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/fa/punctuation.py | .py | from ..char_classes import (
ALPHA_UPPER,
CURRENCY,
LIST_ELLIPSES,
LIST_PUNCT,
LIST_QUOTES,
UNITS,
)
_suffixes = (
LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
+ [
r"(?<=[0-9])\+",
r"(?<=[0-9])%", # 4% -> ["4", "%"]
# Persian is written from Right-To-Left
... | 25 | 508 |
spaCy | spacy/lang/fa/generate_verbs_exc.py | .py | verb_roots = """
#هست
آخت#آهنج
آراست#آرا
آراماند#آرامان
آرامید#آرام
آرمید#آرام
آزرد#آزار
آزمود#آزما
آسود#آسا
آشامید#آشام
آشفت#آشوب
آشوبید#آشوب
آغازید#آغاز
آغشت#آمیز
آفرید#آفرین
آلود#آلا
آمد#آ
آمرزید#آمرز
آموخت#آموز
آموزاند#آموزان
آمیخت#آمیز
آورد#آر
آورد#آور
آویخت#آویز
آکند#آکن
آگاهانید#آگاهان
ارزید#ارز
افتاد#افت
افراخت... | 652 | 14,844 |
spaCy | spacy/lang/fa/__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_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .token... | 53 | 1,307 |
spaCy | spacy/lang/fa/stop_words.py | .py | # Stop words from HAZM package
STOP_WORDS = set(
"""
و
در
به
از
که
این
را
با
است
برای
آن
یک
خود
تا
کرد
بر
هم
نیز
گفت
می‌شود
وی
شد
دارد
ما
اما
یا
شده
باید
هر
آنها
بود
او
دیگر
دو
مورد
می‌کند
شود
کند
وجود
بین
پیش
شده‌است
پس
نظر
اگر
همه
یکی
حال
هستند
من
کنند
نیست
باشد
چه
بی
می
بخش
می‌کنند
همین
افزود
هایی
دارند
راه
همچن... | 394 | 3,768 |
spaCy | spacy/lang/fa/lex_attrs.py | .py | from ...attrs import LIKE_NUM
MIM = "م"
ZWNJ_O_MIM = "ام"
YE_NUN = "ین"
_num_words = set(
"""
صفر
یک
دو
سه
چهار
پنج
شش
شیش
هفت
هشت
نه
ده
یازده
دوازده
سیزده
چهارده
پانزده
پونزده
شانزده
شونزده
هفده
هجده
هیجده
نوزده
بیست
سی
چهل
پنجاه
شصت
هفتاد
هشتاد
نود
صد
یکصد
یکصد
دویست
سیصد
چهارصد
پانصد
پونصد
ششصد
شیشصد
هفتصد
... | 102 | 1,385 |
spaCy | spacy/lang/fa/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.
"... | 52 | 1,556 |
spaCy | spacy/lang/fa/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.fa.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"این یک جمله نمونه می باشد.",
"قرار ما، امروز ساعت ۲:۳۰ بعدازظهر هست!",
"دیروز علی به من ۲۰۰۰.۱﷼ پول نقد داد.",
"چطور می‌توان از تهران به کاشان... | 15 | 514 |
spaCy | spacy/lang/fa/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
TOKENIZER_EXCEPTIONS = {
".ق ": [{ORTH: ".ق "}],
".م": [{ORTH: ".م"}],
".هـ": [{ORTH: ".هـ"}],
"ب.م": [{ORTH: "ب.م"}],
"ق.م": [{ORTH: "ق.م"}],
"آبرویت": [{ORTH: "آبروی", NORM: "آبروی"}, {ORTH: "ت", NORM: "ت"}],
"آب‌نباتش": [{ORTH: "آب‌نبات", NORM: "آب‌نبات"... | 747 | 64,920 |
spaCy | spacy/lang/ca/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
LIST_CURRENCY,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
PUNCT,
_units,
merge_chars,
)
ELISION = " ' ’ ".strip().replace(" ", "").replace("\n", "")
_prefixes = (
["§... | 67 | 1,602 |
spaCy | spacy/lang/ca/__init__.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from .lemmatizer import CatalanLemmatizer
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax... | 54 | 1,344 |
spaCy | spacy/lang/ca/stop_words.py | .py | STOP_WORDS = set(
"""
a abans ací ah així això al aleshores algun alguna algunes alguns alhora allà allí allò
als altra altre altres amb ambdues ambdós anar ans apa aquell aquella aquelles aquells
aquest aquesta aquestes aquests aquí
baix bastant bé
cada cadascuna cadascunes cadascuns cadascú com consegueixo cons... | 53 | 1,619 |
spaCy | spacy/lang/ca/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"zero",
"un",
"dos",
"tres",
"quatre",
"cinc",
"sis",
"set",
"vuit",
"nou",
"deu",
"onze",
"dotze",
"tretze",
"catorze",
"quinze",
"setze",
"disset",
"divuit",
"dinou",
"vint",
"trenta",... | 59 | 955 |
spaCy | spacy/lang/ca/syntax_iterators.py | .py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import NOUN, 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: off
... | 50 | 1,995 |
