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/pl/punctuation.py | spacy/lang/pl/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
LIST_ELLIPSES,
LIST_HYPHENS,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
PUNCT,
UNITS,
)
from ..punctuation import TOKENIZER_PREFIXES as BASE_TOKENIZER_PREFIXES
_quotes = CONCAT_QUOTES.replac... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/pl/examples.py | spacy/lang/pl/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.pl.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Poczuł przyjemną woń mocnej kawy.",
"Istnieje wiele dróg oddziaływania substancji psychoaktywnej na układ nerwowy.",
"Powitał mnie biało-czarny ko... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/pl/lex_attrs.py | spacy/lang/pl/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
"zero",
"jeden",
"dwa",
"trzy",
"cztery",
"pięć",
"sześć",
"siedem",
"osiem",
"dziewięć",
"dziesięć",
"jedenaście",
"dwanaście",
"trzynaście",
"czternaście",
"pietnaście",
"szesnaście",
"siedemnaście",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/pl/stop_words.py | spacy/lang/pl/stop_words.py | # sources: https://github.com/bieli/stopwords/blob/master/polish.stopwords.txt and https://github.com/stopwords-iso/stopwords-pl
STOP_WORDS = set(
"""
a aby ach acz aczkolwiek aj albo ale alez
ależ ani az aż
bardziej bardzo beda bede bedzie bez bo bowiem by
byc byl byla byli bylo byly bym bynajmniej być był
była ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ar/tokenizer_exceptions.py | spacy/lang/ar/tokenizer_exceptions.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",... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ar/__init__.py | spacy/lang/ar/__init__.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ar/punctuation.py | spacy/lang/ar/punctuation.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)... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ar/examples.py | spacy/lang/ar/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ar/lex_attrs.py | spacy/lang/ar/lex_attrs.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ar/stop_words.py | spacy/lang/ar/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/vi/__init__.py | spacy/lang/vi/__init__.py | import re
import string
from pathlib import Path
from typing import Any, Dict, Union
import srsly
from ... import util
from ...language import BaseDefaults, Language
from ...tokens import Doc
from ...util import DummyTokenizer, load_config_from_str, registry
from ...vocab import Vocab
from .lex_attrs import LEX_ATTRS... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/vi/examples.py | spacy/lang/vi/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.vi.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Đây là đâu, tôi là ai?",
"Căn phòng có nhiều cửa sổ nên nó khá sáng",
"Đại dịch COVID vừa qua đã gây ảnh hưởng rất lớn tới nhiều doanh nghiệp lớn n... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/vi/lex_attrs.py | spacy/lang/vi/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
"không", # Zero
"một", # One
"mốt", # Also one, irreplacable in niché cases for unit digit such as "51"="năm mươi mốt"
"hai", # Two
"ba", # Three
"bốn", # Four
"tư", # Also four, used in certain cases for unit digit such as "54"="năm mươi ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/vi/stop_words.py | spacy/lang/vi/stop_words.py | # Source: https://github.com/stopwords/vietnamese-stopwords
STOP_WORDS = set(
"""
a_lô
a_ha
ai
ai_ai
ai_nấy
ai_đó
alô
amen
anh
anh_ấy
ba
ba_bau
ba_bản
ba_cùng
ba_họ
ba_ngày
ba_ngôi
ba_tăng
bao_giờ
bao_lâu
bao_nhiêu
bao_nả
bay_biến
biết
biết_bao
biết_bao_nhiêu
biết_chắc
biết_chừng_nào
biết_mình
biết_mấy
biết_thế
biế... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nl/tokenizer_exceptions.py | spacy/lang/nl/tokenizer_exceptions.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. ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nl/lemmatizer.py | spacy/lang/nl/lemmatizer.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", "... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nl/__init__.py | spacy/lang/nl/__init__.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nl/punctuation.py | spacy/lang/nl/punctuation.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_... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nl/syntax_iterators.py | spacy/lang/nl/syntax_iterators.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nl/examples.py | spacy/lang/nl/examples.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",
"... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nl/lex_attrs.py | spacy/lang/nl/lex_attrs.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/nl/stop_words.py | spacy/lang/nl/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/fr/_tokenizer_exceptions_list.py | spacy/lang/fr/_tokenizer_exceptions_list.py | FR_BASE_EXCEPTIONS = [
"(+)-amphétamine",
"(5R,6S)-7,8-didehydro-4,5-époxy-3-méthoxy-N-méthylmorphinan-6-ol",
"(R)-amphétamine",
"(S)-amphétamine",
"(−)-amphétamine",
"0-day",
"0-days",
"1,1-diméthylhydrazine",
"1,2,3-tris-nitrooxy-propane",
"1,2-diazine",
"1,2-dichloropropan... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | true |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/fr/tokenizer_exceptions.py | spacy/lang/fr/tokenizer_exceptions.py | import re
from ...symbols import ORTH
from ...util import update_exc
from ..char_classes import ALPHA, ALPHA_LOWER
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from .punctuation import ELISION, HYPHENS
# not using the large _tokenizer_exceptions_list by default as it slows down the tokenizer
# from ._tokenizer_... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/fr/lemmatizer.py | spacy/lang/fr/lemmatizer.py | from typing import List, Tuple
from ...pipeline import Lemmatizer
from ...tokens import Token
class FrenchLemmatizer(Lemmatizer):
"""
French language lemmatizer applies the default rule based lemmatization
procedure with some modifications for better French language support.
The parts of speech 'ADV... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/fr/__init__.py | spacy/lang/fr/__init__.py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from .lemmatizer import FrenchLemmatizer
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax_... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/fr/punctuation.py | spacy/lang/fr/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
LIST_ELLIPSES,
LIST_PUNCT,
LIST_QUOTES,
UNITS,
merge_chars,
)
from ..punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES
ELISION = "' ’".replace(" ", "")
HYPHENS = r"- – — ‐ ‑".replace(" ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/fr/syntax_iterators.py | spacy/lang/fr/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/fr/examples.py | spacy/lang/fr/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.fr.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple cherche à acheter une start-up anglaise pour 1 milliard de dollars",
"Les voitures autonomes déplacent la responsabilité de l'assurance vers les... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/fr/lex_attrs.py | spacy/lang/fr/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = set(
"""
zero un une deux trois quatre cinq six sept huit neuf dix
onze douze treize quatorze quinze seize dix-sept dix-huit dix-neuf
vingt trente quarante cinquante soixante soixante-dix septante quatre-vingt huitante quatre-vingt-dix nonante
cent mille mil million milli... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/fr/stop_words.py | spacy/lang/fr/stop_words.py | STOP_WORDS = set(
"""
a à â abord afin ah ai aie ainsi ait allaient allons
alors anterieur anterieure anterieures antérieur antérieure antérieures
apres après as assez attendu au
aupres auquel aura auraient aurait auront
aussi autre autrement autres autrui aux auxquelles auxquels avaient
avais avait avant avec avoi... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/pt/tokenizer_exceptions.py | spacy/lang/pt/tokenizer_exceptions.py | from ...symbols import ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
for orth in [
"Adm.",
"Art.",
"art.",
"Av.",
"av.",
"Cia.",
"dom.",
"Dr.",
"dr.",
"e.g.",
"E.g.",
"E.G.",
"e/ou",
"ed.",
"eng.",
"etc... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/pt/__init__.py | spacy/lang/pt/__init__.py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class PortugueseDefaults(BaseDefaults)... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/pt/punctuation.py | spacy/lang/pt/punctuation.py | from ..punctuation import TOKENIZER_INFIXES as BASE_TOKENIZER_INFIXES
