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	Fix redundant test. 2 failures
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				|  | @ -205,7 +205,7 @@ def test_train_empty(): | |||
|         train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1])) | ||||
|     ner = nlp.add_pipe("ner", last=True) | ||||
|     ner.add_label("PERSON") | ||||
|     nlp.initialize() | ||||
|     nlp.initialize(get_examples=lambda: train_examples) | ||||
|     for itn in range(2): | ||||
|         losses = {} | ||||
|         batches = util.minibatch(train_examples, size=8) | ||||
|  | @ -301,11 +301,10 @@ def test_block_ner(): | |||
|     assert [token.ent_type_ for token in doc] == expected_types | ||||
| 
 | ||||
| 
 | ||||
| @pytest.mark.parametrize("use_upper", [True, False]) | ||||
| def test_overfitting_IO(use_upper): | ||||
| def test_overfitting_IO(): | ||||
|     # Simple test to try and quickly overfit the NER component | ||||
|     nlp = English() | ||||
|     ner = nlp.add_pipe("ner", config={"model": {"use_upper": use_upper}}) | ||||
|     ner = nlp.add_pipe("ner", config={"model": {}}) | ||||
|     train_examples = [] | ||||
|     for text, annotations in TRAIN_DATA: | ||||
|         train_examples.append(Example.from_dict(nlp.make_doc(text), annotations)) | ||||
|  | @ -337,7 +336,6 @@ def test_overfitting_IO(use_upper): | |||
|         assert ents2[0].label_ == "LOC" | ||||
|         # Ensure that the predictions are still the same, even after adding a new label | ||||
|         ner2 = nlp2.get_pipe("ner") | ||||
|         assert ner2.model.attrs["has_upper"] == use_upper | ||||
|         ner2.add_label("RANDOM_NEW_LABEL") | ||||
|         doc3 = nlp2(test_text) | ||||
|         ents3 = doc3.ents | ||||
|  |  | |||
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