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152 lines
6.1 KiB
Plaintext
152 lines
6.1 KiB
Plaintext
//- 💫 DOCS > USAGE > PROCESSING PIPELINES > CUSTOM COMPONENTS
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p
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| A component receives a #[code Doc] object and
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| #[strong performs the actual processing] – for example, using the current
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| weights to make a prediction and set some annotation on the document. By
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| adding a component to the pipeline, you'll get access to the #[code Doc]
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| at any point #[strong during] processing – instead of only being able to
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| modify it afterwards.
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+aside-code("Example").
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def my_component(doc):
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# do something to the doc here
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return doc
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+table(["Argument", "Type", "Description"])
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+row
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+cell #[code doc]
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+cell #[code Doc]
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+cell The #[code Doc] object processed by the previous component.
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+row("foot")
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+cell returns
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+cell #[code Doc]
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+cell The #[code Doc] object processed by this pipeline component.
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p
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| Custom components can be added to the pipeline using the
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| #[+api("language#add_pipe") #[code add_pipe]] method. Optionally, you
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| can either specify a component to add it before or after, tell spaCy
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| to add it first or last in the pipeline, or define a custom name.
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| If no name is set and no #[code name] attribute is present on your
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| component, the function name, e.g. #[code component.__name__] is used.
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+code("Adding pipeline components").
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def my_component(doc):
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print("After tokenization, this doc has %s tokens." % len(doc))
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if len(doc) < 10:
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print("This is a pretty short document.")
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return doc
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nlp = spacy.load('en')
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nlp.pipeline.add_pipe(my_component, name='print_info', first=True)
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print(nlp.pipe_names) # ['print_info', 'tagger', 'parser', 'ner']
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doc = nlp(u"This is a sentence.")
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p
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| Of course, you can also wrap your component as a class to allow
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| initialising it with custom settings and hold state within the component.
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| This is useful for #[strong stateful components], especially ones which
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| #[strong depend on shared data].
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+code.
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class MyComponent(object):
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name = 'print_info'
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def __init__(vocab, short_limit=10):
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self.vocab = nlp.vocab
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self.short_limit = short_limit
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def __call__(doc):
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if len(doc) < self.short_limit:
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print("This is a pretty short document.")
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return doc
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my_component = MyComponent(nlp.vocab, short_limit=25)
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nlp.add_pipe(my_component, first=True)
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+h(3, "custom-components-attributes")
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| Setting attributes on the #[code Doc], #[code Span] and #[code Token]
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+aside("Why ._?")
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| Writing to a #[code ._] attribute instead of to the #[code Doc] directly
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| keeps a clearer separation and makes it easier to ensure backwards
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| compatibility. For example, if you've implemented your own #[code .coref]
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| property and spaCy claims it one day, it'll break your code. Similarly,
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| just by looking at the code, you'll immediately know what's built-in and
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| what's custom – for example, #[code doc.sentiment] is spaCy, while
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| #[code doc._.sent_score] isn't.
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+under-construction
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+h(3, "custom-components-user-hooks") Other user hooks
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p
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| While it's generally recommended to use the #[code Doc._], #[code Span._]
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| and #[code Token._] proxies to add your own custom attributes, spaCy
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| offers a few exceptions to allow #[strong customising the built-in methods]
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| like #[+api("doc#similarity") #[code Doc.similarity]] or
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| #[+api("doc#vector") #[code Doc.vector]]. with your own hooks, which can
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| rely on statistical models you train yourself. For instance, you can
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| provide your own on-the-fly sentence segmentation algorithm or document
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| similarity method.
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p
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| Hooks let you customize some of the behaviours of the #[code Doc],
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| #[code Span] or #[code Token] objects by adding a component to the
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| pipeline. For instance, to customize the
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| #[+api("doc#similarity") #[code Doc.similarity]] method, you can add a
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| component that sets a custom function to
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| #[code doc.user_hooks['similarity']]. The built-in #[code Doc.similarity]
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| method will check the #[code user_hooks] dict, and delegate to your
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| function if you've set one. Similar results can be achieved by setting
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| functions to #[code Doc.user_span_hooks] and #[code Doc.user_token_hooks].
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+aside("Implementation note")
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| The hooks live on the #[code Doc] object because the #[code Span] and
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| #[code Token] objects are created lazily, and don't own any data. They
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| just proxy to their parent #[code Doc]. This turns out to be convenient
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| here — we only have to worry about installing hooks in one place.
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+table(["Name", "Customises"])
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+row
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+cell #[code user_hooks]
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+cell
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+api("doc#vector") #[code Doc.vector]
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+api("doc#has_vector") #[code Doc.has_vector]
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+api("doc#vector_norm") #[code Doc.vector_norm]
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+api("doc#sents") #[code Doc.sents]
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+row
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+cell #[code user_token_hooks]
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+cell
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+api("token#similarity") #[code Token.similarity]
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+api("token#vector") #[code Token.vector]
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+api("token#has_vector") #[code Token.has_vector]
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+api("token#vector_norm") #[code Token.vector_norm]
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+api("token#conjuncts") #[code Token.conjuncts]
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+row
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+cell #[code user_span_hooks]
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+cell
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+api("span#similarity") #[code Span.similarity]
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+api("span#vector") #[code Span.vector]
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+api("span#has_vector") #[code Span.has_vector]
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+api("span#vector_norm") #[code Span.vector_norm]
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+api("span#root") #[code Span.root]
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+code("Add custom similarity hooks").
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class SimilarityModel(object):
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def __init__(self, model):
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self._model = model
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def __call__(self, doc):
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doc.user_hooks['similarity'] = self.similarity
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doc.user_span_hooks['similarity'] = self.similarity
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doc.user_token_hooks['similarity'] = self.similarity
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def similarity(self, obj1, obj2):
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y = self._model([obj1.vector, obj2.vector])
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return float(y[0])
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