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3f58ab7 9678bfe 3f58ab7 9678bfe 3f58ab7 9678bfe 3f58ab7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | # Copyright 2023 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utility library of instructions."""
import os
import re
import immutabledict
from packaging.version import parse as parse_version
RANK = os.environ.get("LOCAL_RANK", "0")
# ISO 639-1 codes to language names.
LANGUAGE_CODES = immutabledict.immutabledict(
{
"en": "English",
"es": "Spanish",
"pt": "Portuguese",
"ar": "Arabic",
"hi": "Hindi",
"fr": "French",
"ru": "Russian",
"de": "German",
"ja": "Japanese",
"it": "Italian",
"bn": "Bengali",
"uk": "Ukrainian",
"th": "Thai",
"ur": "Urdu",
"ta": "Tamil",
"te": "Telugu",
"bg": "Bulgarian",
"ko": "Korean",
"pl": "Polish",
"he": "Hebrew",
"fa": "Persian",
"vi": "Vietnamese",
"ne": "Nepali",
"sw": "Swahili",
"kn": "Kannada",
"mr": "Marathi",
"gu": "Gujarati",
"pa": "Punjabi",
"ml": "Malayalam",
"fi": "Finnish",
}
)
_ALPHABETS = "([A-Za-z])"
_PREFIXES = "(Mr|St|Mrs|Ms|Dr)[.]"
_SUFFIXES = "(Inc|Ltd|Jr|Sr|Co)"
# _STARTERS = r"(Mr|Mrs|Ms|Dr|Prof|Capt|Cpt|Lt|He\s|She\s|It\s|They\s|Their\s|Our\s|We\s|But\s|However\s|That\s|This\s|Wherever)"
# _ACRONYMS = "([A-Z][.][A-Z][.](?:[A-Z][.])?)"
_WEBSITES = "[.](com|net|org|io|gov|edu|me)"
_DIGITS = "([0-9])"
_MULTIPLE_DOTS = r"\.{2,}"
_MIXED_ALPHABETS = "([A-Za-z가-힣])" # 한글과 영어 모두 포함
_KOREAN_LIST = "([가나다라마바사])" # 한글 리스트 마커
_KOREAN_LIST_ITEM = "[가나다라마바사아자차카타파하]" # [hanifeval] v1.1, used line-anchored only
# [hanifeval] two pattern literals below made raw strings (same regex; silences SyntaxWarning).
def split_into_sentences(text):
"""Split the text into sentences. (답변을 문장 단위로 분리합니다.)
기존 함수를 이용합니다. 한국어 문장 생성에서도 중간에 약어 등은 영어로 표기될 수 있습니다.
Args:
text: A string that consists of more than or equal to one sentences.
Returns:
A list of strings where each string is a sentence.
"""
# [hanifeval] v1.1: list markers ("가.", "1.", "2)") are recognised only at the start of a line,
# before newlines are flattened. IFEval-Ko matched Korean markers anywhere after a space, so the
# imperative endings "마." / "가." were read as markers, and numbered lines counted "1." as a sentence.
text = re.sub(r"(?m)^(\s*)(" + _KOREAN_LIST_ITEM + r"|\d{1,3})[.)](\s+)", "\\1\\2<prd>\\3", text)
text = " " + text + " "
text = text.replace("\n", " ")
text = re.sub(_PREFIXES, "\\1<prd>", text)
text = re.sub(_WEBSITES, "<prd>\\1", text)
text = re.sub(_DIGITS + "[.]" + _DIGITS, "\\1<prd>\\2", text)
text = re.sub(
_MULTIPLE_DOTS,
lambda match: "<prd>" * len(match.group(0)) + "<stop>",
text,
)
if "Ph.D" in text:
text = text.replace("Ph.D.", "Ph<prd>D<prd>")
# text = re.sub(_ACRONYMS + " " + _STARTERS, "\\1<stop> \\2", text)
text = re.sub(_MIXED_ALPHABETS + "[.]" + _MIXED_ALPHABETS + "[.]" + _MIXED_ALPHABETS + "[.]", "\\1<prd>\\2<prd>\\3<prd>", text) # 영어/한국어 약어 처리
text = re.sub(_MIXED_ALPHABETS + "[.]" + _MIXED_ALPHABETS + "[.]", "\\1<prd>\\2<prd>", text) # 영어/한국어 약어 처리
# 기존 영어 약어 처리
# text = re.sub(
# _ALPHABETS + "[.]" + _ALPHABETS + "[.]" + _ALPHABETS + "[.]",
# "\\1<prd>\\2<prd>\\3<prd>",
# text,
# )
# text = re.sub(_ALPHABETS + "[.]" + _ALPHABETS + "[.]", "\\1<prd>\\2<prd>", text)
# text = re.sub(" " + _SUFFIXES + "[.] " + _STARTERS, " \\1<stop> \\2", text) # _STARTERS는 사용하지 않음
text = re.sub(" " + _SUFFIXES + "[.]", " \\1<prd>", text)
text = re.sub(" " + _ALPHABETS + "[.]", " \\1<prd>", text)
text = re.sub(r"\s" + _ALPHABETS + r"[.]\s+(?=[가-힣])", " \\1<prd> ", text) # 영어 약어 + 직후 한글이 적힐 시 온점 아님 처리
# [hanifeval] v1.1: the unanchored Korean list-marker rule was removed (see the top of this function).
if "”" in text:
text = text.replace(".”", "”.")
if '"' in text:
text = text.replace('."', '".')
if "!" in text:
text = text.replace('!"', '"!')
if "?" in text:
text = text.replace('?"', '"?')
text = text.replace(".", ".<stop>")
text = text.replace("?", "?<stop>")
text = text.replace("!", "!<stop>")
text = text.replace("<prd>", ".")
sentences = text.split("<stop>")
sentences = [s.strip() for s in sentences]
if sentences and not sentences[-1]:
sentences = sentences[:-1]
return sentences
def count_words(text):
"""Counts the number of words for Korean text.
띄어쓰기를 기준으로 한국어 문장의 단어를 분리합니다."""
# 기존 코드
# tokenizer = nltk.tokenize.RegexpTokenizer(r"\w+")
# tokens = tokenizer.tokenize(text)
# num_words = len(tokens)
text = text.strip()
text = ' '.join(text.split())
if not text:
return 0
return len(text.split())
def count_sentences(text):
"""Count the number of sentences."""
# tokenizer = _get_sentence_tokenizer()
# tokenized_sentences = tokenizer.tokenize(text)
tokenized_sentences = split_into_sentences(text)
return len(tokenized_sentences)
# 제거된 원본 IFEval 함수
# def generate_keywords(num_keywords):
# """Randomly generates a few keywords."""
# return random.sample(WORD_LIST, k=num_keywords)
# @functools.lru_cache(maxsize=None)
# def _get_sentence_tokenizer():
# return nltk.data.load("nltk:tokenizers/punkt/english.pickle") |