78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
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from typing import List, Tuple, cast
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from thinc.api import (
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Linear,
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Logistic,
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Maxout,
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Model,
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chain,
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concatenate,
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glorot_uniform_init,
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list2ragged,
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reduce_first,
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reduce_last,
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reduce_max,
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reduce_mean,
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with_getitem,
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)
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from thinc.types import Floats2d, Ragged
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from ...tokens import Doc
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from ...util import registry
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from ..extract_spans import extract_spans
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@registry.layers("spacy.LinearLogistic.v1")
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def build_linear_logistic(nO=None, nI=None) -> Model[Floats2d, Floats2d]:
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"""An output layer for multi-label classification. It uses a linear layer
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followed by a logistic activation.
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"""
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return chain(Linear(nO=nO, nI=nI, init_W=glorot_uniform_init), Logistic())
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@registry.layers("spacy.mean_max_reducer.v1")
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def build_mean_max_reducer(hidden_size: int) -> Model[Ragged, Floats2d]:
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"""Reduce sequences by concatenating their mean and max pooled vectors,
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and then combine the concatenated vectors with a hidden layer.
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"""
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return chain(
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concatenate(
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cast(Model[Ragged, Floats2d], reduce_last()),
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cast(Model[Ragged, Floats2d], reduce_first()),
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reduce_mean(),
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reduce_max(),
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),
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Maxout(nO=hidden_size, normalize=True, dropout=0.0),
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)
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@registry.architectures("spacy.SpanCategorizer.v1")
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def build_spancat_model(
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tok2vec: Model[List[Doc], List[Floats2d]],
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reducer: Model[Ragged, Floats2d],
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scorer: Model[Floats2d, Floats2d],
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) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
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"""Build a span categorizer model, given a token-to-vector model, a
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reducer model to map the sequence of vectors for each span down to a single
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vector, and a scorer model to map the vectors to probabilities.
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tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
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reducer (Model[Ragged, Floats2d]): The reducer model.
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scorer (Model[Floats2d, Floats2d]): The scorer model.
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"""
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model = chain(
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cast(
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Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
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with_getitem(
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0, chain(tok2vec, cast(Model[List[Floats2d], Ragged], list2ragged()))
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),
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),
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extract_spans(),
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reducer,
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scorer,
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)
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model.set_ref("tok2vec", tok2vec)
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model.set_ref("reducer", reducer)
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model.set_ref("scorer", scorer)
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return model
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