Datasets:

danidarko

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def _prepare_summary(summary):

summary = summary.replace(' . ', '.\n')

return summary

aggregator = scoring.BootstrapAggregator()

for (ref, pred) in zip(refs, preds):

ref = _prepare_summary(ref)

pred = _prepare_summary(pred)

aggregator.add_scores(scorer.score(ref, pred))

result = aggregator.aggregate()

return {type: result[type].mid.fmeasure * 100 for type in rouge_types}

# File: lm-evaluation-harness-main/lm_eval/tasks/tinyBenchmarks/utils_winogrande.py

""""""

def doc_to_text(doc):

answer_to_num = {'1': 0, '2': 1}

return answer_to_num[doc['answer']]

def doc_to_target(doc):

idx = doc['sentence'].index('_') + 1

return doc['sentence'][idx:].strip()

def doc_to_choice(doc):

idx = doc['sentence'].index('_')

options = [doc['option1'], doc['option2']]

return [doc['sentence'][:idx] + opt for opt in options]

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

""""""

import argparse

import os

import pandas as pd

import yaml

from tqdm import tqdm

categories = {'STEM': ['physics', 'chemistry', 'biology', 'computer science', 'math', 'engineering'], 'humanities': ['history', 'philosophy', 'law'], 'social_sciences': ['politics', 'culture', 'economics', 'geography', 'psychology', 'education'], 'other': ['other', 'business', 'health']}

task_list = ['engineering_math', 'dentistry', 'traditional_chinese_medicine_clinical_medicine', 'clinical_psychology', 'technical', 'culinary_skills', 'mechanical', 'logic_reasoning', 'real_estate', 'general_principles_of_law', 'finance_banking', 'anti_money_laundering', 'ttqav2', 'marketing_management', 'business_management', 'organic_chemistry', 'advance_chemistry', 'physics', 'secondary_physics', 'human_behavior', 'national_protection', 'jce_humanities', 'politic_science', 'agriculture', 'official_document_management', 'financial_analysis', 'pharmacy', 'educational_psychology', 'statistics_and_machine_learning', 'management_accounting', 'introduction_to_law', 'computer_science', 'veterinary_pathology', 'accounting', 'fire_science', 'optometry', 'insurance_studies', 'pharmacology', 'taxation', 'education_(profession_level)', 'economics', 'veterinary_pharmacology', 'nautical_science', 'occupational_therapy_for_psychological_disorders', 'trust_practice', 'geography_of_taiwan', 'physical_education', 'auditing', 'administrative_law', 'basic_medical_science', 'macroeconomics', 'trade', 'chinese_language_and_literature', 'tve_design', 'junior_science_exam', 'junior_math_exam', 'junior_chinese_exam', 'junior_social_studies', 'tve_mathematics', 'tve_chinese_language', 'tve_natural_sciences', 'junior_chemistry', 'music', 'education', 'three_principles_of_people', 'taiwanese_hokkien']

subject2name = {}

SUBJECTS = {}

def parse_args():

parser = argparse.ArgumentParser()

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

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

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

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

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

parser.add_argument('--subject_file', default='subject.tsv')

return parser.parse_args()

if __name__ == '__main__':

args = parse_args()

from pathlib import Path

SUBJECT_FILE = Path(__file__).parent / Path(args.subject_file)

df = pd.read_csv(SUBJECT_FILE, delimiter='\t')

for (_, row) in df.iterrows():

for _c in categories:

if row['subject'] in SUBJECTS:

raise ValueError('Duplicate tasks.')

if row['category'] in categories[_c]:

SUBJECTS[row['subject']] = _c

subject2name[row['subject']] = row['name']

break

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

with open(args.base_yaml_path) as f:

base_yaml = yaml.full_load(f)

if args.cot_prompt_path is not None:

import json

with open(args.cot_prompt_path) as f:

cot_file = json.load(f)

ALL_CATEGORIES = []

for (subject, category) in tqdm(SUBJECTS.items()):

if category not in ALL_CATEGORIES:

ALL_CATEGORIES.append(category)

if args.cot_prompt_path is not None:

description = cot_file[subject]

else:

name_of_subject = subject2name[subject].replace('＿', ' ')

description = f'以下為{name_of_subject}的單選題，請提供正確答案的選項。\n\n'

yaml_dict = {'include': base_yaml_name, 'group': f'tmmluplus_{args.task_prefix}_{category}' if args.task_prefix != '' else f'tmmluplus_{category}', 'group_alias': category.replace('_', ' '), 'task': f'tmmluplus_{args.task_prefix}_{subject}' if args.task_prefix != '' else f'tmmluplus_{subject}', 'task_alias': subject.replace('_', ' '), 'dataset_name': subject, 'description': description}

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

with open(file_save_path, 'w') as yaml_file:

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

if args.task_prefix != '':

mmlu_subcategories = [f'tmmluplus_{args.task_prefix}_{category}' for category in ALL_CATEGORIES]

else:

mmlu_subcategories = [f'tmmluplus_{category}' for category in ALL_CATEGORIES]

if args.group_prefix != '':

file_save_path = args.group_prefix + '.yaml'

else:

file_save_path = args.save_prefix_path + '.yaml'

with open(file_save_path, 'w') as yaml_file:

yaml.dump({'group': f'tmmluplus_{args.task_prefix}' if args.task_prefix != '' else 'tmmluplus', 'task': mmlu_subcategories}, yaml_file, indent=4, default_flow_style=False)

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

import datasets

def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:

def _helper(doc):

answer_list = ['A', 'B', 'C', 'D']