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)
conv_depth = kwargs.get("conv_depth", 4)
bilstm_depth = kwargs.get("bilstm_depth", 0)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
with Model.define_operators(
{">>": chain, "|": concatenate, "**": clone, "+": add, "*": reapply}
):
norm = HashEmbed(width, embed_size, column=cols.index(NORM), name="embed_norm")
if subword_features:
prefix = HashEmbed(
width, embed_size // 2, column=cols.index(PREFIX), name="embed_prefix"
)
suffix = HashEmbed(
width, embed_size // 2, column=cols.index(SUFFIX), name="embed_suffix"
)
shape = HashEmbed(
width, embed_size // 2, column=cols.index(SHAPE), name="embed_shape"
)
else:
prefix, suffix, shape = (None, None, None)
if pretrained_vectors is not None:
glove = StaticVectors(pretrained_vectors, width, column=cols.index(ID))
if subword_features:
embed = uniqued(
(glove | norm | prefix | suffix | shape)
>> LN(Maxout(width, width * 5, pieces=3)),
column=cols.index(ORTH),
)
else:
embed = uniqued(
(glove | norm) >> LN(Maxout(width, width * 2, pieces=3)),
column=cols.index(ORTH),
)
elif subword_features:
embed = uniqued(
(norm | prefix | suffix | shape)
>> LN(Maxout(width, width * 4, pieces=3)),
column=cols.index(ORTH),
)
else:
embed = norm
convolution = Residual(
ExtractWindow(nW=1)
>> LN(Maxout(width, width * 3, pieces=cnn_maxout_pieces))
)
tok2vec = FeatureExtracter(cols) >> with_flatten(
embed >> convolution ** conv_depth, pad=conv_depth
)
if bilstm_depth >= 1:
tok2vec = tok2vec >> PyTorchBiLSTM(width, width, bilstm_depth)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed
return tok2vec
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)
conv_depth = kwargs.get("conv_depth", 4)
bilstm_depth = kwargs.get("bilstm_depth", 0)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
with Model.define_operators(
{">>": chain, "|": concatenate, "**": clone, "+": add, "*": reapply}
):
norm = HashEmbed(width, embed_size, column=cols.index(NORM), name="embed_norm")
if subword_features:
prefix = HashEmbed(
width, embed_size // 2, column=cols.index(PREFIX), name="embed_prefix"
)
suffix = HashEmbed(
width, embed_size // 2, column=cols.index(SUFFIX), name="embed_suffix"
)
shape = HashEmbed(
width, embed_size // 2, column=cols.index(SHAPE), name="embed_shape"
)
else:
prefix, suffix, shape = (None, None, None)
if pretrained_vectors is not None:
glove = StaticVectors(pretrained_vectors, width, column=cols.index(ID))
if subword_features:
embed = uniqued(
(glove | norm | prefix | suffix | shape)
>> LN(Maxout(width, width * 5, pieces=3)),
column=cols.index(ORTH),
)
else:
embed = uniqued(
(glove | norm) >> LN(Maxout(width, width * 2, pieces=3)),
column=cols.index(ORTH),
)
elif subword_features:
embed = uniqued(
(norm | prefix | suffix | shape)
>> LN(Maxout(width, width * 4, pieces=3)),
column=cols.index(ORTH),
)
else:
embed = norm
convolution = Residual(
ExtractWindow(nW=1)
>> LN(Maxout(width, width * 3, pieces=cnn_maxout_pieces))
)
tok2vec = FeatureExtracter(cols) >> with_flatten(
embed >> convolution ** conv_depth, pad=conv_depth
)
if bilstm_depth >= 1:
tok2vec = tok2vec >> PyTorchBiLSTM(width, width, bilstm_depth)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed
return tok2vec
def MultiHashEmbed(config):
