repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
spaCy | spacy/lang/da/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
LIST_ELLIPSES,
LIST_ICONS,
)
from ..punctuation import TOKENIZER_SUFFIXES
_quotes = CONCAT_QUOTES.replace("'", "")
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[{al}])\.(?=[{au}])".format(al=A... | 37 | 914 |
spaCy | spacy/lang/da/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class DanishDefaults(BaseDefaults):
... | 24 | 628 |
spaCy | spacy/lang/da/stop_words.py | .py | # Source: Handpicked by Jens Dahl Møllerhøj.
STOP_WORDS = set(
"""
af aldrig alene alle allerede alligevel alt altid anden andet andre at
bag begge blandt blev blive bliver burde bør
da de dem den denne dens der derefter deres derfor derfra deri dermed derpå derved det dette dig din dine disse dog du
efter egen... | 46 | 1,345 |
spaCy | spacy/lang/da/lex_attrs.py | .py | from ...attrs import LIKE_NUM
# Source http://fjern-uv.dk/tal.php
_num_words = """nul
en et to tre fire fem seks syv otte ni ti
elleve tolv tretten fjorten femten seksten sytten atten nitten tyve
enogtyve toogtyve treogtyve fireogtyve femogtyve seksogtyve syvogtyve otteogtyve niogtyve tredive
enogtredive toogtredive t... | 51 | 3,574 |
spaCy | spacy/lang/da/syntax_iterators.py | .py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import AUX, NOUN, PRON, PROPN, VERB
from ...tokens import Doc, Span
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
def is_verb_token(tok):
return tok.pos in [VERB, AUX]
def get_left_... | 75 | 2,189 |
spaCy | spacy/lang/da/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.da.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple overvejer at købe et britisk startup for 1 milliard dollar.",
"Selvkørende biler flytter forsikringsansvaret over på producenterne.",
"San F... | 18 | 568 |
spaCy | spacy/lang/da/tokenizer_exceptions.py | .py | """
Tokenizer Exceptions.
Source: https://forkortelse.dk/ and various others.
"""
from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
# Abbreviations for weekdays "søn." (for "søndag") as well as "Tor." and "Tors."
# (for "torsdag") are left o... | 582 | 8,956 |
spaCy | spacy/lang/lt/punctuation.py | .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... | 32 | 696 |
spaCy | spacy/lang/lt/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class LithuanianDefaults(BaseDefaults):
infixes = TOKENIZER_INFIXES
suffixes ... | 22 | 557 |
spaCy | spacy/lang/lt/stop_words.py | .py | STOP_WORDS = {
"a",
"abejais",
"abejas",
"abejetam",
"abejetame",
"abejetas",
"abejeto",
"abejetu",
"abejetą",
"abeji",
"abejiems",
"abejomis",
"abejoms",
"abejos",
"abejose",
"abejuose",
"abejus",
"abejų",
"abi",
"abidvi",
"abiejose",
... | 1,317 | 19,708 |
spaCy | spacy/lang/lt/lex_attrs.py | .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",
... | 1,151 | 22,464 |
spaCy | spacy/lang/lt/examples.py | .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",
... | 18 | 601 |
spaCy | spacy/lang/lt/tokenizer_exceptions.py | .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)"]... | 15 | 382 |
spaCy | spacy/lang/ml/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class MalayalamDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Malayalam(Language):
lang = "ml"
Defaults = MalayalamDefaults
__all__ = ["Malayalam"]... | 17 | 321 |
spaCy | spacy/lang/ml/stop_words.py | .py | STOP_WORDS = set(
