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Fix missing imports
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0fc13b2f14
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@ -1,8 +1,9 @@
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# cython: infer_types=True, profile=True, binding=True
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# cython: infer_types=True, profile=True, binding=True
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from typing import List
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import numpy
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import numpy
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import srsly
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import srsly
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from thinc.api import Model, set_dropout_rate, SequenceCategoricalCrossentropy, Config
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from thinc.api import Model, set_dropout_rate, SequenceCategoricalCrossentropy, Config
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from thinc.types import Floats2d
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import warnings
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import warnings
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from ..tokens.doc cimport Doc
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from ..tokens.doc cimport Doc
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@ -1,5 +1,6 @@
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from typing import Iterable, Tuple, Optional, Dict, List, Callable, Iterator, Any
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from typing import Iterable, Tuple, Optional, Dict, List, Callable, Iterator, Any
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from thinc.api import get_array_module, Model, Optimizer, set_dropout_rate, Config
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from thinc.api import get_array_module, Model, Optimizer, set_dropout_rate, Config
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from thinc.types import Floats2d
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import numpy
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import numpy
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from .pipe import Pipe
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from .pipe import Pipe
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@ -69,7 +70,10 @@ subword_features = true
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default_score_weights={"cats_score": 1.0},
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default_score_weights={"cats_score": 1.0},
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)
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)
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def make_textcat(
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def make_textcat(
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nlp: Language, name: str, model: Model[List[Doc], List[Floats2d]], labels: Iterable[str]
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nlp: Language,
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name: str,
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model: Model[List[Doc], List[Floats2d]],
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labels: Iterable[str],
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) -> "TextCategorizer":
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) -> "TextCategorizer":
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"""Create a TextCategorizer compoment. The text categorizer predicts categories
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"""Create a TextCategorizer compoment. The text categorizer predicts categories
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over a whole document. It can learn one or more labels, and the labels can
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over a whole document. It can learn one or more labels, and the labels can
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