# For backwards compatibility with models before the architecture registry,
# we have to be careful to get exactly the same model structure. One subtle
# trick is that when we define concatenation with the operator, the operator
# is actually binary associative. So when we write (a | b | c), we're actually
# getting concatenate(concatenate(a, b), c). That's why the implementation
# is a bit ugly here.
cols = config["columns"]
width = config["width"]
rows = config["rows"]
norm = HashEmbed(width,
rows,
column=cols.index("NORM"),
name="embed_norm",
seed=1)
if config["use_subwords"]:
prefix = HashEmbed(width,
rows // 2,
column=cols.index("PREFIX"),
name="embed_prefix",
seed=2)
suffix = HashEmbed(width,
rows // 2,
column=cols.index("SUFFIX"),
name="embed_suffix",
seed=3)
shape = HashEmbed(width,
rows // 2,
column=cols.index("SHAPE"),
name="embed_shape",
seed=4)
if config.get("@pretrained_vectors"):
glove = make_layer(config["@pretrained_vectors"])
mix = make_layer(config["@mix"])
with Model.define_operators({">>": chain, "|": concatenate}):
if config["use_subwords"] and config["@pretrained_vectors"]:
mix._layers[0].nI = width * 5
layer = uniqued(
(glove | norm | prefix | suffix | shape) >> mix,
column=cols.index("ORTH"),
)
elif config["use_subwords"]:
mix._layers[0].nI = width * 4
layer = uniqued((norm | prefix | suffix | shape) >> mix,
column=cols.index("ORTH"))
elif config["@pretrained_vectors"]:
mix._layers[0].nI = width * 2
layer = uniqued(
(glove | norm) >> mix,
column=cols.index("ORTH"),
)
else:
layer = norm
layer.cfg = config
return layer
def Tok2Vec(width, embed_size, **kwargs):
pretrained_vectors = kwargs.get('pretrained_vectors', None)
cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 2)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
with Model.define_operators({
'>>': chain,
'|': concatenate,
'**': clone,
'+': add,
'*': reapply
}):
norm = HashEmbed(width,
embed_size,
column=cols.index(NORM),
name='embed_norm')
prefix = HashEmbed(width,
embed_size // 2,
column=cols.index(PREFIX),
name='embed_prefix')
suffix = HashEmbed(width,
embed_size // 2,
column=cols.index(SUFFIX),
name='embed_suffix')
shape = HashEmbed(width,
embed_size // 2,
column=cols.index(SHAPE),
name='embed_shape')
if pretrained_vectors is not None:
glove = StaticVectors(pretrained_vectors,
width,
column=cols.index(ID))
embed = uniqued((glove | norm | prefix | suffix | shape) >> LN(
Maxout(width, width * 5, pieces=3)),
column=cols.index(ORTH))
else:
embed = uniqued((norm | prefix | suffix | shape) >> LN(
Maxout(width, width * 4, pieces=3)),
column=cols.index(ORTH))
convolution = Residual(
ExtractWindow(
nW=1) >> LN(Maxout(width, width *
3, pieces=cnn_maxout_pieces)))
tok2vec = (FeatureExtracter(cols) >> with_flatten(
embed >> convolution**4, pad=4))
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed
return tok2vec
def build_model(nr_class, width, **kwargs):
with Model.define_operators({'|': concatenate, '>>': chain, '**': clone}):
model = (FeatureExtracter([ORTH]) >> flatten_add_lengths >>
with_getitem(0, uniqued(HashEmbed(width, 10000, column=0))) >>
