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/lg/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.lg.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Mpa ebyafaayo ku byalo Nakatu ne Nkajja",
"Okuyita Ttembo kitegeeza kugwa ddalu",
"Ekifumu kino kyali kya mulimu ki?",
"Ekkovu we liyise wayit... | 18 | 522 |
spaCy | spacy/lang/ga/__init__.py | .py | from typing import Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from .lemmatizer import IrishLemmatizer
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class IrishDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
... | 34 | 819 |
spaCy | spacy/lang/ga/stop_words.py | .py | STOP_WORDS = set(
"""
a ach ag agus an aon ar arna as
ba beirt bhúr
caoga ceathair ceathrar chomh chuig chun cois céad cúig cúigear
daichead dar de deich deichniúr den dhá do don dtí dá dár dó
faoi faoin faoina faoinár fara fiche
gach gan go gur
haon hocht
i iad idir in ina ins inár is
le leis lena lenár
m... | 44 | 608 |
spaCy | spacy/lang/ga/lemmatizer.py | .py | from typing import List, Tuple
from ...pipeline import Lemmatizer
from ...tokens import Token
class IrishLemmatizer(Lemmatizer):
# This is a lookup-based lemmatiser using data extracted from
# BuNaMo (https://github.com/michmech/BuNaMo)
@classmethod
def get_lookups_config(cls, mode: str) -> Tuple[Li... | 163 | 4,930 |
spaCy | spacy/lang/ga/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {
"'acha'n": [{ORTH: "'ach", NORM: "gach"}, {ORTH: "a'n", NORM: "aon"}],
"dem'": [{ORTH: "de", NORM: "de"}, {ORTH: "m'", NORM: "mo"}],
"ded'": [{ORTH: "de", NORM: "de"}, {ORTH: "d'", N... | 72 | 1,882 |
spaCy | spacy/lang/tt/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
HYPHENS,
LIST_ELLIPSES,
LIST_ICONS,
)
_hyphens_no_dash = HYPHENS.replace("-", "").strip("|").replace("||", "")
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[{al}])\.(?=[{au}])".format(al=AL... | 28 | 806 |
spaCy | spacy/lang/tt/__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 TatarDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
infixes = TOKENIZE... | 21 | 483 |
spaCy | spacy/lang/tt/stop_words.py | .py | # Tatar stopwords are from https://github.com/aliiae/stopwords-tt
STOP_WORDS = set(
"""алай алайса алар аларга аларда алардан аларны аларның аларча
алары аларын аларынга аларында аларыннан аларының алтмыш алтмышынчы алтмышынчыга
алтмышынчыда алтмышынчыдан алтмышынчылар алтмышынчыларга алтмышынчыларда
алтмышынчылар... | 174 | 18,567 |
spaCy | spacy/lang/tt/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"нуль",
"ноль",
"бер",
"ике",
"өч",
"дүрт",
"биш",
"алты",
"җиде",
"сигез",
"тугыз",
"ун",
"унбер",
"унике",
"унөч",
"ундүрт",
"унбиш",
"уналты",
