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			84 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			84 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
from typing import List
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from thinc.api import Model, Linear, with_array, softmax_activation, padded2list
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from thinc.api import chain, list2padded, configure_normal_init
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from thinc.api import Dropout
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from thinc.types import Floats2d
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from ...tokens import Doc
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from .._biluo import BILUO
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from .._iob import IOB
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from ...util import registry
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@registry.architectures.register("spacy.BiluoTagger.v1")
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def BiluoTagger(
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    tok2vec: Model[List[Doc], List[Floats2d]]
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) -> Model[List[Doc], List[Floats2d]]:
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    biluo = BILUO()
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    linear = Linear(
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        nO=None, nI=tok2vec.get_dim("nO"), init_W=configure_normal_init(mean=0.02)
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    )
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    model = chain(
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        tok2vec,
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        list2padded(),
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        with_array(chain(Dropout(0.1), linear)),
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        biluo,
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        with_array(softmax_activation()),
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        padded2list(),
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    )
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    return Model(
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        "biluo-tagger",
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        forward,
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        init=init,
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        layers=[model, linear],
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        refs={"tok2vec": tok2vec, "linear": linear, "biluo": biluo},
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        dims={"nO": None},
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        attrs={"get_num_actions": biluo.attrs["get_num_actions"]},
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    )
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@registry.architectures.register("spacy.IOBTagger.v1")
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def IOBTagger(
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    tok2vec: Model[List[Doc], List[Floats2d]]
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) -> Model[List[Doc], List[Floats2d]]:
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    biluo = IOB()
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    linear = Linear(nO=None, nI=tok2vec.get_dim("nO"))
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    model = chain(
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        tok2vec,
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        list2padded(),
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        with_array(linear),
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        biluo,
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        with_array(softmax_activation()),
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        padded2list(),
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    )
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    return Model(
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        "iob-tagger",
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        forward,
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        init=init,
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        layers=[model],
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        refs={"tok2vec": tok2vec, "linear": linear, "biluo": biluo},
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        dims={"nO": None},
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        attrs={"get_num_actions": biluo.attrs["get_num_actions"]},
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    )
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def init(model: Model[List[Doc], List[Floats2d]], X=None, Y=None) -> None:
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    if model.get_dim("nO") is None and Y:
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        model.set_dim("nO", Y[0].shape[1])
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    nO = model.get_dim("nO")
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    biluo = model.get_ref("biluo")
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    linear = model.get_ref("linear")
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    biluo.set_dim("nO", nO)
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    if linear.has_dim("nO") is None:
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        linear.set_dim("nO", nO)
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    model.layers[0].initialize(X=X, Y=Y)
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def forward(model: Model, X: List[Doc], is_train: bool):
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    return model.layers[0](X, is_train)
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__all__ = ["BiluoTagger"]
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