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Update docs [ci skip]

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Ines Montani 2020-09-12 17:40:50 +02:00
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40
website/docs/usage/facts-figures.md
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@ -12,16 +12,32 @@ menu:
### When should I use spaCy? {#comparison-usage}
<!-- TODO: update -->
| Use Cases |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| ✅ **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). |
| ✅ **I want to build an end-to-end production application.** |
| ✅ **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. |
| ✅ **I want to try out different neural network architectures for NLP.** |
| ❌ **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 to research machine learning algorithms.** |
- ✅ **I'm a beginner and just getting started with NLP.** – spaCy makes it easy
to get started and comes with extensive documentation, including a
beginner-friendly [101 guide](/usage/spacy-101), a free interactive
[online course](https://course.spacy.io) and a range of
[video tutorials](https://www.youtube.com/c/ExplosionAI).
- ✅ **I want to build an end-to-end production application.** – spaCy is
specifically designed for production use and lets you build and train powerful
NLP pipelines and package them for easy deployment.
- ✅ **I want my application to be efficient on GPU _and_ CPU.** – While spaCy
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 -->

13
website/docs/usage/spacy-101.md
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@ -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 to
power conversational applications, it's not designed specifically for chat
- ❌ **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
- ❌**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).

41
website/src/components/icon.js
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@ -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 }
}

12
website/src/components/list.js
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@ -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>
)
}

40
website/src/components/table.js
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@ -1,8 +1,7 @@
import React, { Fragment } from 'react'
import classNames from 'classnames'
import Icon from './icon'
import { Help } from './typography'
import { replaceEmoji } from './icon'
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,

6
website/src/styles/list.module.sass
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@ -35,3 +35,9 @@
counter-increment: li
box-sizing: content-box
vertical-align: top
.li-icon
text-indent: calc(-20px - 0.55em)
&:before
content: ""