def build_text_classifier_v2(
tok2vec: Model[List[Doc], List[Floats2d]],
linear_model: Model[List[Doc], Floats2d],
nO: Optional[int] = None,
) -> Model[List[Doc], Floats2d]:
exclusive_classes = not linear_model.attrs["multi_label"]
with Model.define_operators({">>": chain, "|": concatenate}):
width = tok2vec.maybe_get_dim("nO")
attention_layer = ParametricAttention(
width) # TODO: benchmark performance difference of this layer
maxout_layer = Maxout(nO=width, nI=width)
norm_layer = LayerNorm(nI=width)
cnn_model = (
tok2vec >> list2ragged() >> attention_layer >> reduce_sum() >>
residual(maxout_layer >> norm_layer >> Dropout(0.0)))
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) >> 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.set_ref("attention_layer", attention_layer)
model.set_ref("maxout_layer", maxout_layer)
model.set_ref("norm_layer", norm_layer)
model.attrs["multi_label"] = not exclusive_classes
model.init = init_ensemble_textcat
return model
def build_cloze_characters_multi_task_model(
vocab: "Vocab", tok2vec: Model, maxout_pieces: int, hidden_size: int, nr_char: int
) -> Model:
output_layer = chain(
list2array(),
Maxout(nO=hidden_size, nP=maxout_pieces),
LayerNorm(nI=hidden_size),
MultiSoftmax([256] * nr_char, nI=hidden_size),
)
model = build_masked_language_model(vocab, chain(tok2vec, output_layer))
model.set_ref("tok2vec", tok2vec)
model.set_ref("output_layer", output_layer)
return model
def build_multi_task_model(
tok2vec: Model,
maxout_pieces: int,
token_vector_width: int,
nO: Optional[int] = None,
) -> Model:
softmax = Softmax(nO=nO, nI=token_vector_width * 2)
model = chain(
tok2vec,
Maxout(
nO=token_vector_width * 2,
nI=token_vector_width,
nP=maxout_pieces,
dropout=0.0,
),
LayerNorm(token_vector_width * 2),
softmax,
)
model.set_ref("tok2vec", tok2vec)
model.set_ref("output_layer", softmax)
return model
