repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
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spaCy | spacy/tests/lang/en/test_tokenizer.py | .py | import pytest
@pytest.mark.issue(351)
def test_issue351(en_tokenizer):
doc = en_tokenizer(" This is a cat.")
assert doc[0].idx == 0
assert len(doc[0]) == 3
assert doc[1].idx == 3
@pytest.mark.issue(360)
def test_issue360(en_tokenizer):
"""Test tokenization of big ellipsis"""
tokens = en_to... | 179 | 5,938 |
spaCy | spacy/tests/lang/en/test_noun_chunks.py | .py | import pytest
from spacy.tokens import Doc
@pytest.fixture
def doc(en_vocab):
words = ["Peter", "has", "chronic", "command", "and", "control", "issues"]
heads = [1, 1, 6, 6, 3, 3, 1]
deps = ["nsubj", "ROOT", "amod", "nmod", "cc", "conj", "dobj"]
pos = ["PROPN", "VERB", "ADJ", "NOUN", "CCONJ", "NOUN",... | 46 | 1,549 |
spaCy | spacy/tests/lang/en/test_indices.py | .py | def test_en_simple_punct(en_tokenizer):
text = "to walk, do foo"
tokens = en_tokenizer(text)
assert tokens[0].idx == 0
assert tokens[1].idx == 3
assert tokens[2].idx == 7
assert tokens[3].idx == 9
assert tokens[4].idx == 12
def test_en_complex_punct(en_tokenizer):
text = "Tom (D., Ill.... | 26 | 723 |
spaCy | spacy/tests/lang/en/test_text.py | .py | import pytest
from spacy.lang.en.lex_attrs import like_num
def test_en_tokenizer_handles_long_text(en_tokenizer):
text = """Tributes pour in for late British Labour Party leader
Tributes poured in from around the world Thursday
to the late Labour Party leader John Smith, who died earlier from a massive
heart at... | 71 | 1,963 |
spaCy | spacy/tests/lang/bg/test_tokenizer.py | .py | def test_bg_tokenizer_handles_final_diacritics(bg_tokenizer):
text = "Ня̀маше яйца̀. Ня̀маше яйца̀."
tokens = bg_tokenizer(text)
assert tokens[1].text == "яйца̀"
assert tokens[2].text == "."
| 6 | 236 |
spaCy | spacy/tests/lang/bg/test_text.py | .py | import pytest
@pytest.mark.parametrize(
"word,match",
[
("10", True),
("1", True),
("10000", True),
("1.000", True),
("бројка", False),
("999,23", True),
("едно", True),
("две", True),
("цифра", False),
("единайсет", True),
... | 31 | 750 |
spaCy | spacy/tests/lang/is/test_tokenizer.py | .py | import pytest
IS_BASIC_TOKENIZATION_TESTS = [
(
"Enginn maður skal sæta pyndingum eða ómannlegri eða "
"vanvirðandi meðferð eða refsingu. ",
[
"Enginn",
"maður",
"skal",
"sæta",
"pyndingum",
"eða",
"ómannleg... | 31 | 783 |
spaCy | spacy/tests/lang/is/test_text.py | .py | import pytest
def test_long_text(is_tokenizer):
# Excerpt: European Convention on Human Rights
text = """
hafa í huga, að yfirlýsing þessi hefur það markmið að tryggja
almenna og raunhæfa viðurkenningu og vernd þeirra réttinda,
sem þar er lýst;
hafa í huga, að markmið Evrópuráðs er að koma á nánari einingu
að... | 27 | 980 |
spaCy | spacy/tests/lang/tl/test_punct.py | .py | import pytest
from spacy.lang.punctuation import TOKENIZER_PREFIXES
from spacy.util import compile_prefix_regex
PUNCT_OPEN = ["(", "[", "{", "*"]
PUNCT_CLOSE = [")", "]", "}", "*"]
PUNCT_PAIRED = [("(", ")"), ("[", "]"), ("{", "}"), ("*", "*")]
@pytest.mark.parametrize("text", ["(", "((", "<"])
def test_tl_tokenize... | 128 | 4,420 |
spaCy | spacy/tests/lang/tl/test_indices.py | .py | def test_tl_simple_punct(tl_tokenizer):
text = "Sige, punta ka dito"
tokens = tl_tokenizer(text)
assert tokens[0].idx == 0
assert tokens[1].idx == 4
assert tokens[2].idx == 6
assert tokens[3].idx == 12
assert tokens[4].idx == 15
| 9 | 257 |
spaCy | spacy/tests/lang/tl/test_text.py | .py | import pytest
from spacy.lang.tl.lex_attrs import like_num
# https://github.com/explosion/spaCy/blob/master/spacy/tests/lang/en/test_text.py
def test_tl_tokenizer_handles_long_text(tl_tokenizer):
# Excerpt: "Sapagkat ang Pilosopiya ay Ginagawa" by Padre Roque Ferriols
text = """
Tingin tayo nang tingin.... | 75 | 2,480 |
spaCy | spacy/tests/lang/sv/test_exceptions.py | .py | import pytest
SV_TOKEN_EXCEPTION_TESTS = [
(
"Smörsåsen används bl.a. till fisk",
["Smörsåsen", "används", "bl.a.", "till", "fisk"],
),
(
"Jag kommer först kl. 13 p.g.a. diverse förseningar",
["Jag", "kommer", "först", "kl.", "13", "p.g.a.", "diverse", "förseningar"],
),... | 78 | 2,453 |
spaCy | spacy/tests/lang/sv/test_prefix_suffix_infix.py | .py | import pytest
@pytest.mark.parametrize("text", ["(under)"])
def test_tokenizer_splits_no_special(sv_tokenizer, text):
tokens = sv_tokenizer(text)
assert len(tokens) == 3
@pytest.mark.parametrize("text", ["gitta'r", "Björn's", "Lars'"])
def test_tokenizer_handles_no_punct(sv_tokenizer, text):
tokens = sv... | 42 | 1,265 |
spaCy | spacy/tests/lang/sv/test_tokenizer.py | .py | import pytest
SV_TOKEN_EXCEPTION_TESTS = [
(
"Smörsåsen används bl.a. till fisk",
["Smörsåsen", "används", "bl.a.", "till", "fisk"],
),
(
"Jag kommer först kl. 13 p.g.a. diverse förseningar",
["Jag", "kommer", "först", "kl.", "13", "p.g.a.", "diverse", "förseningar"],
),... | 31 | 1,004 |
spaCy | spacy/tests/lang/sv/test_lex_attrs.py | .py | import pytest
from spacy.lang.sv.lex_attrs import like_num
@pytest.mark.parametrize(
"text,match",
[
("10", True),
("1", True),
("10.000", True),
("10.00", True),
("999,0", True),
("en", True),
("två", True),
("miljard", True),
("hund", ... | 32 | 684 |
spaCy | spacy/tests/lang/sv/test_noun_chunks.py | .py | import pytest
from spacy.tokens import Doc
def test_noun_chunks_is_parsed_sv(sv_tokenizer):
"""Test that noun_chunks raises Value Error for 'sv' language if Doc is not parsed."""
