code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
from typing import Pattern
from recognizers_text.utilities import RegExpUtility
from ...resources.french_date_time import FrenchDateTime
from ..base_date import DateTimeUtilityConfiguration
class FrenchDateTimeUtilityConfiguration(DateTimeUtilityConfiguration):
@property
def ago_regex(self) -> Pattern:
... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/base_configs.py | 0.900187 | 0.246137 | base_configs.py | pypi |
from typing import Pattern, Dict
from recognizers_text.utilities import RegExpUtility
from recognizers_number.number.extractors import BaseNumberExtractor
from recognizers_number.number.parsers import BaseNumberParser
from ...resources.french_date_time import FrenchDateTime
from ..extractors import DateTimeExtractor
... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/duration_parser_config.py | 0.782455 | 0.256014 | duration_parser_config.py | pypi |
from typing import Pattern
from recognizers_text.utilities import RegExpUtility
from recognizers_number.number.extractors import BaseNumberExtractor
from recognizers_number.number.french.extractors import FrenchCardinalExtractor
from ...resources.french_date_time import FrenchDateTime
from ..base_duration import Durat... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/duration_extractor_config.py | 0.918503 | 0.350449 | duration_extractor_config.py | pypi |
from typing import Pattern, Dict
from recognizers_text.utilities import RegExpUtility
from ...resources.french_date_time import FrenchDateTime
from ..base_datetimeperiod import DateTimePeriodParserConfiguration, MatchedTimeRange
from ..extractors import DateTimeExtractor
from ..parsers import DateTimeParser
from ..bas... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/datetimeperiod_parser_config.py | 0.838911 | 0.194158 | datetimeperiod_parser_config.py | pypi |
from typing import List, Pattern
from recognizers_text.utilities import RegExpUtility
from recognizers_text.extractor import Extractor
from recognizers_number.number.french.extractors import FrenchIntegerExtractor
from ...resources.french_date_time import FrenchDateTime
from ..extractors import DateTimeExtractor
from ... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/timeperiod_extractor_config.py | 0.908674 | 0.318379 | timeperiod_extractor_config.py | pypi |
from typing import Pattern, List, Dict
from recognizers_number import (BaseNumberExtractor, BaseNumberParser,
FrenchOrdinalExtractor, FrenchIntegerExtractor, FrenchNumberParserConfiguration)
from recognizers_text.utilities import RegExpUtility
from ...resources.french_date_time import Fr... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/date_extractor_config.py | 0.87596 | 0.24469 | date_extractor_config.py | pypi |
from typing import List, Pattern
from recognizers_text.utilities import RegExpUtility
from recognizers_number.number import BaseNumberParser
from recognizers_number.number.french.extractors import FrenchIntegerExtractor
from recognizers_number.number.french.parsers import FrenchNumberParserConfiguration
from ...resour... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/dateperiod_extractor_config.py | 0.894778 | 0.335432 | dateperiod_extractor_config.py | pypi |
from typing import List, Pattern
from recognizers_text.extractor import Extractor
from recognizers_text.utilities import RegExpUtility
from recognizers_number import FrenchIntegerExtractor
from ...resources.french_date_time import FrenchDateTime
from ..extractors import DateTimeExtractor
from ..base_merged import Merg... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/merged_extractor_config.py | 0.838448 | 0.193281 | merged_extractor_config.py | pypi |
from typing import Pattern
import regex
from recognizers_text.utilities import RegExpUtility
from ...resources.french_date_time import FrenchDateTime
from ..extractors import DateTimeExtractor
from ..base_date import BaseDateExtractor
from ..base_time import BaseTimeExtractor
from ..base_duration import BaseDurationEx... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/datetime_extractor_config.py | 0.894361 | 0.209571 | datetime_extractor_config.py | pypi |
from typing import Pattern, Dict
from recognizers_text.utilities import RegExpUtility
from recognizers_text.extractor import Extractor
from recognizers_number.number.french.extractors import FrenchIntegerExtractor
from ...resources.french_date_time import FrenchDateTime
from ..extractors import DateTimeExtractor
from ... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/timeperiod_parser_config.py | 0.879781 | 0.233182 | timeperiod_parser_config.py | pypi |
from typing import List, Dict, Callable
from datetime import datetime
from recognizers_text.utilities import RegExpUtility
from ..utilities import DateUtils
from ..base_holiday import BaseHolidayParserConfiguration
from ...resources.french_date_time import FrenchDateTime
class FrenchHolidayParserConfiguration(BaseHo... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/holiday_parser_config.py | 0.801431 | 0.431884 | holiday_parser_config.py | pypi |
from typing import List, Pattern
from recognizers_number import BaseNumberExtractor, FrenchCardinalExtractor
from recognizers_text.utilities import RegExpUtility
from ...resources.french_date_time import FrenchDateTime
from ..extractors import DateTimeExtractor