spaCy | spacy/lang/ca/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ca.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple està buscant comprar una startup del Regne Unit per mil milions de dòlars",
"Els cotxes autònoms deleguen la responsabilitat de l'assegurança al... | 18 | 594 |
spaCy | spacy/lang/ca/lemmatizer.py | .py | from typing import List, Tuple
from ...pipeline import Lemmatizer
from ...tokens import Token
class CatalanLemmatizer(Lemmatizer):
"""
Copied from French Lemmatizer
Catalan language lemmatizer applies the default rule based lemmatization
procedure with some modifications for better Catalan language s... | 82 | 2,843 |
spaCy | spacy/lang/ca/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
for exc_data in [
{ORTH: "aprox.", NORM: "aproximadament"},
{ORTH: "pàg.", NORM: "pàgina"},
{ORTH: "p.ex.", NORM: "per exemple"},
{ORTH: "gen.", NORM: "gener"},
{ORTH: "feb... | 68 | 1,961 |
spaCy | spacy/lang/eu/punctuation.py | .py | from ..punctuation import TOKENIZER_SUFFIXES
_suffixes = TOKENIZER_SUFFIXES
| 4 | 77 |
spaCy | spacy/lang/eu/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
class BasqueDefaults(BaseDefaults):
suffixes = TOKENIZER_SUFFIXES
stop_words = STOP_WORDS
lex_attr_getters = LEX_ATTRS
class Basque(Language):
... | 19 | 387 |
spaCy | spacy/lang/eu/stop_words.py | .py | # Source: https://github.com/stopwords-iso/stopwords-eu
# https://www.ranks.nl/stopwords/basque
# https://www.mustgo.com/worldlanguages/basque/
STOP_WORDS = set(
"""
al
anitz
arabera
asko
baina
bat
batean
batek
bati
batzuei
batzuek
batzuetan
batzuk
bera
beraiek
berau
berauek
bere
berori
beroriek
beste
bezala
da
dag... | 106 | 760 |
spaCy | spacy/lang/eu/lex_attrs.py | .py | from ...attrs import LIKE_NUM
# Source http://mylanguages.org/basque_numbers.php
_num_words = """
bat
bi
hiru
lau
bost
sei
zazpi
zortzi
bederatzi
hamar
hamaika
hamabi
hamahiru
hamalau
hamabost
hamasei
hamazazpi
Hemezortzi
hemeretzi
hogei
ehun
mila
milioi
""".split()
# source https://www.google.com/intl/ur/inputtool... | 77 | 1,093 |
spaCy | spacy/lang/eu/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.eu.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"bilbon ko castinga egin da eta nik jakin ez zuetako inork egin al du edota parte hartu duen ezagunik ba al du",
"gaur telebistan entzunda denok martet... | 12 | 419 |
spaCy | spacy/lang/gd/__init__.py | .py | from typing import Optional
from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class ScottishDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
stop_words = STOP_WORDS
class Scottish(Language):
lang =... | 19 | 383 |
spaCy | spacy/lang/gd/stop_words.py | .py | STOP_WORDS = set(
"""
'ad
'ar
'd # iad
'g # ag
'ga
'gam
'gan
'gar
'gur
'm # am
'n # an
'n seo
'na
'nad
'nam
'nan
'nar
'nuair
'nur
's
'sa
'san
'sann
'se
'sna
a
a'
a'd # agad
a'm # agam
a-chèile
a-seo
a-sin
a-siud
a chionn
a chionn 's
a chèile
a chéile
a dh'
a h-uile
a seo
ac' # aca
aca
aca-san
acasan
ach
ag
agad
aga... | 387 | 2,354 |
spaCy | spacy/lang/gd/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
"""
All rules and exceptions were taken from the "Gaelic Orthographic Conventions
of 2009" (GOC) and from the "Annotated Reference Corpus of Scottish Gaelic" (ARCOSG). I did
my best to ensure this token... | 1,982 | 24,574 |
spaCy | spacy/lang/cs/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class CzechDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Czech(Language):
lang = "cs"
Defaults = CzechDefaults
__all__ = ["Czech"]
| 17 | 305 |
spaCy | spacy/lang/cs/stop_words.py | .py | # Source: https://github.com/Alir3z4/stop-words
# Source: https://github.com/stopwords-iso/stopwords-cs/blob/master/stopwords-cs.txt
STOP_WORDS = set(
"""
a
aby
ahoj
ačkoli
ale
alespoň
anebo
ani
aniž
ano
atd.
atp.