"""
അത്
ഇത്
ആയിരുന്നു
ആകുന്നു
വരെ
അന്നേരം
അന്ന്
ഇന്ന്
ആണ്
""".split()
)
| 14 | 184 |
spaCy | spacy/lang/ml/lex_attrs.py | .py | from ...attrs import LIKE_NUM
# reference 2: https://www.omniglot.com/language/numbers/malayalam.htm
_num_words = [
"പൂജ്യം ",
"ഒന്ന് ",
"രണ്ട് ",
"മൂന്ന് ",
"നാല് ",
"അഞ്ച് ",
"ആറ് ",
"ഏഴ് ",
"എട്ട് ",
"ഒന്പത് ",
"പത്ത് ",
"പതിനൊന്ന്",
"പന്ത്രണ്ട്",
"പതി മൂന്... | 77 | 2,344 |
spaCy | spacy/lang/ml/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ml.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"അനാവശ്യമായി കണ്ണിലും മൂക്കിലും വായിലും സ്പർശിക്കാതിരിക്കുക",
"പൊതുരംഗത്ത് മലയാള ഭാഷയുടെ സമഗ്രപുരോഗതി ലക്ഷ്യമാക്കി പ്രവർത്തിക്കുന്ന സംഘടനയായ മലയാളഐക്യവ... | 15 | 1,402 |
spaCy | spacy/lang/lv/__init__.py | .py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class LatvianDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Latvian(Language):
lang = "lv"
Defaults = LatvianDefaults
__all__ = ["Latvian"]
| 15 | 247 |
spaCy | spacy/lang/lv/stop_words.py | .py | # Source: https://github.com/stopwords-iso/stopwords-lv
STOP_WORDS = set(
"""
aiz
ap
apakš
apakšpus
ar
arī
augšpus
bet
bez
bija
biji
biju
bijām
bijāt
būs
būsi
būsiet
būsim
būt
būšu
caur
diemžēl
diezin
droši
dēļ
esam
esat
esi
esmu
gan
gar
iekam
iekams
iekām
iekāms
iekš
iekšpus
ik
ir
it
itin
iz
ja
jau
jeb
jebšu
jel
... | 168 | 1,085 |
spaCy | spacy/lang/ky/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
HYPHENS,
LIST_ELLIPSES,
LIST_ICONS,
)
_hyphens_no_dash = HYPHENS.replace("-", "").strip("|").replace("||", "")
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[{al}])\.(?=[{au}])".format(al=AL... | 29 | 855 |
spaCy | spacy/lang/ky/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class KyrgyzDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
infixes = TOKENIZ... | 21 | 487 |
spaCy | spacy/lang/ky/stop_words.py | .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... | 43 | 1,064 |
spaCy | spacy/lang/ky/lex_attrs.py | .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... | 49 | 925 |
spaCy | spacy/lang/ky/examples.py | .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... | 17 | 917 |
spaCy | spacy/lang/ky/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
_abbrev_exc = [
# Weekdays abbreviations
{ORTH: "дүй", NORM: "дүйшөмбү"},
{ORTH: "шей", NORM: "шейшемби"},
{ORTH: "шар", NORM: "шаршемби"},
{ORTH: "бей", NORM: "бейшемби"},... | 54 | 2,049 |
spaCy | spacy/lang/am/punctuation.py | .py | from ..char_classes import (
ALPHA_UPPER,
CURRENCY,
LIST_ELLIPSES,
LIST_PUNCT,
LIST_QUOTES,
UNITS,
)
_list_punct = LIST_PUNCT + "፡ ። ፣ ፤ ፥ ፦ ፧ ፠ ፨".strip().split()
_suffixes = (
_list_punct
+ LIST_ELLIPSES
+ LIST_QUOTES
+ [
r"(?<=[0-9])\+",
# Amharic is written ... | 26 | 547 |
spaCy | spacy/lang/am/__init__.py | .py | from ...attrs import LANG
from ...language import BaseDefaults, Language
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTION... | 27 | 830 |
spaCy | spacy/lang/am/stop_words.py | .py | # Stop words by Teshome Kassie http://etd.aau.edu.et/bitstream/handle/123456789/3315/Teshome%20Kassie.pdf?sequence=1&isAllowed=y
# Stop words by Tihitina Petros http://etd.aau.edu.et/bitstream/handle/123456789/3384/Tihitina%20Petros.pdf?sequence=1&isAllowed=y
STOP_WORDS = set(
"""
ግን አንቺ አንተ እናንተ ያንተ ያንቺ የናንተ ራስህን... | 34 | 3,202 |
spaCy | spacy/lang/am/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"ዜሮ",
"አንድ",