from ..base_datetimeperiod import DateTimePeriodExtractor... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/datetimeperiod_extractor_config.py | 0.907317 | 0.249996 | datetimeperiod_extractor_config.py | pypi |
from typing import Pattern, Dict
from recognizers_text.utilities import RegExpUtility
from ...resources.french_date_time import FrenchDateTime
from ..extractors import DateTimeExtractor
from ..parsers import DateTimeParser
from ..base_configs import BaseDateParserConfiguration
from ..base_dateperiod import DatePeriodP... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/dateperiod_parser_config.py | 0.905196 | 0.397821 | dateperiod_parser_config.py | pypi |
from typing import List, Pattern
from recognizers_text.utilities import RegExpUtility
from ...resources.french_date_time import FrenchDateTime
from ..base_time import TimeExtractorConfiguration
from ..utilities import DateTimeOptions
class FrenchTimeExtractorConfiguration(TimeExtractorConfiguration):
@property
... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/time_extractor_config.py | 0.771327 | 0.273411 | time_extractor_config.py | pypi |
from typing import Pattern, List, Dict
import regex
from recognizers_text.utilities import RegExpUtility
from recognizers_number import BaseNumberExtractor, BaseNumberParser
from ...resources.french_date_time import FrenchDateTime
from ..extractors import DateTimeExtractor
from ..parsers import DateTimeParser
from ..u... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/date_parser_config.py | 0.859987 | 0.213562 | date_parser_config.py | pypi |
from typing import Pattern
from recognizers_text.utilities import RegExpUtility
from ...resources.french_date_time import FrenchDateTime
from ..extractors import DateTimeExtractor
from ..base_set import SetExtractorConfiguration
from ..base_date import BaseDateExtractor
from ..base_time import BaseTimeExtractor
from .... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/set_extractor_config.py | 0.905726 | 0.176672 | set_extractor_config.py | pypi |
from typing import Pattern, Dict
from recognizers_text.utilities import RegExpUtility
from recognizers_number.number.extractors import BaseNumberExtractor
from recognizers_number.number.parsers import BaseNumberParser
from ...resources.french_date_time import FrenchDateTime
from ..extractors import DateTimeExtractor
f... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/datetime_parser_config.py | 0.78016 | 0.243137 | datetime_parser_config.py | pypi |
from typing import List, Pattern, Dict
import regex
from recognizers_text.utilities import RegExpUtility
from ...resources.french_date_time import FrenchDateTime
from ..base_time import TimeParserConfiguration, AdjustParams
from ..base_configs import BaseDateParserConfiguration, DateTimeUtilityConfiguration
from .time... | /recognizers_text_date_time-1.0.2a1-py3-none-any.whl/recognizers_date_time/date_time/french/time_parser_config.py | 0.842248 | 0.154249 | time_parser_config.py | pypi |
# pylint: disable=line-too-long
class BaseCurrency:
CurrencyFractionMapping = dict([("CNY", "FEN|JIAO"),
("__D", "CENT"),
("RUB", "KOPEK"),
("AFN", "PUL"),
("EUR", "CENT... | /recognizers_text_number_with_unit-1.0.2a2-py3-none-any.whl/recognizers_number_with_unit/resources/base_currency.py | 0.435421 | 0.23105 | base_currency.py | pypi |
from .base_numbers import BaseNumbers
# pylint: disable=line-too-long
class EnglishNumericWithUnit:
AgeSuffixList = dict([("Year", "years old|year old|year-old|years-old|-year-old|-years-old|years of age|year of age"),
("Month", "months old|month old|month-old|months-old|-month-old|-mon... | /recognizers_text_number_with_unit-1.0.2a2-py3-none-any.whl/recognizers_number_with_unit/resources/english_numeric_with_unit.py | 0.444083 | 0.644952 | english_numeric_with_unit.py | pypi |
from abc import ABC, abstractmethod
from typing import List, Dict, Set, Pattern, Match
from copy import deepcopy
from collections import namedtuple
from itertools import chain
import regex
from .constants import *
from recognizers_text.utilities import RegExpUtility
from recognizers_text.extractor import Extractor, Ext... | /recognizers_text_number_with_unit-1.0.2a2-py3-none-any.whl/recognizers_number_with_unit/number_with_unit/extractors.py | 0.843219 | 0.229018 | extractors.py | pypi |
from abc import abstractmethod
from typing import List
from recognizers_text.model import Model, ModelResult
from recognizers_text.extractor import Extractor
from recognizers_text.parser import Parser
from recognizers_text.utilities import QueryProcessor
from recognizers_number_with_unit.number_with_unit.parsers impor... | /recognizers_text_number_with_unit-1.0.2a2-py3-none-any.whl/recognizers_number_with_unit/number_with_unit/models.py | 0.84241 | 0.250191 | models.py | pypi |
from typing import Dict, List, Pattern
from recognizers_text.culture import Culture
from recognizers_text.extractor import Extractor
from recognizers_text.utilities import RegExpUtility
from recognizers_number.culture import CultureInfo
from recognizers_number.number.models import NumberMode
from recognizers_number.nu... | /recognizers_text_number_with_unit-1.0.2a2-py3-none-any.whl/recognizers_number_with_unit/number_with_unit/english/extractors.py | 0.859561 | 0.240072 | extractors.py | pypi |
from typing import Dict, List, Pattern
from recognizers_text.culture import Culture
