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Matthew Honnibal 2020-08-07 16:49:00 +02:00
parent 234c52a91e
commit 473504d837
2 changed files with 22 additions and 5 deletions
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15
spacy/ml/models/tagger.py
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@ -1,11 +1,22 @@
from typing import Optional from typing import Optional, List
from thinc.api import zero_init, with_array, Softmax, chain, Model from thinc.api import zero_init, with_array, Softmax, chain, Model
from thinc.types import Floats2d
from ...util import registry from ...util import registry
from ..tokens import Doc
@registry.architectures.register("spacy.Tagger.v1") @registry.architectures.register("spacy.Tagger.v1")
def build_tagger_model(tok2vec: Model, nO: Optional[int] = None) -> Model: def build_tagger_model(
tok2vec: Model[List[Doc], List[Floats2d]], nO: Optional[int] = None
) -> Model[List[Doc], List[Floats2d]]:
"""Build a tagger model, using a provided token-to-vector component. The tagger
model simply adds a linear layer with softmax activation to predict scores
given the token vectors.
tok2vec (Model[List[Doc], List[Floats2d]]): The token-to-vector subnetwork.
nO (int or None): The number of tags to output. Inferred from the data if None.
"""
# TODO: glorot_uniform_init seems to work a bit better than zero_init here?! # TODO: glorot_uniform_init seems to work a bit better than zero_init here?!
t2v_width = tok2vec.get_dim("nO") if tok2vec.has_dim("nO") else None t2v_width = tok2vec.get_dim("nO") if tok2vec.has_dim("nO") else None
output_layer = Softmax(nO, t2v_width, init_W=zero_init) output_layer = Softmax(nO, t2v_width, init_W=zero_init)

12
spacy/ml/models/tok2vec.py
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@ -205,7 +205,9 @@ def CharacterEmbed(width: int, rows: int, nM: int, nC: int):
@registry.architectures.register("spacy.MaxoutWindowEncoder.v1") @registry.architectures.register("spacy.MaxoutWindowEncoder.v1")
def MaxoutWindowEncoder(width: int, window_size: int, maxout_pieces: int, depth: int) -> Model[List[Floats2d], List[Floats2d]]: def MaxoutWindowEncoder(
width: int, window_size: int, maxout_pieces: int, depth: int
) -> Model[List[Floats2d], List[Floats2d]]:
"""Encode context using convolutions with maxout activation, layer """Encode context using convolutions with maxout activation, layer
normalization and residual connections. normalization and residual connections.
@ -235,7 +237,9 @@ def MaxoutWindowEncoder(width: int, window_size: int, maxout_pieces: int, depth:
@registry.architectures.register("spacy.MishWindowEncoder.v1") @registry.architectures.register("spacy.MishWindowEncoder.v1")
def MishWindowEncoder(width: int, window_size: int, depth: int) -> Model[List[Floats2d], List[Floats2d]]: def MishWindowEncoder(
width: int, window_size: int, depth: int
) -> Model[List[Floats2d], List[Floats2d]]:
"""Encode context using convolutions with mish activation, layer """Encode context using convolutions with mish activation, layer
normalization and residual connections. normalization and residual connections.
@ -256,7 +260,9 @@ def MishWindowEncoder(width: int, window_size: int, depth: int) -> Model[List[Fl
@registry.architectures.register("spacy.TorchBiLSTMEncoder.v1") @registry.architectures.register("spacy.TorchBiLSTMEncoder.v1")
def BiLSTMEncoder(width: int, depth: int, dropout: float) -> Model[List[Floats2d], List[Floats2d]]: def BiLSTMEncoder(
width: int, depth: int, dropout: float
) -> Model[List[Floats2d], List[Floats2d]]:
"""Encode context using bidirectonal LSTM layers. Requires PyTorch. """Encode context using bidirectonal LSTM layers. Requires PyTorch.
width (int): The input and output width. These are required to be the same, width (int): The input and output width. These are required to be the same,