"ሁለት",
"ሶስት",
"አራት",
"አምስት",
"ስድስት",
"ሰባት",
"ስምት",
"ዘጠኝ",
"አስር",
"አስራ አንድ",
"አስራ ሁለት",
"አስራ ሶስት",
"አስራ አራት",
"አስራ አምስት",
"አስራ ስድስት",
"አስራ ሰባት",
"አስራ ስምንት",
"አስራ ዘጠኝ",
"ሃያ",
... | 103 | 2,189 |
spaCy | spacy/lang/am/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.am.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"አፕል የዩኬን ጅምር ድርጅት በ 1 ቢሊዮን ዶላር ለመግዛት አስቧል።",
"የራስ ገዝ መኪኖች የኢንሹራንስ ኃላፊነትን ወደ አምራቾች ያዛውራሉ",
"ሳን ፍራንሲስኮ የእግረኛ መንገድ አቅርቦት ሮቦቶችን ማገድን ይመለከታል",
"ለንደ... | 18 | 791 |
spaCy | spacy/lang/am/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
_exc = {}
for exc_data in [
{ORTH: "ት/ቤት"},
{ORTH: "ወ/ሮ", NORM: "ወይዘሮ"},
]:
_exc[exc_data[ORTH]] = [exc_data]
for orth in [
"ዓ.ም.",
"ኪ.ሜ.",
]:
_exc[orth] = [{ORTH: orth}]
TOKENIZER_EXCEPTIONS = _exc
| 21 | 290 |
spaCy | spacy/lang/nb/punctuation.py | .py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
LIST_CURRENCY,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
PUNCT,
UNITS,
)
# Punctuation adapted from Danish
_quotes = CONCAT_QUOTES.replace("'", "")
_list_punct = [x for x in ... | 70 | 1,663 |
spaCy | spacy/lang/nb/__init__.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from ...pipeline import Lemmatizer
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from ... | 52 | 1,274 |
spaCy | spacy/lang/nb/stop_words.py | .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... | 51 | 1,160 |
spaCy | spacy/lang/nb/syntax_iterators.py | .py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import NOUN, PRON, PROPN
from ...tokens import Doc, Span
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""Detect base noun phrases from a dependency parse. Works on Doc and Span."""
# fmt: o... | 40 | 1,523 |
spaCy | spacy/lang/nb/examples.py | .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... | 14 | 427 |
spaCy | spacy/lang/nb/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
for exc_data in [
{ORTH: "jan.", NORM: "januar"},
{ORTH: "feb.", NORM: "februar"},
{ORTH: "mar.", NORM: "mars"},
{ORTH: "apr.", NORM: "april"},
{ORTH: "jun.", NORM: "juni"... | 223 | 3,068 |
spaCy | spacy/lang/hi/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class HindiDefaults(BaseDefaults):
stop_words = STOP_WORDS
lex_attr_getters = LEX_ATTRS
class Hindi(Language):
lang = "hi"
Defaults = HindiDefaults
__all__ = ["Hindi"]
| 17 | 305 |
spaCy | spacy/lang/hi/stop_words.py | .py | # Source: https://github.com/taranjeet/hindi-tokenizer/blob/master/stopwords.txt, https://data.mendeley.com/datasets/bsr3frvvjc/1#file-a21d5092-99d7-45d8-b044-3ae9edd391c6
STOP_WORDS = set(
"""
अंदर
अत
अदि
अप
अपना
अपनि
अपनी
अपने
अभि
अभी
अंदर
आदि
आप
अगर
इंहिं
इंहें
इंहों
इतयादि
इत्यादि
इन
इनका
इन्हीं
इन्हें
इन्हों
... | 240 | 2,975 |
spaCy | spacy/lang/hi/lex_attrs.py | .py | from ...attrs import LIKE_NUM, NORM
from ..norm_exceptions import BASE_NORMS
# fmt: off
_stem_suffixes = [
["ो", "े", "ू", "ु", "ी", "ि", "ा"],
["कर", "ाओ", "िए", "ाई", "ाए", "ने", "नी", "ना", "ते", "ीं", "ती", "ता", "ाँ", "ां", "ों", "ें"],
["ाकर", "ाइए", "ाईं", "ाया", "ेगी", "ेगा", "ोगी", "ोगे", "ाने", "... | 189 | 5,888 |
spaCy | spacy/lang/hi/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.hi.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"एप्पल 1 अरब डॉलर के लिए यू.के. स्टार्टअप खरीदने पर विचार कर रहा है।",
"स्वायत्त कारें निर्माताओं की ओर बीमा दायित्व रखतीं हैं।",
"सैन फ्रांसिस्को ... | 20 | 1,517 |