from recognizers_text.extractor import Extractor
from recognizers_text.utilities import RegExpUtility
from recognizers_number.culture import CultureInfo
from recognizers_number.number.models import NumberMode
from recognizers_number.nu... | /recognizers_text_number_with_unit-1.0.2a2-py3-none-any.whl/recognizers_number_with_unit/number_with_unit/spanish/extractors.py | 0.869174 | 0.24434 | extractors.py | pypi |
from recognizers_text.culture import Culture
from recognizers_text.extractor import Extractor
from recognizers_text.parser import Parser
from recognizers_number.culture import CultureInfo
from recognizers_number.number.spanish.extractors import SpanishNumberExtractor, NumberMode
from recognizers_number.number.parser_fa... | /recognizers_text_number_with_unit-1.0.2a2-py3-none-any.whl/recognizers_number_with_unit/number_with_unit/spanish/parsers.py | 0.807271 | 0.163145 | parsers.py | pypi |
from typing import Dict, List, Pattern
from recognizers_text.culture import Culture
from recognizers_text.extractor import Extractor
from recognizers_text.utilities import RegExpUtility
from recognizers_number.culture import CultureInfo
from recognizers_number.number.chinese.extractors import ChineseNumberExtractor, C... | /recognizers_text_number_with_unit-1.0.2a2-py3-none-any.whl/recognizers_number_with_unit/number_with_unit/chinese/extractors.py | 0.865295 | 0.228974 | extractors.py | pypi |
from typing import Dict, List, Pattern
from recognizers_text.culture import Culture
from recognizers_text.extractor import Extractor
from recognizers_text.utilities import RegExpUtility
from recognizers_number.culture import CultureInfo
from recognizers_number.number.models import NumberMode
from recognizers_number.nu... | /recognizers_text_number_with_unit-1.0.2a2-py3-none-any.whl/recognizers_number_with_unit/number_with_unit/portuguese/extractors.py | 0.861261 | 0.224671 | extractors.py | pypi |
from typing import Dict, List, Pattern
from recognizers_text.culture import Culture
from recognizers_text.extractor import Extractor
from recognizers_text.utilities import RegExpUtility
from recognizers_number.culture import CultureInfo
from recognizers_number.number.models import NumberMode
from recognizers_number.nu... | /recognizers_text_number_with_unit-1.0.2a2-py3-none-any.whl/recognizers_number_with_unit/number_with_unit/french/extractors.py | 0.867822 | 0.229546 | extractors.py | pypi |
from abc import abstractmethod
from typing import List, Dict, Pattern, Optional
from collections import namedtuple
from decimal import Decimal, getcontext
import copy
import regex
from recognizers_text.utilities import RegExpUtility
from recognizers_text.culture import Culture
from recognizers_text.extractor import Ex... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/cjk_parsers.py | 0.842734 | 0.264345 | cjk_parsers.py | pypi |
from abc import abstractmethod
from typing import List, Pattern, Dict, Match
from collections import namedtuple
import regex
from recognizers_text.utilities import RegExpUtility
from recognizers_text.extractor import Extractor, ExtractResult
from recognizers_number.resources.base_numbers import BaseNumbers
from recogn... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/extractors.py | 0.682468 | 0.285599 | extractors.py | pypi |
from typing import Pattern, List, NamedTuple
import regex
from recognizers_text.utilities import RegExpUtility
from recognizers_number.number.models import NumberMode, LongFormatMode
from recognizers_number.resources import BaseNumbers
from recognizers_number.resources.english_numeric import EnglishNumeric
from recogn... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/english/extractors.py | 0.830044 | 0.246307 | extractors.py | pypi |
from typing import Dict, Pattern, List
from recognizers_text.utilities import RegExpUtility
from recognizers_text.culture import Culture
from recognizers_text.parser import ParseResult
from recognizers_number.culture import CultureInfo
from recognizers_number.number.parsers import NumberParserConfiguration
from recogn... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/english/parsers.py | 0.84039 | 0.263351 | parsers.py | pypi |
from typing import Pattern, List, NamedTuple
from recognizers_text.utilities import RegExpUtility
from recognizers_number.number.models import NumberMode, LongFormatMode
from recognizers_number.resources import BaseNumbers
from recognizers_number.resources.spanish_numeric import SpanishNumeric
from recognizers_number.... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/spanish/extractors.py | 0.854597 | 0.270348 | extractors.py | pypi |
from typing import Dict, Pattern, List
import regex
from recognizers_text.utilities import RegExpUtility
from recognizers_text.culture import Culture
from recognizers_text.parser import ParseResult
from recognizers_number.culture import CultureInfo
from recognizers_number.number.parsers import NumberParserConfiguratio... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/spanish/parsers.py | 0.76882 | 0.228028 | parsers.py | pypi |
from typing import List
from enum import Enum
from recognizers_number.number.extractors import ReVal, BaseNumberExtractor
from recognizers_text.utilities import RegExpUtility
from recognizers_number.number.constants import Constants
from recognizers_number.resources.chinese_numeric import ChineseNumeric
