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@ -107,7 +107,9 @@ def span_finder_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
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"getter", lambda doc, key: doc.spans.get(key[len(attr_prefix) :], [])
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"getter", lambda doc, key: doc.spans.get(key[len(attr_prefix) :], [])
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)
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)
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kwargs.setdefault("has_annotation", lambda doc: key in doc.spans)
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kwargs.setdefault("has_annotation", lambda doc: key in doc.spans)
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return Scorer.score_spans(examples, **kwargs)
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scores = Scorer.score_spans(examples, **kwargs)
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scores.pop(f"{kwargs['attr']}_per_type", None)
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return scores
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class _MaxInt(int):
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class _MaxInt(int):
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@ -135,7 +135,6 @@ def test_span_finder_component():
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nlp.initialize()
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nlp.initialize()
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docs = list(span_finder.pipe(docs))
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docs = list(span_finder.pipe(docs))
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# TODO: update hard-coded name
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assert SPANS_KEY in docs[0].spans
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assert SPANS_KEY in docs[0].spans
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@ -252,7 +251,7 @@ def test_overfitting_IO():
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# Test scoring
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# Test scoring
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scores = nlp.evaluate(train_examples)
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scores = nlp.evaluate(train_examples)
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assert f"span_finder_{span_finder.spans_key}_f" in scores
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assert f"span_finder_{span_finder.spans_key}_f" in scores
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# XXX Its not perfect 1.0 F1 because we want it to overgenerate for now.
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# It's not perfect 1.0 F1 because it's designed to overgenerate for now.
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assert scores[f"span_finder_{span_finder.spans_key}_f"] == 0.4
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assert scores[f"span_finder_{span_finder.spans_key}_f"] == 0.4
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assert scores[f"span_finder_{span_finder.spans_key}_r"] == 1.0
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assert scores[f"span_finder_{span_finder.spans_key}_r"] == 1.0
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