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Fix redundant test. 2 failures
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@ -131,6 +131,41 @@ def test_negative_sample_key_is_in_config(vocab, entity_types):
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assert tsys.cfg["neg_key"] == "non_entities"
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@pytest.mark.filterwarnings("ignore::UserWarning")
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def test_get_oracle_moves_negative_entities(tsys, doc, entity_annots):
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entity_annots = [(s, e, "!" + label) for s, e, label in entity_annots]
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example = Example.from_dict(doc, {"entities": entity_annots})
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ex_dict = example.to_dict()
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for i, tag in enumerate(ex_dict["doc_annotation"]["entities"]):
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if tag == "L-!GPE":
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ex_dict["doc_annotation"]["entities"][i] = "-"
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example = Example.from_dict(doc, ex_dict)
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act_classes = tsys.get_oracle_sequence(example)
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names = [tsys.get_class_name(act) for act in act_classes]
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assert names
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def test_get_oracle_moves_negative_entities2(tsys, vocab):
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doc = Doc(vocab, words=["A", "B", "C", "D"])
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entity_annots = ["B-!PERSON", "L-!PERSON", "B-!PERSON", "L-!PERSON"]
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example = Example.from_dict(doc, {"entities": entity_annots})
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act_classes = tsys.get_oracle_sequence(example)
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names = [tsys.get_class_name(act) for act in act_classes]
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assert names
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@pytest.mark.skip(reason="Maybe outdated? Unsure")
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def test_get_oracle_moves_negative_O(tsys, vocab):
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doc = Doc(vocab, words=["A", "B", "C", "D"])
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entity_annots = ["O", "!O", "O", "!O"]
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example = Example.from_dict(doc, {"entities": entity_annots})
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act_classes = tsys.get_oracle_sequence(example)
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names = [tsys.get_class_name(act) for act in act_classes]
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assert names
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# We can't easily represent this on a Doc object. Not sure what the best solution
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# would be, but I don't think it's an important use case?
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@pytest.mark.skip(reason="No longer supported")
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@ -242,7 +277,7 @@ def test_train_empty():
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train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
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ner = nlp.add_pipe("ner", last=True)
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ner.add_label("PERSON")
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nlp.initialize()
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nlp.initialize(get_examples=lambda: train_examples)
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for itn in range(2):
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losses = {}
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batches = util.minibatch(train_examples, size=8)
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@ -372,7 +407,7 @@ def test_block_ner():
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def test_overfitting_IO():
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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")
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ner = nlp.add_pipe("ner", config={"model": {}})
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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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