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 |
|---|---|---|---|---|---|
spaCy | spacy/lang/ms/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class MalayDefault... | 25 | 678 |
spaCy | spacy/lang/ms/stop_words.py | .py | STOP_WORDS = set(
"""
ada adalah adanya adapun agak agaknya agar akan akankah akhir akhiri akhirnya
aku akulah amat amatlah anda andalah antar antara antaranya apa apaan apabila
apakah apalagi apatah artinya asal asalkan atas atau ataukah ataupun awal
awalnya
bagai bagaikan bagaimana bagaimanakah bagaimanapun bagi... | 119 | 6,507 |
spaCy | spacy/lang/ms/lex_attrs.py | .py | import unicodedata
from ...attrs import IS_CURRENCY, LIKE_NUM
from .punctuation import LIST_CURRENCY
_num_words = [
"kosong",
"satu",
"dua",
"tiga",
"empat",
"lima",
"enam",
"tujuh",
"lapan",
"sembilan",
"sepuluh",
"sebelas",
"belas",
"puluh",
"ratus",
"... | 66 | 1,265 |
spaCy | spacy/lang/ms/syntax_iterators.py | .py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import NOUN, PRON, PROPN
from ...tokens import Doc, Span
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"... | 42 | 1,538 |
spaCy | spacy/lang/ms/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ms.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Malaysia ialah sebuah negara yang terletak di Asia Tenggara.",
"Berapa banyak pelajar yang akan menghadiri majlis perpisahan sekolah?",
"Pengeluar... | 17 | 676 |
spaCy | spacy/lang/ms/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from ._tokenizer_exceptions_list import MS_BASE_EXCEPTIONS
# Daftar singkatan dan Akronim dari:
# https://ms.wiktionary.org/wiki/Wiktionary:Senarai_akronim_dan_singkatan
_exc = {}
for orth in MS_BASE_E... | 1,533 | 19,109 |
spaCy | spacy/lang/en/__init__.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from .lemmatizer import EnglishLemmatizer
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from ... | 52 | 1,236 |
spaCy | spacy/lang/en/stop_words.py | .py | # Stop words
STOP_WORDS = set(
"""
a about above across after afterwards again against all almost alone along
already also although always am among amongst amount an and another any anyhow
anyone anything anyway anywhere are around as at
back be became because become becomes becoming been before beforehand behind
... | 74 | 2,148 |
spaCy | spacy/lang/en/lex_attrs.py | .py | from ...attrs import LIKE_NUM
# fmt: off
_num_words = [
"zero", "one", "two", "three", "four", "five", "six", "seven", "eight",
"nine", "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen",
"sixteen", "seventeen", "eighteen", "nineteen", "twenty", "thirty", "forty",
"fifty", "sixty", "seventy",... | 47 | 1,770 |
spaCy | spacy/lang/en/syntax_iterators.py | .py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import NOUN, PRON, PROPN
from ...tokens import Doc, Span
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"... | 51 | 1,570 |
spaCy | spacy/lang/en/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.en.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple is looking at buying U.K. startup for $1 billion",
"Autonomous cars shift insurance liability toward manufacturers",
"San Francisco consider... | 18 | 555 |
spaCy | spacy/lang/en/lemmatizer.py | .py | from ...pipeline import Lemmatizer
from ...tokens import Token
class EnglishLemmatizer(Lemmatizer):
"""English lemmatizer. Only overrides is_base_form."""
def is_base_form(self, token: Token) -> bool:
"""
Check whether we're dealing with an uninflected paradigm, so we can
avoid lemmat... | 41 | 1,480 |
spaCy | spacy/lang/en/tokenizer_exceptions.py | .py | from typing import Dict, List
from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc: Dict[str, List[Dict]] = {}
_exclude = [
"Ill",
"ill",
"Its",
"its",
"Hell",
"hell",
"Shell",
"shell",
"Shed",
"shed",
"wer... | 527 | 14,252 |
spaCy | spacy/lang/bg/__init__.py | .py | from ...attrs import LANG
from ...language import BaseDefaults, Language
from ...util import update_exc
from ..punctuation import (
COMBINING_DIACRITICS_TOKENIZER_INFIXES,
COMBINING_DIACRITICS_TOKENIZER_SUFFIXES,
)
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from .lex_attrs import LEX_ATTRS
from .stop_wo... | 32 | 907 |
spaCy | spacy/lang/bg/stop_words.py | .py | """
References:
https://github.com/Alir3z4/stop-words - Original list, serves as a base.
https://postvai.com/books/stop-dumi.pdf - Additions to the original list in order to improve it.
