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Small fixes to docstrings (#11610) 

* add missing scorer arg to docstring

* fix class names in textcat_multilabel

* add missing scorer to docstrings
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Sofie Van Landeghem 2022-10-12 15:17:40 +02:00 committed by GitHub
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commit 4d869fcc11
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2 changed files with 7 additions and 2 deletions
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3
spacy/pipeline/spancat.py
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@ -133,6 +133,9 @@ def make_spancat(
spans_key (str): Key of the doc.spans dict to save the spans under. During spans_key (str): Key of the doc.spans dict to save the spans under. During
initialization and training, the component will look for spans on the initialization and training, the component will look for spans on the
reference document under the same key. reference document under the same key.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_spans for the Doc.spans[spans_key] with overlapping
spans allowed.
threshold (float): Minimum probability to consider a prediction positive. threshold (float): Minimum probability to consider a prediction positive.
Spans with a positive prediction will be saved on the Doc. Defaults to Spans with a positive prediction will be saved on the Doc. Defaults to
0.5. 0.5.

6
spacy/pipeline/textcat_multilabel.py
View File

@ -96,8 +96,8 @@ def make_multilabel_textcat(
model: Model[List[Doc], List[Floats2d]], model: Model[List[Doc], List[Floats2d]],
threshold: float, threshold: float,
scorer: Optional[Callable], scorer: Optional[Callable],
) -> "TextCategorizer": ) -> "MultiLabel_TextCategorizer":
"""Create a TextCategorizer component. The text categorizer predicts categories """Create a MultiLabel_TextCategorizer component. The text categorizer predicts categories
over a whole document. It can learn one or more labels, and the labels are considered over a whole document. It can learn one or more labels, and the labels are considered
to be non-mutually exclusive, which means that there can be zero or more labels to be non-mutually exclusive, which means that there can be zero or more labels
per doc). per doc).
@ -105,6 +105,7 @@ def make_multilabel_textcat(
model (Model[List[Doc], List[Floats2d]]): A model instance that predicts model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
scores for each category. scores for each category.
threshold (float): Cutoff to consider a prediction "positive". threshold (float): Cutoff to consider a prediction "positive".
scorer (Optional[Callable]): The scoring method.
""" """
return MultiLabel_TextCategorizer( return MultiLabel_TextCategorizer(
nlp.vocab, model, name, threshold=threshold, scorer=scorer nlp.vocab, model, name, threshold=threshold, scorer=scorer
@ -147,6 +148,7 @@ class MultiLabel_TextCategorizer(TextCategorizer):
name (str): The component instance name, used to add entries to the name (str): The component instance name, used to add entries to the
losses during training. losses during training.
threshold (float): Cutoff to consider a prediction "positive". threshold (float): Cutoff to consider a prediction "positive".
scorer (Optional[Callable]): The scoring method.
DOCS: https://spacy.io/api/textcategorizer#init DOCS: https://spacy.io/api/textcategorizer#init
""" """