Pooling(mean_pool) >> Softmax(nr_class))
model.lsuv = False
return model
def build_text_classifier(nr_class, width=64, **cfg):
nr_vector = cfg.get('nr_vector', 5000)
pretrained_dims = cfg.get('pretrained_dims', 0)
with Model.define_operators({
'>>': chain,
'+': add,
'|': concatenate,
'**': clone
}):
if cfg.get('low_data') and pretrained_dims:
model = (SpacyVectors >> flatten_add_lengths >> with_getitem(
0, Affine(width, pretrained_dims)) >>
ParametricAttention(width) >> Pooling(sum_pool) >>
Residual(ReLu(width, width))**2 >> zero_init(
Affine(nr_class, width, drop_factor=0.0)) >> logistic)
return model
lower = HashEmbed(width, nr_vector, column=1)
prefix = HashEmbed(width // 2, nr_vector, column=2)
suffix = HashEmbed(width // 2, nr_vector, column=3)
shape = HashEmbed(width // 2, nr_vector, column=4)
trained_vectors = (FeatureExtracter(
[ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]) >> with_flatten(
uniqued((lower | prefix | suffix | shape) >> LN(
Maxout(width, width + (width // 2) * 3)),
column=0)))
if pretrained_dims:
static_vectors = (
SpacyVectors >> with_flatten(Affine(width, pretrained_dims)))
# TODO Make concatenate support lists
vectors = concatenate_lists(trained_vectors, static_vectors)
vectors_width = width * 2
else:
vectors = trained_vectors
vectors_width = width
static_vectors = None
cnn_model = (
vectors >> with_flatten(
LN(Maxout(width, vectors_width)) >> Residual(
(ExtractWindow(nW=1) >> LN(Maxout(width, width * 3))))**2,
pad=2) >> flatten_add_lengths >> ParametricAttention(width) >>
Pooling(sum_pool) >> Residual(zero_init(Maxout(width, width))) >>
zero_init(Affine(nr_class, width, drop_factor=0.0)))
linear_model = (
_preprocess_doc >> LinearModel(nr_class, drop_factor=0.))
model = ((linear_model | cnn_model) >> zero_init(
Affine(nr_class, nr_class * 2, drop_factor=0.0)) >> logistic)
model.nO = nr_class
model.lsuv = False
return model
def build_model(nr_class, width, **kwargs):
with Model.define_operators({"|": concatenate, ">>": chain, "**": clone}):
model = (
FeatureExtracter([ORTH])
>> flatten_add_lengths
>> with_getitem(0, uniqued(HashEmbed(width, 10000, column=0)))
>> Pooling(mean_pool)
>> Softmax(nr_class)
)
model.lsuv = False
return model
def Tok2Vec(width, embed_size, **kwargs):
pretrained_vectors = kwargs.get('pretrained_vectors', None)
cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 2)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
with Model.define_operators({'>>': chain, '|': concatenate, '**': clone,
'+': add, '*': reapply}):
norm = HashEmbed(width, embed_size, column=cols.index(NORM),
name='embed_norm')
prefix = HashEmbed(width, embed_size//2, column=cols.index(PREFIX),
name='embed_prefix')
suffix = HashEmbed(width, embed_size//2, column=cols.index(SUFFIX),
name='embed_suffix')
shape = HashEmbed(width, embed_size//2, column=cols.index(SHAPE),
name='embed_shape')
if pretrained_vectors is not None:
glove = StaticVectors(pretrained_vectors, width, column=cols.index(ID))
embed = uniqued(
(glove | norm | prefix | suffix | shape)
>> LN(Maxout(width, width*5, pieces=3)), column=cols.index(ORTH))
