179 lines
6.7 KiB
Python
179 lines
6.7 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, with_cpu
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from spacy.attrs import ID, ORTH, PREFIX, SUFFIX, SHAPE, LOWER
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from spacy.util import registry
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from spacy.tokens import Doc
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def TextCatCNN_v1(
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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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chain = registry.get("layers", "chain.v1")
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reduce_mean = registry.get("layers", "reduce_mean.v1")
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Logistic = registry.get("layers", "Logistic.v1")
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Softmax = registry.get("layers", "Softmax.v1")
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Linear = registry.get("layers", "Linear.v1")
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list2ragged = registry.get("layers", "list2ragged.v1")
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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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def TextCatBOW_v1(
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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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chain = registry.get("layers", "chain.v1")
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Logistic = registry.get("layers", "Logistic.v1")
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SparseLinear = registry.get("layers", "SparseLinear.v1")
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softmax_activation = registry.get("layers", "softmax_activation.v1")
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extract_ngrams = registry.get("layers", "spacy.extract_ngrams.v1")
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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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def TextCatEnsemble_v1(
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width: int,
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embed_size: int,
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pretrained_vectors: Optional[bool],
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exclusive_classes: bool,
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ngram_size: int,
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window_size: int,
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conv_depth: int,
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dropout: Optional[float],
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nO: Optional[int] = None,
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) -> Model:
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# Don't document this yet, I'm not sure it's right.
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HashEmbed = registry.get("layers", "HashEmbed.v1")
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FeatureExtractor = registry.get("layers", "spacy.FeatureExtractor.v1")
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Maxout = registry.get("layers", "Maxout.v1")
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StaticVectors = registry.get("layers", "spacy.StaticVectors.v1")
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Softmax = registry.get("layers", "Softmax.v1")
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Linear = registry.get("layers", "Linear.v1")
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ParametricAttention = registry.get("layers", "ParametricAttention.v1")
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Dropout = registry.get("layers", "Dropout.v1")
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Logistic = registry.get("layers", "Logistic.v1")
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build_bow_text_classifier = registry.get("architectures", "spacy.TextCatBOW.v1")
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list2ragged = registry.get("layers", "list2ragged.v1")
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chain = registry.get("layers", "chain.v1")
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concatenate = registry.get("layers", "concatenate.v1")
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clone = registry.get("layers", "clone.v1")
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reduce_sum = registry.get("layers", "reduce_sum.v1")
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with_array = registry.get("layers", "with_array.v1")
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uniqued = registry.get("layers", "uniqued.v1")
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residual = registry.get("layers", "residual.v1")
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expand_window = registry.get("layers", "expand_window.v1")
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cols = [ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]
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with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
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lower = HashEmbed(
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nO=width, nV=embed_size, column=cols.index(LOWER), dropout=dropout, seed=10
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)
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prefix = HashEmbed(
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nO=width // 2,
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nV=embed_size,
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column=cols.index(PREFIX),
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dropout=dropout,
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seed=11,
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)
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suffix = HashEmbed(
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nO=width // 2,
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nV=embed_size,
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column=cols.index(SUFFIX),
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dropout=dropout,
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seed=12,
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)
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shape = HashEmbed(
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nO=width // 2,
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nV=embed_size,
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column=cols.index(SHAPE),
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dropout=dropout,
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seed=13,
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)
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width_nI = sum(layer.get_dim("nO") for layer in [lower, prefix, suffix, shape])
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trained_vectors = FeatureExtractor(cols) >> with_array(
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uniqued(
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(lower | prefix | suffix | shape)
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>> Maxout(nO=width, nI=width_nI, normalize=True),
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column=cols.index(ORTH),
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)
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)
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if pretrained_vectors:
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static_vectors = StaticVectors(width)
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vector_layer = trained_vectors | static_vectors
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vectors_width = width * 2
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else:
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vector_layer = trained_vectors
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vectors_width = width
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tok2vec = vector_layer >> with_array(
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Maxout(width, vectors_width, normalize=True)
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>> residual(
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(
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expand_window(window_size=window_size)
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>> Maxout(
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nO=width, nI=width * ((window_size * 2) + 1), normalize=True
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)
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)
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)
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** conv_depth,
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pad=conv_depth,
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)
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cnn_model = (
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tok2vec
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>> list2ragged()
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>> ParametricAttention(width)
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>> reduce_sum()
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>> residual(Maxout(nO=width, nI=width))
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>> Linear(nO=nO, nI=width)
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>> Dropout(0.0)
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)
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linear_model = build_bow_text_classifier(
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nO=nO,
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ngram_size=ngram_size,
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exclusive_classes=exclusive_classes,
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no_output_layer=False,
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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) >> Dropout(0.0) >> 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.attrs["multi_label"] = not exclusive_classes
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return model
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