mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-15 06:09:01 +03:00
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
from typing import List, Tuple, cast
|
|
|
|
from thinc.api import (
|
|
Linear,
|
|
Logistic,
|
|
Maxout,
|
|
Model,
|
|
chain,
|
|
concatenate,
|
|
glorot_uniform_init,
|
|
list2ragged,
|
|
reduce_first,
|
|
reduce_last,
|
|
reduce_max,
|
|
reduce_mean,
|
|
with_getitem,
|
|
)
|
|
from thinc.types import Floats2d, Ragged
|
|
|
|
from ...tokens import Doc
|
|
from ...util import registry
|
|
from ..extract_spans import extract_spans
|
|
|
|
|
|
@registry.layers("spacy.LinearLogistic.v1")
|
|
def build_linear_logistic(nO=None, nI=None) -> Model[Floats2d, Floats2d]:
|
|
"""An output layer for multi-label classification. It uses a linear layer
|
|
followed by a logistic activation.
|
|
"""
|
|
return chain(Linear(nO=nO, nI=nI, init_W=glorot_uniform_init), Logistic())
|
|
|
|
|
|
@registry.layers("spacy.mean_max_reducer.v1")
|
|
def build_mean_max_reducer(hidden_size: int) -> Model[Ragged, Floats2d]:
|
|
"""Reduce sequences by concatenating their mean and max pooled vectors,
|
|
and then combine the concatenated vectors with a hidden layer.
|
|
"""
|
|
return chain(
|
|
concatenate(
|
|
cast(Model[Ragged, Floats2d], reduce_last()),
|
|
cast(Model[Ragged, Floats2d], reduce_first()),
|
|
reduce_mean(),
|
|
reduce_max(),
|
|
),
|
|
Maxout(nO=hidden_size, normalize=True, dropout=0.0),
|
|
)
|
|
|
|
|
|
@registry.architectures("spacy.SpanCategorizer.v1")
|
|
def build_spancat_model(
|
|
tok2vec: Model[List[Doc], List[Floats2d]],
|
|
reducer: Model[Ragged, Floats2d],
|
|
scorer: Model[Floats2d, Floats2d],
|
|
) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
|
|
"""Build a span categorizer model, given a token-to-vector model, a
|
|
reducer model to map the sequence of vectors for each span down to a single
|
|
vector, and a scorer model to map the vectors to probabilities.
|
|
|
|
tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
|
|
reducer (Model[Ragged, Floats2d]): The reducer model.
|
|
scorer (Model[Floats2d, Floats2d]): The scorer model.
|
|
"""
|
|
model = chain(
|
|
cast(
|
|
Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
|
|
with_getitem(
|
|
0, chain(tok2vec, cast(Model[List[Floats2d], Ragged], list2ragged()))
|
|
),
|
|
),
|
|
extract_spans(),
|
|
reducer,
|
|
scorer,
|
|
)
|
|
model.set_ref("tok2vec", tok2vec)
|
|
model.set_ref("reducer", reducer)
|
|
model.set_ref("scorer", scorer)
|
|
return model
|