exceptions for time
-    |  formats like "1a.m." and "1am" are generated in the English
-    |  #[+src(gh("spaCy", "spacy/en/lang/tokenizer_exceptions.py")) #[code tokenizer_exceptions.py]]:
-
-+code("tokenizer_exceptions.py (excerpt)").
-    # use short, internal variable for readability
-    _exc = {}
-
-    for h in range(1, 12 + 1):
-        for period in ["a.m.", "am"]:
-            # always keep an eye on string interpolation!
-            _exc["%d%s" % (h, period)] = [
-                {ORTH: "%d" % h},
-                {ORTH: period, LEMMA: "a.m."}]
-        for period in ["p.m.", "pm"]:
-            _exc["%d%s" % (h, period)] = [
-                {ORTH: "%d" % h},
-                {ORTH: period, LEMMA: "p.m."}]
-
-    # only declare this at the bottom
-    TOKENIZER_EXCEPTIONS = _exc
-
-+aside("Generating tokenizer exceptions")
-    |  Keep in mind that generating exceptions only makes sense if there's a
-    |  clearly defined and #[strong finite number] of them, like common
-    |  contractions in English. This is not always the case – in Spanish for
-    |  instance, infinitive or imperative reflexive verbs and pronouns are one
-    |  token (e.g. "vestirme"). In cases like this, spaCy shouldn't be
-    |  generating exceptions for #[em all verbs]. Instead, this will be handled
-    |  at a later stage during lemmatization.
-
-p
-    |  When adding the tokenizer exceptions to the #[code Defaults], you can use
-    |  the #[+api("util#update_exc") #[code update_exc()]] helper function to merge
-    |  them with the global base exceptions (including one-letter abbreviations
-    |  and emoticons). The function performs a basic check to make sure
-    |  exceptions are provided in the correct format. It can take any number of
-    |  exceptions dicts as its arguments, and will update and overwrite the
-    |  exception in this order. For example, if your language's tokenizer
-    |  exceptions include a custom tokenization pattern for "a.", it will
-    |  overwrite the base exceptions with the language's custom one.
-
-+code("Example").
-    from ...util import update_exc
-
-    BASE_EXCEPTIONS =  {"a.": [{ORTH: "a."}], ":)": [{ORTH: ":)"}]}
-    TOKENIZER_EXCEPTIONS = {"a.": [{ORTH: "a.", LEMMA: "all"}]}
-
-    tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
-    # {"a.": [{ORTH: "a.", LEMMA: "all"}], ":)": [{ORTH: ":)"}]}
-
-+infobox("About spaCy's custom pronoun lemma")
-    |  Unlike verbs and common nouns, there's no clear base form of a personal
-    |  pronoun. Should the lemma of "me" be "I", or should we normalize person
-    |  as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a
-    |  novel symbol, #[code.u-nowrap -PRON-], which is used as the lemma for
-    |  all personal pronouns.
-
-+h(3, "norm-exceptions") Norm exceptions
-    +tag-new(2)
-
-p
-    |  In addition to #[code ORTH] or #[code LEMMA], tokenizer exceptions can
-    |  also set a #[code NORM] attribute. This is useful to specify a normalised
-    |  version of the token – for example, the norm of "n't" is "not". By default,
-    |  a token's norm equals its lowercase text. If the lowercase spelling of a
-    |  word exists, norms should always be in lowercase.
-
-+aside-code("Norms vs. lemmas").
-    doc = nlp(u"I'm gonna realise")
-    norms = [token.norm_ for token in doc]
-    lemmas = [token.lemma_ for token in doc]
-    assert norms == ['i', 'am', 'going', 'to', 'realize']
-    assert lemmas == ['i', 'be', 'go', 'to', 'realise']
-
-p
-    |  spaCy usually tries to normalise words with different spellings to a single,
-    |  common spelling. This has no effect on any other token attributes, or
-    |  tokenization in general, but it ensures that
-    |  #[strong equivalent tokens receive similar representations]. This can
-    |  improve the model's predictions on words that weren't common in the
-    |  training data, but are equivalent to other words – for example, "realize"
-    |  and "realise", or "thx" and "thanks".
-
-p
-    |  Similarly, spaCy also includes
-    |  #[+src(gh("spaCy", "spacy/lang/norm_exceptions.py")) global base norms]
-    |  for normalising different styles of quotation marks and currency
-    |  symbols. Even though #[code $] and #[code €] are very different, spaCy
-    |  normalises them both to #[code $]. This way, they'll always be seen as
-    |  similar, no matter how common they were in the training data.
-
-p
-    |  Norm exceptions can be provided as a simple dictionary. For more examples,
-    |  see the English
-    |  #[+src(gh("spaCy", "spacy/lang/en/norm_exceptions.py")) #[code norm_exceptions.py]].
-
-+code("Example").
-    NORM_EXCEPTIONS = {
-        "cos": "because",
-        "fav": "favorite",
-        "accessorise": "accessorize",
-        "accessorised": "accessorized"
-    }
-
-p
-    |  To add the custom norm exceptions lookup table, you can use the
-    |  #[code add_lookups()] helper functions. It takes the default attribute
-    |  getter function as its first argument, plus a variable list of
-    |  dictionaries. If a string's norm is found in one of the dictionaries,
-    |  that value is used – otherwise, the default function is called and the
-    |  token is assigned its default norm.
-
-+code.
-    lex_attr_getters[NORM] = add_lookups(Language.Defaults.lex_attr_getters[NORM],
-                                         NORM_EXCEPTIONS, BASE_NORMS)
-
-p
-    |  The order of the dictionaries is also the lookup order – so if your
-    |  language's norm exceptions overwrite any of the global exceptions, they
-    |  should be added first. Also note that the tokenizer exceptions will
-    |  always have priority over the atrribute getters.
-
-+h(3, "lex-attrs") Lexical attributes
-    +tag-new(2)
-
-p
-    |  spaCy provides a range of #[+api("token#attributes") #[code Token] attributes]
-    |  that return useful information on that token – for example, whether it's
-    |  uppercase or lowercase, a left or right punctuation mark, or whether it
-    |  resembles a number or email address. Most of these functions, like
-    |  #[code is_lower] or #[code like_url] should be language-independent.
-    |  Others, like #[code like_num] (which includes both digits and number
-    |  words), requires some customisation.
-
-+aside("Best practices")
-    |  Keep in mind that those functions are only intended to be  an approximation.
-    |  It's always better to prioritise simplicity and performance over covering
-    |  very specific edge cases.#[br]#[br]
-    |  English number words are pretty simple, because even large numbers
-    |  consist of individual tokens, and we can get away with splitting and
-    |  matching strings against a list. In other languages, like German, "two
-    |  hundred and thirty-four" is one word, and thus one token. Here, it's best
-    |  to match a string against a list of number word fragments (instead of a
-    |  technically almost infinite list of possible number words).
-
-p
-    |  Here's an example from the English
-    |  #[+src(gh("spaCy", "spacy/lang/en/lex_attrs.py")) #[code lex_attrs.py]]:
-
-+code("lex_attrs.py").
-    _num_words = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven',
-                  'eight', 'nine', 'ten', 'eleven', 'twelve', 'thirteen', 'fourteen',
-                  'fifteen', 'sixteen', 'seventeen', 'eighteen', 'nineteen', 'twenty',
-                  'thirty', 'forty', 'fifty', 'sixty', 'seventy', 'eighty', 'ninety',
-                  'hundred', 'thousand', 'million', 'billion', 'trillion', 'quadrillion',
-                  'gajillion', 'bazillion']
-
-    def like_num(text):
-        text = text.replace(',', '').replace('.', '')
-        if text.isdigit():
-            return True
-        if text.count('/') == 1:
-            num, denom = text.split('/')
-            if num.isdigit() and denom.isdigit():
-                return True
-        if text.lower() in _num_words:
-            return True
-        return False
-
-    LEX_ATTRS = {
-        LIKE_NUM: like_num
-    }
-
-p
-    |  By updating the default lexical attributes with a custom #[code LEX_ATTRS]
-    |  dictionary in the language's defaults via
-    |  #[code lex_attr_getters.update(LEX_ATTRS)], only the new custom functions
-    |  are overwritten.
-
-+h(3, "syntax-iterators") Syntax iterators
-
-p
-    |  Syntax iterators are functions that compute views of a #[code Doc]
-    |  object based on its syntax. At the moment, this data is only used for
-    |  extracting
-    |  #[+a("/usage/linguistic-features#noun-chunks") noun chunks], which
-    |  are available as the #[+api("doc#noun_chunks") #[code Doc.noun_chunks]]
-    |  property. Because base noun phrases work differently across languages,
-    |  the rules to compute them are part of the individual language's data. If
-    |  a language does not include a noun chunks iterator, the property won't
-    |  be available. For examples, see the existing syntax iterators:
-
-+aside-code("Noun chunks example").
-    doc = nlp(u'A phrase with another phrase occurs.')
-    chunks = list(doc.noun_chunks)
-    assert chunks[0].text == "A phrase"
-    assert chunks[1].text == "another phrase"
-
-+table(["Language", "Code", "Source"])
-    for lang in ["en", "de", "fr", "es"]
-        +row
-            +cell=LANGUAGES[lang]
-            +cell #[code=lang]
-            +cell
-                +src(gh("spaCy", "spacy/lang/" + lang + "/syntax_iterators.py"))
-                    code lang/#{lang}/syntax_iterators.py
-
-+h(3, "lemmatizer") Lemmatizer
-    +tag-new(2)
-
-p
-    |  As of v2.0, spaCy supports simple lookup-based lemmatization. This is
-    |  usually the quickest and easiest way to get started. The data is stored
-    |  in a dictionary mapping a string to its lemma. To determine a token's
-    |  lemma, spaCy simply looks it up in the table. Here's an example from
-    |  the Spanish language data:
-
-+code("lang/es/lemmatizer.py (excerpt)").
-    LOOKUP = {
-        "aba": "abar",
-        "ababa": "abar",
-        "ababais": "abar",
-        "ababan": "abar",
-        "ababanes": "ababán",
-        "ababas": "abar",
-        "ababoles": "ababol",
-        "ababábites": "ababábite"
-    }
-
-p
-    |  To provide a lookup lemmatizer for your language, import the lookup table
-    |  and add it to the #[code Language] class as #[code lemma_lookup]:
-
-+code.
-    lemma_lookup = dict(LOOKUP)
-
-+h(3, "tag-map") Tag map
-
-p
-    |  Most treebanks define a custom part-of-speech tag scheme, striking a
-    |  balance between level of detail and ease of prediction.  While it's
-    |  useful to have custom tagging schemes, it's also useful to have a common
-    |  scheme, to which the more specific tags can be related. The tagger can
-    |  learn a tag scheme with any arbitrary symbols. However, you need to
-    |  define how those symbols map down to the
-    |  #[+a("http://universaldependencies.org/u/pos/all.html") Universal Dependencies tag set].
-    |  This is done by providing a tag map.
-
-p
-    |  The keys of the tag map should be #[strong strings in your tag set]. The
-    |  values should be a dictionary. The dictionary must have an entry POS
-    |  whose value is one of the
-    |  #[+a("http://universaldependencies.org/u/pos/all.html") Universal Dependencies]
-    |  tags. Optionally, you can also include morphological features or other
-    |  token attributes in the tag map as well. This allows you to do simple
-    |  #[+a("/usage/linguistic-features#rule-based-morphology") rule-based morphological analysis].
-
-+code("Example").
-    from ..symbols import POS, NOUN, VERB, DET
-
-    TAG_MAP = {
-        "NNS":  {POS: NOUN, "Number": "plur"},
-        "VBG":  {POS: VERB, "VerbForm": "part", "Tense": "pres", "Aspect": "prog"},
-        "DT":   {POS: DET}
-    }
-
-+h(3, "morph-rules") Morph rules
-
-p
-    |  The morphology rules let you set token attributes such as lemmas, keyed
-    |  by the extended part-of-speech tag and token text. The morphological
-    |  features and their possible values are language-specific and based on the
-    |  #[+a("http://universaldependencies.org") Universal Dependencies scheme].
-
-
-+code("Example").
-    from ..symbols import LEMMA
-
-    MORPH_RULES = {
-        "VBZ": {
-            "am": {LEMMA: "be", "VerbForm": "Fin", "Person": "One", "Tense": "Pres", "Mood": "Ind"},
-            "are": {LEMMA: "be", "VerbForm": "Fin", "Person": "Two", "Tense": "Pres", "Mood": "Ind"},
-            "is": {LEMMA: "be", "VerbForm": "Fin", "Person": "Three", "Tense": "Pres", "Mood": "Ind"},
-            "'re": {LEMMA: "be", "VerbForm": "Fin", "Person": "Two", "Tense": "Pres", "Mood": "Ind"},
-            "'s": {LEMMA: "be", "VerbForm": "Fin", "Person": "Three", "Tense": "Pres", "Mood": "Ind"}
-        }
-    }
-
-p
-    |  In the example of #[code "am"], the attributes look like this:
-
-+table(["Attribute", "Description"])
-    +row
-        +cell #[code LEMMA: "be"]
-        +cell Base form, e.g. "to be".
-
-    +row
-        +cell #[code "VerbForm": "Fin"]
-        +cell
-            |  Finite verb. Finite verbs have a subject and can be the root of
-            |  an independent clause – "I am." is a valid, complete
-            |  sentence.
-
-    +row
-        +cell #[code "Person": "One"]
-        +cell First person, i.e. "#[strong I] am".
-
-    +row
-        +cell #[code "Tense": "Pres"]
-        +cell
-            |  Present tense, i.e. actions that are happening right now or
-            |  actions that usually happen.
-
-    +row
-        +cell #[code "Mood": "Ind"]
-        +cell
-            |  Indicative, i.e. something happens, has happened or will happen
-            |  (as opposed to imperative or conditional).
-
-
-+infobox("Important note", "⚠️")
-    |  The morphological attributes are currently #[strong not all used by spaCy].
-    |  Full integration is still being developed. In the meantime, it can still
-    |  be useful to add them, especially if the language you're adding includes
-    |  important distinctions and special cases. This ensures that as soon as
-    |  full support is introduced, your language will be able to assign all
-    |  possible attributes.
diff --git a/website/usage/_adding-languages/_testing.jade b/website/usage/_adding-languages/_testing.jade
deleted file mode 100644
index 4c63bb8d5..000000000
--- a/website/usage/_adding-languages/_testing.jade
+++ /dev/null
@@ -1,51 +0,0 @@
-//- 💫 DOCS > USAGE > ADDING LANGUAGES > TESTING
-
-p
-    |  Before using the new language or submitting a
-    |  #[+a(gh("spaCy") + "/pulls") pull request] to spaCy, you should make sure
-    |  it works as expected. This is especially important if you've added custom
-    |  regular expressions for token matching or punctuation – you don't want to
-    |  be causing regressions.
-
-+infobox("spaCy's test suite")
-    |  spaCy uses the #[+a("https://docs.pytest.org/en/latest/") pytest framework]
-    |  for testing. For more details on how the tests are structured and best
-    |  practices for writing your own tests, see our
-    |  #[+a(gh("spaCy", "spacy/tests")) tests documentation].
-
-+h(3, "testing-custom") Writing language-specific tests
-
-p
-    |  It's recommended to always add at least some tests with examples specific
-    |  to the language. Language tests should be located in
-    |  #[+src(gh("spaCy", "spacy/tests/lang")) #[code tests/lang]] in a
-    |  directory named after the language ID. You'll also need to create a
-    |  fixture for your tokenizer in the
-    |  #[+src(gh("spaCy", "spacy/tests/conftest.py")) #[code conftest.py]].
-    |  Always use the #[+api("util#get_lang_class") #[code get_lang_class()]]
-    |  helper function within the fixture, instead of importing the class at the
-    |  top of the file. This will load the language data only when it's needed.
-    |  (Otherwise, #[em all data] would be loaded every time you run a test.)
-
-+code.
-    @pytest.fixture
-    def en_tokenizer():
-        return util.get_lang_class('en').Defaults.create_tokenizer()
-
-p
-    |  When adding test cases, always
-    |  #[+a(gh("spaCy", "spacy/tests#parameters")) #[code parametrize]] them –
-    |  this will make it easier for others to add more test cases without having
-    |  to modify the test itself. You can also add parameter tuples, for example,
-    |  a test sentence and its expected length, or a list of expected tokens.
-    |  Here's an example of an English tokenizer test for combinations of
-    |  punctuation and abbreviations:
-
-+code("Example test").
-    @pytest.mark.parametrize('text,length', [
-        ("The U.S. Army likes Shock and Awe.", 8),
-        ("U.N. regulations are not a part of their concern.", 10),
-        ("“Isn't it?”", 6)])
-    def test_en_tokenizer_handles_punct_abbrev(en_tokenizer, text, length):
-        tokens = en_tokenizer(text)
-        assert len(tokens) == length
diff --git a/website/usage/_adding-languages/_training.jade b/website/usage/_adding-languages/_training.jade
deleted file mode 100644
index 054f2a460..000000000
--- a/website/usage/_adding-languages/_training.jade
+++ /dev/null
@@ -1,93 +0,0 @@
-//- 💫 DOCS > USAGE > ADDING LANGUAGES > TRAINING
-
-p
-    |  spaCy expects that common words will be cached in a
-    |  #[+api("vocab") #[code Vocab]] instance. The vocabulary caches lexical
-    |  features, and makes it easy to use information from unlabelled text
-    |  samples in your models. Specifically, you'll usually want to collect
-    |  word frequencies, and train word vectors. To generate the word frequencies
-    |  from a large, raw corpus, you can use the
-    |  #[+src(gh("spacy-dev-resources", "training/word_freqs.py")) #[code word_freqs.py]]
-    |  script from the spaCy developer resources.
-
-+github("spacy-dev-resources", "training/word_freqs.py")
-
-p
-    |  Note that your corpus should not be preprocessed (i.e. you need
-    |  punctuation for example). The word frequencies should be generated as a
-    |  tab-separated file with three columns:
-
-+list("numbers")
-    +item The number of times the word occurred in your language sample.
-    +item The number of distinct documents the word occurred in.
-    +item The word itself.
-
-+code("es_word_freqs.txt", "text").
-    6361109	111	Aunque
-    23598543	111	aunque
-    10097056	111	claro
-    193454	111	aro
-    7711123	111	viene
-    12812323	111	mal
-    23414636	111	momento
-    2014580	111	felicidad
-    233865	111	repleto
-    15527	111	eto
-    235565	111	deliciosos
-    17259079	111	buena
-    71155	111	Anímate
-    37705	111	anímate
-    33155	111	cuéntanos
-    2389171	111	cuál
-    961576	111	típico
-
-+aside("Brown Clusters")
-    |  Additionally, you can use distributional similarity features provided by the
-    |  #[+a("https://github.com/percyliang/brown-cluster") Brown clustering algorithm].
-    |  You should train a model with between 500 and 1000 clusters. A minimum
-    |  frequency threshold of 10 usually works well.
-
-p
-    |  You should make sure you use the spaCy tokenizer for your
-    |  language to segment the text for your word frequencies. This will ensure
-    |  that the frequencies refer to the same segmentation standards you'll be
-    |  using at run-time. For instance, spaCy's English tokenizer segments
-    |  "can't" into two tokens. If we segmented the text by whitespace to
-    |  produce the frequency counts, we'll have incorrect frequency counts for
-    |  the tokens "ca" and "n't".
-
-+h(4, "word-vectors") Training the word vectors
-
-p
-    |  #[+a("https://en.wikipedia.org/wiki/Word2vec") Word2vec] and related
-    |  algorithms let you train useful word similarity models from unlabelled
-    |  text. This is a key part of using
-    |  #[+a("/usage/deep-learning") deep learning] for NLP with limited
-    |  labelled data. The vectors are also useful by themselves – they power
-    |  the #[code .similarity()] methods in spaCy. For best results, you should
-    |  pre-process the text with spaCy before training the Word2vec model. This
-    |  ensures your tokenization will match. You can use our
-    |  #[+src(gh("spacy-dev-resources", "training/word_vectors.py")) word vectors training script],
-    |  which pre-processes the text with your language-specific tokenizer and
-    |  trains the model using #[+a("https://radimrehurek.com/gensim/") Gensim].
-    |  The #[code vectors.bin] file should consist of one word and vector per line.
-
-+github("spacy-dev-resources", "training/word_vectors.py")
-
-+h(3, "train-tagger-parser") Training the tagger and parser
-
-p
-    |  You can now train the model using a corpus for your language annotated
-    |  with #[+a("http://universaldependencies.org/") Universal Dependencies].
-    |  If your corpus uses the
-    |  #[+a("http://universaldependencies.org/docs/format.html") CoNLL-U] format,
-    |  i.e. files with the extension #[code .conllu], you can use the
-    |  #[+api("cli#convert") #[code convert]] command to convert it to spaCy's
-    |  #[+a("/api/annotation#json-input") JSON format] for training.
-    |  Once you have your UD corpus transformed into JSON, you can train your
-    |  model use the using spaCy's #[+api("cli#train") #[code train]] command.
-
-+infobox
-    |  For more details and examples of how to
-    |  #[strong train the tagger and dependency parser], see the
-    |  #[+a("/usage/training#tagger-parser") usage guide on training].
diff --git a/website/usage/_data.json b/website/usage/_data.json
deleted file mode 100644
index ec4c6b6a3..000000000
--- a/website/usage/_data.json
+++ /dev/null
@@ -1,168 +0,0 @@
-{
-    "sidebar": {
-        "Get started": {
-            "Installation": "./",
-            "Models & Languages": "models",
-            "Facts & Figures": "facts-figures",
-            "spaCy 101": "spacy-101",
-            "New in v2.0": "v2"
-        },
-        "Guides": {
-            "Linguistic Features": "linguistic-features",
-            "Processing Pipelines": "processing-pipelines",
-            "Vectors & Similarity": "vectors-similarity",
-            "Training Models": "training",
-            "Adding Languages": "adding-languages",
-            "Visualizers": "visualizers"
-        },
-        "In-depth": {
-            "Code Examples": "examples"
-        }
-    },
-
-    "index": {
-        "title": "Install spaCy",
-        "next": "models",
-        "quickstart": true,
-        "menu": {
-            "Quickstart": "quickstart",
-            "Instructions": "instructions",
-            "Troubleshooting": "troubleshooting",
-            "Changelog": "changelog"
-        }
-    },
-
-    "models": {
-        "title": "Models & Languages",
-        "next": "facts-figures",
-        "quickstart": true,
-        "menu": {
-            "Quickstart": "quickstart",
-            "Available Models": "available",
-            "Installation & Usage": "install",
-            "Language Support": "languages",
-            "Production Use": "production"
-        }
-    },
-
-    "facts-figures": {
-        "title": "Facts & Figures",
-        "teaser": "The hard numbers for spaCy and how it compares to other libraries and tools.",
-        "next": "spacy-101",
-        "menu": {
-            "Feature comparison": "comparison",
-            "Benchmarks": "benchmarks",
-            "Powered by spaCy": "powered-by",
-            "Other Libraries": "other-libraries"
-        }
-    },
-
-    "spacy-101": {
-        "title": "spaCy 101: Everything you need to know",
-        "teaser": "The most important concepts, explained in simple terms.",
-        "next": "index",
-        "quickstart": true,
-        "preview": "101",
-        "menu": {
-            "Features": "features",
-            "Lightning tour": "lightning-tour",
-            "Architecture": "architecture",
-            "Community & FAQ": "community-faq"
-        }
-    },
-
-    "v2": {
-        "title": "What's New in v2.0",
-        "teaser": "New features, backwards incompatibilities and migration guide.",
-        "menu": {
-            "Summary": "summary",
-            "New features": "features",
-            "Backwards Incompatibilities": "incompat",
-            "Migrating from v1.x": "migrating",
-            "Benchmarks": "benchmarks"
-        }
-    },
-
-    "linguistic-features": {
-        "title": "Linguistic Features",
-        "teaser": "Using spaCy to extract linguistic features like part-of-speech tags, dependency labels and named entities, customising the tokenizer and working with the rule-based matcher.",
-        "next": "processing-pipelines",
-        "menu": {
-            "POS Tagging": "pos-tagging",
-            "Dependency Parse": "dependency-parse",
-            "Named Entities": "named-entities",
-            "Tokenization": "tokenization",
-            "Sentence Segmentation": "sbd",
-            "Rule-based Matching": "rule-based-matching"
-        }
-    },
-
-    "processing-pipelines": {
-        "title": "Language Processing Pipelines",
-        "next": "vectors-similarity",
-        "menu": {
-            "How Pipelines Work": "pipelines",
-            "Custom Components": "custom-components",
-            "Extension Attributes": "custom-components-attributes",
-            "Multi-Threading": "multithreading",
-            "Serialization": "serialization"
-        }
-    },
-
-    "vectors-similarity": {
-        "title": "Word Vectors and Semantic Similarity",
-        "menu": {
-            "Basics": "basics",
-            "Custom Vectors": "custom",
-            "GPU Usage": "gpu"
-        }
-    },
-
-    "training": {
-        "title": "Training spaCy's Statistical Models",
-        "next": "adding-languages",
-        "menu": {
-            "Basics": "basics",
-            "NER": "ner",
-            "Tagger & Parser": "tagger-parser",
-            "Text Classification": "textcat",
-            "Tips and Advice": "tips",
-            "Saving & Loading": "saving-loading"
-        }
-    },
-
-    "adding-languages": {
-        "title": "Adding Languages",
-        "teaser": "Adding full support for a language touches many different parts of the spaCy library. This guide explains how to fit everything together, and points you to the specific workflows for each component.",
-        "next": "training",
-        "menu": {
-            "Language data": "language-data",
-            "Testing": "testing",
-            "Training": "training"
-        }
-    },
-
-    "visualizers": {
-        "title": "Visualizers",
-        "tag_new": 2,
-        "teaser": "Visualize dependencies and entities in your browser and notebook, or export HTML.",
-        "menu": {
-            "Dependencies": "dep",
-            "Entities": "ent",
-            "Jupyter Notebooks": "jupyter",
-            "Rendering HTML": "html"
-        }
-    },
-
-    "examples": {
-        "title": "Code Examples",
-        "teaser": "Full code examples you can modify and run.",
-        "menu": {
-            "Information Extraction": "information-extraction",
-            "Pipeline": "pipeline",
-            "Training": "training",
-            "Vectors & Similarity": "vectors",
-            "Deep Learning": "deep-learning"
-        }
-    }
-}
diff --git a/website/usage/_deep-learning.jade b/website/usage/_deep-learning.jade
deleted file mode 100644
index 4c33c0572..000000000
--- a/website/usage/_deep-learning.jade
+++ /dev/null
@@ -1,29 +0,0 @@
-//- 💫 DOCS > USAGE > DEEP LEARNING
-
-include ../_includes/_mixins
-+section
-    +under-construction
-
-+section("pre-processing")
-    +h(2, "pre-processing") Pre-processing text for deep learning
-    include _deep-learning/_pre-processing
-
-+section("thinc")
-    +h(2, "thinc") spaCy and Thinc
-    include _deep-learning/_thinc
-
-+section("tensorflow-keras")
-    +h(2, "tensorflow-keras") Using spaCy with TensorFlow / Keras
-    include _deep-learning/_tensorflow-keras
-
-+section("scikit-learn")
-    +h(2, "scikit-learn") Using spaCy with scikit-learn
-    include _deep-learning/_scikit-learn
-
-+section("pytorch")
-    +h(2, "pytorch") Using spaCy with PyTorch
-    include _deep-learning/_pytorch
-
-+section("dynet")
-    +h(2, "dynet") Using spaCy with DyNet
-    include _deep-learning/_dynet
diff --git a/website/usage/_deep-learning/_dynet.jade b/website/usage/_deep-learning/_dynet.jade
deleted file mode 100644
index 81aa4e066..000000000
--- a/website/usage/_deep-learning/_dynet.jade
+++ /dev/null
@@ -1,11 +0,0 @@
-//- 💫 DOCS > USAGE > DEEP LEARNING > DYNET
-
-+infobox
-    +infobox-logos(["dynet", 80, 34, "http://dynet.readthedocs.io/"])
-    |  #[strong DyNet] is a dynamic neural network library, which can be much
-    |  easier to work with for NLP. Outside of Google, there's a general shift
-    |  among NLP researchers to both DyNet and Pytorch. You can use DyNet to
-    |  create spaCy pipeline components, to add annotations to the #[code Doc]
-    |  object.
-
-+under-construction
diff --git a/website/usage/_deep-learning/_pre-processing.jade b/website/usage/_deep-learning/_pre-processing.jade
deleted file mode 100644
index ca87cee7b..000000000
--- a/website/usage/_deep-learning/_pre-processing.jade
+++ /dev/null
@@ -1,3 +0,0 @@
-//- 💫 DOCS > USAGE > DEEP LEARNING > PRE-PROCESSING
-
-+under-construction
diff --git a/website/usage/_deep-learning/_pytorch.jade b/website/usage/_deep-learning/_pytorch.jade
deleted file mode 100644
index cf0f692f9..000000000
--- a/website/usage/_deep-learning/_pytorch.jade
+++ /dev/null
@@ -1,91 +0,0 @@
-//- 💫 DOCS > USAGE > DEEP LEARNING > PYTORCH
-
-+infobox
-    +infobox-logos(["pytorch", 100, 48, "http://pytorch.org"])
-    |  #[strong PyTorch] is a dynamic neural network library, which can be much
-    |  easier to work with for NLP. Outside of Google, there's a general shift
-    |  among NLP researchers to both Pytorch and DyNet. spaCy is the front-end
-    |  of choice for PyTorch's #[code torch.text] extension. You can use PyTorch
-    |  to create spaCy pipeline components, to add annotations to the
-    |  #[code Doc] object.
-
-+under-construction
-
-p
-    |  Here's how a #[code begin_update] function that wraps an arbitrary
-    |  PyTorch model would look:
-
-+code.
-    class PytorchWrapper(thinc.neural.Model):
-        def __init__(self, pytorch_model):
-            self.pytorch_model = pytorch_model
-
-        def begin_update(self, x_data, drop=0.):
-            x_var = Variable(x_data)
-            # Make prediction
-            y_var = pytorch_model.forward(x_var)
-            def backward(dy_data, sgd=None):
-                dy_var = Variable(dy_data)
-                dx_var = torch.autograd.backward(x_var, dy_var)
-                return dx_var
-            return y_var.data, backward
-
-p
-    |  PyTorch requires data to be wrapped in a container, #[code Variable],
-    |  that tracks the operations performed on the data. This "tape" of
-    |  operations is then used by #[code torch.autograd.backward] to compute the
-    |  gradient with respect to the input. For example, the following code
-    |  constructs a PyTorch Linear layer that takes a vector of shape
-    |  #[code (length, 2)], multiples it by a #[code (2, 2)] matrix of weights,
-    |  adds a #[code (2,)] bias, and returns the resulting #[code (length, 2)]
-    |  vector:
-
-+code("PyTorch Linear").
-    from torch import autograd
-    from torch import nn
-    import torch
-    import numpy
-
-    pt_model = nn.Linear(2, 2)
-    length = 5
-
-    input_data = numpy.ones((5, 2), dtype='f')
-    input_var = autograd.Variable(torch.Tensor(input_data))
-
-    output_var = pt_model(input_var)
-    output_data = output_var.data.numpy()
-
-p
-    |  Given target values we would like the output data to approximate, we can
-    |  then "learn" values of the parameters within #[code pt_model], to give us
-    |  output that's closer to our target. As a trivial example, let's make the
-    |  linear layer compute the negative inverse of the input:
-
-+code.
-    def get_target(input_data):
-        return -(1 / input_data)
-
-p
-    |  To update the PyTorch model, we create an optimizer and give it
-    |  references to the model's parameters. We'll then randomly generate input
-    |  data and get the target result we'd like the function to produce. We then
-    |  compute the #[strong gradient of the error] between the current output
-    |  and the target. Using the most popular definition of "error", this is
-    |  simply the average difference:
-
-+code.
-    from torch import optim
-
-    optimizer = optim.SGD(pt_model.parameters(), lr = 0.01)
-    for i in range(10):
-        input_data = numpy.random.uniform(-1., 1., (length, 2))
-        target = -(1 / input_data)
-
-        output_var = pt_model(autograd.Variable(torch.Tensor(input_data)))
-        output_data = output_var.data.numpy()
-
-        d_output_data = (output_data - target) / length
-        d_output_var = autograd.Variable(torch.Tensor(d_output_data))
-
-        d_input_var = torch.autograg.backward(output_var, d_output_var)
-        optimizer.step()
diff --git a/website/usage/_deep-learning/_scikit-learn.jade b/website/usage/_deep-learning/_scikit-learn.jade
deleted file mode 100644
index 3d0f30397..000000000
--- a/website/usage/_deep-learning/_scikit-learn.jade
+++ /dev/null
@@ -1,15 +0,0 @@
-//- 💫 DOCS > USAGE > DEEP LEARNING > SCIKIT-LEARN
-
-+infobox
-    +infobox-logos(["scikitlearn", 70, 34, "http://scikit-learn.org"])
-    |  #[strong scikit-learn] features a number of useful NLP functions,
-    |  especially for solving text classification problems using linear models
-    |  with bag-of-words features. If you know you need exactly that, it might
-    |  be better to use scikit-learn's built-in pipeline directly. However, if
-    |  you want to extract more detailed features, using part-of-speech tags,
-    |  named entity labels, or string transformations, you can use spaCy as a
-    |  pre-process in your classification system. scikit-learn also provides a
-    |  lot of experiment management and evaluation utilities that people use
-    |  alongside spaCy.
-
-+under-construction
diff --git a/website/usage/_deep-learning/_tensorflow-keras.jade b/website/usage/_deep-learning/_tensorflow-keras.jade
deleted file mode 100644
index 3efb2e2a6..000000000
--- a/website/usage/_deep-learning/_tensorflow-keras.jade
+++ /dev/null
@@ -1,11 +0,0 @@
-//- 💫 DOCS > USAGE > DEEP LEARNING > TENSORFLOW / KERAS
-
-+infobox
-    +infobox-logos(["tensorflow", 35, 42, "https://www.tensorflow.org"], ["keras", 45, 45, "https://www.keras.io"])
-    |  #[strong Tensorflow / Keras] is the most popular deep learning library.
-    |  spaCy provides efficient and powerful feature extraction functionality,
-    |  that can be used as a pre-process to any deep learning library. You can
-    |  also use Tensorflow and Keras to create spaCy pipeline components, to add
-    |  annotations to the #[code Doc] object.
-
-+under-construction
diff --git a/website/usage/_deep-learning/_thinc.jade b/website/usage/_deep-learning/_thinc.jade
deleted file mode 100644
index 6c354f708..000000000
--- a/website/usage/_deep-learning/_thinc.jade
+++ /dev/null
@@ -1,66 +0,0 @@
-//- 💫 DOCS > USAGE > DEEP LEARNING > THINC
-
-p
-    |  #[+a(gh("thinc")) Thinc] is the machine learning library powering spaCy.
-    |  It's a practical toolkit for implementing models that follow the
-    |  #[+a("https://explosion.ai/blog/deep-learning-formula-nlp", true) "Embed, encode, attend, predict"]
-    |  architecture. It's designed to be easy to install, efficient for CPU
-    |  usage and optimised for NLP and deep learning with text – in particular,
-    |  hierarchically structured input and variable-length sequences.
-
-p
-    |  spaCy's built-in pipeline components can all be powered by any object
-    |  that follows Thinc's #[code Model] API. If a wrapper is not yet available
-    |  for the library you're using, you should create a
-    |  #[code thinc.neural.Model] subclass that implements a #[code begin_update]
-    |  method. You'll also want to implement #[code to_bytes], #[code from_bytes],
-    |  #[code to_disk] and #[code from_disk] methods, to save and load your
-    |  model. Here's the tempate you'll need to fill in:
-
-    +code("Thinc Model API").
-        class ThincModel(thinc.neural.Model):
-            def __init__(self, *args, **kwargs):
-                pass
-
-            def begin_update(self, X, drop=0.):
-                def backprop(dY, sgd=None):
-                    return dX
-                return Y, backprop
-
-            def to_disk(self, path, **exclude):
-                return None
-
-            def from_disk(self, path, **exclude):
-                return self
-
-            def to_bytes(self, **exclude):
-                return bytes
-
-            def from_bytes(self, msgpacked_bytes, **exclude):
-                return self
-
-p
-    |  The #[code begin_update] method should return a callback, that takes the
-    |  gradient with respect to the output, and returns the gradient with
-    |  respect to the input.  It's usually convenient to implement the callback
-    |  as a nested function, so you can refer to any intermediate variables from
-    |  the forward computation in the enclosing scope.
-
-+h(3, "how-thinc-works") How Thinc works
-
-p
-    |  Neural networks are all about composing small functions that we know how
-    |  to differentiate into larger functions that we know how to differentiate.
-    |  To differentiate a function efficiently, you usually need to store
-    |  intermediate results, computed during the "forward pass", to reuse them
-    |  during the backward pass. Most libraries require the data passed through
-    |  the network to accumulate these intermediate result. This is the "tape"
-    |  in tape-based differentiation.
-
-p
-    |  In Thinc, a model that computes #[code y = f(x)] is required to also
-    |  return a callback that computes #[code dx = f&apos;(dy)]. The same
-    |  intermediate state needs to be tracked, but this becomes an
-    |  implementation detail for the model to take care of – usually, the
-    |  callback is implemented as a closure, so the intermediate results can be
-    |  read from the enclosing scope.
diff --git a/website/usage/_facts-figures/_benchmarks-choi-2015.jade b/website/usage/_facts-figures/_benchmarks-choi-2015.jade
deleted file mode 100644
index 7df9cf14e..000000000
--- a/website/usage/_facts-figures/_benchmarks-choi-2015.jade
+++ /dev/null
@@ -1,45 +0,0 @@
-//- 💫 DOCS > USAGE > FACTS & FIGURES > BENCHMARKS > CHOI ET AL. (2015)
-
-+table(["System", "Year", "Language", "Accuracy", "Speed (wps)"])
-    +row
-        +cell #[strong spaCy v2.x]
-        +cell 2017
-        +cell Python / Cython
-        +cell("num") #[strong 92.6]
-        +cell("num") #[em n/a]
-            |  #[+help("This table shows speed as benchmarked by Choi et al. We therefore can't provide comparable figures, as we'd be running the benchmark on different hardware.").u-color-dark]
-
-    +row
-        +cell #[strong spaCy v1.x]
-        +cell 2015
-        +cell Python / Cython
-        +cell("num") 91.8
-        +cell("num") 13,963
-
-    +row
-        +cell ClearNLP
-        +cell 2015
-        +cell Java
-        +cell("num") 91.7
-        +cell("num") 10,271
-
-    +row
-        +cell CoreNLP
-        +cell 2015
-        +cell Java
-        +cell("num") 89.6
-        +cell("num") 8,602
-
-    +row
-        +cell MATE
-        +cell 2015
-        +cell Java
-        +cell("num") 92.5
-        +cell("num") 550
-
-    +row
-        +cell Turbo
-        +cell 2015
-        +cell C++
-        +cell("num") 92.4
-        +cell("num") 349
diff --git a/website/usage/_facts-figures/_benchmarks-models.jade b/website/usage/_facts-figures/_benchmarks-models.jade
deleted file mode 100644
index fa2370f65..000000000
--- a/website/usage/_facts-figures/_benchmarks-models.jade
+++ /dev/null
@@ -1,103 +0,0 @@
-//- 💫 DOCS > USAGE > FACTS & FIGURES > BENCHMARKS > MODEL COMPARISON
-
-p
-    |  In this section, we provide benchmark accuracies for the pre-trained
-    |  model pipelines we distribute with spaCy. Evaluations are conducted
-    |  end-to-end from raw text, with no "gold standard" pre-processing, over
-    |  text from a mix of genres where possible.
-
-+aside("Methodology")
-    |  The evaluation was conducted on raw text with no gold standard
-    |  information. The parser, tagger and entity recognizer were trained on the
-    |  #[+a("https://www.gabormelli.com/RKB/OntoNotes_Corpus") OntoNotes 5]
-    |  corpus, the word vectors on #[+a("http://commoncrawl.org") Common Crawl].
-
-+h(4, "benchmarks-models-english") English
-
-+table(["Model", "spaCy", "Type", "UAS", "NER F", "POS", "WPS", "Size"])
-    +row
-        +cell #[+a("/models/en#en_core_web_sm") #[code en_core_web_sm]] 2.0.0
-        +cell("num") 2.x
-        +cell neural
-        +cell("num") 91.7
-        +cell("num") 85.3
-        +cell("num") 97.0
-        +cell("num") 10.1k
-        +cell("num") #[strong 35MB]
-
-    +row
-        +cell #[+a("/models/en#en_core_web_md") #[code en_core_web_md]] 2.0.0
-        +cell("num") 2.x
-        +cell neural
-        +cell("num") 91.7
-        +cell("num") #[strong 85.9]
-        +cell("num") 97.1
-        +cell("num") 10.0k
-        +cell("num") 115MB
-
-    +row
-        +cell #[+a("/models/en#en_core_web_lg") #[code en_core_web_lg]] 2.0.0
-        +cell("num") 2.x
-        +cell neural
-        +cell("num") #[strong 91.9]
-        +cell("num") #[strong 85.9]
-        +cell("num") #[strong 97.2]
-        +cell("num") 10.0k
-        +cell("num") 812MB
-
-    +row("divider")
-        +cell #[code en_core_web_sm] 1.2.0
-        +cell("num") 1.x
-        +cell linear
-        +cell("num") 86.6
-        +cell("num") 78.5
-        +cell("num") 96.6
-        +cell("num") #[strong 25.7k]
-        +cell("num") 50MB
-
-    +row
-        +cell #[code en_core_web_md] 1.2.1
-        +cell("num") 1.x
-        +cell linear
-        +cell("num") 90.6
-        +cell("num") 81.4
-        +cell("num") 96.7
-        +cell("num") 18.8k
-        +cell("num") 1GB
-
-+h(4, "benchmarks-models-spanish") Spanish
-
-+aside("Evaluation note")
-    |  The NER accuracy refers to the "silver standard" annotations in the
-    |  WikiNER corpus. Accuracy on these annotations tends to be higher than
-    |  correct human annotations.
-
-+table(["Model", "spaCy", "Type", "UAS", "NER F", "POS", "WPS", "Size"])
-    +row
-        +cell #[+a("/models/es#es_core_news_sm") #[code es_core_news_sm]] 2.0.0
-        +cell("num") 2.x
-        +cell("num") neural
-        +cell("num") 89.8
-        +cell("num") 88.7
-        +cell("num") #[strong 96.9]
-        +cell("num") #[em n/a]
-        +cell("num") #[strong 35MB]
-
-    +row
-        +cell #[+a("/models/es#es_core_news_md") #[code es_core_news_md]] 2.0.0
-        +cell("num") 2.x
-        +cell("num") neural
-        +cell("num") #[strong 90.2]
-        +cell("num") 89.0
-        +cell("num") 97.8
-        +cell("num") #[em n/a]
-        +cell("num") 93MB
-
-    +row("divider")
-        +cell #[code es_core_web_md] 1.1.0
-        each data in ["1.x", "linear", 87.5]
-            +cell("num")=data
-        +cell("num") #[strong 94.2]
-        +cell("num") 96.7
-        +cell("num") #[em n/a]
-        +cell("num") 377MB
diff --git a/website/usage/_facts-figures/_benchmarks.jade b/website/usage/_facts-figures/_benchmarks.jade
deleted file mode 100644
index 57c23d4a9..000000000
--- a/website/usage/_facts-figures/_benchmarks.jade
+++ /dev/null
@@ -1,212 +0,0 @@
-//- 💫 DOCS > USAGE > FACTS & FIGURES > BENCHMARKS
-
-p
-    |  Two peer-reviewed papers in 2015 confirm that spaCy offers the
-    |  #[strong fastest syntactic parser in the world] and that
-    |  #[strong its accuracy is within 1% of the best] available. The few
-    |  systems that are more accurate are 20&times; slower or more.
-
-+aside("About the evaluation")
-    |  The first of the evaluations was published by #[strong Yahoo! Labs] and
-    |  #[strong Emory University], as part of a survey of current parsing
-    |  technologies #[+a("https://aclweb.org/anthology/P/P15/P15-1038.pdf") (Choi et al., 2015)].
-    |  Their results and subsequent discussions helped us develop a novel
-    |  psychologically-motivated technique to improve spaCy's accuracy, which
-    |  we published in joint work with Macquarie University
-    |  #[+a("https://www.aclweb.org/anthology/D/D15/D15-1162.pdf") (Honnibal and Johnson, 2015)].
-
-include _benchmarks-choi-2015
-
-+h(3, "algorithm") Algorithm comparison
-
-p
-    |  In this section, we compare spaCy's algorithms to recently published
-    |  systems, using some of the most popular benchmarks. These benchmarks are
-    |  designed to help isolate the contributions of specific algorithmic
-    |  decisions, so they promote slightly "idealised" conditions. Specifically,
-    |  the text comes pre-processed with "gold standard" token and sentence
-    |  boundaries. The data sets also tend to be fairly small, to help
-    |  researchers iterate quickly. These conditions mean the models trained on
-    |  these data sets are not always useful for practical purposes.
-
-+h(4, "parse-accuracy-penn") Parse accuracy (Penn Treebank / Wall Street Journal)
-
-p
-    |  This is the "classic" evaluation, so it's the number parsing researchers
-    |  are most easily able to put in context. However, it's quite far removed
-    |  from actual usage: it uses sentences with gold-standard segmentation and
-    |  tokenization, from a pretty specific type of text (articles from a single
-    |  newspaper, 1984-1989).
-
-+aside("Methodology")
-    |  #[+a("http://arxiv.org/abs/1603.06042") Andor et al. (2016)] chose
-    |  slightly different experimental conditions from
-    |  #[+a("https://aclweb.org/anthology/P/P15/P15-1038.pdf") Choi et al. (2015)],
-    |  so the two accuracy tables here do not present directly comparable
-    |  figures.
-
-+table(["System", "Year", "Type", "Accuracy"])
-    +row
-        +cell spaCy v2.0.0
-        +cell 2017
-        +cell neural
-        +cell("num") 94.48
-
-    +row
-        +cell spaCy v1.1.0
-        +cell 2016
-        +cell linear
-        +cell("num") 92.80
-
-    +row("divider")
-        +cell
-            +a("https://arxiv.org/pdf/1611.01734.pdf") Dozat and Manning
-            +cell 2017
-            +cell neural
-            +cell("num") #[strong 95.75]
-
-    +row
-        +cell
-            +a("http://arxiv.org/abs/1603.06042") Andor et al.
-        +cell 2016
-        +cell neural
-        +cell("num") 94.44
-
-    +row
-        +cell
-            +a("https://github.com/tensorflow/models/tree/master/research/syntaxnet") SyntaxNet Parsey McParseface
-        +cell 2016
-        +cell neural
-        +cell("num") 94.15
-
-    +row
-        +cell
-            +a("http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43800.pdf") Weiss et al.
-        +cell 2015
-        +cell neural
-        +cell("num") 93.91
-
-    +row
-        +cell
-            +a("http://research.google.com/pubs/archive/38148.pdf") Zhang and McDonald
-        +cell 2014
-        +cell linear
-        +cell("num") 93.32
-
-    +row
-        +cell
-            +a("http://www.cs.cmu.edu/~ark/TurboParser/") Martins et al.
-        +cell 2013
-        +cell linear
-        +cell("num") 93.10
-
-+h(4, "ner-accuracy-ontonotes5") NER accuracy (OntoNotes 5, no pre-process)
-
-p
-    |  This is the evaluation we use to tune spaCy's parameters to decide which
-    |  algorithms are better than the others. It's reasonably close to actual usage,
-    |  because it requires the parses to be produced from raw text, without any
-    |  pre-processing.
-
-+table(["System", "Year", "Type", "Accuracy"])
-    +row
-        +cell spaCy #[+a("/models/en#en_core_web_lg") #[code en_core_web_lg]] v2.0.0a3
-        +cell 2017
-        +cell neural
-        +cell("num") 85.85
-
-    +row("divider")
-        +cell
-            +a("https://arxiv.org/pdf/1702.02098.pdf") Strubell et al.
-        +cell 2017
-        +cell neural
-        +cell("num") #[strong 86.81]
-
-    +row
-        +cell
-            +a("https://www.semanticscholar.org/paper/Named-Entity-Recognition-with-Bidirectional-LSTM-C-Chiu-Nichols/10a4db59e81d26b2e0e896d3186ef81b4458b93f") Chiu and Nichols
-        +cell 2016
-        +cell neural
-        +cell("num") 86.19
-
-    +row
-        +cell
-            +a("https://www.semanticscholar.org/paper/A-Joint-Model-for-Entity-Analysis-Coreference-Typi-Durrett-Klein/28eb033eee5f51c5e5389cbb6b777779203a6778") Durrett and Klein
-        +cell 2014
-        +cell neural
-        +cell("num") 84.04
-
-    +row
-        +cell
-            +a("http://www.aclweb.org/anthology/W09-1119") Ratinov and Roth
-        +cell 2009
-        +cell linear
-        +cell("num") 83.45
-
-+h(3, "spacy-models") Model comparison
-
-include _benchmarks-models
-
-+h(3, "speed-comparison") Detailed speed comparison
-
-p
-    |  Here we compare the per-document processing time of various spaCy
-    |  functionalities against other NLP libraries. We show both absolute
-    |  timings (in ms) and relative performance (normalized to spaCy). Lower is
-    |  better.
-
-+infobox("Important note", "⚠️")
-    |  This evaluation was conducted in 2015. We're working on benchmarks on
-    |  current CPU and GPU hardware. In the meantime, we're grateful to the
-    |  Stanford folks for drawing our attention to what seems
-    |  to be #[+a("https://nlp.stanford.edu/software/tokenizer.html#Speed") a long-standing error] 
-    |  in our CoreNLP benchmarks, especially for their 
-    |  tokenizer. Until we run corrected experiments, we have updated the table
-    |  using their figures.
-
-
-+aside("Methodology")
-    |  #[strong Set up:] 100,000 plain-text documents were streamed from an
-    |  SQLite3 database, and processed with an NLP library, to one of three
-    |  levels of detail — tokenization, tagging, or parsing. The tasks are
-    |  additive: to parse the text you have to tokenize and tag it. The
-    |  pre-processing was not subtracted from the times — we report the time
-    |  required for the pipeline to complete. We report mean times per document,
-    |  in milliseconds.#[br]#[br]
-    |  #[strong Hardware]: Intel i7-3770 (2012)#[br]
-    |  #[strong Implementation]: #[+src(gh("spacy-benchmarks")) #[code spacy-benchmarks]]
-
-+table
-    +row.u-text-label.u-text-center
-        +head-cell
-        +head-cell(colspan="3") Absolute (ms per doc)
-        +head-cell(colspan="3") Relative (to spaCy)
-
-    +row
-        each column in ["System", "Tokenize", "Tag", "Parse", "Tokenize", "Tag", "Parse"]
-            +head-cell=column
-
-    +row
-        +cell #[strong spaCy]
-        each data in [ "0.2ms", "1ms", "19ms"]
-            +cell("num")=data
-
-        each data in ["1x", "1x", "1x"]
-            +cell("num")=data
-
-    +row
-        +cell CoreNLP
-        each data in ["0.18ms", "10ms", "49ms", "0.9x", "10x", "2.6x"]
-            +cell("num")=data
-    +row
-        +cell ZPar
-        each data in ["1ms", "8ms", "850ms", "5x", "8x", "44.7x"]
-            +cell("num")=data
-    +row
-        +cell NLTK
-        each data in ["4ms", "443ms"]
-            +cell("num")=data
-        +cell("num") #[em n/a]
-        each data in ["20x", "443x"]
-            +cell("num")=data
-        +cell("num") #[em n/a]
diff --git a/website/usage/_facts-figures/_feature-comparison.jade b/website/usage/_facts-figures/_feature-comparison.jade
deleted file mode 100644
index 3f970f16c..000000000
--- a/website/usage/_facts-figures/_feature-comparison.jade
+++ /dev/null
@@ -1,58 +0,0 @@
-//- 💫 DOCS > USAGE > FACTS & FIGURES > FEATURE COMPARISON
-
-p
-    |  Here's a quick comparison of the functionalities offered by spaCy,
-    |  #[+a("https://github.com/tensorflow/models/tree/master/research/syntaxnet") SyntaxNet],
-    |  #[+a("http://www.nltk.org/py-modindex.html") NLTK] and
-    |  #[+a("http://stanfordnlp.github.io/CoreNLP/") CoreNLP].