"унҗиде",
"унсигез",
"унтугыз",
"егерме"... | 59 | 1,117 |
spaCy | spacy/lang/tt/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.tt.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple Бөекбритания стартабын $1 миллиард өчен сатып алыун исәпли.",
"Автоном автомобильләр иминият җаваплылыкны җитештерүчеләргә күчерә.",
"Сан-Фра... | 17 | 897 |
spaCy | spacy/lang/tt/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: "дш", NORM: "дүшәмбе"},
{ORTH: "сш", NORM: "сишәмбе"},
{ORTH: "чш", NORM: "чәршәмбе"},
{ORTH: "пш", NORM: "пәнҗешәмбе"},
... | 48 | 1,760 |
spaCy | spacy/lang/kn/__init__.py | .py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class KannadaDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Kannada(Language):
lang = "kn"
Defaults = KannadaDefaults
__all__ = ["Kannada"]
| 15 | 247 |
spaCy | spacy/lang/kn/stop_words.py | .py | STOP_WORDS = set(
"""
ಹಲವು
ಮೂಲಕ
ಹಾಗೂ
ಅದು
ನೀಡಿದ್ದಾರೆ
ಯಾವ
ಎಂದರು
ಅವರು
ಈಗ
ಎಂಬ
ಹಾಗಾಗಿ
ಅಷ್ಟೇ
ನಾವು
ಇದೇ
ಹೇಳಿ
ತಮ್ಮ
ಹೀಗೆ
ನಮ್ಮ
ಬೇರೆ
ನೀಡಿದರು
ಮತ್ತೆ
ಇದು
ಈ
ನೀವು
ನಾನು
ಇತ್ತು
ಎಲ್ಲಾ
ಯಾವುದೇ
ನಡೆದ
ಅದನ್ನು
ಎಂದರೆ
ನೀಡಿದೆ
ಹೀಗಾಗಿ
ಜೊತೆಗೆ
ಇದರಿಂದ
ನನಗೆ
ಅಲ್ಲದೆ
ಎಷ್ಟು
ಇದರ
ಇಲ್ಲ
ಕಳೆದ
ತುಂಬಾ
ಈಗಾಗಲೇ
ಮಾಡಿ
ಅದಕ್ಕೆ
ಬಗ್ಗೆ
ಅವರ
ಇದನ್ನು
ಆ
ಇದೆ
ಹೆಚ್ಚು
ಇನ್ನು
ಎಲ್ಲ
ಇ... | 87 | 1,253 |
spaCy | spacy/lang/kn/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.en.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"ಆಪಲ್ ಒಂದು ಯು.ಕೆ. ಸ್ಟಾರ್ಟ್ಅಪ್ ಅನ್ನು ೧ ಶತಕೋಟಿ ಡಾಲರ್ಗಳಿಗೆ ಖರೀದಿಸಲು ನೋಡುತ್ತಿದೆ.",
"ಸ್ವಾಯತ್ತ ಕಾರುಗಳು ವಿಮಾ ಹೊಣೆಗಾರಿಕೆಯನ್ನು ತಯಾರಕರ ಕಡೆಗೆ ಬದಲಾಯಿಸುತ್ತವೆ.",
... | 18 | 1,210 |
spaCy | spacy/lang/fi/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
LIST_ELLIPSES,
LIST_HYPHENS,
LIST_ICONS,
)
from ..punctuation import TOKENIZER_SUFFIXES
_quotes = CONCAT_QUOTES.replace("'", "")
DASHES = "|".join(x for x in LIST_HYPHENS if x != "-")
_infixes = (
LIST_ELLIPSE... | 36 | 863 |
spaCy | spacy/lang/fi/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class FinnishDefaults(BaseDefaults):
... | 24 | 632 |
spaCy | spacy/lang/fi/stop_words.py | .py | # Source https://github.com/stopwords-iso/stopwords-fi/blob/master/stopwords-fi.txt
# Reformatted with some minor corrections
STOP_WORDS = set(
"""
aiemmin aika aikaa aikaan aikaisemmin aikaisin aikana aikoina aikoo aikovat
aina ainakaan ainakin ainoa ainoat aiomme aion aiotte aivan ajan alas alemmas
alkuisin alkuu... | 111 | 6,436 |
spaCy | spacy/lang/fi/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"nolla",
"yksi",
"kaksi",
"kolme",
"neljä",
"viisi",
"kuusi",
"seitsemän",
"kahdeksan",
"yhdeksän",
"kymmenen",
"yksitoista",
"kaksitoista",
"kolmetoista",
"neljätoista",
"viisitoista",
"kuusitoista",
"... | 56 | 1,076 |
spaCy | spacy/lang/fi/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."""