doc = sv_tokenizer("Studenten läste den bästa boken")
with pytest.raises(ValueError):
list(doc.noun_chunks)
SV_NP_TE... | 50 | 1,858 |
spaCy | spacy/tests/lang/sv/test_text.py | .py | def test_sv_tokenizer_handles_long_text(sv_tokenizer):
text = """Det var så härligt ute på landet. Det var sommar, majsen var gul, havren grön,
höet var uppställt i stackar nere vid den gröna ängen, och där gick storken på sina långa,
röda ben och snackade engelska, för det språket hade han lärt sig av sin mor.
Ru... | 15 | 723 |
spaCy | spacy/tests/lang/hsb/test_tokenizer.py | .py | import pytest
HSB_BASIC_TOKENIZATION_TESTS = [
(
"Hornjoserbšćina wobsteji resp. wobsteješe z wjacorych dialektow, kotrež so zdźěla chětro wot so rozeznawachu.",
[
"Hornjoserbšćina",
"wobsteji",
"resp.",
"wobsteješe",
"z",
"wja... | 33 | 866 |
spaCy | spacy/tests/lang/hsb/test_text.py | .py | import pytest
@pytest.mark.parametrize(
"text,match",
[
("10", True),
("1", True),
("10,000", True),
("10,00", True),
("jedne", True),
("dwanaće", True),
("milion", True),
("sto", True),
("załožene", False),
("wona", False),
... | 26 | 566 |
spaCy | spacy/tests/lang/et/test_tokenizer.py | .py | import pytest
ET_BASIC_TOKENIZATION_TESTS = [
(
"Kedagi ei või piinata ega ebainimlikult või alandavalt kohelda ega karistada.",
[
"Kedagi",
"ei",
"või",
"piinata",
"ega",
"ebainimlikult",
"või",
"alanda... | 29 | 726 |
spaCy | spacy/tests/lang/et/test_text.py | .py | import pytest
def test_long_text(et_tokenizer):
# Excerpt: European Convention on Human Rights
text = """
arvestades, et nimetatud deklaratsiooni eesmärk on tagada selles
kuulutatud õiguste üldine ja tõhus tunnustamine ning järgimine;
arvestades, et Euroopa Nõukogu eesmärk on saavutada tema
liikmete suurem üh... | 27 | 933 |
spaCy | spacy/tests/lang/ja/test_serialize.py | .py | import pickle
from spacy.lang.ja import Japanese
from ...util import make_tempdir
def test_ja_tokenizer_serialize(ja_tokenizer):
tokenizer_bytes = ja_tokenizer.to_bytes()
nlp = Japanese()
nlp.tokenizer.from_bytes(tokenizer_bytes)
assert tokenizer_bytes == nlp.tokenizer.to_bytes()
assert nlp.toke... | 43 | 1,307 |
spaCy | spacy/tests/lang/ja/test_morphologizer_factory.py | .py | import pytest
from spacy.lang.ja import Japanese
def test_ja_morphologizer_factory():
pytest.importorskip("sudachipy")
nlp = Japanese()
morphologizer = nlp.add_pipe("morphologizer")
assert morphologizer.cfg["extend"] is True
| 11 | 244 |
spaCy | spacy/tests/lang/ja/test_tokenizer.py | .py | import pytest
from spacy.lang.ja import DetailedToken, Japanese
from ...tokenizer.test_naughty_strings import NAUGHTY_STRINGS
# fmt: off
TOKENIZER_TESTS = [
("日本語だよ", ['日本', '語', 'だ', 'よ']),
("東京タワーの近くに住んでいます。", ['東京', 'タワー', 'の', '近く', 'に', '住ん', 'で', 'い', 'ます', '。']),
("吾輩は猫である。", ['吾輩', 'は', '猫', 'で',... | 174 | 7,980 |
spaCy | spacy/tests/lang/ja/test_lemmatization.py | .py | import pytest
@pytest.mark.parametrize(
"word,lemma",
[
("新しく", "新しい"),
("赤く", "赤い"),
("すごく", "すごい"),
("いただきました", "いただく"),
("なった", "なる"),
],
)
def test_ja_lemmatizer_assigns(ja_tokenizer, word, lemma):
test_lemma = ja_tokenizer(word)[0].lemma_
assert test_le... | 31 | 741 |
spaCy | spacy/tests/lang/ru/test_exceptions.py | .py | import pytest
@pytest.mark.parametrize(
"text,norms",
[("пн.", ["понедельник"]), ("пт.", ["пятница"]), ("дек.", ["декабрь"])],
)
def test_ru_tokenizer_abbrev_exceptions(ru_tokenizer, text, norms):
tokens = ru_tokenizer(text)
assert len(tokens) == 1
assert [token.norm_ for token in tokens] == norms... | 12 | 353 |
spaCy | spacy/tests/lang/ru/test_lemmatizer.py | .py | import pytest