-
-+table(["", "spaCy", "SyntaxNet", "NLTK", "CoreNLP"])
-    +row
-        +cell Programming language
-        each lang in ["Python", "C++", "Python", "Java"]
-            +cell.u-text-small.u-text-center=lang
-
-    +row
-        +cell Neural network models
-            each answer in ["yes", "yes", "no", "yes"]
-                +cell.u-text-center #[+procon(answer)]
-
-    +row
-        +cell Integrated word vectors
-        each answer in ["yes", "no", "no", "no"]
-            +cell.u-text-center #[+procon(answer)]
-
-    +row
-        +cell Multi-language support
-        each answer in ["yes", "yes", "yes", "yes"]
-            +cell.u-text-center #[+procon(answer)]
-
-    +row
-        +cell Tokenization
-        each answer in ["yes", "yes", "yes", "yes"]
-            +cell.u-text-center #[+procon(answer)]
-
-    +row
-        +cell Part-of-speech tagging
-        each answer in ["yes", "yes", "yes", "yes"]
-            +cell.u-text-center #[+procon(answer)]
-
-    +row
-        +cell Sentence segmentation
-        each answer in ["yes", "yes", "yes", "yes"]
-            +cell.u-text-center #[+procon(answer)]
-
-    +row
-        +cell Dependency parsing
-        each answer in ["yes", "yes", "no", "yes"]
-            +cell.u-text-center #[+procon(answer)]
-
-    +row
-        +cell Entity recognition
-        each answer in ["yes", "no", "yes", "yes"]
-            +cell.u-text-center #[+procon(answer)]
-
-    +row
-        +cell Coreference resolution
-        each answer in ["no", "no", "no", "yes"]
-            +cell.u-text-center #[+procon(answer)]
diff --git a/website/usage/_facts-figures/_other-libraries.jade b/website/usage/_facts-figures/_other-libraries.jade
deleted file mode 100644
index b91815618..000000000
--- a/website/usage/_facts-figures/_other-libraries.jade
+++ /dev/null
@@ -1,70 +0,0 @@
-//- 💫 DOCS > USAGE > FACTS & FIGURES > OTHER LIBRARIES
-
-p
-    |  Data scientists, researchers and machine learning engineers have
-    |  converged on Python as the language for AI. This gives developers a rich
-    |  ecosystem of NLP libraries to work with. Here's how we think the pieces
-    |  fit together.
-
-//-+aside("Using spaCy with other libraries")
-    |  For details on how to use spaCy together with popular machine learning
-    |  libraries like TensorFlow, Keras or PyTorch, see the
-    |  #[+a("/usage/deep-learning") usage guide on deep learning].
-
-+infobox
-    +infobox-logos(["nltk", 80, 25, "http://nltk.org"])
-    |  #[+label-inline NLTK] offers some of the same functionality as spaCy.
-    |  Although originally developed for teaching and research, its longevity
-    |  and stability has resulted in a large number of industrial users. It's
-    |  the main alternative to spaCy for tokenization and sentence segmentation.
-    |  In comparison to spaCy, NLTK takes a much more "broad church" approach –
-    |  so it has some functions that spaCy doesn't provide, at the expense of a
-    |  bit more clutter to sift through. spaCy is also much more
-    |  performance-focussed than NLTK: where the two libraries provide the same
-    |  functionality, spaCy's implementation will usually be faster and more
-    |  accurate.
-
-+infobox
-    +infobox-logos(["gensim", 40, 40, "https://radimrehurek.com/gensim/"])
-    |  #[+label-inline Gensim] provides unsupervised text modelling algorithms.
-    |  Although Gensim isn't a runtime dependency of spaCy, we use it to train
-    |  word vectors. There's almost no overlap between the libraries – the two
-    |  work together.
-
-+infobox
-    +infobox-logos(["tensorflow", 35, 42, "https://www.tensorflow.org"], ["keras", 45, 45, "https://www.keras.io"])
-    |  #[+label-inline Tensorflow / Keras] is the most popular deep learning library.
-    |  spaCy provides efficient and powerful feature extraction functionality,
-    |  that can be used as a pre-process to any deep learning library. You can
-    |  also use Tensorflow and Keras to create spaCy pipeline components, to add
-    |  annotations to the #[code Doc] object.
-
-+infobox
-    +infobox-logos(["scikitlearn", 90, 44, "http://scikit-learn.org"])
-    |  #[+label-inline scikit-learn] features a number of useful NLP functions,
-    |  especially for solving text classification problems using linear models
-    |  with bag-of-words features. If you know you need exactly that, it might
-    |  be better to use scikit-learn's built-in pipeline directly. However, if
-    |  you want to extract more detailed features, using part-of-speech tags,
-    |  named entity labels, or string transformations, you can use spaCy as a
-    |  pre-process in your classification system. scikit-learn also provides a
-    |  lot of experiment management and evaluation utilities that people use
-    |  alongside spaCy.
-
-+infobox
-    +infobox-logos(["pytorch", 100, 48, "http://pytorch.org"], ["dynet", 80, 34, "http://dynet.readthedocs.io/"], ["chainer", 80, 43, "http://chainer.org"])
-    |  #[+label-inline PyTorch, DyNet and Chainer] are dynamic neural network
-    |  libraries, which can be much easier to work with for NLP. Outside of
-    |  Google, there's a general shift among NLP researchers to both DyNet and
-    |  Pytorch. spaCy is the front-end of choice for PyTorch's
-    |  #[code torch.text] extension. You can use any of these libraries to
-    |  create spaCy pipeline components, to add annotations to the #[code Doc]
-    |  object.
-
-+infobox
-    +infobox-logos(["allennlp", 124, 22, "http://allennlp.org"])
-    |  #[+label-inline AllenNLP] is a new library designed to accelerate NLP
-    |  research, by providing a framework that supports modern deep learning
-    |  workflows for cutting-edge language understanding problems. AllenNLP uses
-    |  spaCy as a preprocessing component. You can also use AllenNLP to develop
-    |  spaCy pipeline components, to add annotations to the #[code Doc] object.
diff --git a/website/usage/_install/_changelog.jade b/website/usage/_install/_changelog.jade
deleted file mode 100644
index 646d746a9..000000000
--- a/website/usage/_install/_changelog.jade
+++ /dev/null
@@ -1,37 +0,0 @@
-//- 💫 DOCS > USAGE > INSTALL > CHANGELOG
-
-+h(2, "changelog") Changelog
-    +button(gh("spacy") + "/releases", false, "secondary", "small").u-float-right.u-nowrap View releases
-
-section(data-vue="changelog")
-    +infobox(v-if="error")
-        |  Unable to load changelog from GitHub. Please see the
-        |  #[+a(gh("spacy") + "/releases") releases page] instead.
-
-    section(v-if="!error" v-cloak="")
-        +table(["Date", "Version", "Title"])(v-if="releases.length")
-            +row(v-for="version in releases")
-                +cell.u-nowrap #[+label(v-text="version.date")]
-                +cell
-                    +a(gh("spacy") + "/releases")(v-bind:href="version.url")
-                        code(v-text="version.tag")
-                +cell.u-text-small(v-text="version.title")
-
-    section(v-if="!error" v-cloak="")
-        +h(3) Pre-releases
-
-        +aside("About pre-releases")
-            .o-block-small
-                |  Pre-releases include alpha and beta versions, as well as release
-                |  candidates. They are not intended for production use. You can
-                |  download spaCy pre-releases via the #[code spacy-nightly] package
-                |  on pip.
-            +badge("https://img.shields.io/pypi/v/spacy-nightly.svg?style=flat-square", "https://pypi.python.org/pypi/spacy-nightly")
-
-        +table(["Date", "Version", "Title"])(v-if="prereleases.length")
-            +row(v-for="version in prereleases")
-                +cell.u-nowrap #[+label(v-text="version.date")]
-                +cell
-                    +a(gh("spacy") + "/releases")(v-bind:href="version.url")
-                        code(v-text="version.tag")
-                +cell.u-text-small(v-text="version.title")
diff --git a/website/usage/_install/_instructions.jade b/website/usage/_install/_instructions.jade
deleted file mode 100644
index f203d7aa8..000000000
--- a/website/usage/_install/_instructions.jade
+++ /dev/null
@@ -1,256 +0,0 @@
-//- 💫 DOCS > USAGE > INSTALL > INSTRUCTIONS
-
-+h(3, "pip") pip
-    +badge("https://img.shields.io/pypi/v/spacy.svg?style=flat-square", "https://pypi.python.org/pypi/spacy")
-
-p
-    |  Using pip, spaCy releases are available as source packages and binary
-    |  wheels (as of #[code v2.0.13]).
-
-+code(false, "bash").
-    pip install -U spacy
-
-+aside("Download models")
-    |  After installation you need to download a language model. For more info
-    |  and available models, see the #[+a("/usage/models") docs on models].
-
-    +code.o-no-block.
-        python -m spacy download en
-
-        &gt;&gt;&gt; import spacy
-        &gt;&gt;&gt; nlp = spacy.load('en')
-
-p
-    |  When using pip it is generally recommended to install packages in a
-    |  virtual environment to avoid modifying system state:
-
-+code(false, "bash").
-    python -m venv .env
-    source .env/bin/activate
-    pip install spacy
-
-+h(3, "conda") conda
-    +badge("https://anaconda.org/conda-forge/spacy/badges/version.svg", "https://anaconda.org/conda-forge/spacy")
-
-p
-    |  Thanks to our great community, we've finally re-added conda support. You
-    |  can now install spaCy via #[code conda-forge]:
-
-+code(false, "bash").
-    conda install -c conda-forge spacy
-
-p
-    |  For the feedstock including the build recipe and configuration, check out
-    |  #[+a("https://github.com/conda-forge/spacy-feedstock") this repository].
-    |  Improvements and pull requests to the recipe and setup are always
-    |  appreciated.
-
-+h(3, "upgrading") Upgrading spaCy
-
-+aside("Upgrading from v1 to v2")
-    |  Although we've tried to keep breaking changes to a minimum, upgrading
-    |  from spaCy v1.x to v2.x may still require some changes to your code base.
-    |  For details see the sections on
-    |  #[+a("/usage/v2#incompat") backwards incompatibilities] and
-    |  #[+a("/usage/v2#migrating") migrating]. Also remember to download the new
-    |  models, and retrain your own models.
-
-p
-    |  When updating to a newer version of spaCy, it's generally recommended to
-    |  start with a clean virtual environment. If you're upgrading to a new
-    |  major version, make sure you have the latest #[strong compatible models]
-    |  installed, and that there are no old shortcut links or incompatible model
-    |  packages left over in your environment, as this can often lead to unexpected
-    |  results and errors.  If you've trained your own models, keep in mind that
-    |  your train and runtime inputs must match. This means you'll have to
-    |  #[strong retrain your models] with the new version.
-
-p
-    |  As of v2.0, spaCy also provides a #[+api("cli#validate") #[code validate]]
-    |  command, which lets you verify that all installed models are compatible
-    |  with your spaCy version. If incompatible models are found, tips and
-    |  installation instructions are printed. The command is also useful to
-    |  detect out-of-sync model links resulting from links created in different
-    |  virtual environments. It's recommended to run the command with
-    |  #[code python -m] to make sure you're executing the correct version of
-    |  spaCy.
-
-+code(false, "bash").
-    pip install -U spacy
-    python -m spacy validate
-
-+h(3, "gpu") Run spaCy with GPU
-    +tag-new("2.0.14")
-
-p
-    |  As of v2.0, spaCy's comes with neural network models that are implemented
-    |  in our machine learning library, #[+a(gh("thinc")) Thinc]. For GPU
-    |  support, we've been grateful to use the work of
-    |  Chainer's #[+a("https://cupy.chainer.org") CuPy] module, which provides
-    |  a NumPy-compatible interface for GPU arrays.
-
-p
-    |  spaCy can be installed on GPU by specifying #[code spacy[cuda]],
-    |  #[code spacy[cuda90]], #[code spacy[cuda91]], #[code spacy[cuda92]] or
-    |  #[code spacy[cuda100]]. If you know your cuda version, using the more
-    |  explicit specifier allows cupy to be installed via wheel, saving some
-    |  compilation time. The specifiers should install two libraries:
-    |  #[+a("https://cupy.chainer.org") #[code cupy]] and
-    |  #[+a(gh("thinc_gpu_ops")) #[code thinc_gpu_ops]].
-
-+code(false, "bash").
-    pip install -U spacy[cuda92]
-
-p
-    |  Once you have a GPU-enabled installation, the best way to activate it is
-    |  to call #[+api("top-level#spacy.prefer_gpu") #[code spacy.prefer_gpu()]]
-    |  or #[+api("top-level#spacy.require_gpu") #[code spacy.require_gpu()]]
-    |  somewhere in your script before any models have been loaded.
-    |  #[code require_gpu] will raise an error if no GPU is available.
-
-+code.
-    import spacy
-
-    spacy.prefer_gpu()
-    nlp = spacy.load('en_core_web_sm')
-
-+h(3, "source") Compile from source
-
-p
-    |  The other way to install spaCy is to clone its
-    |  #[+a(gh("spaCy")) GitHub repository] and build it from source. That is
-    |  the common way if you want to make changes to the code base. You'll need
-    |  to make sure that you have a development environment consisting of a
-    |  Python distribution including header files, a compiler,
-    |  #[+a("https://pip.pypa.io/en/latest/installing/") pip],
-    |  #[+a("https://virtualenv.pypa.io/") virtualenv] and
-    |  #[+a("https://git-scm.com") git] installed. The compiler part is the
-    |  trickiest. How to do that depends on your system. See notes on
-    |  #[a(href="#source-ubuntu") Ubuntu], #[a(href="#source-osx") OS X] and
-    |  #[a(href="#source-windows") Windows] for details.
-
-+code(false, "bash").
-    python -m pip install -U pip                   # update pip
-    git clone #{gh("spaCy")}   # clone spaCy
-    cd spaCy                                       # navigate into directory
-
-    python -m venv .env                            # create environment in .env
-    source .env/bin/activate                       # activate virtual environment
-    export PYTHONPATH=`pwd`                        # set Python path to spaCy directory
-    pip install -r requirements.txt                # install all requirements
-    python setup.py build_ext --inplace            # compile spaCy
-
-p
-    |  Compared to regular install via pip, the
-    |  #[+src(gh("spaCy", "requirements.txt")) #[code requirements.txt]]
-    |  additionally installs developer dependencies such as Cython. See the
-    |  the #[+a("#section-quickstart") quickstart widget] to get the right
-    |  commands for your platform and Python version. Instead of the above
-    |  verbose commands, you can also use the following
-    |  #[+a("http://www.fabfile.org/") Fabric] commands:
-
-+table(["Command", "Description"])
-    +row
-        +cell #[code fab env]
-        +cell Create a virtual environment and delete previous one, if it exists.
-
-    +row
-        +cell #[code fab make]
-        +cell Compile the source.
-
-    +row
-        +cell #[code fab clean]
-        +cell Remove compiled objects, including the generated C++.
-
-    +row
-        +cell #[code fab test]
-        +cell Run basic tests, aborting after first failure.
-
-p
-    |  All commands assume that your virtual environment is located in a
-    |  directory #[code .env]. If you're using a different directory, you can
-    |  change it via the environment variable #[code VENV_DIR], for example:
-
-+code(false, "bash").
-    VENV_DIR=".custom-env" fab clean make
-
-+h(4, "source-ubuntu") Ubuntu
-
-p Install system-level dependencies via #[code apt-get]:
-
-+code(false, "bash").
-    sudo apt-get install build-essential python-dev git
-
-+h(4, "source-osx") macOS / OS X
-
-p
-    |  Install a recent version of
-    |  #[+a("https://developer.apple.com/xcode/") XCode], including the
-    |  so-called "Command Line Tools". macOS and OS X ship with Python and git
-    |  preinstalled. To compile spaCy with multi-threading support on macOS / OS X,
-    |  #[+a("https://github.com/explosion/spaCy/issues/267") see here].
-
-+h(4, "source-windows") Windows
-
-p
-    |  Install a version of the
-    |  #[+a("https://visualstudio.microsoft.com/visual-cpp-build-tools/") Visual C++ Build Tools] or
-    |  #[+a("https://www.visualstudio.com/vs/visual-studio-express/") Visual Studio Express]
-    |  that matches the version that was used to compile your Python
-    |  interpreter. For official distributions these are:
-
-+table([ "Distribution", "Version"])
-    +row
-        +cell Python 2.7
-        +cell Visual Studio 2008
-
-    +row
-        +cell Python 3.4
-        +cell Visual Studio 2010
-
-    +row
-        +cell Python 3.5+
-        +cell Visual Studio 2015
-
-+h(3, "tests") Run tests
-
-p
-    |  spaCy comes with an #[+a(gh("spacy", "spacy/tests")) extensive test suite].
-    |  In order to run the tests, you'll usually want to clone the
-    |  #[+a(gh("spacy")) repository] and #[+a("#source") build spaCy from source].
-    |  This will also install the required development dependencies and test
-    |  utilities defined in the #[code requirements.txt].
-
-p
-    |  Alternatively, you can find out where spaCy is installed and run
-    |  #[code pytest] on that directory. Don't forget to also install the
-    |  test utilities via spaCy's
-    |  #[+a(gh("spacy", "requirements.txt")) #[code requirements.txt]]:
-
-+code(false, "bash").
-    python -c "import os; import spacy; print(os.path.dirname(spacy.__file__))"
-    pip install -r path/to/requirements.txt
-    python -m pytest &lt;spacy-directory&gt;
-
-p
-    |  Calling #[code pytest] on the spaCy directory will run only the basic
-    |  tests. The flags #[code --slow] and #[code --model] are optional and
-    |  enable additional tests that take longer or use specific models.
-
-+code(false, "bash").
-    # make sure you are using recent pytest version
-    python -m pip install -U pytest
-
-    python -m pytest &lt;spacy-directory&gt;                 # basic tests
-    python -m pytest &lt;spacy-directory&gt; --slow          # basic and slow tests
-    python -m pytest &lt;spacy-directory&gt; --models --all  # basic and all model tests
-    python -m pytest &lt;spacy-directory&gt; --models --en   # basic and English model tests
-
-+infobox("Note on model tests", "⚠️")
-    |  The test suite specifies a #[+a(gh("spacy", "spacy/tests/conftest.py")) list of models]
-    |  to run the tests on. If a model is not installed, the tests will be
-    |  skipped. If all models are installed, the respective tests will run once
-    |  for each model. The easiest way to find out which models and model
-    |  versions are available in your current environment is to run
-    |  #[+a("/api/cli#validate") #[code python -m spacy validate]]. This will
-    |  also show whether an installed model is out of date, and how to update it.
diff --git a/website/usage/_install/_quickstart.jade b/website/usage/_install/_quickstart.jade
deleted file mode 100644
index ceab484d5..000000000
--- a/website/usage/_install/_quickstart.jade
+++ /dev/null
@@ -1,26 +0,0 @@
-//- 💫 DOCS > USAGE > INSTALL > QUICKSTART
-
-- QUICKSTART[QUICKSTART.length - 1].options = Object.keys(MODELS).map(m => ({ id: m, title: LANGUAGES[m] }))
-
-+quickstart(QUICKSTART, "Quickstart")
-    +qs({config: 'venv', python: 2}) python -m pip install -U virtualenv
-    +qs({config: 'venv', python: 3}) python -m pip install -U venv
-    +qs({config: 'venv', python: 2}) virtualenv .env
-    +qs({config: 'venv', python: 3}) python -m venv .env
-    +qs({config: 'venv', os: 'mac'}) source .env/bin/activate
-    +qs({config: 'venv', os: 'linux'}) source .env/bin/activate
-    +qs({config: 'venv', os: 'windows'}) .env\Scripts\activate
-
-    +qs({package: 'pip'}) pip install -U spacy
-    +qs({package: 'conda'}) conda install -c conda-forge spacy
-
-    +qs({package: 'source'}) git clone https://github.com/explosion/spaCy
-    +qs({package: 'source'}) cd spaCy
-    +qs({package: 'source', os: 'mac'}) export PYTHONPATH=`pwd`
-    +qs({package: 'source', os: 'linux'}) export PYTHONPATH=`pwd`
-    +qs({package: 'source', os: 'windows'}) set PYTHONPATH=/path/to/spaCy
-    +qs({package: 'source'}) pip install -r requirements.txt
-    +qs({package: 'source'}) python setup.py build_ext --inplace
-
-    for _, model in MODELS
-        +qs({model: model}) python -m spacy download #{model}
diff --git a/website/usage/_install/_troubleshooting.jade b/website/usage/_install/_troubleshooting.jade
deleted file mode 100644
index ee8eb902b..000000000
--- a/website/usage/_install/_troubleshooting.jade
+++ /dev/null
@@ -1,199 +0,0 @@
-//- 💫 DOCS > USAGE > INSTALL > TROUBLESHOOTING
-
-p
-    |  This section collects some of the most common errors you may come
-    |  across when installing, loading and using spaCy, as well as their solutions.
-
-+aside("Help us improve this guide")
-    |  Did you come across a problem like the ones listed here and want to
-    |  share the solution? You can find the "Suggest edits" button at the
-    |  bottom of this page that points you to the source. We always
-    |  appreciate #[+a(gh("spaCy") + "/pulls") pull requests]!
-
-+h(3, "compatible-model") No compatible model found
-
-+code(false, "text").
-    No compatible model found for [lang] (spaCy v#{SPACY_VERSION}).
-
-p
-    |  This usually means that the model you're trying to download does not
-    |  exist, or isn't available for your version of spaCy. Check the
-    |  #[+a(gh("spacy-models", "compatibility.json")) compatibility table]
-    |  to see which models are available for your spaCy version. If you're using
-    |  an old version, consider upgrading to the latest release. Note that while
-    |  spaCy supports tokenization for
-    |  #[+a("/usage/models#languages") a variety of languages],
-    |  not all of them come with statistical models. To only use the tokenizer,
-    |  import the language's #[code Language] class instead, for example
-    |  #[code from spacy.fr import French].
-
-+h(3, "symlink-privilege") Symbolic link privilege not held
-
-+code(false, "text").
-    OSError: symbolic link privilege not held
-
-p
-    |  To create #[+a("/usage/models#usage") shortcut links] that let you
-    |  load models by name, spaCy creates a symbolic link in the
-    |  #[code spacy/data] directory. This means your user needs permission to do
-    |  this. The above error mostly occurs when doing a system-wide installation,
-    |  which will create the symlinks in a system directory. Run the
-    |  #[code download] or #[code link] command as administrator (on Windows,
-    |  you can either right-click on your terminal or shell ans select "Run as
-    |  Administrator"), or use a #[code virtualenv] to install spaCy in a user
-    |  directory, instead of doing a system-wide installation.
-
-+h(3, "no-cache-dir") No such option: --no-cache-dir
-
-+code(false, "text").
-    no such option: --no-cache-dir
-
-p
-    |  The #[code download] command uses pip to install the models and sets the
-    |  #[code --no-cache-dir] flag to prevent it from requiring too much memory.
-    |  #[+a("https://pip.pypa.io/en/stable/reference/pip_install/#caching") This setting]
-    |  requires pip v6.0 or newer. Run #[code pip install -U pip] to upgrade to
-    |  the latest version of pip. To see which version you have installed,
-    |  run #[code pip --version].
-
-+h(3, "unknown-locale") Unknown locale: UTF-8
-
-+code(false, "text").
-    ValueError: unknown locale: UTF-8
-
-p
-    |  This error can sometimes occur on OSX and is likely related to a
-    |  still unresolved #[+a("https://bugs.python.org/issue18378") Python bug].
-    |  However, it's easy to fix: just add the following to your
-    |  #[code ~&sol;.bash_profile] or #[code ~&sol;.zshrc] and then run
-    |  #[code source ~&sol;.bash_profile] or #[code source ~&sol;.zshrc].
-    |  Make sure to add #[strong both lines] for #[code LC_ALL] and
-    |  #[code LANG].
-
-+code(false, "bash").
-    export LC_ALL=en_US.UTF-8
-    export LANG=en_US.UTF-8
-
-+h(3, "import-error") Import error
-
-+code(false, "text").
-    Import Error: No module named spacy
-
-p
-    |  This error means that the spaCy module can't be located on your system, or in
-    |  your environment. Make sure you have spaCy installed. If you're using a
-    |  #[code virtualenv], make sure it's activated and check that spaCy is
-    |  installed in that environment – otherwise, you're trying to load a system
-    |  installation. You can also run #[code which python] to find out where
-    |  your Python executable is located.
-
-+h(3, "import-error-models") Import error: models
-
-+code(false, "text").
-    ImportError: No module named 'en_core_web_sm'
-
-p
-    |  As of spaCy v1.7, all models can be installed as Python packages. This means
-    |  that they'll become importable modules of your application. When creating
-    |  #[+a("/usage/models#usage") shortcut links], spaCy will also try
-    |  to import the model to load its meta data. If this fails, it's usually a
-    |  sign that the package is not installed in the current environment.
-    |  Run #[code pip list] or #[code pip freeze] to check which model packages
-    |  you have installed, and install the
-    |  #[+a("/models") correct models] if necessary. If you're
-    |  importing a model manually at the top of a file, make sure to use the name
-    |  of the package, not the shortcut link you've created.
-
-+h(3, "vocab-strings") File not found: vocab/strings.json
-
-+code(false, "text").
-    FileNotFoundError: No such file or directory: [...]/vocab/strings.json
-
-p
-    |  This error may occur when using #[code spacy.load()] to load
-    |  a language model – either because you haven't set up a
-    |  #[+a("/usage/models#usage") shortcut link] for it, or because it
-    |  doesn't actually exist. Set up a link for the model
-    |  you want to load. This can either be an installed model package, or a
-    |  local directory containing the model data. If you want to use one of the
-    |  #[+a("/usage/models#languages") alpha tokenizers] for
-    |  languages that don't yet have a statistical model, you should import its
-    |  #[code Language] class instead, for example
-    |  #[code from spacy.lang.bn import Bengali]. You can also use
-    |  #[+api("top-level#spacy.blank") #[code spacy.blank]] to create a blank
-    |  instance, e.g. #[code nlp = spacy.blank('bn')].
-
-+h(3, "command-not-found") Command not found
-
-+code(false, "text").
-    command not found: spacy
-
-p
-    |  This error may occur when running the #[code spacy] command from the
-    |  command line. spaCy does not currently add an entry to our #[code PATH]
-    |  environment variable, as this can lead to unexpected results, especially
-    |  when using #[code virtualenv]. Instead, spaCy adds an auto-alias that
-    |  maps #[code spacy] to #[code python -m spacy]. If this is not working as
-    |  expected, run the command with #[code python -m], yourself –
-    |  for example #[code python -m spacy download en]. For more info on this,
-    |  see the #[+api("cli#download") #[code download]] command.
-
-+h(3, "module-load") 'module' object has no attribute 'load'
-
-+code(false, "text").
-    AttributeError: 'module' object has no attribute 'load'
-
-p
-    |  While this could technically have many causes, including spaCy being
-    |  broken, the most likely one is that your script's file or directory name
-    |  is "shadowing" the module – e.g. your file is called #[code spacy.py],
-    |  or a directory you're importing from is called #[code spacy]. So, when
-    |  using spaCy, never call anything else #[code spacy].
-
-+h(3, "pron-lemma") Pronoun lemma is returned as #[code -PRON-]
-
-+code.
-    doc = nlp(u'They are')
-    print(doc[0].lemma_)
-    # -PRON-
-
-p
-    |  This is in fact expected behaviour and not a bug.
-    |  Unlike verbs and common nouns, there's no clear base form of a personal
-    |  pronoun. Should the lemma of "me" be "I", or should we normalize person
-    |  as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a
-    |  novel symbol, #[code -PRON-], which is used as the lemma for
-    |  all personal pronouns. For more info on this, see the
-    |  #[+a("/api/annotation#lemmatization") lemmatization specs].
-
-+h(3, "catastrophic-forgetting") NER model doesn't recognise other entities anymore after training
-
-p
-    |  If your training data only contained new entities and you didn't mix in
-    |  any examples the model previously recognised, it can cause the model to
-    |  "forget" what it had previously learned. This is also referred to as the
-    |  #[+a("https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting", true) "catastrophic forgetting problem"].
-    |  A solution is to pre-label some text, and mix it with the new text in
-    |  your updates. You can also do this by running spaCy over some text,
-    |  extracting a bunch of entities the model previously recognised correctly,
-    |  and adding them to your training examples.
-
-+h(3, "unhashable-list") Unhashable type: 'list'
-
-+code(false, "text").
-    TypeError: unhashable type: 'list'
-
-p
-    |  If you're training models, writing them to disk, and versioning them with
-    |  git, you might encounter this error when trying to load them in a Windows
-    |  environment. This happens because a default install of Git for Windows is
-    |  configured to automatically convert Unix-style end-of-line characters
-    |  (LF) to Windows-style ones (CRLF) during file checkout (and the reverse
-    |  when commiting). While that's mostly fine for text files, a trained model
-    |  written to disk has some binary files that should not go through this
-    |  conversion. When they do, you get the error above. You can fix it by
-    |  either changing your #[+a("https://git-scm.com/book/en/v2/Customizing-Git-Git-Configuration") #[code core.autocrlf]]
-    |  setting to #[code "false"], or by commiting a #[+a("https://git-scm.com/docs/gitattributes") #[code .gitattributes] file]
-    |  to your repository to tell git on which files or folders it shouldn't do
-    |  LF-to-CRLF conversion, with an entry like #[code path&sol;to&sol;spacy&sol;model&sol;** -text].
-    |  After you've done either of these, clone your repository again.
diff --git a/website/usage/_linguistic-features/_dependency-parse.jade b/website/usage/_linguistic-features/_dependency-parse.jade
deleted file mode 100644
index 586e56b30..000000000
--- a/website/usage/_linguistic-features/_dependency-parse.jade
+++ /dev/null
@@ -1,274 +0,0 @@
-//- 💫 DOCS > USAGE > LINGUISTIC FEATURES > DEPENDENCY PARSE
-
-p
-    |  spaCy features a fast and accurate syntactic dependency parser, and has
-    |  a rich API for navigating the tree. The parser also powers the sentence
-    |  boundary detection, and lets you iterate over base noun phrases, or
-    |  "chunks". You can check whether a #[+api("doc") #[code Doc]] object has
-    |  been parsed with the #[code doc.is_parsed] attribute, which returns a
-    |  boolean value. If this attribute is #[code False], the default sentence
-    |  iterator will raise an exception.
-
-+h(3, "noun-chunks") Noun chunks
-
-p
-    |  Noun chunks are "base noun phrases" – flat phrases that have a noun as
-    |  their head. You can think of noun chunks as a noun plus the words describing
-    |  the noun – for example, "the lavish green grass" or "the world’s largest
-    |  tech fund". To get the noun chunks in a document, simply iterate over
-    |  #[+api("doc#noun_chunks") #[code Doc.noun_chunks]].
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u"Autonomous cars shift insurance liability toward manufacturers")
-    for chunk in doc.noun_chunks:
-        print(chunk.text, chunk.root.text, chunk.root.dep_,
-              chunk.root.head.text)
-
-+aside
-    |  #[strong Text:] The original noun chunk text.#[br]
-    |  #[strong Root text:] The original text of the word connecting the noun
-    |  chunk to the rest of the parse.#[br]
-    |  #[strong Root dep:] Dependency relation connecting the root to its head.#[br]
-    |  #[strong Root head text:] The text of the root token's head.#[br]
-
-+table(["Text", "root.text", "root.dep_", "root.head.text"])
-    - var style = [0, 0, 1, 0]
-    +annotation-row(["Autonomous cars", "cars", "nsubj", "shift"], style)
-    +annotation-row(["insurance liability", "liability", "dobj", "shift"], style)
-    +annotation-row(["manufacturers", "manufacturers", "pobj", "toward"], style)
-
-+h(3, "navigating") Navigating the parse tree
-
-p
-    |  spaCy uses the terms #[strong head] and #[strong child] to describe the words
-    |  #[strong connected by a single arc] in the dependency tree. The term
-    |  #[strong dep] is used for the arc label, which describes the type of
-    |  syntactic relation that connects the child to the head. As with other
-    |  attributes, the value of #[code .dep] is a hash value. You can get
-    |  the string value with #[code .dep_].
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u"Autonomous cars shift insurance liability toward manufacturers")
-    for token in doc:
-        print(token.text, token.dep_, token.head.text, token.head.pos_,
-              [child for child in token.children])
-
-+aside
-    |  #[strong Text]: The original token text.#[br]
-    |  #[strong Dep]: The syntactic relation connecting child to head.#[br]
-    |  #[strong Head text]: The original text of the token head.#[br]
-    |  #[strong Head POS]: The part-of-speech tag of the token head.#[br]
-    |  #[strong Children]: The immediate syntactic dependents of the token.
-
-+table(["Text", "Dep", "Head text", "Head POS", "Children"])
-    - var style = [0, 1, 0, 1, 0]
-    +annotation-row(["Autonomous", "amod", "cars", "NOUN", ""], style)
-    +annotation-row(["cars", "nsubj", "shift", "VERB", "Autonomous"], style)
-    +annotation-row(["shift", "ROOT", "shift", "VERB", "cars, liability, toward"], style)
-    +annotation-row(["insurance", "compound", "liability", "NOUN", ""], style)
-    +annotation-row(["liability", "dobj", "shift", "VERB", "insurance"], style)
-    +annotation-row(["toward", "prep", "shift", "NOUN", "manufacturers"], style)
-    +annotation-row(["manufacturers", "pobj", "toward", "ADP", ""], style)
-
-+codepen("dcf8d293367ca185b935ed2ca11ebedd", 370)
-
-p
-    |  Because the syntactic relations form a tree, every word has
-    |  #[strong exactly one head]. You can therefore iterate over the arcs in
-    |  the tree by iterating over the words in the sentence. This is usually
-    |  the best way to match an arc of interest — from below:
-
-+code-exec.
-    import spacy
-    from spacy.symbols import nsubj, VERB
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u"Autonomous cars shift insurance liability toward manufacturers")
-
-    # Finding a verb with a subject from below — good
-    verbs = set()
-    for possible_subject in doc:
-        if possible_subject.dep == nsubj and possible_subject.head.pos == VERB:
-            verbs.add(possible_subject.head)
-    print(verbs)
-
-p
-    |  If you try to match from above, you'll have to iterate twice: once for
-    |  the head, and then again through the children:
-
-+code.
-    # Finding a verb with a subject from above — less good
-    verbs = []
-    for possible_verb in doc:
-        if possible_verb.pos == VERB:
-            for possible_subject in possible_verb.children:
-                if possible_subject.dep == nsubj:
-                    verbs.append(possible_verb)
-                    break
-
-p
-    |  To iterate through the children, use the #[code token.children]
-    |  attribute, which provides a sequence of #[+api("token") #[code Token]]
-    |  objects.
-
-+h(4, "navigating-around") Iterating around the local tree
-
-p
-    |  A few more convenience attributes are provided for iterating around the
-    |  local tree from the token. The #[+api("token#lefts") #[code Token.lefts]]
-    |  and #[+api("token#rights") #[code Token.rights]] attributes provide
-    |  sequences of syntactic children that occur before and after the token.
-    |  Both sequences are in sentence order. There are also two integer-typed
-    |  attributes, #[+api("token#n_rights") #[code Token.n_rights]] and
-    |  #[+api("token#n_lefts") #[code Token.n_lefts]], that give the number of
-    |  left and right children.
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u"bright red apples on the tree")
-    print([token.text for token in doc[2].lefts])  # ['bright', 'red']
-    print([token.text for token in doc[2].rights])  # ['on']
-    print(doc[2].n_lefts)  # 2
-    print(doc[2].n_rights)  # 1
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('de_core_news_sm')
-    doc = nlp(u"schöne rote Äpfel auf dem Baum")
-    print([token.text for token in doc[2].lefts])  # ['schöne', 'rote']
-    print([token.text for token in doc[2].rights])  # ['auf']
-
-p
-    |  You can get a whole phrase by its syntactic head using the
-    |  #[+api("token#subtree") #[code Token.subtree]] attribute. This returns an
-    |  ordered  sequence of tokens. You can walk up the tree with the
-    |  #[+api("token#ancestors") #[code Token.ancestors]] attribute, and
-    |  check dominance with
-    |  #[+api("token#is_ancestor") #[code Token.is_ancestor()]].
-
-+aside("Projective vs. non-projective")
-    |  For the #[+a("/models/en") default English model], the
-    |  parse tree is #[strong projective], which means that there are no crossing
-    |  brackets. The tokens returned by #[code .subtree] are therefore guaranteed
-    |  to be contiguous. This is not true for the German model, which has many
-    |  #[+a(COMPANY_URL + "/blog/german-model#word-order", true) non-projective dependencies].
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u"Credit and mortgage account holders must submit their requests")
-
-    root = [token for token in doc if token.head == token][0]
-    subject = list(root.lefts)[0]
-    for descendant in subject.subtree:
-        assert subject is descendant or subject.is_ancestor(descendant)
-        print(descendant.text, descendant.dep_, descendant.n_lefts,
-              descendant.n_rights,
-              [ancestor.text for ancestor in descendant.ancestors])
-
-+table(["Text", "Dep", "n_lefts", "n_rights", "ancestors"])
-    - var style = [0, 1, 1, 1, 0]
-    +annotation-row(["Credit", "nmod", 0, 2, "holders, submit"], style)
-    +annotation-row(["and", "cc", 0, 0, "Credit, holders, submit"], style)
-    +annotation-row(["mortgage", "compound", 0, 0, "account, Credit, holders, submit"], style)
-    +annotation-row(["account", "conj", 1, 0, "Credit, holders, submit"], style)
-    +annotation-row(["holders", "nsubj", 1, 0, "submit"], style)
-
-p
-    |  Finally, the #[code .left_edge] and #[code .right_edge] attributes
-    |  can be especially useful, because they give you the first and last token
-    |  of the subtree. This is the easiest way to create a #[code Span] object
-    |  for a syntactic phrase. Note that #[code .right_edge] gives a token
-    |  #[strong within] the subtree — so if you use it as the end-point of a
-    |  range, don't forget to #[code +1]!
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u"Credit and mortgage account holders must submit their requests")
-    span = doc[doc[4].left_edge.i : doc[4].right_edge.i+1]
-    span.merge()
-    for token in doc:
-        print(token.text, token.pos_, token.dep_, token.head.text)
-
-+table(["Text", "POS", "Dep", "Head text"])
-    - var style = [0, 1, 1, 0]
-    +annotation-row(["Credit and mortgage account holders", "NOUN", "nsubj", "submit"], style)
-    +annotation-row(["must", "VERB", "aux", "submit"], style)
-    +annotation-row(["submit", "VERB", "ROOT", "submit"], style)
-    +annotation-row(["their", "ADJ", "poss", "requests"], style)
-    +annotation-row(["requests", "NOUN", "dobj", "submit"], style)
-
-+infobox("Dependency label scheme", "📖")
-    |  For a list of the syntactic dependency labels assigned by spaCy's models
-    |  across different languages, see the
-    |  #[+a("/api/annotation#pos-tagging") dependency label scheme documentation].
-
-
-+h(3, "displacy") Visualizing dependencies
-
-p
-    |  The best way to understand spaCy's dependency parser is interactively.
-    |  To make this easier, spaCy v2.0+ comes with a visualization module. You
-    |  can pass a #[code Doc] or a list of #[code Doc] objects to
-    |  displaCy and run #[+api("top-level#displacy.serve") #[code displacy.serve]] to
-    |  run the web server, or #[+api("top-level#displacy.render") #[code displacy.render]]
-    |  to generate the raw markup. If you want to know how to write rules that
-    |  hook into some type of syntactic construction, just plug the sentence into
-    |  the visualizer and see how spaCy annotates it.
-
-+code-exec.
-    import spacy
-    from spacy import displacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u"Autonomous cars shift insurance liability toward manufacturers")
-    displacy.render(doc, style='dep', jupyter=True)
-
-+infobox
-    |  For more details and examples, see the
-    |  #[+a("/usage/visualizers") usage guide on visualizing spaCy]. You
-    |  can also test displaCy in our #[+a(DEMOS_URL + "/displacy", true) online demo].
-
-+h(3, "disabling") Disabling the parser
-
-p
-    |  In the #[+a("/models") default models], the parser is loaded and enabled
-    |  as part of the
-    |  #[+a("/usage/processing-pipelines") standard processing pipeline].
-    |  If you don't need any of the syntactic information, you should disable
-    |  the parser. Disabling the parser will make spaCy load and run much faster.
-    |  If you want to load the parser, but need to disable it for specific
-    |  documents, you can also control its use on the #[code nlp] object.
-
-+code.
-    nlp = spacy.load('en', disable=['parser'])
-    nlp = English().from_disk('/model', disable=['parser'])
-    doc = nlp(u"I don't want parsed", disable=['parser'])
-
-+infobox("Important note: disabling pipeline components")
-    .o-block
-        |  Since spaCy v2.0 comes with better support for customising the
-        |  processing pipeline components, the #[code parser] keyword argument
-        |  has been replaced with #[code disable], which takes a list of
-        |  #[+a("/usage/processing-pipelines") pipeline component names].
-        |  This lets you disable both default and custom components when loading
-        |  a model, or initialising a Language class via
-        |  #[+api("language#from_disk") #[code from_disk]].
-    +code-new.
-        nlp = spacy.load('en', disable=['parser'])
-        doc = nlp(u"I don't want parsed", disable=['parser'])
-    +code-old.
-        nlp = spacy.load('en', parser=False)
-        doc = nlp(u"I don't want parsed", parse=False)
diff --git a/website/usage/_linguistic-features/_named-entities.jade b/website/usage/_linguistic-features/_named-entities.jade
deleted file mode 100644
index 17a270866..000000000
--- a/website/usage/_linguistic-features/_named-entities.jade
+++ /dev/null
@@ -1,220 +0,0 @@
-//- 💫 DOCS > USAGE > LINGUISTIC FEATURES > NAMED ENTITY RECOGNITION
-
-p
-    |  spaCy features an extremely fast statistical entity recognition system,
-    |  that assigns labels to contiguous spans of tokens. The default model
-    |  identifies a variety of named and numeric entities, including companies,
-    |  locations, organizations and products. You can add arbitrary classes to
-    |  the entity recognition system, and update the model with new examples.
-
-+h(3, "101") Named Entity Recognition 101
-
-include ../_spacy-101/_named-entities
-
-+h(3, "accessing") Accessing entity annotations
-
-p
-    |  The standard way to access entity annotations is the
-    |  #[+api("doc#ents") #[code doc.ents]] property, which produces a sequence
-    |  of #[+api("span") #[code Span]] objects. The entity type is accessible
-    |  either as a hash value or as a string, using the attributes
-    |  #[code ent.label] and #[code ent.label_]. The #[code Span] object acts
-    |  as a sequence of tokens, so you can iterate over the entity or index into
-    |  it. You can also get the text form of the whole entity, as though it were
-    |  a single token.
-
-p
-    |  You can also access token entity annotations using the
-    |  #[+api("token#attributes") #[code token.ent_iob]] and
-    |  #[+api("token#attributes") #[code token.ent_type]] attributes.
-    |  #[code token.ent_iob] indicates whether an entity starts, continues or
-    |  ends on the tag. If no entity type is set on a token, it will return an
-    |  empty string.
-
-+aside("IOB Scheme")
-    |  #[code I] – Token is inside an entity.#[br]
-    |  #[code O] – Token is outside an entity.#[br]
-    |  #[code B] – Token is the beginning of an entity.#[br]
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u'San Francisco considers banning sidewalk delivery robots')
-
-    # document level
-    ents = [(e.text, e.start_char, e.end_char, e.label_) for e in doc.ents]
-    print(ents)
-
-    # token level
-    ent_san = [doc[0].text, doc[0].ent_iob_, doc[0].ent_type_]
-    ent_francisco = [doc[1].text, doc[1].ent_iob_, doc[1].ent_type_]
-    print(ent_san)  # [u'San', u'B', u'GPE']
-    print(ent_francisco)  # [u'Francisco', u'I', u'GPE']
-
-+table(["Text", "ent_iob", "ent_iob_", "ent_type_", "Description"])
-    - var style = [0, 1, 1, 1, 0]
-    +annotation-row(["San", 3, "B", "GPE", "beginning of an entity"], style)
-    +annotation-row(["Francisco", 1, "I", "GPE", "inside an entity"], style)
-    +annotation-row(["considers", 2, "O", '""', "outside an entity"], style)
-    +annotation-row(["banning", 2, "O", '""', "outside an entity"], style)
-    +annotation-row(["sidewalk", 2, "O", '""', "outside an entity"], style)
-    +annotation-row(["delivery", 2, "O", '""', "outside an entity"], style)
-    +annotation-row(["robots", 2, "O", '""', "outside an entity"], style)
-
-+h(3, "setting-entities") Setting entity annotations
-
-p
-    |  To ensure that the sequence of token annotations remains consistent, you
-    |  have to set entity annotations #[strong at the document level]. However,
-    |  you can't write directly to the #[code token.ent_iob] or
-    |  #[code token.ent_type] attributes, so the easiest way to set entities is
-    |  to assign to the #[+api("doc#ents") #[code doc.ents]] attribute
-    |  and create the new entity as a #[+api("span") #[code Span]].
-
-+code-exec.
-    import spacy
-    from spacy.tokens import Span
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u"FB is hiring a new Vice President of global policy")
-    ents = [(e.text, e.start_char, e.end_char, e.label_) for e in doc.ents]
-    print('Before', ents)
-    # the model didn't recognise "FB" as an entity :(
-
-    ORG = doc.vocab.strings[u'ORG']  # get hash value of entity label
-    fb_ent = Span(doc, 0, 1, label=ORG) # create a Span for the new entity
-    doc.ents = list(doc.ents) + [fb_ent]
-
-    ents = [(e.text, e.start_char, e.end_char, e.label_) for e in doc.ents]
-    print('After', ents)
-    # [(u'FB', 0, 2, 'ORG')] 🎉
-
-p
-    |  Keep in mind that you need to create a #[code Span] with the start and
-    |  end index of the #[strong token], not the start and end index of the
-    |  entity in the document. In this case, "FB" is token #[code (0, 1)] –
-    |  but at the document level, the entity will have the start and end
-    |  indices #[code (0, 2)].
-
-+h(4, "setting-from-array") Setting entity annotations from array
-
-p
-    |  You can also assign entity annotations using the
-    |  #[+api("doc#from_array") #[code doc.from_array()]] method. To do this,
-    |  you should include both the #[code ENT_TYPE] and the #[code ENT_IOB]
-    |  attributes in the array you're importing from.
-
-+code-exec.
-    import numpy
-    import spacy
-    from spacy.attrs import ENT_IOB, ENT_TYPE
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp.make_doc(u'London is a big city in the United Kingdom.')
-    print('Before', list(doc.ents))  # []
-
-    header = [ENT_IOB, ENT_TYPE]
-    attr_array = numpy.zeros((len(doc), len(header)))
-    attr_array[0, 0] = 3  # B
-    attr_array[0, 1] = doc.vocab.strings[u'GPE']
-    doc.from_array(header, attr_array)
-    print('After', list(doc.ents))  # [London]
-
-+h(4, "setting-cython") Setting entity annotations in Cython
-
-p
-    |  Finally, you can always write to the underlying struct, if you compile
-    |  a #[+a("http://cython.org/") Cython] function. This is easy to do, and
-    |  allows you to write efficient native code.
-
-+code.
-    # cython: infer_types=True
-    from spacy.tokens.doc cimport Doc
-
-    cpdef set_entity(Doc doc, int start, int end, int ent_type):
-        for i in range(start, end):
-            doc.c[i].ent_type = ent_type
-        doc.c[start].ent_iob = 3
-        for i in range(start+1, end):
-            doc.c[i].ent_iob = 2
-
-p
-    |  Obviously, if you write directly to the array of #[code TokenC*] structs,
-    |  you'll have responsibility for ensuring that the data is left in a
-    |  consistent state.
-
-+h(3, "entity-types") Built-in entity types
-
-+aside("Tip: Understanding entity types")
-    |  You can also use #[code spacy.explain()] to get the description for the
-    |  string representation of an entity label. For example,
-    |  #[code spacy.explain("LANGUAGE")] will return "any named language".
-
-include ../../api/_annotation/_named-entities
-
-+h(3, "updating") Training and updating
-
-p
-    |  To provide training examples to the entity recogniser, you'll first need
-    |  to create an instance of the #[+api("goldparse") #[code GoldParse]] class.
-    |  You can specify your annotations in a stand-off format or as token tags.
-    |  If a character offset in your entity annotations doesn't fall on a token
-    |  boundary, the #[code GoldParse] class will treat that annotation as a
-    |  missing value.  This allows for more realistic training, because the
-    |  entity recogniser is allowed to learn from examples that may feature
-    |  tokenizer errors.
-
-+code.
-    train_data = [('Who is Chaka Khan?', [(7, 17, 'PERSON')]),
-                  ('I like London and Berlin.', [(7, 13, 'LOC'), (18, 24, 'LOC')])]
-
-+code.
-    doc = Doc(nlp.vocab, [u'rats', u'make', u'good', u'pets'])
-    gold = GoldParse(doc, entities=[u'U-ANIMAL', u'O', u'O', u'O'])
-
-+infobox
-    |  For more details on #[strong training and updating] the named entity
-    |  recognizer, see the usage guides on #[+a("/usage/training") training]
-    |  or check out the runnable
-    |  #[+src(gh("spaCy", "examples/training/train_ner.py")) training script]
-    |  on GitHub.
-
-+h(4, "updating-biluo") The BILUO Scheme
-
-p
-    |  You can also provide token-level entity annotation, using the
-    |  following tagging scheme to describe the entity boundaries:
-
-include ../../api/_annotation/_biluo
-
-+h(3, "displacy") Visualizing named entities
-
-p
-    |  The #[+a(DEMOS_URL + "/displacy-ent/") displaCy #[sup ENT] visualizer]
-    |  lets you explore an entity recognition model's behaviour interactively.
-    |  If you're training a model, it's very useful to run the visualization
-    |  yourself. To help you do that, spaCy v2.0+ comes with a visualization
-    |  module. You can pass a #[code Doc] or a list of #[code Doc] objects to
-    |  displaCy and run #[+api("top-level#displacy.serve") #[code displacy.serve]] to
-    |  run the web server, or #[+api("top-level#displacy.render") #[code displacy.render]]
-    |  to generate the raw markup.
-
-p
-    |  For more details and examples, see the
-    |  #[+a("/usage/visualizers") usage guide on visualizing spaCy].
-
-+code("Named Entity example").
-    import spacy
-    from spacy import displacy
-
-    text = """But Google is starting from behind. The company made a late push
-    into hardware, and Apple’s Siri, available on iPhones, and Amazon’s Alexa
-    software, which runs on its Echo and Dot devices, have clear leads in
-    consumer adoption."""
-
-    nlp = spacy.load('custom_ner_model')
-    doc = nlp(text)
-    displacy.serve(doc, style='ent')
-
-+codepen("a73f8b68f9af3157855962b283b364e4", 345)
diff --git a/website/usage/_linguistic-features/_pos-tagging.jade b/website/usage/_linguistic-features/_pos-tagging.jade
deleted file mode 100644
index b49a399fd..000000000
--- a/website/usage/_linguistic-features/_pos-tagging.jade
+++ /dev/null
@@ -1,74 +0,0 @@
-//- 💫 DOCS > USAGE > LINGUISTIC FEATURES > PART-OF-SPEECH TAGGING
-
-include ../_spacy-101/_pos-deps
-
-+h(3, "rule-based-morphology") Rule-based morphology
-
-p
-    |  Inflectional morphology is the process by which a root form of a word is
-    |  modified by adding prefixes or suffixes that specify its grammatical
-    |  function but do not changes its part-of-speech. We say that a
-    |  #[strong lemma] (root form) is #[strong inflected] (modified/combined)
-    |  with one or more #[strong morphological features] to create a surface
-    |  form. Here are some examples:
-
-+table(["Context", "Surface", "Lemma", "POS", "Morphological Features"])
-    +row
-        +cell I was reading the paper
-        +cell reading
-        +cell read
-        +cell verb
-        +cell #[code VerbForm=Ger]
-
-    +row
-        +cell I don't watch the news, I read the paper.
-        +cell read
-        +cell read
-        +cell verb
-        +cell #[code VerbForm=Fin], #[code Mood=Ind], #[code Tense=Pres]
-
-    +row
-        +cell I read the paper yesterday
-        +cell read
-        +cell read
-        +cell verb
-        +cell #[code VerbForm=Fin], #[code Mood=Ind], #[code Tense=Past]
-
-p
-    |  English has a relatively simple morphological system, which spaCy
-    |  handles using rules that can be keyed by the token, the part-of-speech
-    |  tag, or the combination of the two. The system works as follows:
-
-+list("numbers")
-    +item
-        |  The tokenizer consults a
-        |  #[+a("/usage/adding-languages#tokenizer-exceptions") mapping table]
-        |  #[code TOKENIZER_EXCEPTIONS], which allows sequences of characters
-        |  to be mapped to multiple tokens. Each token may be assigned a part
-        |  of speech and one or more morphological features.
-
-    +item
-        |  The part-of-speech tagger then assigns each token an
-        |  #[strong extended POS tag]. In the API, these tags are known as
-        |  #[code Token.tag]. They express the part-of-speech (e.g.
-        |  #[code VERB]) and some amount of morphological information, e.g.
-        |  that the verb is past tense.
-
-    +item
-        |  For words whose POS is not set by a prior process, a
-        |  #[+a("/usage/adding-languages#tag-map") mapping table]
-        |  #[code TAG_MAP] maps the tags to a part-of-speech and a set of
-        |  morphological features.
-
-    +item
-        |  Finally, a #[strong rule-based deterministic lemmatizer] maps the
-        |  surface form, to a lemma in light of the previously assigned
-        |  extended part-of-speech and morphological information, without
-        |  consulting the context of the token. The lemmatizer also accepts
-        |  list-based exception files, acquired from
-        |  #[+a("https://wordnet.princeton.edu/") WordNet].