from ..punctuation import TOKENIZER_PREFIXES as BASE_TOKENIZER_PREFIXES
from ..punctuation import TOKENIZER_SUFFIXES as BASE_TOKENIZER_SUFFIXES
_prefixes = [r"\w{1,3}\$"] + BASE_TOKENIZER_PREFIXES
_suffixes = BASE_TOKENIZER_SUFFIXES
_infixes = [r"... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/pt/syntax_iterators.py | spacy/lang/pt/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/pt/examples.py | spacy/lang/pt/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.pt.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple está querendo comprar uma startup do Reino Unido por 100 milhões de dólares",
"Carros autônomos empurram a responsabilidade do seguro para os fa... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/pt/lex_attrs.py | spacy/lang/pt/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
"zero",
"um",
"dois",
"três",
"tres",
"quatro",
"cinco",
"seis",
"sete",
"oito",
"nove",
"dez",
"onze",
"doze",
"dúzia",
"dúzias",
"duzia",
"duzias",
"treze",
"catorze",
"quinze",
"dezas... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/pt/stop_words.py | spacy/lang/pt/stop_words.py | STOP_WORDS = set(
"""
a à às área acerca ademais adeus agora ainda algo algumas alguns ali além ambas ambos antes
ao aos apenas apoia apoio apontar após aquela aquelas aquele aqueles aqui aquilo
as assim através atrás até aí
baixo bastante bem boa bom breve
cada caminho catorze cedo cento certamente certeza cima ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/it/tokenizer_exceptions.py | spacy/lang/it/tokenizer_exceptions.py | from ...symbols import ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {
"all'art.": [{ORTH: "all'"}, {ORTH: "art."}],
"dall'art.": [{ORTH: "dall'"}, {ORTH: "art."}],
"dell'art.": [{ORTH: "dell'"}, {ORTH: "art."}],
"L'art.": [{ORTH: "L'"}, {ORTH: "art."}],
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/it/lemmatizer.py | spacy/lang/it/lemmatizer.py | from typing import Dict, List, Tuple
from ...pipeline import Lemmatizer
from ...tokens import Token
class ItalianLemmatizer(Lemmatizer):
"""This lemmatizer was adapted from the Polish one (version of April 2021).
It implements lookup lemmatization based on the morphological lexicon
morph-it (Baroni and Z... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/it/__init__.py | spacy/lang/it/__init__.py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from .lemmatizer import ItalianLemmatizer
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tokenizer_ex... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/it/punctuation.py | spacy/lang/it/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
HYPHENS,
LIST_ELLIPSES,
LIST_ICONS,
)
from ..punctuation import TOKENIZER_PREFIXES as BASE_TOKENIZER_PREFIXES
ELISION = "'’"
_prefixes = [r"'[0-9][0-9]", r"[0-9]+°"] + BASE_TOKENIZER_PREFIXES
_infixes = (
L... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/it/syntax_iterators.py | spacy/lang/it/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/it/examples.py | spacy/lang/it/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.it.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple vuole comprare una startup del Regno Unito per un miliardo di dollari",
"Le automobili a guida autonoma spostano la responsabilità assicurativa ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/it/stop_words.py | spacy/lang/it/stop_words.py | STOP_WORDS = set(
"""
a abbastanza abbia abbiamo abbiano abbiate accidenti ad adesso affinche agl
agli ahime ahimè ai al alcuna alcuni alcuno all alla alle allo allora altri
altrimenti altro altrove altrui anche ancora anni anno ansa anticipo assai
attesa attraverso avanti avemmo avendo avente aver avere averlo ave... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lt/tokenizer_exceptions.py | spacy/lang/lt/tokenizer_exceptions.py | from ...symbols import ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
for orth in ["n-tosios", "?!"]:
_exc[orth] = [{ORTH: orth}]
mod_base_exceptions = {
exc: val for exc, val in BASE_EXCEPTIONS.items() if not exc.endswith(".")
