danidarko/docs · Datasets at Hugging Face

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if args.cot_prompt_path is not None:
description = cot_file[subject_eng]
else:
description = f'以下是中国关于{subject_zh}的单项选择题，请选出其中的正确答案。\n\n'
yaml_dict = {'include': base_yaml_name, 'task': f'ceval-valid_{args.task_prefix}_{subject_eng}' if args.task_prefix != '' else f'ceval-valid_{subject_eng}', 'dataset_name': subject_eng, 'description': description}
file_save_path = args.save_prefix_path + f'_{subject_eng}.yaml'
eval_logger.info(f'Saving yaml for subset {subject_eng} to {file_save_path}')
with open(file_save_path, 'w', encoding='utf-8') as yaml_file:
yaml.dump(yaml_dict, yaml_file, width=float('inf'), allow_unicode=True, default_style='"')
group_yaml_dict = {'group': 'ceval-valid', 'task': [f'ceval-valid_{task_name}' for task_name in SUBJECTS.keys()], 'aggregate_metric_list': [{'metric': 'acc', 'aggregation': 'mean', 'weight_by_size': True}, {'metric': 'acc_norm', 'aggregation': 'mean', 'weight_by_size': True}], 'metadata': {'version': 1.0}}
file_save_path = '_' + args.save_prefix_path + '.yaml'
with open(file_save_path, 'w', encoding='utf-8') as group_yaml_file:
yaml.dump(group_yaml_dict, group_yaml_file, width=float('inf'), allow_unicode=True, default_style='"')

# File: lm-evaluation-harness-main/lm_eval/tasks/cmmlu/_generate_configs.py
""""""
import argparse
import os
import yaml
from tqdm import tqdm
from lm_eval.utils import eval_logger
SUBJECTS = {'agronomy': '农学', 'anatomy': '解剖学', 'ancient_chinese': '古汉语', 'arts': '艺术学', 'astronomy': '天文学', 'business_ethics': '商业伦理', 'chinese_civil_service_exam': '中国公务员考试', 'chinese_driving_rule': '中国驾驶规则', 'chinese_food_culture': '中国饮食文化', 'chinese_foreign_policy': '中国外交政策', 'chinese_history': '中国历史', 'chinese_literature': '中国文学', 'chinese_teacher_qualification': '中国教师资格', 'clinical_knowledge': '临床知识', 'college_actuarial_science': '大学精算学', 'college_education': '大学教育学', 'college_engineering_hydrology': '大学工程水文学', 'college_law': '大学法律', 'college_mathematics': '大学数学', 'college_medical_statistics': '大学医学统计', 'college_medicine': '大学医学', 'computer_science': '计算机科学', 'computer_security': '计算机安全', 'conceptual_physics': '概念物理学', 'construction_project_management': '建设工程管理', 'economics': '经济学', 'education': '教育学', 'electrical_engineering': '电气工程', 'elementary_chinese': '小学语文', 'elementary_commonsense': '小学常识', 'elementary_information_and_technology': '小学信息技术', 'elementary_mathematics': '初等数学', 'ethnology': '民族学', 'food_science': '食品科学', 'genetics': '遗传学', 'global_facts': '全球事实', 'high_school_biology': '高中生物', 'high_school_chemistry': '高中化学', 'high_school_geography': '高中地理', 'high_school_mathematics': '高中数学', 'high_school_physics': '高中物理学', 'high_school_politics': '高中政治', 'human_sexuality': '人类性行为', 'international_law': '国际法学', 'journalism': '新闻学', 'jurisprudence': '法理学', 'legal_and_moral_basis': '法律与道德基础', 'logical': '逻辑学', 'machine_learning': '机器学习', 'management': '管理学', 'marketing': '市场营销', 'marxist_theory': '马克思主义理论', 'modern_chinese': '现代汉语', 'nutrition': '营养学', 'philosophy': '哲学', 'professional_accounting': '专业会计', 'professional_law': '专业法学', 'professional_medicine': '专业医学', 'professional_psychology': '专业心理学', 'public_relations': '公共关系', 'security_study': '安全研究', 'sociology': '社会学', 'sports_science': '体育学', 'traditional_chinese_medicine': '中医中药', 'virology': '病毒学', 'world_history': '世界历史', 'world_religions': '世界宗教'}