-
-+infobox("Part-of-speech tag scheme", "📖")
-    |  For a list of the fine-grained and coarse-grained part-of-speech tags
-    |  assigned by spaCy's models across different languages, see the
-    |  #[+a("/api/annotation#pos-tagging") POS tag scheme documentation].
diff --git a/website/usage/_linguistic-features/_rule-based-matching.jade b/website/usage/_linguistic-features/_rule-based-matching.jade
deleted file mode 100644
index e96199eec..000000000
--- a/website/usage/_linguistic-features/_rule-based-matching.jade
+++ /dev/null
@@ -1,706 +0,0 @@
-//- 💫 DOCS > USAGE > RULE-BASED MATCHING
-
-p
-    |  spaCy features a rule-matching engine, the #[+api("matcher") #[code Matcher]],
-    |  that operates over tokens, similar
-    |  to regular expressions. The rules can refer to token annotations (e.g.
-    |  the token #[code text] or #[code tag_], and flags (e.g. #[code IS_PUNCT]).
-    |  The rule matcher also lets you pass in a custom callback
-    |  to act on matches – for example, to merge entities and apply custom labels.
-    |  You can also associate patterns with entity IDs, to allow some basic
-    |  entity linking or disambiguation. To match large terminology lists,
-    |  you can use the #[+api("phrasematcher") #[code PhraseMatcher]], which
-    |  accepts #[code Doc] objects as match patterns.
-
-+h(3, "adding-patterns") Adding patterns
-
-p
-    |  Let's say we want to enable spaCy to find a combination of three tokens:
-
-+list("numbers")
-    +item
-        |  A token whose #[strong lowercase form matches "hello"], e.g. "Hello"
-        |  or "HELLO".
-    +item
-        |  A token whose #[strong #[code is_punct] flag is set to #[code True]],
-        |  i.e. any punctuation.
-    +item
-        |  A token whose #[strong lowercase form matches "world"], e.g. "World"
-        |  or "WORLD".
-
-+code.
-    [{'LOWER': 'hello'}, {'IS_PUNCT': True}, {'LOWER': 'world'}]
-
-p
-    |  First, we initialise the #[code Matcher] with a vocab. The matcher must
-    |  always share the same vocab with the documents it will operate on. We
-    |  can now call #[+api("matcher#add") #[code matcher.add()]] with an ID and
-    |  our custom pattern. The second argument lets you pass in an optional
-    |  callback function to invoke on a successful match. For now, we set it
-    |  to #[code None].
-
-+code-exec.
-    import spacy
-    from spacy.matcher import Matcher
-
-    nlp = spacy.load('en_core_web_sm')
-    matcher = Matcher(nlp.vocab)
-    # add match ID "HelloWorld" with no callback and one pattern
-    pattern = [{'LOWER': 'hello'}, {'IS_PUNCT': True}, {'LOWER': 'world'}]
-    matcher.add('HelloWorld', None, pattern)
-
-    doc = nlp(u'Hello, world! Hello world!')
-    matches = matcher(doc)
-    for match_id, start, end in matches:
-        string_id = nlp.vocab.strings[match_id]  # get string representation
-        span = doc[start:end]  # the matched span
-        print(match_id, string_id, start, end, span.text)
-
-p
-    |  The matcher returns a list of #[code (match_id, start, end)] tuples – in
-    |  this case, #[code [('15578876784678163569', 0, 2)]], which maps to the
-    |  span #[code doc[0:2]] of our original document. The #[code match_id]
-    |  is the #[+a("/usage/spacy-101#vocab") hash value] of the string ID
-    |  "HelloWorld". To get the string value, you can look up the ID
-    |  in the #[+api("stringstore") #[code StringStore]].
-
-+code.
-    for match_id, start, end in matches:
-        string_id = nlp.vocab.strings[match_id]  # 'HelloWorld'
-        span = doc[start:end]                    # the matched span
-
-p
-    |  Optionally, we could also choose to add more than one pattern, for
-    |  example to also match sequences without punctuation between "hello" and
-    |  "world":
-
-+code.
-    matcher.add('HelloWorld', None,
-                [{'LOWER': 'hello'}, {'IS_PUNCT': True}, {'LOWER': 'world'}],
-                [{'LOWER': 'hello'}, {'LOWER': 'world'}])
-
-p
-    |  By default, the matcher will only return the matches and
-    |  #[strong not do anything else], like merge entities or assign labels.
-    |  This is all up to you and can be defined individually for each pattern,
-    |  by passing in a callback function as the #[code on_match] argument on
-    |  #[code add()]. This is useful, because it lets you write entirely custom
-    |  and #[strong pattern-specific logic]. For example, you might want to
-    |  merge #[em some] patterns into one token, while adding entity labels for
-    |  other pattern types. You shouldn't have to create different matchers for
-    |  each of those processes.
-
-+h(4, "adding-patterns-attributes") Available token attributes
-
-p
-    |  The available token pattern keys are uppercase versions of the
-    |  #[+api("token#attributes") #[code Token] attributes]. The most relevant
-    |  ones for rule-based matching are:
-
-+table(["Attribute", "Description"])
-    +row
-        +cell #[code ORTH]
-        +cell The exact verbatim text of a token.
-
-    +row
-        +cell.u-nowrap #[code LOWER]
-        +cell The lowercase form of the token text.
-
-    +row
-        +cell #[code LENGTH]
-        +cell The length of the token text.
-
-    +row
-        +cell.u-nowrap #[code IS_ALPHA], #[code IS_ASCII], #[code IS_DIGIT]
-        +cell
-            |  Token text consists of alphanumeric characters, ASCII characters,
-            |  digits.
-
-    +row
-        +cell.u-nowrap #[code IS_LOWER], #[code IS_UPPER], #[code IS_TITLE]
-        +cell Token text is in lowercase, uppercase, titlecase.
-
-    +row
-        +cell.u-nowrap #[code IS_PUNCT], #[code IS_SPACE], #[code IS_STOP]
-        +cell Token is punctuation, whitespace, stop word.
-
-    +row
-        +cell.u-nowrap #[code LIKE_NUM], #[code LIKE_URL], #[code LIKE_EMAIL]
-        +cell Token text resembles a number, URL, email.
-
-    +row
-        +cell.u-nowrap
-            |  #[code POS], #[code TAG], #[code DEP], #[code LEMMA],
-            |  #[code SHAPE]
-        +cell
-            |  The token's simple and extended part-of-speech tag, dependency
-            |  label, lemma, shape.
-
-    +row
-        +cell.u-nowrap #[code ENT_TYPE]
-        +cell The token's entity label.
-
-+h(4, "adding-patterns-wildcard") Using wildcard token patterns
-    +tag-new(2)
-
-p
-    |  While the token attributes offer many options to write highly specific
-    |  patterns, you can also use an empty dictionary, #[code {}] as a wildcard
-    |  representing #[strong any token]. This is useful if you know the context
-    |  of what you're trying to match, but very little about the specific token
-    |  and its characters. For example, let's say you're trying to extract
-    |  people's user names from your data. All you know is that they are listed
-    |  as "User name: {username}". The name itself may contain any character,
-    |  but no whitespace – so you'll know it will be handled as one token.
-
-+code.
-    [{'ORTH': 'User'}, {'ORTH': 'name'}, {'ORTH': ':'}, {}]
-
-+h(4, "quantifiers") Using operators and quantifiers
-
-p
-    |  The matcher also lets you use quantifiers, specified as the #[code 'OP']
-    |  key. Quantifiers let you define sequences of tokens to be mached, e.g.
-    |  one or more punctuation marks, or specify optional tokens. Note that there
-    |  are no nested or scoped quantifiers – instead, you can build those
-    |  behaviours with #[code on_match] callbacks.
-
-+table([ "OP", "Description"])
-    +row
-        +cell #[code !]
-        +cell Negate the pattern, by requiring it to match exactly 0 times.
-
-    +row
-        +cell #[code ?]
-        +cell Make the pattern optional, by allowing it to match 0 or 1 times.
-
-    +row
-        +cell #[code +]
-        +cell Require the pattern to match 1 or more times.
-
-    +row
-        +cell #[code *]
-        +cell Allow the pattern to match zero or more times.
-
-p
-    |  In versions before v2.1.0, the semantics of the #[code +] and #[code *] operators
-    |  behave inconsistently. They were usually interpreted
-    |  "greedily", i.e. longer matches are returned where possible. However, if
-    |  you specify two #[code +] and #[code *] patterns in a row and their
-    |  matches overlap, the first operator will behave non-greedily. This quirk
-    |  in the semantics is corrected in spaCy v2.1.0.
-
-+h(3, "adding-phrase-patterns") Adding phrase patterns
-
-p
-    |  If you need to match large terminology lists, you can also use the
-    |  #[+api("phrasematcher") #[code PhraseMatcher]] and create
-    |  #[+api("doc") #[code Doc]] objects instead of token patterns, which is
-    |  much more efficient overall. The #[code Doc] patterns can contain single
-    |  or multiple tokens.
-
-+code-exec.
-    import spacy
-    from spacy.matcher import PhraseMatcher
-
-    nlp = spacy.load('en_core_web_sm')
-    matcher = PhraseMatcher(nlp.vocab)
-    terminology_list = ['Barack Obama', 'Angela Merkel', 'Washington, D.C.']
-    # Only run nlp.make_doc to speed things up
-    patterns = [nlp.make_doc(text) for text in terminology_list]
-    matcher.add('TerminologyList', None, *patterns)
-
-    doc = nlp(u"German Chancellor Angela Merkel and US President Barack Obama "
-              u"converse in the Oval Office inside the White House in Washington, D.C.")
-    matches = matcher(doc)
-    for match_id, start, end in matches:
-        span = doc[start:end]
-        print(span.text)
-
-p
-    |  Since spaCy is used for processing both the patterns and the text to be
-    |  matched, you won't have to worry about specific tokenization – for
-    |  example, you can simply pass in #[code nlp(u"Washington, D.C.")] and
-    |  won't have to write a complex token pattern covering the exact
-    |  tokenization of the term.
-
-+h(3, "on_match") Adding #[code on_match] rules
-
-p
-    |  To move on to a more realistic example, let's say you're working with a
-    |  large corpus of blog articles, and you want to match all mentions of
-    |  "Google I/O" (which spaCy tokenizes as #[code ['Google', 'I', '/', 'O']]).
-    |  To be safe, you only match on the uppercase versions, in case someone has
-    |  written it as "Google i/o". You also add a second pattern with an added
-    |  #[code {IS_DIGIT: True}] token – this will make sure you also match on
-    |  "Google I/O 2017". If your pattern matches, spaCy should execute your
-    |  custom callback function #[code add_event_ent].
-
-+code-exec.
-    import spacy
-    from spacy.matcher import Matcher
-
-    nlp = spacy.load('en_core_web_sm')
-    matcher = Matcher(nlp.vocab)
-
-    # Get the ID of the 'EVENT' entity type. This is required to set an entity.
-    EVENT = nlp.vocab.strings['EVENT']
-
-    def add_event_ent(matcher, doc, i, matches):
-        # Get the current match and create tuple of entity label, start and end.
-        # Append entity to the doc's entity. (Don't overwrite doc.ents!)
-        match_id, start, end = matches[i]
-        entity = (EVENT, start, end)
-        doc.ents += (entity,)
-        print(doc[start:end].text, entity)
-
-    matcher.add('GoogleIO', add_event_ent,
-                [{'ORTH': 'Google'}, {'ORTH': 'I'}, {'ORTH': '/'}, {'ORTH': 'O'}],
-                [{'ORTH': 'Google'}, {'ORTH': 'I'}, {'ORTH': '/'}, {'ORTH': 'O'}, {'IS_DIGIT': True}])
-
-    doc = nlp(u"This is a text about Google I/O 2015.")
-    matches = matcher(doc)
-
-+aside("Tip: Visualizing matches")
-    |  When working with entities, you can use #[+api("top-level#displacy") displaCy]
-    |  to quickly generate a NER visualization from your updated #[code Doc],
-    |  which can be exported as an HTML file:
-
-    +code.o-no-block.
-        from spacy import displacy
-        html = displacy.render(doc, style='ent', page=True,
-                               options={'ents': ['EVENT']})
-
-    |  For more info and examples, see the usage guide on
-    |  #[+a("/usage/visualizers") visualizing spaCy].
-
-p
-    |  We can now call the matcher on our documents. The patterns will be
-    |  matched in the order they occur in the text. The matcher will then
-    |  iterate over the matches, look up the callback for the match ID
-    |  that was matched, and invoke it.
-
-+code.
-    doc = nlp(YOUR_TEXT_HERE)
-    matcher(doc)
-
-p
-    |  When the callback is invoked, it is
-    |  passed four arguments: the matcher itself, the document, the position of
-    |  the current match, and the total list of matches. This allows you to
-    |  write callbacks that consider the entire set of matched phrases, so that
-    |  you can resolve overlaps and other conflicts in whatever way you prefer.
-
-+table(["Argument", "Type", "Description"])
-    +row
-        +cell #[code matcher]
-        +cell #[code Matcher]
-        +cell The matcher instance.
-
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The document the matcher was used on.
-
-    +row
-        +cell #[code i]
-        +cell int
-        +cell Index of the current match (#[code matches[i]]).
-
-    +row
-        +cell #[code matches]
-        +cell list
-        +cell
-            |  A list of #[code (match_id, start, end)] tuples, describing the
-            |  matches. A match tuple describes a span #[code doc[start:end]].
-
-+h(3, "matcher-pipeline") Using custom pipeline components
-
-p
-    |  Let's say your data also contains some annoying pre-processing artefacts,
-    |  like leftover HTML line breaks (e.g. #[code &lt;br&gt;] or
-    |  #[code &lt;BR/&gt;]). To make your text easier to analyse, you want to
-    |  merge those into one token and flag them, to make sure you
-    |  can ignore them later. Ideally, this should all be done automatically
-    |  as you process the text. You can achieve this by adding a
-    |  #[+a("/usage/processing-pipelines#custom-components") custom pipeline component]
-    |  that's called on each #[code Doc] object, merges the leftover HTML spans
-    |  and sets an attribute #[code bad_html] on the token.
-
-+code-exec.
-    import spacy
-    from spacy.matcher import Matcher
-    from spacy.tokens import Token
-
-    # we're using a class because the component needs to be initialised with
-    # the shared vocab via the nlp object
-    class BadHTMLMerger(object):
-        def __init__(self, nlp):
-            # register a new token extension to flag bad HTML
-            Token.set_extension('bad_html', default=False)
-            self.matcher = Matcher(nlp.vocab)
-            self.matcher.add('BAD_HTML', None,
-                [{'ORTH': '&lt;'}, {'LOWER': 'br'}, {'ORTH': '&gt;'}],
-                [{'ORTH': '&lt;'}, {'LOWER': 'br/'}, {'ORTH': '&gt;'}])
-
-        def __call__(self, doc):
-            # this method is invoked when the component is called on a Doc
-            matches = self.matcher(doc)
-            spans = []  # collect the matched spans here
-            for match_id, start, end in matches:
-                spans.append(doc[start:end])
-            for span in spans:
-                span.merge()   # merge
-                for token in span:
-                    token._.bad_html = True  # mark token as bad HTML
-            return doc
-
-    nlp = spacy.load('en_core_web_sm')
-    html_merger = BadHTMLMerger(nlp)
-    nlp.add_pipe(html_merger, last=True)  # add component to the pipeline
-    doc = nlp(u"Hello&lt;br&gt;world! &lt;br/&gt; This is a test.")
-    for token in doc:
-        print(token.text, token._.bad_html)
-
-p
-    |  Instead of hard-coding the patterns into the component, you could also
-    |  make it take a path to a JSON file containing the patterns. This lets
-    |  you reuse the component with different patterns, depending on your
-    |  application:
-
-+code.
-    html_merger = BadHTMLMerger(nlp, path='/path/to/patterns.json')
-
-+infobox
-    |  For more details and examples of how to
-    |  #[strong create custom pipeline components] and
-    |  #[strong extension attributes], see the
-    |  #[+a("/usage/processing-pipelines") usage guide].
-
-+h(3, "regex") Using regular expressions
-
-p
-    |  In some cases, only matching tokens and token attributes isn't enough –
-    |  for example, you might want to match different spellings of a word,
-    |  without having to add a new pattern for each spelling. A simple solution
-    |  is to match a regular expression on the #[code Doc]'s #[code text] and
-    |  use the #[+api("doc#char_span") #[code Doc.char_span]] method to
-    |  create a #[code Span] from the character indices of the match:
-
-+code-exec.
-    import spacy
-    import re
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u'The spelling is "definitely", not "definately" or "deffinitely".')
-
-    DEFINITELY_PATTERN = re.compile(r'deff?in[ia]tely')
-
-    for match in re.finditer(DEFINITELY_PATTERN, doc.text):
-        start, end = match.span()         # get matched indices
-        span = doc.char_span(start, end)  # create Span from indices
-        print(span.text)
-
-p
-    |  You can also use the regular expression with spaCy's #[code Matcher] by
-    |  converting it to a token flag. To ensure efficiency, the
-    |  #[code Matcher] can only access the C-level data. This means that it can
-    |  either use built-in token attributes or #[strong binary flags].
-    |  #[+api("vocab#add_flag") #[code Vocab.add_flag]] returns a flag ID which
-    |  you can use as a key of a token match pattern. Tokens that match the
-    |  regular expression will return #[code True] for the #[code IS_DEFINITELY]
-    |  flag.
-
-+code-exec.
-    import spacy
-    from spacy.matcher import Matcher
-    import re
-
-    nlp = spacy.load('en_core_web_sm')
-    definitely_flag = lambda text: bool(re.compile(r'deff?in[ia]tely').match(text))
-    IS_DEFINITELY = nlp.vocab.add_flag(definitely_flag)
-
-    matcher = Matcher(nlp.vocab)
-    matcher.add('DEFINITELY', None, [{IS_DEFINITELY: True}])
-
-    doc = nlp(u'The spelling is "definitely", not "definately" or "deffinitely".')
-    matches = matcher(doc)
-    for match_id, start, end in matches:
-        span = doc[start:end]
-        print(span.text)
-
-p
-    |  Providing the regular expressions as binary flags also lets you use them
-    |  in combination with other token patterns – for example, to match the
-    |  word "definitely" in various spellings, followed by a case-insensitive
-    |  "not" and and adjective:
-
-+code.
-    [{IS_DEFINITELY: True}, {'LOWER': 'not'}, {'POS': 'ADJ'}]
-
-+h(3, "example1") Example: Using linguistic annotations
-
-p
-    |  Let's say you're analysing user comments and you want to find out what
-    |  people are saying about Facebook. You want to start off by finding
-    |  adjectives following "Facebook is" or "Facebook was". This is obviously
-    |  a very rudimentary solution, but it'll be fast, and a great way to get an
-    |  idea for what's in your data. Your pattern could look like this:
-
-+code.
-    [{'LOWER': 'facebook'}, {'LEMMA': 'be'}, {'POS': 'ADV', 'OP': '*'}, {'POS': 'ADJ'}]
-
-p
-    |  This translates to a token whose lowercase form matches "facebook"
-    |  (like Facebook, facebook or FACEBOOK), followed by a token with the lemma
-    |  "be" (for example, is, was, or 's), followed by an #[strong optional] adverb,
-    |  followed by an adjective. Using the linguistic annotations here is
-    |  especially useful, because you can tell spaCy to match "Facebook's
-    |  annoying", but #[strong not] "Facebook's annoying ads". The optional
-    |  adverb makes sure you won't miss adjectives with intensifiers, like
-    |  "pretty awful" or "very nice".
-
-p
-    |  To get a quick overview of the results, you could collect all sentences
-    |  containing a match and render them with the
-    |  #[+a("/usage/visualizers") displaCy visualizer].
-    |  In the callback function, you'll have access to the #[code start] and
-    |  #[code end] of each match, as well as the parent #[code Doc]. This lets
-    |  you determine the sentence containing the match,
-    |  #[code doc[start : end].sent], and calculate the start and end of the
-    |  matched span within the sentence. Using displaCy in
-    |  #[+a("/usage/visualizers#manual-usage") "manual" mode] lets you
-    |  pass in a list of dictionaries containing the text and entities to render.
-
-+code-exec.
-    import spacy
-    from spacy import displacy
-    from spacy.matcher import Matcher
-
-    nlp = spacy.load('en_core_web_sm')
-    matcher = Matcher(nlp.vocab)
-    matched_sents = [] # collect data of matched sentences to be visualized
-
-    def collect_sents(matcher, doc, i, matches):
-        match_id, start, end = matches[i]
-        span = doc[start : end]  # matched span
-        sent = span.sent  # sentence containing matched span
-        # append mock entity for match in displaCy style to matched_sents
-        # get the match span by ofsetting the start and end of the span with the
-        # start and end of the sentence in the doc
-        match_ents = [{'start': span.start_char - sent.start_char,
-                       'end': span.end_char - sent.start_char,
-                       'label': 'MATCH'}]
-        matched_sents.append({'text': sent.text, 'ents': match_ents })
-
-    pattern = [{'LOWER': 'facebook'}, {'LEMMA': 'be'}, {'POS': 'ADV', 'OP': '*'},
-               {'POS': 'ADJ'}]
-    matcher.add('FacebookIs', collect_sents, pattern)  # add pattern
-    doc = nlp(u"I'd say that Facebook is evil. – Facebook is pretty cool, right?")
-    matches = matcher(doc)
-
-    # serve visualization of sentences containing match with displaCy
-    # set manual=True to make displaCy render straight from a dictionary
-    # (if you're not running the code within a Jupyer environment, you can
-    # remove jupyter=True and use displacy.serve instead)
-    displacy.render(matched_sents, style='ent', manual=True, jupyter=True)
-
-+h(3, "example2") Example: Phone numbers
-
-p
-    |  Phone numbers can have many different formats and matching them is often
-    |  tricky. During tokenization, spaCy will leave sequences of numbers intact
-    |  and only split on whitespace and punctuation. This means that your match
-    |  pattern will have to look out for number sequences of a certain length,
-    |  surrounded by specific punctuation – depending on the
-    |  #[+a("https://en.wikipedia.org/wiki/National_conventions_for_writing_telephone_numbers") national conventions].
-
-p
-    |  The #[code IS_DIGIT] flag is not very helpful here, because it doesn't
-    |  tell us anything about the length. However, you can use the #[code SHAPE]
-    |  flag, with each #[code d] representing a digit:
-
-+code.
-    [{'ORTH': '('}, {'SHAPE': 'ddd'}, {'ORTH': ')'}, {'SHAPE': 'dddd'},
-     {'ORTH': '-', 'OP': '?'}, {'SHAPE': 'dddd'}]
-
-p
-    |  This will match phone numbers of the format #[strong (123) 4567 8901] or
-    |  #[strong (123) 4567-8901]. To also match formats like #[strong (123) 456 789],
-    |  you can add a second pattern using #[code 'ddd'] in place of #[code 'dddd'].
-    |  By hard-coding some values, you can match only certain, country-specific
-    |  numbers. For example, here's a pattern to match the most common formats of
-    |  #[+a("https://en.wikipedia.org/wiki/National_conventions_for_writing_telephone_numbers#Germany") international German numbers]:
-
-+code.
-    [{'ORTH': '+'}, {'ORTH': '49'}, {'ORTH': '(', 'OP': '?'}, {'SHAPE': 'dddd'},
-     {'ORTH': ')', 'OP': '?'}, {'SHAPE': 'dddddd'}]
-
-p
-    |  Depending on the formats your application needs to match, creating an
-    |  extensive set of rules like this is often better than training a model.
-    |  It'll produce more predictable results, is much easier to modify and
-    |  extend, and doesn't require any training data – only a set of
-    |  test cases.
-
-+code-exec.
-    import spacy
-    from spacy.matcher import Matcher
-
-    nlp = spacy.load('en_core_web_sm')
-    matcher = Matcher(nlp.vocab)
-    pattern = [{'ORTH': '('}, {'SHAPE': 'ddd'}, {'ORTH': ')'}, {'SHAPE': 'ddd'},
-               {'ORTH': '-', 'OP': '?'}, {'SHAPE': 'ddd'}]
-    matcher.add('PHONE_NUMBER', None, pattern)
-
-    doc = nlp(u"Call me at (123) 456 789 or (123) 456 789!")
-    print([t.text for t in doc])
-    matches = matcher(doc)
-    for match_id, start, end in matches:
-        span = doc[start:end]
-        print(span.text)
-
-+h(3, "example3") Example: Hashtags and emoji on social media
-
-p
-    |  Social media posts, especially tweets, can be difficult to work with.
-    |  They're very short and often contain various emoji and hashtags. By only
-    |  looking at the plain text, you'll lose a lot of valuable semantic
-    |  information.
-
-p
-    |  Let's say you've extracted a large sample of social media posts on a
-    |  specific topic, for example posts mentioning a brand name or product.
-    |  As the first step of your data exploration, you want to filter out posts
-    |  containing certain emoji and use them to assign a general sentiment
-    |  score, based on whether the expressed emotion is positive or negative,
-    |  e.g. #[span.o-icon.o-icon--inline 😀] or #[span.o-icon.o-icon--inline 😞].
-    |  You also want to find, merge and label hashtags like
-    |  #[code #MondayMotivation], to be able to ignore or analyse them later.
-
-+aside("Note on sentiment analysis")
-    |  Ultimately, sentiment analysis is not always #[em that] easy. In
-    |  addition to the emoji, you'll also want to take specific words into
-    |  account and check the #[code subtree] for intensifiers like "very", to
-    |  increase the sentiment score. At some point, you might also want to train
-    |  a sentiment model. However, the approach described in this example is
-    |  very useful for #[strong bootstrapping rules to collect training data].
-    |  It's also an incredibly fast way to gather first insights into your data
-    |  – with about 1 million tweets, you'd be looking at a processing time of
-    |  #[strong under 1 minute].
-
-p
-    |  By default, spaCy's tokenizer will split emoji into separate tokens. This
-    |  means that you can create a pattern for one or more emoji tokens.
-    |  Valid hashtags usually consist of a #[code #], plus a sequence of
-    |  ASCII characters with no whitespace, making them easy to match as well.
-
-+code-exec.
-    from spacy.lang.en import English
-    from spacy.matcher import Matcher
-
-    nlp = English()  # we only want the tokenizer, so no need to load a model
-    matcher = Matcher(nlp.vocab)
-
-    pos_emoji = [u'😀', u'😃', u'😂', u'🤣', u'😊', u'😍']  # positive emoji
-    neg_emoji = [u'😞', u'😠', u'😩', u'😢', u'😭', u'😒']  # negative emoji
-
-    # add patterns to match one or more emoji tokens
-    pos_patterns = [[{'ORTH': emoji}] for emoji in pos_emoji]
-    neg_patterns = [[{'ORTH': emoji}] for emoji in neg_emoji]
-
-    # function to label the sentiment
-    def label_sentiment(matcher, doc, i, matches):
-        match_id, start, end = matches[i]
-        if doc.vocab.strings[match_id] == 'HAPPY':  # don't forget to get string!
-            doc.sentiment += 0.1  # add 0.1 for positive sentiment
-        elif doc.vocab.strings[match_id] == 'SAD':
-            doc.sentiment -= 0.1  # subtract 0.1 for negative sentiment
-
-    matcher.add('HAPPY', label_sentiment, *pos_patterns)  # add positive pattern
-    matcher.add('SAD', label_sentiment, *neg_patterns)  # add negative pattern
-
-    # add pattern for valid hashtag, i.e. '#' plus any ASCII token
-    matcher.add('HASHTAG', None, [{'ORTH': '#'}, {'IS_ASCII': True}])
-
-    doc = nlp(u"Hello world 😀 #MondayMotivation")
-    matches = matcher(doc)
-    for match_id, start, end in matches:
-        string_id = doc.vocab.strings[match_id]  # look up string ID
-        span = doc[start:end]
-        print(string_id, span.text)
-
-p
-    |  Because the #[code on_match] callback receives the ID of each match, you
-    |  can use the same function to handle the sentiment assignment for both
-    |  the positive and negative pattern. To keep it simple, we'll either add
-    |  or subtract #[code 0.1] points – this way, the score will also reflect
-    |  combinations of emoji, even positive #[em and] negative ones.
-
-p
-    |  With a library like
-    |  #[+a("https://github.com/bcongdon/python-emojipedia") Emojipedia],
-    |  we can also retrieve a short description for each emoji – for example,
-    |  #[span.o-icon.o-icon--inline 😍]'s official title is "Smiling Face With
-    |  Heart-Eyes". Assigning it to a
-    |  #[+a("/usage/processing-pipelines#custom-components-attributes") custom attribute]
-    |  on the emoji span will make it available as #[code span._.emoji_desc].
-
-+code.
-    from emojipedia import Emojipedia  # installation: pip install emojipedia
-    from spacy.tokens import Span  # get the global Span object
-
-    Span.set_extension('emoji_desc', default=None)  # register the custom attribute
-
-    def label_sentiment(matcher, doc, i, matches):
-        match_id, start, end = matches[i]
-        if doc.vocab.strings[match_id] == 'HAPPY':  # don't forget to get string!
-            doc.sentiment += 0.1  # add 0.1 for positive sentiment
-        elif doc.vocab.strings[match_id] == 'SAD':
-            doc.sentiment -= 0.1  # subtract 0.1 for negative sentiment
-        span = doc[start : end]
-        emoji = Emojipedia.search(span[0].text) # get data for emoji
-        span._.emoji_desc = emoji.title  # assign emoji description
-
-p
-    |  To label the hashtags, we can use a
-    |  #[+a("/usage/processing-pipelines#custom-components-attributes") custom attribute]
-    |  set on the respective token:
-
-+code-exec.
-    import spacy
-    from spacy.matcher import Matcher
-    from spacy.tokens import Token
-
-    nlp = spacy.load('en_core_web_sm')
-    matcher = Matcher(nlp.vocab)
-
-    # add pattern for valid hashtag, i.e. '#' plus any ASCII token
-    matcher.add('HASHTAG', None, [{'ORTH': '#'}, {'IS_ASCII': True}])
-
-    # register token extension
-    Token.set_extension('is_hashtag', default=False)
-
-    doc = nlp(u"Hello world 😀 #MondayMotivation")
-    matches = matcher(doc)
-    hashtags = []
-    for match_id, start, end in matches:
-        if doc.vocab.strings[match_id] == 'HASHTAG':
-            hashtags.append(doc[start:end])
-    for span in hashtags:
-        span.merge()
-        for token in span:
-            token._.is_hashtag = True
-
-    for token in doc:
-        print(token.text, token._.is_hashtag)
-
-p
-    |  To process a stream of social media posts, we can use
-    |  #[+api("language#pipe") #[code Language.pipe()]], which will return a
-    |  stream of #[code Doc] objects that we can pass to
-    |  #[+api("matcher#pipe") #[code Matcher.pipe()]].
-
-+code.
-    docs = nlp.pipe(LOTS_OF_TWEETS)
-    matches = matcher.pipe(docs)
diff --git a/website/usage/_linguistic-features/_sentence-segmentation.jade b/website/usage/_linguistic-features/_sentence-segmentation.jade
deleted file mode 100644
index 98d2883ad..000000000
--- a/website/usage/_linguistic-features/_sentence-segmentation.jade
+++ /dev/null
@@ -1,129 +0,0 @@
-//- 💫 DOCS > USAGE > LINGUISTIC FEATURES > SENTENCE SEGMENTATION
-
-p
-    |  A #[+api("doc") #[code Doc]] object's sentences are available via the
-    |  #[code Doc.sents] property. Unlike other libraries, spaCy uses the
-    |  dependency parse to determine sentence boundaries. This is usually more
-    |  accurate than a rule-based approach, but it also means you'll need a
-    |  #[strong statistical model] and accurate predictions. If your
-    |  texts are closer to general-purpose news or web text, this should work
-    |  well out-of-the-box. For social media or conversational text that
-    |  doesn't follow the same rules, your application may benefit from a custom
-    |  rule-based implementation. You can either plug a rule-based component
-    |  into your #[+a("/usage/processing-pipelines") processing pipeline] or use
-    |  the #[code SentenceSegmenter] component with a custom stategy.
-
-+h(3, "sbd-parser") Default: Using the dependency parse
-    +tag-model("dependency parser")
-
-p
-    |  To view a #[code Doc]'s sentences, you can iterate over the
-    |  #[code Doc.sents], a generator that yields
-    |  #[+api("span") #[code Span]] objects.
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u"This is a sentence. This is another sentence.")
-    for sent in doc.sents:
-        print(sent.text)
-
-+h(3, "sbd-manual") Setting boundaries manually
-
-p
-    |  spaCy's dependency parser respects already set boundaries, so you can
-    |  preprocess your #[code Doc] using custom rules #[code before] it's
-    |  parsed. This can be done by adding a
-    |  #[+a("/usage/processing-pipelines") custom pipeline component]. Depending
-    |  on your text, this may also improve accuracy, since the parser is
-    |  constrained to predict parses consistent with the sentence boundaries.
-
-+infobox("Important note", "⚠️")
-    |  To prevent inconsistent state, you can only set boundaries #[em before] a
-    |  document is parsed (and #[code Doc.is_parsed] is #[code False]). To
-    |  ensure that your component is added in the right place, you can set
-    |  #[code before='parser'] or #[code first=True] when adding it to the
-    |  pipeline using #[+api("language#add_pipe") #[code nlp.add_pipe]].
-
-p
-    |  Here's an example of a component that implements a pre-processing rule
-    |  for splitting on #[code '...'] tokens. The component is added before
-    |  the parser, which is then used to further segment the text. This
-    |  approach can be useful if you want to implement #[em additional] rules
-    |  specific to your data, while still being able to take advantage of
-    |  dependency-based sentence segmentation.
-
-+code-exec.
-    import spacy
-
-    text = u"this is a sentence...hello...and another sentence."
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(text)
-    print('Before:', [sent.text for sent in doc.sents])
-
-    def set_custom_boundaries(doc):
-        for token in doc[:-1]:
-            if token.text == '...':
-                doc[token.i+1].is_sent_start = True
-        return doc
-
-    nlp.add_pipe(set_custom_boundaries, before='parser')
-    doc = nlp(text)
-    print('After:', [sent.text for sent in doc.sents])
-
-+h(3, "sbd-component") Rule-based pipeline component
-
-p
-    |  The #[code sentencizer] component is a
-    |  #[+a("/usage/processing-pipelines") pipeline component] that splits
-    |  sentences on punctuation like #[code &period;], #[code !] or #[code ?].
-    |  You can plug it into your pipeline if you only need sentence boundaries
-    |  without the dependency parse. Note that #[code Doc.sents] will
-    |  #[strong raise an error] if no sentence boundaries are set.
-
-+code-exec.
-    import spacy
-    from spacy.lang.en import English
-
-    nlp = English()  # just the language with no model
-    sentencizer = nlp.create_pipe('sentencizer')
-    nlp.add_pipe(sentencizer)
-    doc = nlp(u"This is a sentence. This is another sentence.")
-    for sent in doc.sents:
-        print(sent.text)
-
-+h(3, "sbd-custom") Custom rule-based strategy
-
-p
-    |  If you want to implement your own strategy that differs from the default
-    |  rule-based approach of splitting on sentences, you can also instantiate
-    |  the #[code SentenceSegmenter] directly and pass in your own strategy.
-    |  The strategy should be a function that takes a #[code Doc] object and
-    |  yields a #[code Span] for each sentence. Here's an example of a custom
-    |  segmentation strategy for splitting on newlines only:
-
-+code-exec.
-    from spacy.lang.en import English
-    from spacy.pipeline import SentenceSegmenter
-
-    def split_on_newlines(doc):
-        start = 0
-        seen_newline = False
-        for word in doc:
-            if seen_newline and not word.is_space:
-                yield doc[start:word.i]
-                start = word.i
-                seen_newline = False
-            elif word.text == '\n':
-                seen_newline = True
-        if start &lt; len(doc):
-            yield doc[start:len(doc)]
-
-    nlp = English()  # just the language with no model
-    sentencizer = SentenceSegmenter(nlp.vocab, strategy=split_on_newlines)
-    nlp.add_pipe(sentencizer)
-    doc = nlp(u"This is a sentence\n\nThis is another sentence\nAnd more")
-    for sent in doc.sents:
-        print([token.text for token in sent])
diff --git a/website/usage/_linguistic-features/_tokenization.jade b/website/usage/_linguistic-features/_tokenization.jade
deleted file mode 100644
index f0896332c..000000000
--- a/website/usage/_linguistic-features/_tokenization.jade
+++ /dev/null
@@ -1,367 +0,0 @@
-//- 💫 DOCS > USAGE > LINGUISTIC FEATURES > TOKENIZATION
-
-p
-    |  Tokenization is the task of splitting a text into meaningful segments,
-    |  called #[em tokens].  The input to the tokenizer is a unicode text, and
-    |  the output is a #[+api("doc") #[code Doc]] object. To construct a
-    |  #[code Doc] object, you need a #[+api("vocab") #[code Vocab]] instance,
-    |  a sequence of #[code word] strings, and optionally a sequence of
-    |  #[code spaces] booleans, which allow you to maintain alignment of the
-    |  tokens into the original string.
-
-include ../_spacy-101/_tokenization
-
-+h(4, "101-data") Tokenizer data
-
-p
-    |  #[strong Global] and #[strong language-specific] tokenizer data is
-    |  supplied via the language data in
-    |  #[+src(gh("spaCy", "spacy/lang")) #[code spacy/lang]].
-    |  The tokenizer exceptions define special cases like "don't" in English,
-    |  which needs to be split into two tokens: #[code {ORTH: "do"}] and
-    |  #[code {ORTH: "n't", LEMMA: "not"}]. The prefixes, suffixes and infixes
-    |  mosty define punctuation rules – for example, when to split off periods
-    |  (at the end of a sentence), and when to leave tokens containing periods
-    |  intact (abbreviations like "U.S.").
-
-+graphic("/assets/img/language_data.svg")
-    include ../../assets/img/language_data.svg
-
-+infobox
-    |  For more details on the language-specific data, see the
-    |  usage guide on #[+a("/usage/adding-languages") adding languages].
-
-+h(3, "special-cases") Adding special case tokenization rules
-
-p
-    |  Most domains have at least some idiosyncrasies that require custom
-    |  tokenization rules. This could be very certain expressions, or
-    |  abbreviations only used in this specific field.
-
-+aside("Language data vs. custom tokenization")
-    |  Tokenization rules that are specific to one language, but can be
-    |  #[strong generalised across that language] should ideally live in the
-    |  language data in #[+src(gh("spaCy", "spacy/lang")) #[code spacy/lang]] – we
-    |  always appreciate pull requests! Anything that's specific to a domain or
-    |  text type – like financial trading abbreviations, or Bavarian youth slang
-    |  – should be added as a special case rule to your tokenizer instance. If
-    |  you're dealing with a lot of customisations, it might make sense to create
-    |  an entirely custom subclass.
-
-p
-    |  Here's how to add a special case rule to an existing
-    |  #[+api("tokenizer") #[code Tokenizer]] instance:
-
-+code-exec.
-    import spacy
-    from spacy.symbols import ORTH, LEMMA, POS, TAG
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u'gimme that')  # phrase to tokenize
-    print([w.text for w in doc])  # ['gimme', 'that']
-
-    # add special case rule
-    special_case = [{ORTH: u'gim', LEMMA: u'give', POS: u'VERB'}, {ORTH: u'me'}]
-    nlp.tokenizer.add_special_case(u'gimme', special_case)
-
-    # check new tokenization
-    print([w.text for w in nlp(u'gimme that')])  # ['gim', 'me', 'that']
-
-    # Pronoun lemma is returned as -PRON-!
-    print([w.lemma_ for w in nlp(u'gimme that')])  # ['give', '-PRON-', 'that']
-
-p
-    |  For details on spaCy's custom pronoun lemma #[code -PRON-],
-    |  #[+a("/usage/#pron-lemma") see here].
-    |  The special case doesn't have to match an entire whitespace-delimited
-    |  substring. The tokenizer will incrementally split off punctuation, and
-    |  keep looking up the remaining substring:
-
-+code.
-    assert 'gimme' not in [w.text for w in nlp(u'gimme!')]
-    assert 'gimme' not in [w.text for w in nlp(u'("...gimme...?")')]
-
-p
-    |  The special case rules have precedence over the punctuation splitting:
-
-+code.
-    special_case = [{ORTH: u'...gimme...?', LEMMA: u'give', TAG: u'VB'}]
-    nlp.tokenizer.add_special_case(u'...gimme...?', special_case)
-    assert len(nlp(u'...gimme...?')) == 1
-
-p
-    |  Because the special-case rules allow you to set arbitrary token
-    |  attributes, such as the part-of-speech, lemma, etc, they make a good
-    |  mechanism for arbitrary fix-up rules. Having this logic live in the
-    |  tokenizer isn't very satisfying from a design perspective, however, so
-    |  the API may eventually be exposed on the
-    |  #[+api("language") #[code Language]] class itself.
-
-
-+h(3, "how-tokenizer-works") How spaCy's tokenizer works
-
-p
-    |  spaCy introduces a novel tokenization algorithm, that gives a better
-    |  balance between performance, ease of definition, and ease of alignment
-    |  into the original string.
-
-p
-    |  After consuming a prefix or infix, we consult the special cases again.
-    |  We want the special cases to handle things like "don't" in English, and
-    |  we want the same rule to work for "(don't)!". We do this by splitting
-    |  off the open bracket, then the exclamation, then the close bracket, and
-    |  finally matching the special-case. Here's an implementation of the
-    |  algorithm in Python, optimized for readability rather than performance:
-
-+code.
-    def tokenizer_pseudo_code(text, special_cases,
-                              find_prefix, find_suffix, find_infixes):
-        tokens = []
-        for substring in text.split(' '):
-            suffixes = []
-            while substring:
-                if substring in special_cases:
-                    tokens.extend(special_cases[substring])
-                    substring = ''
-                elif find_prefix(substring) is not None:
-                    split = find_prefix(substring)
-                    tokens.append(substring[:split])
-                    substring = substring[split:]
-                elif find_suffix(substring) is not None:
-                    split = find_suffix(substring)
-                    suffixes.append(substring[-split:])
-                    substring = substring[:-split]
-                elif find_infixes(substring):
-                    infixes = find_infixes(substring)
-                    offset = 0
-                    for match in infixes:
-                        tokens.append(substring[offset : match.start()])
-                        tokens.append(substring[match.start() : match.end()])
-                        offset = match.end()
-                    substring = substring[offset:]
-                else:
-                    tokens.append(substring)
-                    substring = ''
-            tokens.extend(reversed(suffixes))
-        return tokens
-
-p
-    |  The algorithm can be summarized as follows:
-
-+list("numbers")
-    +item Iterate over space-separated substrings
-    +item
-        |  Check whether we have an explicitly defined rule for this substring.
-        |  If we do, use it.
-    +item Otherwise, try to consume a prefix.
-    +item
-        |  If we consumed a prefix, go back to the beginning of the loop, so
-        |  that special-cases always get priority.
-    +item If we didn't consume a prefix, try to consume a suffix.
-    +item
-        |  If we can't consume a prefix or suffix, look for "infixes" — stuff
-        |  like hyphens etc.
-    +item Once we can't consume any more of the string, handle it as a single token.
-
-+h(3, "native-tokenizers") Customizing spaCy's Tokenizer class
-
-p
-    |  Let's imagine you wanted to create a tokenizer for a new language or
-    |  specific domain. There are five things you would need to define:
-
-+list("numbers")
-    +item
-        |  A dictionary of #[strong special cases]. This handles things like
-        |  contractions, units of measurement, emoticons, certain
-        |  abbreviations, etc.
-
-    +item
-        |  A function #[code prefix_search], to handle
-        |  #[strong preceding punctuation], such as open quotes, open brackets,
-        |  etc
-
-    +item
-        |  A function #[code suffix_search], to handle
-        |  #[strong succeeding punctuation], such as commas, periods, close
-        |  quotes, etc.
-
-    +item
-        |  A function #[code infixes_finditer], to handle non-whitespace
-        |  separators, such as hyphens etc.
-
-    +item
-        |  An optional boolean function #[code token_match] matching strings
-        |  that should never be split, overriding the previous rules.
-        |  Useful for things like URLs or numbers.
-
-p
-    |  You shouldn't usually need to create a #[code Tokenizer] subclass.
-    |  Standard usage is to use #[code re.compile()] to build a regular
-    |  expression object, and pass its #[code .search()] and
-    |  #[code .finditer()] methods:
-
-+code-exec.
-    import re
-    import spacy
-    from spacy.tokenizer import Tokenizer
-
-    prefix_re = re.compile(r'''^[\[\(&quot;&apos;]''')
-    suffix_re = re.compile(r'''[\]\)&quot;&apos;]$''')
-    infix_re = re.compile(r'''[-~]''')
-    simple_url_re = re.compile(r'''^https?://''')
-
-    def custom_tokenizer(nlp):
-        return Tokenizer(nlp.vocab, prefix_search=prefix_re.search,
-                                    suffix_search=suffix_re.search,
-                                    infix_finditer=infix_re.finditer,
-                                    token_match=simple_url_re.match)
-
-    nlp = spacy.load('en_core_web_sm')
-    nlp.tokenizer = custom_tokenizer(nlp)
-    doc = nlp(u"hello-world.")
-    print([t.text for t in doc])
-
-p
-    |  If you need to subclass the tokenizer instead, the relevant methods to
-    |  specialize are #[code find_prefix], #[code find_suffix] and
-    |  #[code find_infix].
-
-+infobox("Important note", "⚠️")
-    |  When customising the prefix, suffix and infix handling, remember that
-    |  you're passing in #[strong functions] for spaCy to execute, e.g.
-    |  #[code prefix_re.search] – not just the regular expressions. This means
-    |  that your functions also need to define how the rules should be applied.
-    |  For example, if you're adding your own prefix rules, you need
-    |  to make sure they're only applied to characters at the
-    |  #[strong beginning of a token], e.g. by adding #[code ^]. Similarly,
-    |  suffix rules should only be applied at the #[strong end of a token],
-    |  so your expression should end with a #[code $].
-
-+h(3, "custom-tokenizer") Hooking an arbitrary tokenizer into the pipeline
-
-p
-    |  The tokenizer is the first component of the processing pipeline and the
-    |  only one that can't be replaced by writing to #[code nlp.pipeline]. This
-    |  is because it has a different signature from all the other components:
-    |  it takes a text and returns a #[code Doc], whereas all other components
-    |  expect to already receive a tokenized #[code Doc].
-
-+graphic("/assets/img/pipeline.svg")
-    include ../../assets/img/pipeline.svg
-
-p
-    |  To overwrite the existing tokenizer, you need to replace
-    |  #[code nlp.tokenizer] with a custom function that takes a text, and
-    |  returns a #[code Doc].
-
-+code.
-    nlp = spacy.load('en')
-    nlp.tokenizer = my_tokenizer
-
-+table(["Argument", "Type", "Description"])
-    +row
-        +cell #[code text]
-        +cell unicode
-        +cell The raw text to tokenize.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Doc]
-        +cell The tokenized document.
-
-+infobox("Important note: using a custom tokenizer")
-    .o-block
-        |  In spaCy v1.x, you had to add a custom tokenizer by passing it to the
-        |  #[code make_doc] keyword argument, or by passing a tokenizer "factory"
-        |  to #[code create_make_doc]. This was unnecessarily complicated. Since
-        |  spaCy v2.0, you can write to #[code nlp.tokenizer] instead. If your
-        |  tokenizer needs the vocab, you can write a function and use
-        |  #[code nlp.vocab].
-
-    +code-new.
-        nlp.tokenizer = my_tokenizer
-        nlp.tokenizer = my_tokenizer_factory(nlp.vocab)
-    +code-old.
-        nlp = spacy.load('en', make_doc=my_tokenizer)
-        nlp = spacy.load('en', create_make_doc=my_tokenizer_factory)
-
-+h(3, "custom-tokenizer-example") Example: A custom whitespace tokenizer
-
-p
-    |  To construct the tokenizer, we usually want attributes of the #[code nlp]
-    |  pipeline. Specifically, we want the tokenizer to hold a reference to the
-    |  vocabulary object. Let's say we have the following class as
-    |  our tokenizer:
-
-+code-exec.
-    import spacy
-    from spacy.tokens import Doc
-
-    class WhitespaceTokenizer(object):
-        def __init__(self, vocab):
-            self.vocab = vocab
-
-        def __call__(self, text):
-            words = text.split(' ')
-            # All tokens 'own' a subsequent space character in this tokenizer
-            spaces = [True] * len(words)
-            return Doc(self.vocab, words=words, spaces=spaces)
-
-    nlp = spacy.load('en_core_web_sm')
-    nlp.tokenizer = WhitespaceTokenizer(nlp.vocab)
-    doc = nlp(u"What's happened to me? he thought. It wasn't a dream.")
-    print([t.text for t in doc])
-
-p
-    |  As you can see, we need a #[code Vocab] instance to construct this — but
-    |  we won't have it until we get back the loaded #[code nlp] object. The
-    |  simplest solution is to build the tokenizer in two steps. This also means
-    |  that you can reuse the "tokenizer factory" and initialise it with
-    |  different instances of #[code Vocab].
-
-+h(3, "own-annotations") Bringing your own annotations
-
-p
-    |  spaCy generally assumes by default that your data is raw text. However,
-    |  sometimes your data is partially annotated, e.g. with pre-existing
-    |  tokenization, part-of-speech tags, etc. The most common situation is
-    |  that you have pre-defined tokenization. If you have a list of strings,
-    |  you can create a #[code Doc] object directly. Optionally, you can also
-    |  specify a list of boolean values, indicating whether each word has a
-    |  subsequent space.
-
-+code-exec.
-    import spacy
-    from spacy.tokens import Doc
-    from spacy.lang.en import English
-
-    nlp = English()
-    doc = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'],
-              spaces=[False, True, False, False])
-    print([(t.text, t.text_with_ws, t.whitespace_) for t in doc])
-
-p
-    |  If provided, the spaces list must be the same length as the words list.
-    |  The spaces list affects the #[code doc.text], #[code span.text],
-    |  #[code token.idx], #[code span.start_char] and #[code span.end_char]
-    |  attributes. If you don't provide a #[code spaces] sequence, spaCy will
-    |  assume that all words are whitespace delimited.
-
-+code-exec.
-    import spacy
-    from spacy.tokens import Doc
-    from spacy.lang.en import English
-
-    nlp = English()
-    bad_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'])
-    good_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'],
-                      spaces=[False, True, False, False])
-
-    print(bad_spaces.text)   # 'Hello , world !'
-    print(good_spaces.text)  # 'Hello, world!'
-
-p
-    |  Once you have a #[+api("doc") #[code Doc]] object, you can write to its
-    |  attributes to set the part-of-speech tags, syntactic dependencies, named
-    |  entities and other attributes. For details, see the respective usage
-    |  pages.
diff --git a/website/usage/_models/_available-models.jade b/website/usage/_models/_available-models.jade
deleted file mode 100644
index 3a5e318d0..000000000
--- a/website/usage/_models/_available-models.jade
+++ /dev/null
@@ -1,19 +0,0 @@
-//- 💫 DOCS > USAGE > MODELS > AVAILABE MODELS
-
-p
-    |  Model differences are mostly statistical. In general, we do expect larger
-    |  models to be "better" and more accurate overall. Ultimately, it depends on
-    |  your use case and requirements. We recommend starting with the default
-    |  models (marked with a star below).
-
-+table(["Name", "Language", "Type"])
-    for models, lang in MODELS
-        for model, i in models
-            - var comps = getModelComponents(model)
-            - var type = comps.size == "sm" && comps.type == "core" ? "core_sm" : comps.type
-            +row
-                +cell #[+a("/models/" + lang + "#" + model) #[code=model]]
-                    if i == 0
-                        +icon("star", 16).o-icon--inline.u-color-theme
-                +cell #{LANGUAGES[comps.lang]}
-                +cell #{MODEL_META[type]}
diff --git a/website/usage/_models/_install-basics.jade b/website/usage/_models/_install-basics.jade
deleted file mode 100644
index 3fb8fa00c..000000000
--- a/website/usage/_models/_install-basics.jade
+++ /dev/null
@@ -1,43 +0,0 @@
-//- 💫 DOCS > USAGE > MODELS > INSTALLATION BASICS
-
-p
-    |  The easiest way to download a model is via spaCy's
-    |  #[+api("cli#download") #[code download]] command. It takes care of
-    |  finding the best-matching model compatible with your spaCy installation.
-
-- var models = Object.keys(MODELS).map(function(lang) { return "python -m spacy download " + lang })
-+code(false, "bash").
-    # out-of-the-box: download best-matching default model
-    #{Object.keys(MODELS).map(function(l) {return "python -m spacy download " + l}).join('\n')}
-
-    # download best-matching version of specific model for your spaCy installation
-    python -m spacy download en_core_web_sm
-
-    # download exact model version (doesn't create shortcut link)
-    python -m spacy download en_core_web_sm-2.0.0 --direct
-
-p
-    |  The download command will #[+a("/usage/models#download-pip") install the model] via
-    |  pip, place the package in your #[code site-packages] directory and create
-    |  a #[+a("/usage/models#usage") shortcut link] that lets you load the model by a custom
-    |  name. The shortcut link will be the same as the model name used in
-    |  #[code spacy download].
-
-+code(false, "bash").
-    pip install spacy
-    python -m spacy download en
-
-+code.
-    import spacy
-    nlp = spacy.load('en')
-    doc = nlp(u'This is a sentence.')
