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use en_vocab fixture

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Peter Baumgartner 2023-01-24 13:36:08 -05:00
parent de6e29ae8f
commit 9387d1573f
1 changed files with 5 additions and 6 deletions
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11
spacy/tests/test_cli_app.py
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@ -44,19 +44,18 @@ def test_benchmark_accuracy_alias():
) )
def test_debug_data_trainable_lemmatizer_cli(): def test_debug_data_trainable_lemmatizer_cli(en_vocab):
nlp = English()
train_docs = [ train_docs = [
Doc(nlp.vocab, words=["I", "like", "cats"], lemmas=["I", "like", "cat"]), Doc(en_vocab, words=["I", "like", "cats"], lemmas=["I", "like", "cat"]),
Doc( Doc(
nlp.vocab, en_vocab,
words=["Dogs", "are", "great", "too"], words=["Dogs", "are", "great", "too"],
lemmas=["dog", "be", "great", "too"], lemmas=["dog", "be", "great", "too"],
), ),
] ]
dev_docs = [ dev_docs = [
Doc(nlp.vocab, words=["Cats", "are", "cute"], lemmas=["cat", "be", "cute"]), Doc(en_vocab, words=["Cats", "are", "cute"], lemmas=["cat", "be", "cute"]),
Doc(nlp.vocab, words=["Pets", "are", "great"], lemmas=["pet", "be", "great"]), Doc(en_vocab, words=["Pets", "are", "great"], lemmas=["pet", "be", "great"]),
] ]
with make_tempdir() as d_in: with make_tempdir() as d_in:
train_bin = DocBin(docs=train_docs) train_bin = DocBin(docs=train_docs)