spaCy | spacy/lang/tn/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES
from .stop_words import STOP_WORDS
class SetswanaDefaults(BaseDefaults):
infixes = TOKENIZER_INFIXES
stop_words = STOP_WORDS
lex_attr_getters = LEX_ATTRS
class Setswana(Language):
... | 19 | 392 |
spaCy | spacy/lang/tn/stop_words.py | .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... | 21 | 816 |
spaCy | spacy/lang/tn/lex_attrs.py | .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",
... | 108 | 2,050 |
spaCy | spacy/lang/tn/examples.py | .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 ... | 15 | 434 |
spaCy | spacy/lang/uk/__init__.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from ..punctuation import (
COMBINING_DIACRITICS_TOKENIZER_INFIXES,
COMBINING_DIACRITICS_TOKENIZER_SUFFIXES,
)
from .lemmatizer import UkrainianLemmatizer
from .lex_attrs import LEX_ATTRS
from .sto... | 54 | 1,320 |
spaCy | spacy/lang/uk/stop_words.py | .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... | 470 | 4,887 |
spaCy | spacy/lang/uk/lex_attrs.py | .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... | 71 | 1,575 |
spaCy | spacy/lang/uk/examples.py | .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... | 19 | 1,797 |
spaCy | spacy/lang/uk/lemmatizer.py | .py | from typing import Callable, Optional
from thinc.api import Model
from ...pipeline.lemmatizer import lemmatizer_score
from ...vocab import Vocab
from ..ru.lemmatizer import RussianLemmatizer
class UkrainianLemmatizer(RussianLemmatizer):
def __init__(
self,
vocab: Vocab,
model: Optional[M... | 46 | 1,716 |
spaCy | spacy/lang/uk/tokenizer_exceptions.py | .py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
for exc_data in [
{ORTH: "обл.", NORM: "область"},
{ORTH: "р-н.", NORM: "район"},
{ORTH: "р-н", NORM: "район"},
{ORTH: "м.", NORM: "місто"},
{ORTH: "вул.", NORM: "вулиця"},... | 37 | 1,388 |
spaCy | spacy/lang/tr/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tokenizer_exceptions import TOKEN_MATCH, TOKENIZER_EXCEPTIONS
class TurkishDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
... | 22 | 546 |
spaCy | spacy/lang/tr/stop_words.py | .py | # Source: https://github.com/stopwords-iso/stopwords-tr
STOP_WORDS = set(
"""
acaba
acep
adamakıllı
adeta
ait
ama
amma
anca
ancak
arada
artık
aslında
aynen
ayrıca
az
açıkça
açıkçası
bana
bari
bazen
bazı
bazısı
bazısına
bazısında
bazısından
bazısını
bazısının
başkası
başkasına
başkasında
başkasından
başkasını
başkas... | 558 | 4,506 |
spaCy | spacy/lang/tr/lex_attrs.py | .py | from ...attrs import LIKE_NUM
# Thirteen, fifteen etc. are written separate: on üç
_num_words = [
"bir",
"iki",
"üç",
"dört",
"beş",
"altı",
"yedi",
"sekiz",
"dokuz",
"on",
"yirmi",
"otuz",
"kırk",
"elli",
"altmış",
"yetmiş",
"seksen",
"doksan",
... | 89 | 1,673 |
spaCy | spacy/lang/tr/syntax_iterators.py | .py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import NOUN, PRON, PROPN
from ...tokens import Doc, Span
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"... | 59 | 1,854 |
spaCy | spacy/lang/tr/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.tr.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Neredesin?",
"Neredesiniz?",
"Bu bir cümledir.",
"Sürücüsüz araçlar sigorta yükümlülüğünü üreticilere kaydırıyor.",
"San Francisco kaldırım... | 21 | 726 |
spaCy | spacy/lang/tr/tokenizer_exceptions.py | .py | import re
from ...symbols import NORM, ORTH
