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[ci skip] Add breakpoint for debugging

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This commit is contained in:
Lj Miranda 2022-08-25 13:23:15 +08:00
parent 5452e71b05
commit d6e56b62b9
1 changed files with 7 additions and 6 deletions
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13
spacy/pipeline/spancat_exclusive.py
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@ -79,9 +79,9 @@ def make_spancat(
spans_key: str, spans_key: str,
scorer: Optional[Callable], scorer: Optional[Callable],
negative_weight: float = 1.0, negative_weight: float = 1.0,
allow_overlap: Optional[bool] = True, allow_overlap: bool = True,
) -> "SpanCategorizerExclusive": ) -> "SpanCategorizerExclusive":
"""Create a SpanCategorizer component. The span categorizer consists of two """Create a SpanCategorizerExclusive component. The span categorizer consists of two
parts: a suggester function that proposes candidate spans, and a labeller parts: a suggester function that proposes candidate spans, and a labeller
model that predicts one or more labels for each span. model that predicts one or more labels for each span.
@ -97,7 +97,7 @@ def make_spancat(
reference document under the same key. reference document under the same key.
negative_weight (float): Multiplier for the loss terms. negative_weight (float): Multiplier for the loss terms.
Can be used to down weigh the negative samples if there are too many. Can be used to down weigh the negative samples if there are too many.
allow_overlap (Optional[bool]): If True the data is assumed to allow_overlap (bool): If True the data is assumed to
contain overlapping spans. contain overlapping spans.
""" """
return SpanCategorizerExclusive( return SpanCategorizerExclusive(
@ -148,8 +148,8 @@ class SpanCategorizerExclusive(TrainablePipe):
*, *,
spans_key: str = "spans", spans_key: str = "spans",
negative_weight: float = 1.0, negative_weight: float = 1.0,
allow_overlap: bool = True,
scorer: Optional[Callable] = spancat_score, scorer: Optional[Callable] = spancat_score,
allow_overlap: Optional[bool] = True,
) -> None: ) -> None:
"""Initialize the span categorizer. """Initialize the span categorizer.
vocab (Vocab): The shared vocabulary. vocab (Vocab): The shared vocabulary.
@ -162,11 +162,11 @@ class SpanCategorizerExclusive(TrainablePipe):
`"spans"`. `"spans"`.
negative_weight (float): Multiplier for the loss terms. negative_weight (float): Multiplier for the loss terms.
Can be used to down weigh the negative samples if there are too many. Can be used to down weigh the negative samples if there are too many.
allow_overlap (bool): If True the data is assumed to
contain overlapping spans.
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.
allow_overlap (Optional[bool]): If True the data is assumed to
contain overlapping spans.
DOCS: https://spacy.io/api/spancategorizerexclusive#init DOCS: https://spacy.io/api/spancategorizerexclusive#init
""" """
@ -376,6 +376,7 @@ class SpanCategorizerExclusive(TrainablePipe):
offset += spans.lengths[i] offset += spans.lengths[i]
target = self.model.ops.asarray(target, dtype="f") # type: ignore target = self.model.ops.asarray(target, dtype="f") # type: ignore
negative_samples = numpy.nonzero(negative_spans)[0] negative_samples = numpy.nonzero(negative_spans)[0]
breakpoint()
target[negative_samples, self._negative_label] = 1.0 target[negative_samples, self._negative_label] = 1.0
d_scores = scores - target d_scores = scores - target
neg_weight = self.cfg["negative_weight"] neg_weight = self.cfg["negative_weight"]