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remove near duplicate reduntant method
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@ -100,7 +100,6 @@ def make_span_finder_scorer():
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def span_finder_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
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def span_finder_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
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kwargs = dict(kwargs)
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kwargs = dict(kwargs)
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print(kwargs)
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attr_prefix = "span_finder_"
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attr_prefix = "span_finder_"
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key = kwargs["spans_key"]
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key = kwargs["spans_key"]
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kwargs.setdefault("attr", f"{attr_prefix}{key}")
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kwargs.setdefault("attr", f"{attr_prefix}{key}")
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@ -295,28 +294,6 @@ class SpanFinder(TrainablePipe):
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masks = ops.xp.concatenate(masks, axis=0)
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masks = ops.xp.concatenate(masks, axis=0)
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return truths, masks
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return truths, masks
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def _get_reference(self, docs) -> List[Tuple[int, int]]:
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"""Create a reference list of token probabilities"""
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reference_probabilities = []
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for doc in docs:
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start_indices = set()
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end_indices = set()
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if self.spans_key in doc.spans:
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for span in doc.spans[self.spans_key]:
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start_indices.add(span.start)
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end_indices.add(span.end - 1)
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for token in doc:
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reference_probabilities.append(
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(
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1 if token.i in start_indices else 0,
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1 if token.i in end_indices else 0,
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)
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)
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return reference_probabilities
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def initialize(
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def initialize(
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self,
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self,
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get_examples: Callable[[], Iterable[Example]],
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get_examples: Callable[[], Iterable[Example]],
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@ -337,7 +314,7 @@ class SpanFinder(TrainablePipe):
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if subbatch:
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if subbatch:
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docs = [eg.reference for eg in subbatch]
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docs = [eg.reference for eg in subbatch]
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Y = self.model.ops.asarray2f(self._get_reference(docs))
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Y, _ = self._get_aligned_truth_scores(subbatch, self.model.ops)
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self.model.initialize(X=docs, Y=Y)
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self.model.initialize(X=docs, Y=Y)
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else:
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else:
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self.model.initialize()
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self.model.initialize()
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