from spacy.tokens import Doc
pytestmark = pytest.mark.filterwarnings("ignore::DeprecationWarning")
def test_ru_doc_lemmatization(ru_lemmatizer):
words = ["мама", "мыла", "раму"]
pos = ["NOUN", "VERB", "NOUN"]
morphs = [
"Animacy=Anim|Case=Nom|Gender=Fem|Number=Sing",
"Aspec... | 110 | 3,958 |
spaCy | spacy/tests/lang/ru/test_tokenizer.py | .py | from string import punctuation
import pytest
PUNCT_OPEN = ["(", "[", "{", "*"]
PUNCT_CLOSE = [")", "]", "}", "*"]
PUNCT_PAIRED = [("(", ")"), ("[", "]"), ("{", "}"), ("*", "*")]
@pytest.mark.parametrize("text", ["(", "((", "<"])
def test_ru_tokenizer_handles_only_punct(ru_tokenizer, text):
tokens = ru_tokenizer... | 159 | 5,983 |
spaCy | spacy/tests/lang/ru/test_text.py | .py | import pytest
from spacy.lang.ru.lex_attrs import like_num
@pytest.mark.parametrize("word", ["одиннадцать"])
def test_ru_lex_attrs_capitals(word):
assert like_num(word)
assert like_num(word.upper())
| 10 | 221 |
spaCy | spacy/tests/lang/vi/test_serialize.py | .py | import pickle
from spacy.lang.vi import Vietnamese
from ...util import make_tempdir
def test_vi_tokenizer_serialize(vi_tokenizer):
tokenizer_bytes = vi_tokenizer.to_bytes()
nlp = Vietnamese()
nlp.tokenizer.from_bytes(tokenizer_bytes)
assert tokenizer_bytes == nlp.tokenizer.to_bytes()
assert nlp.... | 43 | 1,309 |
spaCy | spacy/tests/lang/vi/test_tokenizer.py | .py | import pytest
from spacy.lang.vi import Vietnamese
from ...tokenizer.test_naughty_strings import NAUGHTY_STRINGS
# fmt: off
TOKENIZER_TESTS = [
("Đây là một văn bản bằng tiếng Việt Sau đó, đây là một văn bản khác bằng ngôn ngữ này", ['Đây', 'là', 'một', 'văn bản', 'bằng', 'tiếng', 'Việt', 'Sau', 'đó', ',', 'đâ... | 48 | 1,677 |
spaCy | spacy/tests/lang/fr/test_exceptions.py | .py | import pytest
@pytest.mark.parametrize(
"text",
[
"aujourd'hui",
"Aujourd'hui",
"prud'hommes",
"prud’hommal",
"audio-numérique",
"Audio-numérique",
"entr'amis",
"entr'abat",
"rentr'ouvertes",
"grand'hamien",
"Châteauneuf-l... | 83 | 2,219 |
spaCy | spacy/tests/lang/fr/test_prefix_suffix_infix.py | .py | import pytest
from spacy.lang.char_classes import ALPHA
from spacy.lang.punctuation import TOKENIZER_INFIXES
from spacy.language import BaseDefaults, Language
@pytest.mark.issue(768)
@pytest.mark.parametrize(
"text,expected_tokens", [("l'avion", ["l'", "avion"]), ("j'ai", ["j'", "ai"])]
)
def test_issue768(text,... | 24 | 773 |
spaCy | spacy/tests/lang/fr/test_noun_chunks.py | .py | import pytest
from spacy.tokens import Doc
# fmt: off
@pytest.mark.parametrize(
"words,heads,deps,pos,chunk_offsets",
[
# determiner + noun
# un nom -> un nom
(
["un", "nom"],
[1, 1],
["det", "ROOT"],
["DET", "NOUN"],
[(0, 2)... | 232 | 8,332 |
spaCy | spacy/tests/lang/fr/test_text.py | .py | import pytest
from spacy.lang.fr.lex_attrs import like_num
def test_tokenizer_handles_long_text(fr_tokenizer):
text = """L'histoire du TAL commence dans les années 1950, bien que l'on puisse \
trouver des travaux antérieurs. En 1950, Alan Turing éditait un article \
célèbre sous le titre « Computing machinery an... | 24 | 1,012 |
spaCy | spacy/tests/lang/xx/test_tokenizer.py | .py | import pytest
XX_BASIC_TOKENIZATION_TESTS = [
(
"Lääʹddjânnmest lie nuʹtt 10 000 säʹmmliʹžžed. Seeʹst pâʹjjel",
[
"Lääʹddjânnmest",
"lie",
"nuʹtt",
"10",
"000",
"säʹmmliʹžžed",
".",
"Seeʹst",
... | 26 | 669 |
spaCy | spacy/tests/lang/xx/test_text.py | .py | def test_long_text(xx_tokenizer):
# Excerpt: Text in Skolt Sami taken from https://www.samediggi.fi
text = """
Säʹmmla lie Euroopp unioon oʹdinakai alggmeer. Säʹmmlai alggmeerstatus lij raʹvvjum Lääʹddjânnam vuâđđlääʹjjest.