else:
embed = uniqued(
(norm | prefix | suffix | shape)
>> LN(Maxout(width, width*4, pieces=3)), column=cols.index(ORTH))
convolution = Residual(
ExtractWindow(nW=1)
>> LN(Maxout(width, width*3, pieces=cnn_maxout_pieces))
)
tok2vec = (
FeatureExtracter(cols)
>> with_flatten(
embed
>> convolution ** 4, pad=4
)
)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed
return tok2vec
def my_tok_to_vec(width, embed_size, pretrained_vectors, **kwargs):
# Circular imports :(
from spacy._ml import PyTorchBiLSTM
cnn_maxout_pieces = kwargs.get("cnn_maxout_pieces", 3)
conv_depth = kwargs.get("conv_depth", 4)
bilstm_depth = kwargs.get("bilstm_depth", 0)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
storage = []
with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
# norm = HashEmbed(width, embed_size, column=cols.index(NORM), name="embed_norm")
# prefix = HashEmbed(
# width, embed_size // 2, column=cols.index(PREFIX), name="embed_prefix"
# )
# suffix = HashEmbed(
# width, embed_size // 2, column=cols.index(SUFFIX), name="embed_suffix"
# )
shape = HashEmbed(
width, embed_size // 2, column=cols.index(SHAPE), name="embed_shape"
)
glove = Vectors(storage, pretrained_vectors, width, column=cols.index(NORM), )
vec_width = glove.nV
embed = uniqued(
(glove | shape)
>> LN(Maxout(width, width + vec_width, pieces=3)),
column=cols.index(ORTH),
)
convolution = Residual(
ExtractWindow(nW=1)
>> LN(Maxout(width, width * 3, pieces=cnn_maxout_pieces))
)
tok2vec = SaveDoc(storage) >> FeatureExtracter(cols) >> with_flatten(
embed >> convolution ** conv_depth, pad=conv_depth
)
if bilstm_depth >= 1:
tok2vec = tok2vec >> PyTorchBiLSTM(width, width, bilstm_depth)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed
return tok2vec
def build_model(nr_class, width, depth, conv_depth, **kwargs):
with Model.define_operators({'|': concatenate, '>>': chain, '**': clone}):
embed = ((HashEmbed(width, 5000, column=1)
| HashEmbed(width // 2, 750, column=2)
| HashEmbed(width // 2, 750, column=3)
| HashEmbed(width // 2, 750, column=4)) >> Maxout(width))
sent2vec = (
FeatureExtracter([ORTH, LOWER, PREFIX, SUFFIX, SHAPE]) >>
flatten_add_lengths >> with_getitem(
0,
uniqued(embed, column=0) >>
Residual(ExtractWindow(nW=1) >> SELU(width))**conv_depth) >>
ParametricAttention(width) >> Pooling(sum_pool) >> Residual(
SELU(width))**depth)
model = (
foreach_sentence(sent2vec, drop_factor=2.0) >> flatten_add_lengths
>> ParametricAttention(width, hard=False) >> Pooling(sum_pool) >>
Residual(SELU(width))**depth >> Softmax(nr_class))
model.lsuv = False
return model
def MultiHashEmbed(config):
cols = config["columns"]
width = config["width"]
rows = config["rows"]
tables = [HashEmbed(width, rows, column=cols.index("NORM"), name="embed_norm")]
if config["use_subwords"]:
for feature in ["PREFIX", "SUFFIX", "SHAPE"]:
tables.append(
HashEmbed(
width,
rows // 2,
column=cols.index(feature),
name="embed_%s" % feature.lower(),
)
)
if config.get("@pretrained_vectors"):
tables.append(make_layer(config["@pretrained_vectors"]))
mix = make_layer(config["@mix"])