class Chines... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/chinese/extractors.py | 0.811937 | 0.239483 | extractors.py | pypi |
from typing import List, Dict, Pattern, Optional
from collections import namedtuple
from decimal import Decimal, getcontext
import copy
import regex
from recognizers_text.utilities import RegExpUtility
from recognizers_text.culture import Culture
from recognizers_text.extractor import ExtractResult
from recognizers_te... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/chinese/parsers.py | 0.897847 | 0.297818 | parsers.py | pypi |
import regex
from typing import Pattern, List, NamedTuple
from recognizers_text.utilities import RegExpUtility
from recognizers_number.number.models import NumberMode, LongFormatMode
from recognizers_number.resources import BaseNumbers
from recognizers_number.resources.portuguese_numeric import PortugueseNumeric
from ... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/portuguese/extractors.py | 0.789842 | 0.235944 | extractors.py | pypi |
from typing import Dict, Pattern, List
import regex
from recognizers_text.utilities import RegExpUtility
from recognizers_text.culture import Culture
from recognizers_text.parser import ParseResult
from recognizers_number.culture import CultureInfo
from recognizers_number.number.parsers import NumberParserConfiguratio... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/portuguese/parsers.py | 0.752649 | 0.227362 | parsers.py | pypi |
from typing import List
from enum import Enum
from recognizers_number.number.extractors import ReVal, BaseNumberExtractor
from recognizers_text.utilities import RegExpUtility
from recognizers_number.number.constants import Constants
from recognizers_number.resources.japanese_numeric import JapaneseNumeric
class Japa... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/japanese/extractors.py | 0.741487 | 0.239494 | extractors.py | pypi |
from typing import List, Dict, Pattern, Optional
from collections import namedtuple
from decimal import Decimal, getcontext
import copy
import regex
from recognizers_text.utilities import RegExpUtility
from recognizers_text.culture import Culture
from recognizers_text.extractor import ExtractResult
from recognizers_te... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/japanese/parsers.py | 0.904287 | 0.289152 | parsers.py | pypi |
from typing import Pattern, List, NamedTuple
import regex
from recognizers_text.utilities import RegExpUtility
from recognizers_number.number.models import NumberMode, LongFormatMode
from recognizers_number.resources import BaseNumbers
from recognizers_number.resources.french_numeric import FrenchNumeric
from recogniz... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/french/extractors.py | 0.830216 | 0.244814 | extractors.py | pypi |
from typing import Dict, Pattern, List
import regex
from recognizers_text.utilities import RegExpUtility
from recognizers_text.culture import Culture
from recognizers_text.parser import ParseResult
from recognizers_number.culture import CultureInfo
from recognizers_number.number.parsers import NumberParserConfiguratio... | /recognizers-text-number-1.0.2a2.tar.gz/recognizers-text-number-1.0.2a2/recognizers_number/number/french/parsers.py | 0.827584 | 0.237852 | parsers.py | pypi |
from abc import ABC, abstractmethod
from typing import List, Dict, Set, Pattern, Match
from collections import namedtuple
import regex as re
from recognizers_sequence.sequence.config.url_configuration import URLConfiguration
from recognizers_text.matcher.string_matcher import StringMatcher
from .constants import *
fro... | /recognizers_text_sequence-1.0.2a1-py3-none-any.whl/recognizers_sequence/sequence/extractors.py | 0.685844 | 0.194444 | extractors.py | pypi |
from abc import abstractmethod
from typing import List
from recognizers_sequence.sequence.constants import Constants
from recognizers_text import QueryProcessor
from recognizers_text.model import Model, ModelResult
from recognizers_text.extractor import Extractor
from recognizers_text.parser import Parser, ParseResult... | /recognizers_text_sequence-1.0.2a1-py3-none-any.whl/recognizers_sequence/sequence/models.py | 0.808823 | 0.296654 | models.py | pypi |
from typing import Optional
import regex as re
from recognizers_sequence.sequence.parsers import SequenceParser, BaseIpParser
from recognizers_sequence.resources import BasePhoneNumbers, BaseEmail, BaseGUID
from recognizers_text.parser import Parser, ParseResult
from recognizers_text import ExtractResult, Pattern, reg... | /recognizers_text_sequence-1.0.2a1-py3-none-any.whl/recognizers_sequence/sequence/english/parsers.py | 0.791982 | 0.169991 | parsers.py | pypi |
from recognizers_sequence.sequence.config import PhoneNumberConfiguration, URLConfiguration, IpConfiguration
from recognizers_sequence.sequence.sequence_recognizer import *
from recognizers_sequence.resources.chinese_phone_numbers import ChinesePhoneNumbers
from recognizers_sequence.resources.chinese_url import Chinese... | /recognizers_text_sequence-1.0.2a1-py3-none-any.whl/recognizers_sequence/sequence/chinese/extractors.py | 0.75401 | 0.175114 | extractors.py | pypi |
from abc import ABC, abstractmethod
from typing import List
from .meta_data import MetaData
class ExtractResult:
def __init__(self):
self.start: int = 0
self.length: int = 0
self.text: str = ''
self.type: str = ''
self.data: object = None
