repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/uk/__init__.py | spacy/lang/uk/__init__.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 UkrainianLemmatizer
from .lex_attrs import LEX_ATTRS
from .sto... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/uk/examples.py | spacy/lang/uk/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.uk.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Ніч на середу буде морозною.",
"Чим кращі книги ти читав, тим гірше спиш.", # Serhiy Zhadan
"Найстаріші ґудзики, відомі людству, археологи знайшл... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/uk/lex_attrs.py | spacy/lang/uk/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
"більйон",
"вісім",
"вісімдесят",
"вісімнадцять",
"вісімсот",
"восьмий",
"два",
"двадцять",
"дванадцять",
"двісті",
"дев'яносто",
"дев'ятнадцять",
"дев'ятсот",
"дев'ять",
"десять",
"децильйон",
"квадрильйон... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/uk/stop_words.py | spacy/lang/uk/stop_words.py | STOP_WORDS = set(
"""а
або
адже
аж
але
алло
б
багато
без
безперервно
би
більш
більше
біля
близько
бо
був
буває
буде
будемо
будете
будеш
буду
будуть
будь
була
були
було
бути
в
вам
вами
вас
ваш
ваша
ваше
вашим
вашими
ваших
ваші
вашій
вашого
вашої
вашому
вашою
вашу
вгорі
вгору
вдалині
весь
вже
ви
від
відсотків
він
віс... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/te/__init__.py | spacy/lang/te/__init__.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"]
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/te/examples.py | spacy/lang/te/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/te/lex_attrs.py | spacy/lang/te/lex_attrs.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/te/stop_words.py | spacy/lang/te/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ne/__init__.py | spacy/lang/ne/__init__.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"]
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ne/examples.py | spacy/lang/ne/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ne/lex_attrs.py | spacy/lang/ne/lex_attrs.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",... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ne/stop_words.py | spacy/lang/ne/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ht/tag_map.py | spacy/lang/ht/tag_map.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"... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ht/tokenizer_exceptions.py | spacy/lang/ht/tokenizer_exceptions.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ht/lemmatizer.py | spacy/lang/ht/lemmatizer.py | from typing import List, Tuple
from ...lookups import Lookups
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.
""... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ht/__init__.py | spacy/lang/ht/__init__.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 .... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ht/punctuation.py | spacy/lang/ht/punctuation.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ht/syntax_iterators.py | spacy/lang/ht/syntax_iterators.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ht/examples.py | spacy/lang/ht/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ht/lex_attrs.py | spacy/lang/ht/lex_attrs.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ht/stop_words.py | spacy/lang/ht/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ti/tokenizer_exceptions.py | spacy/lang/ti/tokenizer_exceptions.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
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ti/__init__.py | spacy/lang/ti/__init__.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ti/punctuation.py | spacy/lang/ti/punctuation.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ti/examples.py | spacy/lang/ti/examples.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",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ti/lex_attrs.py | spacy/lang/ti/lex_attrs.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",... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ti/stop_words.py | spacy/lang/ti/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nb/tokenizer_exceptions.py | spacy/lang/nb/tokenizer_exceptions.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"... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nb/__init__.py | spacy/lang/nb/__init__.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 .syntax_iterators import SYNTAX_ITERATORS
from ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nb/punctuation.py | spacy/lang/nb/punctuation.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,
)
# Punctuation adapted from Danish
_quotes = CONCAT_QUOTES.replace("'", "")
_list_punct = [x for x in ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nb/syntax_iterators.py | spacy/lang/nb/syntax_iterators.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nb/examples.py | spacy/lang/nb/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.nb.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple vurderer å kjøpe britisk oppstartfirma for en milliard dollar.",