"""
STOP_WORDS = set(
"""
а автентичен аз ако ала
бе без беше би бивш бивша бившо бивши бил била били било благодаря бли... | 81 | 4,749 |
spaCy | spacy/lang/bg/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"нула",
"едно",
"един",
"една",
"две",
"три",
"четири",
"пет",
"шест",
"седем",
"осем",
"девет",
"десет",
"единадесет",
"единайсет",
"дванадесет",
"дванайсет",
"тринадесет",
"тринайсет",
"четири... | 88 | 2,047 |
spaCy | spacy/lang/bg/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.bg.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Епъл иска да купи английски стартъп за 1 милиард долара."
"Автономните коли прехвърлят застрахователната отговорност към производителите."
"Сан Фр... | 14 | 636 |
spaCy | spacy/lang/bg/tokenizer_exceptions.py | .py | """
References:
https://slovored.com/bg/abbr/grammar/ - Additional refs for abbreviations
(countries, occupations, fields of studies and more).
"""
from ...symbols import NORM, ORTH
_exc = {}
# measurements
for abbr in [
{ORTH: "м", NORM: "метър"},
{ORTH: "мм", NORM: "милиметър"},
{ORTH: "см", NO... | 213 | 9,109 |
spaCy | spacy/lang/is/__init__.py | .py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class IcelandicDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Icelandic(Language):
lang = "is"
Defaults = IcelandicDefaults
__all__ = ["Icelandic"]
| 15 | 255 |
spaCy | spacy/lang/is/stop_words.py | .py | # Source: https://github.com/Xangis/extra-stopwords
STOP_WORDS = set(
"""
afhverju
aftan
aftur
afþví
aldrei
allir
allt
alveg
annað
annars
bara
dag
eða
eftir
eiga
einhver
einhverjir
einhvers
eins
einu
eitthvað
ekkert
ekki
ennþá
eru
fara
fer
finna
fjöldi
fólk
framan
frá
frekar
fyrir
gegnum
geta
getur
gmg
gott
hann
h... | 159 | 1,019 |
spaCy | spacy/lang/tl/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class TagalogDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
... | 19 | 416 |
spaCy | spacy/lang/tl/stop_words.py | .py | STOP_WORDS = set(
"""
akin
aking
ako
alin
am
amin
aming
ang
ano
anumang
apat
at
atin
ating
ay
bababa
bago
bakit
bawat
bilang
dahil
dalawa
dapat
din
dito
doon
gagawin
gayunman
ginagawa
ginawa
ginawang
gumawa
gusto
habang
hanggang
hindi
huwag
iba
ibaba
ibabaw
ibig
ikaw
ilagay
ilalim
ilan
inyong
isa
isang
itaas
ito
iy... | 152 | 965 |
spaCy | spacy/lang/tl/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"sero",
"isa",
"dalawa",
"tatlo",
"apat",
"lima",
"anim",
"pito",
"walo",
"siyam",
"sampu",
"labing-isa",
"labindalawa",
"labintatlo",
"labing-apat",
"labinlima",
"labing-anim",
"labimpito",
"labing... | 57 | 942 |
spaCy | spacy/lang/tl/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {
"tayo'y": [{ORTH: "tayo"}, {ORTH: "'y", NORM: "ay"}],
"isa'y": [{ORTH: "isa"}, {ORTH: "'y", NORM: "ay"}],
"baya'y": [{ORTH: "baya"}, {ORTH: "'y", NORM: "ay"}],
"sa'yo": [{ORTH: "... | 19 | 687 |
spaCy | spacy/lang/sv/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
LIST_ELLIPSES,
LIST_ICONS,
)
from ..punctuation import TOKENIZER_SUFFIXES
_quotes = CONCAT_QUOTES.replace("'", "")
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[{al}])\.(?=[{au}])".format(al=A... | 39 | 1,025 |
spaCy | spacy/lang/sv/__init__.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from ...pipeline import Lemmatizer
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_IT... | 53 | 1,276 |
spaCy | spacy/lang/sv/stop_words.py | .py | STOP_WORDS = set(
"""
aderton adertonde adjö aldrig alla allas allt alltid alltså än andra andras
annan annat ännu artonde arton åtminstone att åtta åttio åttionde åttonde av
även
båda bådas bakom bara bäst bättre behöva behövas behövde behövt beslut beslutat
beslutit bland blev bli blir blivit bort borta bra
då ... | 67 | 2,545 |
spaCy | spacy/lang/sv/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"noll",
"en",
"ett",
"två",
"tre",
"fyra",
"fem",
"sex",
"sju",
"åtta",
"nio",
"tio",
"elva",
"tolv",
"tretton",
"fjorton",
"femton",
"sexton",
"sjutton",
"arton",
"nitton",
"tjugo",
... | 59 | 953 |
spaCy | spacy/lang/sv/syntax_iterators.py | .py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import NOUN, PRON, PROPN
from ...tokens import Doc, Span
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""Detect base noun phrases from a dependency parse. Works on Doc and Span."""