lab... | 81 | 2,380 |
spaCy | spacy/lang/fi/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.fi.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Itseajavat autot siirtävät vakuutusvastuun autojen valmistajille",
"San Francisco harkitsee toimitusrobottien liikkumisen kieltämistä jalkakäytävillä",... | 16 | 545 |
spaCy | spacy/lang/fi/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
# Source https://www.cs.tut.fi/~jkorpela/kielenopas/5.5.html
for exc_data in [
{ORTH: "aik."},
{ORTH: "alk."},
{ORTH: "alv."},
{ORTH: "ark."},
{ORTH: "as."},
{ORTH: "e... | 112 | 2,463 |
spaCy | spacy/lang/ta/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class TamilDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Tamil(Language):
lang = "ta"
Defaults = TamilDefaults
__all__ = ["Tamil"]
| 17 | 305 |
spaCy | spacy/lang/ta/stop_words.py | .py | # Stop words
STOP_WORDS = set(
"""
ஒரு
என்று
மற்றும்
இந்த
இது
என்ற
கொண்டு
என்பது
பல
ஆகும்
அல்லது
அவர்
நான்
உள்ள
அந்த
இவர்
என
முதல்
என்ன
இருந்து
சில
என்
போன்ற
வேண்டும்
வந்து
இதன்
அது
அவன்
தான்
பலரும்
என்னும்
மேலும்
பின்னர்
கொண்ட
இருக்கும்
தனது
உள்ளது
போது
என்றும்
அதன்
தன்
பிறகு
அவர்கள்
வரை
அவள்
நீ
ஆகிய
இருந்தது
உள்... | 132 | 2,018 |
spaCy | spacy/lang/ta/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_numeral_suffixes = {"பத்து": "பது", "ற்று": "று", "ரத்து": "ரம்", "சத்து": "சம்"}
_num_words = [
"பூச்சியம்",
"ஒரு",
"ஒன்று",
"இரண்டு",
"மூன்று",
"நான்கு",
"ஐந்து",
"ஆறு",
"ஏழு",
"எட்டு",
"ஒன்பது",
"பத்து",
"பதினொன்று",
"பன்னிரண்டு"... | 81 | 2,287 |
spaCy | spacy/lang/ta/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ta.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"கிறிஸ்துமஸ் மற்றும் இனிய புத்தாண்டு வாழ்த்துக்கள்",
"எனக்கு என் குழந்தைப் பருவம் நினைவிருக்கிறது",
"உங்கள் பெயர் என்ன?",
"ஏறத்தாழ இலங்கைத் தமி... | 24 | 2,702 |
spaCy | spacy/lang/ht/tag_map.py | .py | from spacy.symbols import (
ADJ,
ADP,
ADV,
AUX,
CCONJ,
DET,
INTJ,
NOUN,
NUM,
PART,
PRON,
PROPN,
PUNCT,
SCONJ,
SYM,
VERB,
X,
)
TAG_MAP = {
"NOUN": {"pos": NOUN},
"VERB": {"pos": VERB},
"AUX": {"pos": AUX},
"ADJ": {"pos": ADJ},
"ADV"... | 40 | 656 |
spaCy | spacy/lang/ht/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
HYPHENS,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
merge_chars,
)
ELISION = "'’".replace(" ", "")
_prefixes_elision = "m n l y t k w"
_prefixes_elision += " " + _prefixes_elision.upper()
TOK... | 59 | 1,561 |
spaCy | spacy/lang/ht/__init__.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from .lemmatizer import HaitianCreoleLemmatizer
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .... | 56 | 1,437 |
spaCy | spacy/lang/ht/stop_words.py | .py | STOP_WORDS = set(
"""
a ak an ankò ant apre ap atò avan avanlè
byen bò byenke
chak
de depi deja deja
e en epi èske
fò fòk
gen genyen
ki kisa kilès kote koukou konsa konbyen konn konnen kounye kouman
la l laa le lè li lye lò
m m' mwen
nan nap nou n'
ou oumenm
pa paske pami pandan pito pou pral preske pwis... | 50 | 760 |
spaCy | spacy/lang/ht/lex_attrs.py | .py | from ...attrs import LIKE_NUM, NORM
# Cardinal numbers in Creole
_num_words = set(
"""
zewo youn en de twa kat senk sis sèt uit nèf dis
onz douz trèz katoz kenz sèz disèt dizwit diznèf
vent trant karant sinkant swasant swasann-dis
san mil milyon milya
""".split()
)
# Ordinal numbers in Creole (some are French-inf... | 82 | 1,852 |
spaCy | spacy/lang/ht/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 for Haitian Creole.
Works on b... | 75 | 2,327 |
spaCy | spacy/lang/ht/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ht.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple ap panse achte yon demaraj nan Wayòm Ini pou $1 milya dola",
"Machin otonòm fè responsablite asirans lan ale sou men fabrikan yo",
"San Fran... | 18 | 539 |
spaCy | spacy/lang/ht/lemmatizer.py | .py | from typing import List, Tuple
from ...pipeline import Lemmatizer
from ...tokens import Token
class HaitianCreoleLemmatizer(Lemmatizer):
"""
Minimal Haitian Creole lemmatizer.
Returns a word's base form based on rules and lookup,
or defaults to the original form.
