mirror of
https://github.com/explosion/spaCy.git
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c95ce96c44
* Update sentence recognizer * rename `sentrec` to `senter` * use `spacy.HashEmbedCNN.v1` by default * update to follow `Tagger` modifications * remove component methods that can be inherited from `Tagger` * add simple initialization and overfitting pipeline tests * Update serialization test for senter
53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
import pytest
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from spacy import util
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from spacy.lang.en import English
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from spacy.language import Language
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from spacy.tests.util import make_tempdir
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def test_label_types():
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nlp = Language()
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nlp.add_pipe(nlp.create_pipe("senter"))
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with pytest.raises(NotImplementedError):
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nlp.get_pipe("senter").add_label("A")
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SENT_STARTS = [0] * 14
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SENT_STARTS[0] = 1
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SENT_STARTS[5] = 1
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SENT_STARTS[9] = 1
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TRAIN_DATA = [
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("I like green eggs. Eat blue ham. I like purple eggs.", {"sent_starts": SENT_STARTS}),
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("She likes purple eggs. They hate ham. You like yellow eggs.", {"sent_starts": SENT_STARTS}),
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]
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def test_overfitting_IO():
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# Simple test to try and quickly overfit the senter - ensuring the ML models work correctly
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nlp = English()
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senter = nlp.create_pipe("senter")
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nlp.add_pipe(senter)
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optimizer = nlp.begin_training()
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for i in range(200):
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losses = {}
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nlp.update(TRAIN_DATA, sgd=optimizer, losses=losses)
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assert losses["senter"] < 0.0001
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# test the trained model
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test_text = "I like eggs. There is ham. She likes ham."
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doc = nlp(test_text)
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gold_sent_starts = [0] * 12
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gold_sent_starts[0] = 1
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gold_sent_starts[4] = 1
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gold_sent_starts[8] = 1
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assert gold_sent_starts == [int(t.is_sent_start) for t in doc]
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# Also test the results are still the same after IO
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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nlp2 = util.load_model_from_path(tmp_dir)
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doc2 = nlp2(test_text)
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assert gold_sent_starts == [int(t.is_sent_start) for t in doc2]
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