}
del mod_base_exceptions["8)"]... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lt/__init__.py | spacy/lang/lt/__init__.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 LithuanianDefaults(BaseDefaults):
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/lt/punctuation.py | spacy/lang/lt/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
HYPHENS,
LIST_ELLIPSES,
LIST_ICONS,
)
from ..punctuation import TOKENIZER_SUFFIXES
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[0-9])[+\*^](?=[0-9-])",
r"(?<=[{al}{q}])\.(?=[{au}{q... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lt/examples.py | spacy/lang/lt/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.lt.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Jaunikis pirmąją vestuvinę naktį iškeitė į areštinės gultą",
"Bepiločiai automobiliai išnaikins vairavimo mokyklas, autoservisus ir eismo nelaimes",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lt/lex_attrs.py | spacy/lang/lt/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = {
"antra",
"antrai",
"antrais",
"antram",
"antrame",
"antras",
"antri",
"antriems",
"antro",
"antroje",
"antromis",
"antroms",
"antros",
"antrose",
"antru",
"antruose",
"antrus",
"antrą",
"antrų",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lt/stop_words.py | spacy/lang/lt/stop_words.py | STOP_WORDS = {
"a",
"abejais",
"abejas",
"abejetam",
"abejetame",
"abejetas",
"abejeto",
"abejetu",
"abejetą",
"abeji",
"abejiems",
"abejomis",
"abejoms",
"abejos",
"abejose",
"abejuose",
"abejus",
"abejų",
"abi",
"abidvi",
"abiejose",
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/hu/tokenizer_exceptions.py | spacy/lang/hu/tokenizer_exceptions.py | import re
from ...symbols import ORTH
from ...util import update_exc
from ..punctuation import ALPHA_LOWER, CURRENCY
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
for orth in [
"-e",
"A.",
"AG.",
"AkH.",
"Aö.",
"B.",
"B.CS.",
"B.S.",
"B.Sc.",
"B.ú.é.k.",
"BE... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/hu/__init__.py | spacy/lang/hu/__init__.py | from ...language import BaseDefaults, Language
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKEN_MATCH, TOKENIZER_EXCEPTIONS
class HungarianDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTION... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/hu/punctuation.py | spacy/lang/hu/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_ICONS,
CONCAT_QUOTES,
LIST_ELLIPSES,
LIST_PUNCT,
LIST_QUOTES,
UNITS,
)
# removing ° from the special icons to keep e.g. 99° as one token
_concat_icons = CONCAT_ICONS.replace("\u00b0", "")
_currency = r"\$¢£€¥฿"
_q... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/hu/examples.py | spacy/lang/hu/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.hu.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Az Apple egy brit startup vásárlását tervezi 1 milliárd dollár értékben.",
"San Francisco vezetése mérlegeli a járdát használó szállító robotok betilt... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/hu/stop_words.py | spacy/lang/hu/stop_words.py | STOP_WORDS = set(
"""
a abban ahhoz ahogy ahol aki akik akkor akár alatt amely amelyek amelyekben
amelyeket amelyet amelynek ami amikor amit amolyan amíg annak arra arról az
azok azon azonban azt aztán azután azzal azért
be belül benne bár
cikk cikkek cikkeket csak
de
e ebben eddig egy egyes egyetlen egyik egyr... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/sa/__init__.py | spacy/lang/sa/__init__.py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class SanskritDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Sanskrit(Language):
lang = "sa"
Defaults = SanskritDefaults
__all__ = ["Sanskrit"]
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/sa/examples.py | spacy/lang/sa/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.sa.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"अभ्यावहति कल्याणं विविधं वाक् सुभाषिता ।",
"मनसि व्याकुले चक्षुः पश्यन्नपि न पश्यति ।",
"यस्य बुद्धिर्बलं तस्य निर्बुद्धेस्तु कुतो बलम्?",
"पर... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/sa/lex_attrs.py | spacy/lang/sa/lex_attrs.py | from ...attrs import LIKE_NUM
# reference 1: https://en.wikibooks.org/wiki/Sanskrit/Numbers
_num_words = [
"एकः",
"द्वौ",
"त्रयः",
"चत्वारः",
"पञ्च",
"षट्",
"सप्त",
"अष्ट",
"नव",
"दश",
"एकादश",
"द्वादश",
"त्रयोदश",
"चतुर्दश",
"पञ्चदश",
"षोडश",
"सप्तद... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/sa/stop_words.py | spacy/lang/sa/stop_words.py | # Source: https://gist.github.com/Akhilesh28/fe8b8e180f64b72e64751bc31cb6d323
STOP_WORDS = set(
"""
अहम्
आवाम्
वयम्
माम् मा
आवाम्
अस्मान् नः
मया
आवाभ्याम्
अस्माभिस्
मह्यम् मे
आवाभ्याम् नौ
अस्मभ्यम् नः
मत्
आवाभ्याम्
अस्मत्
मम मे
आवयोः
अस्माकम् नः
मयि
आवयोः
अस्मासु
त्वम्
युवाम्
यूयम्
त्वाम् त्वा
युवाम् वाम्... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/el/tokenizer_exceptions.py | spacy/lang/el/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'", "αφ'", "Αφ'"]:
_exc[token] = [{ORTH: token, NORM: "από"}]
for token in ["Αλλ'", "αλλ'"]:
_exc[token] = [{ORTH: token, NORM: "αλλά"}]
for token in ["παρ'",... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/el/lemmatizer.py | spacy/lang/el/lemmatizer.py | from typing import List
from ...pipeline import Lemmatizer
from ...tokens import Token
class GreekLemmatizer(Lemmatizer):
"""
Greek language lemmatizer applies the default rule based lemmatization
procedure with some modifications for better Greek language support.
