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42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
from typing import Callable, List, Tuple
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from thinc.api import Model, chain, with_array
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from thinc.types import Floats1d, Floats2d
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from ...tokens import Doc
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from ...util import registry
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InT = List[Doc]
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OutT = Floats2d
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@registry.architectures("spacy.SpanFinder.v1")
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def build_finder_model(
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tok2vec: Model[InT, List[Floats2d]], scorer: Model[OutT, OutT]
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) -> Model[InT, OutT]:
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logistic_layer: Model[List[Floats2d], List[Floats2d]] = with_array(scorer)
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model: Model[InT, OutT] = chain(tok2vec, logistic_layer, flattener())
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model.set_ref("tok2vec", tok2vec)
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model.set_ref("scorer", scorer)
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model.set_ref("logistic_layer", logistic_layer)
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return model
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def flattener() -> Model[List[Floats2d], Floats2d]:
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"""Flattens the input to a 1-dimensional list of scores"""
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def forward(
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model: Model[Floats1d, Floats1d], X: List[Floats2d], is_train: bool
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) -> Tuple[Floats2d, Callable[[Floats2d], List[Floats2d]]]:
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lens = model.ops.asarray1i([len(doc) for doc in X])
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Y = model.ops.flatten(X)
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def backprop(dY: Floats2d) -> List[Floats2d]:
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return model.ops.unflatten(dY, lens)
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return Y, backprop
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return Model("Flattener", forward=forward)
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