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Use spans_ prefix for default span finder scores (#12753)
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@ -53,9 +53,9 @@ DEFAULT_SPAN_FINDER_MODEL = Config().from_str(span_finder_default_config)["model
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"scorer": {"@scorers": "spacy.span_finder_scorer.v1"},
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},
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default_score_weights={
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f"span_finder_{DEFAULT_SPANS_KEY}_f": 1.0,
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f"span_finder_{DEFAULT_SPANS_KEY}_p": 0.0,
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f"span_finder_{DEFAULT_SPANS_KEY}_r": 0.0,
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f"spans_{DEFAULT_SPANS_KEY}_f": 1.0,
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f"spans_{DEFAULT_SPANS_KEY}_p": 0.0,
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f"spans_{DEFAULT_SPANS_KEY}_r": 0.0,
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},
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)
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def make_span_finder(
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@ -104,7 +104,7 @@ def make_span_finder_scorer():
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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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attr_prefix = "span_finder_"
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attr_prefix = "spans_"
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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(
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@ -230,10 +230,10 @@ def test_overfitting_IO():
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# Test scoring
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scores = nlp.evaluate(train_examples)
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assert f"span_finder_{SPANS_KEY}_f" in scores
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assert f"spans_{SPANS_KEY}_f" in scores
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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_{SPANS_KEY}_p"] == 0.75
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assert scores[f"span_finder_{SPANS_KEY}_r"] == 1.0
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assert scores[f"spans_{SPANS_KEY}_p"] == 0.75
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assert scores[f"spans_{SPANS_KEY}_r"] == 1.0
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# also test that the spancat works for just a single entity in a sentence
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doc = nlp("London")
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