"""
def is_base_form(self, t... | 51 | 1,560 |
spaCy | spacy/lang/ht/tokenizer_exceptions.py | .py | from spacy.symbols import NORM, ORTH
def make_variants(base, first_norm, second_orth, second_norm):
return {
base: [
{ORTH: base.split("'")[0] + "'", NORM: first_norm},
{ORTH: second_orth, NORM: second_norm},
],
base.capitalize(): [
{
ORT... | 127 | 3,664 |
spaCy | spacy/lang/de/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
PUNCT,
UNITS,
)
from ..punctuation import TOKENIZER_PREFIXES as BASE_TOKENIZER_PREFIXES
_prefixes = ["``"] + BASE_TOKENIZER_PREFIXES
_s... | 57 | 1,409 |
spaCy | spacy/lang/de/__init__.py | .py | from ...language import BaseDefaults, Language
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 GermanDefaults(BaseDefaults):
tokenizer_e... | 23 | 616 |
spaCy | spacy/lang/de/stop_words.py | .py | STOP_WORDS = set(
"""
á a ab aber ach acht achte achten achter achtes ag alle allein allem allen
aller allerdings alles allgemeinen als also am an andere anderen anderem andern
anders auch auf aus ausser außer ausserdem außerdem
bald bei beide beiden beim beispiel bekannt bereits besonders besser besten bin
bis bi... | 79 | 3,661 |
spaCy | spacy/lang/de/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."""
# this i... | 44 | 1,850 |
spaCy | spacy/lang/de/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.de.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Die ganze Stadt ist ein Startup: Shenzhen ist das Silicon Valley für Hardware-Firmen",
"Wie deutsche Startups die Technologie vorantreiben wollen: Kün... | 18 | 678 |
spaCy | spacy/lang/de/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {
"auf'm": [{ORTH: "auf"}, {ORTH: "'m", NORM: "dem"}],
"du's": [{ORTH: "du"}, {ORTH: "'s", NORM: "es"}],
"er's": [{ORTH: "er"}, {ORTH: "'s", NORM: "es"}],
"hinter'm": [{ORTH: "hint... | 235 | 5,888 |
spaCy | spacy/lang/sr/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
PUNCT,
UNITS,
)
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[0-9])[+\-\*^](?=[0-9-])",
r"(?<=[{al}{q}]... | 46 | 960 |
spaCy | spacy/lang/sr/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class SerbianDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
... | 22 | 545 |
spaCy | spacy/lang/sr/stop_words.py | .py | STOP_WORDS = set(
"""
а
авај
ако
ал
али
арх
ау
ах
аха
ај
бар
би
била
били
било
бисмо
бисте
бих
бијасмо
бијасте
бијах
бијаху
бијаше
биће
близу
број
брр
буде
будимо
будите
буду
будући
бум
бућ
вам
вама
вас
ваша
ваше
вашим
вашима
ваљда
веома
вероватно
већ
већина
ви
видео
више
врло
врх
га
где
гиц
год
горе
гђекоје
да
дак... | 394 | 3,895 |
spaCy | spacy/lang/sr/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"нула",
"један",
"два",
"три",
"четири",
"пет",
"шест",
"седам",
"осам",
"девет",
"десет",
"једанаест",
"дванаест",
"тринаест",
"четрнаест",
"петнаест",
"шеснаест",
"седамнаест",
"осамнаест",
"д... | 66 | 1,425 |
spaCy | spacy/lang/sr/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.sr.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
# Translations from English
"Apple планира куповину америчког стартапа за $1 милијарду.",
"Беспилотни аутомобили пребацују одговорност осигурања на... | 21 | 909 |
spaCy | spacy/lang/sr/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: "пoн", NORM: "понедељак"},
{ORTH: "уто", NORM: "уторак"},
{ORTH: "сре", NORM: "среда"},
{ORTH: "чет", NORM: "четвртак"},
... | 93 | 3,523 |
spaCy | spacy/lang/bo/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class TibetanDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Tibetan(Language):
lang = "bo"
Defaults = TibetanDefaults
__all__ = ["Tibetan"]
| 17 | 313 |
spaCy | spacy/lang/bo/stop_words.py | .py | # Source: https://zenodo.org/records/10148636
STOP_WORDS = set(
"""
འི་
།
དུ་
གིས་
སོགས་
ཏེ
གི་
རྣམས་
ནི
ཀུན་
ཡི་
འདི
ཀྱི་
སྙེད་
པས་
གཞན་
ཀྱིས་
ཡི
ལ
ནི་
དང་
སོགས
ཅིང་
ར
དུ
མི་
སུ་
བཅས་
ཡོངས་
ལས
ཙམ་
གྱིས་
དེ་
ཡང་
མཐའ་དག་
ཏུ་
ཉིད་
ས
ཏེ་
གྱི་
སྤྱི
དེ
ཀ་
ཡིན་
ཞིང་
འདི་
རུང་
རང་
ཞིག་
སྟེ
སྟེ་
ན་རེ
ངམ
ཤིང་
དག་
ཏོ
རེ་
འང... | 199 | 2,199 |
spaCy | spacy/lang/bo/lex_attrs.py | .py | from ...attrs import LIKE_NUM
# reference 1: https://en.wikipedia.org/wiki/Tibetan_numerals
_num_words = [
"ཀླད་ཀོར་",
"གཅིག་",
"གཉིས་",
"གསུམ་",
"བཞི་",
"ལྔ་",
"དྲུག་",
"བདུན་",
"བརྒྱད་",
"དགུ་",
"བཅུ་",
"བཅུ་གཅིག་",
"བཅུ་གཉིས་",
"བཅུ་གསུམ་",
"བཅུ་བཞི་",
... | 66 | 1,694 |