self.meta_data: MetaData = ... | /recognizers-text-1.0.2a2.tar.gz/recognizers-text-1.0.2a2/recognizers_text/extractor.py | 0.892146 | 0.324797 | extractor.py | pypi |
import re
from typing import Pattern, Union, List, Match
import regex
from emoji import UNICODE_EMOJI
class StringUtility:
@staticmethod
def is_emoji(letter):
return letter in UNICODE_EMOJI
@staticmethod
def remove_unicode_matches(string: Pattern):
py_regex = re.sub('\\\\u.{4}[\\|\\\\... | /recognizers-text-1.0.2a2.tar.gz/recognizers-text-1.0.2a2/recognizers_text/utilities.py | 0.665411 | 0.234911 | utilities.py | pypi |
from abc import ABC, abstractmethod
from enum import Flag
from typing import List, Dict, Generic, TypeVar, Callable, Optional
from collections import namedtuple
from .culture import Culture
T_MODEL_OPTIONS = TypeVar('T_MODEL_OPTIONS', bound=Flag)
class ModelResult():
def __init__(self):
self.text: str
... | /recognizers-text-1.0.2a2.tar.gz/recognizers-text-1.0.2a2/recognizers_text/model.py | 0.880534 | 0.160957 | model.py | pypi |
from .tokenizer import Tokenizer
from .match_strategy import MatchStrategy
from .simple_tokenizer import SimpleTokenizer
from .matcher import Matcher
from .trie_tree import TrieTree
from .ac_automaton import AcAutomaton
from multipledispatch import dispatch
from .match_result import MatchResult
class StringMatcher:
... | /recognizers-text-1.0.2a2.tar.gz/recognizers-text-1.0.2a2/recognizers_text/matcher/string_matcher.py | 0.773088 | 0.290339 | string_matcher.py | pypi |
from .simple_tokenizer import SimpleTokenizer
from .token import Token
class NumberWithUnitTokenizer(SimpleTokenizer):
def __init__(self):
self.__special_tokens_characters = ['$']
@property
def special_tokens_characters(self) -> []:
return self.__special_tokens_characters
def tokeni... | /recognizers-text-1.0.2a2.tar.gz/recognizers-text-1.0.2a2/recognizers_text/matcher/number_with_unit_tokenizer.py | 0.654453 | 0.222151 | number_with_unit_tokenizer.py | pypi |
from .tokenizer import Tokenizer
from .token import Token
class SimpleTokenizer(Tokenizer):
def tokenize(self, input: str) -> []:
tokens = []
if not input:
return tokens
in_token = False
token_start = 0
chars = list(input)
for i in range(0, len(chars)... | /recognizers-text-1.0.2a2.tar.gz/recognizers-text-1.0.2a2/recognizers_text/matcher/simple_tokenizer.py | 0.458591 | 0.187709 | simple_tokenizer.py | pypi |
# recohut
<div id="top"></div>
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<!-- PROJECT LOGO -->
<br />
<div align="center">
<a href="ht... | /recohut-0.0.11.tar.gz/recohut-0.0.11/README.md | 0.63375 | 0.70162 | README.md | pypi |
class SparseFeat(namedtuple('SparseFeat', ['name', 'vocabulary_size', 'embedding_dim', 'use_hash',
'dtype', 'embedding_name', 'group_name'])):
__slots__ = ()
def __new__(cls, name, vocabulary_size, embedding_dim=4, use_hash=False, dtype="int32", embedding_name=None,
... | /recohut-0.0.11.tar.gz/recohut-0.0.11/model.py | 0.939206 | 0.346928 | model.py | pypi |
```
#hide
from recohut.datasets import *
from recohut.layers import *
from recohut.models.layerss import *
from recohut.models import *
from recohut.utils import *
from recohut.rl import *
from recohut.visualization import *
from recohut.transforms import *
```
# recohut
<div id="top"></div>
[![Contributors][contrib... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/index.ipynb | 0.513181 | 0.675263 | index.ipynb | pypi |
```
# default_exp transforms.bipartite
```
# Bi-partite Dataset
> Generate bi-partite graph dataset.
```
#hide
from nbdev.showdoc import *
#export
import numpy as np
import time
from tqdm.notebook import tqdm
import torch
from torch.utils.data import Dataset
#export
class BipartiteDataset(Dataset):
def __init... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/transforms/transforms.bipartite.ipynb | 0.495361 | 0.77886 | transforms.bipartite.ipynb | pypi |
```
# default_exp transforms.sampling
```
# Sampling
> Data sampling methods.
```
#hide
from nbdev.showdoc import *
#export
from abc import *
from pathlib import Path
import pickle
import os
from tqdm import trange
from collections import Counter
import numpy as np
import pandas as pd
#export
def simple_negative_sa... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/transforms/transforms.sampling.ipynb | 0.542379 | 0.663328 | transforms.sampling.ipynb | pypi |
```
# default_exp transforms.session
```
# Session
> Create sessions.
```
#hide
from nbdev.showdoc import *
#export
import numpy as np
#export
def construct_session_sequences(df, sessionID, itemID):
"""
Given a dataset in pandas df format, construct a list of lists where each sublist
represents the inte... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/transforms/transforms.session.ipynb | 0.496582 | 0.894698 | transforms.session.ipynb | pypi |
```
# default_exp transforms.user_grouping
```
# User Grouping
> Generate user groups for training group recommenders.
```
#hide
from nbdev.showdoc import *
#export
import os
import shutil
import numpy as np
import pandas as pd
import scipy.sparse as sp
from collections import Counter
#export
class GroupGenerator(o... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/transforms/transforms.user_grouping.ipynb | 0.557002 | 0.623606 | transforms.user_grouping.ipynb | pypi |
```
# default_exp transforms.aggregate
```
# Aggregate
> Aggregation of records.
