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	Don't auto-slugify accordion links [ci skip]
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					@ -78,7 +78,7 @@ assigned by spaCy's [models](/models). The individual mapping is specific to the
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training corpus and can be defined in the respective language data's
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					training corpus and can be defined in the respective language data's
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[`tag_map.py`](/usage/adding-languages#tag-map).
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					[`tag_map.py`](/usage/adding-languages#tag-map).
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<Accordion title="Universal Part-of-speech Tags">
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					<Accordion title="Universal Part-of-speech Tags" id="pos-universal">
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spaCy also maps all language-specific part-of-speech tags to a small, fixed set
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					spaCy also maps all language-specific part-of-speech tags to a small, fixed set
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of word type tags following the
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					of word type tags following the
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					@ -269,7 +269,7 @@ This section lists the syntactic dependency labels assigned by spaCy's
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[models](/models). The individual labels are language-specific and depend on the
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					[models](/models). The individual labels are language-specific and depend on the
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training corpus.
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					training corpus.
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<Accordion title="Universal Dependency Labels">
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					<Accordion title="Universal Dependency Labels" id="dependency-parsing-universal">
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The [Universal Dependencies scheme](http://universaldependencies.org/u/dep/) is
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					The [Universal Dependencies scheme](http://universaldependencies.org/u/dep/) is
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used in all languages trained on Universal Dependency Corpora.
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					used in all languages trained on Universal Dependency Corpora.
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					@ -621,7 +621,7 @@ For more details on the language-specific data, see the usage guide on
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</Infobox>
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					</Infobox>
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<Accordion title="Should I change the language data or add custom tokenizer rules?">
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					<Accordion title="Should I change the language data or add custom tokenizer rules?" id="lang-data-vs-tokenizer">
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Tokenization rules that are specific to one language, but can be **generalized
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					Tokenization rules that are specific to one language, but can be **generalized
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across that language** should ideally live in the language data in
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					across that language** should ideally live in the language data in
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					@ -426,7 +426,7 @@ spaCy, and implement your own models trained with other machine learning
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libraries. It also lets you take advantage of spaCy's data structures and the
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					libraries. It also lets you take advantage of spaCy's data structures and the
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`Doc` object as the "single source of truth".
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					`Doc` object as the "single source of truth".
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<Accordion title="Why ._ and not just a top-level attribute?">
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					<Accordion title="Why ._ and not just a top-level attribute?" id="why-dot-underscore">
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Writing to a `._` attribute instead of to the `Doc` directly keeps a clearer
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					Writing to a `._` attribute instead of to the `Doc` directly keeps a clearer
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separation and makes it easier to ensure backwards compatibility. For example,
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					separation and makes it easier to ensure backwards compatibility. For example,
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					@ -437,7 +437,7 @@ immediately know what's built-in and what's custom – for example,
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</Accordion>
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					</Accordion>
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<Accordion title="How is the ._ implemented?">
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					<Accordion title="How is the ._ implemented?" id="dot-underscore-implementation">
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Extension definitions – the defaults, methods, getters and setters you pass in
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					Extension definitions – the defaults, methods, getters and setters you pass in
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to `set_extension` – are stored in class attributes on the `Underscore` class.
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					to `set_extension` – are stored in class attributes on the `Underscore` class.
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					@ -15,7 +15,7 @@ their relationships. This means you can easily access and analyze the
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surrounding tokens, merge spans into single tokens or add entries to the named
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					surrounding tokens, merge spans into single tokens or add entries to the named
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entities in `doc.ents`.
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					entities in `doc.ents`.
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<Accordion title="Should I use rules or train a model?">
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					<Accordion title="Should I use rules or train a model?" id="rules-vs-model">
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For complex tasks, it's usually better to train a statistical entity recognition
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					For complex tasks, it's usually better to train a statistical entity recognition
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model. However, statistical models require training data, so for many
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					model. However, statistical models require training data, so for many
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					@ -41,7 +41,7 @@ on [rule-based entity recognition](#entityruler).