from ..punctuation import ALPHA, ALPHA_LOWER
_exc = {}
_abbr_period_exc = [
{ORTH: "A.B.D.", NORM: "Amerika"},
{ORTH: "Alb.", NORM: "albay"},
{ORTH: "Ank.", NORM: "Ankara"},
{ORTH: "Ar.Gör."},
{ORTH: "Arş.Gör."},
{ORTH: "Asb.", NORM: "astsubay"},
... | 191 | 6,091 |
spaCy | spacy/lang/la/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class LatinDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
stop_words = S... | 21 | 495 |
spaCy | spacy/lang/la/stop_words.py | .py | # Corrected Perseus list, cf. https://wiki.digitalclassicist.org/Stopwords_for_Greek_and_Latin
STOP_WORDS = set(
"""
ab ac ad adhuc aliqui aliquis an ante apud at atque aut autem
cum cur
de deinde dum
ego enim ergo es est et etiam etsi ex
fio
haud hic
iam idem igitur ille in infra inter interim ipse is... | 38 | 619 |
spaCy | spacy/lang/la/lex_attrs.py | .py | import re
from ...attrs import LIKE_NUM
# cf. Goyvaerts/Levithan 2009; case-insensitive, allow 4
roman_numerals_compile = re.compile(
r"(?i)^(?=[MDCLXVI])M*(C[MD]|D?C{0,4})(X[CL]|L?X{0,4})(I[XV]|V?I{0,4})$"
)
_num_words = """unus una unum duo duae tres tria quattuor quinque sex septem octo novem decem undecim du... | 35 | 2,372 |
spaCy | spacy/lang/la/syntax_iterators.py | .py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import AUX, NOUN, PRON, PROPN, VERB
from ...tokens import Doc, Span
# NB: Modified from da on suggestion from https://github.com/explosion/spaCy/issues/7457#issuecomment-800349751 [PJB]
def noun_chunks(doclike: Union[Doc, Span]) ... | 87 | 2,392 |
spaCy | spacy/lang/la/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.la.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
# > Caes. BG 1.1
# > Cic. De Amic. 1
# > V. Georg. 1.1-5
# > Gen. 1:1
# > Galileo, Sid. Nunc.
# > van Schurman, Opusc. arg. 1
sentences = [
"Gallia est omnis divisa in ... | 23 | 1,146 |
spaCy | spacy/lang/la/tokenizer_exceptions.py | .py | from ...symbols import ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
## TODO: Look into systematically handling u/v
_exc = {
"mecum": [{ORTH: "me"}, {ORTH: "cum"}],
"tecum": [{ORTH: "te"}, {ORTH: "cum"}],
"nobiscum": [{ORTH: "nobis"}, {ORTH: "cum"}],
"vobiscum":... | 26 | 1,235 |
spaCy | spacy/lang/pl/punctuation.py | .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... | 57 | 1,363 |
spaCy | spacy/lang/pl/__init__.py | .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... | 56 | 1,358 |
spaCy | spacy/lang/pl/stop_words.py | .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 ... | 79 | 2,360 |
spaCy | spacy/lang/pl/lex_attrs.py | .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",
... | 66 | 1,192 |
spaCy | spacy/lang/pl/examples.py | .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... | 16 | 684 |
spaCy | spacy/lang/pl/lemmatizer.py | .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... | 87 | 3,566 |
spaCy | spacy/lang/hu/punctuation.py | .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... | 62 | 1,505 |
spaCy | spacy/lang/hu/__init__.py | .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... | 22 | 584 |
spaCy | spacy/lang/hu/stop_words.py | .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... | 63 | 1,384 |
spaCy | spacy/lang/hu/examples.py | .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... | 13 | 406 |
spaCy | spacy/lang/hu/tokenizer_exceptions.py | .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... | 655 | 8,400 |
