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Update span predictor docstrings
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@ -95,6 +95,8 @@ class SpanPredictor(TrainablePipe):
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"""Pipeline component to resolve one-token spans to full spans.
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"""Pipeline component to resolve one-token spans to full spans.
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Used in coreference resolution.
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Used in coreference resolution.
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DOCS: https://spacy.io/api/span_predictor
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"""
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"""
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def __init__(
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def __init__(
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@ -119,6 +121,14 @@ class SpanPredictor(TrainablePipe):
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}
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}
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def predict(self, docs: Iterable[Doc]) -> List[MentionClusters]:
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def predict(self, docs: Iterable[Doc]) -> List[MentionClusters]:
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"""Apply the pipeline's model to a batch of docs, without modifying them.
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Return the list of predicted span clusters.
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docs (Iterable[Doc]): The documents to predict.
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RETURNS (List[MentionClusters]): The model's prediction for each document.
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DOCS: https://spacy.io/api/span_predictor#predict
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"""
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# for now pretend there's just one doc
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# for now pretend there's just one doc
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out = []
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out = []
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@ -151,6 +161,13 @@ class SpanPredictor(TrainablePipe):
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return out
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return out
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def set_annotations(self, docs: Iterable[Doc], clusters_by_doc) -> None:
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def set_annotations(self, docs: Iterable[Doc], clusters_by_doc) -> None:
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"""Modify a batch of Doc objects, using pre-computed scores.
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docs (Iterable[Doc]): The documents to modify.
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clusters: The span clusters, produced by SpanPredictor.predict.
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DOCS: https://spacy.io/api/span_predictor#set_annotations
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"""
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for doc, clusters in zip(docs, clusters_by_doc):
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for doc, clusters in zip(docs, clusters_by_doc):
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for ii, cluster in enumerate(clusters):
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for ii, cluster in enumerate(clusters):
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spans = [doc[mm[0] : mm[1]] for mm in cluster]
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spans = [doc[mm[0] : mm[1]] for mm in cluster]
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@ -166,6 +183,15 @@ class SpanPredictor(TrainablePipe):
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) -> Dict[str, float]:
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) -> Dict[str, float]:
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"""Learn from a batch of documents and gold-standard information,
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"""Learn from a batch of documents and gold-standard information,
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updating the pipe's model. Delegates to predict and get_loss.
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updating the pipe's model. Delegates to predict and get_loss.
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examples (Iterable[Example]): A batch of Example objects.
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drop (float): The dropout rate.
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sgd (thinc.api.Optimizer): The optimizer.
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losses (Dict[str, float]): Optional record of the loss during training.
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Updated using the component name as the key.
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RETURNS (Dict[str, float]): The updated losses dictionary.
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DOCS: https://spacy.io/api/span_predictor#update
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"""
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"""
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if losses is None:
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if losses is None:
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losses = {}
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losses = {}
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@ -222,6 +248,15 @@ class SpanPredictor(TrainablePipe):
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examples: Iterable[Example],
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examples: Iterable[Example],
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span_scores: Floats3d,
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span_scores: Floats3d,
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):
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):
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"""Find the loss and gradient of loss for the batch of documents and
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their predicted scores.
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examples (Iterable[Examples]): The batch of examples.
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scores: Scores representing the model's predictions.
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RETURNS (Tuple[float, float]): The loss and the gradient.
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DOCS: https://spacy.io/api/span_predictor#get_loss
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"""
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ops = self.model.ops
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ops = self.model.ops
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# NOTE This is doing fake batching, and should always get a list of one example
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# NOTE This is doing fake batching, and should always get a list of one example
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@ -258,6 +293,15 @@ class SpanPredictor(TrainablePipe):
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*,
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*,
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nlp: Optional[Language] = None,
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nlp: Optional[Language] = None,
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) -> None:
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) -> None:
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"""Initialize the pipe for training, using a representative set
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of data examples.
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get_examples (Callable[[], Iterable[Example]]): Function that
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returns a representative sample of gold-standard Example objects.
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nlp (Language): The current nlp object the component is part of.
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DOCS: https://spacy.io/api/span_predictor#initialize
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"""
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validate_get_examples(get_examples, "SpanPredictor.initialize")
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validate_get_examples(get_examples, "SpanPredictor.initialize")
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X = []
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X = []
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