mirror of https://github.com/explosion/spaCy.git
134 lines
5.1 KiB
Python
134 lines
5.1 KiB
Python
from typing import Optional, List
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from thinc.types import Floats2d
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from thinc.api import Model, reduce_mean, Linear, list2ragged, Logistic
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from thinc.api import chain, concatenate, clone, Dropout, ParametricAttention
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from thinc.api import SparseLinear, Softmax, softmax_activation, Maxout, reduce_sum
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from thinc.api import with_cpu, Relu, residual, LayerNorm
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from thinc.layers.chain import init as init_chain
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from ...attrs import ORTH
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from ...util import registry
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from ..extract_ngrams import extract_ngrams
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from ..staticvectors import StaticVectors
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from ...tokens import Doc
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from .tok2vec import get_tok2vec_width
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@registry.architectures("spacy.TextCatCNN.v1")
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def build_simple_cnn_text_classifier(
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tok2vec: Model, exclusive_classes: bool, nO: Optional[int] = None
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) -> Model[List[Doc], Floats2d]:
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"""
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Build a simple CNN text classifier, given a token-to-vector model as inputs.
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If exclusive_classes=True, a softmax non-linearity is applied, so that the
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outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
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is applied instead, so that outputs are in the range [0, 1].
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"""
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with Model.define_operators({">>": chain}):
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cnn = tok2vec >> list2ragged() >> reduce_mean()
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if exclusive_classes:
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output_layer = Softmax(nO=nO, nI=tok2vec.maybe_get_dim("nO"))
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model = cnn >> output_layer
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model.set_ref("output_layer", output_layer)
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else:
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linear_layer = Linear(nO=nO, nI=tok2vec.maybe_get_dim("nO"))
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model = cnn >> linear_layer >> Logistic()
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model.set_ref("output_layer", linear_layer)
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model.set_ref("tok2vec", tok2vec)
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model.set_dim("nO", nO)
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model.attrs["multi_label"] = not exclusive_classes
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return model
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@registry.architectures("spacy.TextCatBOW.v1")
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def build_bow_text_classifier(
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exclusive_classes: bool,
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ngram_size: int,
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no_output_layer: bool,
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nO: Optional[int] = None,
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) -> Model[List[Doc], Floats2d]:
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with Model.define_operators({">>": chain}):
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sparse_linear = SparseLinear(nO)
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model = extract_ngrams(ngram_size, attr=ORTH) >> sparse_linear
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model = with_cpu(model, model.ops)
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if not no_output_layer:
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output_layer = softmax_activation() if exclusive_classes else Logistic()
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model = model >> with_cpu(output_layer, output_layer.ops)
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model.set_ref("output_layer", sparse_linear)
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model.attrs["multi_label"] = not exclusive_classes
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return model
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@registry.architectures("spacy.TextCatEnsemble.v2")
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def build_text_classifier_v2(
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tok2vec: Model[List[Doc], List[Floats2d]],
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linear_model: Model[List[Doc], Floats2d],
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nO: Optional[int] = None,
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) -> Model[List[Doc], Floats2d]:
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exclusive_classes = not linear_model.attrs["multi_label"]
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with Model.define_operators({">>": chain, "|": concatenate}):
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width = tok2vec.maybe_get_dim("nO")
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attention_layer = ParametricAttention(
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width
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) # TODO: benchmark performance difference of this layer
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maxout_layer = Maxout(nO=width, nI=width)
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norm_layer = LayerNorm(nI=width)
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cnn_model = (
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tok2vec
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>> list2ragged()
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>> attention_layer
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>> reduce_sum()
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>> residual(maxout_layer >> norm_layer >> Dropout(0.0))
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)
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nO_double = nO * 2 if nO else None
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if exclusive_classes:
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output_layer = Softmax(nO=nO, nI=nO_double)
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else:
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output_layer = Linear(nO=nO, nI=nO_double) >> Logistic()
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model = (linear_model | cnn_model) >> output_layer
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model.set_ref("tok2vec", tok2vec)
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if model.has_dim("nO") is not False:
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model.set_dim("nO", nO)
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model.set_ref("output_layer", linear_model.get_ref("output_layer"))
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model.set_ref("attention_layer", attention_layer)
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model.set_ref("maxout_layer", maxout_layer)
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model.set_ref("norm_layer", norm_layer)
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model.attrs["multi_label"] = not exclusive_classes
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model.init = init_ensemble_textcat
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return model
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def init_ensemble_textcat(model, X, Y) -> Model:
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tok2vec_width = get_tok2vec_width(model)
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model.get_ref("attention_layer").set_dim("nO", tok2vec_width)
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model.get_ref("maxout_layer").set_dim("nO", tok2vec_width)
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model.get_ref("maxout_layer").set_dim("nI", tok2vec_width)
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model.get_ref("norm_layer").set_dim("nI", tok2vec_width)
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model.get_ref("norm_layer").set_dim("nO", tok2vec_width)
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init_chain(model, X, Y)
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return model
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@registry.architectures("spacy.TextCatLowData.v1")
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def build_text_classifier_lowdata(
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width: int, dropout: Optional[float], nO: Optional[int] = None
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) -> Model[List[Doc], Floats2d]:
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# Don't document this yet, I'm not sure it's right.
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# Note, before v.3, this was the default if setting "low_data" and "pretrained_dims"
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with Model.define_operators({">>": chain, "**": clone}):
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model = (
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StaticVectors(width)
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>> list2ragged()
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>> ParametricAttention(width)
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>> reduce_sum()
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>> residual(Relu(width, width)) ** 2
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>> Linear(nO, width)
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)
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if dropout:
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model = model >> Dropout(dropout)
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model = model >> Logistic()
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return model
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