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37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
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# coding: utf8
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from __future__ import unicode_literals
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from spacy.lang.en import English
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from spacy.pipeline import EntityRuler
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def test_issue4849():
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nlp = English()
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ruler = EntityRuler(
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nlp, patterns=[
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{"label": "PERSON", "pattern": 'joe biden', "id": 'joe-biden'},
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{"label": "PERSON", "pattern": 'bernie sanders', "id": 'bernie-sanders'},
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],
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phrase_matcher_attr="LOWER"
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)
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nlp.add_pipe(ruler)
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text = """
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The left is starting to take aim at Democratic front-runner Joe Biden.
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Sen. Bernie Sanders joined in her criticism: "There is no 'middle ground' when it comes to climate policy."
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"""
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# USING 1 PROCESS
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count_ents = 0
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for doc in nlp.pipe([text], n_process=1):
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count_ents += len([ent for ent in doc.ents if ent.ent_id > 0])
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assert(count_ents == 2)
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# USING 2 PROCESSES
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count_ents = 0
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for doc in nlp.pipe([text], n_process=2):
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count_ents += len([ent for ent in doc.ents if ent.ent_id > 0])
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assert (count_ents == 2)
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