-
-+infobox("Important note", "⚠️")
-    |  To allow loading models via convenient shortcuts like #[code 'en'], spaCy
-    |  will create a symlink within the #[code spacy/data] directory. This means
-    |  that your user needs the #[strong required permissions].
-    |  If you've installed spaCy to a system directory and don't have admin
-    |  privileges, the model linking may fail. The easiest solution
-    |  is to re-run the command as admin, or use a #[code virtualenv]. For more
-    |  info on this, see the
-    |  #[+a("/usage/#symlink-privilege") troubleshooting guide].
diff --git a/website/usage/_models/_install.jade b/website/usage/_models/_install.jade
deleted file mode 100644
index 8924bb018..000000000
--- a/website/usage/_models/_install.jade
+++ /dev/null
@@ -1,183 +0,0 @@
-//- 💫 DOCS > USAGE > MODELS > INSTALLATION
-
-+aside("Downloading models in spaCy < v1.7")
-    |  In older versions of spaCy, you can still use the old download commands.
-    |  This will download and install the models into the #[code spacy/data]
-    |  directory.
-
-    +code.o-no-block.
-        python -m spacy.en.download all
-        python -m spacy.de.download all
-        python -m spacy.en.download glove
-
-    |  The old models are also #[+a(gh("spacy") + "/tree/v1.6.0") attached to the v1.6.0 release].
-    |  To download and install them manually, unpack the archive, drop the
-    |  contained directory into #[code spacy/data].
-
-include _install-basics
-
-+h(3, "download-pip") Installation via pip
-
-p
-    |  To download a model directly using #[+a("https://pypi.python.org/pypi/pip") pip],
-    |  point #[code pip install] to the URL or local path of the archive
-    |  file. To find the direct link to a model, head over to the
-    |  #[+a(gh("spacy-models") + "/releases") model releases], right click on the archive
-    |  link and copy it to your clipboard.
-
-+code(false, "bash").
-    # with external URL
-    pip install #{gh("spacy-models")}/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz
-
-    # with local file
-    pip install /Users/you/en_core_web_md-1.2.0.tar.gz
-
-p
-    |  By default, this will install the model into your #[code site-packages]
-    |  directory. You can then use #[code spacy.load()] to load it via its
-    |  package name, create a #[+a("#usage-link") shortcut link] to assign it a
-    |  custom name, or #[+a("#usage-import") import it] explicitly as a module.
-    |  If you need to download models as part of an automated process, we
-    |  recommend using pip with a direct link, instead of relying on spaCy's
-    |  #[+api("cli#download") #[code download]] command.
-
-+infobox
-    |  You can also add the direct download link to your application's
-    |  #[code requirements.txt]. For more details,
-    |  see the section on
-    |  #[+a("/models/#production") working with models in production].
-
-
-+h(3, "download-manual") Manual download and installation
-
-p
-    |  In some cases, you might prefer downloading the data manually, for
-    |  example to place it into a custom directory. You can download the model
-    |  via your browser from the #[+a(gh("spacy-models")) latest releases], or configure
-    |  your own download script using the URL of the archive file. The archive
-    |  consists of a model directory that contains another directory with the
-    |  model data.
-
-+code("Directory structure", "yaml").
-    └── en_core_web_md-1.2.0.tar.gz       # downloaded archive
-        ├── meta.json                     # model meta data
-        ├── setup.py                      # setup file for pip installation
-        └── en_core_web_md                # 📦 model package
-            ├── __init__.py               # init for pip installation
-            ├── meta.json                 # model meta data
-            └── en_core_web_md-1.2.0      # model data
-
-p
-    |  You can place the #[strong model package directory] anywhere on your
-    |  local file system. To use it with spaCy, assign it a name by
-    |  creating a #[+a("#usage") shortcut link] for the data directory.
-
-+h(3, "usage") Using models with spaCy
-
-p
-    |  To load a model, use #[+api("spacy#load") #[code spacy.load()]] with the
-    |  model's shortcut link, package name or a path to the data directory:
-
-+code.
-    import spacy
-    nlp = spacy.load('en')                       # load model with shortcut link "en"
-    nlp = spacy.load('en_core_web_sm')           # load model package "en_core_web_sm"
-    nlp = spacy.load('/path/to/en_core_web_sm')  # load package from a directory
-
-    doc = nlp(u'This is a sentence.')
-
-+infobox("Tip: Preview model info")
-    |  You can use the #[+api("cli#info") #[code info]] command or
-    |  #[+api("spacy#info") #[code spacy.info()]] method to print a model's meta data
-    |  before loading it. Each #[code Language] object with a loaded model also
-    |  exposes the model's meta data as the attribute #[code meta]. For example,
-    |  #[code nlp.meta['version']] will return the model's version.
-
-+h(3, "usage-link") Using custom shortcut links
-
-p
-    |  While previous versions of spaCy required you to maintain a data directory
-    |  containing the models for each installation, you can now choose
-    |  #[strong how and where you want to keep your data]. For example, you could
-    |  download all models manually and put them into a local directory.
-    |  Whenever your spaCy projects need a model, you create a shortcut link to
-    |  tell spaCy to load it from there. This means you'll never end up with
-    |  duplicate data.
-
-p
-    |  The #[+api("cli#link") #[code link]] command will create a symlink
-    |  in the #[code spacy/data] directory.
-
-+aside("Why does spaCy use symlinks?")
-    |  Symlinks were originally introduced to maintain backwards compatibility,
-    |  as older versions expected model data to live within #[code spacy/data].
-    |  However, we decided to keep using them in v2.0 instead of opting for
-    |  a config file. There'll always be a need for assigning and saving custom
-    |  model names or IDs. And your system already comes with a native solution
-    |  to mapping unicode aliases to file paths: symbolic links.
-
-+code(false, "bash", "$").
-    python -m spacy link [package name or path] [shortcut] [--force]
-
-p
-    |  The first argument is the #[strong package name] (if the model was
-    |  installed via pip), or a local path to the the #[strong model package].
-    |  The second argument is the internal name you want to use for the model.
-    |  Setting the #[code --force] flag will overwrite any existing links.
-
-+code("Examples", "bash").
-    # set up shortcut link to load installed package as "en_default"
-    python -m spacy link en_core_web_md en_default
-
-    # set up shortcut link to load local model as "my_amazing_model"
-    python -m spacy link /Users/you/model my_amazing_model
-
-+infobox("Important note", "⚠️")
-    |  In order to create a symlink, your user needs the #[strong required permissions].
-    |  If you've installed spaCy to a system directory and don't have admin
-    |  privileges, the #[code spacy link] command may fail. The easiest solution
-    |  is to re-run the command as admin, or use a #[code virtualenv]. For more
-    |  info on this, see the
-    |  #[+a("/usage/#symlink-privilege") troubleshooting guide].
-
-+h(3, "usage-import") Importing models as modules
-
-p
-    |  If you've installed a model via spaCy's downloader, or directly via pip,
-    |  you can also #[code import] it and then call its #[code load()] method
-    |  with no arguments:
-
-+code-exec.
-    import en_core_web_sm
-
-    nlp = en_core_web_sm.load()
-    doc = nlp(u'This is a sentence.')
-
-p
-    |  How you choose to load your models ultimately depends on personal
-    |  preference. However, #[strong for larger code bases], we usually recommend
-    |  native imports, as this will make it easier to integrate models with your
-    |  existing build process, continuous integration workflow and testing
-    |  framework. It'll also prevent you from ever trying to load a model that
-    |  is not installed, as your code will raise an #[code ImportError]
-    |  immediately, instead of failing somewhere down the line when calling
-    |  #[code spacy.load()].
-
-+infobox
-    |  For more details, see the section on
-    |  #[+a("/models/#production") working with models in production].
-
-+h(3, "own-models") Using your own models
-
-p
-    |  If you've trained your own model, for example for
-    |  #[+a("/usage/adding-languages") additional languages] or
-    |  #[+a("/usage/training#ner") custom named entities], you can save its
-    |  state using the #[+api("language#to_disk") #[code Language.to_disk()]]
-    |  method. To make the model more convenient to deploy, we recommend
-    |  wrapping it as a Python package.
-
-+infobox("Saving and loading models")
-    |  For more information and a detailed guide on how to package your model,
-    |  see the documentation on
-    |  #[+a("/usage/training#saving-loading") saving and loading models].
diff --git a/website/usage/_models/_languages.jade b/website/usage/_models/_languages.jade
deleted file mode 100644
index c2c06f7d9..000000000
--- a/website/usage/_models/_languages.jade
+++ /dev/null
@@ -1,76 +0,0 @@
-//- 💫 DOCS > USAGE > MODELS > LANGUAGE SUPPORT
-
-p spaCy currently provides models for the following languages:
-
-+table(["Language", "Code", "Language data", "Models"])
-    for models, code in MODELS
-        - var count = Object.keys(models).length
-        +row
-            +cell=LANGUAGES[code]
-            +cell #[code=code]
-            +cell
-                +src(gh("spaCy", "spacy/lang/" + code)) #[code lang/#{code}]
-            +cell
-                +a("/models/" + code) #{count} #{(count == 1) ? "model" : "models"}
-
-+h(3, "alpha-support") Alpha tokenization support
-
-p
-    |  Work has started on the following languages. You can help by
-    |  #[+a("/usage/adding-languages#language-data") improving the existing language data]
-    |  and extending the tokenization patterns.
-
-+aside("Usage note")
-    |  Note that the alpha languages don't yet come with a language model. In
-    |  order to use them, you have to import them directly, or use
-    |  #[+api("spacy#blank") #[code spacy.blank]]:
-
-    +code.o-no-block.
-        from spacy.lang.fi import Finnish
-        nlp = Finnish()  # use directly
-        nlp = spacy.blank('fi')  # blank instance
-
-+table(["Language", "Code", "Language data"])
-    - var sorted_langs = Object.assign({}, ...Object.keys(LANGUAGES).filter(key => !MODELS[key]).sort().map(key => ({ [key]: LANGUAGES[key] })))
-    for lang, code in sorted_langs
-        +row
-            +cell #{LANGUAGES[code]}
-            +cell #[code=code]
-            +cell
-                +src(gh("spaCy", "spacy/lang/" + code)) #[code lang/#{code}]
-
-+infobox("Dependencies")
-    .o-block-small Some language tokenizers require external dependencies.
-
-    +list.o-no-block
-        +item #[strong Chinese]: #[+a("https://github.com/fxsjy/jieba") Jieba]
-        +item #[strong Japanese]: #[+a("https://github.com/taku910/mecab") MeCab] with #[+a("http://unidic.ninjal.ac.jp/back_number#unidic_cwj") Unidic]
-        +item #[strong Thai]: #[+a("https://github.com/wannaphongcom/pythainlp") pythainlp]
-        +item #[strong Vietnamese]: #[+a("https://github.com/trungtv/pyvi") Pyvi]
-        +item #[strong Russian]: #[+a("https://github.com/kmike/pymorphy2") pymorphy2]
-
-+h(3, "multi-language") Multi-language support
-    +tag-new(2)
-
-p
-    |  As of v2.0, spaCy supports models trained on more than one language. This
-    |  is especially useful for named entity recognition. The language ID used
-    |  for multi-language or language-neutral models is #[code xx]. The
-    |  language class, a generic subclass containing only the base language data,
-    |  can be found in #[+src(gh("spaCy", "spacy/lang/xx")) #[code lang/xx]].
-
-p
-    |  To load your model with the neutral, multi-language class, simply set
-    |  #[code "language": "xx"] in your
-    |  #[+a("/usage/training#models-generating") model package]'s
-    |  meta.json. You can also import the class directly, or call
-    |  #[+api("util#get_lang_class") #[code util.get_lang_class()]] for
-    |  lazy-loading.
-
-+code("Standard import").
-    from spacy.lang.xx import MultiLanguage
-    nlp = MultiLanguage()
-
-+code("With lazy-loading").
-    from spacy.util import get_lang_class
-    nlp = get_lang_class('xx')
diff --git a/website/usage/_models/_production.jade b/website/usage/_models/_production.jade
deleted file mode 100644
index c5a6cc587..000000000
--- a/website/usage/_models/_production.jade
+++ /dev/null
@@ -1,81 +0,0 @@
-//- 💫 DOCS > USAGE > MODELS > PRODUCTION USE
-
-p
-    |  If your application depends on one or more models,
-    |  you'll usually want to integrate them into your continuous integration
-    |  workflow and build process. While spaCy provides a range of useful helpers
-    |  for downloading, linking and loading models, the underlying functionality
-    |  is entirely based on native Python packages. This allows your application
-    |  to handle a model like any other package dependency.
-
-+infobox("Training models for production")
-    |  For an example of an automated model training and build process, see
-    |  #[+a("/usage/training#example-training-spacy") this example] of how
-    |  we're training and packaging our models for spaCy.
-
-+h(3, "models-download") Downloading and requiring model dependencies
-
-p
-    |  spaCy's built-in #[+api("cli#download") #[code download]] command
-    |  is mostly intended as a convenient, interactive wrapper. It performs
-    |  compatibility checks and prints detailed error messages and warnings.
-    |  However, if you're downloading models as part of an automated build
-    |  process, this only adds an unnecessary layer of complexity. If you know
-    |  which models your application needs, you should be specifying them directly.
-
-p
-    |  Because all models are valid Python packages, you can add them to your
-    |  application's #[code requirements.txt]. If you're running your own
-    |  internal PyPi installation, you can upload the models there. pip's
-    |  #[+a("https://pip.pypa.io/en/latest/reference/pip_install/#requirements-file-format") requirements file format]
-    |  supports both package names to download via a PyPi server, as well as direct
-    |  URLs.
-
-+code("requirements.txt", "text").
-    spacy&gt;=2.0.0,&lt;3.0.0
-    #{gh("spacy-models")}/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz#en_core_web_sm
-
-p
-    |  Specifying #[code #egg=] with the package name tells pip
-    |  which package to expect from the download URL. This way, the
-    |  package won't be re-downloaded and overwritten if it's already
-    |  installed - just like when you're downloading a package from PyPi.
-
-p
-    |  All models are versioned and specify their spaCy dependency. This ensures
-    |  cross-compatibility and lets you specify exact version requirements for
-    |  each model. If you've trained your own model, you can use the
-    |  #[+api("cli#package") #[code package]] command to generate the required
-    |  meta data and turn it into a loadable package.
-
-
-+h(3, "models-loading") Loading and testing models
-
-p
-    |  Downloading models directly via pip won't call spaCy's link
-    |  #[+api("cli#link") #[code link]] command, which creates
-    |  symlinks for model shortcuts. This means that you'll have to run this
-    |  command separately, or use the native #[code import] syntax to load the
-    |  models:
-
-+code.
-    import en_core_web_sm
-    nlp = en_core_web_sm.load()
-
-p
-    |  In general, this approach is recommended for larger code bases, as it's
-    |  more "native", and doesn't depend on symlinks or rely on spaCy's loader
-    |  to resolve string names to model packages. If a model can't be
-    |  imported, Python will raise an #[code ImportError] immediately. And if a
-    |  model is imported but not used, any linter will catch that.
-
-p
-    |  Similarly, it'll give you more flexibility when writing tests that
-    |  require loading models. For example, instead of writing your own
-    |  #[code try] and #[code except] logic around spaCy's loader, you can use
-    |  #[+a("http://pytest.readthedocs.io/en/latest/") pytest]'s
-    |  #[+a("https://docs.pytest.org/en/latest/builtin.html#_pytest.outcomes.importorskip") #[code importorskip()]]
-    |  method to only run a test if a specific model or model version is
-    |  installed. Each model package exposes a #[code __version__] attribute
-    |  which you can also use to perform your own version compatibility checks
-    |  before loading a model.
diff --git a/website/usage/_models/_quickstart.jade b/website/usage/_models/_quickstart.jade
deleted file mode 100644
index 593f70272..000000000
--- a/website/usage/_models/_quickstart.jade
+++ /dev/null
@@ -1,18 +0,0 @@
-//- 💫 DOCS > USAGE > MODELS > QUICKSTART
-
-- QUICKSTART_MODELS[0].options = Object.keys(MODELS).map(m => ({ id: m, title: LANGUAGES[m], checked: m == 'en'}))
-+quickstart(QUICKSTART_MODELS, "Quickstart", "Install a default model, get the code to load it from within spaCy and an example to test it. For more options, see the section on available models below.")
-    for models, lang in MODELS
-        - var package = models[0]
-        +qs({lang: lang}) python -m spacy download #{lang}
-        +qs({lang: lang}, "divider")
-        +qs({lang: lang, load: "module"}, "python") import #{package}
-        +qs({lang: lang, load: "module"}, "python") nlp = #{package}.load()
-        +qs({lang: lang, load: "spacy"}, "python") import spacy
-        +qs({lang: lang, load: "spacy"}, "python") nlp = spacy.load('#{lang}')
-        +qs({lang: lang, config: "example"}, "python") doc = nlp(u"#{EXAMPLE_SENTENCES[lang]}")
-
-        if lang != "xx"
-            +qs({lang: lang, config: "example"}, "python") print([(w.text, w.pos_) for w in doc])
-        else
-            +qs({lang: lang, config: "example"}, "python") print([(ent.text, ent.label) for ent in doc.ents])
diff --git a/website/usage/_processing-pipelines/_custom-components.jade b/website/usage/_processing-pipelines/_custom-components.jade
deleted file mode 100644
index 674722eba..000000000
--- a/website/usage/_processing-pipelines/_custom-components.jade
+++ /dev/null
@@ -1,130 +0,0 @@
-//- 💫 DOCS > USAGE > PROCESSING PIPELINES > CUSTOM COMPONENTS
-
-p
-    |  A component receives a #[code Doc] object and can modify it – for example,
-    |  by using the current weights to make a prediction and set some annotation
-    |  on the document. By adding a component to the pipeline, you'll get access
-    |  to the #[code Doc] at any point #[strong during processing] – instead of
-    |  only being able to modify it afterwards.
-
-+aside-code("Example").
-    def my_component(doc):
-        # do something to the doc here
-        return doc
-
-+table(["Argument", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The #[code Doc] object processed by the previous component.
-
-    +row("foot")
-        +cell returns
-        +cell #[code Doc]
-        +cell The #[code Doc] object processed by this pipeline component.
-
-p
-    |  Custom components can be added to the pipeline using the
-    |  #[+api("language#add_pipe") #[code add_pipe]] method. Optionally, you
-    |  can either specify a component to add it #[strong before or after], tell
-    |  spaCy to add it #[strong first or last] in the pipeline, or define a
-    |  #[strong custom name]. If no name is set and no #[code name] attribute
-    |  is present on your component, the function name is used.
-
-+code-exec.
-    import spacy
-
-    def my_component(doc):
-        print("After tokenization, this doc has %s tokens." % len(doc))
-        if len(doc) &lt; 10:
-            print("This is a pretty short document.")
-        return doc
-
-    nlp = spacy.load('en_core_web_sm')
-    nlp.add_pipe(my_component, name='print_info', first=True)
-    print(nlp.pipe_names) # ['print_info', 'tagger', 'parser', 'ner']
-    doc = nlp(u"This is a sentence.")
-
-p
-    |  Of course, you can also wrap your component as a class to allow
-    |  initialising it with custom settings and hold state within the component.
-    |  This is useful for #[strong stateful components], especially ones which
-    |  #[strong depend on shared data]. In the following example, the custom
-    |  component #[code EntityMatcher] can be initialised with #[code nlp] object,
-    |  a terminology list and an entity label. Using the
-    |  #[+api("phrasematcher") #[code PhraseMatcher]], it then matches the terms
-    |  in the #[code Doc] and adds them to the existing entities.
-
-+aside("Rule-based entities vs. model", "💡")
-    |  For complex tasks, it's usually better to train a statistical entity
-    |  recognition model. However, statistical models require training data, so
-    |  for many situations, rule-based approaches are more practical. This is
-    |  especially true at the start of a project: you can use a rule-based
-    |  approach as part of a data collection process, to help you "bootstrap" a
-    |  statistical model.
-
-+code-exec.
-    import spacy
-    from spacy.matcher import PhraseMatcher
-    from spacy.tokens import Span
-
-    class EntityMatcher(object):
-        name = 'entity_matcher'
-
-        def __init__(self, nlp, terms, label):
-            patterns = [nlp.make_doc(text) for text in terms]
-            self.matcher = PhraseMatcher(nlp.vocab)
-            self.matcher.add(label, None, *patterns)
-
-        def __call__(self, doc):
-            matches = self.matcher(doc)
-            for match_id, start, end in matches:
-                span = Span(doc, start, end, label=match_id)
-                doc.ents = list(doc.ents) + [span]
-            return doc
-
-    nlp = spacy.load('en_core_web_sm')
-    terms = (u'cat', u'dog', u'tree kangaroo', u'giant sea spider')
-    entity_matcher = EntityMatcher(nlp, terms, 'ANIMAL')
-
-    nlp.add_pipe(entity_matcher, after='ner')
-    print(nlp.pipe_names)  # the components in the pipeline
-
-    doc = nlp(u"This is a text about Barack Obama and a tree kangaroo")
-    print([(ent.text, ent.label_) for ent in doc.ents])
-
-+h(3, "custom-components-factories") Adding factories
-
-p
-    |  When spaCy loads a model via its #[code meta.json], it will iterate over
-    |  the #[code "pipeline"] setting, look up every component name in the
-    |  internal factories and call
-    |  #[+api("language#create_pipe") #[code nlp.create_pipe]] to initialise the
-    |  individual components, like the tagger, parser or entity recogniser. If
-    |  your model uses custom components, this won't work – so you'll have to
-    |  tell spaCy #[strong where to find your component]. You can do this by
-    |  writing to the #[code Language.factories]:
-
-+code.
-    from spacy.language import Language
-    Language.factories['entity_matcher'] = lambda nlp, **cfg: EntityMatcher(nlp, **cfg)
-
-p
-    |  You can also ship the above code and your custom component in your
-    |  packaged model's #[code __init__.py], so it's executed when you load your
-    |  model. The #[code **cfg] config parameters are passed all the way down
-    |  from #[+api("spacy#load") #[code spacy.load]], so you can load the model
-    |  and its components with custom settings:
-
-+code.
-    nlp = spacy.load('your_custom_model', terms=(u'tree kangaroo'), label='ANIMAL')
-
-+infobox("Important note", "⚠️")
-    |  When you load a model via its shortcut or package name, like
-    |  #[code en_core_web_sm], spaCy will import the package and then call its
-    |  #[code load()] method. This means that custom code in the model's
-    |  #[code __init__.py] will be executed, too. This is #[strong not the case]
-    |  if you're loading a model from a path containing the model data. Here,
-    |  spaCy will only read in the #[code meta.json]. If you want to use custom
-    |  factories with a model loaded from a path, you need to add them to
-    |  #[code Language.factories] #[em before] you load the model.
diff --git a/website/usage/_processing-pipelines/_extensions.jade b/website/usage/_processing-pipelines/_extensions.jade
deleted file mode 100644
index 6bfdef543..000000000
--- a/website/usage/_processing-pipelines/_extensions.jade
+++ /dev/null
@@ -1,438 +0,0 @@
-//- 💫 DOCS > USAGE > PROCESSING PIPELINES > EXTENSIONS
-
-p
-    |  As of v2.0, spaCy allows you to set any custom attributes and methods
-    |  on the #[code Doc], #[code Span] and #[code Token], which become
-    |  available as #[code Doc._], #[code Span._] and #[code Token._] – for
-    |  example, #[code Token._.my_attr]. This lets you store additional
-    |  information relevant to your application, add new features and
-    |  functionality to spaCy, and implement your own models trained with other
-    |  machine learning libraries. It also lets you take advantage of spaCy's
-    |  data structures and the #[code Doc] object as the "single source of
-    |  truth".
-
-+aside("Why ._?")
-    |  Writing to a #[code ._] attribute instead of to the #[code Doc] directly
-    |  keeps a clearer separation and makes it easier to ensure backwards
-    |  compatibility. For example, if you've implemented your own #[code .coref]
-    |  property and spaCy claims it one day, it'll break your code. Similarly,
-    |  just by looking at the code, you'll immediately know what's built-in and
-    |  what's custom – for example, #[code doc.sentiment] is spaCy, while
-    |  #[code doc._.sent_score] isn't.
-
-p
-    |  There are three main types of extensions, which can be defined using the
-    |  #[+api("doc#set_extension") #[code Doc.set_extension]],
-    |  #[+api("span#set_extension") #[code Span.set_extension]] and
-    |  #[+api("token#set_extension") #[code Token.set_extension]] methods.
-
-+list("numbers")
-    +item #[strong Attribute extensions].
-        |  Set a default value for an attribute, which can be overwritten
-        |  manually at any time. Attribute extensions work like "normal"
-        |  variables and are the quickest way to store arbitrary information
-        |  on a #[code Doc], #[code Span] or #[code Token]. Attribute defaults
-        |  behaves just like argument defaults
-        |  #[+a("http://docs.python-guide.org/en/latest/writing/gotchas/#mutable-default-arguments") in Python functions],
-        |  and should not be used for mutable values like dictionaries or lists.
-
-        +code-wrapper
-            +code.
-                Doc.set_extension('hello', default=True)
-                assert doc._.hello
-                doc._.hello = False
-
-    +item #[strong Property extensions].
-        |  Define a getter and an optional setter function. If no setter is
-        |  provided, the extension is immutable. Since the getter and setter
-        |  functions are only called when you #[em retrieve] the attribute,
-        |  you can also access values of previously added attribute extensions.
-        |  For example, a #[code Doc] getter can average over #[code Token]
-        |   attributes. For #[code Span] extensions, you'll almost always want
-        |  to use a property – otherwise, you'd have to write to
-        |  #[em every possible] #[code Span] in the #[code Doc] to set up the
-        |  values correctly.
-
-        +code-wrapper
-            +code.
-                Doc.set_extension('hello', getter=get_hello_value, setter=set_hello_value)
-                assert doc._.hello
-                doc._.hello = 'Hi!'
-
-    +item #[strong Method extensions].
-        |  Assign a function that becomes available as an object method. Method
-        |  extensions are always immutable. For more details and implementation
-        |  ideas, see
-        |  #[+a("/usage/examples#custom-components-attr-methods") these examples].
-
-        +code-wrapper
-            +code.o-no-block.
-                Doc.set_extension('hello', method=lambda doc, name: 'Hi {}!'.format(name))
-                assert doc._.hello('Bob') == 'Hi Bob!'
-
-p
-    |  Before you can access a custom extension, you need to register it using
-    |  the #[code set_extension] method on the object you want
-    |  to add it to, e.g. the #[code Doc]. Keep in mind that extensions are
-    |  always #[strong added globally] and not just on a particular instance.
-    |  If an attribute of the same name
-    |  already exists, or if you're trying to access an attribute that hasn't
-    |  been registered, spaCy will raise an #[code AttributeError].
-
-+code("Example").
-    from spacy.tokens import Doc, Span, Token
-
-    fruits = ['apple', 'pear', 'banana', 'orange', 'strawberry']
-    is_fruit_getter = lambda token: token.text in fruits
-    has_fruit_getter = lambda obj: any([t.text in fruits for t in obj])
-
-    Token.set_extension('is_fruit', getter=is_fruit_getter)
-    Doc.set_extension('has_fruit', getter=has_fruit_getter)
-    Span.set_extension('has_fruit', getter=has_fruit_getter)
-
-+aside-code("Usage example").
-    doc = nlp(u"I have an apple and a melon")
-    assert doc[3]._.is_fruit      # get Token attributes
-    assert not doc[0]._.is_fruit
-    assert doc._.has_fruit        # get Doc attributes
-    assert doc[1:4]._.has_fruit   # get Span attributes
-
-p
-    |  Once you've registered your custom attribute, you can also use the
-    |  built-in #[code set], #[code get] and #[code has] methods to modify and
-    |  retrieve the attributes. This is especially useful it you want to pass in
-    |  a string instead of calling #[code doc._.my_attr].
-
-+table(["Method", "Description", "Valid for", "Example"])
-    +row
-        +cell #[code ._.set()]
-        +cell Set a value for an attribute.
-        +cell Attributes, mutable properties.
-        +cell #[code.u-break token._.set('my_attr', True)]
-
-    +row
-        +cell #[code ._.get()]
-        +cell Get the value of an attribute.
-        +cell Attributes, mutable properties, immutable properties, methods.
-        +cell #[code.u-break my_attr = span._.get('my_attr')]
-
-    +row
-        +cell #[code ._.has()]
-        +cell Check if an attribute exists.
-        +cell Attributes, mutable properties, immutable properties, methods.
-        +cell #[code.u-break doc._.has('my_attr')]
-
-+infobox("How the ._ is implemented")
-    |  Extension definitions – the defaults, methods, getters and setters you
-    |  pass in to #[code set_extension] – are stored in class attributes on the
-    |  #[code Underscore] class. If you write to an extension attribute, e.g.
-    |  #[code doc._.hello = True], the data is stored within the
-    |  #[+api("doc#attributes") #[code Doc.user_data]] dictionary. To keep the
-    |  underscore data separate from your other dictionary entries, the string
-    |  #[code "._."] is placed before the name, in a tuple.
-
-+h(4, "component-example1") Example: Custom sentence segmentation logic
-
-p
-    |  Let's say you want to implement custom logic to improve spaCy's sentence
-    |  boundary detection. Currently, sentence segmentation is based on the
-    |  dependency parse, which doesn't always produce ideal results. The custom
-    |  logic should therefore be applied #[strong after] tokenization, but
-    |  #[strong before] the dependency parsing – this way, the parser can also
-    |  take advantage of the sentence boundaries.
-
-+code-exec.
-    import spacy
-
-    def custom_sentencizer(doc):
-        for i, token in enumerate(doc[:-2]):
-            # define sentence start if period + titlecase token
-            if token.text == '.' and doc[i+1].is_title:
-                doc[i+1].sent_start = True
-        return doc
-
-    nlp = spacy.load('en_core_web_sm')
-    nlp.add_pipe(custom_sentencizer, before='parser') # insert before parser
-    doc = nlp(u"This is a sentence. This is another sentence.")
-    for sent in doc.sents:
-        print(sent.text)
-
-+h(4, "component-example2")
-    |  Example: Pipeline component for entity matching and tagging with
-    |  custom attributes
-
-p
-    |  This example shows how to create a spaCy extension that takes a
-    |  terminology list (in this case, single- and multi-word company names),
-    |  matches the occurences in a document, labels them as #[code ORG] entities,
-    |  merges the tokens and sets custom #[code is_tech_org] and
-    |  #[code has_tech_org] attributes. For efficient matching, the example uses
-    |  the #[+api("phrasematcher") #[code PhraseMatcher]] which accepts
-    |  #[code Doc] objects as match patterns and works well for large
-    |  terminology lists. It also ensures your patterns will always match, even
-    |  when you customise spaCy's tokenization rules. When you call #[code nlp]
-    |  on a text, the custom pipeline component is applied to the #[code Doc]
-
-+github("spacy", "examples/pipeline/custom_component_entities.py", 500)
-
-p
-    |  Wrapping this functionality in a
-    |  pipeline component allows you to reuse the module with different
-    |  settings, and have all pre-processing taken care of when you call
-    |  #[code nlp] on your text and receive a #[code Doc] object.
-
-+h(4, "component-example3")
-    |  Example: Pipeline component for GPE entities and country meta data via a
-    |  REST API
-
-p
-    |  This example shows the implementation of a pipeline component
-    |  that fetches country meta data via the
-    |  #[+a("https://restcountries.eu") REST Countries API] sets entity
-    |  annotations for countries, merges entities into one token and
-    |  sets custom attributes on the #[code Doc], #[code Span] and
-    |  #[code Token] – for example, the capital, latitude/longitude coordinates
-    |  and even the country flag.
-
-+github("spacy", "examples/pipeline/custom_component_countries_api.py", 500)
-
-p
-    |  In this case, all data can be fetched on initialisation in one request.
-    |  However, if you're working with text that contains incomplete country
-    |  names, spelling mistakes or foreign-language versions, you could also
-    |  implement a #[code like_country]-style getter function that makes a
-    |  request to the search API endpoint and returns the best-matching
-    |  result.
-
-+h(4, "custom-components-usage-ideas") Other usage ideas
-
-+list
-    +item
-        |  #[strong Adding new features and hooking in models]. For example,
-        |  a sentiment analysis model, or your preferred solution for
-        |  lemmatization or sentiment analysis. spaCy's built-in tagger,
-        |  parser and entity recognizer respect annotations that were already
-        |  set on the #[code Doc] in a previous step of the pipeline.
-    +item
-        |  #[strong Integrating other libraries and APIs]. For example, your
-        |  pipeline component can write additional information and data
-        |  directly to the #[code Doc] or #[code Token] as custom attributes,
-        |  while making sure no information is lost in the process. This can
-        |  be output generated by other libraries and models, or an external
-        |  service with a REST API.
-    +item
-        |  #[strong Debugging and logging]. For example, a component which
-        |  stores and/or exports relevant information about the current state
-        |  of the processed document, and insert it at any point of your
-        |  pipeline.
-
-+infobox("Developing third-party extensions")
-    |  The new pipeline management and custom attributes finally make it easy
-    |  to develop your own spaCy extensions and plugins and share them with
-    |  others. Extensions can claim their own #[code ._] namespace and exist as
-    |  standalone packages. If you're developing a tool or library and want to
-    |  make it easy for others to use it with spaCy and add it to their
-    |  pipeline, all you have to do is expose a function that takes a
-    |  #[code Doc], modifies it and returns it. For more details and
-    |  #[strong best practices], see the section on
-    |  #[+a("#extensions") developing spaCy extensions].
-
-+h(3, "custom-components-user-hooks") User hooks
-
-p
-    |  While it's generally recommended to use the #[code Doc._], #[code Span._]
-    |  and #[code Token._] proxies to add your own custom attributes, spaCy
-    |  offers a few exceptions to allow #[strong customising the built-in methods]
-    |  like #[+api("doc#similarity") #[code Doc.similarity]] or
-    |  #[+api("doc#vector") #[code Doc.vector]]. with your own hooks, which can
-    |  rely on statistical models you train yourself. For instance, you can
-    |  provide your own on-the-fly sentence segmentation algorithm or document
-    |  similarity method.
-
-p
-    |  Hooks let you customize some of the behaviours of the #[code Doc],
-    |  #[code Span] or #[code Token] objects by adding a component to the
-    |  pipeline. For instance, to customize the
-    |  #[+api("doc#similarity") #[code Doc.similarity]] method, you can add a
-    |  component that sets a custom function to
-    |  #[code doc.user_hooks['similarity']]. The built-in #[code Doc.similarity]
-    |  method will check the #[code user_hooks] dict, and delegate to your
-    |  function if you've set one. Similar results can be achieved by setting
-    |  functions to #[code Doc.user_span_hooks] and #[code Doc.user_token_hooks].
-
-+aside("Implementation note")
-    |  The hooks live on the #[code Doc] object because the #[code Span] and
-    |  #[code Token] objects are created lazily, and don't own any data. They
-    |  just proxy to their parent #[code Doc]. This turns out to be convenient
-    |  here — we only have to worry about installing hooks in one place.
-
-+table(["Name", "Customises"])
-    +row
-        +cell #[code user_hooks]
-        +cell
-            +api("doc#vector") #[code Doc.vector]
-            +api("doc#has_vector") #[code Doc.has_vector]
-            +api("doc#vector_norm") #[code Doc.vector_norm]
-            +api("doc#sents") #[code Doc.sents]
-
-    +row
-        +cell #[code user_token_hooks]
-        +cell
-            +api("token#similarity") #[code Token.similarity]
-            +api("token#vector") #[code Token.vector]
-            +api("token#has_vector") #[code Token.has_vector]
-            +api("token#vector_norm") #[code Token.vector_norm]
-            +api("token#conjuncts") #[code Token.conjuncts]
-
-    +row
-        +cell #[code user_span_hooks]
-        +cell
-            +api("span#similarity") #[code Span.similarity]
-            +api("span#vector") #[code Span.vector]
-            +api("span#has_vector") #[code Span.has_vector]
-            +api("span#vector_norm") #[code Span.vector_norm]
-            +api("span#root") #[code Span.root]
-
-+code("Add custom similarity hooks").
-    class SimilarityModel(object):
-        def __init__(self, model):
-            self._model = model
-
-        def __call__(self, doc):
-            doc.user_hooks['similarity'] = self.similarity
-            doc.user_span_hooks['similarity'] = self.similarity
-            doc.user_token_hooks['similarity'] = self.similarity
-
-        def similarity(self, obj1, obj2):
-            y = self._model([obj1.vector, obj2.vector])
-            return float(y[0])
-
-+h(3, "extensions") Developing spaCy extensions
-
-p
-    |  We're very excited about all the new possibilities for community
-    |  extensions and plugins in spaCy v2.0, and we can't wait to see what
-    |  you build with it! To get you started, here are a few tips, tricks and
-    |  best practices. #[+a("/universe/?category=pipeline") See here] for
-    |  examples of other spaCy extensions.
-
-+list
-    +item
-        |  Make sure to choose a #[strong descriptive and specific name] for
-        |  your pipeline component class, and set it as its #[code name]
-        |  attribute. Avoid names that are too common or likely to clash with
-        |  built-in or a user's other custom components. While it's fine to call
-        |  your package "spacy_my_extension", avoid component names including
-        |  "spacy", since this can easily lead to confusion.
-
-        +code-wrapper
-            +code-new name = 'myapp_lemmatizer'
-            +code-old name = 'lemmatizer'
-
-    +item
-        |  When writing to #[code Doc], #[code Token] or #[code Span] objects,
-        |  #[strong use getter functions] wherever possible, and avoid setting
-        |  values explicitly. Tokens and spans don't own any data themselves,
-        |  so you should provide a function that allows them to compute the
-        |  values instead of writing static properties to individual objects.
-
-        +code-wrapper
-            +code-new.
-                is_fruit = lambda token: token.text in ('apple', 'orange')
-                Token.set_extension('is_fruit', getter=is_fruit)
-            +code-old.
-                token._.set_extension('is_fruit', default=False)
-                if token.text in ('apple', 'orange'):
-                    token._.set('is_fruit', True)
-
-    +item
-        |  When using #[strong mutable values] like dictionaries or lists as
-        |  the #[code default] argument, keep in mind that they behave just like
-        |  mutable default arguments
-        |  #[+a("http://docs.python-guide.org/en/latest/writing/gotchas/#mutable-default-arguments") in Python functions].
-        |  This can easily cause unintended results, like the same value being
-        |  set on #[em all] objects instead of only one particular instance.
-        |  In most cases, it's better to use #[strong getters and setters], and
-        |  only set the #[code default] for boolean or string values.
-
-        +code-wrapper
-            +code-new.
-                Doc.set_extension('fruits', getter=get_fruits, setter=set_fruits)
-
-            +code-old.
-                Doc.set_extension('fruits', default={})
-                doc._.fruits['apple'] = u'🍎'  # all docs now have {'apple': u'🍎'}
-
-    +item
-        |  Always add your custom attributes to the #[strong global] #[code Doc]
-        |  #[code Token] or #[code Span] objects, not a particular instance of
-        |  them. Add the attributes #[strong as early as possible], e.g. in
-        |  your extension's #[code __init__] method or in the global scope of
-        |  your module. This means that in the case of namespace collisions,
-        |  the user will see an error immediately, not just when they run their
-        |  pipeline.
-
-        +code-wrapper
-            +code-new.
-                from spacy.tokens import Doc
-                def __init__(attr='my_attr'):
-                    Doc.set_extension(attr, getter=self.get_doc_attr)
-            +code-old.
-                def __call__(doc):
-                    doc.set_extension('my_attr', getter=self.get_doc_attr)
-
-    +item
-        |  If your extension is setting properties on the #[code Doc],
-        |  #[code Token] or #[code Span], include an option to
-        |  #[strong let the user to change those attribute names]. This makes
-        |  it easier to avoid namespace collisions and accommodate users with
-        |  different naming preferences. We recommend adding an #[code attrs]
-        |  argument to the #[code __init__] method of your class so you can
-        |  write the names to class attributes and reuse them across your
-        |  component.
-
-        +code-wrapper
-            +code-new Doc.set_extension(self.doc_attr, default='some value')
-            +code-old Doc.set_extension('my_doc_attr', default='some value')
-
-    +item
-        |  Ideally, extensions should be #[strong standalone packages] with
-        |  spaCy and optionally, other packages specified as a dependency. They
-        |  can freely assign to their own #[code ._] namespace, but should stick
-        |  to that. If your extension's only job is to provide a better
-        |  #[code .similarity] implementation, and your docs state this
-        |  explicitly, there's no problem with writing to the
-        |  #[+a("#custom-components-user-hooks") #[code user_hooks]], and
-        |  overwriting spaCy's built-in method. However, a third-party
-        |  extension should #[strong never silently overwrite built-ins], or
-        |  attributes set by other extensions.
-
-    +item
-        |  If you're looking to publish a model that depends on a custom
-        |  pipeline component, you can either #[strong require it] in the model
-        |  package's dependencies, or – if the component is specific and
-        |  lightweight – choose to #[strong ship it with your model package]
-        |  and add it to the #[code Language] instance returned by the
-        |  model's #[code load()] method. For examples of this, check out the
-        |  implementations of spaCy's
-        |  #[+api("util#load_model_from_init_py") #[code load_model_from_init_py]]
-        |  and  #[+api("util#load_model_from_path") #[code load_model_from_path]]
-        |  utility functions.
-
-        +code-wrapper
-            +code-new.
-                nlp.add_pipe(my_custom_component)
-                return nlp.from_disk(model_path)
-
-    +item
-        |  Once you're ready to share your extension with others, make sure to
-        |  #[strong add docs and installation instructions] (you can
-        |  always link to this page for more info). Make it easy for others to
-        |  install and use your extension, for example by uploading it to
-        |  #[+a("https://pypi.python.org") PyPi]. If you're sharing your code on
-        |  GitHub, don't forget to tag it
-        |  with #[+a("https://github.com/topics/spacy?o=desc&s=stars") #[code spacy]]
-        |  and #[+a("https://github.com/topics/spacy-extension?o=desc&s=stars") #[code spacy-extension]]
-        |  to help people find it. If you post it on Twitter, feel free to tag
-        |  #[+a("https://twitter.com/" + SOCIAL.twitter) @#{SOCIAL.twitter}]
-        |  so we can check it out.
diff --git a/website/usage/_processing-pipelines/_multithreading.jade b/website/usage/_processing-pipelines/_multithreading.jade
deleted file mode 100644
index c7fcaac3f..000000000
--- a/website/usage/_processing-pipelines/_multithreading.jade
+++ /dev/null
@@ -1,53 +0,0 @@
-//- 💫 DOCS > USAGE > PROCESSING PIPELINES > MULTI-THREADING
-
-p
-    |  If you have a sequence of documents to process, you should use the
-    |  #[+api("language#pipe") #[code Language.pipe()]] method. The method takes
-    |  an iterator of texts, and accumulates an internal buffer,
-    |  which it works on in parallel. It then yields the documents in order,
-    |  one-by-one. After a long and bitter struggle, the global interpreter
-    |  lock was freed around spaCy's main parsing loop in v0.100.3. This means
-    |  that #[code .pipe()] will be significantly faster in most
-    |  practical situations, because it allows shared memory parallelism.
-
-+code.
-    for doc in nlp.pipe(texts, batch_size=10000, n_threads=3):
-       pass
-
-p
-    |  To make full use of the #[code .pipe()] function, you might want to
-    |  brush up on #[strong Python generators]. Here are a few quick hints:
-
-+list
-    +item
-        |  Generator comprehensions can be written as
-        |  #[code (item for item in sequence)].
-
-    +item
-        |  The
-        |  #[+a("https://docs.python.org/2/library/itertools.html") #[code itertools] built-in library]
-        |  and the
-        |  #[+a("https://github.com/pytoolz/cytoolz") #[code cytoolz] package]
-        |  provide a lot of handy #[strong generator tools].
-
-    +item
-        |  Often you'll have an input stream that pairs text with some
-        |  important meta data, e.g. a JSON document. To
-        |  #[strong pair up the meta data] with the processed #[code Doc]
-        |  object, you should use the #[code itertools.tee] function to split
-        |  the generator in two, and then #[code izip] the extra stream to the
-        |  document stream. Here's
-        |  #[+a(gh("spacy") + "/issues/172#issuecomment-183963403") an example].
-
-+h(3, "multi-processing-example") Example: Multi-processing with Joblib
-
-p
-    |  This example shows how to use multiple cores to process text using
-    |  spaCy and #[+a("https://joblib.readthedocs.io/en/latest/parallel.html") Joblib]. We're
-    |  exporting part-of-speech-tagged, true-cased, (very roughly)
-    |  sentence-separated text, with each "sentence" on a newline, and
-    |  spaces between tokens. Data is loaded from the IMDB movie reviews
-    |  dataset and will be loaded automatically via Thinc's built-in dataset
-    |  loader.
-
-+github("spacy", "examples/pipeline/multi_processing.py", 500)
diff --git a/website/usage/_processing-pipelines/_pipelines.jade b/website/usage/_processing-pipelines/_pipelines.jade
deleted file mode 100644
index 95fe86a22..000000000
--- a/website/usage/_processing-pipelines/_pipelines.jade
+++ /dev/null
@@ -1,154 +0,0 @@
-//- 💫 DOCS > USAGE > PROCESSING PIPELINES > PIPELINES
-
-p
-    |  spaCy makes it very easy to create your own pipelines consisting of
-    |  reusable components – this includes spaCy's default tagger,
-    |  parser and entity recognizer, but also your own custom processing
-    |  functions. A pipeline component can be added to an already existing
-    |  #[code nlp] object, specified when initialising a #[code Language] class,
-    |  or defined within a
-    |  #[+a("/usage/training#saving-loading") model package].
-
-p
-    |  When you load a model, spaCy first consults the model's
-    |  #[+a("/usage/training#saving-loading") #[code meta.json]]. The
-    |  meta typically includes the model details, the ID of a language class,
-    |  and an optional list of pipeline components. spaCy then does the
-    |  following:
-
-+aside-code("meta.json (excerpt)", "json").
-    {
-        "name": "example_model",
-        "lang": "en"
-        "description": "Example model for spaCy",
-        "pipeline": ["tagger", "parser"]
-    }
-
-+list("numbers")
-    +item
-        |  Load the #[strong language class and data] for the given ID via
-        |  #[+api("top-level#util.get_lang_class") #[code get_lang_class]] and initialise
-        |  it. The #[code Language] class contains the shared vocabulary,
-        |  tokenization rules and the language-specific annotation scheme.
-    +item
-        |  Iterate over the #[strong pipeline names] and create each component
-        |  using #[+api("language#create_pipe") #[code create_pipe]], which
-        |  looks them up in #[code Language.factories].
-    +item
-        |  Add each pipeline component to the pipeline in order, using
-        |  #[+api("language#add_pipe") #[code add_pipe]].
-    +item
-        |  Make the #[strong model data] available to the #[code Language] class
-        |  by calling #[+api("language#from_disk") #[code from_disk]] with the
-        |  path to the model data directory.
-
-p
-    |  So when you call this...
-
-+code.
-    nlp = spacy.load('en')
-
-p
-    | ... the model tells spaCy to use the language #[code "en"] and the
-    |  pipeline #[code.u-break ["tagger", "parser", "ner"]]. spaCy will then
-    |  initialise #[code spacy.lang.en.English], and create each pipeline
-    |  component and add it to the processing pipeline. It'll then load in the
-    |  model's data from its data directory and return the modified
-    |  #[code Language] class for you to use as the #[code nlp] object.
-
-p
-    |  Fundamentally, a #[+a("/models") spaCy model] consists of three
-    |  components: #[strong the weights], i.e. binary data loaded in from a
-    |  directory, a #[strong pipeline] of functions called in order,
-    |  and #[strong language data] like the tokenization rules and annotation
-    |  scheme. All of this is specific to each model, and defined in the
-    |  model's #[code meta.json] – for example, a Spanish NER model requires
-    |  different weights, language data and pipeline components than an English
-    |  parsing and tagging model. This is also why the pipeline state is always
-    |  held by the #[code Language] class.
-    |  #[+api("spacy#load") #[code spacy.load]] puts this all together and
-    |  returns an instance of #[code Language]  with a pipeline set and access
-    |  to the binary data:
-
-+code("spacy.load under the hood").
-    lang = 'en'
-    pipeline = ['tagger', 'parser', 'ner']
-    data_path = 'path/to/en_core_web_sm/en_core_web_sm-2.0.0'
-
-    cls = spacy.util.get_lang_class(lang)   # 1. get Language instance, e.g. English()
-    nlp = cls()                             # 2. initialise it
-    for name in pipeline:
-        component = nlp.create_pipe(name)   # 3. create the pipeline components
-        nlp.add_pipe(component)             # 4. add the component to the pipeline
-    nlp.from_disk(model_data_path)          # 5. load in the binary data
-
-p
-    |  When you call #[code nlp] on a text, spaCy will #[strong tokenize] it and
-    |  then #[strong call each component] on the #[code Doc], in order.
-    |  Since the model data is loaded, the components can access it to assign
-    |  annotations to the #[code Doc] object, and subsequently to the
-    |  #[code Token] and #[code Span] which are only views of the #[code Doc],
-    |  and don't own any data themselves. All components return the modified
-    |  document, which is then processed by the component next in the pipeline.
-
-+code("The pipeline under the hood").
-    doc = nlp.make_doc(u'This is a sentence')   # create a Doc from raw text
-    for name, proc in nlp.pipeline:             # iterate over components in order
-        doc = proc(doc)                         # apply each component
-
-p
-    |  The current processing pipeline is available as #[code nlp.pipeline],
-    |  which returns a list of #[code (name, component)] tuples, or
-    |  #[code nlp.pipe_names], which only returns a list of human-readable
-    |  component names.
-
-+code.
-    nlp.pipeline
-    # [('tagger', &lt;spacy.pipeline.Tagger&gt;), ('parser', &lt;spacy.pipeline.DependencyParser&gt;), ('ner', &lt;spacy.pipeline.EntityRecognizer&gt;)]
-    nlp.pipe_names
-    # ['tagger', 'parser', 'ner']
-
-+h(3, "disabling") Disabling and modifying pipeline components
-
-p
-    |  If you don't need a particular component of the pipeline – for
-    |  example, the tagger or the parser, you can disable loading it. This can
-    |  sometimes make a big difference and improve loading speed. Disabled
-    |  component names can be provided to #[+api("spacy#load") #[code spacy.load()]],
-    |  #[+api("language#from_disk") #[code Language.from_disk()]] or the
-    |  #[code nlp] object itself as a list:
-
-+code.
-    nlp = spacy.load('en', disable=['parser', 'tagger'])
-    nlp = English().from_disk('/model', disable=['ner'])
-
-p
-    |  You can also use the #[+api("language#remove_pipe") #[code remove_pipe]]
-    |  method to remove pipeline components from an existing pipeline, the
-    |  #[+api("language#rename_pipe") #[code rename_pipe]] method to rename them,
-    |  or the #[+api("language#replace_pipe") #[code replace_pipe]] method
-    |  to replace them with a custom component entirely (more details on this
-    |  in the section on #[+a("#custom-components") custom components].
-
-+code.
-    nlp.remove_pipe('parser')
-    nlp.rename_pipe('ner', 'entityrecognizer')
-    nlp.replace_pipe('tagger', my_custom_tagger)
-
-+infobox("Important note: disabling pipeline components")
-    .o-block
-        |  Since spaCy v2.0 comes with better support for customising the
-        |  processing pipeline components, the #[code parser], #[code tagger]
-        |  and #[code entity] keyword arguments have been replaced with
-        |  #[code disable], which takes a list of pipeline component names.
-        |  This lets you disable pre-defined components when loading
-        |  a model, or initialising a Language class via
-        |  #[+api("language#from_disk") #[code from_disk]].
-
-    +code-new.
-        nlp = spacy.load('en', disable=['ner'])
-        nlp.remove_pipe('parser')
-        doc = nlp(u"I don't want parsed")
-    +code-old.
-        nlp = spacy.load('en', tagger=False, entity=False)
-        doc = nlp(u"I don't want parsed", parse=False)
diff --git a/website/usage/_processing-pipelines/_serialization.jade b/website/usage/_processing-pipelines/_serialization.jade
deleted file mode 100644
index 8254f4c7f..000000000
--- a/website/usage/_processing-pipelines/_serialization.jade
+++ /dev/null
@@ -1,38 +0,0 @@
-//- 💫 DOCS > USAGE > PROCESSING PIPELINES > SERIALIZATION
-
-include ../_spacy-101/_serialization
-
-+infobox("Important note")
-    |  In spaCy v2.0, the API for saving and loading has changed to only use the
-    |  four methods listed above consistently across objects and classes. For an
-    |  overview of the changes, see #[+a("/usage/v2#incompat") this table]
-    |  and the notes on #[+a("/usage/v2#migrating-saving-loading") migrating].
-
-+h(3, "example-doc") Example: Saving and loading a document
-
-p
-    |  For simplicity, let's assume you've
-    |  #[+a("/usage/linguistic-features#setting-entities") added custom entities] to
-    |  a #[code Doc], either manually, or by using a
-    |  #[+a("/usage/linguistic-features#on_match") match pattern]. You can
-    |  save it locally by calling #[+api("doc#to_disk") #[code Doc.to_disk()]],
-    |  and load it again via #[+api("doc#from_disk") #[code Doc.from_disk()]].
-    |  This will overwrite the existing object and return it.
-
-+code.
