💫 Update website (#3285)
<!--- 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.
2019-02-17 18:31:19 +00:00
|
|
|
|
---
|
|
|
|
|
title: Examples
|
|
|
|
|
teaser: Full code examples you can modify and run
|
|
|
|
|
menu:
|
|
|
|
|
- ['Information Extraction', 'information-extraction']
|
|
|
|
|
- ['Pipeline', 'pipeline']
|
|
|
|
|
- ['Training', 'training']
|
|
|
|
|
- ['Vectors & Similarity', 'vectors']
|
|
|
|
|
- ['Deep Learning', 'deep-learning']
|
|
|
|
|
---
|
|
|
|
|
|
|
|
|
|
## Information Extraction {#information-extraction hidden="true"}
|
|
|
|
|
|
|
|
|
|
### Using spaCy's phrase matcher {#phrase-matcher new="2"}
|
|
|
|
|
|
|
|
|
|
This example shows how to use the new [`PhraseMatcher`](/api/phrasematcher) to
|
|
|
|
|
efficiently find entities from a large terminology list.
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/information_extraction/phrase_matcher.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### Extracting entity relations {#entity-relations}
|
|
|
|
|
|
|
|
|
|
A simple example of extracting relations between phrases and entities using
|
|
|
|
|
spaCy's named entity recognizer and the dependency parse. Here, we extract money
|
|
|
|
|
and currency values (entities labelled as `MONEY`) and then check the dependency
|
|
|
|
|
tree to find the noun phrase they are referring to – for example:
|
|
|
|
|
`"$9.4 million"` → `"Net income"`.
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/information_extraction/entity_relations.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### Navigating the parse tree and subtrees {#subtrees}
|
|
|
|
|
|
|
|
|
|
This example shows how to navigate the parse tree including subtrees attached to
|
|
|
|
|
a word.
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/information_extraction/parse_subtrees.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## Pipeline {#pipeline hidden="true"}
|
|
|
|
|
|
|
|
|
|
### Custom pipeline components and attribute extensions {#custom-components-entities new="2"}
|
|
|
|
|
|
|
|
|
|
This example shows the implementation of a pipeline component that sets entity
|
|
|
|
|
annotations based on a list of single or multiple-word company names, merges
|
|
|
|
|
entities into one token and sets custom attributes on the `Doc`, `Span` and
|
|
|
|
|
`Token`.
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/pipeline/custom_component_entities.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### Custom pipeline components and attribute extensions via a REST API {#custom-components-api new="2"}
|
|
|
|
|
|
|
|
|
|
This example shows the implementation of a pipeline component that fetches
|
|
|
|
|
country meta data via the [REST Countries API](https://restcountries.eu) sets
|
|
|
|
|
entity annotations for countries, merges entities into one token and sets custom
|
|
|
|
|
attributes on the `Doc`, `Span` and `Token` – for example, the capital,
|
|
|
|
|
latitude/longitude coordinates and the country flag.
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/pipeline/custom_component_countries_api.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### Custom method extensions {#custom-components-attr-methods new="2"}
|
|
|
|
|
|
|
|
|
|
A collection of snippets showing examples of extensions adding custom methods to
|
|
|
|
|
the `Doc`, `Token` and `Span`.
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/pipeline/custom_attr_methods.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### Multi-processing with Joblib {#multi-processing}
|
|
|
|
|
|
|
|
|
|
This example shows how to use multiple cores to process text using spaCy and
|
|
|
|
|
[Joblib](https://joblib.readthedocs.io/en/latest/). We're exporting
|
|
|
|
|
part-of-speech-tagged, true-cased, (very roughly) sentence-separated text, with
|
|
|
|
|
each "sentence" on a newline, and spaces between tokens. Data is loaded from the
|
|
|
|
|
IMDB movie reviews dataset and will be loaded automatically via Thinc's built-in
|
|
|
|
|
dataset loader.
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/pipeline/multi_processing.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## Training {#training hidden="true"}
|
|
|
|
|
|
|
|
|
|
### Training spaCy's Named Entity Recognizer {#training-ner}
|
|
|
|
|
|
|
|
|
|
This example shows how to update spaCy's entity recognizer with your own
|
2019-10-02 08:37:39 +00:00
|
|
|
|
examples, starting off with an existing, pretrained model, or from scratch
|
💫 Update website (#3285)
<!--- 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.
2019-02-17 18:31:19 +00:00
|
|
|
|
using a blank `Language` class.
