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remove references to 'single_label'

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kadarakos 2023-03-08 11:36:33 +00:00
parent efea793248
commit a5c407f11e
1 changed files with 8 additions and 5 deletions
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spacy/pipeline/spancat.py
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@ -317,7 +317,8 @@ class SpanCategorizer(TrainablePipe):
scorer: Optional[Callable] = spancat_score, scorer: Optional[Callable] = spancat_score,
) -> None: ) -> None:
"""Initialize the multi-label or multi-class span categorizer. """Initialize the multi-label or multi-class span categorizer.
The 'single_label' argument configures whether the component
argument configures whether the component
should only produce one label per span (multi-class) or if it should only produce one label per span (multi-class) or if it
can produce multiple labels per span (multi-label). In the can produce multiple labels per span (multi-label). In the
multi-label case the classification layer is expected to be multi-label case the classification layer is expected to be
@ -325,6 +326,9 @@ class SpanCategorizer(TrainablePipe):
vocab (Vocab): The shared vocabulary. vocab (Vocab): The shared vocabulary.
model (thinc.api.Model): The Thinc Model powering the pipeline component. model (thinc.api.Model): The Thinc Model powering the pipeline component.
For multi-class classification (single label per span) we recommend
using a Softmax classifier as a the final layer, while for multi-label
classification (multiple possible labels per span) we recommend Logistic.
suggester (Callable[[Iterable[Doc], Optional[Ops]], Ragged]): A function that suggests spans. suggester (Callable[[Iterable[Doc], Optional[Ops]], Ragged]): A function that suggests spans.
Spans are returned as a ragged array with two integer columns, for the Spans are returned as a ragged array with two integer columns, for the
start and end positions. start and end positions.
@ -340,14 +344,13 @@ class SpanCategorizer(TrainablePipe):
positive. Defaults to 0.5. Spans with a positive prediction will be saved positive. Defaults to 0.5. Spans with a positive prediction will be saved
on the Doc. on the Doc.
max_positive (Optional[int]): Maximum number of labels to consider max_positive (Optional[int]): Maximum number of labels to consider
positive per span. Defaults to None, indicating no limit. This is positive per span. Defaults to None, indicating no limit.
unused when single_label is True.
negative_weight (float): Multiplier for the loss terms. negative_weight (float): Multiplier for the loss terms.
Can be used to downweight the negative samples if there are too many Can be used to downweight the negative samples if there are too many
when single_label is True. Otherwise its unused. when add_negative_label is True. Otherwise its unused.
allow_overlap (bool): If True the data is assumed to contain overlapping spans. allow_overlap (bool): If True the data is assumed to contain overlapping spans.
Otherwise it produces non-overlapping spans greedily prioritizing Otherwise it produces non-overlapping spans greedily prioritizing
higher assigned label scores. Only used when single_label is True. higher assigned label scores. Only used when max_positive is 1.
scorer (Optional[Callable]): The scoring method. Defaults to scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_spans for the Doc.spans[spans_key] with overlapping Scorer.score_spans for the Doc.spans[spans_key] with overlapping
spans allowed. spans allowed.