# fmt: o... | 40 | 1,531 |
spaCy | spacy/lang/sv/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.sv.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple överväger att köpa brittisk startup för 1 miljard dollar.",
"Självkörande bilar förskjuter försäkringsansvar mot tillverkare.",
"San Fransis... | 14 | 440 |
spaCy | spacy/lang/sv/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
# Verbs
for verb_data in [
{ORTH: "driver"},
{ORTH: "kör"},
{ORTH: "hörr"},
{ORTH: "fattar"},
{ORTH: "hajar"},
{ORTH: "lever"},
{ORTH: "serr"},
{ORTH: "fixar"... | 157 | 3,655 |
spaCy | spacy/lang/hsb/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class UpperSorbianDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
tokenizer_exceptions = TOKENIZER_EXCEPTIO... | 19 | 437 |
spaCy | spacy/lang/hsb/stop_words.py | .py | STOP_WORDS = set(
"""
a abo ale ani
dokelž
hdyž
jeli jelizo
kaž
pak potom
tež tohodla
zo zoby
""".split()
)
| 20 | 123 |
spaCy | spacy/lang/hsb/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"nul",
"jedyn",
"jedna",
"jedne",
"dwaj",
"dwě",
"tři",
"třo",
"štyri",
"štyrjo",
"pjeć",
"šěsć",
"sydom",
"wosom",
"dźewjeć",
"dźesać",
"jědnaće",
"dwanaće",
"třinaće",
"štyrnaće",
"pjatnać... | 107 | 1,791 |
spaCy | spacy/lang/hsb/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.hsb.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"To běšo wjelgin raźone a jo se wót luźi derje pśiwzeło. Tak som dožywiła wjelgin",
"Jogo pśewóźowarce stej groniłej, až how w serbskich stronach njam... | 15 | 639 |
spaCy | spacy/lang/hsb/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = dict()
for exc_data in [
{ORTH: "mil.", NORM: "milion"},
{ORTH: "wob.", NORM: "wobydler"},
]:
_exc[exc_data[ORTH]] = [exc_data]
for orth in [
"resp.",
]:
_exc[orth] = [{ORTH: ... | 19 | 386 |
spaCy | spacy/lang/et/__init__.py | .py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class EstonianDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Estonian(Language):
lang = "et"
Defaults = EstonianDefaults
__all__ = ["Estonian"]
| 15 | 251 |
spaCy | spacy/lang/et/stop_words.py | .py | # Source: https://github.com/stopwords-iso/stopwords-et
STOP_WORDS = set(
"""
aga
ei
et
ja
jah
kas
kui
kõik
ma
me
mida
midagi
mind
minu
mis
mu
mul
mulle
nad
nii
oled
olen
oli
oma
on
pole
sa
seda
see
selle
siin
siis
ta
te
ära
""".split()
)
| 42 | 246 |
spaCy | spacy/lang/ja/tag_map.py | .py | from ...symbols import (
ADJ,
ADP,
ADV,
AUX,
CCONJ,
DET,
INTJ,
NOUN,
NUM,
PART,
POS,
PRON,
PROPN,
PUNCT,
SCONJ,
SPACE,
SYM,
VERB,
)
TAG_MAP = {
# Explanation of Unidic tags:
# https://www.gavo.t.u-tokyo.ac.jp/~mine/japanese/nlp+slp/UNIDIC_... | 90 | 3,823 |
spaCy | spacy/lang/ja/__init__.py | .py | import re
from collections import namedtuple
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Union
import srsly
from thinc.api import Model
from ... import util
from ...errors import Errors
from ...language import BaseDefaults, Language
from ...pipeline import Morphologizer
from ...pipeline... | 342 | 12,586 |
spaCy | spacy/lang/ja/stop_words.py | .py | # This list was created by taking the top 2000 words from a Wikipedia dump and