Alggmeer kriteeʹr vuâđđâʹvve meeraikõskksaž tuâjjorganisaatio, ILO, suåppmõʹšše nââmar... | 22 | 1,704 |
spaCy | spacy/tests/lang/af/test_tokenizer.py | .py | import pytest
AF_BASIC_TOKENIZATION_TESTS = [
(
"Elkeen het die reg tot lewe, vryheid en sekuriteit van persoon.",
[
"Elkeen",
"het",
"die",
"reg",
"tot",
"lewe",
",",
"vryheid",
"en",
... | 30 | 710 |
spaCy | spacy/tests/lang/af/test_text.py | .py | import pytest
def test_long_text(af_tokenizer):
# Excerpt: Universal Declaration of Human Rights; “'n” changed to “die” in first sentence
text = """
Hierdie Universele Verklaring van Menseregte as die algemene standaard vir die verwesenliking deur alle mense en nasies,
om te verseker dat elke individu en elk... | 23 | 931 |
spaCy | spacy/tests/lang/id/test_prefix_suffix_infix.py | .py | import pytest
@pytest.mark.parametrize("text", ["(Ma'arif)"])
def test_id_tokenizer_splits_no_special(id_tokenizer, text):
tokens = id_tokenizer(text)
assert len(tokens) == 3
@pytest.mark.parametrize("text", ["Ma'arif"])
def test_id_tokenizer_splits_no_punct(id_tokenizer, text):
tokens = id_tokenizer(te... | 112 | 3,492 |
spaCy | spacy/tests/lang/id/test_noun_chunks.py | .py | import pytest
def test_noun_chunks_is_parsed_id(id_tokenizer):
"""Test that noun_chunks raises Value Error for 'id' language if Doc is not parsed."""
doc = id_tokenizer("sebelas")
with pytest.raises(ValueError):
list(doc.noun_chunks)
| 9 | 256 |
spaCy | spacy/tests/lang/id/test_text.py | .py | import pytest
from spacy.lang.id.lex_attrs import like_num
@pytest.mark.parametrize("word", ["sebelas"])
def test_id_lex_attrs_capitals(word):
assert like_num(word)
assert like_num(word.upper())
| 10 | 206 |
spaCy | spacy/tests/lang/yo/test_text.py | .py | import pytest
from spacy.lang.yo.lex_attrs import like_num
def test_yo_tokenizer_handles_long_text(yo_tokenizer):
text = """Àwọn ọmọ ìlú tí wọ́n ń ṣàmúlò ayélujára ti bẹ̀rẹ̀ ìkọkúkọ sórí àwòrán ààrẹ Nkurunziza nínú ìfẹ̀hónúhàn pẹ̀lú àmì ìdámọ̀: Nkurunziza àti Burundi:
Ọmọ ilé ẹ̀kọ́ gíga ní ẹ̀wọ̀n fún kí... | 31 | 1,491 |
spaCy | spacy/tests/lang/pt/test_noun_chunks.py | .py | import pytest
from spacy.tokens import Doc
# fmt: off
@pytest.mark.parametrize(
"words,heads,deps,pos,chunk_offsets",
[
# determiner + noun
# um cachorro -> um cachorro
(
["um", "cachorro"],
[1, 1],
["det", "ROOT"],
["DET", "NOUN"],
... | 223 | 7,728 |
spaCy | spacy/tests/lang/pt/test_text.py | .py | import pytest
from spacy.lang.pt.lex_attrs import like_num
@pytest.mark.parametrize("word", ["onze", "quadragésimo"])
def test_pt_lex_attrs_capitals(word):
assert like_num(word)
assert like_num(word.upper())
| 10 | 220 |
spaCy | spacy/tests/lang/el/test_exception.py | .py | import pytest
@pytest.mark.parametrize("text", ["αριθ.", "τρισ.", "δισ.", "σελ."])
def test_el_tokenizer_handles_abbr(el_tokenizer, text):
tokens = el_tokenizer(text)
assert len(tokens) == 1
def test_el_tokenizer_handles_exc_in_text(el_tokenizer):
text = "Στα 14 τρισ. δολάρια το κόστος από την άνοδο της... | 15 | 514 |
spaCy | spacy/tests/lang/el/test_noun_chunks.py | .py | import pytest
def test_noun_chunks_is_parsed_el(el_tokenizer):
"""Test that noun_chunks raises Value Error for 'el' language if Doc is not parsed."""
doc = el_tokenizer("είναι χώρα της νοτιοανατολικής")
with pytest.raises(ValueError):
list(doc.noun_chunks)
| 9 | 306 |
spaCy | spacy/tests/lang/el/test_text.py | .py | import pytest
def test_el_tokenizer_handles_long_text(el_tokenizer):
text = """Η Ελλάδα (παλαιότερα Ελλάς), επίσημα γνωστή ως Ελληνική Δημοκρατία,\
είναι χώρα της νοτιοανατολικής Ευρώπης στο νοτιότερο άκρο της Βαλκανικής χερσονήσου.\
Συνορεύει στα βορειοδυτικά με την Αλβανία, στα βόρεια με την πρώην\
... | 32 | 1,768 |
spaCy | spacy/tests/lang/da/test_exceptions.py | .py | import pytest
@pytest.mark.parametrize("text", ["ca.", "m.a.o.", "Jan.", "Dec.", "kr.", "jf."])
def test_da_tokenizer_handles_abbr(da_tokenizer, text):
tokens = da_tokenizer(text)
assert len(tokens) == 1
@pytest.mark.parametrize("text", ["Jul.", "jul.", "Tor.", "Tors."])
def test_da_tokenizer_handles_ambigu... | 60 | 1,824 |
spaCy | spacy/tests/lang/da/test_prefix_suffix_infix.py | .py | import pytest
@pytest.mark.parametrize("text", ["(under)"])
def test_da_tokenizer_splits_no_special(da_tokenizer, text):
tokens = da_tokenizer(text)
assert len(tokens) == 3
@pytest.mark.parametrize("text", ["ta'r", "Søren's", "Lars'"])
def test_da_tokenizer_handles_no_punct(da_tokenizer, text):
tokens =... | 169 | 5,423 |
spaCy | spacy/tests/lang/da/test_noun_chunks.py | .py | import pytest
from spacy.tokens import Doc
def test_noun_chunks_is_parsed(da_tokenizer):
"""Test that noun_chunks raises Value Error for 'da' language if Doc is not parsed.
