def build_bow_text_classifier(
exclusive_classes: bool,
ngram_size: int,
no_output_layer: bool,
nO: Optional[int] = None,
) -> Model[List[Doc], Floats2d]:
fill_defaults = {"b": 0, "W": 0}
with Model.define_operators({">>": chain}):
sparse_linear = SparseLinear(nO=nO)
output_layer = None
if not no_output_layer:
fill_defaults["b"] = NEG_VALUE
output_layer = softmax_activation() if exclusive_classes else Logistic()
resizable_layer = resizable(
sparse_linear,
resize_layer=partial(resize_linear_weighted, fill_defaults=fill_defaults),
)
model = extract_ngrams(ngram_size, attr=ORTH) >> resizable_layer
model = with_cpu(model, model.ops)
if output_layer:
model = model >> with_cpu(output_layer, output_layer.ops)
model.set_dim("nO", nO)
model.set_ref("output_layer", sparse_linear)
model.attrs["multi_label"] = not exclusive_classes
model.attrs["resize_output"] = partial(
resize_and_set_ref, resizable_layer=resizable_layer
)
return model
def build_bow_text_classifier(
exclusive_classes: bool,
ngram_size: int,
no_output_layer: bool,
nO: Optional[int] = None,
) -> Model[List[Doc], Floats2d]:
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 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")
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