# filtering out everything that wasn't hiragana. ー (one) was also added.
# Considered keeping some non-hiragana words but too many place names were
# present.
STOP_WORDS = set(
"""
あ あっ あまり あり ある あるいは あれ
い いい いう いく いずれ いっ いつ いる いわ
うち
え
お... | 49 | 1,328 |
spaCy | spacy/lang/ja/tag_bigram_map.py | .py | from ...symbols import ADJ, AUX, NOUN, PART, VERB
# mapping from tag bi-gram to pos of previous token
TAG_BIGRAM_MAP = {
# This covers only small part of AUX.
("形容詞-非自立可能", "助詞-終助詞"): (AUX, None),
("名詞-普通名詞-形状詞可能", "助動詞"): (ADJ, None),
# ("副詞", "名詞-普通名詞-形状詞可能"): (None, ADJ),
# This covers acl, advc... | 29 | 1,647 |
spaCy | spacy/lang/ja/syntax_iterators.py | .py | from typing import Iterator, Set, Tuple, Union
from ...symbols import NOUN, PRON, PROPN, VERB
from ...tokens import Doc, Span
# TODO: this can probably be pruned a bit
# fmt: off
labels = ["nsubj", "nmod", "ddoclike", "nsubjpass", "pcomp", "pdoclike", "doclike", "obl", "dative", "appos", "attr", "ROOT"]
# fmt: on
d... | 42 | 1,638 |
spaCy | spacy/lang/ja/tag_orth_map.py | .py | from ...symbols import DET, PART, PRON, SPACE, X
# mapping from tag bi-gram to pos of previous token
TAG_ORTH_MAP = {
"空白": {" ": SPACE, " ": X},
"助詞-副助詞": {"たり": PART},
"連体詞": {
"あの": DET,
"かの": DET,
"この": DET,
"その": DET,
"どの": DET,
"彼の": DET,
"此の": ... | 23 | 546 |
spaCy | spacy/lang/ja/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ja.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"アップルがイギリスの新興企業を１０億ドルで購入を検討",
"自動運転車の損害賠償責任、自動車メーカーに一定の負担を求める",
"歩道を走る自動配達ロボ、サンフランシスコ市が走行禁止を検討",
"ロンドンはイギリスの大都市です。",
]
| 14 | 498 |
spaCy | spacy/lang/ru/__init__.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from ..punctuation import (
COMBINING_DIACRITICS_TOKENIZER_INFIXES,
COMBINING_DIACRITICS_TOKENIZER_SUFFIXES,
)
from .lemmatizer import RussianLemmatizer
from .lex_attrs import LEX_ATTRS
from .stop_... | 54 | 1,306 |
spaCy | spacy/lang/ru/stop_words.py | .py | STOP_WORDS = set(
"""
а авось ага агу аж ай али алло ау ах ая
б будем будет будете будешь буду будут будучи будь будьте бы был была были было
быть бац без безусловно бишь благо благодаря ближайшие близко более больше
будто бывает бывала бывали бываю бывают бытует
в вам вами вас весь во вот все всё всего всей всем... | 112 | 8,356 |
spaCy | spacy/lang/ru/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = list(
set(
"""
ноль ноля нолю нолём ноле нулевой нулевого нулевому нулевым нулевом нулевая нулевую нулевое нулевые нулевых нулевыми
четверть четверти четвертью четвертей четвертям четвертями четвертях
треть трети третью третей третям третями третях
половина ... | 229 | 18,733 |
spaCy | spacy/lang/ru/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ru.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
# Translations from English:
"Apple рассматривает возможность покупки стартапа из Соединённого Королевства за $1 млрд",
"Беспилотные автомобили пер... | 40 | 4,580 |
spaCy | spacy/lang/ru/lemmatizer.py | .py | from typing import Callable, Dict, List, Optional, Tuple
from thinc.api import Model
from ...pipeline import Lemmatizer
from ...pipeline.lemmatizer import lemmatizer_score
from ...symbols import POS
from ...tokens import Token
from ...vocab import Vocab
PUNCT_RULES = {"«": '"', "»": '"'}
class RussianLemmatizer(Le... | 218 | 7,975 |
spaCy | spacy/lang/ru/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
_abbrev_exc = [
# Weekdays abbreviations
{ORTH: "пн", NORM: "понедельник"},
{ORTH: "вт", NORM: "вторник"},
{ORTH: "ср", NORM: "среда"},
{ORTH: "чт", NORM: "четверг"},
{... | 408 | 25,394 |
spaCy | spacy/lang/vi/__init__.py | .py | import re
import string
from pathlib import Path
from typing import Any, Dict, Union
import srsly
from ... import util
from ...language import BaseDefaults, Language
from ...tokens import Doc
from ...util import DummyTokenizer, load_config_from_str, registry
from ...vocab import Vocab
from .lex_attrs import LEX_ATTRS... | 168 | 5,522 |
spaCy | spacy/lang/vi/stop_words.py | .py | # Source: https://github.com/stopwords/vietnamese-stopwords
