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Remove MDX imports

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Marcus Blättermann 2022-11-17 17:17:26 +01:00
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2
website/docs/api/index.mdx
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@ -3,6 +3,4 @@ title: Library Architecture
next: /api/architectures
---
import Architecture101 from 'usage/101/_architecture.mdx'
<Architecture101 />

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website/docs/models/index.mdx
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@ -16,8 +16,6 @@ menu:
> For more details on how to use trained pipelines with spaCy, see the
> [usage guide](/usage/models).
import QuickstartModels from 'widgets/quickstart-models.js'
<QuickstartModels id="quickstart" />
## Package naming conventions {id="conventions"}

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website/docs/styleguide.mdx
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@ -44,8 +44,6 @@ enough, JSX components can be used.
## Logo {id="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
@ -55,8 +53,6 @@ project is using spaCy, you can grab one of our
## Colors {id="colors"}
import { Colors, Patterns } from 'widgets/styleguide'
<Colors />
### Patterns
@ -65,8 +61,6 @@ import { Colors, Patterns } from 'widgets/styleguide'
## Typography {id="typography"}
import { H1, H2, H3, H4, H5, Label } from 'components/typography'
> #### Markdown
>
> ```markdown_
@ -147,8 +141,6 @@ Special link styles are used depending on the link URL.
### Abbreviations {id="abbr"}
import { Abbr } from 'components/typography'
> #### JSX
>
> ```jsx
@ -161,8 +153,6 @@ abbreviation.
### Tags {id="tags"}
import Tag from 'components/tag'
> ```jsx
> <Tag>method</Tag>
> <Tag variant="version">4</Tag>
@ -186,8 +176,6 @@ installed.
### Buttons {id="buttons"}
import Button from 'components/button'
> ```jsx
> <Button to="#" variant="primary">Primary small</Button>
> <Button to="#" variant="secondary">Secondary small</Button>
@ -477,8 +465,6 @@ https://github.com/explosion/spaCy/tree/master/spacy/language.py
### Infobox {id="infobox"}
import Infobox from 'components/infobox'
> #### JSX
>
> ```jsx
@ -515,8 +501,6 @@ blocks.
### Accordion {id="accordion"}
import Accordion from 'components/accordion'
> #### JSX
>
> ```jsx

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website/docs/usage/101/_named-entities.mdx
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@ -32,8 +32,6 @@ for ent in doc.ents:
Using spaCy's built-in [displaCy visualizer](/usage/visualizers), here's what
our example sentence and its named entities look like:
import { Iframe } from 'components/embed'
<Iframe
title="displaCy visualization of entities"
src="/images/displacy-ent1.html"

