Add textcat docstring
@@ -69,8 +69,19 @@ subword_features = true
default_score_weights={"cats_score": 1.0},
)
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":
+ """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)