def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--base_yaml_path', required=True)
parser.add_argument('--save_prefix_path', default='cmmlu')
parser.add_argument('--cot_prompt_path', default=None)
parser.add_argument('--task_prefix', default='')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
base_yaml_name = os.path.split(args.base_yaml_path)[-1]
with open(args.base_yaml_path, encoding='utf-8') as f:
base_yaml = yaml.full_load(f)
if args.cot_prompt_path is not None:
import json
with open(args.cot_prompt_path, encoding='utf-8') as f:
cot_file = json.load(f)
for (subject_eng, subject_zh) in tqdm(SUBJECTS.items()):
if args.cot_prompt_path is not None:
description = cot_file[subject_eng]
else:
description = f'以下是关于{subject_zh}的单项选择题，请直接给出正确答案的选项。\n\n'
yaml_dict = {'include': base_yaml_name, 'task': f'cmmlu_{args.task_prefix}_{subject_eng}' if args.task_prefix != '' else f'cmmlu_{subject_eng}', 'dataset_name': subject_eng, 'description': description}
file_save_path = args.save_prefix_path + f'_{subject_eng}.yaml'
eval_logger.info(f'Saving yaml for subset {subject_eng} to {file_save_path}')
with open(file_save_path, 'w', encoding='utf-8') as yaml_file:
yaml.dump(yaml_dict, yaml_file, width=float('inf'), allow_unicode=True, default_style='"')
group_yaml_dict = {'group': 'cmmlu', 'task': [f'cmmlu_{args.task_prefix}_{subject_eng}' if args.task_prefix != '' else f'cmmlu_{subject_eng}' for subject_eng in SUBJECTS.keys()], 'aggregate_metric_list': [{'metric': 'acc', 'aggregation': 'mean', 'weight_by_size': True}, {'metric': 'acc_norm', 'aggregation': 'mean', 'weight_by_size': True}], 'metadata': {'version': 0.0}}
file_save_path = '_' + args.save_prefix_path + '.yaml'
with open(file_save_path, 'w', encoding='utf-8') as group_yaml_file:
yaml.dump(group_yaml_dict, group_yaml_file, width=float('inf'), allow_unicode=True, default_style='"')

# File: lm-evaluation-harness-main/lm_eval/tasks/code_x_glue/code-text/bleu.py
import math
import re
import sys
import xml.sax.saxutils
from typing import Any, Dict, List, Optional, Pattern, Tuple, Union
''
''
nonorm = 0
preserve_case = False
eff_ref_len = 'shortest'
normalize1: List[Tuple[Union[Pattern[str], str], str]] = [('<skipped>', ''), ('-\\n', ''), ('\\n', ' ')]
normalize1 = [(re.compile(pattern), replace) for (pattern, replace) in normalize1]
normalize2: List[Tuple[Union[Pattern[str], str], str]] = [('([\\{-\\~\\[-\\` -\\&\\(-\\+\\:-\\@\\/])', ' \\1 '), ('([^0-9])([\\.,])', '\\1 \\2 '), ('([\\.,])([^0-9])', ' \\1 \\2'), ('([0-9])(-)', '\\1 \\2 ')]
normalize2 = [(re.compile(pattern), replace) for (pattern, replace) in normalize2]

def normalize(s):
if nonorm:
return s.split()
if not isinstance(s, str):
s = ' '.join(s)
for (pattern, replace) in normalize1:
s = re.sub(pattern, replace, s)
s = xml.sax.saxutils.unescape(s, {'"': '"'})
s = ' %s ' % s
if not preserve_case:
s = s.lower()
for (pattern, replace) in normalize2:
s = re.sub(pattern, replace, s)
return s.split()

def count_ngrams(words, n=4):
counts: Dict[Any, int] = {}
for k in range(1, n + 1):
for i in range(len(words) - k + 1):
ngram = tuple(words[i:i + k])
counts[ngram] = counts.get(ngram, 0) + 1
return counts

def cook_refs(refs, n=4):
refs = [normalize(ref) for ref in refs]
maxcounts: Dict[Tuple[str], int] = {}
for ref in refs:
counts = count_ngrams(ref, n)
for (ngram, count) in counts.items():
maxcounts[ngram] = max(maxcounts.get(ngram, 0), count)

if args.cot_prompt_path is not None:

