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use en_vocab fixture
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@ -44,19 +44,18 @@ def test_benchmark_accuracy_alias():
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)
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def test_debug_data_trainable_lemmatizer_cli():
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nlp = English()
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def test_debug_data_trainable_lemmatizer_cli(en_vocab):
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train_docs = [
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Doc(nlp.vocab, words=["I", "like", "cats"], lemmas=["I", "like", "cat"]),
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Doc(en_vocab, words=["I", "like", "cats"], lemmas=["I", "like", "cat"]),
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Doc(
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nlp.vocab,
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en_vocab,
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words=["Dogs", "are", "great", "too"],
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lemmas=["dog", "be", "great", "too"],
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),
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]
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dev_docs = [
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Doc(nlp.vocab, words=["Cats", "are", "cute"], lemmas=["cat", "be", "cute"]),
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Doc(nlp.vocab, words=["Pets", "are", "great"], lemmas=["pet", "be", "great"]),
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Doc(en_vocab, words=["Cats", "are", "cute"], lemmas=["cat", "be", "cute"]),
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Doc(en_vocab, words=["Pets", "are", "great"], lemmas=["pet", "be", "great"]),
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]
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with make_tempdir() as d_in:
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train_bin = DocBin(docs=train_docs)
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