```
#hide
from nbdev.showdoc import *
#export
import numpy as np
import pandas as pd
#export
def simple_aggregate(data,
drop_duplicates=True,
by='AFFINITY',
aggregation='me... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/transforms/transforms.aggregate.ipynb | 0.439266 | 0.631509 | transforms.aggregate.ipynb | pypi |
```
# default_exp transforms.datasets.movielens
```
# MovieLens Dataset Transformation
> Implementation of transformation functions specific to movielens datasets.
```
#hide
from nbdev.showdoc import *
#export
import pandas as pd
import numpy as np
import random
from tqdm import tqdm
from collections import defaultd... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/transforms/datasets/transforms.datasets.movielens.ipynb | 0.654453 | 0.726814 | transforms.datasets.movielens.ipynb | pypi |
```
# default_exp transforms.datasets.criteo
```
# Criteo Dataset Transformation
> Implementation of transformation functions specific to criteo ad-display dataset.
```
#hide
from nbdev.showdoc import *
#export
import os
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, KBinsDisc... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/transforms/datasets/transforms.datasets.criteo.ipynb | 0.754282 | 0.841631 | transforms.datasets.criteo.ipynb | pypi |
```
# default_exp models.nmf
```
# NMF
> Neural Matrix Factorization.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
from typing import Any, Iterable, List, Optional, Tuple, Union, Callable
import os
import torch
from torch import nn
from recohut.models.... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.nmf.ipynb | 0.885511 | 0.75274 | models.nmf.ipynb | pypi |
```
# default_exp models.sasrec
```
# SASRec
> Self-Attentive Sequential Recommendation Model.
Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the ‘context’ of users’ activities on the basis of actions they have performed recently. To capture such patterns, two approache... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.sasrec.ipynb | 0.883078 | 0.935641 | models.sasrec.ipynb | pypi |
```
# default_exp models.pnn
```
# Product-based Neural Network (PNN)
> A pytorch implementation of inner/outer Product Neural Network.
PNN uses an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between inter-field categories, and further ... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.pnn.ipynb | 0.863938 | 0.947721 | models.pnn.ipynb | pypi |
```
# default_exp models.mpm
```
# MPM
> Multi-Preferences Model.
MPM tries to eliminate the effects of unexpected behaviors by first extracting the users’ instant preferences from their recent historical interactions by a fine-grained preferences module. Then an unexpected-behaviors detector is trained to judge wheth... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.mpm.ipynb | 0.832917 | 0.85741 | models.mpm.ipynb | pypi |
```
# default_exp models.lr
```
# LR
> A pytorch implementation of Logistic Regression.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
import torch
from recohut.models.layers.common import FeaturesLinear
#export
class LR(torch.nn.Module):
"""
A p... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.lr.ipynb | 0.785267 | 0.778607 | models.lr.ipynb | pypi |
```
# default_exp models.ffm
```
# FFM
> A pytorch implementation of Field-aware Factorization Machine.
Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive. For example, the interactions with usel... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.ffm.ipynb | 0.872877 | 0.976288 | models.ffm.ipynb | pypi |
```
# default_exp models.groupim
```
# GroupIM
> Implementation of GroupIM recommendation model - A Mutual Information Maximization Framework for Neural Group Recommendation.
Group interactions are sparse in nature which makes it difficult to provide relevant recommendation to the group. GroupIM regularize the user-gr... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.groupim.ipynb | 0.934073 | 0.871092 | models.groupim.ipynb | pypi |
```
# default_exp models.afn
```
# AFN
> A pytorch implementation of AFN.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
import math
import torch
import torch.nn.functional as F
from recohut.models.layers.common import FeaturesEmbedding, FeaturesLinear, M... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.afn.ipynb | 0.906545 | 0.90291 | models.afn.ipynb | pypi |
```
# default_exp models.tagnn_pp
```
# TAGNN++
> [Mitheran et. al. Improved Representation Learning for Session-based Recommendation. arXiv, 2021.](https://arxiv.org/abs/2107.01516v2)
TAGNN models item interactions with GNN, and both local and global user interactions with a Transformer.
<img src='https://raw.githu... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.tagnn_pp.ipynb | 0.884987 | 0.828245 | models.tagnn_pp.ipynb | pypi |
```
# default_exp models.xdeepfm
```
# xDeepFM
> A pytorch implementation of Extreme Deep Factorization Machines (xDeepFM).
xDeepFM combines the CIN and a classical DNN into one unified model. xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.xdeepfm.ipynb | 0.85449 | 0.969642 | models.xdeepfm.ipynb | pypi |
```
# default_exp models.spop
```
# S-Pop
> Session Popularity
Session popularity predictor that gives higher scores to items with higher number of occurrences in the session. Ties are broken up by adding the popularity score of the item.
The score is given by $r_{s,i} = supp_{s,i} + \frac{supp_i}{(1+supp_i)}$.
```... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.spop.ipynb | 0.578686 | 0.734191 | models.spop.ipynb | pypi |
```
# default_exp models.fnfm
```
# FNFM
> A pytorch implementation of Field-aware Neural Factorization Machine.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
import numpy as np
import torch
from recohut.models.layers.common import FeaturesLinear, MultiL... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.fnfm.ipynb | 0.882927 | 0.819641 | models.fnfm.ipynb | pypi |
```
# default_exp models.bandits
```
# Bandits
> Bandit models including beta bandit, and epsilon bandit.