# This is a pretty ugly hack. Not sure what the best solution should be.
mix._layers[0].nI = sum(table.nO for table in tables)
layer = uniqued(chain(concatenate(*tables), mix), column=cols.index("ORTH"))
layer.cfg = config
return layer
def build_text_classifier(nr_class, width=64, **cfg):
depth = cfg.get("depth", 2)
nr_vector = cfg.get("nr_vector", 5000)
pretrained_dims = cfg.get("pretrained_dims", 0)
with Model.define_operators({
">>": chain,
"+": add,
"|": concatenate,
"**": clone
}):
if cfg.get("low_data") and pretrained_dims:
model = (SpacyVectors >> flatten_add_lengths >> with_getitem(
0, Affine(width, pretrained_dims)) >>
ParametricAttention(width) >> Pooling(sum_pool) >>
Residual(ReLu(width, width))**2 >> zero_init(
Affine(nr_class, width, drop_factor=0.0)) >> logistic)
return model
lower = HashEmbed(width, nr_vector, column=1)
prefix = HashEmbed(width // 2, nr_vector, column=2)
suffix = HashEmbed(width // 2, nr_vector, column=3)
shape = HashEmbed(width // 2, nr_vector, column=4)
trained_vectors = FeatureExtracter(
[ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]) >> with_flatten(
uniqued(
(lower | prefix | suffix | shape) >> LN(
Maxout(width, width + (width // 2) * 3)),
column=0,
))
if pretrained_dims:
static_vectors = SpacyVectors >> with_flatten(
Affine(width, pretrained_dims))
# TODO Make concatenate support lists
vectors = concatenate_lists(trained_vectors, static_vectors)
vectors_width = width * 2
else:
vectors = trained_vectors
vectors_width = width
static_vectors = None
tok2vec = vectors >> with_flatten(
LN(Maxout(width, vectors_width)) >> Residual(
(ExtractWindow(nW=1) >> LN(Maxout(width, width * 3))))**depth,
pad=depth,
)
cnn_model = (
tok2vec >> flatten_add_lengths >> ParametricAttention(width) >>
Pooling(sum_pool) >> Residual(zero_init(Maxout(width, width))) >>
zero_init(Affine(nr_class, width, drop_factor=0.0)))
linear_model = build_bow_text_classifier(nr_class,
ngram_size=cfg.get(
"ngram_size", 1),
exclusive_classes=False)
if cfg.get("exclusive_classes"):
output_layer = Softmax(nr_class, nr_class * 2)
else:
output_layer = (zero_init(
Affine(nr_class, nr_class * 2, drop_factor=0.0)) >> logistic)
model = (linear_model | cnn_model) >> output_layer
model.tok2vec = chain(tok2vec, flatten)
model.nO = nr_class
model.lsuv = False
return model
def build_text_classifier(nr_class, width=64, **cfg):
depth = cfg.get("depth", 2)
nr_vector = cfg.get("nr_vector", 5000)
pretrained_dims = cfg.get("pretrained_dims", 0)
with Model.define_operators({">>": chain, "+": add, "|": concatenate, "**": clone}):
if cfg.get("low_data") and pretrained_dims:
model = (
SpacyVectors
>> flatten_add_lengths
>> with_getitem(0, Affine(width, pretrained_dims))
>> ParametricAttention(width)
>> Pooling(sum_pool)
>> Residual(ReLu(width, width)) ** 2
>> zero_init(Affine(nr_class, width, drop_factor=0.0))
>> logistic
)
return model
lower = HashEmbed(width, nr_vector, column=1)
prefix = HashEmbed(width // 2, nr_vector, column=2)
suffix = HashEmbed(width // 2, nr_vector, column=3)
shape = HashEmbed(width // 2, nr_vector, column=4)
trained_vectors = FeatureExtracter(
[ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]
) >> with_flatten(
uniqued(
(lower | prefix | suffix | shape)
>> LN(Maxout(width, width + (width // 2) * 3)),
column=0,
)
)
if pretrained_dims:
static_vectors = SpacyVectors >> with_flatten(
Affine(width, pretrained_dims)
)
# TODO Make concatenate support lists
vectors = concatenate_lists(trained_vectors, static_vectors)
vectors_width = width * 2
else:
vectors = trained_vectors
vectors_width = width