asi
aspoň
až
během
bez
beze
blízko
bohužel
brzo
bude
budeme
budeš
budete
budou
budu
by
byl
byla
byli
... | 366 | 2,263 |
spaCy | spacy/lang/cs/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"nula",
"jedna",
"dva",
"tři",
"čtyři",
"pět",
"šest",
"sedm",
"osm",
"devět",
"deset",
"jedenáct",
"dvanáct",
"třináct",
"čtrnáct",
"patnáct",
"šestnáct",
"sedmnáct",
"osmnáct",
"devatenáct",
... | 62 | 1,074 |
spaCy | spacy/lang/cs/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.cs.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Máma mele maso.",
"Příliš žluťoučký kůň úpěl ďábelské ódy.",
"ArcGIS je geografický informační systém určený pro práci s prostorovými daty.",
"... | 38 | 1,513 |
spaCy | spacy/lang/ne/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class NepaliDefaults(BaseDefaults):
stop_words = STOP_WORDS
lex_attr_getters = LEX_ATTRS
class Nepali(Language):
lang = "ne"
Defaults = NepaliDefaults
__all__ = ["Nepali"]
| 17 | 309 |
spaCy | spacy/lang/ne/stop_words.py | .py | # Source: https://github.com/sanjaalcorps/NepaliStopWords/blob/master/NepaliStopWords.txt
STOP_WORDS = set(
"""
अक्सर
अगाडि
अगाडी
अघि
अझै
अठार
अथवा
अनि
अनुसार
अन्तर्गत
अन्य
अन्यत्र
अन्यथा
अब
अरु
अरुलाई
अरू
अर्को
अर्थात
अर्थात्
अलग
अलि
अवस्था
अहिले
आए
आएका
आएको
आज
आजको
आठ
आत्म
आदि
आदिलाई
आफनो
आफू
आफूलाई
आफै
आफैँ
आफ... | 495 | 7,135 |
spaCy | spacy/lang/ne/lex_attrs.py | .py | from ...attrs import LIKE_NUM, NORM
from ..norm_exceptions import BASE_NORMS
# fmt: off
_stem_suffixes = [
["ा", "ि", "ी", "ु", "ू", "ृ", "े", "ै", "ो", "ौ"],
["ँ", "ं", "्", "ः"],
["लाई", "ले", "बाट", "को", "मा", "हरू"],
["हरूलाई", "हरूले", "हरूबाट", "हरूको", "हरूमा"],
["इलो", "िलो", "नु", "ाउनु",... | 95 | 3,275 |
spaCy | spacy/lang/ne/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ne.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"एप्पलले अमेरिकी स्टार्टअप १ अर्ब डलरमा किन्ने सोच्दै छ",
"स्वायत्त कारहरूले बीमा दायित्व निर्माताहरु तिर बदल्छन्",
"स्यान फ्रांसिस्कोले फुटपाथ वित... | 18 | 1,103 |
spaCy | spacy/lang/ur/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
class UrduDefaults(BaseDefaults):
suffixes = TOKENIZER_SUFFIXES
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
writing_system = {"directi... | 20 | 461 |
spaCy | spacy/lang/ur/stop_words.py | .py | # Source: collected from different resource on internet
STOP_WORDS = set(
"""
ثھی
خو
گی
اپٌے
گئے
ثہت
طرف
ہوبری
پبئے
اپٌب
دوضری
گیب
کت
گب
ثھی
ضے
ہر
پر
اش
دی
گے
لگیں
ہے
ثعذ
ضکتے
تھی
اى
دیب
لئے
والے
یہ
ثدبئے
ضکتی
تھب
اًذر
رریعے
لگی
ہوبرا
ہوًے
ثبہر
ضکتب
ًہیں
تو
اور
رہب
لگے
ہوضکتب
ہوں
کب
ہوبرے
توبم
کیب
ایطے
رہی
هگر
ہوضک... | 514 | 4,722 |
spaCy | spacy/lang/ur/lex_attrs.py | .py | from ...attrs import LIKE_NUM
# Source https://quizlet.com/4271889/1-100-urdu-number-wordsurdu-numerals-flash-cards/
# http://www.urduword.com/lessons.php?lesson=numbers
# https://en.wikibooks.org/wiki/Urdu/Vocabulary/Numbers
# https://www.urdu-english.com/lessons/beginner/numbers
_num_words = """ایک دو تین چار پانچ ... | 46 | 2,237 |
spaCy | spacy/lang/ur/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.da.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"اردو ہے جس کا نام ہم جانتے ہیں داغ",
"سارے جہاں میں دھوم ہماری زباں کی ہے",
]
| 12 | 302 |
spaCy | spacy/lang/ti/punctuation.py | .py | from ..char_classes import (
ALPHA_UPPER,
CURRENCY,
LIST_ELLIPSES,
LIST_PUNCT,
LIST_QUOTES,
UNITS,
)
_list_punct = LIST_PUNCT + "፡ ። ፣ ፤ ፥ ፦ ፧ ፠ ፨".strip().split()
_suffixes = (
_list_punct
+ LIST_ELLIPSES
+ LIST_QUOTES
+ [
r"(?<=[0-9])\+",
# Tigrinya is written... | 26 | 548 |
spaCy | spacy/lang/ti/__init__.py | .py | from ...attrs import LANG
from ...language import BaseDefaults, Language
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTION... | 27 | 834 |
