spaCy/spacy/ml/models/simple_ner.py
2020-08-07 18:40:54 +02:00

105 lines
3.5 KiB
Python

from typing import List
from thinc.api import Model, Linear, with_array, softmax_activation, padded2list
from thinc.api import chain, list2padded, configure_normal_init
from thinc.api import Dropout
from thinc.types import Floats2d
from ...tokens import Doc
from .._biluo import BILUO
from .._iob import IOB
from ...util import registry
@registry.architectures.register("spacy.BILUOTagger.v1")
def BiluoTagger(
tok2vec: Model[List[Doc], List[Floats2d]]
) -> Model[List[Doc], List[Floats2d]]:
"""Construct a simple NER tagger, that predicts BILUO tag scores for each
token and uses greedy decoding with transition-constraints to return a valid
BILUO tag sequence.
A BILUO tag sequence encodes a sequence of non-overlapping labelled spans
into tags assigned to each token. The first token of a span is given the
tag B-LABEL, the last token of the span is given the tag L-LABEL, and tokens
within the span are given the tag U-LABEL. Single-token spans are given
the tag U-LABEL. All other tokens are assigned the tag O.
The BILUO tag scheme generally results in better linear separation between
classes, especially for non-CRF models, because there are more distinct classes
for the different situations (Ratinov et al., 2009).
"""
biluo = BILUO()
linear = Linear(
nO=None, nI=tok2vec.get_dim("nO"), init_W=configure_normal_init(mean=0.02)
)
model = chain(
tok2vec,
list2padded(),
with_array(chain(Dropout(0.1), linear)),
biluo,
with_array(softmax_activation()),
padded2list(),
)
return Model(
"biluo-tagger",
forward,
init=init,
layers=[model, linear],
refs={"tok2vec": tok2vec, "linear": linear, "biluo": biluo},
dims={"nO": None},
attrs={"get_num_actions": biluo.attrs["get_num_actions"]},
)
@registry.architectures.register("spacy.IOBTagger.v1")
def IOBTagger(
tok2vec: Model[List[Doc], List[Floats2d]]
) -> Model[List[Doc], List[Floats2d]]:
"""Construct a simple NER tagger, that predicts IOB tag scores for each
token and uses greedy decoding with transition-constraints to return a valid
IOB tag sequence.
An IOB tag sequence encodes a sequence of non-overlapping labelled spans
into tags assigned to each token. The first token of a span is given the
tag B-LABEL, and subsequent tokens are given the tag I-LABEL.
All other tokens are assigned the tag O.
"""
biluo = IOB()
linear = Linear(nO=None, nI=tok2vec.get_dim("nO"))
model = chain(
tok2vec,
list2padded(),
with_array(linear),
biluo,
with_array(softmax_activation()),
padded2list(),
)
return Model(
"iob-tagger",
forward,
init=init,
layers=[model],
refs={"tok2vec": tok2vec, "linear": linear, "biluo": biluo},
dims={"nO": None},
attrs={"get_num_actions": biluo.attrs["get_num_actions"]},
)
def init(model: Model[List[Doc], List[Floats2d]], X=None, Y=None) -> None:
if model.get_dim("nO") is None and Y:
model.set_dim("nO", Y[0].shape[1])
nO = model.get_dim("nO")
biluo = model.get_ref("biluo")
linear = model.get_ref("linear")
biluo.set_dim("nO", nO)
if linear.has_dim("nO") is None:
linear.set_dim("nO", nO)
model.layers[0].initialize(X=X, Y=Y)
def forward(model: Model, X: List[Doc], is_train: bool):
return model.layers[0](X, is_train)
__all__ = ["BiluoTagger"]