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/training/train_ner.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### Training an additional entity type {#new-entity-type}
|
|
|
|
|
|
2019-10-02 08:37:39 +00:00
|
|
|
|
This script shows how to add a new entity type to an existing pretrained NER
|
💫 Update website (#3285)
<!--- 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.
2019-02-17 18:31:19 +00:00
|
|
|
|
model. To keep the example short and simple, only four sentences are provided as
|
|
|
|
|
examples. In practice, you'll need many more — a few hundred would be a good
|
|
|
|
|
start.
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/training/train_new_entity_type.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### Training spaCy's Dependency Parser {#parser}
|
|
|
|
|
|
|
|
|
|
This example shows how to update spaCy's dependency parser, starting off with an
|
2019-10-02 08:37:39 +00:00
|
|
|
|
existing, pretrained model, or from scratch using a blank `Language` class.
|
💫 Update website (#3285)
<!--- 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.
2019-02-17 18:31:19 +00:00
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/training/train_parser.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### Training spaCy's Part-of-speech Tagger {#tagger}
|
|
|
|
|
|
|
|
|
|
In this example, we're training spaCy's part-of-speech tagger with a custom tag
|
|
|
|
|
map, mapping our own tags to the mapping those tags to the
|
|
|
|
|
[Universal Dependencies scheme](http://universaldependencies.github.io/docs/u/pos/index.html).
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/training/train_tagger.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### Training a custom parser for chat intent semantics {#intent-parser}
|
|
|
|
|
|
|
|
|
|
spaCy's parser component can be used to trained to predict any type of tree
|
|
|
|
|
structure over your input text. You can also predict trees over whole documents
|
|
|
|
|
or chat logs, with connections between the sentence-roots used to annotate
|
|
|
|
|
discourse structure. In this example, we'll build a message parser for a common
|
|
|
|
|
"chat intent": finding local businesses. Our message semantics will have the
|
|
|
|
|
following types of relations: `ROOT`, `PLACE`, `QUALITY`, `ATTRIBUTE`, `TIME`
|
|
|
|
|
and `LOCATION`.
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/training/train_intent_parser.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### Training spaCy's text classifier {#textcat new="2"}
|
|
|
|
|
|
|
|
|
|
This example shows how to train a multi-label convolutional neural network text
|
|
|
|
|
classifier on IMDB movie reviews, using spaCy's new
|
|
|
|
|
[`TextCategorizer`](/api/textcategorizer) component. The dataset will be loaded
|
|
|
|
|
automatically via Thinc's built-in dataset loader. Predictions are available via
|
|
|
|
|
[`Doc.cats`](/api/doc#attributes).
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/training/train_textcat.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## Vectors {#vectors hidden="true"}
|
|
|
|
|
|
|
|
|
|
### Visualizing spaCy vectors in TensorBoard {#tensorboard}
|
|
|
|
|
|
2019-04-03 07:59:17 +00:00
|
|
|
|
This script lets you load any spaCy model containing word vectors into
|
💫 Update website (#3285)
<!--- 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.
2019-02-17 18:31:19 +00:00
|
|
|
|
[TensorBoard](https://projector.tensorflow.org/) to create an
|
2020-04-21 18:35:12 +00:00
|
|
|
|
[embedding visualization](https://github.com/tensorflow/tensorboard/blob/master/docs/tensorboard_projector_plugin.ipynb).
|
💫 Update website (#3285)
<!--- 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.
2019-02-17 18:31:19 +00:00
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/vectors_tensorboard.py
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## Deep Learning {#deep-learning hidden="true"}
|
|
|
|
|
|
|
|
|
|
### Text classification with Keras {#keras}
|
|
|
|
|
|
|
|
|
|
This example shows how to use a [Keras](https://keras.io) LSTM sentiment
|
|
|
|
|
classification model in spaCy. spaCy splits the document into sentences, and
|
|
|
|
|
each sentence is classified using the LSTM. The scores for the sentences are
|
|
|
|
|
then aggregated to give the document score. This kind of hierarchical model is
|
|
|
|
|
quite difficult in "pure" Keras or TensorFlow, but it's very effective. The
|
|
|
|
|
Keras example on this dataset performs quite poorly, because it cuts off the
|
|
|
|
|
documents so that they're a fixed size. This hurts review accuracy a lot,
|
|
|
|
|
because people often summarize their rating in the final sentence.
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
https://github.com/explosion/spaCy/tree/master/examples/deep_learning_keras.py
|
|
|
|
|
```
|