spaCy | spacy/lang/bo/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.bo.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"དོན་དུ་རྒྱ་མཚོ་བླ་མ་ཞེས་བྱ་ཞིང༌།",
"ཏཱ་ལའི་ཞེས་པ་ནི་སོག་སྐད་ཡིན་པ་དེ་བོད་སྐད་དུ་རྒྱ་མཚོའི་དོན་དུ་འཇུག",
"སོག་པོ་ཨལ་ཐན་རྒྱལ་པོས་རྒྱལ་དབང་བསོད་ནམས་ར... | 16 | 1,640 |
spaCy | spacy/lang/nn/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,
)
from ..punctuation import TOKENIZER_SUFFIXES
_quotes = CONCAT_QUOTES.replace("'", "")
_list_punct = [... | 75 | 1,796 |
spaCy | spacy/lang/nn/__init__.py | .py | from ...language import BaseDefaults, Language
from ..nb import SYNTAX_ITERATORS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class NorwegianNynorskDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
p... | 21 | 580 |
spaCy | spacy/lang/nn/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.nn.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
# sentences taken from Omsetjingsminne frå Nynorsk pressekontor 2022 (https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-80/)
sentences = [
"Konseptet går... | 15 | 674 |
spaCy | spacy/lang/nn/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: "jan.", NORM: "januar"},
{ORTH: "feb.", NORM: "februar"},
{ORTH: "mar.", NORM: "mars"},
{ORTH: "apr.", NORM: "april"},
{ORTH: "jun.", NORM: "juni"... | 229 | 3,298 |
spaCy | spacy/lang/dsb/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class LowerSorbianDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class LowerSorbian(Language):
lang = "dsb"
Defaults = LowerSorbianDefaults
__all__ = ["L... | 17 | 334 |
spaCy | spacy/lang/dsb/stop_words.py | .py | STOP_WORDS = set(
"""
a abo aby ako ale až
daniž dokulaž
gaž
jolic
pak pótom
teke togodla
""".split()
)
| 16 | 118 |
spaCy | spacy/lang/dsb/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"nul",
"jaden",
"jadna",
"jadno",
"dwa",
"dwě",
"tśi",
"tśo",
"styri",
"styrjo",
"pěś",
"pěśo",
"šesć",
"šesćo",
"sedym",
"sedymjo",
"wósym",
"wósymjo",
"źewjeś",
"źewjeśo",
"źaseś",
"źa... | 114 | 1,906 |
spaCy | spacy/lang/dsb/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.dsb.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Z tym stwori so wuměnjenje a zakład za dalše wobdźěłanje přez analyzu tekstoweje struktury a semantisku anotaciju a z tym tež za tu předstajenu digitalnu... | 15 | 552 |
spaCy | spacy/lang/hr/__init__.py | .py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class CroatianDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Croatian(Language):
lang = "hr"
Defaults = CroatianDefaults
__all__ = ["Croatian"]
| 15 | 251 |
spaCy | spacy/lang/hr/stop_words.py | .py | # Source: https://github.com/stopwords-iso/stopwords-hr
STOP_WORDS = set(
"""
a
ah
aha
aj
ako
al
ali
arh
au
avaj
bar
baš
bez
bi
bih
bijah
bijahu
bijaše
bijasmo
bijaste
bila
bili
bilo
bio
bismo
biste
biti
brr
buć
budavši
bude
budimo
budite
budu
budući
bum
bumo
će
ćemo
ćeš
ćete
čijem
čijim
čijima
ću
da
daj
dakle
de
d... | 345 | 1,999 |
spaCy | spacy/lang/hr/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.hr.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Ovo je rečenica.",
"Kako se popravlja auto?",
"Zagreb je udaljen od Ljubljane svega 150 km.",
"Nećete vjerovati što se dogodilo na ovogodišnje... | 16 | 489 |
spaCy | spacy/lang/es/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
LIST_UNITS,
PUNCT,
merge_chars,
)
_list_units = [u for u in LIST_UNITS if u != "%"]
_units = merge_chars(" ".join(_list_units))
_con... | 54 | 1,208 |
spaCy | spacy/lang/es/__init__.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from .lemmatizer import SpanishLemmatizer
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SY... | 53 | 1,290 |
spaCy | spacy/lang/es/stop_words.py | .py | STOP_WORDS = set(
"""
a acuerdo adelante ademas además afirmó agregó ahi ahora ahí al algo alguna
algunas alguno algunos algún alli allí alrededor ambos ante anterior antes
apenas aproximadamente aquel aquella aquellas aquello aquellos aqui aquél
aquélla aquéllas aquéllos aquí arriba aseguró asi así atras aun aunqu... | 81 | 3,388 |
spaCy | spacy/lang/es/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"cero",
"uno",
"dos",
"tres",
"cuatro",
"cinco",
"seis",
"siete",
"ocho",
"nueve",
"diez",
"once",
"doce",
"trece",
"catorce",
"quince",
"dieciséis",
"diecisiete",
"dieciocho",
"diecinueve",
... | 104 | 1,795 |
spaCy | spacy/lang/es/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.