-    import spacy
-    from spacy.tokens import Span
-
-    text = u'Netflix is hiring a new VP of global policy'
-
-    nlp = spacy.load('en')
-    doc = nlp(text)
-    assert len(doc.ents) == 0 # Doc has no entities
-    doc.ents += ((Span(doc, 0, 1, label=doc.vocab.strings[u'ORG'])) # add entity
-    doc.to_disk('/path/to/doc') # save Doc to disk
-
-    new_doc = nlp(text)
-    assert len(new_doc.ents) == 0 # new Doc has no entities
-    new_doc = new_doc.from_disk('path/to/doc') # load from disk and overwrite
-    assert len(new_doc.ents) == 1 # entity is now recognised!
-    assert [(ent.text, ent.label_) for ent in new_doc.ents] == [(u'Netflix', u'ORG')]
diff --git a/website/usage/_spacy-101/_architecture.jade b/website/usage/_spacy-101/_architecture.jade
deleted file mode 100644
index 9c6255420..000000000
--- a/website/usage/_spacy-101/_architecture.jade
+++ /dev/null
@@ -1,134 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101 > ARCHITECTURE
-
-p
-    |  The central data structures in spaCy are the #[code Doc] and the
-    |  #[code Vocab]. The #[code Doc] object owns the
-    |  #[strong sequence of tokens] and all their annotations. The #[code Vocab]
-    |  object owns a set of #[strong look-up tables] that make common
-    |  information available across documents. By centralising strings, word
-    |  vectors and lexical attributes, we avoid storing multiple copies of this
-    |  data. This saves memory, and ensures there's a
-    |  #[strong single source of truth].
-
-p
-    |  Text annotations are also designed to allow a single source of truth: the
-    |  #[code Doc] object owns the data, and #[code Span] and #[code Token] are
-    |  #[strong views that point into it]. The #[code Doc] object is constructed
-    |  by the #[code Tokenizer], and then #[strong modified in place] by the
-    |  components of the pipeline. The #[code Language] object coordinates these
-    |  components. It takes raw text and sends it through the pipeline,
-    |  returning an #[strong annotated document]. It also orchestrates training
-    |  and serialization.
-
-+graphic("/assets/img/architecture.svg")
-    include ../../assets/img/architecture.svg
-
-+h(3, "architecture-containers") Container objects
-
-+table(["Name", "Description"])
-    +row
-        +cell #[+api("doc") #[code Doc]]
-        +cell A container for accessing linguistic annotations.
-
-    +row
-        +cell #[+api("span") #[code Span]]
-        +cell A slice from a #[code Doc] object.
-
-    +row
-        +cell #[+api("token") #[code Token]]
-        +cell
-            |  An individual token — i.e. a word, punctuation symbol, whitespace,
-            |  etc.
-
-    +row
-        +cell #[+api("lexeme") #[code Lexeme]]
-        +cell
-            |  An entry in the vocabulary. It's a word type with no context, as
-            |  opposed to a word token. It therefore has no part-of-speech tag,
-            |  dependency parse etc.
-
-+h(3, "architecture-pipeline") Processing pipeline
-
-+table(["Name", "Description"])
-    +row
-        +cell #[+api("language") #[code Language]]
-        +cell
-            |  A text-processing pipeline. Usually you'll load this once per
-            |  process as #[code nlp] and pass the instance around your application.
-
-    +row
-        +cell #[+api("pipe") #[code Pipe]]
-        +cell Base class for processing pipeline components.
-
-    +row
-        +cell #[+api("tagger") #[code Tagger]]
-        +cell Annotate part-of-speech tags on #[code Doc] objects.
-
-    +row
-        +cell #[+api("dependencyparser") #[code DependencyParser]]
-        +cell Annotate syntactic dependencies on #[code Doc] objects.
-
-    +row
-        +cell #[+api("entityrecognizer") #[code EntityRecognizer]]
-        +cell
-            |  Annotate named entities, e.g. persons or products, on #[code Doc]
-            |  objects.
-
-    +row
-        +cell #[+api("textcategorizer") #[code TextCategorizer]]
-        +cell  Assigning categories or labels to #[code Doc] objects.
-
-    +row
-        +cell #[+api("tokenizer") #[code Tokenizer]]
-        +cell
-            |  Segment text, and create #[code Doc] objects with the discovered
-            |  segment boundaries.
-
-    +row
-        +cell #[+api("lemmatizer") #[code Lemmatizer]]
-        +cell
-            |  Determine the base forms of words.
-
-    +row
-        +cell #[code Morphology]
-        +cell
-            |  Assign linguistic features like lemmas, noun case, verb tense etc.
-            |  based on the word and its part-of-speech tag.
-
-    +row
-        +cell #[+api("matcher") #[code Matcher]]
-        +cell
-            |  Match sequences of tokens, based on pattern rules, similar to
-            |  regular expressions.
-
-    +row
-        +cell #[+api("phrasematcher") #[code PhraseMatcher]]
-        +cell Match sequences of tokens based on phrases.
-
-
-+h(3, "architecture-other") Other classes
-
-+table(["Name", "Description"])
-    +row
-        +cell #[+api("vocab") #[code Vocab]]
-        +cell
-            |  A lookup table for the vocabulary that allows you to access
-            |  #[code Lexeme] objects.
-
-    +row
-        +cell #[+api("stringstore") #[code StringStore]]
-        +cell Map strings to and from hash values.
-
-    +row
-        +cell #[+api("vectors") #[code Vectors]]
-        +cell Container class for vector data keyed by string.
-
-    +row
-        +cell #[+api("goldparse") #[code GoldParse]]
-        +cell Collection for training annotations.
-
-    +row
-        +cell #[+api("goldcorpus") #[code GoldCorpus]]
-        +cell
-            |  An annotated corpus, using the JSON file format. Manages
-            |  annotations for tagging, dependency parsing and NER.
diff --git a/website/usage/_spacy-101/_community-faq.jade b/website/usage/_spacy-101/_community-faq.jade
deleted file mode 100644
index 58804dccc..000000000
--- a/website/usage/_spacy-101/_community-faq.jade
+++ /dev/null
@@ -1,133 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101 > COMMUNITY & FAQ
-
-p
-    |  We're very happy to see the spaCy community grow and include a mix of
-    |  people from all kinds of different backgrounds – computational
-    |  linguistics, data science, deep learning, research and more. If you'd
-    |  like to get involved, below are some answers to the most important
-    |  questions and resources for further reading.
-
-+h(3, "faq-help-code") Help, my code isn't working!
-
-p
-    |  Bugs suck, and we're doing our best to continuously improve the tests
-    |  and fix bugs as soon as possible. Before you submit an issue, do a
-    |  quick search and check if the problem has already been reported. If
-    |  you're having installation or loading problems, make sure to also check
-    |  out the #[+a("/usage/#troubleshooting") troubleshooting guide]. Help
-    |  with spaCy is available via the following platforms:
-
-+aside("How do I know if something is a bug?")
-    |  Of course, it's always hard to know for sure, so don't worry – we're not
-    |  going to be mad if a bug report turns out to be a typo in your
-    |  code. As a simple rule, any C-level error without a Python traceback,
-    |  like a #[strong segmentation fault] or #[strong memory error],
-    |  is #[strong always] a spaCy bug.#[br]#[br]
-    |  Because models are statistical, their performance will never be
-    |  #[em perfect]. However, if you come across
-    |  #[strong patterns that might indicate an underlying issue], please do
-    |  file a report. Similarly, we also care about behaviours that
-    |  #[strong contradict our docs].
-
-+table(["Platform", "Purpose"])
-    +row
-        +cell #[+a("https://stackoverflow.com/questions/tagged/spacy") Stack Overflow]
-        +cell
-            |  #[strong Usage questions] and everything related to problems with
-            |  your specific code. The Stack Overflow community is much larger
-            |  than ours, so if your problem can be solved by others, you'll
-            |  receive help much quicker.
-
-    +row
-        +cell #[+a("https://gitter.im/" + SOCIAL.gitter) Gitter chat]
-        +cell
-            |  #[strong General discussion] about spaCy, meeting other community
-            |  members and exchanging #[strong tips, tricks and best practices].
-            |  If we're working on experimental models and features, we usually
-            |  share them on Gitter first.
-
-    +row
-        +cell #[+a(gh("spaCy") + "/issues") GitHub issue tracker]
-        +cell
-            |  #[strong Bug reports] and #[strong improvement suggestions], i.e.
-            |  everything that's likely spaCy's fault. This also includes
-            |  problems with the models beyond statistical imprecisions, like
-            |  patterns that point to a bug.
-
-+infobox
-    |  Please understand that we won't be able to provide individual support via
-    |  email. We also believe that help is much more valuable if it's shared
-    |  publicly, so that #[strong more people can benefit from it]. If you come
-    |  across an issue and you think you might be able to help, consider posting
-    |  a quick update with your solution. No matter how simple, it can easily
-    |  save someone a lot of time and headache – and the next time you need help,
-    |  they might repay the favour.
-
-+h(3, "faq-contributing") How can I contribute to spaCy?
-
-p
-    |  You don't have to be an NLP expert or Python pro to contribute, and we're
-    |  happy to help you get started. If you're new to spaCy, a good place to
-    |  start is the
-    |  #[+a(gh("spaCy") + '/issues?q=is%3Aissue+is%3Aopen+label%3A"help+wanted+%28easy%29"') #[code help wanted (easy)] label]
-    |  on GitHub, which we use to tag bugs and feature requests that are easy
-    |  and self-contained. We also appreciate contributions to the docs – whether
-    |  it's fixing a typo, improving an example or adding additional explanations.
-    |  You'll find a "Suggest edits" link at the bottom of each page that points
-    |  you to the source.
-
-p
-    |  Another way of getting involved is to help us improve the
-    |  #[+a("/usage/adding-languages#language-data") language data] –
-    |  especially if you happen to speak one of the languages currently in
-    |  #[+a("/usage/models#languages") alpha support]. Even
-    |  adding simple tokenizer exceptions, stop words or lemmatizer data
-    |  can make a big difference. It will also make it easier for us to provide
-    |  a statistical model for the language in the future. Submitting a test
-    |  that documents a bug or performance issue, or covers functionality that's
-    |  especially important for your application is also very helpful. This way,
-    |  you'll also make sure we never accidentally introduce regressions to the
-    |  parts of the library that you care about the most.
-
-p
-    strong
-        |  For more details on the types of contributions we're looking for, the
-        |  code conventions and other useful tips, make sure to check out the
-        |  #[+a(gh("spaCy", "CONTRIBUTING.md")) contributing guidelines].
-
-+infobox("Code of Conduct")
-    |  spaCy adheres to the
-    |  #[+a("http://contributor-covenant.org/version/1/4/") Contributor Covenant Code of Conduct].
-    |  By participating, you are expected to uphold this code.
-
-+h(3, "faq-project-with-spacy")
-    |  I've built something cool with spaCy – how can I get the word out?
-
-p
-    |  First, congrats – we'd love to check it out! When you share your
-    |  project on Twitter, don't forget to tag
-    |  #[+a("https://twitter.com/" + SOCIAL.twitter) @#{SOCIAL.twitter}] so we
-    |  don't miss it. If you think your project would be a good fit for the
-    |  #[+a("/universe") spaCy Universe], #[strong feel free to submit it!]
-    |  Tutorials are also incredibly valuable to other users and a great way to
-    |  get exposure. So we strongly encourage #[strong writing up your experiences],
-    |  or sharing your code and some tips and tricks on your blog. Since our
-    |  website is open-source, you can add your project or tutorial by making a
-    |  pull request on GitHub.
-
-p
-    |  If you would like to use the spaCy logo on your site, please get in touch
-    |  and ask us first. However, if you want to show support and tell others
-    |  that your project is using spaCy, you can grab one of our
-    |  #[strong spaCy badges] here:
-
-- SPACY_BADGES =  ["built%20with-spaCy-09a3d5.svg", "made%20with%20❤%20and-spaCy-09a3d5.svg", "spaCy-v2-09a3d5.svg"]
-+quickstart([{id: "badge", input_style: "check", options: SPACY_BADGES.map(function(badge, i) { return {id: i, title: "<img class='o-icon' src='https://img.shields.io/badge/" + badge + "' height='20'/>", checked: (i == 0) ? true : false}}) }], false, false, true)
-    .c-code-block(data-qs-results)
-        for badge, i in SPACY_BADGES
-            - var url = "https://img.shields.io/badge/" + badge
-            +code(false, "text", false, false, "star").o-no-block(data-qs-badge=i)=url
-            +code(false, "text", false, false, "code").o-no-block(data-qs-badge=i).
-                &lt;a href="#{SITE_URL}"&gt;&lt;img src="#{url}" height="20"&gt;&lt;/a&gt;
-            +code(false, "text", false, false, "markdown").o-no-block(data-qs-badge=i).
-                [![spaCy](#{url})](#{SITE_URL})
diff --git a/website/usage/_spacy-101/_language-data.jade b/website/usage/_spacy-101/_language-data.jade
deleted file mode 100644
index 628152524..000000000
--- a/website/usage/_spacy-101/_language-data.jade
+++ /dev/null
@@ -1,107 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101 > LANGUAGE DATA
-
-p
-    |  Every language is different – and usually full of
-    |  #[strong exceptions and special cases], especially amongst the most
-    |  common words. Some of these exceptions are shared across languages, while
-    |  others are #[strong entirely specific] – usually so specific that they need
-    |  to be hard-coded. The #[+src(gh("spaCy", "spacy/lang")) #[code lang]] module
-    |  contains all language-specific data, organised in simple Python files.
-    |  This makes the data easy to update and extend.
-
-p
-    |  The #[strong shared language data] in the directory root includes rules
-    |  that can be generalised across languages – for example, rules for basic
-    |  punctuation, emoji, emoticons, single-letter abbreviations and norms for
-    |  equivalent tokens with different spellings, like #[code &quot;] and
-    |  #[code ”]. This helps the models make more accurate predictions.
-    |  The #[strong individual language data] in a submodule contains
-    |  rules that are only relevant to a particular language. It also takes
-    |  care of putting together all components and creating the #[code Language]
-    |  subclass – for example, #[code English] or #[code German].
-
-+aside-code.
-    from spacy.lang.en import English
-    from spacy.lang.de import German
-
-    nlp_en = English() # includes English data
-    nlp_de = German() # includes German data
-
-+graphic("/assets/img/language_data.svg")
-    include ../../assets/img/language_data.svg
-
-+table(["Name", "Description"])
-    +row
-        +cell #[strong Stop words]#[br]
-            |  #[+src(gh("spacy-dev-resources", "templates/new_language/stop_words.py")) #[code stop_words.py]]
-        +cell
-            |  List of most common words of a language that are often useful to
-            |  filter out, for example "and" or "I". Matching tokens will
-            |  return #[code True] for #[code is_stop].
-
-    +row
-        +cell #[strong Tokenizer exceptions]#[br]
-            |  #[+src(gh("spacy-dev-resources", "templates/new_language/tokenizer_exceptions.py")) #[code tokenizer_exceptions.py]]
-        +cell
-            |  Special-case rules for the tokenizer, for example, contractions
-            |  like "can't" and abbreviations with punctuation, like "U.K.".
-
-    +row
-        +cell #[strong Norm exceptions]
-            |  #[+src(gh("spaCy", "spacy/lang/norm_exceptions.py")) #[code norm_exceptions.py]]
-        +cell
-            |  Special-case rules for normalising tokens to improve the model's
-            |  predictions, for example on American vs. British spelling.
-
-    +row
-        +cell #[strong Punctuation rules]
-            |  #[+src(gh("spaCy", "spacy/lang/punctuation.py")) #[code punctuation.py]]
-        +cell
-            |  Regular expressions for splitting tokens, e.g. on punctuation or
-            |  special characters like emoji. Includes rules for prefixes,
-            |  suffixes and infixes.
-
-    +row
-        +cell #[strong Character classes]
-            |  #[+src(gh("spaCy", "spacy/lang/char_classes.py")) #[code char_classes.py]]
-        +cell
-            |  Character classes to be used in regular expressions, for example,
-            |  latin characters, quotes, hyphens or icons.
-
-    +row
-        +cell #[strong Lexical attributes]
-            |  #[+src(gh("spacy-dev-resources", "templates/new_language/lex_attrs.py")) #[code lex_attrs.py]]
-        +cell
-            |  Custom functions for setting lexical attributes on tokens, e.g.
-            |  #[code like_num], which includes language-specific words like "ten"
-            |  or "hundred".
-
-    +row
-        +cell #[strong Syntax iterators]
-            |  #[+src(gh("spaCy", "spacy/lang/en/syntax_iterators.py")) #[code syntax_iterators.py]]
-        +cell
-            |  Functions that compute views of a #[code Doc] object based on its
-            |  syntax. At the moment, only used for
-            |  #[+a("/usage/linguistic-features#noun-chunks") noun chunks].
-
-    +row
-        +cell #[strong Lemmatizer]
-            |  #[+src(gh("spacy-dev-resources", "templates/new_language/lemmatizer.py")) #[code lemmatizer.py]]
-        +cell
-            |  Lemmatization rules or a lookup-based lemmatization table to
-            |  assign base forms, for example "be" for "was".
-
-    +row
-        +cell #[strong Tag map]#[br]
-            |  #[+src(gh("spacy-dev-resources", "templates/new_language/tag_map.py")) #[code tag_map.py]]
-        +cell
-            |  Dictionary mapping strings in your tag set to
-            |  #[+a("http://universaldependencies.org/u/pos/all.html") Universal Dependencies]
-            |  tags.
-
-    +row
-        +cell #[strong Morph rules]
-            |  #[+src(gh("spaCy", "spacy/lang/en/morph_rules.py")) #[code morph_rules.py]]
-        +cell
-            |  Exception rules for morphological analysis of irregular words like
-            |  personal pronouns.
diff --git a/website/usage/_spacy-101/_lightning-tour.jade b/website/usage/_spacy-101/_lightning-tour.jade
deleted file mode 100644
index 7b999ccaf..000000000
--- a/website/usage/_spacy-101/_lightning-tour.jade
+++ /dev/null
@@ -1,362 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101 > LIGHTNING TOUR
-
-p
-    |  The following examples and code snippets give you an overview of spaCy's
-    |  functionality and its usage.
-
-+h(3, "lightning-tour-models") Install models and process text
-
-+code(false, "bash").
-    python -m spacy download en_core_web_sm
-    python -m spacy download de_core_news_sm
-
-+code-exec.
-    import spacy
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u'Hello, world. Here are two sentences.')
-    print([t.text for t in doc])
-
-    nlp_de = spacy.load('de_core_news_sm')
-    doc_de = nlp_de(u'Ich bin ein Berliner.')
-    print([t.text for t in doc_de])
-
-+infobox
-    |  #[+label-inline API:] #[+api("spacy#load") #[code spacy.load()]]
-    |  #[+label-inline Usage:] #[+a("/usage/models") Models],
-    |  #[+a("/usage/spacy-101") spaCy 101]
-
-+h(3, "lightning-tour-tokens-sentences") Get tokens, noun chunks & sentences
-    +tag-model("dependency parse")
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u"Peach emoji is where it has always been. Peach is the superior "
-              u"emoji. It's outranking eggplant 🍑 ")
-    print(doc[0].text)          # Peach
-    print(doc[1].text)          # emoji
-    print(doc[-1].text)         # 🍑
-    print(doc[17:19].text)      # outranking eggplant
-
-    noun_chunks = list(doc.noun_chunks)
-    print(noun_chunks[0].text)  # Peach emoji
-
-    sentences = list(doc.sents)
-    assert len(sentences) == 3
-    print(sentences[1].text)    # 'Peach is the superior emoji.'
-
-+infobox
-    |  #[+label-inline API:] #[+api("doc") #[code Doc]], #[+api("token") #[code Token]]
-    |  #[+label-inline Usage:] #[+a("/usage/spacy-101") spaCy 101]
-
-+h(3, "lightning-tour-pos-tags") Get part-of-speech tags and flags
-    +tag-model("tagger")
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
-    apple = doc[0]
-    print('Fine-grained POS tag', apple.pos_, apple.pos)
-    print('Coarse-grained POS tag', apple.tag_, apple.tag)
-    print('Word shape', apple.shape_, apple.shape)
-    print('Alphanumeric characters?', apple.is_alpha)
-    print('Punctuation mark?', apple.is_punct)
-
-    billion = doc[10]
-    print('Digit?', billion.is_digit)
-    print('Like a number?', billion.like_num)
-    print('Like an email address?', billion.like_email)
-
-+infobox
-    |  #[+label-inline API:] #[+api("token") #[code Token]]
-    |  #[+label-inline Usage:] #[+a("/usage/linguistic-features#pos-tagging") Part-of-speech tagging]
-
-+h(3, "lightning-tour-hashes") Use hash values for any string
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u'I love coffee')
-
-    coffee_hash = nlp.vocab.strings[u'coffee']  # 3197928453018144401
-    coffee_text = nlp.vocab.strings[coffee_hash]  # 'coffee'
-    print(coffee_hash, coffee_text)
-    print(doc[2].orth, coffee_hash)  # 3197928453018144401
-    print(doc[2].text, coffee_text)  # 'coffee'
-
-    beer_hash = doc.vocab.strings.add(u'beer')  # 3073001599257881079
-    beer_text = doc.vocab.strings[beer_hash]  # 'beer'
-    print(beer_hash, beer_text)
-
-    unicorn_hash = doc.vocab.strings.add(u'🦄 ')  # 18234233413267120783
-    unicorn_text = doc.vocab.strings[unicorn_hash]  # '🦄 '
-    print(unicorn_hash, unicorn_text)
-
-+infobox
-    |  #[+label-inline API:] #[+api("stringstore") #[code StringStore]]
-    |  #[+label-inline Usage:] #[+a("/usage/spacy-101#vocab") Vocab, hashes and lexemes 101]
-
-+h(3, "lightning-tour-entities") Recognise and update named entities
-    +tag-model("NER")
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u'San Francisco considers banning sidewalk delivery robots')
-    for ent in doc.ents:
-        print(ent.text, ent.start_char, ent.end_char, ent.label_)
-
-    from spacy.tokens import Span
-    doc = nlp(u'FB is hiring a new VP of global policy')
-    doc.ents = [Span(doc, 0, 1, label=doc.vocab.strings[u'ORG'])]
-    for ent in doc.ents:
-        print(ent.text, ent.start_char, ent.end_char, ent.label_)
-
-+infobox
-    |  #[+label-inline Usage:] #[+a("/usage/linguistic-features#named-entities") Named entity recognition]
-
-+h(3, "lightning-tour-training") Train and update neural network models
-    +tag-model
-
-+code.
-    import spacy
-    import random
-
-    nlp = spacy.load('en')
-    train_data = [("Uber blew through $1 million", {'entities': [(0, 4, 'ORG')]})]
-
-    with nlp.disable_pipes(*[pipe for pipe in nlp.pipe_names if pipe != 'ner']):
-        optimizer = nlp.begin_training()
-        for i in range(10):
-            random.shuffle(train_data)
-            for text, annotations in train_data:
-                nlp.update([text], [annotations], sgd=optimizer)
-    nlp.to_disk('/model')
-
-+infobox
-    |  #[+label-inline API:] #[+api("language#update") #[code Language.update]]
-    |  #[+label-inline Usage:] #[+a("/usage/training") Training spaCy&apos;s statistical models]
-
-+h(3, "lightning-tour-displacy") Visualize a dependency parse and named entities in your browser
-    +tag-model("dependency parse", "NER")
-    +tag-new(2)
-
-+aside
-    .u-text-center(style="overflow: auto").
-        <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" class="o-svg" viewBox="270 35 125 240" width="400" height="150" style="max-width: none; color: #fff; background: #1a1e23; font-family: inherit; font-size: 2rem">
-            <text fill="currentColor" text-anchor="middle" y="222.0">
-                <tspan style="font-weight: bold" fill="currentColor" x="50">This</tspan>
-                <tspan dy="2em" class="u-color-theme" style="font-weight: bold" fill="currentColor" x="50">DT</tspan>
-            </text>
-            <text fill="currentColor" text-anchor="middle" y="222.0">
-                <tspan style="font-weight: bold" fill="currentColor" x="225">is</tspan>
-                <tspan dy="2em" class="u-color-theme" style="font-weight: bold" fill="currentColor" x="225">VBZ</tspan>
-            </text>
-            <text fill="currentColor" text-anchor="middle" y="222.0">
-                <tspan style="font-weight: bold" fill="currentColor" x="400">a</tspan>
-                <tspan dy="2em" class="u-color-theme" style="font-weight: bold" fill="currentColor" x="400">DT</tspan>
-            </text>
-            <text fill="currentColor" text-anchor="middle" y="222.0">
-                <tspan style="font-weight: bold" fill="currentColor" x="575">sentence.</tspan>
-                <tspan dy="2em" class="u-color-theme" style="font-weight: bold" fill="currentColor" x="575">NN</tspan>
-            </text>
-            <path id="arrow-0-0" stroke-width="2px" d="M70,177.0 C70,89.5 220.0,89.5 220.0,177.0" fill="none" stroke="currentColor"/>
-            <text dy="1.25em" style="font-size: 0.9em; letter-spacing: 2px">
-                <textPath xlink:href="#arrow-0-0" startOffset="50%" fill="currentColor" text-anchor="middle">nsubj</textPath>
-            </text>
-            <path d="M70,179.0 L62,167.0 78,167.0" fill="currentColor"/>
-            <path id="arrow-0-1" stroke-width="2px" d="M420,177.0 C420,89.5 570.0,89.5 570.0,177.0" fill="none" stroke="currentColor"/>
-            <text dy="1.25em" style="font-size: 0.9em; letter-spacing: 2px">
-                <textPath xlink:href="#arrow-0-1" startOffset="50%" fill="currentColor" text-anchor="middle">det</textPath>
-            </text>
-            <path d="M420,179.0 L412,167.0 428,167.0" fill="currentColor"/>
-            <path id="arrow-0-2" stroke-width="2px" d="M245,177.0 C245,2.0 575.0,2.0 575.0,177.0" fill="none" stroke="currentColor"/>
-            <text dy="1.25em" style="font-size: 0.9em; letter-spacing: 2px">
-                <textPath xlink:href="#arrow-0-2" startOffset="50%" fill="currentColor" text-anchor="middle">attr</textPath>
-            </text>
-            <path d="M575.0,179.0 L583.0,167.0 567.0,167.0" fill="currentColor"/>
-        </svg>
-
-+code.
-    from spacy import displacy
-
-    doc_dep = nlp(u'This is a sentence.')
-    displacy.serve(doc_dep, style='dep')
-
-    doc_ent = nlp(u'When Sebastian Thrun started working on self-driving cars at Google '
-                  u'in 2007, few people outside of the company took him seriously.')
-    displacy.serve(doc_ent, style='ent')
-
-+infobox
-    |  #[+label-inline API:] #[+api("top-level#displacy") #[code displacy]]
-    |  #[+label-inline Usage:] #[+a("/usage/visualizers") Visualizers]
-
-+h(3, "lightning-tour-word-vectors") Get word vectors and similarity
-    +tag-model("word vectors")
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_md')
-    doc = nlp(u"Apple and banana are similar. Pasta and hippo aren't.")
-
-    apple = doc[0]
-    banana = doc[2]
-    pasta = doc[6]
-    hippo = doc[8]
-
-    print('apple <-> banana', apple.similarity(banana))
-    print('pasta <-> hippo', pasta.similarity(hippo))
-    print(apple.has_vector, banana.has_vector, pasta.has_vector, hippo.has_vector)
-
-p
-    |  For the best results, you should run this example using the
-    |  #[+a("/models/en#en_vectors_web_lg") #[code en_vectors_web_lg]] model
-    |  (currently not available in the live demo).
-
-+infobox
-    |  #[+label-inline Usage:] #[+a("/usage/vectors-similarity") Word vectors and similarity]
-
-+h(3, "lightning-tour-serialization") Simple and efficient serialization
-
-+code.
-    import spacy
-    from spacy.tokens import Doc
-    from spacy.vocab import Vocab
-
-    nlp = spacy.load('en')
-    customer_feedback = open('customer_feedback_627.txt').read()
-    doc = nlp(customer_feedback)
-    doc.to_disk('/tmp/customer_feedback_627.bin')
-
-    new_doc = Doc(Vocab()).from_disk('/tmp/customer_feedback_627.bin')
-
-+infobox
-    |  #[+label-inline API:] #[+api("language") #[code Language]],
-    |  #[+api("doc") #[code Doc]]
-    |  #[+label-inline Usage:] #[+a("/usage/models#saving-loading") Saving and loading models]
-
-+h(3, "lightning-tour-rule-matcher") Match text with token rules
-
-+code-exec.
-    import spacy
-    from spacy.matcher import Matcher
-
-    nlp = spacy.load('en_core_web_sm')
-    matcher = Matcher(nlp.vocab)
-
-    def set_sentiment(matcher, doc, i, matches):
-        doc.sentiment += 0.1
-
-    pattern1 = [{'ORTH': 'Google'}, {'ORTH': 'I'}, {'ORTH': '/'}, {'ORTH': 'O'}]
-    pattern2 = [[{'ORTH': emoji, 'OP': '+'}] for emoji in ['😀', '😂', '🤣', '😍']]
-    matcher.add('GoogleIO', None, pattern1) # match "Google I/O" or "Google i/o"
-    matcher.add('HAPPY', set_sentiment, *pattern2) # match one or more happy emoji
-
-    doc = nlp(u"A text about Google I/O 😀😀")
-    matches = matcher(doc)
-
-    for match_id, start, end in matches:
-       string_id = nlp.vocab.strings[match_id]
-       span = doc[start:end]
-       print(string_id, span.text)
-    print('Sentiment', doc.sentiment)
-
-+infobox
-    |  #[+label-inline API:] #[+api("matcher") #[code Matcher]]
-    |  #[+label-inline Usage:] #[+a("/usage/linguistic-features#rule-based-matching") Rule-based matching]
-
-+h(3, "lightning-tour-multi-threaded") Multi-threaded generator
-
-+code.
-    texts = [u'One document.', u'...', u'Lots of documents']
-    # .pipe streams input, and produces streaming output
-    iter_texts = (texts[i % 3] for i in xrange(100000000))
-    for i, doc in enumerate(nlp.pipe(iter_texts, batch_size=50, n_threads=4)):
-        assert doc.is_parsed
-        if i == 100:
-            break
-
-+infobox
-    |  #[+label-inline API:] #[+api("doc") #[code Doc]]
-    |  #[+label-inline Usage:] #[+a("/usage/processing-pipelines#multithreading") Processing pipelines]
-
-+h(3, "lightning-tour-dependencies") Get syntactic dependencies
-    +tag-model("dependency parse")
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u"When Sebastian Thrun started working on self-driving cars at Google "
-              u"in 2007, few people outside of the company took him seriously.")
-
-    dep_labels = []
-    for token in doc:
-        while token.head != token:
-            dep_labels.append(token.dep_)
-            token = token.head
-    print(dep_labels)
-
-+infobox
-    |  #[+label-inline API:] #[+api("token") #[code Token]]
-    |  #[+label-inline Usage:] #[+a("/usage/linguistic-features#dependency-parse") Using the dependency parse]
-
-+h(3, "lightning-tour-numpy-arrays") Export to numpy arrays
-
-+code-exec.
-    import spacy
-    from spacy.attrs import ORTH, LIKE_URL
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u"Check out https://spacy.io")
-    for token in doc:
-        print(token.text, token.orth, token.like_url)
-
-    attr_ids = [ORTH, LIKE_URL]
-    doc_array = doc.to_array(attr_ids)
-    print(doc_array.shape)
-    print(len(doc), len(attr_ids))
-
-    assert doc[0].orth == doc_array[0, 0]
-    assert doc[1].orth == doc_array[1, 0]
-    assert doc[0].like_url == doc_array[0, 1]
-
-    assert list(doc_array[:, 1]) == [t.like_url for t in doc]
-    print(list(doc_array[:, 1]))
-
-+h(3, "lightning-tour-inline") Calculate inline markup on original string
-
-+code-exec.
-    import spacy
-
-    def put_spans_around_tokens(doc):
-        """Here, we're building a custom "syntax highlighter" for
-        part-of-speech tags and dependencies. We put each token in a
-        span element, with the appropriate classes computed. All whitespace is
-        preserved, outside of the spans. (Of course, HTML will only display
-        multiple whitespace if enabled – but the point is, no information is lost
-        and you can calculate what you need, e.g. &lt;br /&gt;, &lt;p&gt; etc.)
-        """
-        output = []
-        html = '&lt;span class="{classes}"&gt;{word}&lt;/span&gt;{space}'
-        for token in doc:
-            if token.is_space:
-                output.append(token.text)
-            else:
-                classes = 'pos-{} dep-{}'.format(token.pos_, token.dep_)
-                output.append(html.format(classes=classes, word=token.text, space=token.whitespace_))
-        string = ''.join(output)
-        string = string.replace('\n', '')
-        string = string.replace('\t', '    ')
-        return '&lt;pre&gt;{}&lt;/pre&gt;'.format(string)
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u"This is a test.\n\nHello   world.")
-    html = put_spans_around_tokens(doc)
-    print(html)
diff --git a/website/usage/_spacy-101/_named-entities.jade b/website/usage/_spacy-101/_named-entities.jade
deleted file mode 100644
index 69d2a7692..000000000
--- a/website/usage/_spacy-101/_named-entities.jade
+++ /dev/null
@@ -1,41 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101 > NAMED ENTITIES
-
-p
-    |  A named entity is a "real-world object" that's assigned a name – for
-    |  example, a person, a country, a product or a book title. spaCy can
-    |  #[strong recognise] #[+a("/api/annotation#named-entities") various types]
-    |  of named entities in a document, by asking the model for a
-    |  #[strong prediction]. Because models are statistical and strongly depend
-    |  on the examples they were trained on, this doesn't always work
-    |  #[em perfectly] and might need some tuning later, depending on your use
-    |  case.
-
-p
-    |  Named entities are available as the #[code ents] property of a #[code Doc]:
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
-
-    for ent in doc.ents:
-        print(ent.text, ent.start_char, ent.end_char, ent.label_)
-
-+aside
-    |  #[strong Text]: The original entity text.#[br]
-    |  #[strong Start]: Index of start of entity in the #[code Doc].#[br]
-    |  #[strong End]: Index of end of entity in the #[code Doc].#[br]
-    |  #[strong Label]: Entity label, i.e. type.
-
-+table(["Text", "Start", "End", "Label", "Description"])
-    - var style = [0, 1, 1, 1, 0]
-    +annotation-row(["Apple", 0, 5, "ORG", "Companies, agencies, institutions."], style)
-    +annotation-row(["U.K.", 27, 31, "GPE", "Geopolitical entity, i.e. countries, cities, states."], style)
-    +annotation-row(["$1 billion", 44, 54, "MONEY", "Monetary values, including unit."], style)
-
-p
-    |  Using spaCy's built-in #[+a("/usage/visualizers") displaCy visualizer],
-    |  here's what our example sentence and its named entities look like:
-
-+codepen("2f2ad1408ff79fc6a326ea3aedbb353b", 160)
diff --git a/website/usage/_spacy-101/_pipelines.jade b/website/usage/_spacy-101/_pipelines.jade
deleted file mode 100644
index d1e5453a7..000000000
--- a/website/usage/_spacy-101/_pipelines.jade
+++ /dev/null
@@ -1,82 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101 > PIPELINES
-
-p
-    |  When you call #[code nlp] on a text, spaCy first tokenizes the text to
-    |  produce a #[code Doc] object. The #[code Doc] is then processed in several
-    |  different steps – this is also referred to as the
-    |  #[strong processing pipeline]. The pipeline used by the
-    |  #[+a("/models") default models] consists of a tagger, a parser and an
-    |  entity recognizer. Each pipeline component returns the processed
-    |  #[code Doc], which is then passed on to the next component.
-
-+graphic("/assets/img/pipeline.svg")
-    include ../../assets/img/pipeline.svg
-
-+aside
-    |  #[strong Name:] ID of the pipeline component.#[br]
-    |  #[strong Component:] spaCy's implementation of the component.#[br]
-    |  #[strong Creates:] Objects, attributes and properties modified and set by
-    |  the component.
-
-+table(["Name", "Component", "Creates", "Description"])
-    +row
-        +cell #[strong tokenizer]
-        +cell #[+api("tokenizer") #[code Tokenizer]]
-        +cell #[code Doc]
-        +cell Segment text into tokens.
-
-    +row("divider")
-        +cell #[strong tagger]
-        +cell #[+api("tagger") #[code Tagger]]
-        +cell #[code Doc[i].tag]
-        +cell Assign part-of-speech tags.
-
-    +row
-        +cell #[strong parser]
-        +cell #[+api("dependencyparser") #[code DependencyParser]]
-        +cell
-            |  #[code Doc[i].head],
-            |  #[code Doc[i].dep],
-            |  #[code Doc.sents],
-            |  #[code Doc.noun_chunks]
-        +cell Assign dependency labels.
-
-    +row
-        +cell #[strong ner]
-        +cell #[+api("entityrecognizer") #[code EntityRecognizer]]
-        +cell #[code Doc.ents], #[code Doc[i].ent_iob], #[code Doc[i].ent_type]
-        +cell Detect and label named entities.
-
-    +row
-        +cell #[strong textcat]
-        +cell #[+api("textcategorizer") #[code TextCategorizer]]
-        +cell #[code Doc.cats]
-        +cell Assign document labels.
-
-    +row("divider")
-        +cell #[strong &period;&period;&period;]
-        +cell #[+a("/usage/processing-pipelines#custom-components") custom components]
-        +cell #[code Doc._.xxx], #[code Token._.xxx], #[code Span._.xxx]
-        +cell Assign custom attributes, methods or properties.
-
-p
-    |  The processing pipeline always #[strong depends on the statistical model]
-    |  and its capabilities. For example, a pipeline can only include an entity
-    |  recognizer component if the model includes data to make predictions of
-    |  entity labels. This is why each model will specify the pipeline to use
-    |  in its meta data, as a simple list containing the component names:
-
-+code(false, "json").
-    "pipeline": ["tagger", "parser", "ner"]
-
-p
-    |  Although you can mix and match pipeline components, their
-    |  #[strong order and combination] is usually important. Some components may
-    |  require certain modifications on the #[code Doc] to process it. As the
-    |  processing pipeline is applied, spaCy encodes the document's internal
-    |  #[strong meaning representations] as an array of floats, also called a
-    |  #[strong tensor]. This includes the tokens and their context, which is
-    |  required for the first component, the tagger, to make predictions of the
-    |  part-of-speech tags. Because spaCy's models are neural network models,
-    |  they only "speak" tensors and expect the input #[code Doc] to have
-    |  a #[code tensor].
diff --git a/website/usage/_spacy-101/_pos-deps.jade b/website/usage/_spacy-101/_pos-deps.jade
deleted file mode 100644
index e280ce058..000000000
--- a/website/usage/_spacy-101/_pos-deps.jade
+++ /dev/null
@@ -1,65 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101 > POS TAGGING AND DEPENDENCY PARSING
-
-p
-    |  After tokenization, spaCy can #[strong parse] and #[strong tag] a
-    |  given #[code Doc]. This is where the statistical model comes in, which
-    |  enables spaCy to #[strong make a prediction] of which tag or label most
-    |  likely applies in this context. A model consists of binary data and is
-    |  produced by showing a system enough examples for it to make predictions
-    |  that generalise across the language – for example, a word following "the"
-    |  in English is most likely a noun.
-
-p
-    |  Linguistic annotations are available as
-    |  #[+api("token#attributes") #[code Token] attributes]. Like many NLP
-    |  libraries, spaCy #[strong encodes all strings to hash values] to reduce
-    |  memory usage and improve efficiency. So to get the readable string
-    |  representation of an attribute, we need to add an underscore #[code _]
-    |  to its name:
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
-
-    for token in doc:
-        print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
-              token.shape_, token.is_alpha, token.is_stop)
-
-+aside
-    |  #[strong Text:] The original word text.#[br]
-    |  #[strong Lemma:] The base form of the word.#[br]
-    |  #[strong POS:] The simple part-of-speech tag.#[br]
-    |  #[strong Tag:] The detailed part-of-speech tag.#[br]
-    |  #[strong Dep:] Syntactic dependency, i.e. the relation between tokens.#[br]
-    |  #[strong Shape:] The word shape – capitalisation, punctuation, digits.#[br]
-    |  #[strong is alpha:] Is the token an alpha character?#[br]
-    |  #[strong is stop:] Is the token part of a stop list, i.e. the most common
-    |  words of the language?#[br]
-
-+table(["Text", "Lemma", "POS", "Tag", "Dep", "Shape", "alpha", "stop"])
-    - var style = [0, 0, 1, 1, 1, 1, 1, 1]
-    +annotation-row(["Apple", "apple", "PROPN", "NNP", "nsubj", "Xxxxx", true, false], style)
-    +annotation-row(["is", "be", "VERB", "VBZ", "aux", "xx", true, true], style)
-    +annotation-row(["looking", "look", "VERB", "VBG", "ROOT", "xxxx", true, false], style)
-    +annotation-row(["at", "at", "ADP", "IN", "prep", "xx", true, true], style)
-    +annotation-row(["buying", "buy", "VERB", "VBG", "pcomp", "xxxx", true, false], style)
-    +annotation-row(["U.K.", "u.k.", "PROPN", "NNP", "compound", "X.X.", false, false], style)
-    +annotation-row(["startup", "startup", "NOUN", "NN", "dobj", "xxxx", true, false], style)
-    +annotation-row(["for", "for", "ADP", "IN", "prep", "xxx", true, true], style)
-    +annotation-row(["$", "$", "SYM", "$", "quantmod", "$", false, false], style)
-    +annotation-row(["1", "1", "NUM", "CD", "compound", "d", false, false], style)
-    +annotation-row(["billion", "billion", "NUM", "CD", "pobj", "xxxx", true, false], style)
-
-+aside("Tip: Understanding tags and labels")
-    |  Most of the tags and labels look pretty abstract, and they vary between
-    |  languages. #[code spacy.explain()] will show you a short description –
-    |  for example, #[code spacy.explain("VBZ")] returns "verb, 3rd person
-    |  singular present".
-
-p
-    |  Using spaCy's built-in #[+a("/usage/visualizers") displaCy visualizer],
-    |  here's what our example sentence and its dependencies look like:
-
-+codepen("030d1e4dfa6256cad8fdd59e6aefecbe", 460)
diff --git a/website/usage/_spacy-101/_serialization.jade b/website/usage/_spacy-101/_serialization.jade
deleted file mode 100644
index eba2d573e..000000000
--- a/website/usage/_spacy-101/_serialization.jade
+++ /dev/null
@@ -1,64 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101 > SERIALIZATION
-
-p
-    |  If you've been modifying the pipeline, vocabulary, vectors and entities,
-    |  or made updates to the model, you'll eventually want to
-    |  #[strong save your progress] – for example, everything that's in your
-    |  #[code nlp] object. This means you'll have to translate its contents and
-    |  structure into a format that can be saved, like a file or a byte string.
-    |  This process is called serialization. spaCy comes with
-    |  #[strong built-in serialization methods] and supports the
-    |  #[+a("http://www.diveintopython3.net/serializing.html#dump") Pickle protocol].
-
-+aside("What's pickle?")
-    |  Pickle is Python's built-in object persistance system. It lets you
-    |  transfer arbitrary Python objects between processes. This is usually used
-    |  to load an object to and from disk, but it's also used for distributed
-    |  computing, e.g. with
-    |  #[+a("https://spark.apache.org/docs/0.9.0/python-programming-guide.html") PySpark]
-    |  or #[+a("http://dask.pydata.org/en/latest/") Dask]. When you unpickle an
-    |  object, you're agreeing to execute whatever code it contains. It's like
-    |  calling #[code eval()] on a string – so don't unpickle objects from
-    |  untrusted sources.
-
-p
-    |  All container classes, i.e. #[+api("language") #[code Language]],
-    |  #[+api("doc") #[code Doc]], #[+api("vocab") #[code Vocab]] and
-    |  #[+api("stringstore") #[code StringStore]] have the following methods
-    |  available:
-
-+table(["Method", "Returns", "Example"])
-    - style = [1, 0, 1]
-    +annotation-row(["to_bytes", "bytes", "nlp.to_bytes()"], style)
-    +annotation-row(["from_bytes", "object", "nlp.from_bytes(bytes)"], style)
-    +annotation-row(["to_disk", "-", "nlp.to_disk('/path')"], style)
-    +annotation-row(["from_disk", "object", "nlp.from_disk('/path')"], style)
-
-p
-    |  For example, if you've processed a very large document, you can use
-    |  #[+api("doc#to_disk") #[code Doc.to_disk]] to save it to a file on your
-    |  local machine. This will save the document and its tokens, as well as
-    |  the vocabulary associated with the #[code Doc].
-
-+aside("Why saving the vocab?")
-    |  Saving the vocabulary with the #[code Doc] is important, because the
-    |  #[code Vocab] holds the context-independent information about the words,
-    |  tags and labels, and their #[strong hash values]. If the #[code Vocab]
-    |  wasn't saved with the #[code Doc], spaCy wouldn't know how to resolve
-    |  those IDs back to strings.
-
-+code.
-    text = open('customer_feedback_627.txt', 'r').read() # open a document
-    doc = nlp(text) # process it
-    doc.to_disk('/customer_feedback_627.bin') # save the processed Doc
-
-p
-    |  If you need it again later, you can load it back into an empty #[code Doc]
-    |  with an empty #[code Vocab] by calling
-    |  #[+api("doc#from_disk") #[code from_disk()]]:
-
-+code.
-    from spacy.tokens import Doc # to create empty Doc
-    from spacy.vocab import Vocab # to create empty Vocab
-
-    doc = Doc(Vocab()).from_disk('/customer_feedback_627.bin') # load processed Doc
diff --git a/website/usage/_spacy-101/_similarity.jade b/website/usage/_spacy-101/_similarity.jade
deleted file mode 100644
index 09b255a97..000000000
--- a/website/usage/_spacy-101/_similarity.jade
+++ /dev/null
@@ -1,51 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101 > SIMILARITY
-
-p
-    |  spaCy is able to compare two objects, and make a prediction of
-    |  #[strong how similar they are]. Predicting similarity is useful for
-    |  building recommendation systems or flagging duplicates. For example, you
-    |  can suggest a user content that's similar to what they're currently
-    |  looking at, or label a support ticket as a duplicate if it's very
-    |  similar to an already existing one.
-
-p
-    |  Each #[code Doc], #[code Span] and #[code Token] comes with a
-    |  #[+api("token#similarity") #[code .similarity()]] method that lets you
-    |  compare it with another object, and determine the similarity. Of course
-    |  similarity is always subjective – whether "dog" and "cat" are similar
-    |  really depends on how you're looking at it. spaCy's similarity model
-    |  usually assumes a pretty general-purpose definition of similarity.
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_md')  # make sure to use larger model!
-    tokens = nlp(u'dog cat banana')
-
-    for token1 in tokens:
-        for token2 in tokens:
-            print(token1.text, token2.text, token1.similarity(token2))
-
-+aside
-    |  #[strong #[+procon("neutral", "identical", false, 16)] similarity:] identical#[br]
-    |  #[strong #[+procon("yes", "similar", false, 16)] similarity:] similar (higher is more similar) #[br]
-    |  #[strong #[+procon("no", "dissimilar", false, 16)] similarity:] dissimilar (lower is less similar)
-
-+table
-    +row("head")
-        for column in ["", "dog", "cat", "banana"]
-            +head-cell.u-text-center=column
-    each cells, label in {"dog": [1, 0.8, 0.24], "cat": [0.8, 1, 0.28], "banana": [0.24, 0.28, 1]}
-        +row
-            +cell.u-text-label.u-color-theme=label
-            for cell in cells
-                +cell.u-text-center
-                    - var result = cell < 0.5 ? ["no", "dissimilar"] : cell != 1 ? ["yes", "similar"] : ["neutral", "identical"]
-                    |  #[code=cell.toFixed(2)] #[+procon(...result)]
-
-p
-    |  In this case, the model's predictions are pretty on point. A dog is very
-    |  similar to a cat, whereas a banana is not very similar to either of them.
-    |  Identical tokens are obviously 100% similar to each other (just not always
-    |  exactly #[code 1.0], because of vector math and floating point
-    |  imprecisions).
diff --git a/website/usage/_spacy-101/_tokenization.jade b/website/usage/_spacy-101/_tokenization.jade
deleted file mode 100644
index 9eaaa1b60..000000000
--- a/website/usage/_spacy-101/_tokenization.jade
+++ /dev/null
@@ -1,64 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101 > TOKENIZATION
-
-p
-    |  During processing, spaCy first #[strong tokenizes] the text, i.e.
-    |  segments it into words, punctuation and so on. This is done by applying
-    |  rules specific to each language. For example, punctuation at the end of a
-    |  sentence should be split off – whereas "U.K." should remain one token.
-    |  Each #[code Doc] consists of individual tokens, and we can iterate
-    |  over them:
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
-    for token in doc:
-        print(token.text)
-
-+table([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).u-text-center
-    +row
-        for cell in ["Apple", "is", "looking", "at", "buying", "U.K.", "startup", "for", "$", "1", "billion"]
-            +cell=cell
-
-p
-    |  First, the raw text is split on whitespace characters, similar to
-    |  #[code text.split(' ')]. Then, the tokenizer processes the text from
-    |  left to right. On each substring, it performs two checks:
-
-+list("numbers")
-    +item
-        |  #[strong Does the substring match a tokenizer exception rule?] For
-        |  example, "don't" does not contain whitespace, but should be split
-        |  into two tokens, "do" and "n't", while "U.K." should always
-        |  remain one token.
-    +item
-        |  #[strong Can a prefix, suffix or infix be split off?] For example
-        |  punctuation like commas, periods, hyphens or quotes.
-
-p
-    |  If there's a match, the rule is applied and the tokenizer continues its
-    |  loop, starting with the newly split substrings. This way, spaCy can split
-    |  #[strong complex, nested tokens] like combinations of abbreviations and
-    |  multiple punctuation marks.
-
-+aside
-    |  #[strong Tokenizer exception:] Special-case rule to split a string into
-    |  several tokens or prevent a token from being split when punctuation rules
-    |  are applied.#[br]
-    |  #[strong Prefix:] Character(s) at the beginning, e.g.
-    |  #[code $], #[code (], #[code “], #[code ¿].#[br]
-    |  #[strong Suffix:] Character(s) at the end, e.g.
-    |  #[code km], #[code &#41;], #[code ”], #[code !].#[br]
-    |  #[strong Infix:] Character(s) in between, e.g.
-    |  #[code -], #[code --], #[code /], #[code …].#[br]
-
-+graphic("/assets/img/tokenization.svg")
-    include ../../assets/img/tokenization.svg
-
-p
-    |  While punctuation rules are usually pretty general, tokenizer exceptions
-    |  strongly depend on the specifics of the individual language. This is
-    |  why each #[+a("/usage/models#languages") available language] has its
-    |  own subclass like #[code English] or #[code German], that loads in lists
-    |  of hard-coded data and exception rules.
diff --git a/website/usage/_spacy-101/_training.jade b/website/usage/_spacy-101/_training.jade
deleted file mode 100644
index 5d97a86df..000000000
--- a/website/usage/_spacy-101/_training.jade
+++ /dev/null
@@ -1,50 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101 > TRAINING
-
-p
-    |  spaCy's models are #[strong statistical] and every "decision" they make –
-    |  for example, which part-of-speech tag to assign, or whether a word is a
-    |  named entity – is a #[strong prediction]. This prediction is based
-    |  on the examples the model has seen during #[strong training]. To train
-    |  a model, you first need training data – examples of text, and the
-    |  labels you want the model to predict. This could be a part-of-speech tag,
-    |  a named entity or any other information.
-
-p
-    |  The model is then shown the unlabelled text and will make a prediction.
-    |  Because we know the correct answer, we can give the model feedback on its
-    |  prediction in the form of an #[strong error gradient] of the
-    |  #[strong loss function] that calculates the difference between the training
-    |  example and the expected output. The greater the difference, the more
-    |  significant the gradient and the updates to our model.
-
-+aside
-    |  #[strong Training data:] Examples and their annotations.#[br]
-    |  #[strong Text:] The input text the model should predict a label for.#[br]
-    |  #[strong Label:] The label the model should predict.#[br]
-    |  #[strong Gradient:] Gradient of the loss function calculating the
-    |  difference between input and expected output.
-
-+graphic("/assets/img/training.svg")
-    include ../../assets/img/training.svg
-
-p
-    |  When training a model, we don't just want it to memorise our examples –
-    |  we want it to come up with theory that can be
-    |  #[strong generalised across other examples]. After all, we don't just want
-    |  the model to learn that this one instance of "Amazon" right here is a
-    |  company – we want it to learn that "Amazon", in contexts #[em like this],
-    |  is most likely a company. That's why the training data should always be
-    |  representative of the data we want to process. A model trained on
-    |  Wikipedia, where sentences in the first person are extremely rare, will
-    |  likely perform badly on Twitter. Similarly, a model trained on romantic
-    |  novels will likely perform badly on legal text.