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</Accordion>
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					</Accordion>
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<Accordion title="When should I use the token matcher vs. the phrase matcher?">
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					<Accordion title="When should I use the token matcher vs. the phrase matcher?" id="matcher-vs-phrase-matcher">
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The `PhraseMatcher` is useful if you already have a large terminology list or
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					The `PhraseMatcher` is useful if you already have a large terminology list or
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gazetteer consisting of single or multi-token phrases that you want to find
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					gazetteer consisting of single or multi-token phrases that you want to find
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					@ -12,7 +12,6 @@
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        "@mdx-js/tag": "^0.17.5",
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					        "@mdx-js/tag": "^0.17.5",
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        "@phosphor/widgets": "^1.6.0",
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					        "@phosphor/widgets": "^1.6.0",
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        "@rehooks/online-status": "^1.0.0",
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					        "@rehooks/online-status": "^1.0.0",
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        "@sindresorhus/slugify": "^0.8.0",
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        "@svgr/webpack": "^4.1.0",
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					        "@svgr/webpack": "^4.1.0",
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        "autoprefixer": "^9.4.7",
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					        "autoprefixer": "^9.4.7",
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        "classnames": "^2.2.6",
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					        "classnames": "^2.2.6",
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					@ -62,7 +61,8 @@
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        "md-attr-parser": "^1.2.1",
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					        "md-attr-parser": "^1.2.1",
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        "prettier": "^1.16.4",
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					        "prettier": "^1.16.4",
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        "raw-loader": "^1.0.0",
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					        "raw-loader": "^1.0.0",
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        "unist-util-visit": "^1.4.0"
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					        "unist-util-visit": "^1.4.0",
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					        "@sindresorhus/slugify": "^0.8.0"
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    },
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					    },
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    "repository": {
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					    "repository": {
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        "type": "git",
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					        "type": "git",
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					@ -1,13 +1,11 @@
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import React, { useState } from 'react'
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					import React, { useState } from 'react'
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import PropTypes from 'prop-types'
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					import PropTypes from 'prop-types'
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import classNames from 'classnames'
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					import classNames from 'classnames'
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import slugify from '@sindresorhus/slugify'
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import Link from './link'
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					import Link from './link'
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import classes from '../styles/accordion.module.sass'
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					import classes from '../styles/accordion.module.sass'
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const Accordion = ({ title, id, expanded, children }) => {
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					const Accordion = ({ title, id, expanded, children }) => {
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    const anchorId = id || slugify(title)
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    const [isExpanded, setIsExpanded] = useState(expanded)
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					    const [isExpanded, setIsExpanded] = useState(expanded)
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    const contentClassNames = classNames(classes.content, {
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					    const contentClassNames = classNames(classes.content, {
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        [classes.hidden]: !isExpanded,
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					        [classes.hidden]: !isExpanded,
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					@ -16,7 +14,7 @@ const Accordion = ({ title, id, expanded, children }) => {
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        [classes.hidden]: isExpanded,
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					        [classes.hidden]: isExpanded,
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    })
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					    })
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    return (
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					    return (
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        <section id={anchorId}>
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					        <section id={id}>
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            <div className={classes.root}>
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					            <div className={classes.root}>
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                <h3>
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					                <h3>
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                    <button
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					                    <button
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					@ -26,8 +24,8 @@ const Accordion = ({ title, id, expanded, children }) => {
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                    >
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					                    >
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                        <span>
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					                        <span>
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                            {title}
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					                            {title}
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                            {isExpanded && (
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					                            {isExpanded && !!id && (
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                                <Link to={`#${anchorId}`} className={classes.anchor} hidden>
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					                                <Link to={`#${id}`} className={classes.anchor} hidden>
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                                    ¶
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					                                    ¶
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                                </Link>
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					                                </Link>
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                            )}
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					                            )}
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