spaCy | spacy/lang/ti/stop_words.py | .py | # Stop words from Tigrinya Wordcount: https://github.com/fgaim/Tigrinya-WordCount/blob/main/ti_stop_words.txt
# Stop words
STOP_WORDS = set(
"""
'ምበር 'ሞ 'ቲ 'ታ 'ኳ 'ውን 'ዚ 'የ 'ዩ 'ያ 'ዮም 'ዮን
ልዕሊ ሒዙ ሒዛ ሕጂ መበል መን መንጎ መጠን ማለት ምስ ምባል
ምእንቲ ምኽንያቱ ምኽንያት ምዃኑ ምዃንና ምዃኖም
ስለ ስለዚ ስለዝበላ ሽዑ ቅድሚ በለ በቲ በዚ ብምባል ብተወሳኺ ብኸመይ
ብዘይ ብዘይካ ብዙሕ ብ... | 28 | 1,977 |
spaCy | spacy/lang/ti/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"ዜሮ",
"ሓደ",
"ክልተ",
"ሰለስተ",
"ኣርባዕተ",
"ሓሙሽተ",
"ሽድሽተ",
"ሸውዓተ",
"ሽሞንተ",
"ትሽዓተ",
"ዓሰርተ",
"ዕስራ",
"ሰላሳ",
"ኣርብዓ",
"ሓምሳ",
"ሱሳ",
"ሰብዓ",
"ሰማንያ",
"ቴስዓ",
"ሚእቲ",
"ሺሕ",
"ሚልዮን",
"ቢልዮን",
"ትሪልዮን",... | 74 | 1,508 |
spaCy | spacy/lang/ti/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ti.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"አፕል ብዩኬ ትርከብ ንግድ ብ1 ቢሊዮን ዶላር ንምግዛዕ ሐሲባ።",
"ፈላማይ ክታበት ኮቪድ 19 ተጀሚሩ፤ሓዱሽ ተስፋ ሂቡ ኣሎ",
"ቻንስለር ጀርመን ኣንገላ መርከል ዝርግሓ ቫይረስ ኮሮና ንምክልካል ጽኑዕ እገዳ ክግበር ጸዊዓ",
... | 18 | 820 |
spaCy | spacy/lang/ti/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
_exc = {}
for exc_data in [
{ORTH: "ት/ቤት"},
{ORTH: "ወ/ሮ", NORM: "ወይዘሮ"},
{ORTH: "ወ/ሪ", NORM: "ወይዘሪት"},
]:
_exc[exc_data[ORTH]] = [exc_data]
for orth in [
"ዓ.ም.",
"ኪ.ሜ.",
]:
_exc[orth] = [{ORTH: orth}]
TOKENIZER_EXCEPTIONS = _exc
| 22 | 338 |
spaCy | spacy/lang/mk/__init__.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...attrs import LANG
from ...language import BaseDefaults, Language
from ...lookups import Lookups
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from .lemmatizer import MacedonianLemmatizer
from .lex_attrs impor... | 58 | 1,511 |
spaCy | spacy/lang/mk/stop_words.py | .py | STOP_WORDS = set(
"""
а
абре
aв
аи
ако
алало
ам
ама
аман
ами
амин
априли-ли-ли
ау
аух
ауч
ах
аха
аха-ха
аш
ашколсум
ашколсун
ај
ајде
ајс
аџаба
бавно
бам
бам-бум
бап
бар
баре
барем
бау
бау-бау
баш
бај
бе
беа
бев
бевме
бевте
без
безбели
бездруго
белки
беше
би
бидејќи
бим
бис
бла
блазе
богами
божем
боц
браво
бравос
бр... | 816 | 9,106 |
spaCy | spacy/lang/mk/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"нула",
"еден",
"една",
"едно",
"два",
"две",
"три",
"четири",
"пет",
"шест",
"седум",
"осум",
"девет",
"десет",
"единаесет",
"дванаесет",
"тринаесет",
"четиринаесет",
"петнаесет",
"шеснаесет",
... | 139 | 3,468 |
spaCy | spacy/lang/mk/lemmatizer.py | .py | from collections import OrderedDict
from typing import List
from ...pipeline import Lemmatizer
from ...tokens import Token
class MacedonianLemmatizer(Lemmatizer):
def rule_lemmatize(self, token: Token) -> List[str]:
string = token.text
univ_pos = token.pos_.lower()
if univ_pos in ("", "e... | 59 | 1,718 |
spaCy | spacy/lang/mk/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
_exc = {}
_abbr_exc = [
{ORTH: "м", NORM: "метар"},
{ORTH: "мм", NORM: "милиметар"},
{ORTH: "цм", NORM: "центиметар"},
{ORTH: "см", NORM: "сантиметар"},
{ORTH: "дм", NORM: "дециметар"},
{ORTH: "км", NORM: "километар"},
{ORTH: "кг", NORM: "килограм"},
... | 94 | 3,576 |
spaCy | spacy/lang/hy/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class ArmenianDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Armenian(Language):
lang = "hy"
Defaults = ArmenianDefaults
__all__ = ["Armenian"]
| 17 | 317 |
spaCy | spacy/lang/hy/stop_words.py | .py | STOP_WORDS = set(
"""
նա
ողջը
այստեղ
ենք
նա
էիր
որպես
ուրիշ
բոլորը
այն
այլ
նույնչափ
էի
մի
և
ողջ
ես
ոմն
հետ
նրանք
ամենքը
ըստ
ինչ-ինչ
այսպես
համայն
մի
նաև
նույնքան
դա
ովևէ
համար
այնտեղ
էին
որոնք
սույն
ինչ-որ
ամենը
նույնպիսի
ու
իր
որոշ
միևնույն
ի
այնպիսի
մենք
ամեն ոք
նույն
երբևէ
այն
որևէ
ին
այդպես
նրա
որը
վրա
դու
էինք... | 108 | 1,067 |
spaCy | spacy/lang/hy/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"զրո",
"մեկ",
"երկու",
"երեք",
"չորս",
"հինգ",
"վեց",
"յոթ",
"ութ",
"ինը",
"տասը",
"տասնմեկ",
"տասներկու",
"տասներեք",
"տասնչորս",
"տասնհինգ",
"տասնվեց",
"տասնյոթ",
"տասնութ",
"տասնինը",
"քս... | 57 | 1,159 |