2
website/docs/usage/101/_pipelines.mdx
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@ -35,8 +35,6 @@ the [config](/usage/training#config):
pipeline = ["tok2vec", "tagger", "parser", "ner"]
```
import Accordion from 'components/accordion.js'
<Accordion title="Does the order of pipeline components matter?" id="pipeline-components-order">
The statistical components like the tagger or parser are typically independent

2
website/docs/usage/101/_pos-deps.mdx
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@ -57,8 +57,6 @@ for token in doc:
Using spaCy's built-in [displaCy visualizer](/usage/visualizers), here's what
our example sentence and its dependencies look like:
import { Iframe } from 'components/embed'
<Iframe
title="displaCy visualization of dependencies and entities"
src="/images/displacy-long.html"

2
website/docs/usage/101/_vectors-similarity.mdx
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@ -1,5 +1,3 @@
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

3
website/docs/usage/_benchmarks-models.mdx
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@ -1,6 +1,3 @@
import { Help } from 'components/typography'
import Link from 'components/link'
<figure>
| Pipeline | Parser | Tagger | NER |

4
website/docs/usage/facts-figures.mdx
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@ -18,8 +18,6 @@ understanding systems.
### Feature overview {id="comparison-features"}
import Features from 'widgets/features.js'
<Features />
### When should I use spaCy? {id="comparison-usage"}
@ -69,8 +67,6 @@ pipeline, which is less accurate but much cheaper to run.
> gold-standard segmentation and tokenization, from a pretty specific type of
> text (articles from a single newspaper, 1984-1989).
import Benchmarks from 'usage/_benchmarks-models.mdx'
<Benchmarks />
<figure>

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website/docs/usage/index.mdx
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@ -16,8 +16,6 @@ menu:
> website to [**v2.spacy.io**](https://v2.spacy.io/docs). To see what's changed
> and how to migrate, see the [v3.0 guide](/usage/v3).
import QuickstartInstall from 'widgets/quickstart-install.js'
<QuickstartInstall id="quickstart" />
## Installation instructions {id="installation"}
@ -449,6 +447,4 @@ either of these, clone your repository again.
## Changelog {id="changelog"}
import Changelog from 'widgets/changelog.js'
<Changelog />

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website/docs/usage/linguistic-features.mdx
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@ -28,8 +28,6 @@ annotations.
## Part-of-speech tagging {id="pos-tagging",model="tagger, parser"}
import PosDeps101 from 'usage/101/_pos-deps.mdx'
<PosDeps101 />
<Infobox title="Part-of-speech tag scheme" emoji="📖">
@ -538,8 +536,6 @@ with new examples.
### Named Entity Recognition 101 {id="named-entities-101"}
import NER101 from 'usage/101/_named-entities.mdx'
<NER101 />
### Accessing entity annotations and labels {id="accessing-ner"}
@ -789,8 +785,6 @@ during tokenization. This is kind of a core principle of spaCy's `Doc` object:
</Infobox>
import Tokenization101 from 'usage/101/_tokenization.mdx'
<Tokenization101 />
<Accordion title="Algorithm details: How spaCy's tokenizer works" id="how-tokenizer-works" spaced>
@ -1872,8 +1866,6 @@ initialized before training. See the
## Word vectors and semantic similarity {id="vectors-similarity"}
import Vectors101 from 'usage/101/_vectors-similarity.mdx'
<Vectors101 />
### Adding word vectors {id="adding-vectors"}
@ -2002,8 +1994,6 @@ for word, vector in vector_data.items():
## Language Data {id="language-data"}
import LanguageData101 from 'usage/101/_language-data.mdx'
<LanguageData101 />
### Creating a custom language subclass {id="language-subclass"}

4
website/docs/usage/models.mdx
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@ -23,8 +23,6 @@ located anywhere on your file system.
## Quickstart {hidden="true"}
import QuickstartModels from 'widgets/quickstart-models.js'
<QuickstartModels
title="Quickstart"
id="quickstart"
@ -70,8 +68,6 @@ contribute to development. Also see the
[training documentation](/usage/training) for how to train your own pipelines on
your data.
import Languages from 'widgets/languages.js'
<Languages />
### Multi-language support {id="multi-language",version="2"}

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website/docs/usage/processing-pipelines.mdx
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@ -12,8 +12,6 @@ menu:
- ['Plugins & Wrappers', 'plugins']
---
import Pipelines101 from 'usage/101/_pipelines.mdx'
<Pipelines101 />
## Processing text {id="processing"}

2
website/docs/usage/saving-loading.mdx
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@ -10,8 +10,6 @@ menu:
## Basics {id="basics",hidden="true"}
import Serialization101 from 'usage/101/_serialization.mdx'
<Serialization101 />
### Serializing the pipeline {id="pipeline"}

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website/docs/usage/spacy-101.mdx
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@ -195,8 +195,6 @@ text with spaCy.
### Tokenization {id="annotations-token"}
import Tokenization101 from 'usage/101/_tokenization.mdx'
<Tokenization101 />
<Infobox title="Tokenization rules" emoji="📖">
@ -211,8 +209,6 @@ language-specific data**, see the usage guides on
### Part-of-speech tags and dependencies {id="annotations-pos-deps",model="parser"}
import PosDeps101 from 'usage/101/_pos-deps.mdx'
<PosDeps101 />
<Infobox title="Part-of-speech tagging and morphology" emoji="📖">
@ -226,8 +222,6 @@ how to **navigate and use the parse tree** effectively, see the usage guides on
### Named Entities {id="annotations-ner",model="ner"}
import NER101 from 'usage/101/_named-entities.mdx'
<NER101 />
<Infobox title="Named Entity Recognition" emoji="📖">
@ -242,8 +236,6 @@ of a model, see the usage guides on
### Word vectors and similarity {id="vectors-similarity",model="vectors"}
import Vectors101 from 'usage/101/_vectors-similarity.mdx'
<Vectors101 />
<Infobox title="Word vectors" emoji="📖">
@ -256,8 +248,6 @@ To learn more about word vectors, how to **customize them** and how to load
## Pipelines {id="pipelines"}
import Pipelines101 from 'usage/101/_pipelines.mdx'
<Pipelines101 />
<Infobox title="Processing pipelines" emoji="📖">
@ -270,8 +260,6 @@ guide on [language processing pipelines](/usage/processing-pipelines).
## Architecture {id="architecture"}
import Architecture101 from 'usage/101/_architecture.mdx'
<Architecture101 />
## Vocab, hashes and lexemes {id="vocab"}
@ -388,8 +376,6 @@ it.
## Serialization {id="serialization"}
import Serialization101 from 'usage/101/_serialization.mdx'
<Serialization101 />
<Infobox title="Saving and loading" emoji="📖">
@ -401,8 +387,6 @@ guide on [saving and loading](/usage/saving-loading#models).
## Training {id="training"}
import Training101 from 'usage/101/_training.mdx'
<Training101 />
<Infobox title="Training pipelines and models" emoji="📖">
@ -480,8 +464,6 @@ for trainable components.
## Language data {id="language-data"}
import LanguageData101 from 'usage/101/_language-data.mdx'
<LanguageData101 />
## Community & FAQ {id="community-faq"}

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website/docs/usage/training.mdx
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@ -17,8 +17,6 @@ menu:
## Introduction to training {id="basics",hidden="true"}
import Training101 from 'usage/101/_training.mdx'
<Training101 />
<Infobox title="Tip: Try the Prodigy annotation tool">
@ -69,8 +67,6 @@ config.
> requirements and settings as CLI arguments.
> 2. Run [`train`](/api/cli#train) with the exported config and data.
import QuickstartTraining from 'widgets/quickstart-training.js'
<QuickstartTraining />
After you've saved the starter config to a file `base_config.cfg`, you can use

2
website/docs/usage/v3.mdx
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@ -88,8 +88,6 @@ giving you access to thousands of pretrained models for your pipelines.
![Pipeline components listening to shared embedding component](/images/tok2vec-listener.svg)
import Benchmarks from 'usage/_benchmarks-models.mdx'
<Benchmarks />
#### New trained transformer-based pipelines {id="features-transformers-pipelines"}