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38 lines
1.0 KiB
Python
38 lines
1.0 KiB
Python
# 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,
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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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