56 lines
2.5 KiB
Python
56 lines
2.5 KiB
Python
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import pytest
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from spacy.lang.en import English
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from spacy.training import Example
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from thinc.api import Config
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default_tok2vec_config = """
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[model]
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@architectures = "spacy-legacy.HashEmbedCNN.v1"
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pretrained_vectors = null
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width = 96
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depth = 4
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embed_size = 2000
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window_size = 1
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maxout_pieces = 3
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subword_features = true
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"""
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DEFAULT_TOK2VEC_MODEL = Config().from_str(default_tok2vec_config)["model"]
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TRAIN_DATA_SINGLE_LABEL = [
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("I'm so happy.", {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}),
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("I'm so angry", {"cats": {"POSITIVE": 0.0, "NEGATIVE": 1.0}}),
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]
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TRAIN_DATA_MULTI_LABEL = [
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("I'm angry and confused", {"cats": {"ANGRY": 1.0, "CONFUSED": 1.0, "HAPPY": 0.0}}),
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("I'm confused but happy", {"cats": {"ANGRY": 0.0, "CONFUSED": 1.0, "HAPPY": 1.0}}),
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]
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# fmt: off
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@pytest.mark.parametrize(
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"name,train_data,textcat_config",
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[
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("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy-legacy.TextCatCNN.v1", "exclusive_classes": True, "tok2vec": DEFAULT_TOK2VEC_MODEL}),
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("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy-legacy.TextCatCNN.v1", "exclusive_classes": False, "tok2vec": DEFAULT_TOK2VEC_MODEL}),
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("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy-legacy.TextCatBOW.v1", "exclusive_classes": False,"ngram_size": 2, "no_output_layer": True}),
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("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy-legacy.TextCatBOW.v1", "exclusive_classes": True,"ngram_size": 3, "no_output_layer": True}),
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("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy-legacy.TextCatEnsemble.v1", "width": 32, "embed_size":16, "exclusive_classes": True,"ngram_size":2,"window_size":4,"conv_depth":1,"pretrained_vectors":False,"dropout":0.1}),
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("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy-legacy.TextCatEnsemble.v1", "width": 32, "embed_size":16, "exclusive_classes": False,"ngram_size":3,"window_size":6,"conv_depth":2,"pretrained_vectors":False,"dropout":0.2}),
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],
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)
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# fmt: on
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def test_textcat(name, train_data, textcat_config):
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pipe_config = {"model": textcat_config}
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nlp = English()
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textcat = nlp.add_pipe(name, config=pipe_config)
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train_examples = []
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for text, annotations in train_data:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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for label, value in annotations.get("cats").items():
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textcat.add_label(label)
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optimizer = nlp.initialize()
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for i in range(5):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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