static_vectors = None
tok2vec = vectors >> with_flatten(
LN(Maxout(width, vectors_width))
>> Residual((ExtractWindow(nW=1) >> LN(Maxout(width, width * 3)))) ** depth,
pad=depth,
)
cnn_model = (
tok2vec
>> flatten_add_lengths
>> ParametricAttention(width)
>> Pooling(sum_pool)
>> Residual(zero_init(Maxout(width, width)))
>> zero_init(Affine(nr_class, width, drop_factor=0.0))
)
linear_model = build_bow_text_classifier(
nr_class, ngram_size=cfg.get("ngram_size", 1), exclusive_classes=False
)
if cfg.get("exclusive_classes"):
output_layer = Softmax(nr_class, nr_class * 2)
else:
output_layer = (
zero_init(Affine(nr_class, nr_class * 2, drop_factor=0.0)) >> logistic
)
model = (linear_model | cnn_model) >> output_layer
model.tok2vec = chain(tok2vec, flatten)
model.nO = nr_class
model.lsuv = False
return model
def build_text_classifier(nr_class, width=64, **cfg):
nr_vector = cfg.get('nr_vector', 5000)
pretrained_dims = cfg.get('pretrained_dims', 0)
with Model.define_operators({'>>': chain, '+': add, '|': concatenate,
'**': clone}):
if cfg.get('low_data') and pretrained_dims:
model = (
SpacyVectors
>> flatten_add_lengths
>> with_getitem(0, Affine(width, pretrained_dims))
>> ParametricAttention(width)
>> Pooling(sum_pool)
>> Residual(ReLu(width, width)) ** 2
>> zero_init(Affine(nr_class, width, drop_factor=0.0))
>> logistic
)
return model
lower = HashEmbed(width, nr_vector, column=1)
prefix = HashEmbed(width//2, nr_vector, column=2)
suffix = HashEmbed(width//2, nr_vector, column=3)
shape = HashEmbed(width//2, nr_vector, column=4)
trained_vectors = (
FeatureExtracter([ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID])
>> with_flatten(
uniqued(
(lower | prefix | suffix | shape)
>> LN(Maxout(width, width+(width//2)*3)),
column=0
)
)
)
if pretrained_dims:
static_vectors = (
SpacyVectors
>> with_flatten(Affine(width, pretrained_dims))
)
# TODO Make concatenate support lists
vectors = concatenate_lists(trained_vectors, static_vectors)
vectors_width = width*2
else:
vectors = trained_vectors
vectors_width = width
static_vectors = None
cnn_model = (
vectors
>> with_flatten(
LN(Maxout(width, vectors_width))
>> Residual(
(ExtractWindow(nW=1) >> LN(Maxout(width, width*3)))
) ** 2, pad=2
)
>> flatten_add_lengths
>> ParametricAttention(width)
>> Pooling(sum_pool)
>> Residual(zero_init(Maxout(width, width)))
>> zero_init(Affine(nr_class, width, drop_factor=0.0))
)
linear_model = (
_preprocess_doc
>> LinearModel(nr_class)
)
#model = linear_model >> logistic
model = (
(linear_model | cnn_model)
>> zero_init(Affine(nr_class, nr_class*2, drop_factor=0.0))
>> logistic
)
model.nO = nr_class
model.lsuv = False
return model
def Tok2Vec(width, embed_size, **kwargs):
# Circular imports :(
from .._ml import CharacterEmbed
from .._ml import PyTorchBiLSTM
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)
if char_embed:
subword_features = False
conv_depth = kwargs.get("conv_depth", 4)
bilstm_depth = kwargs.get("bilstm_depth", 0)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
norm = HashEmbed(width,
embed_size,
column=cols.index(NORM),
name="embed_norm",
seed=6)
if subword_features:
prefix = HashEmbed(width,
embed_size // 2,
column=cols.index(PREFIX),
name="embed_prefix",
seed=7)
suffix = HashEmbed(width,
embed_size // 2,
column=cols.index(SUFFIX),
name="embed_suffix",
seed=8)
shape = HashEmbed(width,
embed_size // 2,
column=cols.index(SHAPE),
name="embed_shape",
seed=9)
else:
prefix, suffix, shape = (None, None, None)
if pretrained_vectors is not None:
glove = StaticVectors(pretrained_vectors,
width,
column=cols.index(ID))
