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Fix tok2vec

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This commit is contained in:
Matthew Honnibal 2020-07-28 15:51:40 +02:00
parent fe0cdcd461
commit 099e9331c5
1 changed files with 28 additions and 17 deletions
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45
spacy/ml/models/tok2vec.py
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@ -1,13 +1,15 @@
from typing import Optional, List
from thinc.api import chain, clone, concatenate, with_array, with_padded
from thinc.api import Model, noop
from thinc.api import FeatureExtractor, HashEmbed, StaticVectors
from thincapi import expand_window, residual, Maxout, Mish
from thinc.api import Model, noop, list2ragged, ragged2list
from thinc.api import FeatureExtractor, HashEmbed
from thinc.api import expand_window, residual, Maxout, Mish
from thinc.types import Floats2d
from ...tokens import Doc
from ... import util
from ...util import registry
from ...ml import _character_embed
from ..staticvectors import StaticVectors
from ...pipeline.tok2vec import Tok2VecListener
from ...attrs import ID, ORTH, NORM, PREFIX, SUFFIX, SHAPE
@ -21,20 +23,19 @@ def tok2vec_listener_v1(width, upstream="*"):
@registry.architectures.register("spacy.Tok2Vec.v1")
def Tok2Vec(
embed: Model[List[Doc], List[Floats2d]],
encode: Model[List[Floats2d], List[Floats2d]
encode: Model[List[Floats2d], List[Floats2d]]
) -> Model[List[Doc], List[Floats2d]]:
tok2vec = with_array(
chain(embed, encode),
pad=encode.attrs.get("receptive_field", 0)
)
receptive_field = encode.attrs.get("receptive_field", 0)
tok2vec = chain(embed, with_array(encode, pad=receptive_field))
tok2vec.set_dim("nO", encode.get_dim("nO"))
tok2vec.set_ref("embed", embed)
tok2vec.set_ref("encode", encode)
return tok2vec
@registry.architectures.register("spacy.HashEmbed.v1")
def HashEmbed(
@registry.architectures.register("spacy.MultiHashEmbed.v1")
def MultiHashEmbed(
width: int,
rows: int,
also_embed_subwords: bool,
@ -56,9 +57,9 @@ def HashEmbed(
if also_embed_subwords:
embeddings = [
make_hash_embed(NORM)
make_hash_embed(PREFIX)
make_hash_embed(SUFFIX)
make_hash_embed(NORM),
make_hash_embed(PREFIX),
make_hash_embed(SUFFIX),
make_hash_embed(SHAPE)
]
else:
@ -67,15 +68,25 @@ def HashEmbed(
if also_use_static_vectors:
model = chain(
concatenate(
chain(FeatureExtractor(cols), concatenate(*embeddings)),
chain(
FeatureExtractor(cols),
list2ragged(),
with_array(concatenate(*embeddings))
),
StaticVectors(width, dropout=0.0)
),
Maxout(width, pieces=3, dropout=0.0, normalize=True)
with_array(Maxout(width, nP=3, dropout=0.0, normalize=True)),
ragged2list()
)
else:
model = chain(
chain(FeatureExtractor(cols), concatenate(*embeddings)),
Maxout(width, pieces=3, dropout=0.0, normalize=True)
chain(
FeatureExtractor(cols),
list2ragged(),
with_array(concatenate(*embeddings))
),
with_array(Maxout(width, nP=3, dropout=0.0, normalize=True)),
ragged2list()
)
return model