spaCy/spacy/tests/parser/test_preset_sbd.py

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'''Test that the parser respects preset sentence boundaries.'''
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from __future__ import unicode_literals
import pytest
from thinc.neural.optimizers import Adam
from thinc.neural.ops import NumpyOps
from ...attrs import NORM
from ...gold import GoldParse
from ...vocab import Vocab
from ...tokens import Doc
from ...pipeline import DependencyParser
@pytest.fixture
def vocab():
return Vocab(lex_attr_getters={NORM: lambda s: s})
@pytest.fixture
def parser(vocab):
parser = DependencyParser(vocab)
parser.cfg['token_vector_width'] = 4
parser.cfg['hidden_width'] = 32
#parser.add_label('right')
parser.add_label('left')
parser.begin_training([], **parser.cfg)
sgd = Adam(NumpyOps(), 0.001)
for i in range(10):
losses = {}
doc = Doc(vocab, words=['a', 'b', 'c', 'd'])
gold = GoldParse(doc, heads=[1, 1, 3, 3],
deps=['left', 'ROOT', 'left', 'ROOT'])
parser.update([doc], [gold], sgd=sgd, losses=losses)
return parser
def test_no_sentences(parser):
doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
doc = parser(doc)
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assert len(list(doc.sents)) >= 1
def test_sents_1(parser):
doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
doc[2].sent_start = True
doc = parser(doc)
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assert len(list(doc.sents)) >= 2
doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
doc[1].sent_start = False
doc[2].sent_start = True
doc[3].sent_start = False
doc = parser(doc)
assert len(list(doc.sents)) == 2
def test_sents_1_2(parser):
doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
doc[1].sent_start = True
doc[2].sent_start = True
doc = parser(doc)
assert len(list(doc.sents)) == 3
def test_sents_1_3(parser):
doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
doc[1].sent_start = True
doc[3].sent_start = True
doc = parser(doc)
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assert len(list(doc.sents)) >= 3
doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
doc[1].sent_start = True
doc[2].sent_start = False
doc[3].sent_start = True
doc = parser(doc)
assert len(list(doc.sents)) == 3