if subword_features:
embed = uniqued(
(glove | norm | prefix | suffix | shape) >> LN(
Maxout(width, width * 5, pieces=3)),
column=cols.index(ORTH),
)
elif char_embed:
embed = concatenate_lists(
CharacterEmbed(nM=64, nC=8),
FeatureExtracter(cols) >> with_flatten(glove),
)
reduce_dimensions = LN(
Maxout(width, 64 * 8 + width, pieces=cnn_maxout_pieces))
else:
embed = uniqued(
(glove | norm) >> LN(Maxout(width, width * 2, pieces=3)),
column=cols.index(ORTH),
)
elif subword_features:
embed = uniqued(
(norm | prefix | suffix | shape) >> LN(
Maxout(width, width * 4, pieces=3)),
column=cols.index(ORTH),
)
elif char_embed:
embed = concatenate_lists(
CharacterEmbed(nM=64, nC=8),
FeatureExtracter(cols) >> with_flatten(norm),
)
reduce_dimensions = LN(
Maxout(width, 64 * 8 + width, pieces=cnn_maxout_pieces))
else:
embed = norm
convolution = Residual(
ExtractWindow(
nW=1) >> LN(Maxout(width, width *
3, pieces=cnn_maxout_pieces)))
if char_embed:
tok2vec = embed >> with_flatten(
reduce_dimensions >> convolution**conv_depth, pad=conv_depth)
else:
tok2vec = FeatureExtracter(cols) >> with_flatten(
embed >> convolution**conv_depth, pad=conv_depth)
if bilstm_depth >= 1:
tok2vec = tok2vec >> PyTorchBiLSTM(width, width, bilstm_depth)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed
return tok2vec
def TextCatEnsemble_v1(
width: int,
embed_size: int,
pretrained_vectors: Optional[bool],
exclusive_classes: bool,
ngram_size: int,
window_size: int,
conv_depth: int,
dropout: Optional[float],
nO: Optional[int] = None,
) -> Model:
# Don't document this yet, I'm not sure it's right.
cols = [ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]
with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
lower = HashEmbed(nO=width,
nV=embed_size,
column=cols.index(LOWER),
dropout=dropout,
seed=10)
prefix = HashEmbed(
nO=width // 2,
nV=embed_size,
column=cols.index(PREFIX),
dropout=dropout,
seed=11,
)
suffix = HashEmbed(
nO=width // 2,
nV=embed_size,
column=cols.index(SUFFIX),
dropout=dropout,
seed=12,
)
shape = HashEmbed(
nO=width // 2,
nV=embed_size,
column=cols.index(SHAPE),
dropout=dropout,
seed=13,
)
width_nI = sum(
layer.get_dim("nO") for layer in [lower, prefix, suffix, shape])
trained_vectors = FeatureExtractor(cols) >> with_array(
uniqued(
(lower | prefix | suffix | shape) >> Maxout(
nO=width, nI=width_nI, normalize=True),
column=cols.index(ORTH),
))
if pretrained_vectors:
static_vectors = StaticVectors(width)
vector_layer = trained_vectors | static_vectors
vectors_width = width * 2
else:
vector_layer = trained_vectors
vectors_width = width
tok2vec = vector_layer >> with_array(
Maxout(width, vectors_width, normalize=True) >>
residual((expand_window(window_size=window_size) >> Maxout(
nO=width, nI=width *
((window_size * 2) + 1), normalize=True)))**conv_depth,
pad=conv_depth,
)
cnn_model = (tok2vec >> list2ragged() >> ParametricAttention(width) >>
reduce_sum() >> residual(Maxout(nO=width, nI=width)) >>
Linear(nO=nO, nI=width) >> Dropout(0.0))
linear_model = build_bow_text_classifier(
nO=nO,
ngram_size=ngram_size,
exclusive_classes=exclusive_classes,
no_output_layer=False,
)
nO_double = nO * 2 if nO else None
if exclusive_classes:
output_layer = Softmax(nO=nO, nI=nO_double)
else:
output_layer = Linear(nO=nO,
nI=nO_double) >> Dropout(0.0) >> Logistic()
model = (linear_model | cnn_model) >> output_layer
model.set_ref("tok2vec", tok2vec)
if model.has_dim("nO") is not False:
model.set_dim("nO", nO)
model.set_ref("output_layer", linear_model.get_ref("output_layer"))
model.attrs["multi_label"] = not exclusive_classes
return model
