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43 lines
1.1 KiB
Python
43 lines
1.1 KiB
Python
from __future__ import unicode_literals
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import spacy
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from spacy.vocab import Vocab
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from spacy.matcher import Matcher
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from spacy.tokens.doc import Doc
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from spacy.attrs import *
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from spacy.pipeline import EntityRecognizer
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import pytest
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@pytest.fixture(scope="module")
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def en_vocab():
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return spacy.get_lang_class('en').Defaults.create_vocab()
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@pytest.fixture(scope="module")
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def entity_recognizer(en_vocab):
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return EntityRecognizer(en_vocab, features=[(2,), (3,)])
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@pytest.fixture
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def animal(en_vocab):
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return nlp.vocab.strings[u"ANIMAL"]
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@pytest.fixture
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def doc(en_vocab, entity_recognizer):
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doc = Doc(en_vocab, words=[u"this", u"is", u"a", u"lion"])
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entity_recognizer(doc)
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return doc
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def test_set_ents_iob(doc):
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assert len(list(doc.ents)) == 0
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tags = [w.ent_iob_ for w in doc]
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assert tags == (['O'] * len(doc))
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doc.ents = [(doc.vocab.strings['ANIMAL'], 3, 4)]
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tags = [w.ent_iob_ for w in doc]
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assert tags == ['O', 'O', 'O', 'B']
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doc.ents = [(doc.vocab.strings['WORD'], 0, 2)]
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tags = [w.ent_iob_ for w in doc]
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assert tags == ['B', 'I', 'O', 'O']
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