To check this test, we're constructing a Doc
with a new Vocab here and forcing is_parsed to 'False'
to make sure the noun chunks... | 72 | 2,064 |
spaCy | spacy/tests/lang/da/test_text.py | .py | import pytest
from spacy.lang.da.lex_attrs import like_num
def test_da_tokenizer_handles_long_text(da_tokenizer):
text = """Der var så dejligt ude på landet. Det var sommer, kornet stod gult, havren grøn,
høet var rejst i stakke nede i de grønne enge, og der gik storken på sine lange,
røde ben og snakkede ægypti... | 42 | 1,220 |
spaCy | spacy/tests/lang/lt/test_text.py | .py | import pytest
def test_lt_tokenizer_handles_long_text(lt_tokenizer):
text = """Tokios sausros kriterijus atitinka pirmadienį atlikti skaičiavimai, palyginus faktinį ir žemiausią vidutinį daugiametį vandens lygį. Nustatyta, kad iš 48 šalies vandens matavimo stočių 28-iose stotyse vandens lygis yra žemesnis arba ly... | 54 | 1,644 |
spaCy | spacy/tests/lang/ml/test_text.py | .py | import pytest
def test_ml_tokenizer_handles_long_text(ml_tokenizer):
text = """അനാവശ്യമായി കണ്ണിലും മൂക്കിലും വായിലും സ്പർശിക്കാതിരിക്കുക"""
tokens = ml_tokenizer(text)
assert len(tokens) == 5
@pytest.mark.parametrize(
"text,length",
[
(
"എന്നാൽ അച്ചടിയുടെ ആവിർഭാവം ലിപിയിൽ കാ... | 23 | 1,075 |
spaCy | spacy/tests/lang/lv/test_tokenizer.py | .py | import pytest
LV_BASIC_TOKENIZATION_TESTS = [
(
"Nevienu nedrīkst spīdzināt vai cietsirdīgi vai pazemojoši ar viņu "
"apieties vai sodīt.",
[
"Nevienu",
"nedrīkst",
"spīdzināt",
"vai",
"cietsirdīgi",
"vai",
... | 31 | 778 |
spaCy | spacy/tests/lang/lv/test_text.py | .py | import pytest
def test_long_text(lv_tokenizer):
# Excerpt: European Convention on Human Rights
text = """
Ievērodamas, ka šī deklarācija paredz nodrošināt vispārēju un
efektīvu tajā pasludināto tiesību atzīšanu un ievērošanu;
Ievērodamas, ka Eiropas Padomes mērķis ir panākt lielāku vienotību
tās dalībvalstu s... | 28 | 1,018 |
spaCy | spacy/tests/lang/ky/test_tokenizer.py | .py | import pytest
INFIX_HYPHEN_TESTS = [
("Бала-чака жакшыбы?", "Бала-чака жакшыбы ?".split()),
("Кыз-келиндер кийими.", "Кыз-келиндер кийими .".split()),
]
PUNC_INSIDE_WORDS_TESTS = [
(
"Пассажир саны - 2,13 млн — киши/күнүнө (2010), 783,9 млн. киши/жылына.",
"Пассажир саны - 2,13 млн — киши ... | 86 | 4,013 |
spaCy | spacy/tests/lang/am/test_text.py | .py | import pytest
def test_am_tokenizer_handles_long_text(am_tokenizer):
text = """ሆሴ ሙጂካ በበጋ ወቅት በኦክስፎርድ ንግግር አንድያቀርቡ ሲጋበዙ ጭንቅላታቸው "ፈነዳ"።
“እጅግ ጥንታዊ” የእንግሊዝኛ ተናጋሪ ዩኒቨርስቲ፣ በአስር ሺዎች የሚቆጠሩ ዩሮዎችን ለተማሪዎች በማስተማር የሚያስከፍለው
እና ከማርጋሬት ታቸር እስከ ስቲቨን ሆኪንግ በአዳራሾቻቸው ውስጥ ንግግር ያደረጉበት የትምህርት ማዕከል፣ በሞንቴቪዴኦ
በሚገኘው የመንግስት ትምህርት ቤት የሰለጠ... | 52 | 1,837 |
spaCy | spacy/tests/lang/nb/test_tokenizer.py | .py | import pytest
NB_TOKEN_EXCEPTION_TESTS = [
(
"Smørsausen brukes bl.a. til fisk",
["Smørsausen", "brukes", "bl.a.", "til", "fisk"],
),
(
"Jeg kommer først kl. 13 pga. diverse forsinkelser",
["Jeg", "kommer", "først", "kl.", "13", "pga.", "diverse", "forsinkelser"],
),
]
... | 20 | 629 |
spaCy | spacy/tests/lang/nb/test_noun_chunks.py | .py | import pytest
def test_noun_chunks_is_parsed_nb(nb_tokenizer):
"""Test that noun_chunks raises Value Error for 'nb' language if Doc is not parsed."""
doc = nb_tokenizer("Smørsausen brukes bl.a. til")
with pytest.raises(ValueError):
list(doc.noun_chunks)
| 9 | 277 |
spaCy | spacy/tests/lang/hi/test_lex_attrs.py | .py | import pytest
from spacy.lang.hi.lex_attrs import like_num, norm
def test_hi_tokenizer_handles_long_text(hi_tokenizer):
text = """
ये कहानी 1900 के दशक की है। कौशल्या (स्मिता जयकर) को पता चलता है कि उसका
छोटा बेटा, देवदास (शाहरुख खान) वापस घर आ रहा है। देवदास 10 साल पहले कानून की
पढ़ाई करने के लिए इंग्लैंड गया थ... | 45 | 1,842 |
spaCy | spacy/tests/lang/hi/test_text.py | .py | import pytest
from spacy.lang.hi import Hindi
@pytest.mark.issue(3625)
def test_issue3625():
"""Test that default punctuation rules applies to hindi unicode characters"""
nlp = Hindi()
doc = nlp("hi. how हुए. होटल, होटल")
expected = ["hi", ".", "how", "हुए", ".", "होटल", ",", "होटल"]
assert [toke... | 13 | 401 |
spaCy | spacy/tests/lang/uk/test_lemmatizer.py | .py | import pytest
from spacy.tokens import Doc
pytestmark = pytest.mark.filterwarnings("ignore::DeprecationWarning")
def test_uk_lemmatizer(uk_lemmatizer):
"""Check that the default uk lemmatizer runs."""