spaCy | spacy/lang/hy/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.hy.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Լոնդոնը Միացյալ Թագավորության մեծ քաղաք է։",
"Ո՞վ է Ֆրանսիայի նախագահը։",
"Ո՞րն է Միացյալ Նահանգների մայրաքաղաքը։",
"Ե՞րբ է ծնվել Բարաք Օբաման։... | 13 | 440 |
spaCy | spacy/lang/sk/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class SlovakDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Slovak(Language):
lang = "sk"
Defaults = SlovakDefaults
__all__ = ["Slovak"]
| 17 | 309 |
spaCy | spacy/lang/sk/stop_words.py | .py | # Source: https://github.com/Ardevop-sk/stopwords-sk
STOP_WORDS = set(
"""
a
aby
aj
ak
akej
akejže
ako
akom
akomže
akou
akouže
akože
aká
akáže
aké
akého
akéhože
akému
akémuže
akéže
akú
akúže
aký
akých
akýchže
akým
akými
akýmiže
akýmže
akýže
ale
alebo
ani
asi
avšak
až
ba
bez
bezo
bol
bola
boli
bolo
bude
budem
budem... | 425 | 2,639 |
spaCy | spacy/lang/sk/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"nula",
"jeden",
"dva",
"tri",
"štyri",
"päť",
"šesť",
"sedem",
"osem",
"deväť",
"desať",
"jedenásť",
"dvanásť",
"trinásť",
"štrnásť",
"pätnásť",
"šestnásť",
"sedemnásť",
"osemnásť",
"devätnásť"... | 60 | 1,070 |
spaCy | spacy/lang/sk/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.sk.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Ardevop, s.r.o. je malá startup firma na území SR.",
"Samojazdiace autá presúvajú poistnú zodpovednosť na výrobcov automobilov.",
"Košice sú na vý... | 23 | 728 |
spaCy | spacy/lang/lij/punctuation.py | .py | from ..char_classes import ALPHA
from ..punctuation import TOKENIZER_INFIXES
ELISION = " ' ’ ".strip().replace(" ", "").replace("\n", "")
_infixes = TOKENIZER_INFIXES + [
r"(?<=[{a}][{el}])(?=[{a}])".format(a=ALPHA, el=ELISION)
]
TOKENIZER_INFIXES = _infixes
| 12 | 269 |
spaCy | spacy/lang/lij/__init__.py | .py | from ...language import BaseDefaults, Language
from .punctuation import TOKENIZER_INFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class LigurianDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
infixes = TOKENIZER_INFIXES
stop_words = STO... | 19 | 430 |
spaCy | spacy/lang/lij/stop_words.py | .py | STOP_WORDS = set(
"""
a à â a-a a-e a-i a-o aiva aloa an ancheu ancon apreuvo ascì atra atre atri atro avanti avei
bella belle belli bello ben
ch' che chì chi ciù co-a co-e co-i co-o comm' comme con cösa coscì cöse
d' da da-a da-e da-i da-o dapeu de delongo derê di do doe doî donde dòppo
é e ê ea ean emmo en ës... | 40 | 853 |
spaCy | spacy/lang/lij/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.lij.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Sciusciâ e sciorbî no se peu.",
"Graçie di çetroin, che me son arrivæ.",
"Vegnime apreuvo, che ve fasso pescâ di òmmi.",
"Bella pe sempre l'æ... | 14 | 396 |
spaCy | spacy/lang/lij/tokenizer_exceptions.py | .py | from ...symbols import ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
for raw in [
"a-e",
"a-o",
"a-i",
"a-a",
"co-a",
"co-e",
"co-i",
"co-o",
"da-a",
"da-e",
"da-i",
"da-o",
"pe-a",
"pe-e",
"pe-i",
"pe-o... | 50 | 870 |
spaCy | spacy/lang/sl/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
HYPHENS,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
PUNCT,
UNITS,
)
from ..punctuation import TOKENIZER_PREFIXES as BASE_TOKENIZER_PREFIXES
INCLUDE_SPECIAL = ["\\+", "\\/", "\... | 84 | 3,186 |
spaCy | spacy/lang/sl/__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 .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class SlovenianDefaults(BaseDefaults):
stop_words = STOP_WORD... | 23 | 607 |
spaCy | spacy/lang/sl/stop_words.py | .py | # Source: https://github.com/stopwords-iso/stopwords-sl
STOP_WORDS = set(
"""
a ali
b bi bil bila bile bili bilo biti blizu bo bodo bojo bolj bom bomo
boste bova boš brez
c cel cela celi celo
č če često četrta četrtek četrti četrto čez čigav
