Customizing the pipeline
spaCy provides several linguistic annotation functions by default. Each
function takes a Doc object, and modifies it in-place. The default pipeline is
[nlp.tagger, nlp.entity, nlp.parser]. spaCy 1.0 introduced the ability to customise this pipeline with arbitrary
def arbitrary_fixup_rules(doc): for token in doc: if token.text == u'bill' and token.tag_ == u'NNP': token.tag_ = u'NN' def custom_pipeline(nlp): return (nlp.tagger, arbitrary_fixup_rules, nlp.parser, nlp.entity) nlp = spacy.load('en', create_pipeline=custom_pipeline)
The easiest way to customise the pipeline is to pass a
create_pipeline callback to the
The callback you pass to
create_pipeline should take a single argument, and return a sequence of callables. Each callable in the sequence should accept a
Doc object and modify it in place.
Instead of passing a callback, you can also write to the
.pipeline attribute directly.
nlp = spacy.load('en') nlp.pipeline = [nlp.tagger]