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Add character-based bilstm tok2vec

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This commit is contained in:
Matthw Honnibal 2019-10-18 17:23:37 +02:00
parent 727ede6599
commit 49c0adc706
1 changed files with 40 additions and 7 deletions
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47
spacy/_ml.py
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@ -344,13 +344,35 @@ def Tok2Vec_chars_selfattention(width, embed_size, **kwargs):
tok2vec = (
embed
>> PositionEncode(10000, width)
>> SelfAttention(width, sa_depth, 4)
>> SelfAttention(width, 1, 4) ** sa_depth
)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed
return tok2vec
def Tok2Vec_chars_bilstm(width, embed_size, **kwargs):
cnn_maxout_pieces = kwargs.get("cnn_maxout_pieces", 3)
depth = kwargs.get("bilstm_depth", 2)
with Model.define_operators(
{">>": chain, "|": concatenate, "**": clone, "+": add, "*": reapply}
):
embed = (
CharacterEmbed(nM=64, nC=8)
>> with_flatten(LN(Maxout(width, 64*8, pieces=cnn_maxout_pieces))))
tok2vec = (
embed
>> Residual(PyTorchBiLSTM(width, width, depth))
>> with_flatten(LN(nO=width))
)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed
return tok2vec
def CNN(width, depth, pieces):
layer = chain(
@ -361,10 +383,10 @@ def CNN(width, depth, pieces):
def SelfAttention(width, depth, pieces):
layer = chain(
prepare_self_attention(Affine(width * 3, width), nM=width, nH=pieces),
prepare_self_attention(Affine(width * 3, width), nM=width, nH=pieces, window=None),
MultiHeadedAttention(),
with_flatten(Maxout(width, width)))
return clone(Residual(layer) >> with_flatten(LN(nO=width)), depth)
return clone(Residual(chain(layer, with_flatten(LN(nO=width)))), depth)
def PositionEncode(L, D):
@ -384,12 +406,17 @@ def Tok2Vec(width, embed_size, **kwargs):
pretrained_vectors = kwargs.get("pretrained_vectors", None)
cnn_maxout_pieces = kwargs.get("cnn_maxout_pieces", 3)
subword_features = kwargs.get("subword_features", True)
char_embed = kwargs.get("char_embed", False)
char_embed = util.env_opt("char_embed", kwargs.get("char_embed", False))
conv_depth = kwargs.get("conv_depth", 4)
bilstm_depth = kwargs.get("bilstm_depth", 0)
self_attn_depth = kwargs.get("self_attn_depth", 0)
bilstm_depth = util.env_opt("bilstm_depth", kwargs.get("bilstm_depth", 0))
self_attn_depth = util.env_opt("self_attn_depth", kwargs.get("self_attn_depth", 0))
kwargs.setdefault("bilstm_depth", bilstm_depth)
kwargs.setdefault("self_attn_depth", self_attn_depth)
kwargs.setdefault("char_embed", char_embed)
if char_embed and self_attn_depth:
return Tok2Vec_chars_selfattention(width, embed_size, **kwargs)
elif char_embed and bilstm_depth:
return Tok2Vec_chars_bilstm(width, embed_size, **kwargs)
elif char_embed and conv_depth:
return Tok2Vec_chars_cnn(width, embed_size, **kwargs)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
@ -445,7 +472,13 @@ def Tok2Vec(width, embed_size, **kwargs):
)
if bilstm_depth >= 1:
tok2vec = tok2vec >> PyTorchBiLSTM(width, width, bilstm_depth)
tok2vec = (
tok2vec
>> Residual(
PyTorchBiLSTM(width, width, 1)
>> LN(nO=width)
) ** bilstm_depth
)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed