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Update pipelines section in v2 overview

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ines 2017-10-10 06:33:50 +02:00
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commit 9c96a6e131
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website/usage/v2.jade
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@ -102,30 +102,36 @@ p
+h(3, "features-pipelines") Improved processing pipelines
+aside-code("Example").
# Modify an existing pipeline
nlp = spacy.load('en')
nlp.pipeline.append(my_component)
# Set custom attributes
Doc.set_extension('my_attr', default=False)
Token.set_extension('my_attr', getter=my_token_getter)
assert doc._.my_attr, token._.my_attr
# Register a factory to create a component
spacy.set_factory('my_factory', my_factory)
nlp = Language(pipeline=['my_factory', mycomponent])
# Add components to the pipeline
my_component = lambda doc: doc
nlp.add_pipe(my_component)
p
| It's now much easier to #[strong customise the pipeline] with your own
| components, functions that receive a #[code Doc] object, modify and
| return it. If your component is stateful, you can define and register a
| factory which receives the shared #[code Vocab] object and returns a
|  component. spaCy's default components can be added to your pipeline by
| using their string IDs. This way, you won't have to worry about finding
| and implementing them – simply add #[code "tagger"] to the pipeline,
| and spaCy will know what to do.
| components: functions that receive a #[code Doc] object, modify and
| return it. Extensions let you write any
| #[strong attributes, properties and methods] to the #[code Doc],
| #[code Token] and #[code Span]. You can add data, implement new
| features, integrate other libraries with spaCy or plug in your own
| machine learning models.
+image
include ../assets/img/pipeline.svg
+infobox
| #[+label-inline API:] #[+api("language") #[code Language]]
| #[+label-inline Usage:] #[+a("/usage/language-processing-pipeline") Processing text]
| #[+label-inline API:] #[+api("language") #[code Language]],
| #[+api("doc#set_extension") #[code Doc.set_extension]],
| #[+api("span#set_extension") #[code Span.set_extension]],
| #[+api("token#set_extension") #[code Token.set_extension]]
| #[+label-inline Usage:]
| #[+a("/usage/processing-pipelines") Processing pipelines]
| #[+label-inline Code:]
| #[+src("/usage/examples#section-pipeline") Pipeline examples]
+h(3, "features-text-classification") Text classification