danidarko/docs · Datasets at Hugging Face

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duplicate_tasks = {task_name for task_name in subtask_names if subtask_names.count(task_name) > 1}
competing_groups = [group for group in task_dict.keys() if len(set(task_dict[group]).intersection(duplicate_tasks)) > 0]
if len(duplicate_tasks) > 0:
raise ValueError(f'Found 1 or more tasks while trying to call get_task_dict() that were members of more than 1 called group: {list(duplicate_tasks)}. Offending groups: {competing_groups}. Please call groups which overlap their constituent tasks in separate evaluation runs.')

def get_task_dict(task_name_list: Union[str, List[Union[str, Dict, Task]]], task_manager: Optional[TaskManager]=None):
task_name_from_string_dict = {}
task_name_from_config_dict = {}
task_name_from_object_dict = {}
if isinstance(task_name_list, str):
task_name_list = [task_name_list]
elif isinstance(task_name_list, list):
if not all([isinstance(task, (str, dict, Task)) for task in task_name_list]):
raise TypeError("Expected all list items to be of types 'str', 'dict', or 'Task', but at least one entry did not match.")
else:
raise TypeError(f"Expected a 'str' or 'list' but received {type(task_name_list)}.")
string_task_name_list = [task for task in task_name_list if isinstance(task, str)]
others_task_name_list = [task for task in task_name_list if not isinstance(task, str)]
if len(string_task_name_list) > 0:
if task_manager is None:
task_manager = TaskManager()
task_name_from_string_dict = task_manager.load_task_or_group(string_task_name_list)
for task_element in others_task_name_list:
if isinstance(task_element, dict):
task_name_from_config_dict = {task_name_from_config_dict, task_manager.load_config(config=task_element)}
elif isinstance(task_element, Task):
task_name_from_object_dict = {**task_name_from_object_dict, get_task_name_from_object(task_element): task_element}
if not set(task_name_from_string_dict.keys()).isdisjoint(set(task_name_from_object_dict.keys())):
raise ValueError
final_task_dict = {task_name_from_string_dict, task_name_from_config_dict, **task_name_from_object_dict}
_check_duplicates(get_subtask_list(final_task_dict))
return final_task_dict

# File: lm-evaluation-harness-main/lm_eval/tasks/aclue/_generate_configs.py
""""""
import argparse
import os
import yaml
from tqdm import tqdm
from lm_eval.utils import eval_logger
SUBJECTS = {'古文单字多义': 'polysemy_resolution', '诗词情感分类': 'poetry_sentiment_analysis', '古汉语命名体识别': 'named_entity_recognition', '古汉语知识': 'basic_ancient_chinese', '古诗词上下句预测': 'poetry_context_prediction', '古文断句': 'sentence_segmentation', '对联': 'couplet_prediction', '古诗词曲鉴赏': 'poetry_appreciate', '国学常识': 'ancient_chinese_culture', '古音学': 'ancient_phonetics', '通假字': 'homographic_character_resolution', '古代文学知识': 'ancient_literature', '医古文': 'ancient_medical', '古诗词质量评估': 'poetry_quality_assessment', '古文阅读理解': 'reading_comprehension'}

def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--base_yaml_path', required=True)
parser.add_argument('--save_prefix_path', default='aclue')
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_zh, subject_eng) 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'aclue_{args.task_prefix}_{subject_eng}' if args.task_prefix != '' else f'aclue_{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='"')

