Update docs [ci skip]

website/docs/usage/facts-figures.md

@@ -12,16 +12,32 @@ menu:
 
 ### When should I use spaCy? {#comparison-usage}
 
-<!-- TODO: update -->
+- ✅ **I'm a beginner and just getting started with NLP.** – spaCy makes it easy
-
+  to get started and comes with extensive documentation, including a
-| Use Cases                                                                                                                                                                                                                                                                                                  |
+  beginner-friendly [101 guide](/usage/spacy-101), a free interactive
-| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+  [online course](https://course.spacy.io) and a range of
-| ✅ **I'm a beginner and just getting started with NLP.**<br />spaCy makes it easy to get started and comes with extensive documentation, including a beginner-friendly [101 guide](/usage/spacy-101) and a free interactive [online course](https://course.spacy.io).                                      |
+  [video tutorials](https://www.youtube.com/c/ExplosionAI).
-| ✅ **I want to build an end-to-end production application.**                                                                                                                                                                                                                                               |
+- ✅ **I want to build an end-to-end production application.** – spaCy is
-| ✅ **I want my application to be efficient on CPU.**<br />While spaCy lets you train modern NLP models that are best run on GPU, it also offers CPU-optimized pipelines, which may be less accurate but much cheaper to run.                                                                               |
+  specifically designed for production use and lets you build and train powerful
-| ✅ **I want to try out different neural network architectures for NLP.**                                                                                                                                                                                                                                   |
+  NLP pipelines and package them for easy deployment.
-| ❌ **I want to build a language generation application.**<br />spaCy's focus is natural language _processing_ and extracting information from large volumes of text. While you can use it to help you re-write existing text, it doesn't include any specific functionality for language generation tasks. |
+- ✅ **I want my application to be efficient on GPU _and_ CPU.** – While spaCy
-| ❌ **I want to research machine learning algorithms.**                                                                                                                                                                                                                                                     |
+  lets you train modern NLP models that are best run on GPU, it also offers
+  CPU-optimized pipelines, which are less accurate but much cheaper to run.
+- ✅ **I want to try out different neural network architectures for NLP.** –
+  spaCy lets you customize and swap out the model architectures powering its
+  components, and implement your own using a framework like PyTorch or
+  TensorFlow. The declarative configuration system makes it easy to mix and
+  match functions and keep track of your hyperparameters to make sure your
+  experiments are reproducible.
+- ❌ **I want to build a language generation application.** – spaCy's focus is
+  natural language _processing_ and extracting information from large volumes of
+  text. While you can use it to help you re-write existing text, it doesn't
+  include any specific functionality for language generation tasks.
+- ❌ **I want to research machine learning algorithms.** spaCy is built on the
+  latest research, but it's not a research library. If your goal is to write
+  papers and run benchmarks, spaCy is probably not a good choice. However, you
+  can use it to make the results of your research easily available for others to
+  use, e.g. via a custom spaCy component.
 
 ## Benchmarks {#benchmarks}
 
@@ -45,7 +61,7 @@ import Benchmarks from 'usage/\_benchmarks-models.md'
 
 <Benchmarks />
 
-<!-- TODO: update -->
+<!-- TODO:
 
 <Project id="benchmarks/penn_treebank">
 
@@ -54,6 +70,8 @@ our project template.
 
 </Project>
 
+-->
+
 <!-- ## Citing spaCy {#citation}
 
 <!-- TODO: update -->

website/docs/usage/spacy-101.md

@@ -84,15 +84,13 @@ systems, or to pre-process text for **deep learning**.
 
 ### What spaCy isn't {#what-spacy-isnt}
 
-- **spaCy is not a platform or "an API"**. Unlike a platform, spaCy does not
+- ❌ **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
-- **spaCy is not an out-of-the-box chat bot engine**. While spaCy can be used to
+  to power conversational applications, it's not designed specifically for chat
-  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
-- **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
@@ -101,8 +99,7 @@ systems, or to pre-process text for **deep learning**.
   between multiple algorithms that deliver equivalent functionality. Keeping the
   menu small lets spaCy deliver generally better performance and developer
   experience.
-
+- ❌ **spaCy is not a company**. It's an open-source library. Our company
-- **spaCy is not a company**. It's an open-source library. Our company
   publishing spaCy and other software is called
   [Explosion](https://explosion.ai).
 

website/src/components/icon.js

@@ -1,4 +1,4 @@
-import React from 'react'
+import React, { Fragment } from 'react'
 import PropTypes from 'prop-types'
 import classNames from 'classnames'
 
@@ -25,6 +25,7 @@ import { ReactComponent as NetworkIcon } from '../images/icons/network.svg'
 import { ReactComponent as DownloadIcon } from '../images/icons/download.svg'
 import { ReactComponent as PackageIcon } from '../images/icons/package.svg'
 
