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more docstring and fix negative_label

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kadarakos 2023-02-01 11:16:34 +00:00
parent edf9134e45
commit 5ccb154972
1 changed files with 23 additions and 11 deletions
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34
spacy/pipeline/spancat.py
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@ -1,4 +1,4 @@
from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any, cast
from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any, cast, Union
from dataclasses import dataclass
from thinc.api import Config, Model, get_current_ops, set_dropout_rate, Ops
from thinc.api import Optimizer
@ -309,7 +309,12 @@ class SpanCategorizer(TrainablePipe):
threshold: Optional[float] = None,
scorer: Optional[Callable] = spancat_score,
) -> None:
"""Initialize the span categorizer.
"""Initialize the multilabel or multiclass span categorizer.
The 'single_label' argument configures whether the component
should only produce one label per span (multiclass) or if it
can produce multiple labels per span (multilabel). In the
multilabel case the classification layer is expected to be
Logistic and Softmax in the multiclass case.
vocab (Vocab): The shared vocabulary.
model (thinc.api.Model): The Thinc Model powering the pipeline component.
suggester (Callable[[Iterable[Doc], Optional[Ops]], Ragged]): A function that suggests spans.
@ -321,16 +326,23 @@ class SpanCategorizer(TrainablePipe):
During initialization and training, the component will look for
spans on the reference document under the same key. Defaults to
`"spans"`.
single_label (bool): Whether to configure SpanCategorizer to produce
a single label per span. In this case its expected that the scorer
layer is Softmax. Otherwise its expected to be Logistic. When single_label
is true the SpanCategorizer internally has a negative-label indicating
that a span should not receive any of the labels found in the corpus.
threshold (Optional[float]): Minimum probability to consider a prediction
positive. Spans with a positive prediction will be saved on the Doc.
Defaults to 0.5.
positive in the multilabel usecase.Defaults to 0.5 when single_label is
False otherwise its None. Spans with a positive prediction will be saved on the Doc.
max_positive (Optional[int]): Maximum number of labels to consider
positive per span. Defaults to None, indicating no limit.
positive per span. Defaults to None, indicating no limit. This is
unused when single_label is True.
negative_weight (float): Multiplier for the loss terms.
Can be used to downweight the negative samples if there are too many.
Can be used to downweight the negative samples if there are too many
when single_label is True. Otherwise its unused.
allow_overlap (bool): If True the data is assumed to contain overlapping spans.
Otherwise it produces non-overlapping spans greedily prioritizing
higher assigned label scores.
higher assigned label scores. Only used when single_label is True.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_spans for the Doc.spans[spans_key] with overlapping
spans allowed.
@ -422,12 +434,12 @@ class SpanCategorizer(TrainablePipe):
return len(self.labels)
@property
def _negative_label(self) -> int:
"""RETURNS (int): Index of the negative label."""
def _negative_label(self) -> Union[int, None]:
"""RETURNS (Union[int, None]): Index of the negative label."""
if self.single_label:
return -1
else:
return len(self.label_data)
else:
return None
def predict(self, docs: Iterable[Doc]):
"""Apply the pipeline's model to a batch of docs, without modifying them.