# File: lm-evaluation-harness-main/lm_eval/tasks/afrimgsm/utils.py
import argparse
import yaml
languages = ['eng', 'amh', 'ibo', 'fra', 'sna', 'lin', 'wol', 'ewe', 'lug', 'xho', 'kin', 'twi', 'zul', 'orm', 'yor', 'hau', 'sot', 'swa']
languages_REGEX = {'eng': 'The answer is (\\-?[0-9\\.\\,]+)', 'amh': 'መልሱ (\\-?[0-9\\.\\,]+)', 'ibo': 'Azịza ya bụ (\\-?[0-9\\.\\,]+)', 'fra': 'La réponse est(\\-?[0-9\\.\\,]+)', 'sna': 'Mhinduro kumubvunzo ndi (\\-?[0-9\\.\\,]+)', 'lin': 'Eyano ezali (\\-?[0-9\\.\\,]+)', 'wol': 'Tontu li (\\-?[0-9\\.\\,]+)', 'ewe': 'ŋuɖoɖoae nye (\\-?[0-9\\.\\,]+)', 'lug': 'Ansa eri (\\-?[0-9\\.\\,]+)', 'xho': 'Impendulo ngu (\\-?[0-9\\.\\,]+)', 'kin': 'Igisubizo ni (\\-?[0-9\\.\\,]+)', 'twi': 'Ne nnyiano yɛ (\\-?[0-9\\.\\,]+)', 'zul': 'Impendulo ithi (\\-?[0-9\\.\\,]+)', 'orm': 'Deebiin isaa (\\-?[0-9\\.\\,]+)', 'yor': 'Ìdáhùn náà ni (\\-?[0-9\\.\\,]+)', 'hau': 'Amsar ita ce (\\-?[0-9\\.\\,]+)', 'sot': 'Karabo ke (\\-?[0-9\\.\\,]+)', 'swa': 'Jibu ni (\\-?[0-9\\.\\,]+)'}
LANGUAGES = {}
for lang in languages:
if lang == 'amh':
LANGUAGES[lang] = {'QUESTION': 'ጥያቄ:', 'ANSWER': 'በቅደም ተከተል መልስ:', 'DIRECT': 'Answer:', 'REGEX': languages_REGEX[lang]}
elif lang == 'yor':
LANGUAGES[lang] = {'QUESTION': 'Ìbéèrè:', 'ANSWER': 'Ìdáhùn lẹ́sẹsẹ:', 'DIRECT': 'Answer:', 'REGEX': languages_REGEX[lang]}
else:
LANGUAGES[lang] = {'QUESTION': 'Question:', 'ANSWER': 'Step-by-Step Answer:', 'DIRECT': 'Answer:', 'REGEX': languages_REGEX[lang]}

def add_regex_pattern(regex_pattern):
if regex_pattern is None:
return {}
return {'filter_list': [{'name': 'strict-match', 'filter': [{'function': 'regex', 'regex_pattern': f'{regex_pattern}'}, {'function': 'take_first'}]}, {'name': 'flexible-extract', 'filter': [{'function': 'regex', 'regex_pattern': '(-?[$0-9.,]{2,})\|(-?[0-9]+)', 'group_select': -1}, {'function': 'take_first'}]}]}

def gen_lang_yamls(output_dir: str, overwrite: bool, mode: str) -> None:
err = []
for lang in LANGUAGES.keys():
try:
yaml_template = 'cot_yaml'
filter_list = {}
DELIMITER = None
if mode == 'direct':
ANSWER = LANGUAGES['eng']['DIRECT']
QUESTION = LANGUAGES['eng']['QUESTION']
REGEX = None
task_name = f'afrimgsm_direct_{lang}'

duplicate_tasks = {task_name for task_name in subtask_names if subtask_names.count(task_name) > 1}

competing_groups = [group for group in task_dict.keys() if len(set(task_dict[group]).intersection(duplicate_tasks)) > 0]

if len(duplicate_tasks) > 0:

raise ValueError(f'Found 1 or more tasks while trying to call get_task_dict() that were members of more than 1 called group: {list(duplicate_tasks)}. Offending groups: {competing_groups}. Please call groups which overlap their constituent tasks in separate evaluation runs.')

def get_task_dict(task_name_list: Union[str, List[Union[str, Dict, Task]]], task_manager: Optional[TaskManager]=None):

task_name_from_string_dict = {}

task_name_from_config_dict = {}

task_name_from_object_dict = {}

if isinstance(task_name_list, str):

task_name_list = [task_name_list]

elif isinstance(task_name_list, list):

if not all([isinstance(task, (str, dict, Task)) for task in task_name_list]):

raise TypeError("Expected all list items to be of types 'str', 'dict', or 'Task', but at least one entry did not match.")

else:

raise TypeError(f"Expected a 'str' or 'list' but received {type(task_name_list)}.")

string_task_name_list = [task for task in task_name_list if isinstance(task, str)]

others_task_name_list = [task for task in task_name_list if not isinstance(task, str)]

if len(string_task_name_list) > 0:

if task_manager is None:

task_manager = TaskManager()

task_name_from_string_dict = task_manager.load_task_or_group(string_task_name_list)

for task_element in others_task_name_list:

if isinstance(task_element, dict):

task_name_from_config_dict = {**task_name_from_config_dict, **task_manager.load_config(config=task_element)}

elif isinstance(task_element, Task):

task_name_from_object_dict = {**task_name_from_object_dict, get_task_name_from_object(task_element): task_element}

if not set(task_name_from_string_dict.keys()).isdisjoint(set(task_name_from_object_dict.keys())):

raise ValueError

final_task_dict = {**task_name_from_string_dict, **task_name_from_config_dict, **task_name_from_object_dict}