spaCy | spacy/lang/he/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class HebrewDefaults(BaseDefaults):
stop_words = STOP_WORDS
lex_attr_getters = LEX_ATTRS
writing_system = {"direction": "rtl", "has_case": False, "has_letters": True}
class Hebrew(Language)... | 18 | 391 |
spaCy | spacy/lang/he/stop_words.py | .py | STOP_WORDS = set(
"""
אני
את
אתה
אנחנו
אתן
אתם
הם
הן
היא
הוא
שלי
שלו
שלך
שלה
שלנו
שלכם
שלכן
שלהם
שלהן
לי
לו
לה
לנו
לכם
לכן
להם
להן
אותה
אותו
זה
זאת
אלה
אלו
תחת
מתחת
מעל
בין
עם
עד
על
אל
מול
של
אצל
כמו
אחר
אותו
בלי
לפני
אחרי
מאחורי
עלי
עליו
עליה
עליך
עלינו
עליכם
עליכן
עליהם
עליהן
כל
כולם
כולן
כך
ככה
כזה
כזאת
זה
אותי
... | 223 | 1,856 |
spaCy | spacy/lang/he/lex_attrs.py | .py | from ...attrs import LIKE_NUM
_num_words = [
"אפס",
"אחד",
"אחת",
"שתיים",
"שתים",
"שניים",
"שנים",
"שלוש",
"שלושה",
"ארבע",
"ארבעה",
"חמש",
"חמישה",
"שש",
"שישה",
"שבע",
"שבעה",
"שמונה",
"תשע",
"תשעה",
"עשר",
"עשרה",
"אחד עשר"... | 96 | 1,759 |
spaCy | spacy/lang/he/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.he.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"סין מקימה קרן של 440 מיליון דולר להשקעה בהייטק בישראל",
'רה"מ הודיע כי יחרים טקס בחסותו',
"הכנסת צפויה לאשר איכון אוטומטי של שיחות למוקד 100",
... | 24 | 993 |
spaCy | spacy/lang/sa/__init__.py | .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"]
| 17 | 317 |
spaCy | spacy/lang/sa/stop_words.py | .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... | 516 | 9,364 |
spaCy | spacy/lang/sa/lex_attrs.py | .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... | 128 | 4,211 |
spaCy | spacy/lang/sa/examples.py | .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... | 15 | 780 |
spaCy | spacy/lang/az/__init__.py | .py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class AzerbaijaniDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Azerbaijani(Language):
lang = "az"
Defaults = AzerbaijaniDefaults
__all__ = ["Azerb... | 17 | 329 |
spaCy | spacy/lang/az/stop_words.py | .py | # Source: https://github.com/eliasdabbas/advertools/blob/master/advertools/stopwords.py
STOP_WORDS = set(
"""
amma
arasında
artıq
ay
az
bax
belə
beş
bilər
bir
biraz
biri
birşey
biz
bizim
bizlər
bu
buna
bundan
bunların
bunu
bunun
buradan
bütün
bəli
bəlkə
bəy
bəzi
bəzən
daha
dedi
deyil
dir
düz
də
dək
dən
dəqiqə
edir
... | 146 | 966 |
spaCy | spacy/lang/az/lex_attrs.py | .py | from ...attrs import LIKE_NUM
# Eleven, twelve etc. are written separate: on bir, on iki
_num_words = [
"bir",
"iki",
"üç",
"dörd",
"beş",
"altı",
"yeddi",
"səkkiz",
"doqquz",
"on",
"iyirmi",
"otuz",
"qırx",
"əlli",
"altmış",
"yetmiş",
"səksən",
... | 89 | 1,696 |
spaCy | spacy/lang/az/examples.py | .py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.az.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Bu bir cümlədir.",
"Necəsən?",
"Qarabağ ordeni vətən müharibəsində qələbə münasibəti ilə təsis edilmişdir.",
"Məktəbimizə Bakıdan bir tarix müə... | 18 | 746 |
spaCy | spacy/lang/zh/__init__.py | .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... | 337 | 12,678 |
spaCy | spacy/lang/zh/stop_words.py | .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
—
——
———
‘
’
’‘
“
”
”，
…... | 1,900 | 13,409 |
spaCy | spacy/lang/zh/lex_attrs.py | .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",
... | 98 | 1,612 |
spaCy | spacy/lang/zh/examples.py | .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... | 17 | 792 |