d da daleč dan danes datum deset deseta deseti deseto devet
deveta ... | 85 | 2,478 |
spaCy | spacy/lang/sl/lex_attrs.py | .py | import unicodedata
from ...attrs import IS_CURRENCY, LIKE_NUM
_num_words = set(
"""
nula ničla nič ena dva tri štiri pet šest sedem osem
devet deset enajst dvanajst trinajst štirinajst petnajst
šestnajst sedemnajst osemnajst devetnajst dvajset trideset štirideset
petdeset šestdest sedemdeset osemdeset devedes... | 145 | 6,583 |
spaCy | spacy/lang/sl/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.sl.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple načrtuje nakup britanskega startupa za 1 bilijon dolarjev",
"France Prešeren je umrl 8. februarja 1849 v Kranju",
"Staro ljubljansko letališ... | 18 | 574 |
spaCy | spacy/lang/sl/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]] = {}
_other_exc = {
"t.i.": [{ORTH: "t.", NORM: "tako"}, {ORTH: "i.", NORM: "imenovano"}],
"t.j.": [{ORTH: "t.", NORM: "to"}, {ORTH: "j... | 274 | 13,429 |
spaCy | spacy/lang/ko/tag_map.py | .py | from ...symbols import (
ADJ,
ADP,
ADV,
AUX,
CONJ,
DET,
INTJ,
NOUN,
NUM,
POS,
PRON,
PROPN,
PUNCT,
SYM,
VERB,
X,
)
# 은전한닢(mecab-ko-dic)의 품사 태그를 universal pos tag로 대응시킴
# https://docs.google.com/spreadsheets/d/1-9blXKjtjeKZqsf4NzHeYJCrr49-nXeRF6D80udfcwY/ed... | 73 | 1,975 |
spaCy | spacy/lang/ko/punctuation.py | .py | from ..char_classes import LIST_QUOTES
from ..punctuation import TOKENIZER_INFIXES as BASE_TOKENIZER_INFIXES
_infixes = (
["·", "ㆍ", r"\(", r"\)"]
+ [r"(?<=[0-9])~(?=[0-9-])"]
+ LIST_QUOTES
+ BASE_TOKENIZER_INFIXES
)
TOKENIZER_INFIXES = _infixes
| 12 | 267 |
spaCy | spacy/lang/ko/__init__.py | .py | from typing import Any, Dict, Iterator
from ...language import BaseDefaults, Language
from ...scorer import Scorer
from ...symbols import POS, X
from ...tokens import Doc
from ...training import validate_examples
from ...util import DummyTokenizer, load_config_from_str, registry
from ...vocab import Vocab
from .lex_at... | 126 | 4,251 |
spaCy | spacy/lang/ko/stop_words.py | .py | STOP_WORDS = set(
"""
이
있
하
것
들
그
되
수
이
보
않
없
나
주
아니
등
같
때
년
가
한
지
오
말
일
그렇
위하
때문
그것
두
말하
알
그러나
받
못하
일
그런
또
더
많
그리고
좋
크
시키
그러
하나
살
데
안
어떤
번
나
다른
어떻
들
이렇
점
싶
말
좀
원
잘
놓
""".split()
)
| 68 | 349 |
spaCy | spacy/lang/ko/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"영",
"공",
# Native Korean number system
"하나",
"둘",
"셋",
"넷",
"다섯",
"여섯",
"일곱",
"여덟",
"아홉",
"열",
"스물",
"서른",
"마흔",
"쉰",
"예순",
"일흔",
"여든",
"아흔",
# Sino-Korean number system
"일",
"이... | 64 | 1,051 |
spaCy | spacy/lang/ko/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ko.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"애플이 영국의 스타트업을 10억 달러에 인수하는 것을 알아보고 있다.",
"자율주행 자동차의 손해 배상 책임이 제조 업체로 옮겨 가다",
"샌프란시스코 시가 자동 배달 로봇의 보도 주행 금지를 검토 중이라고 합니다.",
"런던은 영국의 수도이자 가장 큰 ... | 14 | 531 |
spaCy | spacy/lang/sq/__init__.py | .py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class AlbanianDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Albanian(Language):
lang = "sq"
Defaults = AlbanianDefaults
__all__ = ["Albanian"]
| 15 | 251 |
spaCy | spacy/lang/sq/stop_words.py | .py | # Source: https://github.com/andrixh/index-albanian
STOP_WORDS = set(
"""
a
afert
ai
ajo
andej
anes
aq
as
asaj
ashtu
ata
ate
atij
atje
ato
aty
atyre
b
be
behem
behet
bej
beje
bejne
ben
bene
bere
beri
bie
c
ca
cdo
cfare
cila
cilat
cilave
cilen
ciles
cilet
cili
cilin
cilit
deri
dhe
dic
dicka
dickaje
dike
dikujt
diku... | 230 | 1,210 |
spaCy | spacy/lang/sq/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.sq.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple po shqyrton blerjen e nje shoqërie të U.K. për 1 miliard dollarë",
"Makinat autonome ndryshojnë përgjegjësinë e sigurimit ndaj prodhuesve",
... | 14 | 466 |
spaCy | spacy/lang/si/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class SinhalaDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Sinhala(Language):