-
-p
-    |  This also means that in order to know how the model is performing,
-    |  and whether it's learning the right things, you don't only need
-    |  #[strong training data] – you'll also need #[strong evaluation data]. If
-    |  you only test the model with the data it was trained on, you'll have no
-    |  idea how well it's generalising. If you want to train a model from scratch,
-    |  you usually need at least a few hundred examples for both training and
-    |  evaluation. To update an existing model, you can already achieve decent
-    |  results with very few examples – as long as they're representative.
diff --git a/website/usage/_spacy-101/_vocab.jade b/website/usage/_spacy-101/_vocab.jade
deleted file mode 100644
index 3237778ea..000000000
--- a/website/usage/_spacy-101/_vocab.jade
+++ /dev/null
@@ -1,122 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101 > VOCAB & STRINGSTORE
-
-p
-    |  Whenever possible, spaCy tries to store data in a vocabulary, the
-    |  #[+api("vocab") #[code Vocab]], that will be
-    |  #[strong shared by multiple documents]. To save memory, spaCy also
-    |  encodes all strings to #[strong hash values] – in this case for example,
-    |  "coffee" has the hash #[code 3197928453018144401]. Entity labels like
-    |  "ORG" and part-of-speech tags like "VERB" are also encoded. Internally,
-    |  spaCy only "speaks" in hash values.
-
-+aside
-    |  #[strong Token]: A word, punctuation mark etc. #[em in context], including
-    |  its attributes, tags and dependencies.#[br]
-    |  #[strong Lexeme]: A "word type" with no context. Includes the word shape
-    |  and flags, e.g. if it's lowercase, a digit or punctuation.#[br]
-    |  #[strong Doc]: A processed container of tokens in context.#[br]
-    |  #[strong Vocab]: The collection of lexemes.#[br]
-    |  #[strong StringStore]: The dictionary mapping hash values to strings, for
-    |  example #[code 3197928453018144401] &rarr; "coffee".
-
-+graphic("/assets/img/vocab_stringstore.svg")
-    include ../../assets/img/vocab_stringstore.svg
-
-p
-    |  If you process lots of documents containing the word "coffee" in all
-    |  kinds of different contexts, storing the exact string "coffee" every time
-    |  would take up way too much space. So instead, spaCy hashes the string
-    |  and stores it in the #[+api("stringstore") #[code StringStore]]. You can
-    |  think of the #[code StringStore] as a
-    |  #[strong lookup table that works in both directions] – you can look up a
-    |  string to get its hash, or a hash to get its string:
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u'I love coffee')
-    print(doc.vocab.strings[u'coffee'])  # 3197928453018144401
-    print(doc.vocab.strings[3197928453018144401])  # 'coffee'
-
-+aside("What does 'L' at the end of a hash mean?")
-    |  If you return a hash value in the #[strong Python 2 interpreter], it'll
-    |  show up as #[code 3197928453018144401L]. The #[code L] just means "long
-    |  integer" – it's #[strong not] actually a part of the hash value.
-
-p
-    |  Now that all strings are encoded, the entries in the vocabulary
-    |  #[strong don&apos;t need to include the word text] themselves. Instead,
-    |  they can look it up in the #[code StringStore] via its hash value. Each
-    |  entry in the vocabulary, also called #[+api("lexeme") #[code Lexeme]],
-    |  contains the #[strong context-independent] information about a word.
-    |  For example, no matter if "love" is used as a verb or a noun in some
-    |  context, its spelling and whether it consists of alphabetic characters
-    |  won't ever change. Its hash value will also always be the same.
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u'I love coffee')
-    for word in doc:
-        lexeme = doc.vocab[word.text]
-        print(lexeme.text, lexeme.orth, lexeme.shape_, lexeme.prefix_, lexeme.suffix_,
-              lexeme.is_alpha, lexeme.is_digit, lexeme.is_title, lexeme.lang_)
-
-+aside
-    |  #[strong Text]: The original text of the lexeme.#[br]
-    |  #[strong Orth]: The hash value of the lexeme.#[br]
-    |  #[strong Shape]: The abstract word shape of the lexeme.#[br]
-    |  #[strong Prefix]: By default, the first letter of the word string.#[br]
-    |  #[strong Suffix]: By default, the last three letters of the word string.#[br]
-    |  #[strong is alpha]: Does the lexeme consist of alphabetic characters?#[br]
-    |  #[strong is digit]: Does the lexeme consist of digits?#[br]
-
-+table(["text", "orth", "shape", "prefix", "suffix", "is_alpha", "is_digit"])
-    - var style = [0, 1, 1, 0, 0, 1, 1]
-    +annotation-row(["I", "4690420944186131903", "X", "I", "I", true, false], style)
-    +annotation-row(["love", "3702023516439754181", "xxxx", "l", "ove", true, false], style)
-    +annotation-row(["coffee", "3197928453018144401", "xxxx", "c", "fee", true, false], style)
-
-p
-    |  The mapping of words to hashes doesn't depend on any state. To make sure
-    |  each value is unique, spaCy uses a
-    |  #[+a("https://en.wikipedia.org/wiki/Hash_function") hash function] to
-    |  calculate the hash #[strong based on the word string]. This also means
-    |  that the hash for "coffee" will always be the same, no matter which model
-    |  you're using or how you've configured spaCy.
-
-p
-    |  However, hashes #[strong cannot be reversed] and there's no way to
-    |  resolve #[code 3197928453018144401] back to "coffee". All spaCy can do
-    |  is look it up in the vocabulary. That's why you always need to make
-    |  sure all objects you create have access to the same vocabulary. If they
-    |  don't, spaCy might not be able to find the strings it needs.
-
-+code-exec.
-    import spacy
-    from spacy.tokens import Doc
-    from spacy.vocab import Vocab
-
-    nlp = spacy.load('en_core_web_sm')
-    doc = nlp(u'I love coffee') # original Doc
-    print(doc.vocab.strings[u'coffee'])  # 3197928453018144401
-    print(doc.vocab.strings[3197928453018144401])  # 'coffee' 👍
-
-    empty_doc = Doc(Vocab())  # new Doc with empty Vocab
-    # empty_doc.vocab.strings[3197928453018144401] will raise an error :(
-
-    empty_doc.vocab.strings.add(u'coffee')  # add "coffee" and generate hash
-    print(empty_doc.vocab.strings[3197928453018144401])  # 'coffee' 👍
-
-    new_doc = Doc(doc.vocab)  # create new doc with first doc's vocab
-    print(new_doc.vocab.strings[3197928453018144401])  # 'coffee' 👍
-
-p
-    |  If the vocabulary doesn't contain a string for #[code 3197928453018144401],
-    |  spaCy will raise an error. You can re-add "coffee" manually, but this
-    |  only works if you actually #[em know] that the document contains that
-    |  word. To prevent this problem, spaCy will also export the #[code Vocab]
-    |  when you save a #[code Doc] or #[code nlp] object. This will give you
-    |  the object and its encoded annotations, plus the "key" to decode it.
diff --git a/website/usage/_spacy-101/_word-vectors.jade b/website/usage/_spacy-101/_word-vectors.jade
deleted file mode 100644
index 16654e4e1..000000000
--- a/website/usage/_spacy-101/_word-vectors.jade
+++ /dev/null
@@ -1,159 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101 > WORD VECTORS
-
-p
-    |  Similarity is determined by comparing #[strong word vectors] or "word
-    |  embeddings", multi-dimensional meaning representations of a word. Word
-    |  vectors can be generated using an algorithm like
-    |  #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec] and usually
-    |  look like this:
-
-+code("banana.vector", false, false, 250).
-    array([2.02280000e-01,  -7.66180009e-02,   3.70319992e-01,
-           3.28450017e-02,  -4.19569999e-01,   7.20689967e-02,
-          -3.74760002e-01,   5.74599989e-02,  -1.24009997e-02,
-           5.29489994e-01,  -5.23800015e-01,  -1.97710007e-01,
-          -3.41470003e-01,   5.33169985e-01,  -2.53309999e-02,
-           1.73800007e-01,   1.67720005e-01,   8.39839995e-01,
-           5.51070012e-02,   1.05470002e-01,   3.78719985e-01,
-           2.42750004e-01,   1.47449998e-02,   5.59509993e-01,
-           1.25210002e-01,  -6.75960004e-01,   3.58420014e-01,
-          -4.00279984e-02,   9.59490016e-02,  -5.06900012e-01,
-          -8.53179991e-02,   1.79800004e-01,   3.38669986e-01,
-           1.32300004e-01,   3.10209990e-01,   2.18779996e-01,
-           1.68530002e-01,   1.98740005e-01,  -5.73849976e-01,
-          -1.06490001e-01,   2.66689986e-01,   1.28380001e-01,
-          -1.28030002e-01,  -1.32839993e-01,   1.26570001e-01,
-           8.67229998e-01,   9.67210010e-02,   4.83060002e-01,
-           2.12709993e-01,  -5.49900010e-02,  -8.24249983e-02,
-           2.24079996e-01,   2.39749998e-01,  -6.22599982e-02,
-           6.21940017e-01,  -5.98999977e-01,   4.32009995e-01,
-           2.81430006e-01,   3.38420011e-02,  -4.88150001e-01,
-          -2.13589996e-01,   2.74010003e-01,   2.40950003e-01,
-           4.59500015e-01,  -1.86049998e-01,  -1.04970002e+00,
-          -9.73049998e-02,  -1.89080000e-01,  -7.09290028e-01,
-           4.01950002e-01,  -1.87680006e-01,   5.16870022e-01,
-           1.25200003e-01,   8.41499984e-01,   1.20970003e-01,
-           8.82389992e-02,  -2.91959997e-02,   1.21510006e-03,
-           5.68250008e-02,  -2.74210006e-01,   2.55640000e-01,
-           6.97930008e-02,  -2.22580001e-01,  -3.60060006e-01,
-          -2.24020004e-01,  -5.36990017e-02,   1.20220006e+00,
-           5.45350015e-01,  -5.79980016e-01,   1.09049998e-01,
-           4.21669990e-01,   2.06619993e-01,   1.29360005e-01,
-          -4.14570011e-02,  -6.67770028e-01,   4.04670000e-01,
-          -1.52179999e-02,  -2.76400000e-01,  -1.56110004e-01,
-          -7.91980028e-02,   4.00369987e-02,  -1.29439995e-01,
-          -2.40900001e-04,  -2.67850012e-01,  -3.81150007e-01,
-          -9.72450018e-01,   3.17259997e-01,  -4.39509988e-01,
-           4.19340014e-01,   1.83530003e-01,  -1.52600005e-01,
-          -1.08080000e-01,  -1.03579998e+00,   7.62170032e-02,
-           1.65189996e-01,   2.65259994e-04,   1.66160002e-01,
-          -1.52810007e-01,   1.81229994e-01,   7.02740014e-01,
-           5.79559989e-03,   5.16639985e-02,  -5.97449988e-02,
-          -2.75510013e-01,  -3.90489995e-01,   6.11319989e-02,
-           5.54300010e-01,  -8.79969969e-02,  -4.16810006e-01,
-           3.28260005e-01,  -5.25489986e-01,  -4.42880005e-01,
-           8.21829960e-03,   2.44859993e-01,  -2.29819998e-01,
-          -3.49810004e-01,   2.68940002e-01,   3.91660005e-01,
-          -4.19039994e-01,   1.61909997e-01,  -2.62630010e+00,
-           6.41340017e-01,   3.97430003e-01,  -1.28680006e-01,
-          -3.19460005e-01,  -2.56330013e-01,  -1.22199997e-01,
-           3.22750002e-01,  -7.99330026e-02,  -1.53479993e-01,
-           3.15050006e-01,   3.05909991e-01,   2.60120004e-01,
-           1.85530007e-01,  -2.40429997e-01,   4.28860001e-02,
-           4.06219989e-01,  -2.42559999e-01,   6.38700008e-01,
-           6.99829996e-01,  -1.40430003e-01,   2.52090007e-01,
-           4.89840001e-01,  -6.10670000e-02,  -3.67659986e-01,
-          -5.50890028e-01,  -3.82649988e-01,  -2.08430007e-01,
-           2.28320003e-01,   5.12179971e-01,   2.78679997e-01,
-           4.76520002e-01,   4.79510017e-02,  -3.40079993e-01,
-          -3.28729987e-01,  -4.19669986e-01,  -7.54989982e-02,
-          -3.89539987e-01,  -2.96219997e-02,  -3.40700001e-01,
-           2.21699998e-01,  -6.28560036e-02,  -5.19029975e-01,
-          -3.77739996e-01,  -4.34770016e-03,  -5.83010018e-01,
-          -8.75459984e-02,  -2.39289999e-01,  -2.47109994e-01,
-          -2.58870006e-01,  -2.98940003e-01,   1.37150005e-01,
-           2.98919994e-02,   3.65439989e-02,  -4.96650010e-01,
-          -1.81600004e-01,   5.29389977e-01,   2.19919994e-01,
-          -4.45140004e-01,   3.77979994e-01,  -5.70620000e-01,
-          -4.69460003e-02,   8.18059966e-02,   1.92789994e-02,
-           3.32459986e-01,  -1.46200001e-01,   1.71560004e-01,
-           3.99809986e-01,   3.62170011e-01,   1.28160000e-01,
-           3.16439986e-01,   3.75690013e-01,  -7.46899992e-02,
-          -4.84800003e-02,  -3.14009994e-01,  -1.92860007e-01,
-          -3.12940001e-01,  -1.75529998e-02,  -1.75139993e-01,
-          -2.75870003e-02,  -1.00000000e+00,   1.83870003e-01,
-           8.14339995e-01,  -1.89129993e-01,   5.09989977e-01,
-          -9.19600017e-03,  -1.92950002e-03,   2.81890005e-01,
-           2.72470005e-02,   4.34089988e-01,  -5.49669981e-01,
-          -9.74259973e-02,  -2.45399997e-01,  -1.72030002e-01,
-          -8.86500031e-02,  -3.02980006e-01,  -1.35910004e-01,
-          -2.77649999e-01,   3.12860007e-03,   2.05559999e-01,
-          -1.57720000e-01,  -5.23079991e-01,  -6.47010028e-01,
-          -3.70139986e-01,   6.93930015e-02,   1.14009999e-01,
-           2.75940001e-01,  -1.38750002e-01,  -2.72680014e-01,
-           6.68910027e-01,  -5.64539991e-02,   2.40170002e-01,
-          -2.67300010e-01,   2.98599988e-01,   1.00830004e-01,
-           5.55920005e-01,   3.28489989e-01,   7.68579990e-02,
-           1.55279994e-01,   2.56359994e-01,  -1.07720003e-01,
-          -1.23590000e-01,   1.18270002e-01,  -9.90289971e-02,
-          -3.43279988e-01,   1.15019999e-01,  -3.78080010e-01,
-          -3.90120000e-02,  -3.45930010e-01,  -1.94040000e-01,
-          -3.35799992e-01,  -6.23340011e-02,   2.89189994e-01,
-           2.80319989e-01,  -5.37410021e-01,   6.27939999e-01,
-           5.69549985e-02,   6.21469975e-01,  -2.52819985e-01,
-           4.16700006e-01,  -1.01079997e-02,  -2.54339993e-01,
-           4.00029987e-01,   4.24320012e-01,   2.26720005e-01,
-           1.75530002e-01,   2.30489999e-01,   2.83230007e-01,
-           1.38820007e-01,   3.12180002e-03,   1.70570001e-01,
-           3.66849989e-01,   2.52470002e-03,  -6.40089989e-01,
-          -2.97650009e-01,   7.89430022e-01,   3.31680000e-01,
-          -1.19659996e+00,  -4.71559986e-02,   5.31750023e-01], dtype=float32)
-
-+infobox("Important note", "⚠️")
-    |  To make them compact and fast, spaCy's small #[+a("/models") models]
-    |  (all packages that end in #[code sm]) #[strong don&apos;t ship with word vectors], and
-    |  only include context-sensitive #[strong tensors]. This means you can
-    |  still use the #[code similarity()] methods to compare documents, spans
-    |  and tokens – but the result won't be as good, and individual tokens won't
-    |  have any vectors assigned. So in order to use #[em real] word vectors,
-    |  you need to download a larger model:
-
-    +code-wrapper
-        +code-new(false, "bash", "$") python -m spacy download en_core_web_lg
-
-p
-    |  Models that come with built-in word vectors make them available as the
-    |  #[+api("token#vector") #[code Token.vector]] attribute.
-    |  #[+api("doc#vector") #[code Doc.vector]] and
-    |  #[+api("span#vector") #[code Span.vector]] will default to an average
-    |  of their token vectors. You can also check if a token has a vector
-    |  assigned, and get the L2 norm, which can be used to normalise
-    |  vectors.
-
-+code-exec.
-    import spacy
-
-    nlp = spacy.load('en_core_web_md')
-    tokens = nlp(u'dog cat banana afskfsd')
-
-    for token in tokens:
-        print(token.text, token.has_vector, token.vector_norm, token.is_oov)
-
-+aside
-    |  #[strong Text]: The original token text.#[br]
-    |  #[strong has vector]: Does the token have a vector representation?#[br]
-    |  #[strong Vector norm]: The L2 norm of the token's vector (the square root
-    |  of the sum of the values squared)#[br]
-    |  #[strong OOV]: Out-of-vocabulary
-
-p
-    |  The words "dog", "cat" and "banana" are all pretty common in English, so
-    |  they're part of the model's vocabulary, and come with a vector. The word
-    |  "afskfsd" on the other hand is a lot less common and out-of-vocabulary
-    |  – so its vector representation consists of 300 dimensions of #[code 0],
-    |  which means it's practically nonexistent. If your application will
-    |  benefit from a #[strong large vocabulary] with more vectors, you should
-    |  consider using one of the larger models or loading in a full vector
-    |  package, for example,
-    |  #[+a("/models/en#en_vectors_web_lg") #[code en_vectors_web_lg]], which
-    |  includes over #[strong 1 million unique vectors].
diff --git a/website/usage/_training/_basics.jade b/website/usage/_training/_basics.jade
deleted file mode 100644
index d2f74c7a5..000000000
--- a/website/usage/_training/_basics.jade
+++ /dev/null
@@ -1,280 +0,0 @@
-//- 💫 DOCS > USAGE > TRAINING > BASICS
-
-include ../_spacy-101/_training
-
-+h(3, "spacy-train-cli") Training via the command-line interface
-
-p
-    |  For most purposes, the best way to train spaCy is via the command-line
-    |  interface. The #[+api("cli#train") #[code spacy train]] command takes
-    |  care of many details for you, including making sure that the data is
-    |  minibatched and shuffled correctly, progress is printed, and models are
-    |  saved after each epoch. You can prepare your data for use in
-    |  #[+api("cli#train") #[code spacy train]] using the
-    |  #[+api("cli#convert") #[code spacy convert]] command, which accepts many
-    |  common NLP data formats, including #[code .iob] for named entities, and
-    |  the CoNLL format for dependencies:
-
-+code("Example", "bash").
-    git clone https://github.com/UniversalDependencies/UD_Spanish-AnCora
-    mkdir ancora-json
-    python -m spacy convert UD_Spanish-AnCora/es_ancora-ud-train.conllu ancora-json
-    python -m spacy convert UD_Spanish-AnCora/es_ancora-ud-dev.conllu ancora-json
-    mkdir models
-    python -m spacy train es models ancora-json/es_ancora-ud-train.json ancora-json/es_ancora-ud-dev.json
-
-+h(3, "training-data") How do I get training data?
-
-p
-    |  Collecting training data may sound incredibly painful – and it can be,
-    |  if you're planning a large-scale annotation project. However, if your main
-    |  goal is to update an existing model's predictions – for example, spaCy's
-    |  named entity recognition – the hard part is usually not creating the
-    |  actual annotations. It's finding representative examples and
-    |  #[strong extracting potential candidates]. The good news is, if you've
-    |  been noticing bad performance on your data, you likely
-    |  already have some relevant text, and you can use spaCy to
-    |  #[strong bootstrap a first set of training examples]. For example,
-    |  after processing a few sentences, you may end up with the following
-    |  entities, some correct, some incorrect.
-
-+aside("How many examples do I need?")
-    |  As a rule of thumb, you should allocate at least 10% of your project
-    |  resources to creating training and evaluation data. If you're looking to
-    |  improve an existing model, you might be able to start off with only a
-    |  handful of examples. Keep in mind that you'll always want a lot more than
-    |  that for #[strong evaluation] – especially previous errors the model has
-    |  made. Otherwise, you won't be able to sufficiently verify that the model
-    |  has actually made the #[strong correct generalisations] required for your
-    |  use case.
-
-+table(["Text", "Entity", "Start", "End", "Label", ""])
-    - var style = [0, 0, 1, 1, 1]
-    +annotation-row(["Uber blew through $1 million a week", "Uber", 0, 4, "ORG"], style)
-        +cell #[+procon("yes", "right", true)]
-    +annotation-row(["Android Pay expands to Canada", "Android", 0, 7, "PERSON"], style)
-        +cell #[+procon("no", "wrong", true)]
-    +annotation-row(["Android Pay expands to Canada", "Canada", 23, 30, "GPE"], style)
-        +cell #[+procon("yes", "right", true)]
-    +annotation-row(["Spotify steps up Asia expansion", "Spotify", 0, 8, "ORG"], style)
-        +cell #[+procon("yes", "right", true)]
-    +annotation-row(["Spotify steps up Asia expansion", "Asia", 17, 21, "NORP"], style)
-        +cell #[+procon("no", "wrong", true)]
-
-p
-    |  Alternatively, the
-    |  #[+a("/usage/linguistic-features#rule-based-matching") rule-based matcher]
-    |  can be a useful tool to extract tokens or combinations of tokens, as
-    |  well as their start and end index in a document. In this case, we'll
-    |  extract mentions of Google and assume they're an #[code ORG].
-
-+table(["Text", "Entity", "Start", "End", "Label", ""])
-    - var style = [0, 0, 1, 1, 1]
-    +annotation-row(["let me google this for you", "google", 7, 13, "ORG"], style)
-        +cell #[+procon("no", "wrong", true)]
-    +annotation-row(["Google Maps launches location sharing", "Google", 0, 6, "ORG"], style)
-        +cell #[+procon("no", "wrong", true)]
-    +annotation-row(["Google rebrands its business apps", "Google", 0, 6, "ORG"], style)
-        +cell #[+procon("yes", "right", true)]
-    +annotation-row(["look what i found on google! 😂", "google", 21, 27, "ORG"], style)
-        +cell #[+procon("no", "wrong", true)]
-
-p
-    |  Based on the few examples above, you can already create six training
-    |  sentences with eight entities in total. Of course, what you consider a
-    |  "correct annotation" will always depend on
-    |  #[strong what you want the model to learn]. While there are some entity
-    |  annotations that are more or less universally correct – like Canada being
-    |  a geopolitical entity – your application may have its very own definition
-    |  of the #[+a("/api/annotation#named-entities") NER annotation scheme].
-
-+code.
-    train_data = [
-        ("Uber blew through $1 million a week", [(0, 4, 'ORG')]),
-        ("Android Pay expands to Canada", [(0, 11, 'PRODUCT'), (23, 30, 'GPE')]),
-        ("Spotify steps up Asia expansion", [(0, 8, "ORG"), (17, 21, "LOC")]),
-        ("Google Maps launches location sharing", [(0, 11, "PRODUCT")]),
-        ("Google rebrands its business apps", [(0, 6, "ORG")]),
-        ("look what i found on google! 😂", [(21, 27, "PRODUCT")])]
-
-+infobox("Tip: Try the Prodigy annotation tool")
-    +infobox-logos(["prodigy", 100, 29, "https://prodi.gy"])
-    |  If you need to label a lot of data, check out
-    |  #[+a("https://prodi.gy", true) Prodigy], a new, active learning-powered
-    |  annotation tool we've developed. Prodigy is fast and extensible, and
-    |  comes with a modern  #[strong web application] that helps you collect
-    |  training data faster. It integrates seamlessly with spaCy, pre-selects
-    |  the #[strong most relevant examples] for annotation, and lets you
-    |  train and evaluate ready-to-use spaCy models.
-
-+h(3, "annotations") Training with annotations
-
-p
-    |  The #[+api("goldparse") #[code GoldParse]] object collects the annotated
-    |  training examples, also called the #[strong gold standard]. It's
-    |  initialised with the #[+api("doc") #[code Doc]] object it refers to,
-    |  and keyword arguments specifying the annotations, like #[code tags]
-    |  or #[code entities]. Its job is to encode the annotations, keep them
-    |  aligned and create the C-level data structures required for efficient access.
-    |  Here's an example of a simple #[code GoldParse] for part-of-speech tags:
-
-+code.
-    vocab = Vocab(tag_map={'N': {'pos': 'NOUN'}, 'V': {'pos': 'VERB'}})
-    doc = Doc(vocab, words=['I', 'like', 'stuff'])
-    gold = GoldParse(doc, tags=['N', 'V', 'N'])
-
-p
-    |  Using the #[code Doc] and its gold-standard annotations, the model can be
-    |  updated to learn a sentence of three words with their assigned
-    |  part-of-speech tags. The #[+a("/usage/adding-languages#tag-map") tag map]
-    |  is part of the vocabulary and defines the annotation scheme. If you're
-    |  training a new language model, this will let you map the tags present in
-    |  the treebank you train on to spaCy's tag scheme.
-
-+code.
-    doc = Doc(Vocab(), words=['Facebook', 'released', 'React', 'in', '2014'])
-    gold = GoldParse(doc, entities=['U-ORG', 'O', 'U-TECHNOLOGY', 'O', 'U-DATE'])
-
-p
-    |  The same goes for named entities. The letters added before the labels
-    |  refer to the tags of the
-    |  #[+a("/usage/linguistic-features#updating-biluo") BILUO scheme] –
-    |  #[code O] is a token outside an entity, #[code U] an single entity unit,
-    |  #[code B] the beginning of an entity, #[code I] a token inside an entity
-    |  and #[code L] the last token of an entity.
-
-+aside
-    |  #[strong Training data]: The training examples.#[br]
-    |  #[strong Text and label]: The current example.#[br]
-    |  #[strong Doc]: A #[code Doc] object created from the example text.#[br]
-    |  #[strong GoldParse]: A #[code GoldParse] object of the #[code Doc] and label.#[br]
-    |  #[strong nlp]: The #[code nlp] object with the model.#[br]
-    |  #[strong Optimizer]: A function that holds state between updates.#[br]
-    |  #[strong Update]: Update the model's weights.#[br]
-    |  #[strong ]
-
-+graphic("/assets/img/training-loop.svg")
-    include ../../assets/img/training-loop.svg
-
-p
-    |  Of course, it's not enough to only show a model a single example once.
-    |  Especially if you only have few examples, you'll want to train for a
-    |  #[strong number of iterations]. At each iteration, the training data is
-    |  #[strong shuffled] to ensure the model doesn't make any generalisations
-    |  based on the order of examples. Another technique to improve the learning
-    |  results is to set a #[strong dropout rate], a rate at which to randomly
-    |  "drop" individual features and representations. This makes it harder for
-    |  the model to memorise the training data. For example, a #[code 0.25]
-    |  dropout means that each feature or internal representation has a 1/4
-    |  likelihood of being dropped.
-
-+aside
-    |  #[+api("language#begin_training") #[code begin_training()]]: Start the
-    |  training and return an optimizer function to update the model's weights.
-    |  Can take an optional function converting the training data to spaCy's
-    |  training format.#[br]
-    |  #[+api("language#update") #[code update()]]: Update the model with the
-    |  training example and gold data.#[br]
-    |  #[+api("language#to_disk") #[code to_disk()]]: Save the updated model to
-    |  a directory.
-
-+code("Example training loop").
-    optimizer = nlp.begin_training(get_data)
-    for itn in range(100):
-        random.shuffle(train_data)
-        for raw_text, entity_offsets in train_data:
-            doc = nlp.make_doc(raw_text)
-            gold = GoldParse(doc, entities=entity_offsets)
-            nlp.update([doc], [gold], drop=0.5, sgd=optimizer)
-    nlp.to_disk('/model')
-
-p
-    |  The #[+api("language#update") #[code nlp.update]] method takes the
-    |  following arguments:
-
-+table(["Name", "Description"])
-    +row
-        +cell #[code docs]
-        +cell
-            |  #[+api("doc") #[code Doc]] objects. The #[code update] method
-            |  takes a sequence of them, so you can batch up your training
-            |  examples. Alternatively, you can also pass in a sequence of
-            |  raw texts.
-
-    +row
-        +cell #[code golds]
-        +cell
-            |  #[+api("goldparse") #[code GoldParse]] objects. The #[code update]
-            |  method takes a sequence of them, so you can batch up your
-            |  training examples. Alternatively, you can also pass in a
-            |  dictionary containing the annotations.
-
-    +row
-        +cell #[code drop]
-        +cell
-            |  Dropout rate. Makes it harder for the model to just memorise
-            |  the data.
-
-    +row
-        +cell #[code sgd]
-        +cell
-            |  An optimizer, i.e. a callable to update the model's weights. If
-            |  not set, spaCy will create a new one and save it for further use.
-
-p
-    |  Instead of writing your own training loop, you can also use the
-    |  built-in #[+api("cli#train") #[code train]] command, which expects data
-    |  in spaCy's #[+a("/api/annotation#json-input") JSON format]. On each epoch,
-    |  a model will be saved out to the directory. After training, you can
-    |  use the #[+api("cli#package") #[code package]] command to generate an
-    |  installable Python package from your model.
-
-+code(false, "bash").
-    python -m spacy convert /tmp/train.conllu /tmp/data
-    python -m spacy train en /tmp/model /tmp/data/train.json -n 5
-
-+h(3, "training-simple-style") Simple training style
-    +tag-new(2)
-
-p
-    |  Instead of sequences of #[code Doc] and #[code GoldParse] objects,
-    |  you can also use the "simple training style" and pass
-    |  #[strong raw texts] and #[strong dictionaries of annotations]
-    |  to #[+api("language#update") #[code nlp.update]].
-    |  The dictionaries can have the keys #[code entities], #[code heads],
-    |  #[code deps], #[code tags] and #[code cats]. This is generally
-    |  recommended, as it removes one layer of abstraction, and avoids
-    |  unnecessary imports. It also makes it easier to structure and load
-    |  your training data.
-
-+aside-code("Example Annotations").
-    {
-        'entities': [(0, 4, 'ORG')],
-        'heads': [1, 1, 1, 5, 5, 2, 7, 5],
-        'deps': ['nsubj', 'ROOT', 'prt', 'quantmod', 'compound', 'pobj', 'det', 'npadvmod'],
-        'tags': ['PROPN', 'VERB', 'ADP', 'SYM', 'NUM', 'NUM', 'DET', 'NOUN'],
-        'cats': {'BUSINESS': 1.0}
-    }
-
-+code("Simple training loop").
-    TRAIN_DATA = [
-         ("Uber blew through $1 million a week", {'entities': [(0, 4, 'ORG')]}),
-         ("Google rebrands its business apps", {'entities': [(0, 6, "ORG")]})]
-
-    nlp = spacy.blank('en')
-    optimizer = nlp.begin_training()
-    for i in range(20):
-        random.shuffle(TRAIN_DATA)
-        for text, annotations in TRAIN_DATA:
-            nlp.update([text], [annotations], sgd=optimizer)
-    nlp.to_disk('/model')
-
-p
-    |  The above training loop leaves out a few details that can really
-    |  improve accuracy – but the principle really is #[em that] simple. Once
-    |  you've got your pipeline together and you want to tune the accuracy,
-    |  you usually want to process your training examples in batches, and
-    |  experiment with #[+api("top-level#util.minibatch") #[code minibatch]]
-    |  sizes and dropout rates, set via the #[code drop] keyword argument. See
-    |  the #[+api("language") #[code Language]] and #[+api("pipe") #[code Pipe]]
-    |  API docs for available options.
diff --git a/website/usage/_training/_ner.jade b/website/usage/_training/_ner.jade
deleted file mode 100644
index ec01b769d..000000000
--- a/website/usage/_training/_ner.jade
+++ /dev/null
@@ -1,127 +0,0 @@
-//- 💫 DOCS > USAGE > TRAINING > NER
-
-p
-    |  All #[+a("/models") spaCy models] support online learning, so
-    |  you can update a pre-trained model with new examples. You'll usually
-    |  need to provide many #[strong examples] to meaningfully improve the
-    |  system — a few hundred is a good start, although more is better.
-
-p
-    |  You should avoid iterating over the same few examples multiple times, or
-    |  the model is likely to "forget" how to annotate other examples. If you
-    |  iterate over the same few examples, you're effectively changing the loss
-    |  function. The optimizer will find a way to minimize the loss on your
-    |  examples, without regard for the consequences on the examples it's no
-    |  longer paying attention to. One way to avoid this
-    |  #[+a("https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting", true) "catastrophic forgetting" problem]
-    |  is to "remind"
-    |  the model of other examples by augmenting your annotations with sentences
-    |  annotated with entities automatically recognised by the original model.
-    |  Ultimately, this is an empirical process: you'll need to
-    |  #[strong experiment on your data] to find a solution that works best
-    |  for you.
-
-+aside("Tip: Converting entity annotations", "💡")
-    |  You can train the entity recognizer with entity offsets or
-    |  annotations in the #[+a("/api/annotation#biluo") BILUO scheme]. The
-    |  #[code spacy.gold] module also exposes
-    |  #[+a("/api/goldparse#util") two helper functions] to convert offsets to
-    |  BILUO tags, and BILUO tags to entity offsets.
-
-+h(3, "example-train-ner") Updating the Named Entity Recognizer
-
-p
-    |  This example shows how to update spaCy's entity recognizer
-    |  with your own examples, starting off with an existing, pre-trained
-    |  model, or from scratch using a blank #[code Language] class. To do
-    |  this, you'll need #[strong example texts] and the
-    |  #[strong character offsets] and #[strong labels] of each entity contained
-    |  in the texts.
-
-    +github("spacy", "examples/training/train_ner.py", 500)
-
-+h(4) Step by step guide
-
-+list("numbers")
-    +item
-        |  #[strong Load the model] you want to start with, or create an
-        |  #[strong empty model] using
-        |  #[+api("spacy#blank") #[code spacy.blank]] with the ID of your
-        |  language. If you're using a blank model, don't forget to add the
-        |  entity recognizer to the pipeline. If you're using an existing model,
-        |  make sure to disable all other pipeline components during training
-        |  using #[+api("language#disable_pipes") #[code nlp.disable_pipes]].
-        |  This way, you'll only be training the entity recognizer.
-
-    +item
-        |  #[strong Shuffle and loop over] the examples. For each example,
-        |  #[strong update the model] by calling
-        |  #[+api("language#update") #[code nlp.update]], which steps
-        |  through the words of the input. At each word, it makes a
-        |  #[strong prediction]. It then consults the annotations to see whether
-        |  it was right. If it was wrong, it adjusts its weights so that the
-        |  correct action will score higher next time.
-
-    +item
-        |  #[strong Save] the trained model using
-        |  #[+api("language#to_disk") #[code nlp.to_disk]].
-
-    +item
-        |  #[strong Test] the model to make sure the entities in the training
-        |  data are recognised correctly.
-
-+h(3, "example-new-entity-type") Training an additional entity type
-
-p
-    |  This script shows how to add a new entity type #[code ANIMAL] to an
-    |  existing pre-trained NER model, or an empty #[code Language] class. To
-    |  keep the example short and simple, only a few sentences are
-    |  provided as examples. In practice, you'll need many more — a few hundred
-    |  would be a good start. You will also likely need to mix in examples of
-    |  other entity types, which might be obtained by running the entity
-    |  recognizer over unlabelled sentences, and adding their annotations to the
-    |  training set.
-
-+github("spacy", "examples/training/train_new_entity_type.py", 500)
-
-+aside("Important note", "⚠️")
-    |  If you're using an existing model, make sure to mix in examples of
-    |  #[strong other entity types] that spaCy correctly recognized before.
-    |  Otherwise, your model might learn the new type, but "forget" what it
-    |  previously knew. This is also referred to as the
-    |  #[+a("https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting", true) "catastrophic forgetting" problem].
-
-+h(4) Step by step guide
-
-+list("numbers")
-    +item
-        |  #[strong Load the model] you want to start with, or create an
-        |  #[strong empty model] using
-        |  #[+api("spacy#blank") #[code spacy.blank]] with the ID of your
-        |  language. If you're using a blank model, don't forget to add the
-        |  entity recognizer to the pipeline. If you're using an existing model,
-        |  make sure to disable all other pipeline components during training
-        |  using #[+api("language#disable_pipes") #[code nlp.disable_pipes]].
-        |  This way, you'll only be training the entity recognizer.
-
-    +item
-        |  #[strong Add the new entity label] to the entity recognizer using the
-        |  #[+api("entityrecognizer#add_label") #[code add_label]] method. You
-        |  can access the entity recognizer in the pipeline via
-        |  #[code nlp.get_pipe('ner')].
-
-    +item
-        |  #[strong Loop over] the examples and call
-        |  #[+api("language#update") #[code nlp.update]], which steps through
-        |  the words of the input. At each word, it makes a
-        |  #[strong prediction]. It then consults the annotations, to see
-        |  whether it was right. If it was wrong, it adjusts its weights so that
-        |  the correct action will score higher next time.
-
-    +item
-        |  #[strong Save] the trained model using
-        |  #[+api("language#to_disk") #[code nlp.to_disk]].
-
-    +item
-        |  #[strong Test] the model to make sure the new entity is recognised
-        |  correctly.
diff --git a/website/usage/_training/_saving-loading.jade b/website/usage/_training/_saving-loading.jade
deleted file mode 100644
index cb65a5f48..000000000
--- a/website/usage/_training/_saving-loading.jade
+++ /dev/null
@@ -1,192 +0,0 @@
-//- 💫 DOCS > USAGE > TRAINING > SAVING & LOADING
-
-p
-    |  After training your model, you'll usually want to save its state, and load
-    |  it back later. You can do this with the
-    |  #[+api("language#to_disk") #[code Language.to_disk()]]
-    |  method:
-
-+code.
-    nlp.to_disk('/home/me/data/en_example_model')
-
-p
-    |  The directory will be created if it doesn't exist, and the whole pipeline
-    |  will be written out. To make the model more convenient to deploy, we
-    |  recommend wrapping it as a Python package.
-
-+h(3, "models-generating") Generating a model package
-
-+infobox("Important note")
-    |  The model packages are #[strong not suitable] for the public
-    |  #[+a("https://pypi.python.org") pypi.python.org] directory, which is not
-    |  designed for binary data and files over 50 MB. However, if your company
-    |  is running an #[strong internal installation] of PyPi, publishing your
-    |  models on there can be a convenient way to share them with your team.
-
-p
-    |  spaCy comes with a handy CLI command that will create all required files,
-    |  and walk you through generating the meta data. You can also create the
-    |  meta.json manually and place it in the model data directory, or supply a
-    |  path to it using the #[code --meta] flag. For more info on this, see
-    |  the #[+api("cli#package") #[code package]] docs.
-
-+aside-code("meta.json", "json").
-    {
-        "name": "example_model",
-        "lang": "en",
-        "version": "1.0.0",
-        "spacy_version": "&gt;=2.0.0,&lt;3.0.0",
-        "description": "Example model for spaCy",
-        "author": "You",
-        "email": "you@example.com",
-        "license": "CC BY-SA 3.0",
-        "pipeline": ["tagger", "parser", "ner"]
-    }
-
-+code(false, "bash").
-    python -m spacy package /home/me/data/en_example_model /home/me/my_models
-
-p This command will create a model package directory that should look like this:
-
-+code("Directory structure", "yaml").
-    └── /
-        ├── MANIFEST.in                   # to include meta.json
-        ├── meta.json                     # model meta data
-        ├── setup.py                      # setup file for pip installation
-        └── en_example_model              # model directory
-            ├── __init__.py               # init for pip installation
-            └── en_example_model-1.0.0    # model data
-
-p
-    |  You can also find templates for all files on
-    |  #[+src(gh("spacy-models", "template")) GitHub].
-    |  If you're creating the package manually, keep in mind that the directories
-    |  need to be named according to the naming conventions of
-    |  #[code lang_name] and #[code lang_name-version].
-
-
-+h(3, "models-custom") Customising the model setup
-
-p
-    |  The meta.json includes the model details, like name, requirements and
-    |  license, and lets you customise how the model should be initialised and
-    |  loaded. You can define the language data to be loaded and the
-    |  #[+a("/usage/processing-pipelines") processing pipeline] to
-    |  execute.
-
-+table(["Setting", "Type", "Description"])
-    +row
-        +cell #[code lang]
-        +cell unicode
-        +cell ID of the language class to initialise.
-
-    +row
-        +cell #[code pipeline]
-        +cell list
-        +cell
-            |  A list of strings mapping to the IDs of pipeline factories to
-            |  apply in that order. If not set, spaCy's
-            |  #[+a("/usage/processing-pipelines") default pipeline]
-            |  will be used.
-
-p
-    |  The #[code load()] method that comes with our model package
-    |  templates will take care of putting all this together and returning a
-    |  #[code Language] object with the loaded pipeline and data. If your model
-    |  requires custom #[+a("/usage/processing-pipelines") pipeline components]
-    |  or a custom language class, you can also
-    |  #[strong ship the code with your model]. For examples of this, check out
-    |  the implementations of spaCy's
-    |  #[+api("util#load_model_from_init_py") #[code load_model_from_init_py]]
-    |  and  #[+api("util#load_model_from_path") #[code load_model_from_path]]
-    |  utility functions.
-
-+h(3, "models-building") Building the model package
-
-p
-    |  To build the package, run the following command from within the
-    |  directory. For more information on building Python packages, see the
-    |  docs on Python's
-    |  #[+a("https://setuptools.readthedocs.io/en/latest/") Setuptools].
-
-+code(false, "bash").
-    python setup.py sdist
-
-p
-    |  This will create a #[code .tar.gz] archive in a directory #[code /dist].
-    |  The model can be installed by pointing pip to the path of the archive:
-
-+code(false, "bash").
-    pip install /path/to/en_example_model-1.0.0.tar.gz
-
-p
-    |  You can then load the model via its name, #[code en_example_model], or
-    |  import it directly as a module and then call its #[code load()] method.
-
-+h(3, "loading") Loading a custom model package
-
-p
-    |  To load a model from a data directory, you can use
-    |  #[+api("spacy#load") #[code spacy.load()]] with the local path. This will
-    |  look for a meta.json in the directory and use the #[code lang] and
-    |  #[code pipeline] settings to initialise a #[code Language] class with a
-    |  processing pipeline and load in the model data.
-
-+code.
-    nlp = spacy.load('/path/to/model')
-
-p
-    |  If you want to #[strong load only the binary data], you'll have to create
-    |  a #[code Language] class and call
-    |  #[+api("language#from_disk") #[code from_disk]] instead.
-
-+code.
-    nlp = spacy.blank('en').from_disk('/path/to/data')
-
-+infobox("Important note: Loading data in v2.x")
-    .o-block
-        |  In spaCy 1.x, the distinction between #[code spacy.load()] and the
-        |  #[code Language] class constructor was quite unclear. You could call
-        |  #[code spacy.load()] when no model was present, and it would silently
-        |  return an empty object. Likewise, you could pass a path to
-        |  #[code English], even if the mode required a different language.
-        |  spaCy v2.0 solves this with a clear distinction between setting up
-        |  the instance and loading the data.
-
-    +code-new nlp = spacy.blank('en').from_disk('/path/to/data')
-    +code-old nlp = spacy.load('en', path='/path/to/data')
-
-+h(3, "example-training-spacy") Example: How we're training and packaging models for spaCy
-
-p
-    |  Publishing a new version of spaCy often means re-training all available
-    |  models – currently, that's #{MODEL_COUNT} models for #{MODEL_LANG_COUNT}
-    |  languages. To make this run smoothly, we're using an automated build
-    |  process and a #[+api("cli#train") #[code spacy train]] template that
-    |  looks like this:
-
-+code(false, "bash", "$", false, false, true).
-    python -m spacy train {lang} {models_dir}/{name} {train_data} {dev_data} -m meta/{name}.json -V {version} -g {gpu_id} -n {n_epoch} -ns {n_sents}
-
-+aside-code("meta.json template", "json").
-    {
-        "lang": "en",
-        "name": "core_web_sm",
-        "license":"CC BY-SA 3.0",
-        "author":"Explosion AI",
-        "url":"https://explosion.ai",
-        "email":"contact@explosion.ai",
-        "sources": ["OntoNotes 5", "Common Crawl"],
-        "description":"English multi-task CNN trained on OntoNotes, with GloVe vectors trained on common crawl. Assigns word vectors, context-specific token vectors, POS tags, dependency parse and named entities."
-    }
-
-p In a directory #[code meta], we keep #[code meta.json] templates for the individual models, containing all relevant information that doesn't change across versions, like the name, description, author info and training data sources. When we train the model, we pass in the file to the meta template as the #[code --meta] argument, and specify the current model version as the #[code --version] argument.
-
-p On each epoch, the model is saved out with a #[code meta.json] using our template and added properties, like the #[code pipeline], #[code accuracy] scores and the #[code spacy_version] used to train the model. After training completion, the best model is selected automatically and packaged using the #[+api("cli#package") #[code package]] command. Since a full meta file is already present on the trained model, no further setup is required to build a valid model package.
-
-+code(false, "bash").
-    python -m spacy package -f {best_model} dist/
-    cd dist/{model_name}
-    python setup.py sdist
-
-p This process allows us to quickly trigger the model training and build process for all available models and languages, and generate the correct meta data automatically.
diff --git a/website/usage/_training/_tagger-parser.jade b/website/usage/_training/_tagger-parser.jade
deleted file mode 100644
index be1210ace..000000000
--- a/website/usage/_training/_tagger-parser.jade
+++ /dev/null
@@ -1,184 +0,0 @@
-//- 💫 DOCS > USAGE > TRAINING > TAGGER & PARSER
-
-+h(3, "example-train-parser") Updating the Dependency Parser
-
-p
-    |  This example shows how to train spaCy's dependency parser, starting off
-    |  with an existing model or a blank model. You'll need a set of
-    |  #[strong training examples] and the respective #[strong heads] and
-    |  #[strong dependency label] for each token of the example texts.
-
-+github("spacy", "examples/training/train_parser.py", 500)
-
-+h(4) Step by step guide
-
-+list("numbers")
-    +item
-        |  #[strong Load the model] you want to start with, or create an
-        |  #[strong empty model] using
-        |  #[+api("spacy#blank") #[code spacy.blank]] with the ID of your
-        |  language. If you're using a blank model, don't forget to add the
-        |  parser to the pipeline. If you're using an existing model,
-        |  make sure to disable all other pipeline components during training
-        |  using #[+api("language#disable_pipes") #[code nlp.disable_pipes]].
-        |  This way, you'll only be training the parser.
-
-    +item
-        |  #[strong Add the dependency labels] to the parser using the
-        |  #[+api("dependencyparser#add_label") #[code add_label]] method. If
-        |  you're starting off with a pre-trained spaCy model, this is usually
-        |  not necessary – but it doesn't hurt either, just to be safe.
-
-    +item
-        |  #[strong Shuffle and loop over] the examples. For each example,
-        |  #[strong update the model] by calling
-        |  #[+api("language#update") #[code nlp.update]], which steps through
-        |  the words of the input. At each word, it makes a
-        |  #[strong prediction]. It then consults the annotations  to see
-        |  whether it was right. If it was wrong, it adjusts its weights so
-        |  that the correct action will score higher next time.
-
-    +item
-        |  #[strong Save] the trained model using
-        |  #[+api("language#to_disk") #[code nlp.to_disk]].
-
-    +item
-        |  #[strong Test] the model to make sure the parser works as expected.
-
-+h(3, "example-train-tagger") Updating the Part-of-speech Tagger
-
-p
-    |  In this example, we're training spaCy's part-of-speech tagger with a
-    |  custom tag map. We start off with a blank #[code Language] class, update
-    |  its defaults with our custom tags and then train the tagger. You'll need
-    |  a set of #[strong training examples] and the respective
-    |  #[strong custom tags], as well as a dictionary mapping those tags to the
-    |  #[+a("http://universaldependencies.github.io/docs/u/pos/index.html") Universal Dependencies scheme].
-
-+github("spacy", "examples/training/train_tagger.py", 500)
-
-+h(4) Step by step guide
-
-+list("numbers")
-    +item
-        |  #[strong Load the model] you want to start with, or create an
-        |  #[strong empty model] using
-        |  #[+api("spacy#blank") #[code spacy.blank]] with the ID of your
-        |  language. If you're using a blank model, don't forget to add the
-        |  tagger to the pipeline. If you're using an existing model,
-        |  make sure to disable all other pipeline components during training
-        |  using #[+api("language#disable_pipes") #[code nlp.disable_pipes]].
-        |  This way, you'll only be training the tagger.
-
-    +item
-        |  #[strong Add the tag map] to the tagger using the
-        |  #[+api("tagger#add_label") #[code add_label]] method. The first
-        |  argument is the new tag name, the second the mapping to spaCy's
-        |  coarse-grained tags, e.g. #[code {'pos': 'NOUN'}].
-
-    +item
-        |  #[strong Shuffle and loop over] the examples. For each example,
-        |  #[strong update the model] by calling
-        |  #[+api("language#update") #[code nlp.update]], which steps through
-        |  the words of the input. At each word, it makes a
-        |  #[strong prediction]. It then consults the annotations to see whether
-        |  it was right. If it was wrong, it adjusts its weights so that the
-        |  correct action will score higher next time.
-
-    +item
-        |  #[strong Save] the trained model using
-        |  #[+api("language#to_disk") #[code nlp.to_disk]].
-
-    +item
-        |  #[strong Test] the model to make sure the parser works as expected.
-
-+h(3, "intent-parser") Training a parser for custom semantics
-
-p
-    |  spaCy's parser component can be used to be trained to predict any type
-    |  of tree structure over your input text – including
-    |  #[strong semantic relations] that are not syntactic dependencies. This
-    |  can be useful to for #[strong conversational applications], which need to
-    |  predict trees over whole documents or chat logs, with connections between
-    |  the sentence roots used to annotate discourse structure. For example, you
-    |  can train spaCy's parser to label intents and their targets, like
-    |  attributes, quality, time and locations. The result could look like this:
-
-+codepen("991f245ef90debb78c8fc369294f75ad", 300)
-
-+code.
-    doc = nlp(u"find a hotel with good wifi")
-    print([(t.text, t.dep_, t.head.text) for t in doc if t.dep_ != '-'])
-    # [('find', 'ROOT', 'find'), ('hotel', 'PLACE', 'find'),
-    #  ('good', 'QUALITY', 'wifi'), ('wifi', 'ATTRIBUTE', 'hotel')]
-
-p
-    |  The above tree attaches "wifi" to "hotel" and assigns the dependency
-    |  label #[code ATTRIBUTE]. This may not be a correct syntactic dependency –
-    |  but in this case, it expresses exactly what we need: the user is looking
-    |  for a hotel with the attribute "wifi" of the quality "good". This query
-    |  can then be processed by your application and used to trigger the
-    |  respective action – e.g. search the database for hotels with high ratings
-    |  for their wifi offerings.
-
-+aside("Tip: merge phrases and entities", "💡")
-    |  To achieve even better accuracy, try merging multi-word tokens and
-    |  entities specific to your domain into one token before parsing your text.
-    |  You can do this by running the entity recognizer or
-    |  #[+a("/usage/linguistic-features#rule-based-matching") rule-based matcher]
-    |  to find relevant spans, and merging them using
-    |  #[+api("span#merge") #[code Span.merge]]. You could even add your own
-    |  custom #[+a("/usage/processing-pipelines#custom-components") pipeline component]
-    |  to do this automatically – just make sure to add it #[code before='parser'].
-
-p
-    |  The following example shows a full implementation of a training
-    |  loop for a custom message parser for a common "chat intent": finding
-    |  local businesses. Our message semantics will have the following types
-    |  of relations: #[code ROOT], #[code PLACE], #[code QUALITY],
-    |  #[code ATTRIBUTE], #[code TIME] and #[code LOCATION].
-
-+github("spacy", "examples/training/train_intent_parser.py", 500)
-
-+h(4) Step by step guide
-
-+list("numbers")
-    +item
-        |  #[strong Create the training data] consisting of words, their heads
-        |  and their dependency labels in order. A token's head is the index
-        |  of the token it is attached to. The heads don't need to be
-        |  syntactically correct – they should express the
-        |  #[strong semantic relations] you want the parser to learn. For words
-        |  that shouldn't receive a label, you can choose an arbitrary
-        |  placeholder, for example #[code -].
-
-    +item
-        |  #[strong Load the model] you want to start with, or create an
-        |  #[strong empty model] using
-        |  #[+api("spacy#blank") #[code spacy.blank]] with the ID of your
-        |  language. If you're using a blank model, don't forget to add the
-        |  custom parser to the pipeline. If you're using an existing model,
-        |  make sure to #[strong remove the old parser] from the pipeline, and
-        |  disable all other pipeline components during training using
-        |  #[+api("language#disable_pipes") #[code nlp.disable_pipes]].
-        |  This way, you'll only be training the parser.
-
-    +item
-        |  #[strong Add the dependency labels] to the parser using the
-        |  #[+api("dependencyparser#add_label") #[code add_label]] method.
-
-    +item
-        |  #[strong Shuffle and loop over] the examples. For each example,
-        |  #[strong update the model] by calling
-        |  #[+api("language#update") #[code nlp.update]], which steps
-        |  through the words of the input. At each word, it makes a
-        |  #[strong prediction]. It then consults the annotations to see whether
-        |  it was right. If it was wrong, it adjusts its weights so that the
-        |  correct action will score higher next time.
-
-    +item
-        |  #[strong Save] the trained model using
-        |  #[+api("language#to_disk") #[code nlp.to_disk]].
