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Use spans_ prefix for default span finder scores (#12753)

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Adriane Boyd 2023-06-27 19:32:17 +02:00 committed by GitHub
parent 65f6c9cd10
commit 337a360cc7
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2 changed files with 7 additions and 7 deletions
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8
spacy/pipeline/span_finder.py
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@ -53,9 +53,9 @@ DEFAULT_SPAN_FINDER_MODEL = Config().from_str(span_finder_default_config)["model
"scorer": {"@scorers": "spacy.span_finder_scorer.v1"}, "scorer": {"@scorers": "spacy.span_finder_scorer.v1"},
}, },
default_score_weights={ default_score_weights={
f"span_finder_{DEFAULT_SPANS_KEY}_f": 1.0, f"spans_{DEFAULT_SPANS_KEY}_f": 1.0,
f"span_finder_{DEFAULT_SPANS_KEY}_p": 0.0, f"spans_{DEFAULT_SPANS_KEY}_p": 0.0,
f"span_finder_{DEFAULT_SPANS_KEY}_r": 0.0, f"spans_{DEFAULT_SPANS_KEY}_r": 0.0,
}, },
) )
def make_span_finder( def make_span_finder(
@ -104,7 +104,7 @@ def make_span_finder_scorer():
def span_finder_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]: def span_finder_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
kwargs = dict(kwargs) kwargs = dict(kwargs)
attr_prefix = "span_finder_" attr_prefix = "spans_"
key = kwargs["spans_key"] key = kwargs["spans_key"]
kwargs.setdefault("attr", f"{attr_prefix}{key}") kwargs.setdefault("attr", f"{attr_prefix}{key}")
kwargs.setdefault( kwargs.setdefault(

6
spacy/tests/pipeline/test_span_finder.py
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@ -230,10 +230,10 @@ def test_overfitting_IO():
# Test scoring # Test scoring
scores = nlp.evaluate(train_examples) scores = nlp.evaluate(train_examples)
assert f"span_finder_{SPANS_KEY}_f" in scores assert f"spans_{SPANS_KEY}_f" in scores
# It's not perfect 1.0 F1 because it's designed to overgenerate for now. # It's not perfect 1.0 F1 because it's designed to overgenerate for now.
assert scores[f"span_finder_{SPANS_KEY}_p"] == 0.75 assert scores[f"spans_{SPANS_KEY}_p"] == 0.75
assert scores[f"span_finder_{SPANS_KEY}_r"] == 1.0 assert scores[f"spans_{SPANS_KEY}_r"] == 1.0
# also test that the spancat works for just a single entity in a sentence # also test that the spancat works for just a single entity in a sentence
doc = nlp("London") doc = nlp("London")