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[ci skip] Add breakpoint for debugging
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@ -79,9 +79,9 @@ def make_spancat(
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spans_key: str,
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scorer: Optional[Callable],
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negative_weight: float = 1.0,
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allow_overlap: Optional[bool] = True,
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allow_overlap: bool = True,
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) -> "SpanCategorizerExclusive":
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"""Create a SpanCategorizer component. The span categorizer consists of two
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"""Create a SpanCategorizerExclusive component. The span categorizer consists of two
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parts: a suggester function that proposes candidate spans, and a labeller
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model that predicts one or more labels for each span.
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@ -97,7 +97,7 @@ def make_spancat(
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reference document under the same key.
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negative_weight (float): Multiplier for the loss terms.
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Can be used to down weigh the negative samples if there are too many.
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allow_overlap (Optional[bool]): If True the data is assumed to
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allow_overlap (bool): If True the data is assumed to
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contain overlapping spans.
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"""
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return SpanCategorizerExclusive(
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@ -148,8 +148,8 @@ class SpanCategorizerExclusive(TrainablePipe):
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*,
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spans_key: str = "spans",
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negative_weight: float = 1.0,
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allow_overlap: bool = True,
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scorer: Optional[Callable] = spancat_score,
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allow_overlap: Optional[bool] = True,
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) -> None:
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"""Initialize the span categorizer.
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vocab (Vocab): The shared vocabulary.
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@ -162,11 +162,11 @@ class SpanCategorizerExclusive(TrainablePipe):
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`"spans"`.
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negative_weight (float): Multiplier for the loss terms.
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Can be used to down weigh the negative samples if there are too many.
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allow_overlap (bool): If True the data is assumed to
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contain overlapping spans.
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scorer (Optional[Callable]): The scoring method. Defaults to
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Scorer.score_spans for the Doc.spans[spans_key] with overlapping
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spans allowed.
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allow_overlap (Optional[bool]): If True the data is assumed to
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contain overlapping spans.
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DOCS: https://spacy.io/api/spancategorizerexclusive#init
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"""
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@ -376,6 +376,7 @@ class SpanCategorizerExclusive(TrainablePipe):
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offset += spans.lengths[i]
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target = self.model.ops.asarray(target, dtype="f") # type: ignore
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negative_samples = numpy.nonzero(negative_spans)[0]
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breakpoint()
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target[negative_samples, self._negative_label] = 1.0
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d_scores = scores - target
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neg_weight = self.cfg["negative_weight"]
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