description = cot_file[subject_eng]

else:

description = f'以下是中国关于{subject_zh}的单项选择题，请选出其中的正确答案。\n\n'

yaml_dict = {'include': base_yaml_name, 'task': f'ceval-valid_{args.task_prefix}_{subject_eng}' if args.task_prefix != '' else f'ceval-valid_{subject_eng}', 'dataset_name': subject_eng, 'description': description}

file_save_path = args.save_prefix_path + f'_{subject_eng}.yaml'

eval_logger.info(f'Saving yaml for subset {subject_eng} to {file_save_path}')

with open(file_save_path, 'w', encoding='utf-8') as yaml_file:

yaml.dump(yaml_dict, yaml_file, width=float('inf'), allow_unicode=True, default_style='"')

group_yaml_dict = {'group': 'ceval-valid', 'task': [f'ceval-valid_{task_name}' for task_name in SUBJECTS.keys()], 'aggregate_metric_list': [{'metric': 'acc', 'aggregation': 'mean', 'weight_by_size': True}, {'metric': 'acc_norm', 'aggregation': 'mean', 'weight_by_size': True}], 'metadata': {'version': 1.0}}

file_save_path = '_' + args.save_prefix_path + '.yaml'

with open(file_save_path, 'w', encoding='utf-8') as group_yaml_file:

yaml.dump(group_yaml_dict, group_yaml_file, width=float('inf'), allow_unicode=True, default_style='"')

# File: lm-evaluation-harness-main/lm_eval/tasks/cmmlu/_generate_configs.py

""""""

import argparse

import os

import yaml

from tqdm import tqdm

from lm_eval.utils import eval_logger

SUBJECTS = {'agronomy': '农学', 'anatomy': '解剖学', 'ancient_chinese': '古汉语', 'arts': '艺术学', 'astronomy': '天文学', 'business_ethics': '商业伦理', 'chinese_civil_service_exam': '中国公务员考试', 'chinese_driving_rule': '中国驾驶规则', 'chinese_food_culture': '中国饮食文化', 'chinese_foreign_policy': '中国外交政策', 'chinese_history': '中国历史', 'chinese_literature': '中国文学', 'chinese_teacher_qualification': '中国教师资格', 'clinical_knowledge': '临床知识', 'college_actuarial_science': '大学精算学', 'college_education': '大学教育学', 'college_engineering_hydrology': '大学工程水文学', 'college_law': '大学法律', 'college_mathematics': '大学数学', 'college_medical_statistics': '大学医学统计', 'college_medicine': '大学医学', 'computer_science': '计算机科学', 'computer_security': '计算机安全', 'conceptual_physics': '概念物理学', 'construction_project_management': '建设工程管理', 'economics': '经济学', 'education': '教育学', 'electrical_engineering': '电气工程', 'elementary_chinese': '小学语文', 'elementary_commonsense': '小学常识', 'elementary_information_and_technology': '小学信息技术', 'elementary_mathematics': '初等数学', 'ethnology': '民族学', 'food_science': '食品科学', 'genetics': '遗传学', 'global_facts': '全球事实', 'high_school_biology': '高中生物', 'high_school_chemistry': '高中化学', 'high_school_geography': '高中地理', 'high_school_mathematics': '高中数学', 'high_school_physics': '高中物理学', 'high_school_politics': '高中政治', 'human_sexuality': '人类性行为', 'international_law': '国际法学', 'journalism': '新闻学', 'jurisprudence': '法理学', 'legal_and_moral_basis': '法律与道德基础', 'logical': '逻辑学', 'machine_learning': '机器学习', 'management': '管理学', 'marketing': '市场营销', 'marxist_theory': '马克思主义理论', 'modern_chinese': '现代汉语', 'nutrition': '营养学', 'philosophy': '哲学', 'professional_accounting': '专业会计', 'professional_law': '专业法学', 'professional_medicine': '专业医学', 'professional_psychology': '专业心理学', 'public_relations': '公共关系', 'security_study': '安全研究', 'sociology': '社会学', 'sports_science': '体育学', 'traditional_chinese_medicine': '中医中药', 'virology': '病毒学', 'world_history': '世界历史', 'world_religions': '世界宗教'}

def parse_args():

parser = argparse.ArgumentParser()

parser.add_argument('--base_yaml_path', required=True)