-
-    +item
-        |  #[strong Test] the model to make sure the parser works as expected.
diff --git a/website/usage/_training/_textcat.jade b/website/usage/_training/_textcat.jade
deleted file mode 100644
index fffbb5518..000000000
--- a/website/usage/_training/_textcat.jade
+++ /dev/null
@@ -1,63 +0,0 @@
-//- 💫 DOCS > USAGE > TRAINING > TEXT CLASSIFICATION
-
-+h(3, "example-textcat") Adding a text classifier to a spaCy model
-    +tag-new(2)
-
-p
-    |  This example shows how to train a convolutional neural
-    |  network text classifier on IMDB movie reviews, using spaCy's new
-    |  #[+api("textcategorizer") #[code TextCategorizer]] component. The
-    |  dataset will be loaded automatically via Thinc's built-in dataset
-    |  loader. Predictions are available via
-    |  #[+api("doc#attributes") #[code Doc.cats]].
-
-+github("spacy", "examples/training/train_textcat.py", 500)
-
-+h(4) Step by step guide
-
-+list("numbers")
-    +item
-        |  #[strong Load the model] you want to start with, or create an
-        |  #[strong empty model] using
-        |  #[+api("spacy#blank") #[code spacy.blank]] with the ID of your
-        |  language. If you're using an existing model, make sure to disable all
-        |  other pipeline components during training using
-        |  #[+api("language#disable_pipes") #[code nlp.disable_pipes]]. This
-        |  way, you'll only be training the text classifier.
-
-    +item
-        |  #[strong Add the text classifier] to the pipeline, and add the labels
-        |  you want to train – for example, #[code POSITIVE].
-
-    +item
-        |  #[strong Load and pre-process the dataset], shuffle the data and
-        |  split off a part of it to hold back for evaluation. This way, you'll
-        |  be able to see results on each training iteration.
-
-    +item
-        |  #[strong Loop over] the training examples and partition them into
-        |  batches using spaCy's
-        |  #[+api("top-level#util.minibatch") #[code minibatch]] and
-        |  #[+api("top-level#util.compounding") #[code compounding]] helpers.
-
-    +item
-        |  #[strong Update the model] by calling
-        |  #[+api("language#update") #[code nlp.update]], which steps
-        |  through the examples and makes a #[strong prediction]. It then
-        |  consults the annotations to see whether it was right. If it was
-        |  wrong, it adjusts its weights so that the correct prediction will
-        |  score higher next time.
-
-    +item
-        |  Optionally, you can also #[strong evaluate the text classifier] on
-        |  each iteration, by checking how it performs on the development data
-        |  held back from the dataset. This lets you print the
-        |  #[strong precision], #[strong recall] and #[strong F-score].
-
-    +item
-        |  #[strong Save] the trained model using
-        |  #[+api("language#to_disk") #[code nlp.to_disk]].
-
-    +item
-        |  #[strong Test] the model to make sure the text classifier works as
-        |  expected.
diff --git a/website/usage/_training/_tips.jade b/website/usage/_training/_tips.jade
deleted file mode 100644
index ce637a247..000000000
--- a/website/usage/_training/_tips.jade
+++ /dev/null
@@ -1,162 +0,0 @@
-//- 💫 DOCS > USAGE > TRAINING > OPTIMIZATION TIPS AND ADVICE
-
-p
-    |  There are lots of conflicting "recipes" for training deep neural
-    |  networks at the moment. The cutting-edge models take a very long time to
-    |  train, so most researchers can't run enough experiments to figure out
-    |  what's #[em really] going on. For what it's worth, here's a recipe that seems
-    |  to work well on a lot of NLP problems:
-
-+list("numbers")
-    +item
-        |  Initialise with batch size 1, and compound to a maximum determined
-        |  by your data size and problem type.
-    +item
-        |  Use Adam solver with fixed learning rate.
-
-    +item
-        |  Use averaged parameters
-
-    +item
-        |  Use L2 regularization.
-
-    +item
-        |  Clip gradients by L2 norm to 1.
-
-    +item
-        |  On small data sizes, start at a high dropout rate, with linear decay.
-
-p
-    |  This recipe has been cobbled together experimentally. Here's why the
-    |  various elements of the recipe made enough sense to try initially, and
-    |  what you might try changing, depending on your problem.
-
-+h(3, "tips-batch-size") Compounding batch size
-
-p
-    |  The trick of increasing the batch size is starting to become quite
-    |  popular (see #[+a("https://arxiv.org/abs/1711.00489") Smith et al., 2017]).
-    |  Their recipe is quite different from how spaCy's models are being
-    |  trained, but there are some similarities. In training the various spaCy
-    |  models, we haven't found much advantage from decaying the learning
-    |  rate – but starting with a low batch size has definitely helped. You
-    |  should try it out on your data, and see how you go. Here's our current
-    |  strategy:
-
-+code("Batch heuristic").
-    def get_batches(train_data, model_type):
-        max_batch_sizes = {'tagger': 32, 'parser': 16, 'ner': 16, 'textcat': 64}
-        max_batch_size = max_batch_sizes[model_type]
-        if len(train_data) &lt; 1000:
-            max_batch_size /= 2
-        if len(train_data) &lt; 500:
-            max_batch_size /= 2
-        batch_size = compounding(1, max_batch_size, 1.001)
-        batches = minibatch(train_data, size=batch_size)
-        return batches
-
-p
-    |  This will set the batch size to start at #[code 1], and increase each
-    |  batch until it reaches a maximum size. The tagger, parser and entity
-    |  recognizer all take whole sentences as input, so they're learning a lot
-    |  of labels in a single example. You therefore need smaller batches for
-    |  them. The batch size for the text categorizer should be somewhat larger,
-    |  especially if your documents are long.
-
-+h(3, "tips-hyperparams") Learning rate, regularization and gradient clipping
-
-p
-    |  By default spaCy uses the Adam solver, with default settings
-    |  (learning rate #[code 0.001], #[code beta1=0.9], #[code beta2=0.999]).
-    |  Some researchers have said they found these settings terrible on their
-    |  problems – but they've always performed very well in training spaCy's
-    |  models, in combination with the rest of our recipe. You can change these
-    |  settings directly, by modifying the corresponding attributes on the
-    |  #[code optimizer] object. You can also set environment variables, to
-    |  adjust the defaults.
-
-p
-    |  There are two other key hyper-parameters of the solver: #[code L2]
-    |  #[strong regularization], and #[strong gradient clipping]
-    |  (#[code max_grad_norm]). Gradient clipping is a hack that's not discussed
-    |  often, but everybody seems to be using. It's quite important in helping
-    |  to ensure the network doesn't diverge, which is a fancy way of saying
-    |  "fall over during training". The effect is sort of similar to setting the
-    |  learning rate low. It can also compensate for a large batch size (this is
-    |  a good example of how the choices of all these hyper-parameters
-    |  intersect).
-
-+h(3, "tips-dropout") Dropout rate
-
-p
-    |  For small datasets, it's useful to set a
-    |  #[strong high dropout rate at first], and #[strong decay] it down towards
-    |  a more reasonable value. This helps avoid the network immediately
-    |  overfitting, while still encouraging it to learn some of the more
-    |  interesting things in your data. spaCy comes with a
-    |  #[+api("top-level#util.decaying") #[code decaying]] utility function to
-    |  facilitate this. You might try setting:
-
-+code.
-    from spacy.util import decaying
-    dropout = decaying(0.6, 0.2, 1e-4)
-
-p
-    |  You can then draw values from the iterator with #[code next(dropout)],
-    |  which you would pass to the #[code drop] keyword argument of
-    |  #[+api("language#update") #[code nlp.update]]. It's pretty much always a
-    |  good idea to use at least #[strong some dropout]. All of the models
-    |  currently use Bernoulli dropout, for no particularly principled reason –
-    |  we just haven't experimented with another scheme like Gaussian dropout
-    |  yet.
-
-+h(3, "tips-param-avg") Parameter averaging
-
-p
-    |  The last part of our optimization recipe is #[strong parameter averaging],
-    |  an old trick introduced by
-    |  #[+a("https://cseweb.ucsd.edu/~yfreund/papers/LargeMarginsUsingPerceptron.pdf") Freund and Schapire (1999)],
-    |  popularised in the NLP community by
-    |  #[+a("http://www.aclweb.org/anthology/P04-1015") Collins (2002)],
-    |  and explained in more detail by
-    |  #[+a("http://leon.bottou.org/projects/sgd") Leon Bottou]. Just about the
-    |  only other people who seem to be using this for neural network training
-    |  are the SyntaxNet team (one of whom is Michael Collins) – but it really
-    |  seems to work great on every problem.
-
-p
-    |  The trick is to store the moving average of the weights during training.
-    |  We don't optimize this average – we just track it. Then  when we want to
-    |  actually use the model, we use the averages, not the most recent value.
-    |  In spaCy (and #[+a(gh("thinc")) Thinc]) this is done by using a
-    |  context manager, #[+api("language#use_params") #[code use_params]], to
-    |  temporarily replace the weights:
-
-+code.
-    with nlp.use_params(optimizer.averages):
-        nlp.to_disk('/model')
-
-p
-    |  The context manager is handy because you naturally want to evaluate and
-    |  save the model at various points during training (e.g. after each epoch).
-    |  After evaluating and saving, the context manager will exit and the
-    |  weights will be restored, so you resume training from the most recent
-    |  value, rather than the average. By evaluating the model after each epoch,
-    |  you can remove one hyper-parameter from consideration (the number of
-    |  epochs). Having one less magic number to guess is extremely nice – so
-    |  having the averaging under a context manager is very convenient.
-
-+h(3, "tips-transfer-learning") Transfer learning
-
-p
-    |  Finally, if you're training from a small data set, it's very useful to
-    |  start off with some knowledge already in the model. #[strong Word vectors]
-    |  are an easy and reliable way to do that, but depending on the
-    |  application, you may also be able to start with useful knowledge from one
-    |  of spaCy's #[+a("/models") pre-trained models], such as the parser,
-    |  entity recogniser and tagger. If you're adapting a pre-trained model and
-    |  you want it to retain accuracy on the tasks it was originally trained
-    |  for, you should consider the  "catastrophic forgetting" problem.
-    |  #[+a("https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting", true) See this blog post]
-    |  to read more about the problem and our suggested solution,
-    |  pseudo-rehearsal.
diff --git a/website/usage/_v2/_features.jade b/website/usage/_v2/_features.jade
deleted file mode 100644
index b0139e3b0..000000000
--- a/website/usage/_v2/_features.jade
+++ /dev/null
@@ -1,249 +0,0 @@
-//- 💫 DOCS > USAGE > WHAT'S NEW IN V2.0 > NEW FEATURES
-
-p
-    |  This section contains an overview of the most important
-    |  #[strong new features and improvements]. The #[+a("/api") API docs]
-    |  include additional  deprecation notes. New methods and functions that
-    |  were introduced in this version are marked with a
-    |  #[span.u-text-tag.u-text-tag--spaced v2.0] tag.
-
-+h(3, "features-models") Convolutional neural network models
-
-+aside-code("Example", "bash")
-    for _, lang in MODELS
-        if lang != "xx"
-            | python -m spacy download #{lang}  # default #{LANGUAGES[lang]} model!{'\n'}
-    | python -m spacy download xx_ent_wiki_sm  # multi-language NER
-
-p
-    |  spaCy v2.0 features new neural models for tagging,
-    |  parsing and entity recognition. The models have
-    |  been designed and implemented from scratch specifically for spaCy, to
-    |  give you an unmatched balance of speed, size and accuracy. The new
-    |  models are #[strong 10&times; smaller], #[strong 20% more accurate],
-    |  and #[strong even cheaper to run] than the previous generation.
-
-p
-    |  spaCy v2.0's new neural network models bring significant improvements in
-    |  accuracy, especially for English Named Entity Recognition. The new
-    |  #[+a("/models/en#en_core_web_lg") #[code en_core_web_lg]] model makes
-    |  about #[strong 25% fewer mistakes] than the corresponding v1.x model and
-    |  is within #[strong 1% of the current state-of-the-art]
-    |  (#[+a("https://arxiv.org/pdf/1702.02098.pdf") Strubell et al., 2017]).
-    |  The v2.0 models are also cheaper to run at scale, as they require
-    |  #[strong under 1 GB of memory] per process.
-
-+infobox
-    |  #[+label-inline Usage:] #[+a("/models") Models directory]
-    |  #[+a("#benchmarks") Benchmarks]
-
-+h(3, "features-pipelines") Improved processing pipelines
-
-+aside-code("Example").
-    # Set custom attributes
-    Doc.set_extension('my_attr', default=False)
-    Token.set_extension('my_attr', getter=my_token_getter)
-    assert doc._.my_attr, token._.my_attr
-
-    # Add components to the pipeline
-    my_component = lambda doc: doc
-    nlp.add_pipe(my_component)
-
-p
-    |  It's now much easier to #[strong customise the pipeline] with your own
-    |  components: functions that receive a #[code Doc] object, modify and
-    |  return it. Extensions let you write any
-    |  #[strong attributes, properties and methods] to the #[code Doc],
-    |  #[code Token] and #[code Span]. You can add data, implement new
-    |  features, integrate other libraries with spaCy or plug in your own
-    |  machine learning models.
-
-+image
-    include ../../assets/img/pipeline.svg
-
-+infobox
-    |  #[+label-inline API:] #[+api("language") #[code Language]],
-    |  #[+api("doc#set_extension") #[code Doc.set_extension]],
-    |  #[+api("span#set_extension") #[code Span.set_extension]],
-    |  #[+api("token#set_extension") #[code Token.set_extension]]
-    |  #[+label-inline Usage:]
-    |  #[+a("/usage/processing-pipelines") Processing pipelines]
-    |  #[+label-inline Code:]
-    |  #[+src("/usage/examples#section-pipeline") Pipeline examples]
-
-+h(3, "features-text-classification") Text classification
-
-+aside-code("Example").
-    textcat = nlp.create_pipe('textcat')
-    nlp.add_pipe(textcat, last=True)
-    optimizer = nlp.begin_training()
-    for itn in range(100):
-        for doc, gold in train_data:
-            nlp.update([doc], [gold], sgd=optimizer)
-    doc = nlp(u'This is a text.')
-    print(doc.cats)
-
-p
-    |  spaCy v2.0 lets you add text categorization models to spaCy pipelines.
-    |  The model supports classification with multiple, non-mutually
-    |  exclusive labels – so multiple labels can apply at once. You can
-    |  change the model architecture rather easily, but by default, the
-    |  #[code TextCategorizer] class uses a convolutional neural network to
-    |  assign position-sensitive vectors to each word in the document.
-
-+infobox
-    |  #[+label-inline API:] #[+api("textcategorizer") #[code TextCategorizer]],
-    |  #[+api("doc#attributes") #[code Doc.cats]],
-    |  #[+api("goldparse#attributes") #[code GoldParse.cats]]#[br]
-    |  #[+label-inline Usage:] #[+a("/usage/training#textcat") Training a text classication model]
-
-+h(3, "features-hash-ids") Hash values instead of integer IDs
-
-+aside-code("Example").
-    doc = nlp(u'I love coffee')
-    assert doc.vocab.strings[u'coffee'] == 3197928453018144401
-    assert doc.vocab.strings[3197928453018144401] == u'coffee'
-
-    beer_hash = doc.vocab.strings.add(u'beer')
-    assert doc.vocab.strings[u'beer'] == beer_hash
-    assert doc.vocab.strings[beer_hash] == u'beer'
-
-p
-    |  The #[+api("stringstore") #[code StringStore]] now resolves all strings
-    |  to hash values instead of integer IDs. This means that the string-to-int
-    |  mapping #[strong no longer depends on the vocabulary state], making a lot
-    |  of workflows much simpler, especially during training. Unlike integer IDs
-    |  in spaCy v1.x, hash values will #[strong always match] – even across
-    |  models. Strings can now be added explicitly using the new
-    |  #[+api("stringstore#add") #[code Stringstore.add]] method. A token's hash
-    |  is available via #[code token.orth].
-
-+infobox
-    |  #[+label-inline API:] #[+api("stringstore") #[code StringStore]]
-    |  #[+label-inline Usage:] #[+a("/usage/spacy-101#vocab") Vocab, hashes and lexemes 101]
-
-+h(3, "features-vectors") Improved word vectors support
-
-+aside-code("Example").
-    for word, vector in vector_data:
-        nlp.vocab.set_vector(word, vector)
-    nlp.vocab.vectors.from_glove('/path/to/vectors')
-    # keep 10000 unique vectors and remap the rest
-    nlp.vocab.prune_vectors(10000)
-    nlp.to_disk('/model')
-
-p
-    |  The new #[+api("vectors") #[code Vectors]] class helps the
-    |  #[code Vocab] manage the vectors assigned to strings, and lets you
-    |  assign vectors individually, or
-    |  #[+a("/usage/vectors-similarity#custom-loading-glove") load in GloVe vectors]
-    |  from a directory. To help you strike a good balance between coverage
-    |  and memory usage, the #[code Vectors] class lets you map
-    |  #[strong multiple keys] to the #[strong same row] of the table. If
-    |  you're using the #[+api("cli#vocab") #[code spacy vocab]] command to
-    |  create a vocabulary, pruning the vectors will be taken care of
-    |  automatically. Otherwise, you can use the new
-    |  #[+api("vocab#prune_vectors") #[code Vocab.prune_vectors]].
-
-+infobox
-    |  #[+label-inline API:] #[+api("vectors") #[code Vectors]],
-    |  #[+api("vocab") #[code Vocab]]
-    |  #[+label-inline Usage:] #[+a("/usage/vectors-similarity") Word vectors and semantic similarity]
-
-+h(3, "features-serializer") Saving, loading and serialization
-
-+aside-code("Example").
-    nlp = spacy.load('en') # shortcut link
-    nlp = spacy.load('en_core_web_sm') # package
-    nlp = spacy.load('/path/to/en') # unicode path
-    nlp = spacy.load(Path('/path/to/en')) # pathlib Path
-
-    nlp.to_disk('/path/to/nlp')
-    nlp = English().from_disk('/path/to/nlp')
-
-p
-    |  spaCy's serialization API has been made consistent across classes and
-    |  objects. All container classes, i.e. #[code Language], #[code Doc],
-    |  #[code Vocab] and #[code StringStore] now have a #[code to_bytes()],
-    |  #[code from_bytes()], #[code to_disk()] and #[code from_disk()] method
-    |  that supports the Pickle protocol.
-
-p
-    |  The improved #[code spacy.load] makes loading models easier and more
-    |  transparent. You can load a model by supplying its
-    |  #[+a("/usage/models#usage") shortcut link], the name of an installed
-    |  #[+a("/models") model package] or a path. The #[code Language] class to
-    |  initialise will be determined based on the model's settings. For a blank
-    |  language, you can import the class directly, e.g.
-    |  #[code.u-break from spacy.lang.en import English] or use
-    |  #[+api("spacy#blank") #[code spacy.blank()]].
-
-+infobox
-    |  #[+label-inline API:] #[+api("spacy#load") #[code spacy.load]],
-    |  #[+api("language#to_disk") #[code Language.to_disk]]
-    |  #[+label-inline Usage:] #[+a("/usage/models#usage") Models],
-    |  #[+a("/usage/training#saving-loading") Saving and loading]
-
-+h(3, "features-displacy") displaCy visualizer with Jupyter support
-
-+aside-code("Example").
-    from spacy import displacy
-    doc = nlp(u'This is a sentence about Facebook.')
-    displacy.serve(doc, style='dep') # run the web server
-    html = displacy.render(doc, style='ent') # generate HTML
-
-p
-    |  Our popular dependency and named entity visualizers are now an official
-    |  part of the spaCy library. displaCy can run a simple web server, or
-    |  generate raw HTML markup or SVG files to be exported. You can pass in one
-    |  or more docs, and customise the style. displaCy also auto-detects whether
-    |  you're running #[+a("https://jupyter.org") Jupyter] and will render the
-    |  visualizations in your notebook.
-
-+infobox
-    |  #[+label-inline API:] #[+api("top-level#displacy") #[code displacy]]
-    |  #[+label-inline Usage:] #[+a("/usage/visualizers") Visualizing spaCy]
-
-+h(3, "features-language") Improved language data and lazy loading
-
-p
-    |  Language-specfic data now lives in its own submodule, #[code spacy.lang].
-    |  Languages are lazy-loaded, i.e. only loaded when you import a
-    |  #[code Language] class, or load a model that initialises one. This allows
-    |  languages to contain more custom data, e.g. lemmatizer lookup tables, or
-    |  complex regular expressions. The language data has also been tidied up
-    |  and simplified. spaCy now also supports simple lookup-based
-    |  lemmatization – and #[strong #{LANG_COUNT} languages] in total!
-
-+infobox
-    |  #[+label-inline API:] #[+api("language") #[code Language]]
-    |  #[+label-inline Code:] #[+src(gh("spaCy", "spacy/lang")) #[code spacy/lang]]
-    |  #[+label-inline Usage:] #[+a("/usage/adding-languages") Adding languages]
-
-+h(3, "features-matcher") Revised matcher API and phrase matcher
-
-+aside-code("Example").
-    from spacy.matcher import Matcher, PhraseMatcher
-
-    matcher = Matcher(nlp.vocab)
-    matcher.add('HEARTS', None, [{'ORTH': '❤️', 'OP': '+'}])
-
-    phrasematcher = PhraseMatcher(nlp.vocab)
-    phrasematcher.add('OBAMA', None, nlp(u"Barack Obama"))
-
-p
-    |  Patterns can now be added to the matcher by calling
-    |  #[+api("matcher#add") #[code matcher.add()]] with a match ID, an optional
-    |  callback function to be invoked on each match, and one or more patterns.
-    |  This allows you to write powerful, pattern-specific logic using only one
-    |  matcher. For example, you might only want to merge some entity types,
-    |  and set custom flags for other matched patterns. The new
-    |  #[+api("phrasematcher") #[code PhraseMatcher]] lets you efficiently
-    |  match very large terminology lists using #[code Doc] objects as match
-    |  patterns.
-
-+infobox
-    |  #[+label-inline API:] #[+api("matcher") #[code Matcher]],
-    |  #[+api("phrasematcher") #[code PhraseMatcher]]
-    |  #[+label-inline Usage:]
-    |  #[+a("/usage/linguistic-features#rule-based-matching") Rule-based matching]
diff --git a/website/usage/_v2/_incompat.jade b/website/usage/_v2/_incompat.jade
deleted file mode 100644
index e5cbc3043..000000000
--- a/website/usage/_v2/_incompat.jade
+++ /dev/null
@@ -1,170 +0,0 @@
-//- 💫 DOCS > USAGE > WHAT'S NEW IN V2.0 > BACKWARDS INCOMPATIBILITIES
-
-p
-    |  The following modules, classes and methods have changed between v1.x
-    |  and v2.0.
-
-+table(["Old", "New"])
-    +row
-        +cell #[code spacy.download.en], #[code spacy.download.de]
-        +cell #[+api("cli#download") #[code cli.download]]
-
-    +row
-        +cell #[code spacy.en] etc.
-        +cell #[code spacy.lang.en] etc.
-
-    +row
-        +cell #[code spacy.en.word_sets]
-        +cell #[code spacy.lang.en.stop_words]
-
-    +row
-        +cell #[code spacy.orth]
-        +cell #[code spacy.lang.xx.lex_attrs]
-
-    +row
-        +cell #[code spacy.syntax.iterators]
-        +cell #[code spacy.lang.xx.syntax_iterators]
-
-    +row
-        +cell #[code spacy.tagger.Tagger]
-        +cell #[code spacy.pipeline.Tagger]
-
-    +row
-        +cell #[code spacy.cli.model]
-        +cell #[+api("cli#vocab") #[code spacy.cli.vocab]]
-
-    +row
-        +cell #[code Language.save_to_directory]
-        +cell #[+api("language#to_disk") #[code Language.to_disk]]
-
-    +row
-        +cell #[code Language.end_training]
-        +cell #[+api("language#begin_training") #[code Language.begin_training]]
-
-    +row
-        +cell #[code Language.create_make_doc]
-        +cell #[+api("language#attributes") #[code Language.tokenizer]]
-
-    +row
-        +cell #[code Vocab.resize_vectors]
-        +cell #[+api("vectors#resize") #[code Vectors.resize]]
-
-    +row
-        +cell
-            |  #[code Vocab.load]
-            |  #[code Vocab.load_lexemes]
-        +cell
-            |  #[+api("vocab#from_disk") #[code Vocab.from_disk]]
-            |  #[+api("vocab#from_bytes") #[code Vocab.from_bytes]]
-
-    +row
-        +cell
-            |  #[code Vocab.dump]
-        +cell
-            |  #[+api("vocab#to_disk") #[code Vocab.to_disk]]#[br]
-            |  #[+api("vocab#to_bytes") #[code Vocab.to_bytes]]
-
-    +row
-        +cell
-            |  #[code Vocab.load_vectors]
-            |  #[code Vocab.load_vectors_from_bin_loc]
-        +cell
-            |  #[+api("vectors#from_disk") #[code Vectors.from_disk]]
-            |  #[+api("vectors#from_bytes") #[code Vectors.from_bytes]]
-            |  #[+api("vectors#from_glove") #[code Vectors.from_glove]]
-
-    +row
-        +cell
-            |  #[code Vocab.dump_vectors]
-        +cell
-            |  #[+api("vectors#to_disk") #[code Vectors.to_disk]]
-            |  #[+api("vectors#to_bytes") #[code Vectors.to_bytes]]
-
-    +row
-        +cell
-            |  #[code StringStore.load]
-        +cell
-            |  #[+api("stringstore#from_disk") #[code StringStore.from_disk]]
-            |  #[+api("stringstore#from_bytes") #[code StringStore.from_bytes]]
-
-    +row
-        +cell
-            |  #[code StringStore.dump]
-        +cell
-            |  #[+api("stringstore#to_disk") #[code StringStore.to_disk]]
-            |  #[+api("stringstore#to_bytes") #[code StringStore.to_bytes]]
-
-    +row
-        +cell #[code Tokenizer.load]
-        +cell
-            |  #[+api("tokenizer#from_disk") #[code Tokenizer.from_disk]]
-            |  #[+api("tokenizer#from_bytes") #[code Tokenizer.from_bytes]]
-
-    +row
-        +cell #[code Tagger.load]
-        +cell
-            |  #[+api("tagger#from_disk") #[code Tagger.from_disk]]
-            |  #[+api("tagger#from_bytes") #[code Tagger.from_bytes]]
-
-    +row
-        +cell #[code Tagger.tag_names]
-        +cell #[code Tagger.labels]
-
-    +row
-        +cell #[code DependencyParser.load]
-        +cell
-            |  #[+api("dependencyparser#from_disk") #[code DependencyParser.from_disk]]
-            |  #[+api("dependencyparser#from_bytes") #[code DependencyParser.from_bytes]]
-
-    +row
-        +cell #[code EntityRecognizer.load]
-        +cell
-            |  #[+api("entityrecognizer#from_disk") #[code EntityRecognizer.from_disk]]
-            |  #[+api("entityrecognizer#from_bytes") #[code EntityRecognizer.from_bytes]]
-
-    +row
-        +cell #[code Matcher.load]
-        +cell -
-
-    +row
-        +cell
-            |  #[code Matcher.add_pattern]
-            |  #[code Matcher.add_entity]
-        +cell
-            |  #[+api("matcher#add") #[code Matcher.add]]
-            |  #[+api("phrasematcher#add") #[code PhraseMatcher.add]]
-
-    +row
-        +cell #[code Matcher.get_entity]
-        +cell #[+api("matcher#get") #[code Matcher.get]]
-
-    +row
-        +cell #[code Matcher.has_entity]
-        +cell #[+api("matcher#has_key") #[code Matcher.has_key]]
-
-    +row
-        +cell #[code Doc.read_bytes]
-        +cell
-            |  #[+api("doc#to_bytes") #[code Doc.to_bytes]]
-            |  #[+api("doc#from_bytes") #[code Doc.from_bytes]]
-            |  #[+api("doc#to_disk") #[code Doc.to_disk]]
-            |  #[+api("doc#from_disk") #[code Doc.from_disk]]
-
-    +row
-        +cell #[code Token.is_ancestor_of]
-        +cell #[+api("token#is_ancestor") #[code Token.is_ancestor]]
-
-+h(3, "deprecated") Deprecated
-
-p
-    |  The following methods are deprecated. They can still be used,
-    |  but should be replaced.
-
-+table(["Old", "New"])
-    +row
-        +cell #[code Tokenizer.tokens_from_list]
-        +cell #[+api("doc") #[code Doc]]
-
-    +row
-        +cell #[code Span.sent_start]
-        +cell #[+api("span#is_sent_start") #[code Span.is_sent_start]]
diff --git a/website/usage/_v2/_migrating.jade b/website/usage/_v2/_migrating.jade
deleted file mode 100644
index f54ceb9cf..000000000
--- a/website/usage/_v2/_migrating.jade
+++ /dev/null
@@ -1,250 +0,0 @@
-//- 💫 DOCS > USAGE > WHAT'S NEW IN V2.0 > MIGRATING FROM SPACY 1.X
-
-p
-    |  Because we'e made so many architectural changes to the library, we've
-    |  tried to #[strong keep breaking changes to a minimum]. A lot of projects
-    |  follow the philosophy that if you're going to break anything, you may as
-    |  well break everything. We think migration is easier if there's a logic to
-    |  what has changed. We've therefore followed a policy of avoiding
-    |  breaking changes to the #[code Doc], #[code Span] and #[code Token]
-    |  objects. This way, you can focus on only migrating the code that
-    |  does training, loading and serialization — in other words, code that
-    |  works with the #[code nlp] object directly. Code that uses the
-    |  annotations should continue to work.
-
-+infobox("Important note", "⚠️")
-    |  If you've trained your own models, keep in mind that your train and
-    |  runtime inputs must match. This means you'll have to
-    |  #[strong retrain your models] with spaCy v2.0.
-
-+h(3, "migrating-document-processing") Document processing
-
-p
-    |  The #[+api("language#pipe") #[code Language.pipe]] method allows spaCy
-    |  to batch documents, which brings a
-    |  #[strong significant performance advantage] in v2.0. The new neural
-    |  networks introduce some overhead per batch, so if you're processing a
-    |  number of documents in a row, you should use #[code nlp.pipe] and process
-    |  the texts as a stream.
-
-+code-new docs = nlp.pipe(texts)
-+code-old docs = (nlp(text) for text in texts)
-
-p
-    |  To make usage easier, there's now a boolean #[code as_tuples]
-    |  keyword argument, that lets you pass in an iterator of
-    |  #[code (text, context)] pairs, so you can get back an iterator of
-    |  #[code (doc, context)] tuples.
-
-+h(3, "migrating-saving-loading") Saving, loading and serialization
-
-p
-    |  Double-check all calls to #[code spacy.load()] and make sure they don't
-    |  use the #[code path] keyword argument. If you're only loading in binary
-    |  data and not a model package that can construct its own #[code Language]
-    |  class and pipeline, you should now use the
-    |  #[+api("language#from_disk") #[code Language.from_disk()]] method.
-
-+code-new.
-    nlp = spacy.load('/model')
-    nlp = spacy.blank('en').from_disk('/model/data')
-+code-old nlp = spacy.load('en', path='/model')
-
-p
-    |  Review all other code that writes state to disk or bytes.
-    |  All containers, now share the same, consistent API for saving and
-    |  loading. Replace saving with #[code to_disk()] or #[code to_bytes()], and
-    |  loading with #[code from_disk()] and #[code from_bytes()].
-
-+code-new.
-    nlp.to_disk('/model')
-    nlp.vocab.to_disk('/vocab')
-
-+code-old.
-    nlp.save_to_directory('/model')
-    nlp.vocab.dump('/vocab')
-
-p
-    |  If you've trained models with input from v1.x, you'll need to
-    |  #[strong retrain them] with spaCy v2.0. All previous models will not
-    |  be compatible with the new version.
-
-+h(3, "migrating-languages") Processing pipelines and language data
-
-p
-    |  If you're importing language data or #[code Language] classes, make sure
-    |  to change your import statements to import from #[code spacy.lang]. If
-    |  you've added your own custom language, it needs to be moved to
-    |  #[code spacy/lang/xx] and adjusted accordingly.
-
-.o-block
-    +code-new from spacy.lang.en import English
-    +code-old from spacy.en import English
-
-p
-    |  If you've been using custom pipeline components, check out the new
-    |  guide on #[+a("/usage/processing-pipelines") processing pipelines].
-    |  Pipeline components are now #[code (name, func)] tuples. Appending
-    |  them to the pipeline still works – but the
-    |  #[+api("language#add_pipe") #[code add_pipe]] method now makes this
-    |  much more convenient. Methods for removing, renaming, replacing and
-    |  retrieving components have been added as well. Components can now
-    |  be disabled by passing a list of their names to the #[code disable]
-    |  keyword argument on load, or by using
-    |  #[+api("language#disable_pipes") #[code disable_pipes]] as a method
-    |  or contextmanager:
-
-.o-block
-    +code-new.
-        nlp = spacy.load('en', disable=['tagger', 'ner'])
-        with nlp.disable_pipes('parser'):
-            doc = nlp(u"I don't want parsed")
-    +code-old.
-        nlp = spacy.load('en', tagger=False, entity=False)
-        doc = nlp(u"I don't want parsed", parse=False)
-
-p
-    |  To add spaCy's built-in pipeline components to your pipeline,
-    |  you can still import and instantiate them directly – but it's more
-    |  convenient to use the new
-    |  #[+api("language#create_pipe") #[code create_pipe]] method with the
-    |  component name, i.e. #[code 'tagger'], #[code 'parser'], #[code 'ner']
-    |  or #[code 'textcat'].
-
-+code-new.
-    tagger = nlp.create_pipe('tagger')
-    nlp.add_pipe(tagger, first=True)
-
-+code-old.
-    from spacy.pipeline import Tagger
-    tagger = Tagger(nlp.vocab)
-    nlp.pipeline.insert(0, tagger)
-
-+h(3, "migrating-training") Training
-
-p
-    |  All built-in pipeline components are now subclasses of
-    |  #[+api("pipe") #[code Pipe]], fully trainable and serializable,
-    |  and follow the same API. Instead of updating the model and telling
-    |  spaCy when to #[em stop], you can now explicitly call
-    |  #[+api("language#begin_training") #[code begin_training]], which
-    |  returns an optimizer you can pass into the
-    |  #[+api("language#update") #[code update]] function. While #[code update]
-    |  still accepts sequences of #[code Doc] and #[code GoldParse] objects,
-    |  you can now also pass in a list of strings and dictionaries describing
-    |  the annotations. We call this the #[+a("/usage/training#training-simple-style") "simple training style"].
-    |  This is also the recommended usage, as it removes one layer of
-    |  abstraction from the training.
-
-+code-new.
-    optimizer = nlp.begin_training()
-    for itn in range(1000):
-        for texts, annotations in train_data:
-            nlp.update(texts, annotations, sgd=optimizer)
-    nlp.to_disk('/model')
-+code-old.
-    for itn in range(1000):
-        for text, entities in train_data:
-            doc = Doc(text)
-            gold = GoldParse(doc, entities=entities)
-            nlp.update(doc, gold)
-    nlp.end_training()
-    nlp.save_to_directory('/model')
-
-+h(3, "migrating-doc") Attaching custom data to the Doc
-
-p
-    |  Previously, you had to create a new container in order to attach custom
-    |  data to a #[code Doc] object. This often required converting the
-    | #[code Doc] objects to and from arrays. In spaCy v2.0, you can set your
-    |  own attributes, properties and methods on the #[code Doc], #[code Token]
-    |  and #[code Span] via
-    |  #[+a("/usage/processing-pipelines#custom-components-attributes") custom extensions].
-    |  This means that your application can – and should – only pass around
-    |  #[code Doc] objects and refer to them as the single source of truth.
-
-+code-new.
-    Doc.set_extension('meta', getter=get_doc_meta)
-    doc_with_meta = nlp(u'This is a doc with meta data')
-    meta = doc._.meta
-
-+code-old.
-    doc = nlp(u'This is a regular doc')
-    doc_array = doc.to_array(['ORTH', 'POS'])
-    doc_with_meta = {'doc_array': doc_array, 'meta': get_doc_meta(doc_array)}
-
-p
-    |  If you wrap your extension attributes in a
-    |  #[+a("/usage/processing-pipelines#custom-components") custom pipeline component],
-    |  they will be assigned automatically when you call #[code nlp] on a text.
-    |  If your application assigns custom data to spaCy's container objects,
-    |  or includes other utilities that interact with the pipeline, consider
-    |  moving this logic into its own extension module.
-
-+code-new.
-    nlp.add_pipe(meta_component)
-    doc = nlp(u'Doc with a custom pipeline that assigns meta')
-    meta = doc._.meta
-
-+code-old.
-    doc = nlp(u'Doc with a standard pipeline')
-    meta = get_meta(doc)
-
-+h(3, "migrating-strings") Strings and hash values
-
-p
-    |  The change from integer IDs to hash values may not actually affect your
-    |  code very much. However, if you're adding strings to the vocab manually,
-    |  you now need to call #[+api("stringstore#add") #[code StringStore.add()]]
-    |  explicitly. You can also now be sure that the string-to-hash mapping will
-    |  always match across vocabularies.
-
-+code-new.
-    nlp.vocab.strings.add(u'coffee')
-    nlp.vocab.strings[u'coffee']       # 3197928453018144401
-    other_nlp.vocab.strings[u'coffee'] # 3197928453018144401
-
-+code-old.
-    nlp.vocab.strings[u'coffee']       # 3672
-    other_nlp.vocab.strings[u'coffee'] # 40259
-
-+h(3, "migrating-matcher") Adding patterns and callbacks to the matcher
-
-p
-    |  If you're using the matcher, you can now add patterns in one step. This
-    |  should be easy to update – simply merge the ID, callback and patterns
-    |  into one call to #[+api("matcher#add") #[code Matcher.add()]]. The
-    |  matcher now also supports string keys, which saves you an extra import.
-    |  If you've been using #[strong acceptor functions], you'll need to move
-    |  this logic into the
-    |  #[+a("/usage/linguistic-features#on_match") #[code on_match] callbacks].
-    |  The callback function is invoked on every match and will give you access to
-    |  the doc, the index of the current match and all total matches. This lets
-    |  you both accept or reject the match, and define the actions to be
-    |  triggered.
-
-.o-block
-    +code-new.
-        matcher.add('GoogleNow', merge_phrases, [{'ORTH': 'Google'}, {'ORTH': 'Now'}])
-
-    +code-old.
-        matcher.add_entity('GoogleNow', on_match=merge_phrases)
-        matcher.add_pattern('GoogleNow', [{ORTH: 'Google'}, {ORTH: 'Now'}])
-
-p
-    |  If you need to match large terminology lists, you can now also
-    |  use the #[+api("phrasematcher") #[code PhraseMatcher]], which accepts
-    |  #[code Doc] objects as match patterns and is more efficient than the
-    |  regular, rule-based matcher.
-
-+code-new.
-    from spacy.matcher import PhraseMatcher
-    matcher = PhraseMatcher(nlp.vocab)
-    patterns = [nlp.make_doc(text) for text in large_terminology_list]
-    matcher.add('PRODUCT', None, *patterns)
-
-+code-old.
-    matcher = Matcher(nlp.vocab)
-    matcher.add_entity('PRODUCT')
-    for text in large_terminology_list
-        matcher.add_pattern('PRODUCT', [{ORTH: text}])
diff --git a/website/usage/_v2/_summary.jade b/website/usage/_v2/_summary.jade
deleted file mode 100644
index a836a77ba..000000000
--- a/website/usage/_v2/_summary.jade
+++ /dev/null
@@ -1,76 +0,0 @@
-//- 💫 DOCS > USAGE > WHAT'S NEW IN V2.0 > SUMMARY
-
-p
-    |  We're very excited to finally introduce spaCy v2.0! On this page, you'll
-    |  find a summary of the new features, information on the backwards
-    |  incompatibilities, including a handy overview of what's been renamed or
-    |  deprecated. To help you make the most of v2.0, we also
-    |  #[strong re-wrote almost all of the usage guides and API docs], and added
-    |  more #[+a("/usage/examples") real-world examples]. If you're new to
-    |  spaCy, or just want to brush up on some NLP basics and the details of
-    |  the library, check out the
-    |  #[+a("/usage/spacy-101") spaCy 101 guide] that explains the most
-    |  important concepts with examples and illustrations.
-
-+legacy
-
-+h(2, "summary") Summary
-
-+grid.o-no-block
-    +grid-col("half")
-
-        p
-            |  This release features entirely new
-            |  #[strong deep learning-powered models] for spaCy's tagger,
-            |  parser and entity recognizer. The new models are
-            |  #[strong 10&times; smaller], #[strong 20% more accurate] and
-            |  #[strong even cheaper to run] than the previous generation.
-
-        p
-            |  We've also made several usability improvements that are
-            |  particularly helpful for #[strong production deployments].
-            |  spaCy v2 now fully supports the Pickle protocol, making it
-            |  easy to use spaCy with
-            |  #[+a("https://spark.apache.org/") Apache Spark]. The
-            |  string-to-integer mapping is #[strong no longer stateful],
-            |  making it easy to reconcile annotations made in different
-            |  processes. Models are smaller and use less memory, and the
-            |  APIs for serialization are now much more consistent. Custom
-            |  pipeline components let you modify the #[code Doc] at any
-            |  stage in the pipeline. You can now also add your own
-            |  custom attributes, properties and methods to the #[code Doc],
-            |  #[code Token] and #[code Span].
-
-    +table-of-contents
-        +item #[+a("#summary") Summary]
-        +item #[+a("#features") New features]
-        +item #[+a("#features-models") Neural network models]
-        +item #[+a("#features-pipelines") Improved processing pipelines]
-        +item #[+a("#features-text-classification") Text classification]
-        +item #[+a("#features-hash-ids") Hash values as IDs]
-        +item #[+a("#features-vectors") Improved word vectors support]
-        +item #[+a("#features-serializer") Saving, loading and serialization]
-        +item #[+a("#features-displacy") displaCy visualizer]
-        +item #[+a("#features-language") Language data and lazy loading]
-        +item #[+a("#features-matcher") Revised matcher API and phrase matcher]
-        +item #[+a("#incompat") Backwards incompatibilities]
-        +item #[+a("#migrating") Migrating from spaCy v1.x]
-        +item #[+a("#benchmarks") Benchmarks]
-
-p
-    |  The main usability improvements you'll notice in spaCy v2.0 are around
-    |  #[strong defining, training and loading your own models] and components.
-    |  The new neural network models make it much easier to train a model from
-    |  scratch, or update an existing model with a few examples. In v1.x, the
-    |  statistical models depended on the state of the #[code Vocab]. If you
-    |  taught the model a new word, you would have to save and load a lot of
-    |  data — otherwise the model wouldn't correctly recall the features of your
-    |  new example. That's no longer the case.
-
-p
-    |  Due to some clever use of hashing, the statistical models
-    |  #[strong never change size], even as they learn new vocabulary items.
-    |  The whole pipeline is also now fully differentiable. Even if you don't
-    |  have explicitly annotated data, you can update spaCy using all the
-    |  #[strong latest deep learning tricks] like adversarial training, noise
-    |  contrastive estimation or reinforcement learning.
diff --git a/website/usage/_vectors-similarity/_custom.jade b/website/usage/_vectors-similarity/_custom.jade
deleted file mode 100644
index d3c610e71..000000000
--- a/website/usage/_vectors-similarity/_custom.jade
+++ /dev/null
@@ -1,241 +0,0 @@
-//- 💫 DOCS > USAGE > VECTORS & SIMILARITY > CUSTOM VECTORS
-
-p
-    |  Word vectors let you import knowledge from raw text into your model. The
-    |  knowledge is represented as a table of numbers, with one row per term in
-    |  your vocabulary. If two terms are used in similar contexts, the algorithm
-    |  that learns the vectors should assign them
-    |  #[strong rows that are quite similar], while words that are used in
-    |  different contexts will have quite different values. This lets you use
-    |  the row-values assigned to the words as a kind of dictionary, to tell you
-    |  some things about what the words in your text mean.
-
-p
-    |  Word vectors are particularly useful for terms which
-    |  #[strong aren&apos;t well represented in your labelled training data].
-    |  For instance, if you're doing named entity recognition, there will always
-    |  be lots of names that you don't have examples of. For instance, imagine
-    |  your training data happens to contain some examples of the term
-    |  "Microsoft", but it doesn't contain any examples of the term "Symantec".
-    |  In your raw text sample, there are plenty of examples of both terms, and
-    |  they're used in similar contexts. The word vectors make that fact
-    |  available to the entity recognition model. It still won't see examples of
-    |  "Symantec" labelled as a company. However, it'll see that "Symantec" has
-    |  a word vector that usually corresponds to company terms, so it can
-    |  #[strong make the inference].
-
-p
-    |  In order to make best use of the word vectors, you want the word vectors
-    |  table to cover a #[strong very large vocabulary]. However, most words are
-    |  rare, so most of the rows in a large word vectors table will be accessed
-    |  very rarely, or never at all. You can usually cover more than
-    |  #[strong 95% of the tokens] in your corpus with just
-    |  #[strong a few thousand rows] in the vector table. However, it's those
-    |  #[strong 5% of rare terms] where the word vectors are
-    |  #[strong most useful]. The problem is that increasing the size of the
-    |  vector table produces rapidly diminishing returns in coverage over these
-    |  rare terms.
-
-+h(3, "converting") Converting word vectors for use in spaCy
-    +tag-new("2.0.10")
-
-p
-    |  Custom word vectors can be trained using a number of open-source libraries,
-    |  such as #[+a("https://radimrehurek.com/gensim") Gensim],
-    |  #[+a("https://fasttext.cc") Fast Text], or Tomas Mikolov's original
-    |  #[+a("https://code.google.com/archive/p/word2vec/") word2vec implementation].
-    |  Most word vector libraries output an easy-to-read text-based format, where
-    |  each line consists of the word followed by its vector. For everyday use,
-    |  we want to convert the vectors model into a binary format that loads faster
-    |  and takes up less space on disk. The easiest way to do this is the
-    |  #[+api("cli#init-model") #[code init-model]] command-line utility:
-
-+code(false, "bash").
-    wget https://s3-us-west-1.amazonaws.com/fasttext-vectors/word-vectors-v2/cc.la.300.vec.gz
-    python -m spacy init-model en /tmp/la_vectors_wiki_lg --vectors-loc cc.la.300.vec.gz
-
-p
-    |  This will output a spaCy model in the directory
-    |  #[code /tmp/la_vectors_wiki_lg], giving you access to some nice Latin
-    |  vectors 😉 You can then pass the directory path to
-    |  #[+api("spacy#load") #[code spacy.load()]].
-
-+code.
-    nlp_latin = spacy.load('/tmp/la_vectors_wiki_lg')
-    doc1 = nlp_latin(u"Caecilius est in horto")
-    doc2 = nlp_latin(u"servus est in atrio")
-    doc1.similarity(doc2)
-
-p
-    |  The model directory will have a #[code /vocab] directory with the strings,
-    |  lexical entries and word vectors from the input vectors model. The
-    |  #[+api("cli#init-model") #[code init-model]] command supports a number of
-    |  archive formats for the word vectors: the vectors can be in plain text
-    |  (#[code .txt]), zipped (#[code .zip]), or tarred and zipped
-    |  (#[code .tgz]).
-
-+h(3, "custom-vectors-coverage") Optimising vector coverage
-    +tag-new(2)
-
-p
-    |  To help you strike a good balance between coverage and memory usage,
-    |  spaCy's #[+api("vectors") #[code Vectors]] class lets you map
-    |  #[strong multiple keys] to the #[strong same row] of the table. If
-    |  you're using the #[+api("cli#vocab") #[code spacy vocab]] command to
-    |  create a vocabulary, pruning the vectors will be taken care of
-    |  automatically. You can also do it manually in the following steps:
-
-+list("numbers")
-    +item
-        |  Start with a #[strong word vectors model] that covers a huge
-        |  vocabulary. For instance, the
-        |  #[+a("/models/en#en_vectors_web_lg") #[code en_vectors_web_lg]] model
-        |  provides 300-dimensional GloVe vectors for over 1 million terms of
-        |  English.
-
-    +item
-        |  If your vocabulary has values set for the #[code Lexeme.prob]
-        |  attribute, the lexemes will be sorted by descending probability to
-        |  determine which vectors to prune. Otherwise, lexemes will be sorted
-        |  by their order in the #[code Vocab].
-
-    +item
-        |  Call #[+api("vocab#prune_vectors") #[code Vocab.prune_vectors]] with
-        |  the number of vectors you want to keep.
-
-+code.
-    nlp = spacy.load('en_vectors_web_lg')
-    n_vectors = 105000  # number of vectors to keep
-    removed_words = nlp.vocab.prune_vectors(n_vectors)
-
-    assert len(nlp.vocab.vectors) &lt;= n_vectors  # unique vectors have been pruned
-    assert nlp.vocab.vectors.n_keys &gt; n_vectors  # but not the total entries
-
-p
-    |  #[+api("vocab#prune_vectors") #[code Vocab.prune_vectors]] reduces the
-    |  current vector table to a given number of unique entries, and returns a
-    |  dictionary containing the removed words, mapped to #[code (string, score)]
-    |  tuples, where #[code string] is the entry the removed word was mapped
-    |  to, and #[code score] the similarity score between the two words.
-
-+code("Removed words").
-    {
-        'Shore': ('coast', 0.732257),
-        'Precautionary': ('caution', 0.490973),
-        'hopelessness': ('sadness', 0.742366),
-        'Continous': ('continuous', 0.732549),
-        'Disemboweled': ('corpse', 0.499432),
-        'biostatistician': ('scientist', 0.339724),
-        'somewheres': ('somewheres', 0.402736),
-        'observing': ('observe', 0.823096),
-        'Leaving': ('leaving', 1.0)
-    }
-
-p
-    |  In the example above, the vector for "Shore" was removed and remapped
-    |  to the vector of "coast", which is deemed about 73% similar. "Leaving"
-    |  was remapped to the vector of "leaving", which is identical.
-
-p
-    |  If you're using the #[+api("cli#init-model") #[code init-model]] command,
-    |  you can set the #[code --prune-vectors] option to easily reduce the size
-    |  of the vectors as you add them to a spaCy model:
-
-+code(false, "bash", "$").
-    python -m spacy init-model /tmp/la_vectors_web_md --vectors-loc la.300d.vec.tgz --prune-vectors 10000
-
-p
-    |  This will create a spaCy model with vectors for the first 10,000 words in
-    |  the vectors model. All other words in the vectors model are mapped to the
-    |  closest vector among those retained.
-
-+h(3, "custom-vectors-add") Adding vectors
-    +tag-new(2)
-
-p
-    |  spaCy's new #[+api("vectors") #[code Vectors]] class greatly improves the
-    |  way word vectors are stored, accessed and used. The data is stored in
-    |  two structures:
-
-+list
-    +item
-        |  An array, which can be either on CPU or #[+a("#gpu") GPU].
-
-    +item
-        |  A dictionary mapping string-hashes to rows in the table.
-
-p
-    |  Keep in mind that the #[code Vectors] class itself has no
-    |  #[+api("stringstore") #[code StringStore]], so you have to store the
-    |  hash-to-string mapping separately. If you need to manage the strings,
-    |  you should use the #[code Vectors] via the
-    |  #[+api("vocab") #[code Vocab]] class, e.g. #[code vocab.vectors]. To
-    |  add vectors to the vocabulary, you can use the
-    |  #[+api("vocab#set_vector") #[code Vocab.set_vector]] method.
-
-+code("Adding vectors").
-    from spacy.vocab import Vocab
-
-    vector_data = {u'dog': numpy.random.uniform(-1, 1, (300,)),
-                   u'cat': numpy.random.uniform(-1, 1, (300,)),
-                   u'orange': numpy.random.uniform(-1, 1, (300,))}
-
-    vocab = Vocab()
-    for word, vector in vector_data.items():
-        vocab.set_vector(word, vector)
-
-+h(3, "custom-loading-glove") Loading GloVe vectors
-    +tag-new(2)
-
-p
-    |  spaCy comes with built-in support for loading
-    |  #[+a("https://nlp.stanford.edu/projects/glove/") GloVe] vectors from
-    |  a directory. The #[+api("vectors#from_glove") #[code Vectors.from_glove]]
-    |  method assumes a binary format, the vocab provided in a
-    |  #[code vocab.txt], and the naming scheme of
-    |  #[code vectors.{size}.[fd].bin]. For example:
-
-+aside-code("Directory structure", "yaml").
-    └── vectors
-        ├── vectors.128.f.bin  # vectors file
-        └── vocab.txt          # vocabulary
-
-+table(["File name", "Dimensions", "Data type"])
-    +row
-        +cell #[code vectors.128.f.bin]
-        +cell 128
-        +cell float32
-
-    +row
-        +cell #[code vectors.300.d.bin]
-        +cell 300
-        +cell float64 (double)
-
-+code.
-    nlp = spacy.load('en')
-    nlp.vocab.vectors.from_glove('/path/to/vectors')
-
-p
-    |  If your instance of #[code Language] already contains vectors, they will
-    |  be overwritten. To create your own GloVe vectors model package like
-    |  spaCy's #[+a("/models/en#en_vectors_web_lg") #[code en_vectors_web_lg]],
-    |  you can call #[+api("language#to_disk") #[code nlp.to_disk]], and then
-    |  package the model using the #[+api("cli#package") #[code package]]
-    |  command.