STOP_WORDS = set(
"""
a_lô
a_ha
ai
ai_ai
ai_nấy
ai_đó
alô
amen
anh
anh_ấy
ba
ba_bau
ba_bản
ba_cùng
ba_họ
ba_ngày
ba_ngôi
ba_tăng
bao_giờ
bao_lâu
bao_nhiêu
bao_nả
bay_biến
biết
biết_bao
biết_bao_nhiêu
biết_chắc
biết_chừng_nào
biết_mình
biết_mấy
biết_thế
biế... | 1,948 | 20,581 |
spaCy | spacy/lang/vi/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"không", # Zero
"một", # One
"mốt", # Also one, irreplacable in niché cases for unit digit such as "51"="năm mươi mốt"
"hai", # Two
"ba", # Three
"bốn", # Four
"tư", # Also four, used in certain cases for unit digit such as "54"="năm mươi ... | 46 | 1,396 |
spaCy | spacy/lang/vi/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.vi.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Đây là đâu, tôi là ai?",
"Căn phòng có nhiều cửa sổ nên nó khá sáng",
"Đại dịch COVID vừa qua đã gây ảnh hưởng rất lớn tới nhiều doanh nghiệp lớn n... | 17 | 768 |
spaCy | spacy/lang/fr/_tokenizer_exceptions_list.py | .py | FR_BASE_EXCEPTIONS = [
"(+)-amphétamine",
"(5R,6S)-7,8-didehydro-4,5-époxy-3-méthoxy-N-méthylmorphinan-6-ol",
"(R)-amphétamine",
"(S)-amphétamine",
"(−)-amphétamine",
"0-day",
"0-days",
"1,1-diméthylhydrazine",
"1,2,3-tris-nitrooxy-propane",
"1,2-diazine",
"1,2-dichloropropan... | 15,631 | 360,608 |
spaCy | spacy/lang/fr/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
LIST_ELLIPSES,
LIST_PUNCT,
LIST_QUOTES,
UNITS,
merge_chars,
)
from ..punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES
ELISION = "' ’".replace(" ", "")
HYPHENS = r"- – — ‐ ‑".replace(" ... | 57 | 1,468 |
spaCy | spacy/lang/fr/__init__.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from .lemmatizer import FrenchLemmatizer
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax_... | 55 | 1,380 |
spaCy | spacy/lang/fr/stop_words.py | .py | STOP_WORDS = set(
"""
a à â abord afin ah ai aie ainsi ait allaient allons
alors anterieur anterieure anterieures antérieur antérieure antérieures
apres après as assez attendu au
aupres auquel aura auraient aurait auront
aussi autre autrement autres autrui aux auxquelles auxquels avaient
avais avait avant avec avoi... | 85 | 3,504 |
spaCy | spacy/lang/fr/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = set(
"""
zero un une deux trois quatre cinq six sept huit neuf dix
onze douze treize quatorze quinze seize dix-sept dix-huit dix-neuf
vingt trente quarante cinquante soixante soixante-dix septante quatre-vingt huitante quatre-vingt-dix nonante
cent mille mil million milli... | 44 | 1,626 |
spaCy | spacy/lang/fr/syntax_iterators.py | .py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import NOUN, PRON, PROPN
from ...tokens import Doc, Span
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"... | 86 | 3,124 |
spaCy | spacy/lang/fr/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.fr.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple cherche à acheter une start-up anglaise pour 1 milliard de dollars",
"Les voitures autonomes déplacent la responsabilité de l'assurance vers les... | 22 | 937 |
spaCy | spacy/lang/fr/lemmatizer.py | .py | from typing import List, Tuple
from ...pipeline import Lemmatizer
from ...tokens import Token
class FrenchLemmatizer(Lemmatizer):
"""
French language lemmatizer applies the default rule based lemmatization
procedure with some modifications for better French language support.
The parts of speech 'ADV... | 88 | 3,016 |
spaCy | spacy/lang/fr/tokenizer_exceptions.py | .py | import re
from ...symbols import ORTH
from ...util import update_exc
from ..char_classes import ALPHA, ALPHA_LOWER
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from .punctuation import ELISION, HYPHENS
# not using the large _tokenizer_exceptions_list by default as it slows down the tokenizer
# from ._tokenizer_... | 444 | 11,231 |
spaCy | spacy/lang/xx/__init__.py | .py | from ...language import Language
class MultiLanguage(Language):
"""Language class to be used for models that support multiple languages.
This module allows models to specify their language ID as 'xx'.