doc = Doc(uk_lemmatizer.vocab, words=["a", "b", "c"])
assert uk_lemmatizer.mode == "pymorphy3"
uk_l... | 28 | 837 |
spaCy | spacy/tests/lang/uk/test_tokenizer.py | .py | import pytest
PUNCT_OPEN = ["(", "[", "{", "*"]
PUNCT_CLOSE = [")", "]", "}", "*"]
PUNCT_PAIRED = [("(", ")"), ("[", "]"), ("{", "}"), ("*", "*")]
@pytest.mark.parametrize("text", ["(", "((", "<"])
def test_uk_tokenizer_handles_only_punct(uk_tokenizer, text):
tokens = uk_tokenizer(text)
assert len(tokens) ==... | 149 | 5,415 |
spaCy | spacy/tests/lang/uk/test_tokenizer_exc.py | .py | import pytest
@pytest.mark.parametrize(
"text,norms,lemmas",
[("ім.", ["імені"], ["ім'я"]), ("проф.", ["професор"], ["професор"])],
)
def test_uk_tokenizer_abbrev_exceptions(uk_tokenizer, text, norms, lemmas):
tokens = uk_tokenizer(text)
assert len(tokens) == 1
assert [token.norm_ for token in tok... | 12 | 364 |
spaCy | spacy/tests/lang/tr/test_parser.py | .py | from spacy.tokens import Doc
def test_tr_noun_chunks_amod_simple(tr_tokenizer):
text = "sarı kedi"
heads = [1, 1]
deps = ["amod", "ROOT"]
pos = ["ADJ", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
... | 576 | 19,784 |
spaCy | spacy/tests/lang/tr/test_tokenizer.py | .py | import pytest
ABBREV_TESTS = [
("Dr. Murat Bey ile görüştüm.", ["Dr.", "Murat", "Bey", "ile", "görüştüm", "."]),
("Dr.la görüştüm.", ["Dr.la", "görüştüm", "."]),
("Dr.'la görüştüm.", ["Dr.'la", "görüştüm", "."]),
("TBMM'de çalışıyormuş.", ["TBMM'de", "çalışıyormuş", "."]),
(
"Hem İst. hem A... | 697 | 19,588 |
spaCy | spacy/tests/lang/tr/test_noun_chunks.py | .py | import pytest
def test_noun_chunks_is_parsed(tr_tokenizer):
"""Test that noun_chunks raises Value Error for 'tr' language if Doc is not parsed.
To check this test, we're constructing a Doc
with a new Vocab here and forcing is_parsed to 'False'
to make sure the noun chunks don't run.
"""
doc = ... | 13 | 422 |
spaCy | spacy/tests/lang/tr/test_text.py | .py | import pytest
from spacy.lang.tr.lex_attrs import like_num
def test_tr_tokenizer_handles_long_text(tr_tokenizer):
text = """Pamuk nasıl ipliğe dönüştürülür?
Sıkıştırılmış balyalar halindeki pamuk, iplik fabrikasına getirildiğinde hem
lifleri birbirine dolaşmıştır, hem de tarladan toplanırken araya bitkinin
parç... | 49 | 1,797 |
spaCy | spacy/tests/lang/la/test_exception.py | .py | def test_la_tokenizer_handles_exc_in_text(la_tokenizer):
text = "scio te omnia facturum, ut nobiscum quam primum sis"
tokens = la_tokenizer(text)
assert len(tokens) == 11
assert tokens[6].text == "nobis"
| 6 | 220 |
spaCy | spacy/tests/lang/la/test_noun_chunks.py | .py | import pytest
from spacy.tokens import Doc
def test_noun_chunks_is_parsed(la_tokenizer):
"""Test that noun_chunks raises Value Error for 'la' language if Doc is not parsed.
To check this test, we're constructing a Doc
with a new Vocab here and forcing is_parsed to 'False'
to make sure the noun chunks... | 54 | 1,628 |
spaCy | spacy/tests/lang/la/test_text.py | .py | import pytest
from spacy.lang.la.lex_attrs import like_num
@pytest.mark.parametrize(
"text,match",
[
("IIII", True),
("VI", True),
("vi", True),
("IV", True),
("iv", True),
("IX", True),
("ix", True),
("MMXXII", True),
("0", True),
... | 37 | 804 |
spaCy | spacy/tests/lang/pl/test_tokenizer.py | .py | import pytest
DOT_TESTS = [
("tel.", ["tel", "."]),
("0 zł 99 gr", ["0", "zł", "99", "gr"]),
]
HYPHEN_TESTS = [
("cztero-", ["cztero-"]),
("jedno-", ["jedno-"]),
("dwu-", ["dwu-"]),
("trzy-", ["trzy-"]),
]
TESTCASES = DOT_TESTS + HYPHEN_TESTS
@pytest.mark.parametrize("text,expected_tokens"... | 24 | 555 |
spaCy | spacy/tests/lang/pl/test_text.py | .py | """Words like numbers are recognized correctly."""
