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use en_vocab fixture

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This commit is contained in:
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():
nlp = English()
def test_debug_data_trainable_lemmatizer_cli(en_vocab):
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(
nlp.vocab,
en_vocab,
words=["Dogs", "are", "great", "too"],
lemmas=["dog", "be", "great", "too"],
),
]
dev_docs = [
Doc(nlp.vocab, words=["Cats", "are", "cute"], lemmas=["cat", "be", "cute"]),
Doc(nlp.vocab, words=["Pets", "are", "great"], lemmas=["pet", "be", "great"]),
Doc(en_vocab, words=["Cats", "are", "cute"], lemmas=["cat", "be", "cute"]),
Doc(en_vocab, words=["Pets", "are", "great"], lemmas=["pet", "be", "great"]),
]
with make_tempdir() as d_in:
train_bin = DocBin(docs=train_docs)