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e2b70df012
* Use isort with Black profile * isort all the things * Fix import cycles as a result of import sorting * Add DOCBIN_ALL_ATTRS type definition * Add isort to requirements * Remove isort from build dependencies check * Typo
52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
from thinc.api import Model, noop
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from ..util import registry
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from .parser_model import ParserStepModel
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@registry.layers("spacy.TransitionModel.v1")
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def TransitionModel(
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tok2vec, lower, upper, resize_output, dropout=0.2, unseen_classes=set()
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):
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"""Set up a stepwise transition-based model"""
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if upper is None:
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has_upper = False
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upper = noop()
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else:
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has_upper = True
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# don't define nO for this object, because we can't dynamically change it
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return Model(
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name="parser_model",
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forward=forward,
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dims={"nI": tok2vec.maybe_get_dim("nI")},
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layers=[tok2vec, lower, upper],
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refs={"tok2vec": tok2vec, "lower": lower, "upper": upper},
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init=init,
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attrs={
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"has_upper": has_upper,
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"unseen_classes": set(unseen_classes),
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"resize_output": resize_output,
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},
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)
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def forward(model, X, is_train):
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step_model = ParserStepModel(
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X,
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model.layers,
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unseen_classes=model.attrs["unseen_classes"],
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train=is_train,
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has_upper=model.attrs["has_upper"],
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)
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return step_model, step_model.finish_steps
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def init(model, X=None, Y=None):
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model.get_ref("tok2vec").initialize(X=X)
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lower = model.get_ref("lower")
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lower.initialize()
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if model.attrs["has_upper"]:
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statevecs = model.ops.alloc2f(2, lower.get_dim("nO"))
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model.get_ref("upper").initialize(X=statevecs)
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