import pytest
@pytest.mark.parametrize(
"text,match",
[
("10", True),
("1", True),
("10,000", True),
("10,00", True),
("jeden", True),
("dwa", True),
("milion", True),
("pies", False),
(... | 25 | 523 |
spaCy | spacy/tests/lang/hu/test_tokenizer.py | .py | import pytest
DEFAULT_TESTS = [
("N. kormányzósági\nszékhely.", ["N.", "kormányzósági", "székhely", "."]),
pytest.param(
"A .hu egy tld.", ["A", ".hu", "egy", "tld", "."], marks=pytest.mark.xfail()
),
("Az egy.ketto pelda.", ["Az", "egy.ketto", "pelda", "."]),
("A pl. rovidites.", ["A", "pl... | 322 | 14,523 |
spaCy | spacy/tests/lang/he/test_tokenizer.py | .py | import pytest
from spacy.lang.he.lex_attrs import like_num
@pytest.mark.parametrize(
"text,expected_tokens",
[("פייתון היא שפת תכנות דינמית", ["פייתון", "היא", "שפת", "תכנות", "דינמית"])],
)
def test_he_tokenizer_handles_abbreviation(he_tokenizer, text, expected_tokens):
tokens = he_tokenizer(text)
t... | 71 | 2,256 |
spaCy | spacy/tests/lang/sa/test_text.py | .py | import pytest
def test_sa_tokenizer_handles_long_text(sa_tokenizer):
text = """नानाविधानि दिव्यानि नानावर्णाकृतीनि च।।"""
tokens = sa_tokenizer(text)
assert len(tokens) == 6
@pytest.mark.parametrize(
"text,length",
[
("श्री भगवानुवाच पश्य मे पार्थ रूपाणि शतशोऽथ सहस्रशः।", 9),
("ग... | 43 | 1,297 |
spaCy | spacy/tests/lang/zh/test_serialize.py | .py | import pytest
from spacy.lang.zh import Chinese
from ...util import make_tempdir
def zh_tokenizer_serialize(zh_tokenizer):
tokenizer_bytes = zh_tokenizer.to_bytes()
nlp = Chinese()
nlp.tokenizer.from_bytes(tokenizer_bytes)
assert tokenizer_bytes == nlp.tokenizer.to_bytes()
with make_tempdir() a... | 48 | 1,247 |
spaCy | spacy/tests/lang/zh/test_tokenizer.py | .py | import pytest
from confection import ConfigValidationError
from spacy.lang.zh import Chinese, _get_pkuseg_trie_data
# fmt: off
TEXTS = ("作为语言而言，为世界使用人数最多的语言，目前世界有五分之一人口做为母语。",)
JIEBA_TOKENIZER_TESTS = [
(TEXTS[0],
['作为', '语言', '而言', '，', '为', '世界', '使用', '人', '数最多',
'的', '语言', '，', '目前', '世界', '有... | 79 | 2,820 |
spaCy | spacy/tests/lang/zh/test_text.py | .py | import pytest
@pytest.mark.parametrize(
"text,match",
[
("10", True),
("1", True),
("999.0", True),
("一", True),
("二", True),
("〇", True),
("十一", True),
("狗", False),
(",", False),
],
)
def test_lex_attrs_like_number(zh_tokenizer_jieb... | 22 | 454 |
spaCy | spacy/tests/morphology/test_morph_converters.py | .py | from spacy.morphology import Morphology
def test_feats_converters():
feats = "Case=dat,gen|Number=sing"
feats_dict = {"Case": "dat,gen", "Number": "sing"}
# simple conversions
assert Morphology.dict_to_feats(feats_dict) == feats
assert Morphology.feats_to_dict(feats) == feats_dict
# roundtri... | 22 | 856 |
spaCy | spacy/tests/morphology/test_morph_pickle.py | .py | import pickle
import pytest
from spacy.morphology import Morphology
from spacy.strings import StringStore
@pytest.fixture
def morphology():
morphology = Morphology(StringStore())
morphology.add("Feat1=Val1|Feat2=Val2")
morphology.add("Feat3=Val3|Feat4=Val4")
return morphology
def test_morphology_p... | 24 | 670 |
spaCy | spacy/tests/morphology/test_morph_features.py | .py | import pytest
from spacy.morphology import Morphology
from spacy.strings import StringStore, get_string_id
@pytest.fixture
def morphology():
return Morphology(StringStore())
def test_init(morphology):
pass
def test_add_morphology_with_string_names(morphology):
morphology.add({"Case": "gen", "Number":... | 51 | 1,349 |
spaCy | spacy/tests/matcher/test_levenshtein.py | .py | import pytest
from spacy.matcher import levenshtein
from spacy.matcher.levenshtein import levenshtein_compare
# empty string plus 10 random ASCII, 10 random unicode, and 2 random long tests
# from polyleven
@pytest.mark.parametrize(
"dist,a,b",
[
(0, "", ""),
(4, "bbcb", "caba"),
(3, ... | 75 | 2,798 |
spaCy | spacy/tests/matcher/test_phrase_matcher.py | .py | import warnings
import pytest
import srsly
from mock import Mock
from spacy.lang.en import English
from spacy.matcher import Matcher, PhraseMatcher
from spacy.tokens import Doc, Span
from spacy.vocab import Vocab
from ..util import make_tempdir
@pytest.mark.issue(3248)
def test_issue3248_1():
"""Test that the ... | 510 | 17,965 |
spaCy | spacy/tests/matcher/test_matcher_api.py | .py | import pytest
from mock import Mock
from spacy.matcher import Matcher
from spacy.tokens import Doc, Span, Token
from ..doc.test_underscore import clean_underscore # noqa: F401
@pytest.fixture
def matcher(en_vocab):
rules = {
"JS": [[{"ORTH": "JavaScript"}]],
"GoogleNow": [[{"ORTH": "Google"}, {... | 910 | 29,917 |
spaCy | spacy/tests/matcher/test_pattern_validation.py | .py | import pytest
from spacy.errors import MatchPatternError
from spacy.matcher import Matcher
from spacy.schemas import validate_token_pattern
# (pattern, num errors with validation, num errors identified with minimal
# checks)
TEST_PATTERNS = [
# Bad patterns flagged in all cases
([{"XX": "foo"}], 1, 1),
(... | 103 | 3,940 |