The first modification is that it ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/el/__init__.py | spacy/lang/el/__init__.py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from .lemmatizer import GreekLemmatizer
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax_i... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/el/punctuation.py | spacy/lang/el/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
HYPHENS,
LIST_CURRENCY,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
)
_units = (
"km km² km³ m m² m³ dm dm² dm³ cm cm² cm³ mm mm² mm³ ha µm nm yd in ft "
"kg g mg µg t lb o... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/el/syntax_iterators.py | spacy/lang/el/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."""
# It fol... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/el/get_pos_from_wiktionary.py | spacy/lang/el/get_pos_from_wiktionary.py | def get_pos_from_wiktionary():
import re
from gensim.corpora.wikicorpus import extract_pages
regex = re.compile(r"==={{(\w+)\|el}}===")
regex2 = re.compile(r"==={{(\w+ \w+)\|el}}===")
# get words based on the Wiktionary dump
# check only for specific parts
# ==={{κύριο όνομα|el}}===
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/el/examples.py | spacy/lang/el/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.el.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"""Η άνιση κατανομή του πλούτου και του εισοδήματος, η οποία έχει λάβει
τρομερές διαστάσεις, δεν δείχνει τάσεις βελτίωσης.""",
"""Ο στόχος της σύντο... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/el/lex_attrs.py | spacy/lang/el/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
"μηδέν",
"ένας",
"δυο",
"δυό",
"τρεις",
"τέσσερις",
"πέντε",
"έξι",
"εφτά",
"επτά",
"οκτώ",
"οχτώ",
"εννιά",
"εννέα",
"δέκα",
"έντεκα",
"ένδεκα",
"δώδεκα",
"δεκατρείς",
"δεκατέσσερις",
"δεκα... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/el/stop_words.py | spacy/lang/el/stop_words.py | # Stop words
# Link to greek stop words: https://www.translatum.gr/forum/index.php?topic=3550.0?topic=3550.0
STOP_WORDS = set(
"""
αδιάκοπα αι ακόμα ακόμη ακριβώς άλλα αλλά αλλαχού άλλες άλλη άλλην
άλλης αλλιώς αλλιώτικα άλλο άλλοι αλλοιώς αλλοιώτικα άλλον άλλος άλλοτε αλλού
άλλους άλλων άμα άμεσα αμέσως αν ανά ανά... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/af/__init__.py | spacy/lang/af/__init__.py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class AfrikaansDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Afrikaans(Language):
lang = "af"
Defaults = AfrikaansDefaults
__all__ = ["Afrikaans"]
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/af/stop_words.py | spacy/lang/af/stop_words.py | # Source: https://github.com/stopwords-iso/stopwords-af
STOP_WORDS = set(
"""
'n
aan
af
al
as
baie
by
daar
dag
dat
die
dit
een
ek
en
gaan
gesê
haar
het
hom
hulle
hy
in
is
jou
jy
kan
kom
ma
maar
met
my
na
nie
om
ons
op
saam
sal
se
sien
so
sy
te
toe
uit
van
vir
was
wat
ʼn
""".split()
)
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ca/tokenizer_exceptions.py | spacy/lang/ca/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: "aprox.", NORM: "aproximadament"},
{ORTH: "pàg.", NORM: "pàgina"},
{ORTH: "p.ex.", NORM: "per exemple"},
{ORTH: "gen.", NORM: "gener"},
{ORTH: "feb... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ca/lemmatizer.py | spacy/lang/ca/lemmatizer.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ca/__init__.py | spacy/lang/ca/__init__.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ca/punctuation.py | spacy/lang/ca/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,
merge_chars,
)
ELISION = " ' ’ ".strip().replace(" ", "").replace("\n", "")
_prefixes = (
["§... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ca/syntax_iterators.py | spacy/lang/ca/syntax_iterators.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