"""
lang = "xx"
__all__ = ["MultiLanguage"]
| 13 | 266 |
spaCy | spacy/lang/xx/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.de.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
# combined examples from de/en/es/fr/it/nl/pl/pt/ru
sentences = [
"Die ganze Stadt ist ein Startup: Shenzhen ist das Silicon Valley für Hardware-Firmen",
"Wie deuts... | 96 | 8,161 |
spaCy | spacy/lang/af/__init__.py | .py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class AfrikaansDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Afrikaans(Language):
lang = "af"
Defaults = AfrikaansDefaults
__all__ = ["Afrikaans"]
| 15 | 255 |
spaCy | spacy/lang/af/stop_words.py | .py | # Source: https://github.com/stopwords-iso/stopwords-af
STOP_WORDS = set(
"""
'n
aan
af
al
as
baie
by
daar
dag
dat
die
dit
een
ek
en
gaan
gesê
haar
het
hom
hulle
hy
in
is
jou
jy
kan
kom
ma
maar
met
my
na
nie
om
ons
op
saam
sal
se
sien
so
sy
te
toe
uit
van
vir
was
wat
ʼn
""".split()
)
| 58 | 291 |
spaCy | spacy/lang/id/_tokenizer_exceptions_list.py | .py | ID_BASE_EXCEPTIONS = set(
"""
aba-aba
abah-abah
abal-abal
abang-abang
abar-abar
abong-abong
abrit-abrit
abrit-abritan
abu-abu
abuh-abuhan
abuk-abuk
abun-abun
acak-acak
acak-acakan
acang-acang
acap-acap
aci-aci
aci-acian
aci-acinya
aco-acoan
ad-blocker
ad-interim
ada-ada
ada-adanya
ada-adanyakah
adang-adang
adap-ada... | 3,903 | 53,599 |
spaCy | spacy/lang/id/punctuation.py | .py | from ..char_classes import ALPHA, _currency, _units, merge_chars, split_chars
from ..punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
_units = (
_units + "s bit Gbps Mbps mbps Kbps kbps ƒ ppi px "
"Hz kHz MHz GHz mAh "
"ratus rb ribu ribuan "
"juta jt jutaan mill?iar million... | 61 | 2,138 |
spaCy | spacy/lang/id/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class IndonesianDe... | 25 | 698 |
spaCy | spacy/lang/id/lex_attrs.py | .py | import unicodedata
from ...attrs import IS_CURRENCY, LIKE_NUM
from .punctuation import LIST_CURRENCY
_num_words = [
"nol",
"satu",
"dua",
"tiga",
"empat",
"lima",
"enam",
"tujuh",
"delapan",
"sembilan",
"sepuluh",
"sebelas",
"belas",
"puluh",
"ratus",
"r... | 67 | 1,276 |
spaCy | spacy/lang/id/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.id.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Indonesia merupakan negara kepulauan yang kaya akan budaya.",
"Berapa banyak warga yang dibutuhkan saat kerja bakti?",
"Penyaluran pupuk berasal d... | 18 | 725 |
spaCy | spacy/lang/id/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from ._tokenizer_exceptions_list import ID_BASE_EXCEPTIONS
# Daftar singkatan dan Akronim dari:
# https://id.wiktionary.org/wiki/Wiktionary:Daftar_singkatan_dan_akronim_bahasa_Indonesia#A
_exc = {}
for... | 221 | 4,198 |
spaCy | spacy/lang/yo/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class YorubaDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Yoruba(Language):
lang = "yo"
Defaults = YorubaDefaults
__all__ = ["Yoruba"]
| 17 | 309 |
spaCy | spacy/lang/yo/stop_words.py | .py | # stop words as whitespace-separated list.
