mirror of
https://github.com/explosion/spaCy.git
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75a202ce65
* small fix in example imports * throw error when train_corpus or dev_corpus is not a string * small fix in custom logger example * limit macro_auc to labels with 2 annotations * fix typo * also create parents of output_dir if need be * update documentation of textcat scores * refactor TextCatEnsemble * fix tests for new AUC definition * bump to 3.0.0a42 * update docs * rename to spacy.TextCatEnsemble.v2 * spacy.TextCatEnsemble.v1 in legacy * cleanup * small fix * update to 3.0.0rc2 * fix import that got lost in merge * cursed IDE * fix two typos
212 lines
7.7 KiB
Python
212 lines
7.7 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, 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 HashEmbed, with_array, with_cpu, uniqued
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from thinc.api import Relu, residual, expand_window
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from ...attrs import ID, ORTH, PREFIX, SUFFIX, SHAPE, LOWER
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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 ..featureextractor import FeatureExtractor
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from ...tokens import Doc
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@registry.architectures.register("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.register("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.register("spacy.TextCatEnsemble.v2")
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def build_text_classifier(
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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.get_dim("nO")
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cnn_model = (
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tok2vec
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>> list2ragged()
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>> ParametricAttention(width) # TODO: benchmark performance difference of this layer
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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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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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# TODO: move to legacy
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@registry.architectures.register("spacy.TextCatEnsemble.v1")
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def build_text_classifier(
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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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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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@registry.architectures.register("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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