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ca/examples.py | spacy/lang/ca/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ca/lex_attrs.py | spacy/lang/ca/lex_attrs.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",... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ca/stop_words.py | spacy/lang/ca/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ky/tokenizer_exceptions.py | spacy/lang/ky/tokenizer_exceptions.py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
_abbrev_exc = [
# Weekdays abbreviations
{ORTH: "дүй", NORM: "дүйшөмбү"},
{ORTH: "шей", NORM: "шейшемби"},
{ORTH: "шар", NORM: "шаршемби"},
{ORTH: "бей", NORM: "бейшемби"},... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ky/__init__.py | spacy/lang/ky/__init__.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 KyrgyzDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
infixes = TOKENIZ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ky/punctuation.py | spacy/lang/ky/punctuation.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ky/examples.py | spacy/lang/ky/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ky.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple Улуу Британия стартабын $1 миллиардга сатып алууну көздөөдө.",
"Автоном автомобилдерди камсыздоо жоопкерчилиги өндүрүүчүлөргө артылды.",
"Сан... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ky/lex_attrs.py | spacy/lang/ky/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
"нөл",
"ноль",
"бир",
"эки",
"үч",
"төрт",
"беш",
"алты",
"жети",
"сегиз",
"тогуз",
"он",
"жыйырма",
"отуз",
"кырк",
"элүү",
"алтымыш",
"жетмиш",
"сексен",
"токсон",
"жүз",
"миң",
"м... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/ky/stop_words.py | spacy/lang/ky/stop_words.py | STOP_WORDS = set(
"""
ага адам айтты айтымында айтып ал алар
алардын алган алуу алып анда андан аны
анын ар
бар басма баш башка башкы башчысы берген
биз билдирген билдирди бир биринчи бирок
бишкек болгон болот болсо болуп боюнча
буга бул
гана
да дагы деген деди деп
жана жатат жаткан жаңы же жогорку жок жол
жолу... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lb/tokenizer_exceptions.py | spacy/lang/lb/tokenizer_exceptions.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lb/__init__.py | spacy/lang/lb/__init__.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 = ... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lb/punctuation.py | spacy/lang/lb/punctuation.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}])... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lb/examples.py | spacy/lang/lb/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lb/lex_attrs.py | spacy/lang/lb/lex_attrs.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/lb/stop_words.py | spacy/lang/lb/stop_words.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
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/sr/tokenizer_exceptions.py | spacy/lang/sr/tokenizer_exceptions.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"},
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/sr/__init__.py | spacy/lang/sr/__init__.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
... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/sr/punctuation.py | spacy/lang/sr/punctuation.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}]... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/sr/examples.py | spacy/lang/sr/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/sr/lex_attrs.py | spacy/lang/sr/lex_attrs.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/sr/stop_words.py | spacy/lang/sr/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/hr/__init__.py | spacy/lang/hr/__init__.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"]
| python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/hr/examples.py | spacy/lang/hr/examples.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
explosion/spaCy | https://github.com/explosion/spaCy/blob/c1e7cb2ebf387a833d56e3b0bee23788d54b1448/spacy/lang/hr/stop_words.py | spacy/lang/hr/stop_words.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... | python | MIT | c1e7cb2ebf387a833d56e3b0bee23788d54b1448 | 2026-01-04T14:42:48.113774Z | false |
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