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Add textcat docstring

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This commit is contained in:
Matthew Honnibal 2020-08-09 15:07:09 +02:00
parent 8a13f510d6
commit ebf9a7acbf
1 changed files with 12 additions and 1 deletions
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13
spacy/pipeline/textcat.py
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@ -69,8 +69,19 @@ subword_features = true
default_score_weights={"cats_score": 1.0}, default_score_weights={"cats_score": 1.0},
) )
def make_textcat( def make_textcat(
nlp: Language, name: str, model: Model, labels: Iterable[str] nlp: Language, name: str, model: Model[List[Doc], List[Floats2d]], labels: Iterable[str]
) -> "TextCategorizer": ) -> "TextCategorizer":
"""Create a TextCategorizer compoment. The text categorizer predicts categories
over a whole document. It can learn one or more labels, and the labels can
be mutually exclusive (i.e. one true label per doc) or non-mutually exclusive
(i.e. zero or more labels may be true per doc). The multi-label setting is
controlled by the model instance that's provided.
model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
scores for each category.
labels (list): A list of categories to learn. If empty, the model infers the
categories from the data.
"""
return TextCategorizer(nlp.vocab, model, name, labels=labels) return TextCategorizer(nlp.vocab, model, name, labels=labels)