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Hack at tests
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@ -198,10 +198,10 @@ def test_overfitting_IO():
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nlp.add_pipe(parser)
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optimizer = nlp.begin_training()
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for i in range(50):
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for i in range(100):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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assert losses["parser"] < 0.00001
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assert losses["parser"] < 0.0001
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# test the trained model
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test_text = "I like securities."
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