2020-06-26 17:34:12 +00:00
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from thinc.api import (
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Model,
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reduce_mean,
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Linear,
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list2ragged,
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Logistic,
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ParametricAttention,
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)
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from thinc.api import chain, concatenate, clone, Dropout
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from thinc.api import (
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SparseLinear,
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Softmax,
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softmax_activation,
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Maxout,
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reduce_sum,
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Relu,
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residual,
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expand_window,
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)
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from thinc.api import (
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HashEmbed,
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with_ragged,
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with_array,
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with_cpu,
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uniqued,
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FeatureExtractor,
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)
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2020-02-27 17:42:27 +00:00
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2020-03-29 17:40:36 +00:00
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from ..spacy_vectors import SpacyVectors
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from ... import util
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2020-06-20 12:15:04 +00:00
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from ...attrs import ID, ORTH, PREFIX, SUFFIX, SHAPE, LOWER
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2020-02-28 10:57:41 +00:00
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from ...util import registry
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from ..extract_ngrams import extract_ngrams
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2020-02-27 17:42:27 +00:00
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@registry.architectures.register("spacy.TextCatCNN.v1")
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def build_simple_cnn_text_classifier(tok2vec, exclusive_classes, nO=None):
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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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if exclusive_classes:
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output_layer = Softmax(nO=nO, nI=tok2vec.get_dim("nO"))
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model = tok2vec >> list2ragged() >> reduce_mean() >> 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.get_dim("nO"))
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2020-02-28 10:57:41 +00:00
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model = (
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tok2vec >> list2ragged() >> reduce_mean() >> linear_layer >> Logistic()
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)
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2020-02-27 17:42:27 +00:00
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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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2020-06-12 00:02:07 +00:00
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model.attrs["multi_label"] = not exclusive_classes
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2020-02-27 17:42:27 +00:00
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return model
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@registry.architectures.register("spacy.TextCatBOW.v1")
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def build_bow_text_classifier(exclusive_classes, ngram_size, no_output_layer, nO=None):
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with Model.define_operators({">>": chain}):
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2020-03-29 17:40:36 +00:00
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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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2020-02-27 17:42:27 +00:00
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if not no_output_layer:
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2020-03-29 17:40:36 +00:00
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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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2020-06-12 00:02:07 +00:00
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model.attrs["multi_label"] = not exclusive_classes
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2020-03-29 17:40:36 +00:00
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return model
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@registry.architectures.register("spacy.TextCat.v1")
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2020-06-20 12:15:04 +00:00
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def build_text_classifier(
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width,
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embed_size,
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pretrained_vectors,
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exclusive_classes,
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ngram_size,
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window_size,
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conv_depth,
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dropout,
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nO=None,
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):
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2020-03-29 17:40:36 +00:00
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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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2020-06-20 12:15:04 +00:00
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lower = HashEmbed(
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nO=width, nV=embed_size, column=cols.index(LOWER), dropout=dropout
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)
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prefix = HashEmbed(
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nO=width // 2, nV=embed_size, column=cols.index(PREFIX), dropout=dropout
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)
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suffix = HashEmbed(
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nO=width // 2, nV=embed_size, column=cols.index(SUFFIX), dropout=dropout
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)
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shape = HashEmbed(
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nO=width // 2, nV=embed_size, column=cols.index(SHAPE), dropout=dropout
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)
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2020-03-29 17:40:36 +00:00
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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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nlp = util.load_model(pretrained_vectors)
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vectors = nlp.vocab.vectors
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vector_dim = vectors.data.shape[1]
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static_vectors = SpacyVectors(vectors) >> with_array(
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Linear(width, vector_dim)
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)
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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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2020-06-20 12:15:04 +00:00
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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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2020-03-29 17:40:36 +00:00
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pad=conv_depth,
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)
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cnn_model = (
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2020-06-20 12:15:04 +00:00
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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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2020-03-29 17:40:36 +00:00
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)
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linear_model = build_bow_text_classifier(
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2020-06-20 12:15:04 +00:00
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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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2020-03-29 17:40:36 +00:00
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)
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2020-06-20 12:15:04 +00:00
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nO_double = nO * 2 if nO else None
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2020-03-29 17:40:36 +00:00
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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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2020-06-20 12:15:04 +00:00
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output_layer = Linear(nO=nO, nI=nO_double) >> Dropout(0.0) >> Logistic()
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2020-03-29 17:40:36 +00:00
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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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2020-06-12 00:02:07 +00:00
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model.attrs["multi_label"] = not exclusive_classes
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2020-03-29 17:40:36 +00:00
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return model
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@registry.architectures.register("spacy.TextCatLowData.v1")
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2020-06-03 09:50:16 +00:00
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def build_text_classifier_lowdata(width, pretrained_vectors, dropout, nO=None):
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2020-03-29 17:40:36 +00:00
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nlp = util.load_model(pretrained_vectors)
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vectors = nlp.vocab.vectors
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vector_dim = vectors.data.shape[1]
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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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SpacyVectors(vectors)
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>> list2ragged()
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>> with_ragged(0, Linear(width, vector_dim))
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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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2020-06-03 09:50:16 +00:00
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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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2020-02-27 17:42:27 +00:00
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return model
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