_check_duplicates(get_subtask_list(final_task_dict))

return final_task_dict

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

""""""

import argparse

import os

import yaml

from tqdm import tqdm

from lm_eval.utils import eval_logger

SUBJECTS = {'古文单字多义': 'polysemy_resolution', '诗词情感分类': 'poetry_sentiment_analysis', '古汉语命名体识别': 'named_entity_recognition', '古汉语知识': 'basic_ancient_chinese', '古诗词上下句预测': 'poetry_context_prediction', '古文断句': 'sentence_segmentation', '对联': 'couplet_prediction', '古诗词曲鉴赏': 'poetry_appreciate', '国学常识': 'ancient_chinese_culture', '古音学': 'ancient_phonetics', '通假字': 'homographic_character_resolution', '古代文学知识': 'ancient_literature', '医古文': 'ancient_medical', '古诗词质量评估': 'poetry_quality_assessment', '古文阅读理解': 'reading_comprehension'}

def parse_args():

parser = argparse.ArgumentParser()

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

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

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_zh, subject_eng) 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'aclue_{args.task_prefix}_{subject_eng}' if args.task_prefix != '' else f'aclue_{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='"')

# File: lm-evaluation-harness-main/lm_eval/tasks/afrimgsm/utils.py

import argparse

import yaml

languages = ['eng', 'amh', 'ibo', 'fra', 'sna', 'lin', 'wol', 'ewe', 'lug', 'xho', 'kin', 'twi', 'zul', 'orm', 'yor', 'hau', 'sot', 'swa']

languages_REGEX = {'eng': 'The answer is (\\-?[0-9\\.\\,]+)', 'amh': 'መልሱ (\\-?[0-9\\.\\,]+)', 'ibo': 'Azịza ya bụ (\\-?[0-9\\.\\,]+)', 'fra': 'La réponse est(\\-?[0-9\\.\\,]+)', 'sna': 'Mhinduro kumubvunzo ndi (\\-?[0-9\\.\\,]+)', 'lin': 'Eyano ezali (\\-?[0-9\\.\\,]+)', 'wol': 'Tontu li (\\-?[0-9\\.\\,]+)', 'ewe': 'ŋuɖoɖoae nye (\\-?[0-9\\.\\,]+)', 'lug': 'Ansa eri (\\-?[0-9\\.\\,]+)', 'xho': 'Impendulo ngu (\\-?[0-9\\.\\,]+)', 'kin': 'Igisubizo ni (\\-?[0-9\\.\\,]+)', 'twi': 'Ne nnyiano yɛ (\\-?[0-9\\.\\,]+)', 'zul': 'Impendulo ithi (\\-?[0-9\\.\\,]+)', 'orm': 'Deebiin isaa (\\-?[0-9\\.\\,]+)', 'yor': 'Ìdáhùn náà ni (\\-?[0-9\\.\\,]+)', 'hau': 'Amsar ita ce (\\-?[0-9\\.\\,]+)', 'sot': 'Karabo ke (\\-?[0-9\\.\\,]+)', 'swa': 'Jibu ni (\\-?[0-9\\.\\,]+)'}

LANGUAGES = {}

for lang in languages:

if lang == 'amh':

LANGUAGES[lang] = {'QUESTION': 'ጥያቄ:', 'ANSWER': 'በቅደም ተከተል መልስ:', 'DIRECT': 'Answer:', 'REGEX': languages_REGEX[lang]}

elif lang == 'yor':

LANGUAGES[lang] = {'QUESTION': 'Ìbéèrè:', 'ANSWER': 'Ìdáhùn lẹ́sẹsẹ:', 'DIRECT': 'Answer:', 'REGEX': languages_REGEX[lang]}

else:

LANGUAGES[lang] = {'QUESTION': 'Question:', 'ANSWER': 'Step-by-Step Answer:', 'DIRECT': 'Answer:', 'REGEX': languages_REGEX[lang]}

def add_regex_pattern(regex_pattern):

if regex_pattern is None:

return {}

return {'filter_list': [{'name': 'strict-match', 'filter': [{'function': 'regex', 'regex_pattern': f'{regex_pattern}'}, {'function': 'take_first'}]}, {'name': 'flexible-extract', 'filter': [{'function': 'regex', 'regex_pattern': '(-?[$0-9.,]{2,})|(-?[0-9]+)', 'group_select': -1}, {'function': 'take_first'}]}]}

def gen_lang_yamls(output_dir: str, overwrite: bool, mode: str) -> None:

err = []

for lang in LANGUAGES.keys():

try:

yaml_template = 'cot_yaml'

filter_list = {}

DELIMITER = None

if mode == 'direct':

ANSWER = LANGUAGES['eng']['DIRECT']

QUESTION = LANGUAGES['eng']['QUESTION']

REGEX = None

task_name = f'afrimgsm_direct_{lang}'