lang = "si"
Defaults = SinhalaDefaults
__all__ = ["Sinhala"]
| 17 | 313 |
spaCy | spacy/lang/si/stop_words.py | .py | STOP_WORDS = set(
"""
සහ
සමග
සමඟ
අහා
ආහ්
ආ
ඕහෝ
අනේ
අඳෝ
අපොයි
අපෝ
අයියෝ
ආයි
ඌයි
චී
චිහ්
චික්
හෝ
දෝ
දෝහෝ
මෙන්
සේ
වැනි
බඳු
වන්
අයුරු
අයුරින්
ලෙස
වැඩි
ශ්රී
හා
ය
නිසා
නිසාවෙන්
බවට
බව
බවෙන්
නම්
වැඩි
සිට
දී
මහා
මහ
පමණ
පමණින්
පමන
වන
විට
විටින්
මේ
මෙලෙස
මෙයින්
ඇති
ලෙස
සිදු
වශයෙන්
යන
සඳහා
මගින්
හෝ
ඉතා
ඒ
එම
ද
අතර
විසින්
සම... | 196 | 2,407 |
spaCy | spacy/lang/si/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"බින්දුව",
"බිංදුව",
"එක",
"දෙක",
"තුන",
"හතර",
"පහ",
"හය",
"හත",
"අට",
"නවය",
"නමය",
"දහය",
"එකොළහ",
"දොළහ",
"දහතුන",
"දහහතර",
"දාහතර",
"පහළව",
"පහළොව",
"දහසය",
"දහහත",
"දාහත",
... | 62 | 1,253 |
spaCy | spacy/lang/si/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.si.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"මෙය වාක්යයකි.",
"ඔබ කවුද?",
"ගූගල් සමාගම ඩොලර් මිලියන 500 කට එම ආයතනය මිලදී ගන්නා ලදී.",
"කොළඹ ශ්රී ලංකාවේ ප්රධානතම නගරය යි.",
"ප්රංශය... | 19 | 943 |
spaCy | spacy/lang/ro/punctuation.py | .py | import itertools
from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
LIST_CURRENCY,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
PUNCT,
)
_list_icons = [x for x in LIST_ICONS if x != "°"]
_list_icons = [x.replace("\\u00B0", "") for ... | 171 | 3,154 |
spaCy | spacy/lang/ro/__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 .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
# Lemma data note:
# Original pairs downloaded from http://www.lex... | 27 | 778 |
spaCy | spacy/lang/ro/stop_words.py | .py | # Source: https://github.com/stopwords-iso/stopwords-ro
STOP_WORDS = set(
"""
a
abia
acea
aceasta
această
aceea
aceeasi
aceeași
acei
aceia
acel
acela
acelasi
același
acele
acelea
acest
acesta
aceste
acestea
acestei
acestia
acestui
aceşti
aceştia
acolo
acord
acum
adica
adică
ai
aia
aibă
aici
aiurea
al
ala
alaturi
al... | 500 | 2,945 |
spaCy | spacy/lang/ro/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = set(
"""
zero unu doi două trei patru cinci șase șapte opt nouă zece
unsprezece doisprezece douăsprezece treisprezece patrusprezece cincisprezece șaisprezece șaptesprezece optsprezece nouăsprezece
douăzeci treizeci patruzeci cincizeci șaizeci șaptezeci optzeci nouăzeci
su... | 43 | 1,679 |
spaCy | spacy/lang/ro/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ro import Romanian
>>> from spacy.lang.ro.examples import sentences
>>> nlp = Romanian()
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple plănuiește să cumpere o companie britanică pentru un miliard de dolari",
"Municipali... | 19 | 600 |
spaCy | spacy/lang/ro/tokenizer_exceptions.py | .py | from ...symbols import ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from .punctuation import _make_ro_variants
_exc = {}
# Source: https://en.wiktionary.org/wiki/Category:Romanian_abbreviations
for orth in [
"1-a",
"2-a",
"3-a",
"4-a",
"5-a",
"6-a",
... | 96 | 1,460 |
spaCy | spacy/lang/gu/__init__.py | .py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class GujaratiDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Gujarati(Language):
lang = "gu"
Defaults = GujaratiDefaults
__all__ = ["Gujarati"]
| 15 | 251 |
spaCy | spacy/lang/gu/stop_words.py | .py | STOP_WORDS = set(
"""
એમ
આ
એ
રહી
છે
છો
હતા
હતું
હતી
હોય
હતો
શકે
તે
તેના
તેનું
તેને
તેની
તેઓ
તેમને
તેમના
તેમણે
તેમનું
તેમાં
અને
અહીં
થી
થઈ
થાય
જે
ને
કે
ના
ની
નો
ને
નું
શું
માં
પણ
પર
જેવા
જેવું
જાય
જેમ
જેથી
માત્ર
માટે
પરથી
આવ્યું
એવી
આવી
રીતે
સુધી
થાય
થઈ
સાથે
લાગે
હોવા
છતાં
રહેલા
કરી
કરે
કેટલા
કોઈ
કેમ
કર્યો
કર્યુ
કર... | 89 | 1,004 |
spaCy | spacy/lang/gu/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.gu.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"લોકશાહી એ સરકારનું એક એવું તંત્ર છે જ્યાં નાગરિકો મત દ્વારા સત્તાનો ઉપયોગ કરે છે.",
