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Remove debugging

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This commit is contained in:
Adriane Boyd 2023-06-05 12:46:01 +02:00
parent dac12fb684
commit d52d7d9c87
1 changed files with 10 additions and 8 deletions
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18
spacy/pipeline/span_finder.py
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@ -138,18 +138,21 @@ class SpanFinder(TrainablePipe):
min_length: Optional[int] = None, min_length: Optional[int] = None,
scorer: Optional[Callable] = span_finder_score, scorer: Optional[Callable] = span_finder_score,
) -> None: ) -> None:
"""Initialize the span boundary detector. """Initialize the span finder.
model (thinc.api.Model): The Thinc Model powering the pipeline component. model (thinc.api.Model): The Thinc Model powering the pipeline
component.
name (str): The component instance name, used to add entries to the name (str): The component instance name, used to add entries to the
losses during training. losses during training.
threshold (float): Minimum probability to consider a prediction threshold (float): Minimum probability to consider a prediction
positive. positive.
scorer (Optional[Callable]): The scoring method. scorer (Optional[Callable]): The scoring method.
spans_key (str): Key of the doc.spans dict to save the spans under. During spans_key (str): Key of the doc.spans dict to save the spans under.
initialization and training, the component will look for spans on the During initialization and training, the component will look for
reference document under the same key. spans on the reference document under the same key.
max_length (Optional[int]): Max length of the produced spans, defaults to None meaning unlimited length. max_length (Optional[int]): Maximum length of the produced spans,
min_length (Optional[int]): Min length of the produced spans, defaults to None meaning shortest span is length 1. defaults to None meaning unlimited length.
min_length (Optional[int]): Minimum length of the produced spans,
defaults to None meaning shortest span length is 1.
""" """
self.vocab = nlp.vocab self.vocab = nlp.vocab
if (max_length is not None and max_length < 1) or ( if (max_length is not None and max_length < 1) or (
@ -194,7 +197,6 @@ class SpanFinder(TrainablePipe):
if token_score[1] >= self.cfg["threshold"]: if token_score[1] >= self.cfg["threshold"]:
ends.append(token.i) ends.append(token.i)
print(self.cfg)
for start in starts: for start in starts:
for end in ends: for end in ends:
span_length = end + 1 - start span_length = end + 1 - start