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Also test max cuts with NER overfitting
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@ -540,11 +540,13 @@ def test_block_ner():
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assert [token.ent_type_ for token in doc] == expected_types
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def test_overfitting_IO():
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@pytest.mark.parametrize("max_moves", [0, 1, 5, 100])
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def test_overfitting_IO(max_moves):
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fix_random_seed(1)
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# Simple test to try and quickly overfit the NER component
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nlp = English()
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ner = nlp.add_pipe("ner", config={"model": {}})
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ner.cfg["update_with_oracle_cut_size"] = max_moves
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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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