"તે ગુજરાત રાજ્યના ધરમપુર શહેરમાં આવેલું હતું",
"કર્ણદેવ પહેલો... | 18 | 1,214 |
spaCy | spacy/lang/lb/punctuation.py | .py | from ..char_classes import ALPHA, ALPHA_LOWER, ALPHA_UPPER, LIST_ELLIPSES, LIST_ICONS
ELISION = " ' ’ ".strip().replace(" ", "")
abbrev = ("d", "D")
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=^[{ab}][{el}])(?=[{a}])".format(ab=abbrev, a=ALPHA, el=ELISION),
r"(?<=[{al}])\.(?=[{au}])... | 22 | 641 |
spaCy | spacy/lang/lb/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class LuxembourgishDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
infixes = ... | 21 | 515 |
spaCy | spacy/lang/lb/stop_words.py | .py | STOP_WORDS = set(
"""
a
à
äis
är
ärt
äert
ären
all
allem
alles
alleguer
als
also
am
an
anerefalls
ass
aus
awer
bei
beim
bis
bis
d'
dach
datt
däin
där
dat
de
dee
den
deel
deem
deen
deene
déi
den
deng
denger
dem
der
dësem
di
dir
do
da
dann
domat
dozou
drop
du
duerch
duerno
e
ee
em
een
eent
ë
en
ënner
ëm
ech
eis
eise
... | 212 | 1,127 |
spaCy | spacy/lang/lb/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = set(
"""
null eent zwee dräi véier fënnef sechs ziwen aacht néng zéng eelef zwielef dräizéng
véierzéng foffzéng siechzéng siwwenzéng uechtzeng uechzeng nonnzéng nongzéng zwanzeg drësseg véierzeg foffzeg sechzeg siechzeg siwenzeg achtzeg achzeg uechtzeg uechzeg nonnzeg
hon... | 41 | 1,342 |
spaCy | spacy/lang/lb/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.lb.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"An der Zäit hunn sech den Nordwand an d’Sonn gestridden, wie vun hinnen zwee wuel méi staark wier, wéi e Wanderer, deen an ee waarme Mantel agepak war, iw... | 16 | 906 |
spaCy | spacy/lang/lb/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
# TODO
# treat other apostrophes within words as part of the word: [op d'mannst], [fir d'éischt] (= exceptions)
_exc = {}
# translate / delete what is not necessary
for exc_data in [
{ORTH: "’t", N... | 53 | 1,167 |
spaCy | spacy/lang/th/__init__.py | .py | 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
from .stop_words import STOP_WORDS
DEFAULT_CONFIG = """
[nlp]
[nlp.tokenizer]
@tokenizers = "spacy.th.ThaiTokenizer"
"... | 53 | 1,324 |
spaCy | spacy/lang/th/stop_words.py | .py | STOP_WORDS = set(
"""
ทั้งนี้ ดัง ขอ รวม หลังจาก เป็น หลัง หรือ ๆ เกี่ยวกับ ซึ่งได้แก่ ด้วยเพราะ ด้วยว่า ด้วยเหตุเพราะ
ด้วยเหตุว่า สุดๆ เสร็จแล้ว เช่น เข้า ถ้า ถูก ถึง ต่างๆ ใคร เปิดเผย ครา รือ ตาม ใน ได้แก่ ได้แต่
ได้ที่ ตลอดถึง นอกจากว่า นอกนั้น จริง อย่างดี ส่วน เพียงเพื่อ เดียว จัด ทั้งที ทั้งคน ทั้งตัว ไกลๆ
ถึ... | 76 | 19,463 |
spaCy | spacy/lang/th/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"ศูนย์",
"หนึ่ง",
"สอง",
"สาม",
"สี่",
"ห้า",
"หก",
"เจ็ด",
"แปด",
"เก้า",
"สิบ",
"สิบเอ็ด",
"ยี่สิบ",
"ยี่สิบเอ็ด",
"สามสิบ",
"สามสิบเอ็ด",
"สี่สิบ",
"สี่สิบเอ็ด",
"ห้าสิบ",
"ห้าสิบเอ็ด",
"... | 59 | 1,477 |
spaCy | spacy/lang/th/tokenizer_exceptions.py | .py | from ...symbols import ORTH
_exc = {
# หน่วยงานรัฐ / government agency
"กกต.": [{ORTH: "กกต."}],
"กทท.": [{ORTH: "กทท."}],
"กทพ.": [{ORTH: "กทพ."}],
"กบข.": [{ORTH: "กบข."}],
"กบว.": [{ORTH: "กบว."}],
"กปน.": [{ORTH: "กปน."}],
"กปภ.": [{ORTH: "กปภ."}],
"กปส.": [{ORTH: "กปส."}],
... | 439 | 18,333 |
spaCy | spacy/lang/ms/_tokenizer_exceptions_list.py | .py | # from https://prpm.dbp.gov.my/cari1?keyword=
# dbp https://en.wikipedia.org/wiki/Dewan_Bahasa_dan_Pustaka
MS_BASE_EXCEPTIONS = set(
"""
aba-aba
abah-abah
abar-abar
abrit-abritan
abu-abu
abuk-abuk
abun-abun
acak-acak
acak-acakan
acang-acang
aci-aci
aci-acian
aci-acinya
adang-adang
adap-adapan
adik-beradik
aduk-aduk... | 1,944 | 27,445 |
spaCy | spacy/lang/ms/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 | 2,134 |
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