"... | 77 | 2,714 |
spaCy | spacy/lang/es/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.es.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple está buscando comprar una startup del Reino Unido por mil millones de dólares.",
"Los coches autónomos delegan la responsabilidad del seguro en ... | 22 | 777 |
spaCy | spacy/lang/es/lemmatizer.py | .py | import re
from typing import List, Optional, Tuple
from ...pipeline import Lemmatizer
from ...tokens import Token
class SpanishLemmatizer(Lemmatizer):
"""
Spanish rule-based lemmatizer with morph-based rule selection.
"""
@classmethod
def get_lookups_config(cls, mode: str) -> Tuple[List[str], Li... | 432 | 16,097 |
spaCy | spacy/lang/es/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {
"pal": [{ORTH: "pa"}, {ORTH: "l", NORM: "el"}],
}
for exc_data in [
{ORTH: "n°"},
{ORTH: "°C"},
{ORTH: "aprox."},
{ORTH: "dna."},
{ORTH: "dpto."},
{ORTH: "ej."},
... | 83 | 1,457 |
spaCy | spacy/lang/fo/__init__.py | .py | from ...language import BaseDefaults, Language
from ..punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class FaroeseDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
infixes = TOKENIZER_INFIXES
suffixes = ... | 19 | 471 |
spaCy | spacy/lang/fo/tokenizer_exceptions.py | .py | from ...symbols import ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
for orth in [
"apr.",
"aug.",
"avgr.",
"árg.",
"ávís.",
"beinl.",
"blkv.",
"blaðkv.",
"blm.",
"blaðm.",
"bls.",
"blstj.",
"blaðstj.",
"des.",
... | 91 | 1,304 |
spaCy | spacy/lang/grc/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
HYPHENS,
LIST_CURRENCY,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
)
_prefixes = (
[
"†",
"⸏",
"〈",
]
+ LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
... | 58 | 1,105 |
spaCy | spacy/lang/grc/__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 AncientGreekDefaults(BaseDefaults):
tokenizer_exception... | 23 | 620 |
spaCy | spacy/lang/grc/stop_words.py | .py | STOP_WORDS = set(
"""
αὐτῷ αὐτοῦ αὐτῆς αὐτόν αὐτὸν αὐτῶν αὐτὸς αὐτὸ αὐτό αὐτός αὐτὴν αὐτοῖς αὐτοὺς αὔτ' αὐτὰ αὐτῇ αὐτὴ
αὐτὼ αὑταὶ καὐτὸς αὐτά αὑτός αὐτοῖσι αὐτοῖσιν αὑτὸς αὐτήν αὐτοῖσί αὐτοί αὐτοὶ αὐτοῖο αὐτάων αὐτὰς
αὐτέων αὐτώ αὐτάς αὐτούς αὐτή αὐταί αὐταὶ αὐτῇσιν τὠυτῷ τὠυτὸ ταὐτὰ ταύτῃ αὐτῇσι αὐτῇς αὐταῖς αὐτᾶς... | 62 | 9,364 |
spaCy | spacy/lang/grc/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
# CARDINALS
"εἷς",
"ἑνός",
"ἑνί",
"ἕνα",
"μία",
"μιᾶς",
"μιᾷ",
"μίαν",
"ἕν",
"δύο",
"δυοῖν",
"τρεῖς",
"τριῶν",
"τρισί",
"τρία",
"τέτταρες",
"τεττάρων",
"τέτταρσι",
"τέτταρα",
"τέτταρας",
... | 314 | 6,640 |
spaCy | spacy/lang/grc/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.grc.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"ἐρᾷ μὲν ἁγνὸς οὐρανὸς τρῶσαι χθόνα, ἔρως δὲ γαῖαν λαμβάνει γάμου τυχεῖν·",
"εὐδαίμων Χαρίτων καὶ Μελάνιππος ἔφυ, θείας ἁγητῆρες ἐφαμερίοις φιλότατος.... | 17 | 1,065 |
spaCy | spacy/lang/grc/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'", "ἀφ'", "᾽Αφ", "ἀπὸ"]:
_exc[token] = [{ORTH: token, NORM: "από"}]
for token in ["᾽Αλλ'", "ἀλλ'", "ἀλλὰ"]:
_exc[token] = [{ORTH: token, NORM: "ἀλλά"}]
for... | 112 | 6,768 |
spaCy | spacy/lang/nl/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
LIST_UNITS,