spaCy | spacy/tokens/_serialize.py | .py | import zlib
from pathlib import Path
from typing import Dict, Iterable, Iterator, List, Optional, Set, Union
import numpy
import srsly
from numpy import ndarray
from thinc.api import NumpyOps
from ..attrs import IDS, ORTH, SPACY, intify_attr
from ..compat import copy_reg
from ..errors import Errors
from ..util import... | 307 | 11,969 |
spaCy | spacy/tokens/_dict_proxies.py | .py | import warnings
import weakref
from collections import UserDict
from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Tuple, Union
import srsly
from ..errors import Errors, Warnings
from .span_group import SpanGroup
if TYPE_CHECKING:
# This lets us add type hints for mypy etc. without causing circula... | 111 | 4,515 |
spaCy | spacy/tokens/underscore.py | .py | import copy
import functools
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
from ..errors import Errors
if TYPE_CHECKING:
from .doc import Doc
from .span import Span
from .token import Token
class Underscore:
mutable_types = (dict, list, set)
doc_extensions: Dict[Any, ... | 140 | 5,590 |
spaCy | spacy/kb/__init__.py | .py | from .candidate import Candidate, get_candidates, get_candidates_batch
from .kb import KnowledgeBase
from .kb_in_memory import InMemoryLookupKB
__all__ = [
"Candidate",
"KnowledgeBase",
"InMemoryLookupKB",
"get_candidates",
"get_candidates_batch",
]
| 12 | 271 |
spaCy | spacy/tests/test_misc.py | .py | import ctypes
import os
from pathlib import Path
import pytest
from pydantic import ValidationError
from thinc.api import (
Config,
ConfigValidationError,
CupyOps,
MPSOps,
NumpyOps,
Optimizer,
get_current_ops,
set_current_ops,
)
from thinc.compat import has_cupy_gpu, has_torch_mps_gpu
... | 493 | 15,406 |
spaCy | spacy/tests/test_registry_population.py | .py | import json
from pathlib import Path
import pytest
from spacy.util import registry
# Path to the reference registry contents, relative to this file
REFERENCE_FILE = Path(__file__).parent / "registry_contents.json"
@pytest.fixture
def reference_registry():
"""Load reference registry contents from JSON file"""
... | 55 | 1,900 |
spaCy | spacy/tests/test_errors.py | .py | from inspect import isclass
import pytest
from spacy.errors import ErrorsWithCodes
class Errors(metaclass=ErrorsWithCodes):
E001 = "error description"
def test_add_codes():
assert Errors.E001 == "[E001] error description"
with pytest.raises(AttributeError):
Errors.E002
assert isclass(Error... | 17 | 333 |
spaCy | spacy/tests/test_factory_imports.py | .py | # coding: utf-8
"""Test factory import compatibility from original and new locations."""
import importlib
import pytest
@pytest.mark.parametrize(
"factory_name,original_module,compat_module",
[
("make_tagger", "spacy.pipeline.factories", "spacy.pipeline.tagger"),
("make_sentencizer", "spacy.... | 86 | 3,861 |
spaCy | spacy/tests/test_cli_app.py | .py | import os
from pathlib import Path
import pytest
import srsly
from typer.testing import CliRunner
from spacy.cli._util import app, get_git_version
from spacy.tokens import Doc, DocBin, Span
from .util import make_tempdir, normalize_whitespace
def has_git():
try:
get_git_version()
return True
... | 429 | 13,974 |
spaCy | spacy/tests/enable_gpu.py | .py | from spacy import require_gpu
require_gpu()
| 4 | 45 |
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