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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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13
spacy/pipeline/spancat.py
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@ -317,7 +317,8 @@ class SpanCategorizer(TrainablePipe):
scorer: Optional[Callable] = spancat_score,
) -> None:
"""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
can produce multiple labels per span (multi-label). In the
multi-label case the classification layer is expected to be
@ -325,6 +326,9 @@ class SpanCategorizer(TrainablePipe):
vocab (Vocab): The shared vocabulary.
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.
Spans are returned as a ragged array with two integer columns, for the
start and end positions.
@ -340,14 +344,13 @@ class SpanCategorizer(TrainablePipe):
positive. Defaults to 0.5. Spans with a positive prediction will be saved
on the Doc.
max_positive (Optional[int]): Maximum number of labels to consider
positive per span. Defaults to None, indicating no limit. This is
unused when single_label is True.
positive per span. Defaults to None, indicating no limit.
negative_weight (float): Multiplier for the loss terms.
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.
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.score_spans for the Doc.spans[spans_key] with overlapping
spans allowed.