+import { isString } from './util'
 import classes from '../styles/icon.module.sass'
 
 const icons = {
@@ -88,3 +89,41 @@ Icon.propTypes = {
     variant: PropTypes.oneOf(['success', 'error', 'subtle']),
     className: PropTypes.string,
 }
+
+export function replaceEmoji(cellChildren) {
+    const icons = {
+        '✅': { name: 'yes', variant: 'success', 'aria-label': 'positive' },
+        '❌': { name: 'no', variant: 'error', 'aria-label': 'negative' },
+    }
+    const iconRe = new RegExp(`^(${Object.keys(icons).join('|')})`, 'g')
+    let children = isString(cellChildren) ? [cellChildren] : cellChildren
+    let hasIcon = false
+    if (Array.isArray(children)) {
+        children = children.map((child, i) => {
+            if (isString(child)) {
+                const icon = icons[child.trim()]
+                const props = {
+                    inline: i < children.length,
+                    'aria-hidden': undefined,
+                }
+                if (icon) {
+                    hasIcon = true
+                    return <Icon {...icon} {...props} key={i} />
+                } else if (iconRe.test(child)) {
+                    hasIcon = true
+                    const [, iconName, text] = child.split(iconRe)
+                    return (
+                        <Fragment key={i}>
+                            <Icon {...icons[iconName]} {...props} />
+                            {text.trim()}
+                        </Fragment>
+                    )
+                }
+                // Work around prettier auto-escape
+                if (child.startsWith('\\')) return child.slice(1)
+            }
+            return child
+        })
+    }
+    return { content: children, hasIcon }
+}

website/src/components/list.js

@@ -1,7 +1,17 @@
 import React from 'react'
+import classNames from 'classnames'
 
 import classes from '../styles/list.module.sass'
+import { replaceEmoji } from './icon'
 
 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} />
+export const Li = ({ children, ...props }) => {
+    const { hasIcon, content } = replaceEmoji(children)
+    const liClassNames = classNames(classes.li, { [classes.liIcon]: hasIcon })
+    return (
+        <li className={liClassNames} {...props}>
+            {content}
+        </li>
+    )
+}

website/src/components/table.js

@@ -1,8 +1,7 @@
 import React, { Fragment } from 'react'
 import classNames from 'classnames'
 
-import Icon from './icon'
+import { replaceEmoji } from './icon'
-import { Help } from './typography'
 import { isString } from './util'
 import classes from '../styles/table.module.sass'
 
@@ -12,41 +11,6 @@ function isNum(children) {
     return isString(children) && /^\d+[.,]?[\dx]+?(|x|ms|mb|gb|k|m)?$/i.test(children)
 }
 
-function getCellContent(cellChildren) {
-    const icons = {
-        '✅': { name: 'yes', variant: 'success', 'aria-label': 'positive' },
-        '❌': { name: 'no', variant: 'error', 'aria-label': 'negative' },
-    }
-    const iconRe = new RegExp(`^(${Object.keys(icons).join('|')})`, 'g')
-    let children = isString(cellChildren) ? [cellChildren] : cellChildren
-    if (Array.isArray(children)) {
-        return children.map((child, i) => {
-            if (isString(child)) {
-                const icon = icons[child.trim()]
-                const props = {
-                    inline: i < children.length,
-                    'aria-hidden': undefined,
-                }
-                if (icon) {
-                    return <Icon {...icon} {...props} key={i} />
-                } else if (iconRe.test(child)) {
-                    const [, iconName, text] = child.split(iconRe)
-                    return (
-                        <Fragment key={i}>
-                            <Icon {...icons[iconName]} {...props} />
-                            {text.trim()}
-                        </Fragment>
-                    )
-                }
-                // Work around prettier auto-escape
-                if (child.startsWith('\\')) return child.slice(1)
-            }
-            return child
-        })
-    }
-    return children
-}
-
 function isDividerRow(children) {
     if (children.length && children[0].props && children[0].props.name == 'td') {
         const tdChildren = children[0].props.children
@@ -114,7 +78,7 @@ export const Tr = ({ evenodd = true, children, ...props }) => {
 }
 
 export const Td = ({ num, nowrap, className, children, ...props }) => {
-    const content = getCellContent(children)
+    const { content } = replaceEmoji(children)
     const tdClassNames = classNames(classes.td, className, {
         [classes.num]: num || isNum(children),
         [classes.nowrap]: nowrap,

website/src/styles/list.module.sass

@@ -35,3 +35,9 @@
         counter-increment: li
         box-sizing: content-box
         vertical-align: top
+
+.li-icon
+    text-indent: calc(-20px - 0.55em)
+
+    &:before
+        content: ""