```
#hide
from nbdev.showdoc import *
#export
import math
import numpy as np
#export
class BetaBandit:
"""
Bandit class that is used in Thompson sampling.
Attributes:
alpha: Alpha parameter ... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.bandits.ipynb | 0.874874 | 0.985172 | models.bandits.ipynb | pypi |
```
# default_exp models.prod2vec
```
# Prod2Vec
> Implementation of Prod2vec model.
> References
1. [https://nbviewer.org/github/sparsh-ai/stanza/blob/S543002/2021-07-19-session-based-prod2vec-coveo.ipynb](https://nbviewer.org/github/sparsh-ai/stanza/blob/S543002/2021-07-19-session-based-prod2vec-coveo.ipynb)
2. [htt... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.prod2vec.ipynb | 0.621311 | 0.870762 | models.prod2vec.ipynb | pypi |
```
# default_exp models.ultragcn
```
# UltraGCN
> An efficient graph-convolutional recommendation model.
Industrial recommender systems usually involve massive graphs due to the large numbers of users and items. However, current GCN-based models are hard to train with large graphs, which hinders their wide adoption i... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.ultragcn.ipynb | 0.87637 | 0.986675 | models.ultragcn.ipynb | pypi |
```
# default_exp models.sr
```
# SR
> Sequential Rules
The SR method as proposed in [Kamehkhosh et al. 2017] is a variation of MC and AR. It also takes the order of actions into account, but in a less restrictive manner. In contrast to the MC method, we create a rule when an item q appeared after an item p in a sessi... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.sr.ipynb | 0.472197 | 0.932576 | models.sr.ipynb | pypi |
```
# default_exp models.dssm
```
# DSSM
> An implementation of DSSM, Deep Structured Semantic Model.
Reference: https://github.com/massquantity/DBRL/blob/master/dbrl/models/dssm.py
```
#hide
from nbdev.showdoc import *
#export
import torch
import torch.nn as nn
import torch.nn.functional as F
#export
class DSSM(n... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.dssm.ipynb | 0.806929 | 0.74704 | models.dssm.ipynb | pypi |
```
# default_exp models.fm
```
# FM
> A pytorch implementation of Factorization Machine.
Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Factorization Machine type algorithms are a combination of linear r... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.fm.ipynb | 0.884508 | 0.984782 | models.fm.ipynb | pypi |
```
# default_exp models.siren
```
# SiReN
> Sign-Aware Recommendation Systems with Graph Neural Networks (SiReN).
SiRen first constructs a signed bipartite graph $\mathcal{E}^s = (u,v,w^s_{uv})|w^s_{uv} = w_{uv} − w_o,(u,v,w_{uv}) ∈ \mathcal{E}$. Then it split this into 2 graphs. $\mathcal{E}^p = (u,v,w^s_{uv})|w^s_{... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.siren.ipynb | 0.875628 | 0.944791 | models.siren.ipynb | pypi |
```
# default_exp models.bert
```
# BERT
> Bidirectional Encoder Representations from Transformers.
BERT was one of the first autoencoding language models to utilize the encoder Transformer stack with slight modifications for language modeling. Its architecture is a multilayer Transformer encoder based on the Transfor... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.bert.ipynb | 0.88852 | 0.824462 | models.bert.ipynb | pypi |
```
# default_exp models.autoint
```
# AutoInt
> A pytorch implementation of AutoInt.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
import torch
import torch.nn.functional as F
from recohut.models.layers.common import FeaturesEmbedding, FeaturesLinear, M... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.autoint.ipynb | 0.852874 | 0.877004 | models.autoint.ipynb | pypi |
```
# default_exp models.dcn
```
# DCN
> A pytorch implementation of Deep & Cross Network.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
```
## v1
```
#export
import torch
from recohut.models.layers.common import FeaturesEmbedding, MultiLayerPerceptron
class ... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.dcn.ipynb | 0.900647 | 0.87982 | models.dcn.ipynb | pypi |
```
# default_exp models.tagnn
```
# TAGNN
> [Yu et. al. Target Attentive Graph Neural Networks for Session-based Recommendation. SIGIR, 2020.](https://arxiv.org/abs/2005.02844)
TAGNN first models all session sequences as session graphs. Then, graph neural networks capture rich item transitions in sessions. Lastly, fr... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.tagnn.ipynb | 0.90198 | 0.930015 | models.tagnn.ipynb | pypi |
```
# default_exp models.wide_and_deep
```
# Wide & Deep
> A pytorch implementation of wide and deep learning.
Wide and Deep Learning Model, proposed by Google, 2016, is a DNN-Linear mixed model, which combines the strength of memorization and generalization. It's useful for generic large-scale regression and classifi... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.wide_and_deep.ipynb | 0.809012 | 0.988939 | models.wide_and_deep.ipynb | pypi |
```
# default_exp models.narm
```
# NARM
> Neural Attentive Session-based Recommendation.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from torch.nn.utils.rnn import pack_padd... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.narm.ipynb | 0.893414 | 0.779112 | models.narm.ipynb | pypi |
```
# default_exp models.epsilon
```
# Epsilon
> ϵ-greedy algorithmic modules.