parser.add_argument('--save_prefix_path', default='cmmlu')

parser.add_argument('--cot_prompt_path', default=None)

parser.add_argument('--task_prefix', default='')

return parser.parse_args()

if __name__ == '__main__':

args = parse_args()

base_yaml_name = os.path.split(args.base_yaml_path)[-1]

with open(args.base_yaml_path, encoding='utf-8') as f:

base_yaml = yaml.full_load(f)

if args.cot_prompt_path is not None:

import json

with open(args.cot_prompt_path, encoding='utf-8') as f:

cot_file = json.load(f)

for (subject_eng, subject_zh) in tqdm(SUBJECTS.items()):

if args.cot_prompt_path is not None:

description = cot_file[subject_eng]

else:

description = f'以下是关于{subject_zh}的单项选择题，请直接给出正确答案的选项。\n\n'

yaml_dict = {'include': base_yaml_name, 'task': f'cmmlu_{args.task_prefix}_{subject_eng}' if args.task_prefix != '' else f'cmmlu_{subject_eng}', 'dataset_name': subject_eng, 'description': description}

file_save_path = args.save_prefix_path + f'_{subject_eng}.yaml'

eval_logger.info(f'Saving yaml for subset {subject_eng} to {file_save_path}')

with open(file_save_path, 'w', encoding='utf-8') as yaml_file:

yaml.dump(yaml_dict, yaml_file, width=float('inf'), allow_unicode=True, default_style='"')

group_yaml_dict = {'group': 'cmmlu', 'task': [f'cmmlu_{args.task_prefix}_{subject_eng}' if args.task_prefix != '' else f'cmmlu_{subject_eng}' for subject_eng in SUBJECTS.keys()], 'aggregate_metric_list': [{'metric': 'acc', 'aggregation': 'mean', 'weight_by_size': True}, {'metric': 'acc_norm', 'aggregation': 'mean', 'weight_by_size': True}], 'metadata': {'version': 0.0}}

file_save_path = '_' + args.save_prefix_path + '.yaml'

with open(file_save_path, 'w', encoding='utf-8') as group_yaml_file:

yaml.dump(group_yaml_dict, group_yaml_file, width=float('inf'), allow_unicode=True, default_style='"')

# File: lm-evaluation-harness-main/lm_eval/tasks/code_x_glue/code-text/bleu.py

import math

import re

import sys

import xml.sax.saxutils

from typing import Any, Dict, List, Optional, Pattern, Tuple, Union

''

nonorm = 0

preserve_case = False

eff_ref_len = 'shortest'

normalize1: List[Tuple[Union[Pattern[str], str], str]] = [('<skipped>', ''), ('-\\n', ''), ('\\n', ' ')]

normalize1 = [(re.compile(pattern), replace) for (pattern, replace) in normalize1]

normalize2: List[Tuple[Union[Pattern[str], str], str]] = [('([\\{-\\~\\[-\\` -\\&\\(-\\+\\:-\\@\\/])', ' \\1 '), ('([^0-9])([\\.,])', '\\1 \\2 '), ('([\\.,])([^0-9])', ' \\1 \\2'), ('([0-9])(-)', '\\1 \\2 ')]

normalize2 = [(re.compile(pattern), replace) for (pattern, replace) in normalize2]

def normalize(s):

if nonorm:

return s.split()

if not isinstance(s, str):

s = ' '.join(s)

for (pattern, replace) in normalize1:

s = re.sub(pattern, replace, s)

s = xml.sax.saxutils.unescape(s, {'"': '"'})

s = ' %s ' % s

if not preserve_case:

s = s.lower()

for (pattern, replace) in normalize2:

s = re.sub(pattern, replace, s)

return s.split()

def count_ngrams(words, n=4):

counts: Dict[Any, int] = {}

for k in range(1, n + 1):

for i in range(len(words) - k + 1):

ngram = tuple(words[i:i + k])

counts[ngram] = counts.get(ngram, 0) + 1

return counts

def cook_refs(refs, n=4):

refs = [normalize(ref) for ref in refs]

maxcounts: Dict[Tuple[str], int] = {}

for ref in refs:

counts = count_ngrams(ref, n)

for (ngram, count) in counts.items():

maxcounts[ngram] = max(maxcounts.get(ngram, 0), count)