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add docstrings

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This commit is contained in:
kadarakos 2023-01-31 17:06:20 +00:00
parent 079f09b97c
commit edf9134e45
1 changed files with 18 additions and 8 deletions
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26
spacy/pipeline/spancat.py
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@ -149,7 +149,9 @@ def make_spancat(
threshold: float,
max_positive: Optional[int],
) -> "SpanCategorizer":
"""Create a SpanCategorizer component. The span categorizer consists of two
"""Create a SpanCategorizer component and configure it for multilabel
classification to be able to assign multiple labels for each span.
The span categorizer consists of two
parts: a suggester function that proposes candidate spans, and a labeller
model that predicts one or more labels for each span.
@ -207,7 +209,9 @@ def make_spancat_singlelabel(
allow_overlap: bool,
scorer: Optional[Callable],
) -> "SpanCategorizer":
"""Create a SpanCategorizer component. The span categorizer consists of two
"""Create a SpanCategorizer component and configure it for multiclass
classification. With this configuration each span can get at most one
label. The span categorizer consists of two
parts: a suggester function that proposes candidate spans, and a labeller
model that predicts one or more labels for each span.
@ -224,11 +228,11 @@ def make_spancat_singlelabel(
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.
Spans with a positive prediction will be saved on the Doc. Defaults to
0.5.
max_positive (Optional[int]): Maximum number of labels to consider 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.
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.
"""
return SpanCategorizer(
nlp.vocab,
@ -317,11 +321,16 @@ class SpanCategorizer(TrainablePipe):
During initialization and training, the component will look for
spans on the reference document under the same key. Defaults to
`"spans"`.
threshold (float): Minimum probability to consider a prediction
threshold (Optional[float]): Minimum probability to consider a prediction
positive. Spans with a positive prediction will be saved on the Doc.
Defaults to 0.5.
max_positive (Optional[int]): Maximum number of labels to consider
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.
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.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_spans for the Doc.spans[spans_key] with overlapping
spans allowed.
@ -640,6 +649,7 @@ class SpanCategorizer(TrainablePipe):
indices: Ints2d,
scores: Floats2d,
labels: List[str],
# XXX Unused, does it make sense?
allow_overlap: bool = True,
) -> SpanGroup:
spans = SpanGroup(doc, name=self.key)