spaCy | spacy/tests/matcher/test_matcher_logic.py | .py | import re
import pytest
from spacy.attrs import IS_PUNCT, LOWER, ORTH
from spacy.errors import MatchPatternError
from spacy.lang.en import English
from spacy.lang.lex_attrs import LEX_ATTRS
from spacy.matcher import Matcher
from spacy.tokens import Doc, Span, Token
from spacy.vocab import Vocab
pattern1 = [{"ORTH": ... | 789 | 27,181 |
spaCy | spacy/tests/matcher/test_dependency_matcher.py | .py | import copy
import pickle
import re
import pytest
from mock import Mock
from spacy.matcher import DependencyMatcher
from spacy.tokens import Doc, Token
from ..doc.test_underscore import clean_underscore # noqa: F401
@pytest.fixture
def doc(en_vocab):
words = ["The", "quick", "brown", "fox", "jumped", "over", ... | 494 | 15,046 |
spaCy | spacy/tests/vocab_vectors/test_lexeme.py | .py | import numpy
import pytest
from spacy.attrs import IS_ALPHA, IS_DIGIT
from spacy.tokens import Doc
from spacy.util import OOV_RANK
from spacy.vocab import Vocab
@pytest.mark.issue(361)
@pytest.mark.parametrize("text1,text2", [("cat", "dog")])
def test_issue361(en_vocab, text1, text2):
"""Test Issue #361: Equalit... | 84 | 2,819 |
spaCy | spacy/tests/vocab_vectors/test_stringstore.py | .py | import pytest
from spacy.strings import StringStore
@pytest.fixture
def stringstore():
return StringStore()
def test_string_hash(stringstore):
"""Test that string hashing is stable across platforms"""
assert stringstore.add("apple") == 8566208034543834098
heart = "\U0001f499"
h = stringstore.ad... | 99 | 3,339 |
spaCy | spacy/tests/vocab_vectors/test_lookups.py | .py | import pytest
from spacy.lookups import Lookups, Table
from spacy.strings import get_string_id
from spacy.vocab import Vocab
from ..util import make_tempdir
def test_lookups_api():
table_name = "test"
data = {"foo": "bar", "hello": "world"}
lookups = Lookups()
lookups.add_table(table_name, data)
... | 143 | 4,652 |
spaCy | spacy/tests/vocab_vectors/test_memory_zone.py | .py | from spacy.vocab import Vocab
def test_memory_zone_no_insertion():
vocab = Vocab()
with vocab.memory_zone():
pass
lex = vocab["horse"]
assert lex.text == "horse"
def test_memory_zone_insertion():
vocab = Vocab()
_ = vocab["dog"]
assert "dog" in vocab
assert "horse" not in voc... | 60 | 1,661 |
spaCy | spacy/tests/vocab_vectors/test_similarity.py | .py | import numpy
import pytest
from spacy.tokens import Doc
from spacy.vocab import Vocab
from ..util import add_vecs_to_vocab, get_cosine
@pytest.fixture
def vectors():
return [("apple", [1, 2, 3]), ("orange", [-1, -2, -3])]
@pytest.fixture()
def vocab(en_vocab, vectors):
add_vecs_to_vocab(en_vocab, vectors)... | 112 | 3,835 |
spaCy | spacy/tests/vocab_vectors/test_vectors.py | .py | import numpy
import pytest
from numpy.testing import assert_allclose, assert_almost_equal, assert_equal
from thinc.api import NumpyOps, get_current_ops
from spacy.lang.en import English
from spacy.strings import hash_string # type: ignore
from spacy.tokenizer import Tokenizer
from spacy.tokens import Doc
from spacy.t... | 679 | 23,814 |
spaCy | spacy/tests/vocab_vectors/test_vocab_api.py | .py | import os
import pytest
from spacy.attrs import IS_ALPHA, LEMMA, ORTH
from spacy.lang.en import English
from spacy.parts_of_speech import NOUN, VERB
from spacy.vocab import Vocab
from ..util import make_tempdir
@pytest.mark.issue(1868)
def test_issue1868():
"""Test Vocab.__contains__ works with int keys."""
... | 84 | 2,148 |
spaCy | spacy/tests/doc/test_pickle_doc.py | .py | from spacy.compat import pickle
from spacy.language import Language
def test_pickle_single_doc():
nlp = Language()
doc = nlp("pickle roundtrip")
data = pickle.dumps(doc, 1)
doc2 = pickle.loads(data)
assert doc2.text == "pickle roundtrip"
def test_list_of_docs_pickles_efficiently():
nlp = Lan... | 55 | 1,470 |
spaCy | spacy/tests/doc/test_add_entities.py | .py | import pytest
from spacy import registry
from spacy.pipeline import EntityRecognizer
from spacy.pipeline.ner import DEFAULT_NER_MODEL
from spacy.tokens import Doc, Span
from spacy.training import Example
def _ner_example(ner):
doc = Doc(
ner.vocab,
words=["Joe", "loves", "visiting", "London", "du... | 58 | 1,804 |
spaCy | spacy/tests/doc/test_graph.py | .py | from spacy.tokens.doc import Doc
from spacy.tokens.graph import Graph
from spacy.vocab import Vocab
def test_graph_init():
doc = Doc(Vocab(), words=["a", "b", "c", "d"])
graph = Graph(doc, name="hello")
assert graph.name == "hello"
assert graph.doc is doc
def test_graph_edges_and_nodes():
doc = ... | 49 | 1,819 |
spaCy | spacy/tests/doc/test_retokenize_split.py | .py | import numpy
import pytest
from spacy.tokens import Doc, Token
from spacy.vocab import Vocab
@pytest.mark.issue(3540)
def test_issue3540(en_vocab):
words = ["I", "live", "in", "NewYork", "right", "now"]
tensor = numpy.asarray(
[[1.0, 1.1], [2.0, 2.1], [3.0, 3.1], [4.0, 4.1], [5.0, 5.1], [6.0, 6.1]],
... | 297 | 10,937 |
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