"Selvkjørende biler flytter forsikringsansvaret over på produsentene.",
"Sa... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nb/stop_words.py | spacy/lang/nb/stop_words.py | STOP_WORDS = set(
"""
alle allerede alt and andre annen annet at av
bak bare bedre beste blant ble bli blir blitt bris by både
da dag de del dem den denne der dermed det dette disse du
eller en enn er et ett etter
fem fikk fire fjor flere folk for fortsatt fra fram
funnet få får fått før først første
gang gi g... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/zh/__init__.py | spacy/lang/zh/__init__.py | import tempfile
import warnings
from enum import Enum
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional
import srsly
from ... import util
from ...errors import Errors, Warnings
from ...language import BaseDefaults, Language
from ...scorer import Scorer
from ...tokens import Doc... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/zh/examples.py | spacy/lang/zh/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.zh.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
# from https://zh.wikipedia.org/wiki/汉语
sentences = [
"作为语言而言，为世界使用人数最多的语言，目前世界有五分之一人口做为母语。",
"汉语有多种分支，当中官话最为流行，为中华人民共和国的国家通用语言（又称为普通话）、以及中华民国的国语。",
"此外，中文还是联合国正... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/zh/lex_attrs.py | spacy/lang/zh/lex_attrs.py | import re
from ...attrs import LIKE_NUM
_single_num_words = [
"〇",
"一",
"二",
"三",
"四",
"五",
"六",
"七",
"八",
"九",
"十",
"十一",
"十二",
"十三",
"十四",
"十五",
"十六",
"十七",
"十八",
"十九",
"廿",
"卅",
"卌",
"皕",
"零",
"壹",
"贰",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/zh/stop_words.py | spacy/lang/zh/stop_words.py | # stop words as whitespace-separated list
# Chinese stop words,maybe not enough
STOP_WORDS = set(
"""
!
"
#
$
%
&
'
(
)
*
+
,
-
--
.
..
...
......
...................
./
.一
.数
.日
/
//
0
1
2
3
4
5
6
7
8
9
:
://
::
;
<
=
>
>>
?
@
A
Lex
[
\
]
^
_
`
exp
sub
sup
|
}
~
~~~~
·
×
×××
Δ
Ψ
γ
μ
φ
φ．
В
—
——
———
‘
’
’‘
“
”
”，
…... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/grc/tokenizer_exceptions.py | spacy/lang/grc/tokenizer_exceptions.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/grc/__init__.py | spacy/lang/grc/__init__.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/grc/punctuation.py | spacy/lang/grc/punctuation.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
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/grc/examples.py | spacy/lang/grc/examples.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.... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/grc/lex_attrs.py | spacy/lang/grc/lex_attrs.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",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/grc/stop_words.py | spacy/lang/grc/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/bg/tokenizer_exceptions.py | spacy/lang/bg/tokenizer_exceptions.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/bg/__init__.py | spacy/lang/bg/__init__.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/bg/examples.py | spacy/lang/bg/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/bg/lex_attrs.py | spacy/lang/bg/lex_attrs.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/bg/stop_words.py | spacy/lang/bg/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/cs/__init__.py | spacy/lang/cs/__init__.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"]
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/cs/examples.py | spacy/lang/cs/examples.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.",
"... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/cs/lex_attrs.py | spacy/lang/cs/lex_attrs.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",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/cs/stop_words.py | spacy/lang/cs/stop_words.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
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/en/tokenizer_exceptions.py | spacy/lang/en/tokenizer_exceptions.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/en/lemmatizer.py | spacy/lang/en/lemmatizer.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/en/__init__.py | spacy/lang/en/__init__.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 ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/en/punctuation.py | spacy/lang/en/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
HYPHENS,
LIST_ELLIPSES,
LIST_ICONS,
)
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[0-9])[+\-\*^](?=[0-9-])",
r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
al=ALPHA_LOWER, au... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/en/syntax_iterators.py | spacy/lang/en/syntax_iterators.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.
"... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/en/examples.py | spacy/lang/en/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/en/lex_attrs.py | spacy/lang/en/lex_attrs.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",... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/en/stop_words.py | spacy/lang/en/stop_words.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
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/fo/tokenizer_exceptions.py | spacy/lang/fo/tokenizer_exceptions.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.",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/fo/__init__.py | spacy/lang/fo/__init__.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 = ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lij/tokenizer_exceptions.py | spacy/lang/lij/tokenizer_exceptions.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lij/__init__.py | spacy/lang/lij/__init__.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lij/punctuation.py | spacy/lang/lij/punctuation.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
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lij/examples.py | spacy/lang/lij/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lij/stop_words.py | spacy/lang/lij/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ja/tag_bigram_map.py | spacy/lang/ja/tag_bigram_map.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ja/tag_map.py | spacy/lang/ja/tag_map.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_... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ja/__init__.py | spacy/lang/ja/__init__.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ja/syntax_iterators.py | spacy/lang/ja/syntax_iterators.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ja/examples.py | spacy/lang/ja/examples.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。",
]
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ja/tag_orth_map.py | spacy/lang/ja/tag_orth_map.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": ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ja/stop_words.py | spacy/lang/ja/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/hy/__init__.py | spacy/lang/hy/__init__.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"]
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/hy/examples.py | spacy/lang/hy/examples.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։... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/hy/lex_attrs.py | spacy/lang/hy/lex_attrs.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/hy/stop_words.py | spacy/lang/hy/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/tn/__init__.py | spacy/lang/tn/__init__.py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES
from .stop_words import STOP_WORDS
class SetswanaDefaults(BaseDefaults):
infixes = TOKENIZER_INFIXES
stop_words = STOP_WORDS
lex_attr_getters = LEX_ATTRS
class Setswana(Language):
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/tn/punctuation.py | spacy/lang/tn/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
HYPHENS,
LIST_ELLIPSES,
LIST_ICONS,
)
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[0-9])[+\-\*^](?=[0-9-])",
r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
al=ALPHA_LOWER, au... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/tn/examples.py | spacy/lang/tn/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.tn.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple e nyaka go reka JSE ka tlhwatlhwa ta R1 billion",
"Johannesburg ke toropo e kgolo mo Afrika Borwa.",
"O ko kae?",
"ke mang presidente ya ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/tn/lex_attrs.py | spacy/lang/tn/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
"lefela",
"nngwe",
"pedi",
"tharo",
"nne",
"tlhano",
"thataro",
"supa",
"robedi",
"robongwe",
"lesome",
"lesomenngwe",
"lesomepedi",
"sometharo",
"somenne",
"sometlhano",
"somethataro",
"somesupa",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/tn/stop_words.py | spacy/lang/tn/stop_words.py | # Stop words
STOP_WORDS = set(
"""
ke gareng ga selekanyo tlhwatlhwa yo mongwe se
sengwe fa go le jalo gongwe ba na mo tikologong
jaaka kwa morago nna gonne ka sa pele nako teng
tlase fela ntle magareng tsona feta bobedi kgabaganya
moo gape kgatlhanong botlhe tsotlhe bokana e esi
setseng mororo dinako golo kgolo nn... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/yo/__init__.py | spacy/lang/yo/__init__.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"]
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/yo/examples.py | spacy/lang/yo/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/yo/lex_attrs.py | spacy/lang/yo/lex_attrs.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",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/yo/stop_words.py | spacy/lang/yo/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/sq/__init__.py | spacy/lang/sq/__init__.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"]
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/sq/examples.py | spacy/lang/sq/examples.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",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/sq/stop_words.py | spacy/lang/sq/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/kmr/__init__.py | spacy/lang/kmr/__init__.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"]
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/kmr/examples.py | spacy/lang/kmr/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/kmr/lex_attrs.py | spacy/lang/kmr/lex_attrs.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",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/kmr/stop_words.py | spacy/lang/kmr/stop_words.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()
)
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/si/__init__.py | spacy/lang/si/__init__.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"]
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/si/examples.py | spacy/lang/si/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/si/lex_attrs.py | spacy/lang/si/lex_attrs.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",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/si/stop_words.py | spacy/lang/si/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/pl/lemmatizer.py | spacy/lang/pl/lemmatizer.py | from typing import Dict, List, Tuple
from ...pipeline import Lemmatizer
from ...tokens import Token
class PolishLemmatizer(Lemmatizer):
# This lemmatizer implements lookup lemmatization based on the Morfeusz
# dictionary (morfeusz.sgjp.pl/en) by Institute of Computer Science PAS.
# It utilizes some prefi... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/pl/__init__.py | spacy/lang/pl/__init__.py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from .lemmatizer import PolishLemmatizer
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIX... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
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