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Update entry points docs [ci skip]
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Ines Montani
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website/docs/images/displacy-ent-snek.html
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18
website/docs/images/displacy-ent-snek.html
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@ -0,0 +1,18 @@
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<div
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class="entities"
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style="line-height: 2.5; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; font-size: 16px"
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>
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🌱🌿 <mark
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class="entity"
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style="background: #3dff74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone"
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>🐍 <span
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style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem"
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>SNEK</span
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></mark> ____ 🌳🌲 ____ <mark
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class="entity"
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style="background: #cfc5ff; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone"
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>👨🌾 <span
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style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem"
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>HUMAN</span
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></mark> 🏘️
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</div>
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@ -238,13 +238,31 @@ custom components to spaCy automatically.
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## Using entry points {#entry-points new="2.1"}
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Entry points let you expose parts of a Python package you write to other Python
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packages. This lets one application easily customize the behavior of another, by
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exposing an entry point in its `setup.py`. For a quick and fun intro to entry
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points in Python, check out
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[this excellent blog post](https://amir.rachum.com/blog/2017/07/28/python-entry-points/).
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spaCy can load custom function from several different entry points to add
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pipeline component factories, language classes and other settings. To make spaCy
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use your entry points, your package needs to expose them and it needs to be
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installed in the same environment – that's it.
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| Entry point | Description |
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| ------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [`spacy_factories`](#entry-points-components) | Group of entry points for pipeline component factories to add to [`Language.factories`](/usage/processing-pipelines#custom-components-factories), keyed by component name. |
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| [`spacy_languages`](#entry-points-languages) | Group of entry points for custom [`Language` subclasses](/usage/adding-languages), keyed by language shortcut. |
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| [`spacy_displacy_colors`](#entry-points-displacy) <Tag variant="new">2.2</Tag> | Group of entry points of custom label colors for the [displaCy visualizer](/usage/visualizers#ent). The key name doesn't matter, but it should point to a dict of labels and color values. Useful for custom models that predict different entity types. |
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### Custom components via entry points {#entry-points-components}
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When you load a model, spaCy will generally use the model's `meta.json` to set
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up the language class and construct the pipeline. The pipeline is specified as a
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list of strings, e.g. `"pipeline": ["tagger", "paser", "ner"]`. For each of
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those strings, spaCy will call `nlp.create_pipe` and look up the name in the
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[built-in factories](#custom-components-factories). If your model wanted to
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specify its own custom components, you usually have to write to
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`Language.factories` _before_ loading the model.
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[built-in factories](/usage/processing-pipelines#custom-components-factories).
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If your model wanted to specify its own custom components, you usually have to
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write to `Language.factories` _before_ loading the model.
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```python
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pipe = nlp.create_pipe("custom_component") # fails 👎
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@ -260,13 +278,11 @@ added to the built-in factories when the `Language` class is initialized. If a
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package in the same environment exposes spaCy entry points, all of this happens
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automatically and no further user action is required.
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#### Custom components via entry points {#entry-points-components}
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For a quick and fun intro to entry points in Python, I recommend
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[this excellent blog post](https://amir.rachum.com/blog/2017/07/28/python-entry-points/).
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To stick with the theme of the post, consider the following custom spaCy
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extension which is initialized with the shared `nlp` object and will print a
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snake when it's called as a pipeline component.
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To stick with the theme of
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[this entry points blog post](https://amir.rachum.com/blog/2017/07/28/python-entry-points/),
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consider the following custom spaCy extension which is initialized with the
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shared `nlp` object and will print a snake when it's called as a pipeline
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component.
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> #### Package directory structure
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>
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@ -304,15 +320,13 @@ entry to the factories, you can now expose it in your `setup.py` via the
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`entry_points` dictionary:
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```python
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### setup.py {highlight="5-8"}
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### setup.py {highlight="5-7"}
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from setuptools import setup
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setup(
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name="snek",
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entry_points={
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"spacy_factories": [
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"snek = snek:SnekFactory"
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]
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"spacy_factories": ["snek = snek:SnekFactory"]
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}
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)
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```
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@ -410,7 +424,7 @@ The above example will serialize the current snake in a `snek.txt` in the model
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data directory. When a model using the `snek` component is loaded, it will open
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the `snek.txt` and make it available to the component.
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#### Custom language classes via entry points {#entry-points-components}
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### Custom language classes via entry points {#entry-points-languages}
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To stay with the theme of the previous example and
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[this blog post on entry points](https://amir.rachum.com/blog/2017/07/28/python-entry-points/),
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@ -446,12 +460,8 @@ from setuptools import setup
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setup(
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name="snek",
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entry_points={
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"spacy_factories": [
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"snek = snek:SnekFactory"
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]
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+ "spacy_languages": [
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+ "sk = snek:SnekLanguage"
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+ ]
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"spacy_factories": ["snek = snek:SnekFactory"],
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+ "spacy_languages": ["snk = snek:SnekLanguage"]
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}
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)
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```
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@ -481,6 +491,50 @@ SnekLanguage = get_lang_class("snk")
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nlp = SnekLanguage()
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```
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### Custom displaCy colors via entry points {#entry-points-displacy}
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If you're training a named entity recognition model for a custom domain, you may
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end up training different labels that don't have pre-defined colors in the
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[`displacy` visualizer](/usage/visualizers#ent). The `spacy_displacy_colors`
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entry point lets you define a dictionary of entity labels mapped to their color
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values. It's added to the existing pre-defined colors and can also overwrite
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existing values.
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> #### Domain-specific NER labels
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>
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> Good examples of models with domain-specific label schemes are
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> [scispaCy](/universe/project/scispacy) and
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> [Blackstone](/universe/project/blackstone).
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```python
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### snek.py
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displacy_colors = {"SNEK": "#3dff74", "HUMAN": "#cfc5ff"}
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```
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Given the above colors, the entry point can be defined as follows. Entry points
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need to have a name, so we use the key `colors`. However, the name doesn't
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matter and whatever is defined in the entry point group will be used.
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```diff
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### setup.py
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from setuptools import setup
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setup(
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name="snek",
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entry_points={
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+ "spacy_displacy_colors": ["colors = snek:displacy_colors"]
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}
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)
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```
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After installing the package, the the custom colors will be used when
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visualizing text with `displacy`. Whenever the label `SNEK` is assigned, it
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will be displayed in `#3dff74`.
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import DisplaCyEntSnekHtml from 'images/displacy-ent-snek.html'
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<Iframe title="displaCy visualization of entities" html={DisplaCyEntSnekHtml} height={100} />
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## Saving, loading and distributing models {#models}
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After training your model, you'll usually want to save its state, and load it
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