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d854f69fe3
Allows adding those components to the pipeline out-of-the-box if they're defined in a model's meta.json. Also allows usage as nlp.add_pipe(nlp.create_pipe('merge_entities')).
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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import pytest
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from ..util import get_doc
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from ...language import Language
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from ...tokens import Span
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from ... import util
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@pytest.fixture
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def doc(en_tokenizer):
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text = 'I like New York in Autumn.'
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heads = [1, 0, 1, -2, -3, -1, -5]
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tags = ['PRP', 'IN', 'NNP', 'NNP', 'IN', 'NNP', '.']
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pos = ['PRON', 'VERB', 'PROPN', 'PROPN', 'ADP', 'PROPN', 'PUNCT']
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deps = ['ROOT', 'prep', 'compound', 'pobj', 'prep', 'pobj', 'punct']
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads,
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tags=tags, pos=pos, deps=deps)
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doc.ents = [Span(doc, 2, 4, doc.vocab.strings['GPE'])]
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doc.is_parsed = True
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doc.is_tagged = True
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return doc
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def test_factories_merge_noun_chunks(doc):
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assert len(doc) == 7
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nlp = Language()
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merge_noun_chunks = nlp.create_pipe('merge_noun_chunks')
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merge_noun_chunks(doc)
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assert len(doc) == 6
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assert doc[2].text == 'New York'
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def test_factories_merge_ents(doc):
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assert len(doc) == 7
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assert len(list(doc.ents)) == 1
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nlp = Language()
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merge_entities = nlp.create_pipe('merge_entities')
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merge_entities(doc)
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assert len(doc) == 6
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assert len(list(doc.ents)) == 1
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assert doc[2].text == 'New York'
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