mirror of https://github.com/explosion/spaCy.git
Allow more components to use labels
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@ -160,16 +160,12 @@ class TextCategorizer(Pipe):
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self.cfg["labels"] = tuple(value)
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self.cfg["labels"] = tuple(value)
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@property
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@property
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def label_data(self) -> Dict:
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def label_data(self) -> List[str]:
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"""RETURNS (Dict): Information about the component's labels.
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"""RETURNS (List[str]): Information about the component's labels.
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DOCS: https://nightly.spacy.io/api/textcategorizer#labels
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DOCS: https://nightly.spacy.io/api/textcategorizer#labels
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"""
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"""
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return {
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return self.labels
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"labels": self.labels,
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"positive": self.cfg["positive_label"],
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"threshold": self.cfg["threshold"]
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}
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def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
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def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
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"""Apply the pipe to a stream of documents. This usually happens under
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"""Apply the pipe to a stream of documents. This usually happens under
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@ -354,6 +350,7 @@ class TextCategorizer(Pipe):
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get_examples: Callable[[], Iterable[Example]],
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get_examples: Callable[[], Iterable[Example]],
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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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labels: Optional[Dict] = None
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):
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):
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"""Initialize the pipe for training, using a representative set
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"""Initialize the pipe for training, using a representative set
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of data examples.
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of data examples.
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@ -365,12 +362,14 @@ class TextCategorizer(Pipe):
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DOCS: https://nightly.spacy.io/api/textcategorizer#initialize
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DOCS: https://nightly.spacy.io/api/textcategorizer#initialize
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"""
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"""
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self._ensure_examples(get_examples)
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self._ensure_examples(get_examples)
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subbatch = [] # Select a subbatch of examples to initialize the model
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if labels is None:
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for example in islice(get_examples(), 10):
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for example in get_examples():
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if len(subbatch) < 2:
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subbatch.append(example)
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for cat in example.y.cats:
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for cat in example.y.cats:
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self.add_label(cat)
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self.add_label(cat)
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else:
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for label in labels:
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self.add_label(label)
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subbatch = list(islice(get_examples(), 10))
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doc_sample = [eg.reference for eg in subbatch]
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doc_sample = [eg.reference for eg in subbatch]
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label_sample, _ = self._examples_to_truth(subbatch)
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label_sample, _ = self._examples_to_truth(subbatch)
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self._require_labels()
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self._require_labels()
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@ -409,12 +409,15 @@ cdef class Parser(Pipe):
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def set_output(self, nO):
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def set_output(self, nO):
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self.model.attrs["resize_output"](self.model, nO)
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self.model.attrs["resize_output"](self.model, nO)
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def initialize(self, get_examples, nlp=None):
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def initialize(self, get_examples, *, nlp=None, labels=None):
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self._ensure_examples(get_examples)
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self._ensure_examples(get_examples)
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lexeme_norms = self.vocab.lookups.get_table("lexeme_norm", {})
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lexeme_norms = self.vocab.lookups.get_table("lexeme_norm", {})
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if len(lexeme_norms) == 0 and self.vocab.lang in util.LEXEME_NORM_LANGS:
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if len(lexeme_norms) == 0 and self.vocab.lang in util.LEXEME_NORM_LANGS:
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langs = ", ".join(util.LEXEME_NORM_LANGS)
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langs = ", ".join(util.LEXEME_NORM_LANGS)
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util.logger.debug(Warnings.W033.format(model="parser or NER", langs=langs))
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util.logger.debug(Warnings.W033.format(model="parser or NER", langs=langs))
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if labels is not None:
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actions = dict(labels)
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else:
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actions = self.moves.get_actions(
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actions = self.moves.get_actions(
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examples=get_examples(),
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examples=get_examples(),
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min_freq=self.cfg['min_action_freq'],
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min_freq=self.cfg['min_action_freq'],
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