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Fix mypy errors
However, I ignored line 370 because it opened up a bunch of type errors that might be trickier to solve and might lead to a more complicated codebase.
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@ -268,7 +268,7 @@ class SpanCategorizerExclusive(TrainablePipe):
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DOCS: https://spacy.io/api/spancategorizerexclusive#set_annotations
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DOCS: https://spacy.io/api/spancategorizerexclusive#set_annotations
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"""
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"""
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allow_overlap = self.cfg["allow_overlap"]
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allow_overlap = cast(bool, self.cfg["allow_overlap"])
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labels = self.labels
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labels = self.labels
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indices, scores = indices_scores
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indices, scores = indices_scores
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offset = 0
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offset = 0
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@ -278,9 +278,9 @@ class SpanCategorizerExclusive(TrainablePipe):
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doc,
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doc,
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indices_i,
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indices_i,
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scores[offset : offset + indices.lengths[i]],
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scores[offset : offset + indices.lengths[i]],
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labels,
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labels, # type: ignore[arg-type]
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allow_overlap,
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allow_overlap,
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) # type: ignore[arg-type]
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)
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offset += indices.lengths[i]
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offset += indices.lengths[i]
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def update(
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def update(
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@ -367,9 +367,9 @@ class SpanCategorizerExclusive(TrainablePipe):
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offset += spans.lengths[i]
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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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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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negative_samples = numpy.nonzero(negative_spans)[0]
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target[negative_samples, self._negative_label] = 1.0
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target[negative_samples, self._negative_label] = 1.0 # type: ignore
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d_scores = scores - target
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d_scores = scores - target
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neg_weight = self.cfg["negative_weight"]
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neg_weight = cast(float, self.cfg["negative_weight"])
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d_scores[negative_samples] *= neg_weight
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d_scores[negative_samples] *= neg_weight
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loss = float((d_scores**2).sum())
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loss = float((d_scores**2).sum())
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return loss, d_scores
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return loss, d_scores
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