179 lines
6.7 KiB
Python
179 lines
6.7 KiB
Python
|
from typing import Optional, List
|
||
|
|
from thinc.types import Floats2d
|
||
|
|
from thinc.api import Model, with_cpu
|
||
|
|
from spacy.attrs import ID, ORTH, PREFIX, SUFFIX, SHAPE, LOWER
|
||
|
|
from spacy.util import registry
|
||
|
|
from spacy.tokens import Doc
|
||
|
|
|
||
|
|
|
||
|
|
def TextCatCNN_v1(
|
||
|
|
tok2vec: Model, exclusive_classes: bool, nO: Optional[int] = None
|
||
|
|
) -> Model[List[Doc], Floats2d]:
|
||
|
|
"""
|
||
|
|
Build a simple CNN text classifier, given a token-to-vector model as inputs.
|
||
|
|
If exclusive_classes=True, a softmax non-linearity is applied, so that the
|
||
|
|
outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
|
||
|
|
is applied instead, so that outputs are in the range [0, 1].
|
||
|
|
"""
|
||
|
|
chain = registry.get("layers", "chain.v1")
|
||
|
|
reduce_mean = registry.get("layers", "reduce_mean.v1")
|
||
|
|
Logistic = registry.get("layers", "Logistic.v1")
|
||
|
|
Softmax = registry.get("layers", "Softmax.v1")
|
||
|
|
Linear = registry.get("layers", "Linear.v1")
|
||
|
|
list2ragged = registry.get("layers", "list2ragged.v1")
|
||
|
|
|
||
|
|
with Model.define_operators({">>": chain}):
|
||
|
|
cnn = tok2vec >> list2ragged() >> reduce_mean()
|
||
|
|
if exclusive_classes:
|
||
|
|
output_layer = Softmax(nO=nO, nI=tok2vec.maybe_get_dim("nO"))
|
||
|
|
model = cnn >> output_layer
|
||
|
|
model.set_ref("output_layer", output_layer)
|
||
|
|
else:
|
||
|
|
linear_layer = Linear(nO=nO, nI=tok2vec.maybe_get_dim("nO"))
|
||
|
|
model = cnn >> linear_layer >> Logistic()
|
||
|
|
model.set_ref("output_layer", linear_layer)
|
||
|
|
model.set_ref("tok2vec", tok2vec)
|
||
|
|
model.set_dim("nO", nO)
|
||
|
|
model.attrs["multi_label"] = not exclusive_classes
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
def TextCatBOW_v1(
|
||
|
|
exclusive_classes: bool,
|
||
|
|
ngram_size: int,
|
||
|
|
no_output_layer: bool,
|
||
|
|
nO: Optional[int] = None,
|
||
|
|
) -> Model[List[Doc], Floats2d]:
|
||
|
|
chain = registry.get("layers", "chain.v1")
|
||
|
|
Logistic = registry.get("layers", "Logistic.v1")
|
||
|
|
SparseLinear = registry.get("layers", "SparseLinear.v1")
|
||
|
|
softmax_activation = registry.get("layers", "softmax_activation.v1")
|
||
|
|
extract_ngrams = registry.get("layers", "spacy.extract_ngrams.v1")
|
||
|
|
|
||
|
|
with Model.define_operators({">>": chain}):
|
||
|
|
sparse_linear = SparseLinear(nO)
|
||
|
|
model = extract_ngrams(ngram_size, attr=ORTH) >> sparse_linear
|
||
|
|
model = with_cpu(model, model.ops)
|
||
|
|
if not no_output_layer:
|
||
|
|
output_layer = softmax_activation() if exclusive_classes else Logistic()
|
||
|
|
model = model >> with_cpu(output_layer, output_layer.ops)
|
||
|
|
model.set_ref("output_layer", sparse_linear)
|
||
|
|
model.attrs["multi_label"] = not exclusive_classes
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
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.
|
||
|
|
HashEmbed = registry.get("layers", "HashEmbed.v1")
|
||
|
|
FeatureExtractor = registry.get("layers", "spacy.FeatureExtractor.v1")
|
||
|
|
Maxout = registry.get("layers", "Maxout.v1")
|
||
|
|
StaticVectors = registry.get("layers", "spacy.StaticVectors.v1")
|
||
|
|
Softmax = registry.get("layers", "Softmax.v1")
|
||
|
|
Linear = registry.get("layers", "Linear.v1")
|
||
|
|
ParametricAttention = registry.get("layers", "ParametricAttention.v1")
|
||
|
|
Dropout = registry.get("layers", "Dropout.v1")
|
||
|
|
Logistic = registry.get("layers", "Logistic.v1")
|
||
|
|
build_bow_text_classifier = registry.get("architectures", "spacy.TextCatBOW.v1")
|
||
|
|
list2ragged = registry.get("layers", "list2ragged.v1")
|
||
|
|
chain = registry.get("layers", "chain.v1")
|
||
|
|
concatenate = registry.get("layers", "concatenate.v1")
|
||
|
|
clone = registry.get("layers", "clone.v1")
|
||
|
|
reduce_sum = registry.get("layers", "reduce_sum.v1")
|
||
|
|
with_array = registry.get("layers", "with_array.v1")
|
||
|
|
uniqued = registry.get("layers", "uniqued.v1")
|
||
|
|
residual = registry.get("layers", "residual.v1")
|
||
|
|
expand_window = registry.get("layers", "expand_window.v1")
|
||
|
|
|
||
|
|
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
|