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if __name__ == '__main__':
args = parse_args()
base_yaml_name = os.path.basename(args.base_yaml_path)
with open(args.base_yaml_path, 'r', 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 group in GROUPS:
for (subject, category) in tqdm(SUBJECTS.items()):
if args.cot_prompt_path is not None:
description = cot_file[subject]
else:
description = f"The following are multiple choice questions (with answers) about {' '.join(subject.split('_'))}.\n\n"
yaml_dict = {'include': base_yaml_name, 'tag': f'mmlusr_{args.group_prefix}{group}_{category}' if args.group_prefix else f'mmlusr_{group}_{category}', 'task': f'mmlusr_{args.task_prefix}{group}_{subject}' if args.task_prefix else f'mmlusr_{group}_{subject}', 'task_alias': subject.replace('_', ' '), 'description': description, 'dataset_name': f'{group}_{subject}'}
file_save_path = os.path.join(args.save_dir, f'{group}_{subject}.yaml')
with open(file_save_path, 'w', encoding='utf-8') as yaml_file:
yaml.dump(yaml_dict, yaml_file, allow_unicode=True, default_style='"')
eval_logger.info(f'Saved YAML for {group} {subject} to {file_save_path}')
if args.group_prefix:
file_save_path = os.path.join(args.save_prefix_path, args.group_prefix + '.yaml')
eval_logger.info(f'Saving benchmark config to {file_save_path}')
with open(file_save_path, 'w', encoding='utf-8') as yaml_file:
yaml.dump(yaml_dict, yaml_file, indent=4, default_flow_style=False)
# File: lm-evaluation-harness-main/lm_eval/tasks/mmlusr/question_and_answer/utils.py
import datasets
def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
def _helper(doc):
answer_list = ['A', 'B', 'C', 'D']
answer_index = int(doc['answer'])
answer_letter = answer_list[answer_index]
out_doc = {'questions': doc['question'], 'choices': [doc['choice1'], doc['choice2'], doc['choice3'], doc['choice4']], 'answer': answer_letter}
return out_doc
return dataset.map(_helper)
# File: lm-evaluation-harness-main/lm_eval/tasks/mmlusr/question_only/utils.py
import datasets
def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
def _helper(doc):
answer_list = ['A', 'B', 'C', 'D']
answer_index = int(doc['answer'])
answer_letter = answer_list[answer_index]
out_doc = {'questions': doc['question'], 'choices': [doc['choice1'], doc['choice2'], doc['choice3'], doc['choice4']], 'answer': answer_letter}
return out_doc
return dataset.map(_helper)
# File: lm-evaluation-harness-main/lm_eval/tasks/model_written_evals/advanced_ai_risk/_generate_configs.py
import datasets
import yaml
from tqdm import tqdm
def main() -> None:
dataset_path = 'EleutherAI/advanced_ai_risk'
for task in tqdm(datasets.get_dataset_infos(dataset_path).keys()):
file_name = f'{task}.yaml'
try:
with open(f'{file_name}', 'w', encoding='utf-8') as f:
f.write('# Generated by _generate_configs.py\n')
yaml.dump({'include': '_template_yaml', 'task': f"{dataset_path.split('/')[-1]}_{task}", 'dataset_name': task}, f)
except FileExistsError:
pass
if __name__ == '__main__':
main()
# File: lm-evaluation-harness-main/lm_eval/tasks/model_written_evals/persona/_generate_configs.py
import datasets
import yaml
from tqdm import tqdm
def main() -> None:
dataset_path = 'EleutherAI/persona'
for task in tqdm(datasets.get_dataset_infos(dataset_path).keys()):
file_name = f'{task}.yaml'
try:
with open(f'{file_name}', 'w', encoding='utf-8') as f:
f.write('# Generated by _generate_configs.py\n')
yaml.dump({'include': '_template_yaml', 'task': f"{dataset_path.split('/')[-1]}_{task}", 'dataset_name': task}, f)
except FileExistsError:
pass
if __name__ == '__main__':
main()
# File: lm-evaluation-harness-main/lm_eval/tasks/mutual/utils.py
import numpy as np
def process_docs(dataset):
def _detokenize(text):
text = text.replace(" '", "'")
text = text.replace(' \n', '\n')
text = text.replace('\n ', '\n')
text = text.replace(" n't", "n't")
text = text.replace('`` ', '"')
text = text.replace("''", '"')