e597110d31
<!--- 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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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 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.
- 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 |
Doc |
Segment text into tokens. |
| tagger | Tagger |
Doc[i].tag |
Assign part-of-speech tags. |
| parser | DependencyParser |
Doc[i].head, Doc[i].dep, Doc.sents, Doc.noun_chunks |
Assign dependency labels. |
| ner | EntityRecognizer |
Doc.ents, Doc[i].ent_iob, Doc[i].ent_type |
Detect and label named entities. |
| textcat | TextCategorizer |
Doc.cats |
Assign document labels. |
| ... | 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:
"pipeline": ["tagger", "parser", "ner"]
import Accordion from 'components/accordion.js'
No