PUNCT,
merge_chars,
)
from ..punctuation import TOKENIZER_PREFIXES as BASE_TOKENIZER_PREFIXES
_prefixes = [",,"] + BASE_... | 65 | 1,538 |
spaCy | spacy/lang/nl/__init__.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from .lemmatizer import DutchLemmatizer
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/nl/stop_words.py | .py | # The original stop words list (added in f46ffe3) was taken from
# http://www.damienvanholten.com/downloads/dutch-stop-words.txt
# and consisted of about 100 tokens.
# In order to achieve parity with some of the better-supported
# languages, e.g., English, French, and German, this original list has been
# extended with... | 73 | 3,090 |
spaCy | spacy/lang/nl/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = set(
"""
nul een één twee drie vier vijf zes zeven acht negen tien elf twaalf dertien
veertien twintig dertig veertig vijftig zestig zeventig tachtig negentig honderd
duizend miljoen miljard biljoen biljard triljoen triljard
""".split()
)
_ordinal_words = set(
"""
ee... | 41 | 1,302 |
spaCy | spacy/lang/nl/syntax_iterators.py | .py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import NOUN, PRON
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.
The definitio... | 76 | 2,868 |
spaCy | spacy/lang/nl/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.nl.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple overweegt om voor 1 miljard een U.K. startup te kopen",
"Autonome auto's verschuiven de verzekeringverantwoordelijkheid naar producenten",
"... | 14 | 440 |
spaCy | spacy/lang/nl/lemmatizer.py | .py | from typing import List, Tuple
from ...pipeline import Lemmatizer
from ...tokens import Token
class DutchLemmatizer(Lemmatizer):
@classmethod
def get_lookups_config(cls, mode: str) -> Tuple[List[str], List[str]]:
if mode == "rule":
required = ["lemma_lookup", "lemma_rules", "lemma_exc", "... | 123 | 4,618 |
spaCy | spacy/lang/nl/tokenizer_exceptions.py | .py | from ...symbols import ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
# Extensive list of both common and uncommon dutch abbreviations copied from
# github.com/diasks2/pragmatic_segmenter, a Ruby library for rule-based
# sentence boundary detection (MIT, Copyright 2015 Kevin S. ... | 1,608 | 24,298 |
spaCy | spacy/lang/kmr/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class KurmanjiDefaults(BaseDefaults):
stop_words = STOP_WORDS
lex_attr_getters = LEX_ATTRS
class Kurmanji(Language):
lang = "kmr"
Defaults = KurmanjiDefaults
__all__ = ["Kurmanji"]
| 17 | 318 |
spaCy | spacy/lang/kmr/stop_words.py | .py | STOP_WORDS = set(
"""
û
li
bi
di
da
de
ji
ku
ew
ez
tu
em
hûn
ew
ev
min
te
wî
wê
me
we
wan
vê
vî
va
çi
kî
kê
çawa
çima
kengî
li ku
çend
çiqas
her
hin
gelek
hemû
kes
tişt
""".split()
)
| 45 | 203 |
spaCy | spacy/lang/kmr/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"sifir",
"yek",
"du",
"sê",
"çar",
"pênc",
"şeş",
"heft",
"heşt",
"neh",
"deh",
"yazde",
"dazde",
"sêzde",
"çarde",
"pazde",
"şazde",
"hevde",
"hejde",
"nozde",
"bîst",
"sî",
"çil",
... | 139 | 2,328 |
spaCy | spacy/lang/kmr/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.kmr.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Berê mirovan her tim li geşedana pêşerojê ye", # People's gaze is always on the development of the future