-
-+h(3, "custom-similarity") Using custom similarity methods
-
-p
-    |  By default, #[+api("token#vector") #[code Token.vector]] returns the
-    |  vector for its underlying #[+api("lexeme") #[code Lexeme]], while
-    |  #[+api("doc#vector") #[code Doc.vector]] and
-    |  #[+api("span#vector") #[code Span.vector]] return an average of the
-    |  vectors of their tokens. You can customise these
-    |  behaviours by modifying the #[code doc.user_hooks],
-    |  #[code doc.user_span_hooks] and #[code doc.user_token_hooks]
-    |  dictionaries.
-
-+infobox
-    |  For more details on #[strong adding hooks] and #[strong overwriting] the
-    |  built-in #[code Doc], #[code Span] and #[code Token] methods, see the
-    |  usage guide on #[+a("/usage/processing-pipelines#user-hooks") user hooks].
diff --git a/website/usage/_vectors-similarity/_gpu.jade b/website/usage/_vectors-similarity/_gpu.jade
deleted file mode 100644
index 9f1201da9..000000000
--- a/website/usage/_vectors-similarity/_gpu.jade
+++ /dev/null
@@ -1,30 +0,0 @@
-//- 💫 DOCS > USAGE > VECTORS & SIMILARITY > GPU
-
-p
-    |  If you're using a GPU, it's much more efficient to keep the word vectors
-    |  on the device. You can do that by setting the
-    |  #[+api("vectors#attributes") #[code Vectors.data]] attribute to a
-    |  #[code cupy.ndarray] object if you're using spaCy
-    |  or #[+a("https://chainer.org") Chainer], or a
-    |  #[code torch.Tensor] object if you're using
-    |  #[+a("http://pytorch.org") PyTorch]. The #[code data] object just needs
-    |  to support #[code __iter__] and #[code __getitem__], so if you're using
-    |  another library such as #[+a("https://www.tensorflow.org") TensorFlow],
-    |  you could also create a wrapper for your vectors data.
-
-+code("spaCy, Thinc or Chainer").
-    import cupy.cuda
-    from spacy.vectors import Vectors
-
-    vector_table = numpy.zeros((3, 300), dtype='f')
-    vectors = Vectors([u'dog', u'cat', u'orange'], vector_table)
-    with cupy.cuda.Device(0):
-        vectors.data = cupy.asarray(vectors.data)
-
-+code("PyTorch").
-    import torch
-    from spacy.vectors import Vectors
-
-    vector_table = numpy.zeros((3, 300), dtype='f')
-    vectors = Vectors([u'dog', u'cat', u'orange'], vector_table)
-    vectors.data = torch.Tensor(vectors.data).cuda(0)
diff --git a/website/usage/_visualizers/_dep.jade b/website/usage/_visualizers/_dep.jade
deleted file mode 100644
index be316c5cb..000000000
--- a/website/usage/_visualizers/_dep.jade
+++ /dev/null
@@ -1,85 +0,0 @@
-//- 💫 DOCS > USAGE > VISUALIZERS > DEPENDENCIES
-
-p
-    |  The dependency visualizer, #[code dep], shows part-of-speech tags
-    |  and syntactic dependencies.
-
-+code("Dependency example").
-    import spacy
-    from spacy import displacy
-
-    nlp = spacy.load('en')
-    doc = nlp(u'This is a sentence.')
-    displacy.serve(doc, style='dep')
-
-+codepen("f0e85b64d469d6617251d8241716d55f", 370)
-
-p
-    |  The argument #[code options] lets you specify a dictionary of settings
-    |  to customise the layout, for example:
-
-+aside("Important note")
-    |  There's currently a known issue with the #[code compact] mode for
-    |  sentences with short arrows and long dependency labels, that causes labels
-    |  longer than the arrow to wrap. So if you come across this problem,
-    |  especially when using custom labels, you'll have to increase the
-    |  #[code distance] setting in the #[code options] to allow longer arcs.
-
-+table(["Name", "Type", "Description", "Default"])
-    +row
-        +cell #[code compact]
-        +cell bool
-        +cell "Compact mode" with square arrows that takes up less space.
-        +cell #[code False]
-
-    +row
-        +cell #[code color]
-        +cell unicode
-        +cell Text color (HEX, RGB or color names).
-        +cell #[code '#000000']
-
-    +row
-        +cell #[code bg]
-        +cell unicode
-        +cell Background color (HEX, RGB or color names).
-        +cell #[code '#ffffff']
-
-    +row
-        +cell #[code font]
-        +cell unicode
-        +cell Font name or font family for all text.
-        +cell #[code 'Arial']
-
-p
-    |  For a list of all available options, see the
-    |  #[+api("top-level#displacy_options") #[code displacy] API documentation].
-
-+aside-code("Options example").
-    options = {'compact': True, 'bg': '&#35;09a3d5',
-               'color': 'white', 'font': 'Source Sans Pro'}
-    displacy.serve(doc, style='dep', options=options)
-
-+codepen("39c02c893a84794353de77a605d817fd", 360)
-
-+h(3, "dep-long-text") Visualizing long texts
-    +tag-new("2.0.12")
-
-p
-    |  Long texts can become difficult to read when displayed in one row, so
-    |  it's often better to visualize them sentence-by-sentence instead. As of
-    |  v2.0.12, #[code displacy] supports rendering both
-    |  #[+api("doc") #[code Doc]] and #[+api("span") #[code Span]] objects, as
-    |  well as lists of #[code Doc]s or #[code Span]s. Instead of passing the
-    |  full #[code Doc] to #[code displacy.serve], you can also pass in a list
-    |  of the #[code doc.sents]. This will create one visualization for each
-    |  sentence.
-
-+code.
-    import spacy
-    from spacy import displacy
-
-    nlp = spacy.load('en')
-    text = u"""In ancient Rome, some neighbors live in three adjacent houses. In the center is the house of Senex, who lives there with wife Domina, son Hero, and several slaves, including head slave Hysterium and the musical's main character Pseudolus. A slave belonging to Hero, Pseudolus wishes to buy, win, or steal his freedom. One of the neighboring houses is owned by Marcus Lycus, who is a buyer and seller of beautiful women; the other belongs to the ancient Erronius, who is abroad searching for his long-lost children (stolen in infancy by pirates). One day, Senex and Domina go on a trip and leave Pseudolus in charge of Hero. Hero confides in Pseudolus that he is in love with the lovely Philia, one of the courtesans in the House of Lycus (albeit still a virgin)."""
-    doc = nlp(text)
-    sentence_spans = list(doc.sents)
-    displacy.serve(sentence_spans, style='dep')
diff --git a/website/usage/_visualizers/_ent.jade b/website/usage/_visualizers/_ent.jade
deleted file mode 100644
index 6a3e8538f..000000000
--- a/website/usage/_visualizers/_ent.jade
+++ /dev/null
@@ -1,80 +0,0 @@
-//- 💫 DOCS > USAGE > VISUALIZERS > ENTITIES
-
-p
-    |  The entity visualizer, #[code ent], highlights named entities and
-    |  their labels in a text.
-
-+code("Named Entity example").
-    import spacy
-    from spacy import displacy
-
-    text = """But Google is starting from behind. The company made a late push
-    into hardware, and Apple’s Siri, available on iPhones, and Amazon’s Alexa
-    software, which runs on its Echo and Dot devices, have clear leads in
-    consumer adoption."""
-
-    nlp = spacy.load('custom_ner_model')
-    doc = nlp(text)
-    displacy.serve(doc, style='ent')
-
-+codepen("a73f8b68f9af3157855962b283b364e4", 345)
-
-p The entity visualizer lets you customise the following #[code options]:
-
-+table(["Name", "Type", "Description", "Default"])
-    +row
-        +cell #[code ents]
-        +cell list
-        +cell
-            |  Entity types to highlight (#[code None] for all types).
-        +cell #[code None]
-
-    +row
-        +cell #[code colors]
-        +cell dict
-        +cell
-            |  Color overrides. Entity types in lowercase should be mapped to
-            |  color names or values.
-        +cell #[code {}]
-
-p
-    |  If you specify a list of #[code ents], only those entity types will be
-    |  rendered – for example, you can choose to display #[code PERSON] entities.
-    |  Internally, the visualizer knows nothing about available entity types and
-    |  will render whichever spans and labels it receives. This makes it
-    |  especially easy to work with custom entity types. By default, displaCy
-    |  comes with colours for all
-    |  #[+a("/api/annotation#named-entities") entity types supported by spaCy].
-    |  If you're using custom entity types, you can use the #[code colors]
-    |  setting to add your own colours for them.
-
-+aside-code("Options example").
-    colors = {'ORG': 'linear-gradient(90deg, #aa9cfc, #fc9ce7)'}
-    options = {'ents': ['ORG'], 'colors': colors}
-    displacy.serve(doc, style='ent', options=options)
-
-+codepen("f42ec690762b6f007022a7acd6d0c7d4", 300)
-
-p
-    |  The above example uses a little trick: Since the background colour values
-    |  are added as the #[code background] style attribute, you can use any
-    |  #[+a("https://tympanus.net/codrops/css_reference/background/") valid background value]
-    |  or shorthand — including gradients and even images!
-
-+h(3, "ent-titles") Adding titles to documents
-
-p
-    |  Rendering several large documents on one page can easily become confusing.
-    |  To add a headline to each visualization, you can add a #[code title] to
-    |  its #[code user_data]. User data is never touched or modified by spaCy.
-
-+code.
-    doc = nlp(u'This is a sentence about Google.')
-    doc.user_data['title'] = 'This is a title'
-    displacy.serve(doc, style='ent')
-
-p
-    |  This feature is especially handy if you're using displaCy to compare
-    |  performance at different stages of a process, e.g. during training. Here
-    |  you could use the title for a brief description of the text example and
-    |  the number of iterations.
diff --git a/website/usage/_visualizers/_html.jade b/website/usage/_visualizers/_html.jade
deleted file mode 100644
index 8e6948ebf..000000000
--- a/website/usage/_visualizers/_html.jade
+++ /dev/null
@@ -1,164 +0,0 @@
-//- 💫 DOCS > USAGE > VISUALIZERS > HTML
-
-p
-    |  If you don't need the web server and just want to generate the markup
-    |  – for example, to export it to a file or serve it in a custom
-    |  way – you can use #[+api("displacy#render") #[code displacy.render]].
-    |  It works the same way, but returns a string containing the markup.
-
-+code("Example").
-    import spacy
-    from spacy import displacy
-
-    nlp = spacy.load('en')
-    doc1 = nlp(u'This is a sentence.')
-    doc2 = nlp(u'This is another sentence.')
-    html = displacy.render([doc1, doc2], style='dep', page=True)
-
-p
-    |  #[code page=True] renders the markup wrapped as a full HTML page.
-    |  For minified and more compact HTML markup, you can set #[code minify=True].
-    |  If you're rendering a dependency parse, you can also export it as an
-    |  #[code .svg] file.
-
-+aside("What's SVG?")
-    |  Unlike other image formats, the SVG (Scalable Vector Graphics) uses XML
-    |  markup that's easy to manipulate
-    |  #[+a("https://www.smashingmagazine.com/2014/11/styling-and-animating-svgs-with-css/") using CSS] or
-    |  #[+a("https://css-tricks.com/smil-is-dead-long-live-smil-a-guide-to-alternatives-to-smil-features/") JavaScript].
-    |  Essentially, SVG lets you design with code, which makes it a perfect fit
-    |  for visualizing dependency trees. SVGs can be embedded online in an
-    |  #[code &lt;img&gt;] tag, or inlined in an HTML document. They're also
-    |  pretty easy to #[+a("https://convertio.co/image-converter/") convert].
-
-+code.
-    svg = displacy.render(doc, style='dep')
-    output_path = Path('/images/sentence.svg')
-    output_path.open('w', encoding='utf-8').write(svg)
-
-+infobox("Important note")
-    |  Since each visualization is generated as a separate SVG, exporting
-    |  #[code .svg] files only works if you're rendering #[strong one single doc]
-    |  at a time. (This makes sense – after all, each visualization should be
-    |  a standalone graphic.) So instead of rendering all #[code Doc]s at one,
-    |  loop over them and export them separately.
-
-
-+h(3, "examples-export-svg") Example: Export SVG graphics of dependency parses
-
-+code("Example").
-    import spacy
-    from spacy import displacy
-    from pathlib import Path
-
-    nlp = spacy.load('en')
-    sentences = ["This is an example.", "This is another one."]
-    for sent in sentences:
-        doc = nlp(sentence)
-        svg = displacy.render(doc, style='dep')
-        file_name = '-'.join([w.text for w in doc if not w.is_punct]) + '.svg'
-        output_path = Path('/images/' + file_name)
-        output_path.open('w', encoding='utf-8').write(svg)
-
-p
-    |  The above code will generate the dependency visualizations as
-    |  two files, #[code This-is-an-example.svg] and #[code This-is-another-one.svg].
-
-
-+h(3, "manual-usage") Rendering data manually
-
-p
-    |  You can also use displaCy to manually render data. This can be useful if
-    |  you want to visualize output from other libraries, like
-    |  #[+a("http://www.nltk.org") NLTK] or
-    |  #[+a("https://github.com/tensorflow/models/tree/master/research/syntaxnet") SyntaxNet].
-    |  If you set #[code manual=True] on either #[code render()] or
-    |  #[code serve()], you can pass in data in displaCy's format (instead of
-    |  #[code Doc] objects). When setting #[code ents] manually, make sure to
-    |  supply them in the right order, i.e. starting with the lowest start
-    |  position.
-
-+aside-code("Example").
-    ex = [{'text': 'But Google is starting from behind.',
-           'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
-           'title': None}]
-    html = displacy.render(ex, style='ent', manual=True)
-
-+code("DEP input").
-    {
-        'words': [
-            {'text': 'This', 'tag': 'DT'},
-            {'text': 'is', 'tag': 'VBZ'},
-            {'text': 'a', 'tag': 'DT'},
-            {'text': 'sentence', 'tag': 'NN'}],
-        'arcs': [
-            {'start': 0, 'end': 1, 'label': 'nsubj', 'dir': 'left'},
-            {'start': 2, 'end': 3, 'label': 'det', 'dir': 'left'},
-            {'start': 1, 'end': 3, 'label': 'attr', 'dir': 'right'}]
-    }
-
-+code("ENT input").
-    {
-        'text': 'But Google is starting from behind.',
-        'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
-        'title': None
-    }
-
-+h(3, "webapp") Using displaCy in a web application
-
-p
-    |  If you want to use the visualizers as part of a web application, for
-    |  example to create something like our
-    |  #[+a(DEMOS_URL + "/displacy") online demo], it's not recommended to
-    |  only wrap and serve the displaCy renderer. Instead, you should only
-    |  rely on the server to perform spaCy's processing capabilities, and use
-    |  #[+a(gh("displacy")) displaCy.js] to render the JSON-formatted output.
-
-+aside("Why not return the HTML by the server?")
-    |  It's certainly possible to just have your server return the markup.
-    |  But outputting raw, unsanitised HTML is risky and makes your app vulnerable to
-    |  #[+a("https://en.wikipedia.org/wiki/Cross-site_scripting") cross-site scripting]
-    |  (XSS). All your user needs to do is find a way to make spaCy return text
-    |  like #[code &lt;script src="malicious-code.js"&gt;&lt;script&gt;], which
-    |  is pretty easy in NER mode. Instead of relying on the server to render
-    |  and sanitise HTML, you can do this on the client in JavaScript.
-    |  displaCy.js creates the markup as DOM nodes and will never insert raw
-    |  HTML.
-
-p
-    |  The #[code parse_deps] function takes a #[code Doc] object and returns
-    |  a dictionary in a format that can be rendered by displaCy.
-
-+code("Example").
-    import spacy
-    from spacy import displacy
-
-    nlp = spacy.load('en')
-
-    def displacy_service(text):
-        doc = nlp(text)
-        return displacy.parse_deps(doc)
-
-p
-    |  Using a library like #[+a("https://falconframework.org/") Falcon] or
-    |  #[+a("http://www.hug.rest/") Hug], you can easily turn the above code
-    |  into a simple REST API that receives a text and returns a JSON-formatted
-    |  parse. In your front-end, include #[+a(gh("displacy")) displacy.js] and
-    |  initialise it with the API URL and the ID or query selector of the
-    |  container to render the visualisation in, e.g. #[code '#displacy'] for
-    |  #[code &lt;div id="displacy"&gt;].
-
-+code("script.js", "javascript").
-    var displacy = new displaCy('http://localhost:8080', {
-        container: '#displacy'
-    })
-
-    function parse(text) {
-        displacy.parse(text);
-    }
-
-p
-    |  When you call #[code parse()], it will make a request to your API,
-    |  receive the JSON-formatted parse and render it in your container. To
-    |  create an interactive experience, you could trigger this function by
-    |  a button and read the text from an #[code &lt;input&gt;] field.
diff --git a/website/usage/_visualizers/_jupyter.jade b/website/usage/_visualizers/_jupyter.jade
deleted file mode 100644
index f80871f07..000000000
--- a/website/usage/_visualizers/_jupyter.jade
+++ /dev/null
@@ -1,36 +0,0 @@
-//- 💫 DOCS > USAGE > VISUALIZERS > JUPYTER
-
-p
-    |  displaCy is able to detect whether you're working in a
-    |  #[+a("https://jupyter.org") Jupyter] notebook, and will return markup
-    |  that can be rendered in a cell straight away. When you export your
-    |  notebook, the visualizations will be included as HTML.
-
-+code("Jupyter Example").
-    # don't forget to install a model, e.g.: python -m spacy download en
-    import spacy
-    from spacy import displacy
-
-    doc = nlp(u'Rats are various medium-sized, long-tailed rodents.')
-    displacy.render(doc, style='dep', jupyter=True)
-
-    doc2 = nlp(LONG_NEWS_ARTICLE)
-    displacy.render(doc2, style='ent', jupyter=True)
-
-+aside("Enabling or disabling Jupyter mode")
-    |  To explicitly enable or disable "Jupyter mode", you can use the
-    |  #[code jupyter] keyword argument – e.g. to return raw HTML in a notebook,
-    |  or to force Jupyter rendering if auto-detection fails.
-
-+image("/assets/img/displacy_jupyter.jpg", 700, false, "Example of using the displaCy dependency and named entity visualizer in a Jupyter notebook")
-
-p
-    |  Internally, displaCy imports #[code display] and #[code HTML] from
-    |  #[code IPython.core.display] and returns a Jupyter HTML object. If you
-    |  were doing it manually, it'd look like this:
-
-+code.
-    from IPython.core.display import display, HTML
-
-    html = displacy.render(doc, style='dep')
-    display(HTML(html))
diff --git a/website/usage/adding-languages.jade b/website/usage/adding-languages.jade
deleted file mode 100644
index 1e200015f..000000000
--- a/website/usage/adding-languages.jade
+++ /dev/null
@@ -1,58 +0,0 @@
-//- 💫 DOCS > USAGE > ADDING LANGUAGES
-
-include ../_includes/_mixins
-
-+aside("Working on spaCy's source")
-    |  To add a new language to spaCy, you'll need to
-    |  #[strong modify the library&apos;s code]. The easiest way to do this is to
-    |  clone the #[+src(gh("spaCy")) repository] and #[strong build spaCy from source].
-    |  For more information on this, see the #[+a("/usage") installation guide].
-    |  Unlike spaCy's core, which is mostly written in Cython, all language
-    |  data is stored in regular Python files. This means that you won't have to
-    |  rebuild anything in between – you can simply make edits and reload spaCy
-    |  to test them.
-
-+grid.o-no-block
-    +grid-col("half")
-        p
-            |  Obviously, there are lots of ways you can organise your code when
-            |  you implement your own language data. This guide will focus on
-            |  how it's done within spaCy. For full language support, you'll
-            |  need to create a #[code Language] subclass, define custom
-            |  #[strong language data], like a stop list and tokenizer
-            |  exceptions and test the new tokenizer. Once the language is set
-            |  up, you can #[strong build the vocabulary], including word
-            |  frequencies, Brown clusters and word vectors. Finally, you can
-            |  #[strong train the tagger and parser], and save the model to a
-            |  directory.
-
-        p
-            |  For some languages, you may also want to develop a solution for
-            |  lemmatization and morphological analysis.
-
-    +table-of-contents
-        +item #[+a("#101") Language data 101]
-        +item #[+a("#language-subclass") The Language subclass]
-        +item #[+a("#stop-words") Stop words]
-        +item #[+a("#tokenizer-exceptions") Tokenizer exceptions]
-        +item #[+a("#norm-exceptions") Norm exceptions]
-        +item #[+a("#lex-attrs") Lexical attributes]
-        +item #[+a("#syntax-iterators") Syntax iterators]
-        +item #[+a("#lemmatizer") Lemmatizer]
-        +item #[+a("#tag-map") Tag map]
-        +item #[+a("#morph-rules") Morph rules]
-        +item #[+a("#testing") Testing the language]
-        +item #[+a("#training") Training]
-
-+section("language-data")
-    +h(2, "language-data") Language data
-    include _spacy-101/_language-data
-    include _adding-languages/_language-data
-
-+section("testing")
-    +h(2, "testing") Testing the new language
-    include _adding-languages/_testing
-
-+section("training")
-    +h(2, "training") Training a language model
-    include _adding-languages/_training
diff --git a/website/usage/examples.jade b/website/usage/examples.jade
deleted file mode 100644
index cc79ab5fc..000000000
--- a/website/usage/examples.jade
+++ /dev/null
@@ -1,181 +0,0 @@
-//- 💫 DOCS > USAGE > EXAMPLES
-
-include ../_includes/_mixins
-
-+section("information-extraction")
-    +h(3, "phrase-matcher") Using spaCy's phrase matcher
-        +tag-new(2)
-
-    p
-        |  This example shows how to use the new
-        |  #[+api("phrasematcher") #[code PhraseMatcher]] to efficiently find
-        |  entities from a large terminology list.
-
-    +github("spacy", "examples/information_extraction/phrase_matcher.py")
-
-    +h(3, "entity-relations") Extracting entity relations
-
-    p
-        |  A simple example of extracting relations between phrases and
-        |  entities using spaCy's named entity recognizer and the dependency
-        |  parse. Here, we extract money and currency values (entities labelled
-        |  as #[code MONEY]) and then check the dependency tree to find the
-        |  noun phrase they are referring to – for example: "$9.4 million"
-        |  &rarr; "Net income".
-
-    +github("spacy", "examples/information_extraction/entity_relations.py")
-
-    +h(3, "subtrees") Navigating the parse tree and subtrees
-
-    p
-        |  This example shows how to navigate the parse tree including subtrees
-        |  attached to a word.
-
-    +github("spacy", "examples/information_extraction/parse_subtrees.py")
-
-+section("pipeline")
-    +h(3, "custom-components-entities") Custom pipeline components and attribute extensions
-        +tag-new(2)
-
-    p
-        |  This example shows the implementation of a pipeline component
-        |  that sets entity annotations based on a list of single or
-        |  multiple-word company names, merges entities into one token and
-        |  sets custom attributes on the #[code Doc], #[code Span] and
-        |  #[code Token].
-
-    +github("spacy", "examples/pipeline/custom_component_entities.py")
-
-    +h(3, "custom-components-api")
-        |  Custom pipeline components and attribute extensions via a REST API
-        +tag-new(2)
-
-    p
-        |  This example shows the implementation of a pipeline component
-        |  that fetches country meta data via the
-        |  #[+a("https://restcountries.eu") REST Countries API] sets entity
-        |  annotations for countries, merges entities into one token and
-        |  sets custom attributes on the #[code Doc], #[code Span] and
-        |  #[code Token] – for example, the capital, latitude/longitude
-        |  coordinates and the country flag.
-
-    +github("spacy", "examples/pipeline/custom_component_countries_api.py")
-
-    +h(3, "custom-components-attr-methods") Custom method extensions
-        +tag-new(2)
-
-    p
-        |  A collection of snippets showing examples of extensions adding
-        |  custom methods to the #[code Doc], #[code Token] and
-        |  #[code Span].
-
-    +github("spacy", "examples/pipeline/custom_attr_methods.py")
-
-    +h(3, "multi-processing") Multi-processing with Joblib
-
-    p
-        |  This example shows how to use multiple cores to process text using
-        |  spaCy and #[+a("https://joblib.readthedocs.io/en/latest/") Joblib]. We're
-        |  exporting part-of-speech-tagged, true-cased, (very roughly)
-        |  sentence-separated text, with each "sentence" on a newline, and
-        |  spaces between tokens. Data is loaded from the IMDB movie reviews
-        |  dataset and will be loaded automatically via Thinc's built-in dataset
-        |  loader.
-
-    +github("spacy", "examples/pipeline/multi_processing.py")
-
-+section("training")
-    +h(3, "training-ner") Training spaCy's Named Entity Recognizer
-
-    p
-        |  This example shows how to update spaCy's entity recognizer
-        |  with your own examples, starting off with an existing, pre-trained
-        |  model, or from scratch using a blank #[code Language] class.
-
-    +github("spacy", "examples/training/train_ner.py")
-
-    +h(3, "new-entity-type") Training an additional entity type
-
-    p
-        |  This script shows how to add a new entity type to an existing
-        |  pre-trained NER model. To keep the example short and simple, only
-        |  four sentences are provided as examples. In practice, you'll need
-        |  many more — a few hundred would be a good start.
-
-    +github("spacy", "examples/training/train_new_entity_type.py")
-
-    +h(3, "parser") Training spaCy's Dependency Parser
-
-    p
-        |  This example shows how to update spaCy's dependency parser,
-        |  starting off with an existing, pre-trained model, or from scratch
-        |  using a blank #[code Language] class.
-
-    +github("spacy", "examples/training/train_parser.py")
-
-    +h(3, "tagger") Training spaCy's Part-of-speech Tagger
-
-    p
-        |  In this example, we're training spaCy's part-of-speech tagger with a
-        |  custom tag map, mapping our own tags to the mapping those tags to the
-        |  #[+a("http://universaldependencies.github.io/docs/u/pos/index.html") Universal Dependencies scheme].
-
-    +github("spacy", "examples/training/train_tagger.py")
-
-    +h(3, "intent-parser") Training a custom parser for chat intent semantics
-
-    p
-        |  spaCy's parser component can be used to trained to predict any type
-        |  of tree structure over your input text. You can also predict trees
-        |  over whole documents or chat logs, with connections between the
-        |  sentence-roots used to annotate discourse structure. In this example,
-        |  we'll build a message parser for a common "chat intent": finding
-        |  local businesses. Our message semantics will have the following types
-        |  of relations: #[code ROOT], #[code PLACE], #[code QUALITY],
-        |  #[code ATTRIBUTE], #[code TIME] and #[code LOCATION].
-
-    +github("spacy", "examples/training/train_intent_parser.py")
-
-    +h(3, "textcat") Training spaCy's text classifier
-        +tag-new(2)
-
-    p
-        |  This example shows how to train a multi-label convolutional neural
-        |  network text classifier on IMDB movie reviews, using spaCy's new
-        |  #[+api("textcategorizer") #[code TextCategorizer]] component. The
-        |  dataset will be loaded automatically via Thinc's built-in dataset
-        |  loader. Predictions are available via
-        |  #[+api("doc#attributes") #[code Doc.cats]].
-
-    +github("spacy", "examples/training/train_textcat.py")
-
-+section("vectors")
-    +h(3, "tensorboard") Visualizing spaCy vectors in TensorBoard
-
-    p
-        |  These two scripts let you load any spaCy model containing word vectors
-        |  into #[+a("https://projector.tensorflow.org/") TensorBoard] to create
-        |  an #[+a("https://www.tensorflow.org/versions/r1.1/get_started/embedding_viz") embedding visualization].
-        |  The first example uses TensorBoard, the second example TensorBoard's
-        |  standalone embedding projector.
-
-    +github("spacy", "examples/vectors_tensorboard.py")
-
-    +github("spacy", "examples/vectors_tensorboard_standalone.py")
-
-+section("deep-learning")
-    +h(3, "keras") Text classification with Keras
-
-    p
-        |  This example shows how to use a #[+a("https://keras.io") Keras]
-        |  LSTM sentiment classification model in spaCy. spaCy splits
-        |  the document into sentences, and each sentence is classified using
-        |  the LSTM. The scores for the sentences are then aggregated to give
-        |  the document score. This kind of hierarchical model is quite
-        |  difficult in "pure" Keras or Tensorflow, but it's very effective.
-        |  The Keras example on this dataset performs quite poorly, because it
-        |  cuts off the documents so that they're a fixed size. This hurts
-        |  review accuracy a lot, because people often summarise their rating
-        |  in the final sentence.
-
-    +github("spacy", "examples/deep_learning_keras.py")
diff --git a/website/usage/facts-figures.jade b/website/usage/facts-figures.jade
deleted file mode 100644
index b6a548121..000000000
--- a/website/usage/facts-figures.jade
+++ /dev/null
@@ -1,32 +0,0 @@
-//- 💫 DOCS > USAGE > FACTS & FIGURES
-
-include ../_includes/_mixins
-
-+section("comparison")
-    +h(2, "comparison") Feature comparison
-    include _facts-figures/_feature-comparison
-
-+section("benchmarks")
-    +h(2, "benchmarks") Benchmarks
-    include _facts-figures/_benchmarks
-
-
-+section("powered-by")
-    +h(2, "powered-by") Powered by spaCy
-
-    p
-        |  Here's an overview of other tools and libraries that are using spaCy
-        |  behind the scenes.
-
-    +grid
-        +card("torchtext", "https://github.com/pytorch/text", "PyTorch", "github")
-            |  PyTorch's NLP datasets and loaders use spaCy for pre-processing
-            |  and tokenization.
-
-        +card("allennlp", "https://github.com/allenai/allennlp", "Allen Institute for Artificial Intelligence", "github")
-            |  The open-source NLP research library based on PyTorch uses spaCy
-            |  for pre-processing and tokenization.
-
-+section("other-libraries")
-    +h(2, "other-libraries") spaCy and other libraries
-    include _facts-figures/_other-libraries
diff --git a/website/usage/index.jade b/website/usage/index.jade
deleted file mode 100644
index 936cb8e52..000000000
--- a/website/usage/index.jade
+++ /dev/null
@@ -1,29 +0,0 @@
-//- 💫 DOCS > USAGE
-
-include ../_includes/_mixins
-
-p
-    |  spaCy is compatible with #[strong 64-bit CPython 2.6+&#8725;3.3+] and
-    |  runs on #[strong Unix/Linux], #[strong macOS/OS X] and
-    |  #[strong Windows]. The latest spaCy releases are
-    |  available over #[+a("https://pypi.python.org/pypi/spacy") pip] (source
-    |  packages only) and #[+a("https://anaconda.org/conda-forge/spacy") conda].
-    |  Installation requires a working build environment. See notes on
-    |  #[a(href="#source-ubuntu") Ubuntu], #[a(href="#source-osx") macOS/OS X]
-    |  and #[a(href="#source-windows") Windows] for details.
-
-+legacy
-
-+section("quickstart")
-    include _install/_quickstart
-
-+section("instructions")
-    +h(2, "installation") Installation instructions
-    include _install/_instructions
-
-+section("troubleshooting")
-    +h(2, "troubleshooting") Troubleshooting guide
-    include _install/_troubleshooting
-
-+section("changelog")
-    include _install/_changelog
diff --git a/website/usage/linguistic-features.jade b/website/usage/linguistic-features.jade
deleted file mode 100644
index 4cd1fb3a9..000000000
--- a/website/usage/linguistic-features.jade
+++ /dev/null
@@ -1,42 +0,0 @@
-//- 💫 DOCS > USAGE > LINGUISTIC FEATURES
-
-include ../_includes/_mixins
-
-p
-    |  Processing raw text intelligently is difficult: most words are rare, and
-    |  it's common for words that look completely different to mean almost the
-    |  same thing. The same words in a different order can mean something
-    |  completely different. Even splitting text into useful word-like units can
-    |  be difficult in many languages. While it's possible to solve some
-    |  problems starting from only the raw characters, it's usually better to
-    |  use linguistic knowledge to add useful information. That's exactly what
-    |  spaCy is designed to do: you put in raw text, and get back a
-    |  #[+api("doc") #[code Doc]] object, that comes with a variety of
-    |  annotations.
-
-+section("pos-tagging")
-    +h(2, "pos-tagging") Part-of-speech tagging
-        +tag-model("tagger", "dependency parse")
-    include _linguistic-features/_pos-tagging
-
-+section("dependency-parse")
-    +h(2, "dependency-parse") Dependency parsing
-        +tag-model("dependency parse")
-    include _linguistic-features/_dependency-parse
-
-+section("named-entities")
-    +h(2, "named-entities") Named Entities
-        +tag-model("named entities")
-    include _linguistic-features/_named-entities
-
-+section("tokenization")
-    +h(2, "tokenization") Tokenization
-    include _linguistic-features/_tokenization
-
-+section("sbd")
-    +h(2, "sbd") Sentence Segmentation
-    include _linguistic-features/_sentence-segmentation
-
-+section("rule-based-matching")
-    +h(2, "rule-based-matching") Rule-based matching
-    include _linguistic-features/_rule-based-matching
diff --git a/website/usage/models.jade b/website/usage/models.jade
deleted file mode 100644
index 0880e1840..000000000
--- a/website/usage/models.jade
+++ /dev/null
@@ -1,41 +0,0 @@
-//- 💫 DOCS > USAGE > MODELS
-
-include ../_includes/_mixins
-
-p
-    |  spaCy's models can be installed as #[strong Python packages].
-    |  This means that they're a component of your application, just like any
-    |  other module. They're versioned and can be defined as a dependency in your
-    |  #[code requirements.txt]. Models can be installed from a download URL or
-    |  a local directory, manually or via #[+a("https://pypi.python.org/pypi/pip") pip].
-    |  Their data can be located anywhere on your file system.
-
-+aside("Important note")
-    |  If you're upgrading to spaCy v1.7.x or v2.x, you need to
-    |  #[strong download the new models]. If you've trained statistical models
-    |  that use spaCy's annotations, you should #[strong retrain your models]
-    |  after updating spaCy. If you don't retrain, you may suffer train/test
-    |  skew, which might decrease your accuracy.
-
-+section("quickstart")
-    include _models/_quickstart
-
-+section("available")
-    +h(2, "available") Available models
-    include _models/_available-models
-
-    .u-text-right
-        +button("/models", true, "primary", "small") View models directory
-
-
-+section("install")
-    +h(2, "download") Installing and using models
-    include _models/_install
-
-+section("languages")
-    +h(2, "languages") Language support
-    include _models/_languages
-
-+section("production")
-    +h(2, "production") Using models in production
-    include _models/_production
diff --git a/website/usage/processing-pipelines.jade b/website/usage/processing-pipelines.jade
deleted file mode 100644
index 8a890113e..000000000
--- a/website/usage/processing-pipelines.jade
+++ /dev/null
@@ -1,26 +0,0 @@
-//- 💫 DOCS > USAGE > PIPELINE
-
-include ../_includes/_mixins
-
-include _spacy-101/_pipelines
-
-+section("pipelines")
-    +h(2, "pipelines") How pipelines work
-    include _processing-pipelines/_pipelines
-
-+section("custom-components")
-    +h(2, "custom-components") Creating custom pipeline components
-    include _processing-pipelines/_custom-components
-
-+section("custom-components-attributes")
-    +h(2, "custom-components-attributes") Extension attributes
-        +tag-new(2)
-    include _processing-pipelines/_extensions
-
-+section("multithreading")
-    +h(2, "multithreading") Multi-threading
-    include _processing-pipelines/_multithreading
-
-+section("serialization")
-    +h(2, "serialization") Serialization
-    include _processing-pipelines/_serialization
diff --git a/website/usage/spacy-101.jade b/website/usage/spacy-101.jade
deleted file mode 100644
index f356495cd..000000000
--- a/website/usage/spacy-101.jade
+++ /dev/null
@@ -1,321 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101
-
-include ../_includes/_mixins
-
-p
-    |  Whether you're new to spaCy, or just want to brush up on some
-    |  NLP basics and implementation details – this page should have you covered.
-    |  Each section will explain one of spaCy's features in simple terms and
-    |  with examples or illustrations. Some sections will also reappear across
-    |  the usage guides as a quick introduction.
-
-+aside("Help us improve the docs")
-    |  Did you spot a mistake or come across explanations that
-    |  are unclear? We always appreciate improvement
-    |  #[+a(gh("spaCy") + "/issues") suggestions] or
-    |  #[+a(gh("spaCy") + "/pulls") pull requests]. You can find a "Suggest
-    |  edits" link at the bottom of each page that points you to the source.
-
-+h(2, "whats-spacy") What's spaCy?
-
-+grid.o-no-block
-    +grid-col("half")
-        p
-            |  spaCy is a #[strong free, open-source library] for advanced
-            |  #[strong Natural Language Processing] (NLP) in Python.
-
-        p
-            |  If you're working with a lot of text, you'll eventually want to
-            |  know more about it. For example, what's it about? What do the
-            |  words mean in context? Who is doing what to whom? What companies
-            |  and products are mentioned? Which texts are similar to each other?
-
-        p
-            |  spaCy is designed specifically for #[strong production use] and
-            |  helps you build applications that process and "understand"
-            |  large volumes of text. It can be used to build
-            |  #[strong information extraction] or
-            |  #[strong natural language understanding] systems, or to
-            |  pre-process text for #[strong deep learning].
-
-    +table-of-contents
-        +item #[+a("#features") Features]
-        +item #[+a("#annotations") Linguistic annotations]
-        +item #[+a("#annotations-token") Tokenization]
-        +item #[+a("#annotations-pos-deps") POS tags and dependencies]
-        +item #[+a("#annotations-ner") Named entities]
-        +item #[+a("#vectors-similarity") Word vectors and similarity]
-        +item #[+a("#pipelines") Pipelines]
-        +item #[+a("#vocab") Vocab, hashes and lexemes]
-        +item #[+a("#serialization") Serialization]
-        +item #[+a("#training") Training]
-        +item #[+a("#language-data") Language data]
-        +item #[+a("#lightning-tour") Lightning tour]
-        +item #[+a("#architecture") Architecture]
-        +item #[+a("#community") Community & FAQ]
-
-+h(3, "what-spacy-isnt") What spaCy isn't
-
-+list
-    +item #[strong spaCy is not a platform or "an API"].
-        |  Unlike a platform, spaCy does not provide a software as a service, or
-        |  a web application. It's an open-source library designed to help you
-        |  build NLP applications, not a consumable service.
-    +item #[strong spaCy is not an out-of-the-box chat bot engine].
-        |  While spaCy can be used to power conversational applications, it's
-        |  not designed specifically for chat bots, and only provides the
-        |  underlying text processing capabilities.
-    +item #[strong spaCy is not research software].
-        |  It's built on the latest research, but it's designed to get
-        |  things done. This leads to fairly different design decisions than
-        |  #[+a("https://github.com/nltk/nltk") NLTK]
-        |  or #[+a("https://stanfordnlp.github.io/CoreNLP/") CoreNLP], which were
-        |  created as platforms for teaching and research. The main difference
-        |  is that spaCy is integrated and opinionated. spaCy tries to avoid asking
-        |  the user to choose between multiple algorithms that deliver equivalent
-        |  functionality. Keeping the menu small lets spaCy deliver generally better
-        |  performance and developer experience.
-    +item #[strong spaCy is not a company].
-        |  It's an open-source library. Our company publishing spaCy and other
-        |  software is called #[+a(COMPANY_URL, true) Explosion AI].
-
-+section("features")
-    +h(2, "features") Features
-
-    p
-        |  In the documentation, you'll come across mentions of spaCy's
-        |  features and capabilities. Some of them refer to linguistic concepts,
-        |  while others are related to more general machine learning
-        |  functionality.
-
-    +table(["Name", "Description"])
-        +row
-            +cell #[strong Tokenization]
-            +cell Segmenting text into words, punctuations marks etc.
-
-        +row
-            +cell #[strong Part-of-speech] (POS) #[strong Tagging]
-            +cell Assigning word types to tokens, like verb or noun.
-
-        +row
-            +cell #[strong Dependency Parsing]
-            +cell
-                |  Assigning syntactic dependency labels, describing the
-                |  relations between individual tokens, like subject or object.
-
-        +row
-            +cell #[strong Lemmatization]
-            +cell
-                |  Assigning the base forms of words. For example, the lemma of
-                |  "was" is "be", and the lemma of "rats" is "rat".
-
-        +row
-            +cell #[strong Sentence Boundary Detection] (SBD)
-            +cell Finding and segmenting individual sentences.
-
-        +row
-            +cell #[strong Named Entity Recognition] (NER)
-            +cell
-                |  Labelling named "real-world" objects, like persons, companies
-                |  or locations.
-
-        +row
-            +cell #[strong Similarity]
-            +cell
-                |  Comparing words, text spans and documents and how similar
-                |  they are to each other.
-
-        +row
-            +cell #[strong Text Classification]
-            +cell
-                |  Assigning categories or labels to a whole document, or parts
-                |  of a document.
-
-        +row
-            +cell #[strong Rule-based Matching]
-            +cell
-                |  Finding sequences of tokens based on their texts and
-                |  linguistic annotations, similar to regular expressions.
-
-        +row
-            +cell #[strong Training]
-            +cell Updating and improving a statistical model's predictions.
-
-        +row
-            +cell #[strong Serialization]
-            +cell Saving objects to files or byte strings.
-
-    +h(3, "statistical-models") Statistical models
-
-    p
-        |  While some of spaCy's features work independently, others require
-        |  #[+a("/models")  statistical models] to be loaded, which enable spaCy
-        |  to #[strong predict] linguistic annotations – for example,
-        |  whether a word is a verb or a noun. spaCy currently offers statistical
-        |  models for #[strong #{MODEL_LANG_COUNT} languages], which can be
-        |  installed as individual Python modules. Models can differ in size,
-        |  speed, memory usage, accuracy and the data they include. The model
-        |  you choose always depends on your use case and the texts you're
-        |  working with. For a general-purpose use case, the small, default
-        |  models are always a good start. They typically include the following
-        |  components:
-
-    +list
-        +item
-            |  #[strong Binary weights] for the part-of-speech tagger,
-            |  dependency parser and named entity recognizer to predict those
-            |  annotations in context.
-        +item
-            |  #[strong Lexical entries] in the vocabulary, i.e. words and their
-            |  context-independent attributes like the shape or spelling.
-        +item
-            |  #[strong Word vectors], i.e. multi-dimensional meaning
-            |  representations of words that let you determine how similar they
-            |  are to each other.
-        +item
-            |  #[strong Configuration] options, like the language and
-            |  processing pipeline settings, to put spaCy in the correct state
-            |  when you load in the model.
-
-    +h(2, "annotations") Linguistic annotations
-
-    p
-        |  spaCy provides a variety of linguistic annotations to give you
-        |  #[strong insights into a text&apos;s grammatical structure]. This
-        |  includes the word types, like the parts of speech, and how the words
-        |  are related to each other. For example, if you're analysing text, it
-        |  makes a huge difference whether a noun is the subject of a sentence,
-        |  or the object – or whether "google" is used as a verb, or refers to
-        |  the website or company in a specific context.
-
-    +aside-code("Loading models", "bash", "$").
-        python -m spacy download en_core_web_sm
-        &gt;&gt;&gt; import spacy
-        &gt;&gt;&gt; nlp = spacy.load('en_core_web_sm')
-
-    p
-        |  Once you've #[+a("/usage/models") downloaded and installed] a model,
-        |  you can load it via #[+api("spacy#load") #[code spacy.load()]]. This will
-        |  return a #[code Language] object contaning all components and data needed
-        |  to process text. We usually call it #[code nlp]. Calling the #[code nlp]
-        |  object on a string of text will return a processed #[code Doc]:
-
-    +code-exec.
-        import spacy
-
-        nlp = spacy.load('en_core_web_sm')
-        doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
-        for token in doc:
-            print(token.text, token.pos_, token.dep_)
-
-    p
-        |  Even though a #[code Doc] is processed – e.g. split into individual words
-        |  and annotated – it still holds #[strong all information of the original text],
-        |  like whitespace characters. You can always get the offset of a token into the
-        |  original string, or reconstruct the original by joining the tokens and their
-        |  trailing whitespace. This way, you'll never lose any information
-        |  when processing text with spaCy.
-
-    +h(3, "annotations-token") Tokenization
-
-    include _spacy-101/_tokenization
-
-    +infobox
-        |  To learn more about how spaCy's tokenization rules work in detail,
-        |  how to #[strong customise and replace] the default tokenizer and how to
-        |  #[strong add language-specific data], see the usage guides on
-        |  #[+a("/usage/adding-languages") adding languages] and
-        |  #[+a("/usage/linguistic-features#tokenization") customising the tokenizer].
-
-    +h(3, "annotations-pos-deps") Part-of-speech tags and dependencies
-        +tag-model("dependency parse")
-
-    include _spacy-101/_pos-deps
-
-    +infobox
-        |  To learn more about #[strong part-of-speech tagging] and rule-based
-        |  morphology, and how to #[strong navigate and use the parse tree]
-        |  effectively, see the usage guides on
-        |  #[+a("/usage/linguistic-features#pos-tagging") part-of-speech tagging] and
-        |  #[+a("/usage/linguistic-features#dependency-parse") using the dependency parse].
-
-    +h(3, "annotations-ner") Named Entities
-        +tag-model("named entities")
-
-    include _spacy-101/_named-entities
-
-    +infobox
-        |  To learn more about entity recognition in spaCy, how to
-        |  #[strong add your own entities] to a document and how to
-        |  #[strong train and update] the entity predictions of a model, see the
-        |  usage guides on
-        |  #[+a("/usage/linguistic-features#named-entities") named entity recognition] and
-        |  #[+a("/usage/training#ner") training the named entity recognizer].
-
-    +h(2, "vectors-similarity") Word vectors and similarity
-        +tag-model("vectors")
-
-    include _spacy-101/_similarity
-
-    include _spacy-101/_word-vectors
-
-    +infobox
-        |  To learn more about word vectors, how to #[strong customise them] and
-        |  how to load #[strong your own vectors] into spaCy, see the usage
-        |  guide on
-        |  #[+a("/usage/vectors-similarity") using word vectors and semantic similarities].
-
-    +h(2, "pipelines") Pipelines
-
-    include _spacy-101/_pipelines
-
-    +infobox
-        |  To learn more about #[strong how processing pipelines work] in detail,
-        |  how to enable and disable their components, and how to
-        |  #[strong create your own], see the usage guide on
-        |  #[+a("/usage/processing-pipelines") language processing pipelines].
-
-    +h(2, "vocab") Vocab, hashes and lexemes
-
-    include _spacy-101/_vocab
-
-    +h(2, "serialization") Serialization
-
-    include _spacy-101/_serialization
-
-    +infobox
-        |  To learn more about how to #[strong save and load your own models],
-        |  see the usage guide on
-        |  #[+a("/usage/training#saving-loading") saving and loading].
-
-    +h(2, "training") Training
-
-    include _spacy-101/_training
-
-    +infobox
-        |  To learn more about #[strong training and updating] models, how to create
-        |  training data and how to improve spaCy's named entity recognition models,
-        |  see the usage guides on #[+a("/usage/training") training].
-
-    +h(2, "language-data") Language data
-
-    include _spacy-101/_language-data
-
-    +infobox
-        |  To learn more about the individual components of the language data and
-        |  how to #[strong add a new language] to spaCy in preparation for training
-        |  a language model, see the usage guide on
-        |  #[+a("/usage/adding-languages") adding languages].
-
-
-+section("lightning-tour")
-    +h(2, "lightning-tour") Lightning tour
-    include _spacy-101/_lightning-tour
-
-+section("architecture")
-    +h(2, "architecture") Architecture
-    include _spacy-101/_architecture
-
-+section("community-faq")
-    +h(2, "community") Community & FAQ
-    include _spacy-101/_community-faq
diff --git a/website/usage/training.jade b/website/usage/training.jade
deleted file mode 100644
index c726d400f..000000000
--- a/website/usage/training.jade
+++ /dev/null
@@ -1,33 +0,0 @@
-//- 💫 DOCS > USAGE > TRAINING
-
-include ../_includes/_mixins
-
-p
-    |  This guide describes how to train new statistical models for spaCy's
-    |  part-of-speech tagger, named entity recognizer and dependency parser.
-    |  Once the model is trained, you can then
-    |  #[+a("/usage/models#saving-loading") save and load] it.
-
-+section("basics")
-    +h(2, "basics") Training basics
-    include _training/_basics
-
-+section("ner")
-    +h(2, "ner") Training the named entity recognizer
-    include _training/_ner
-
-+section("tagger-parser")
-    +h(2, "tagger-parser") Training the tagger and parser
-    include _training/_tagger-parser
-
-+section("textcat")
-    +h(2, "textcat") Training a text classification model
-    include _training/_textcat
-
-+section("tips")
-    +h(2, "tips") Optimization tips and advice
-    include _training/_tips
-
-+section("saving-loading")
-    +h(2, "saving-loading") Saving and loading models
-    include _training/_saving-loading
diff --git a/website/usage/v2.jade b/website/usage/v2.jade
deleted file mode 100644
index c42a046e3..000000000
--- a/website/usage/v2.jade
+++ /dev/null
@@ -1,22 +0,0 @@
-//- 💫 DOCS > USAGE > WHAT'S NEW IN V2.0
-
-include ../_includes/_mixins
-
-+section("summary")
-    include _v2/_summary
-
-+section("features")
-    +h(2, "features") New features
-    include _v2/_features
-
-+section("incompat")
-    +h(2, "incompat") Backwards incompatibilities
-    include _v2/_incompat
-
-+section("migrating")
-    +h(2, "migrating") Migrating from spaCy 1.x
-    include _v2/_migrating
-
-+section("benchmarks")
-    +h(2, "benchmarks") Benchmarks
-    include _facts-figures/_benchmarks-models
diff --git a/website/usage/vectors-similarity.jade b/website/usage/vectors-similarity.jade
deleted file mode 100644
index efee0ec10..000000000
--- a/website/usage/vectors-similarity.jade
+++ /dev/null
@@ -1,23 +0,0 @@
-//- 💫 DOCS > USAGE > WORD VECTORS & SIMILARITIES
-
-include ../_includes/_mixins
-
-+section("basics")
-    +aside("Training word vectors")
-        |  Dense, real valued vectors representing distributional similarity
-        |  information are now a cornerstone of practical NLP. The most common way
-        |  to train these vectors is the #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]
-        |  family of algorithms. If you need to train a word2vec model, we recommend
-        |  the implementation in the Python library
-        |  #[+a("https://radimrehurek.com/gensim/") Gensim].
-
-    include _spacy-101/_similarity
-    include _spacy-101/_word-vectors
-
-+section("custom")
-    +h(2, "custom") Customising word vectors
-    include _vectors-similarity/_custom
-
-+section("gpu")
-    +h(2, "gpu") Storing vectors on a GPU
-    include _vectors-similarity/_gpu
diff --git a/website/usage/visualizers.jade b/website/usage/visualizers.jade
deleted file mode 100644
index 6da78aa87..000000000
--- a/website/usage/visualizers.jade
+++ /dev/null
@@ -1,48 +0,0 @@
-//- 💫 DOCS > USAGE > VISUALIZERS
-
-include ../_includes/_mixins
-
-+section
-    p
-        |  As of v2.0, our popular visualizers, #[+a(DEMOS_URL + "/displacy") displaCy]
-        |  and #[+a(DEMOS_URL + "/displacy-ent") displaCy #[sup ENT]] are finally an
-        |  official part of the library. Visualizing a dependency parse or named
-        |  entities in a text is not only a fun NLP demo – it can also be incredibly
-        |  helpful in speeding up development and debugging your code and training
-        |  process. If you're running a #[+a("https://jupyter.org") Jupyter] notebook,
-        |  displaCy will detect this and return the markup in a format
-        |  #[+a("#jupyter") ready to be rendered and exported].
-
-    +aside("What about the old visualizers?")
-        |  Our JavaScript-based visualizers #[+src(gh("displacy")) #[code displacy.js]] and
-        |  #[+src(gh("displacy-ent")) #[code displacy-ent.js]] will still be available on
-        |  GitHub. If you're looking to implement web-based visualizations, we
-        |  generally recommend using those instead of spaCy's built-in
-        |  #[code displacy] module. It'll allow your application to perform all
-        |  rendering on the client and only rely on the server for the text
-        |  processing. The generated markup is also more compatible with modern web
-        |  standards.
-
-    p
-        |  The quickest way to visualize  #[code Doc] is to use
-        |  #[+api("displacy#serve") #[code displacy.serve]]. This will spin up a
-        |  simple web server and let you view the result straight from your browser.
-        |  displaCy can either take a single #[code Doc] or a list of #[code Doc]
-        |  objects as its first argument. This lets you construct them however you
-        |  like – using any model or modifications you like.
-
-+section("dep")
-    +h(2, "dep") Visualizing the dependency parse
-    include _visualizers/_dep
-
-+section("ent")
-    +h(2, "ent") Visualizing the entity recognizer
-    include _visualizers/_ent
-
-+section("jupyter")
-    +h(2, "jupyter") Using displaCy in Jupyter notebooks
-    include _visualizers/_jupyter
-
-+section("html")
-    +h(2, "html") Rendering HTML
-    include _visualizers/_html