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Merge branch 'feature/vectors' of https://github.com/explosion/spaCy into feature/vectors

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This commit is contained in:
Matthew Honnibal 2020-07-29 14:56:27 +02:00
commit a2d573c039
8 changed files with 12 additions and 10 deletions
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2
spacy/pipeline/entity_linker.py
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@ -387,7 +387,7 @@ class EntityLinker(Pipe):
docs (Iterable[Doc]): The documents to modify.
kb_ids (List[str]): The IDs to set, produced by EntityLinker.predict.
DOCS: https://spacy.io/api/entitylinker#predict
DOCS: https://spacy.io/api/entitylinker#set_annotations
"""
count_ents = len([ent for doc in docs for ent in doc.ents])
if count_ents != len(kb_ids):

2
spacy/pipeline/morphologizer.pyx
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@ -165,7 +165,7 @@ class Morphologizer(Tagger):
docs (Iterable[Doc]): The documents to modify.
batch_tag_ids: The IDs to set, produced by Morphologizer.predict.
DOCS: https://spacy.io/api/morphologizer#predict
DOCS: https://spacy.io/api/morphologizer#set_annotations
"""
if isinstance(docs, Doc):
docs = [docs]

8
spacy/pipeline/pipe.pyx
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@ -32,7 +32,9 @@ class Pipe:
raise NotImplementedError
def __call__(self, Doc doc):
"""Add context-sensitive embeddings to the Doc.tensor attribute.
"""Apply the pipe to one document. The document is modified in place,
and returned. This usually happens under the hood when the nlp object
is called on a text and all components are applied to the Doc.
docs (Doc): The Doc to preocess.
RETURNS (Doc): The processed Doc.
@ -74,9 +76,9 @@ class Pipe:
"""Modify a batch of documents, using pre-computed scores.
docs (Iterable[Doc]): The documents to modify.
tokvecses: The tensors to set, produced by Pipe.predict.
scores: The scores to assign.
DOCS: https://spacy.io/api/pipe#predict
DOCS: https://spacy.io/api/pipe#set_annotations
"""
raise NotImplementedError

2
spacy/pipeline/senter.pyx
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@ -76,7 +76,7 @@ class SentenceRecognizer(Tagger):
docs (Iterable[Doc]): The documents to modify.
batch_tag_ids: The IDs to set, produced by SentenceRecognizer.predict.
DOCS: https://spacy.io/api/sentencerecognizer#predict
DOCS: https://spacy.io/api/sentencerecognizer#set_annotations
"""
if isinstance(docs, Doc):
docs = [docs]

2
spacy/pipeline/tagger.pyx
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@ -145,7 +145,7 @@ class Tagger(Pipe):
docs (Iterable[Doc]): The documents to modify.
batch_tag_ids: The IDs to set, produced by Tagger.predict.
DOCS: https://spacy.io/api/tagger#predict
DOCS: https://spacy.io/api/tagger#set_annotations
"""
if isinstance(docs, Doc):
docs = [docs]

2
spacy/pipeline/textcat.py
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@ -163,7 +163,7 @@ class TextCategorizer(Pipe):
docs (Iterable[Doc]): The documents to modify.
scores: The scores to set, produced by TextCategorizer.predict.
DOCS: https://spacy.io/api/textcategorizer#predict
DOCS: https://spacy.io/api/textcategorizer#set_annotations
"""
for i, doc in enumerate(docs):
for j, label in enumerate(self.labels):

2
spacy/pipeline/tok2vec.py
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@ -109,7 +109,7 @@ class Tok2Vec(Pipe):
docs (Iterable[Doc]): The documents to modify.
tokvecses: The tensors to set, produced by Tok2Vec.predict.
DOCS: https://spacy.io/api/tok2vec#predict
DOCS: https://spacy.io/api/tok2vec#set_annotations
"""
for doc, tokvecs in zip(docs, tokvecses):
assert tokvecs.shape[0] == len(doc)

2
spacy/tokens/_serialize.py
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@ -50,7 +50,7 @@ class DocBin:
self,
attrs: Iterable[str] = ALL_ATTRS,
store_user_data: bool = False,
docs: Iterable[Doc]=[],
docs: Iterable[Doc] = tuple(),
) -> None:
"""Create a DocBin object to hold serialized annotations.