mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
7d50804644
* Migrate regressions 1-1000 * Move serialize test to correct file * Remove tests that won't work in v3 * Migrate regressions 1000-1500 Removed regression test 1250 because v3 doesn't support the old LEX scheme anymore. * Add missing imports in serializer tests * Migrate tests 1500-2000 * Migrate regressions from 2000-2500 * Migrate regressions from 2501-3000 * Migrate regressions from 3000-3501 * Migrate regressions from 3501-4000 * Migrate regressions from 4001-4500 * Migrate regressions from 4501-5000 * Migrate regressions from 5001-5501 * Migrate regressions from 5501 to 7000 * Migrate regressions from 7001 to 8000 * Migrate remaining regression tests * Fixing missing imports * Update docs with new system [ci skip] * Update CONTRIBUTING.md - Fix formatting - Update wording * Remove lemmatizer tests in el lang * Move a few tests into the general tokenizer * Separate Doc and DocBin tests
74 lines
2.0 KiB
Python
74 lines
2.0 KiB
Python
import pytest
|
|
from spacy.lang.es.lex_attrs import like_num
|
|
from spacy.lang.es import Spanish
|
|
|
|
|
|
@pytest.mark.issue(3803)
|
|
def test_issue3803():
|
|
"""Test that spanish num-like tokens have True for like_num attribute."""
|
|
nlp = Spanish()
|
|
text = "2 dos 1000 mil 12 doce"
|
|
doc = nlp(text)
|
|
|
|
assert [t.like_num for t in doc] == [True, True, True, True, True, True]
|
|
|
|
|
|
def test_es_tokenizer_handles_long_text(es_tokenizer):
|
|
text = """Cuando a José Mujica lo invitaron a dar una conferencia
|
|
|
|
en Oxford este verano, su cabeza hizo "crac". La "más antigua" universidad de habla
|
|
|
|
inglesa, esa que cobra decenas de miles de euros de matrícula a sus alumnos
|
|
|
|
y en cuyos salones han disertado desde Margaret Thatcher hasta Stephen Hawking,
|
|
|
|
reclamaba los servicios de este viejo de 81 años, formado en un colegio público
|
|
|
|
en Montevideo y que pregona las bondades de la vida austera."""
|
|
tokens = es_tokenizer(text)
|
|
assert len(tokens) == 90
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"text,length",
|
|
[
|
|
("¿Por qué José Mujica?", 6),
|
|
("“¿Oh no?”", 6),
|
|
("""¡Sí! "Vámonos", contestó José Arcadio Buendía""", 11),
|
|
("Corrieron aprox. 10km.", 5),
|
|
("Y entonces por qué...", 5),
|
|
],
|
|
)
|
|
def test_es_tokenizer_handles_cnts(es_tokenizer, text, length):
|
|
tokens = es_tokenizer(text)
|
|
assert len(tokens) == length
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"text,match",
|
|
[
|
|
("10", True),
|
|
("1", True),
|
|
("10.000", True),
|
|
("1000", True),
|
|
("999,0", True),
|
|
("uno", True),
|
|
("dos", True),
|
|
("billón", True),
|
|
("veintiséis", True),
|
|
("perro", False),
|
|
(",", False),
|
|
("1/2", True),
|
|
],
|
|
)
|
|
def test_lex_attrs_like_number(es_tokenizer, text, match):
|
|
tokens = es_tokenizer(text)
|
|
assert len(tokens) == 1
|
|
assert tokens[0].like_num == match
|
|
|
|
|
|
@pytest.mark.parametrize("word", ["once"])
|
|
def test_es_lex_attrs_capitals(word):
|
|
assert like_num(word)
|
|
assert like_num(word.upper())
|