# Source: https://raw.githubusercontent.com/dohliam/more-stoplists/master/yo/yo.txt
STOP_WORDS = set(
"a an b bá bí bẹ̀rẹ̀ d e f fún fẹ́ g gbogbo i inú j jù jẹ jẹ́ k kan kì kí kò "
"l láti lè lọ m mi mo máa mọ̀ n ni náà ní nígbà nítorí nǹkan o p padà pé "
"púpọ̀ p... | 10 | 608 |
spaCy | spacy/lang/yo/lex_attrs.py | .py | import unicodedata
from ...attrs import LIKE_NUM
_num_words = [
"ení",
"oókàn",
"ọ̀kanlá",
"ẹ́ẹdọ́gbọ̀n",
"àádọ́fà",
"ẹ̀walélúɡba",
"egbèje",
"ẹgbàárin",
"èjì",
"eéjì",
"èjìlá",
"ọgbọ̀n,",
"ọgọ́fà",
"ọ̀ọ́dúrún",
"ẹgbẹ̀jọ",
"ẹ̀ẹ́dẹ́ɡbàárùn",
"ẹ̀ta",
... | 112 | 2,522 |
spaCy | spacy/lang/yo/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.yo.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
# 1. https://yo.wikipedia.org/wiki/Wikipedia:%C3%80y%E1%BB%8Dk%C3%A0_p%C3%A0t%C3%A0k%C3%AC
# 2.https://yo.wikipedia.org/wiki/Oj%C3%BAew%C3%A9_%C3%80k%E1%BB%8D%CC%81k%E1%BB%8... | 23 | 1,269 |
spaCy | spacy/lang/mr/__init__.py | .py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class MarathiDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Marathi(Language):
lang = "mr"
Defaults = MarathiDefaults
__all__ = ["Marathi"]
| 15 | 247 |
spaCy | spacy/lang/mr/stop_words.py | .py | # Source: https://github.com/stopwords-iso/stopwords-mr/blob/master/stopwords-mr.txt, https://github.com/6/stopwords-json/edit/master/dist/mr.json
STOP_WORDS = set(
"""
न
अतरी
तो
हें
तें
कां
आणि
जें
जे
मग
ते
मी
जो
परी
गा
हे
ऐसें
आतां
नाहीं
तेथ
हा
तया
असे
म्हणे
काय
कीं
जैसें
तंव
तूं
होय
जैसा
आहे
पैं
तैसा
जरी
म्हणोनि... | 193 | 2,456 |
spaCy | spacy/lang/te/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class TeluguDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Telugu(Language):
lang = "te"
Defaults = TeluguDefaults
__all__ = ["Telugu"]
| 17 | 309 |
spaCy | spacy/lang/te/stop_words.py | .py | # Source: https://github.com/Xangis/extra-stopwords (MIT License)
STOP_WORDS = set(
"""
అందరూ
అందుబాటులో
అడగండి
అడగడం
అడ్డంగా
అనుగుణంగా
అనుమతించు
అనుమతిస్తుంది
అయితే
ఇప్పటికే
ఉన్నారు
ఎక్కడైనా
ఎప్పుడు
ఎవరైనా
ఎవరో ఒకరు
ఏ
ఏదైనా
ఏమైనప్పటికి
ఏమైనప్పటికి
ఒక
ఒక ప్రక్కన
కనిపిస్తాయి
కాదు
కాదు
కూడా
గా
గురించి
చుట్టూ
చేయగలిగ... | 57 | 1,107 |
spaCy | spacy/lang/te/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"సున్నా",
"శూన్యం",
"ఒకటి",
"రెండు",
"మూడు",
"నాలుగు",
"ఐదు",
"ఆరు",
"ఏడు",
"ఎనిమిది",
"తొమ్మిది",
"పది",
"పదకొండు",
"పన్నెండు",
"పదమూడు",
"పద్నాలుగు",
"పదిహేను",
"పదహారు",
"పదిహేడు",
"పద్దెనిమి... | 55 | 1,263 |
spaCy | spacy/lang/te/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.te import Telugu
>>> nlp = Telugu()
>>> from spacy.lang.te.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"ఆపిల్ 1 బిలియన్ డాలర్స్ కి యూ.కె. స్టార్ట్అప్ ని కొనాలని అనుకుంటుంది.",
"ఆటోనోమోస్ కార్లు భీమా... | 20 | 1,242 |
spaCy | spacy/lang/pt/punctuation.py | .py | from ..punctuation import (
TOKENIZER_INFIXES as BASE_TOKENIZER_INFIXES,
TOKENIZER_PREFIXES as BASE_TOKENIZER_PREFIXES,
TOKENIZER_SUFFIXES as BASE_TOKENIZER_SUFFIXES,
)
_prefixes = [r"\w{1,3}\$"] + BASE_TOKENIZER_PREFIXES
_suffixes = BASE_TOKENIZER_SUFFIXES
_infixes = [r"(\w+-\w+(-\w+)*)"] + BASE_TOKENIZ... | 16 | 423 |
spaCy | spacy/lang/pt/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class PortugueseDefaults(BaseDefaults)... | 24 | 644 |
spaCy | spacy/lang/pt/stop_words.py | .py | STOP_WORDS = set(
"""
a à às área acerca ademais adeus agora ainda algo algumas alguns ali além ambas ambos antes
ao aos apenas apoia apoio apontar após aquela aquelas aquele aqueles aqui aquilo
as assim através atrás até aí
baixo bastante bem boa bom breve
cada caminho catorze cedo cento certamente certeza cima ... | 67 | 2,566 |
spaCy | spacy/lang/pt/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"zero",
"um",
"dois",
"três",
"tres",
"quatro",
"cinco",
"seis",