"Kawa Nemir di 14 salan de Ulysses wergera... | 18 | 1,326 |
spaCy | spacy/lang/ar/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])\+",
# Arabic is written from Right-To-Left
r"(?<=[0-9])(?:{c})".format(c=CURRENCY)... | 24 | 463 |
spaCy | spacy/lang/ar/__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
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class ArabicDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
suffixes = TOKEN... | 22 | 572 |
spaCy | spacy/lang/ar/stop_words.py | .py | STOP_WORDS = set(
"""
من
نحو
لعل
بما
بين
وبين
ايضا
وبينما
تحت
مثلا
لدي
عنه
مع
هي
وهذا
واذا
هذان
انه
بينما
أمسى
وسوف
ولم
لذلك
إلى
منه
منها
كما
ظل
هنا
به
كذلك
اما
هما
بعد
بينهم
التي
أبو
اذا
بدلا
لها
أمام
يلي
حين
ضد
الذي
قد
صار
إذا
مابرح
قبل
كل
وليست
الذين
لهذا
وثي
انهم
باللتي
مافتئ
ولا
بهذه
بحيث
كيف
وله
علي
بات
لاسيم... | 391 | 3,180 |
spaCy | spacy/lang/ar/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = set(
"""
صفر
واحد
إثنان
اثنان
ثلاثة
ثلاثه
أربعة
أربعه
خمسة
خمسه
ستة
سته
سبعة
سبعه
ثمانية
ثمانيه
تسعة
تسعه
ﻋﺸﺮﺓ
ﻋﺸﺮه
عشرون
عشرين
ثلاثون
ثلاثين
اربعون
اربعين
أربعون
أربعين
خمسون
خمسين
ستون
ستين
سبعون
سبعين
ثمانون
ثمانين
تسعون
تسعين
مائتين
مائتان
ثلاثمائة
خمسمائة
سبعمائة
الف... | 98 | 1,309 |
spaCy | spacy/lang/ar/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ar.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"نال الكاتب خالد توفيق جائزة الرواية العربية في معرض الشارقة الدولي للكتاب",
"أين تقع دمشق ؟",
"كيف حالك ؟",
"هل يمكن ان نلتقي على الساعة الثا... | 18 | 890 |
spaCy | spacy/lang/ar/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
# Time
for exc_data in [
{NORM: "قبل الميلاد", ORTH: "ق.م"},
{NORM: "بعد الميلاد", ORTH: "ب. م"},
{NORM: "ميلادي", ORTH: ".م"},
{NORM: "هجري", ORTH: ".هـ"},
{NORM: "توفي",... | 48 | 1,501 |
spaCy | spacy/lang/bn/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
CONCAT_QUOTES,
HYPHENS,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
UNITS,
)
_currency = r"\$¢£€¥฿৳"
_quotes = CONCAT_QUOTES.replace("'", "")
_list_punct = LIST_PUNCT + "। ॥".strip().split()
_prefixes = [r"\+"] + _list_punct... | 54 | 1,284 |
spaCy | spacy/lang/bn/__init__.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from ...pipeline import Lemmatizer
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIO... | 50 | 1,177 |
spaCy | spacy/lang/bn/stop_words.py | .py | STOP_WORDS = set(
"""
অতএব অথচ অথবা অনুযায়ী অনেক অনেকে অনেকেই অন্তত অবধি অবশ্য অর্থাৎ অন্য অনুযায়ী অর্ধভাগে
আগামী আগে আগেই আছে আজ আদ্যভাগে আপনার আপনি আবার আমরা আমাকে আমাদের আমার আমি আর আরও
ইত্যাদি ইহা
উচিত উনি উপর উপরে উত্তর
এ এঁদের এঁরা এই এক একই একজন একটা একটি একবার একে এখন এখনও এখানে এখানেই এটা এসো
এটাই এটি ... | 43 | 6,156 |
spaCy | spacy/lang/bn/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.bn.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = ["তুই খুব ভালো", "আজ আমরা ডাক্তার দেখতে যাবো", "আমি জানি না "]
| 9 | 304 |
spaCy | spacy/lang/bn/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: "ডঃ", NORM: "ডক্টর"},
{ORTH: "ডাঃ", NORM: "ডাক্তার"},
{ORTH: "ড.", NORM: "ডক্টর"},
{ORTH: "ডা.", NORM: "ডাক্তার"},
{ORTH: "মোঃ", NORM: "মোহাম্মদ"}... | 26 | 970 |
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