```
#hide
from nbdev.showdoc import *
#export
from typing import List, Tuple
import math
import numpy as np
import pandas as pd
from recohut.models.bandits import EpsilonBandit
#export
class EpsilonGreedy:
"""
Class that is use... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.epsilon.ipynb | 0.861174 | 0.933249 | models.epsilon.ipynb | pypi |
```
# default_exp models.bcq
```
# BCQ
> Batch-Constrained Deep Q-Learning.
References:
1. https://www.cnblogs.com/massquantity/p/13842139.html
2. https://github.com/massquantity/DBRL/blob/master/dbrl/models/bcq.py
Current off-policy deep reinforcement learning algorithms fail to address extrapolation error by select... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.bcq.ipynb | 0.877437 | 0.92944 | models.bcq.ipynb | pypi |
```
# default_exp models.ncf
```
# NCF
> A pytorch implementation of Neural Collaborative Filtering.
NCF is a deep learning-based framework for making recommendations. The key idea is to learn the user-item interaction using neural networks. Despite the effectiveness of MF for collaborative filtering, it is well-known... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.ncf.ipynb | 0.835047 | 0.985594 | models.ncf.ipynb | pypi |
```
# default_exp models.vsknn
```
# VSKNN
> Vector Multiplication Session-Based kNN
The idea of this variant is to put more emphasis on the more recent events of a session when computing the similarities. Instead of encoding a session as a binary vector, we use real-valued vectors to encode the current session. Only ... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.vsknn.ipynb | 0.604399 | 0.87153 | models.vsknn.ipynb | pypi |
```
# default_exp models.fnn
```
# FNN
> A pytorch implementation of FNN.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
import torch
from recohut.models.layers.common import FeaturesEmbedding, MultiLayerPerceptron
#export
class FNN(torch.nn.Module):
... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.fnn.ipynb | 0.777131 | 0.826817 | models.fnn.ipynb | pypi |
```
# default_exp models.actor_critic
```
# Actor-critic Model
> RL Actor-critic model framework.
```
#hide
from nbdev.showdoc import *
#export
from typing import Tuple
import torch
from torch import nn
#export
class Actor(nn.Module):
"""
Actor Network
"""
def __init__(self, embedded_state_size: in... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.actor_critic.ipynb | 0.928214 | 0.80502 | models.actor_critic.ipynb | pypi |
```
# default_exp models.mlp
```
# MLP
> Multi-layer Perceptron for Recommendations.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
from typing import Any, Iterable, List, Optional, Tuple, Union, Callable
import os
import torch
from torch import nn
from ... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.mlp.ipynb | 0.893846 | 0.786479 | models.mlp.ipynb | pypi |
```
# default_exp models.mf
#hide
!pip install pytorch-lightning
!git clone --branch US632593 https://github.com/RecoHut-Projects/recohut.git
%cd recohut
!pip install -U .
!apt-get -qq install tree
!pip install -q watermark
```
# MF
> Implementation of Matrix Factorization model in PyTorch Lightning.
```
#hide
from ... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.mf.ipynb | 0.906068 | 0.668953 | models.mf.ipynb | pypi |
```
# default_exp models.afm
```
# AFM
> A pytorch implementation of Attentional Factorization Machines (AFM).
Improves FM by discriminating the importance of different feature interactions. It learns the importance of each feature interaction from data via a neural attention network. Empirically, it is shown on regre... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.afm.ipynb | 0.897805 | 0.90882 | models.afm.ipynb | pypi |
```
# default_exp models.srgnn
```
# SRGNN
> [Wu et. al. Session-based Recommendation with Graph Neural Networks. AAAI, 2019.](https://arxiv.org/abs/1811.00855)
SR-GNN first models all session sequences as session graphs. Then, each session graph is proceeded one by one and the resulting node vectors can be obtained t... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.srgnn.ipynb | 0.904681 | 0.916335 | models.srgnn.ipynb | pypi |
```
# default_exp models.hofm
```
# HOFM
> A pytorch implementation of Higher-Order Factorization Machines.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
import torch
from recohut.models.layers.common import FeaturesEmbedding, FeaturesLinear
#export
cla... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.hofm.ipynb | 0.893427 | 0.901921 | models.hofm.ipynb | pypi |
```
# default_exp models.dnn
```
# DNNs
> Deep Neural Networks (DNNs) based Recommender systems.
```
#hide
from nbdev.showdoc import *
#export
import torch
import torch.nn as nn
import torch.nn.functional as F
#export
class Multi_Layer_Perceptron(nn.Module):
def __init__(self, args, num_users, num_items):
... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/models.dnn.ipynb | 0.909832 | 0.7874 | models.dnn.ipynb | pypi |
```
# default_exp models.layers.message_passing
```
# Message Passing
> Implementation of message passing graph network layers like LightGCN, LR-GCCF etc.
```
#hide
from nbdev.showdoc import *
#export
import torch
from torch import Tensor
from torch import nn
from torch_geometric.nn import MessagePassing
from torch_... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/layers/models.layers.message_passing.ipynb | 0.822118 | 0.798029 | models.layers.message_passing.ipynb | pypi |
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