"sete",
"oito",
"nove",
"dez",
"onze",
"doze",
"dúzia",
"dúzias",
"duzia",
"duzias",
"treze",
"catorze",
"quinze",
"dezas... | 119 | 2,037 |
spaCy | spacy/lang/pt/syntax_iterators.py | .py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import NOUN, PRON, PROPN
from ...tokens import Doc, Span
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"... | 86 | 3,088 |
spaCy | spacy/lang/pt/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.pt.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple está querendo comprar uma startup do Reino Unido por 100 milhões de dólares",
"Carros autônomos empurram a responsabilidade do seguro para os fa... | 14 | 471 |
spaCy | spacy/lang/pt/tokenizer_exceptions.py | .py | from ...symbols import ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
for orth in [
"Adm.",
"Art.",
"art.",
"Av.",
"av.",
"Cia.",
"dom.",
"Dr.",
"dr.",
"e.g.",
"E.g.",
"E.G.",
"e/ou",
"ed.",
"eng.",
"etc... | 55 | 715 |
spaCy | spacy/lang/el/get_pos_from_wiktionary.py | .py | def get_pos_from_wiktionary():
import re
from gensim.corpora.wikicorpus import extract_pages
regex = re.compile(r"==={{(\w+)\|el}}===")
regex2 = re.compile(r"==={{(\w+ \w+)\|el}}===")
# get words based on the Wiktionary dump
# check only for specific parts
# ==={{κύριο όνομα|el}}===
... | 65 | 2,087 |
spaCy | spacy/lang/el/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
HYPHENS,
LIST_CURRENCY,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
)
_units = (
"km km² km³ m m² m³ dm dm² dm³ cm cm² cm³ mm mm² mm³ ha µm nm yd in ft "
"kg g mg µg t lb o... | 106 | 3,408 |
spaCy | spacy/lang/el/__init__.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from .lemmatizer import GreekLemmatizer
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax_i... | 54 | 1,330 |
spaCy | spacy/lang/el/stop_words.py | .py | # Stop words
# Link to greek stop words: https://www.translatum.gr/forum/index.php?topic=3550.0?topic=3550.0
STOP_WORDS = set(
"""
αδιάκοπα αι ακόμα ακόμη ακριβώς άλλα αλλά αλλαχού άλλες άλλη άλλην
άλλης αλλιώς αλλιώτικα άλλο άλλοι αλλοιώς αλλοιώτικα άλλον άλλος άλλοτε αλλού
άλλους άλλων άμα άμεσα αμέσως αν ανά ανά... | 88 | 8,100 |
spaCy | spacy/lang/el/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"μηδέν",
"ένας",
"δυο",
"δυό",
"τρεις",
"τέσσερις",
"πέντε",
"έξι",
"εφτά",
"επτά",
"οκτώ",
"οχτώ",
"εννιά",
"εννέα",
"δέκα",
"έντεκα",
"ένδεκα",
"δώδεκα",
"δεκατρείς",
"δεκατέσσερις",
"δεκα... | 100 | 2,320 |
spaCy | spacy/lang/el/syntax_iterators.py | .py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import NOUN, PRON, PROPN
from ...tokens import Doc, Span
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""Detect base noun phrases from a dependency parse. Works on Doc and Span."""
# It fol... | 54 | 2,290 |
spaCy | spacy/lang/el/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.el.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"""Η άνιση κατανομή του πλούτου και του εισοδήματος, η οποία έχει λάβει
τρομερές διαστάσεις, δεν δείχνει τάσεις βελτίωσης.""",
"""Ο στόχος της σύντο... | 25 | 1,972 |
spaCy | spacy/lang/el/lemmatizer.py | .py | from typing import List
from ...pipeline import Lemmatizer
from ...tokens import Token
class GreekLemmatizer(Lemmatizer):
"""
Greek language lemmatizer applies the default rule based lemmatization
procedure with some modifications for better Greek language support.
The first modification is that it ... | 63 | 2,195 |
spaCy | spacy/lang/el/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
for token in ["Απ'", "ΑΠ'", "αφ'", "Αφ'"]:
_exc[token] = [{ORTH: token, NORM: "από"}]
for token in ["Αλλ'", "αλλ'"]:
_exc[token] = [{ORTH: token, NORM: "αλλά"}]
for token in ["παρ'",... | 394 | 10,076 |
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