LLaMA-Factory/evaluation/cmmlu/cmmlu.py

168 lines
4.9 KiB
Python
Raw Normal View History

2023-09-23 13:10:17 +00:00
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
import os
import datasets
import pandas as pd
_CITATION = """\
@article{li2023cmmlu,
title={CMMLU: Measuring massive multitask language understanding in Chinese},
author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and Hai Zhao and Yeyun Gong and Nan Duan and Timothy Baldwin},
journal={arXiv preprint arXiv:2306.09212},
year={2023}
}
"""
_DESCRIPTION = """\
CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge and reasoning abilities of LLMs within the Chinese language and cultural context.
"""
_HOMEPAGE = "https://github.com/haonan-li/CMMLU"
_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License"
_URL = "cmmlu.zip"
task_list = [
'agronomy',
'anatomy',
'ancient_chinese',
'arts',
'astronomy',
'business_ethics',
'chinese_civil_service_exam',
'chinese_driving_rule',
'chinese_food_culture',
'chinese_foreign_policy',
'chinese_history',
'chinese_literature',
'chinese_teacher_qualification',
'clinical_knowledge',
'college_actuarial_science',
'college_education',
'college_engineering_hydrology',
'college_law',
'college_mathematics',
'college_medical_statistics',
'college_medicine',
'computer_science',
'computer_security',
'conceptual_physics',
'construction_project_management',
'economics',
'education',
'electrical_engineering',
'elementary_chinese',
'elementary_commonsense',
'elementary_information_and_technology',
'elementary_mathematics',
'ethnology',
'food_science',
'genetics',
'global_facts',
'high_school_biology',
'high_school_chemistry',
'high_school_geography',
'high_school_mathematics',
'high_school_physics',
'high_school_politics',
'human_sexuality',
'international_law',
'journalism',
'jurisprudence',
'legal_and_moral_basis',
'logical',
'machine_learning',
'management',
'marketing',
'marxist_theory',
'modern_chinese',
'nutrition',
'philosophy',
'professional_accounting',
'professional_law',
'professional_medicine',
'professional_psychology',
'public_relations',
'security_study',
'sociology',
'sports_science',
'traditional_chinese_medicine',
'virology',
'world_history',
'world_religions',
]
class CMMLUConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super().__init__(version=datasets.Version("1.0.1"), **kwargs)
class CMMLU(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
CMMLUConfig(
name=task_name,
)
for task_name in task_list
]
def _info(self):
features = datasets.Features(
{
"question": datasets.Value("string"),
"A": datasets.Value("string"),
"B": datasets.Value("string"),
"C": datasets.Value("string"),
"D": datasets.Value("string"),
"answer": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_URL)
task_name = self.config.name
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, f"test/{task_name}.csv"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, f"dev/{task_name}.csv"),
},
),
]
def _generate_examples(self, filepath):
df = pd.read_csv(filepath, header=0, index_col=0, encoding="utf-8")
for i, instance in enumerate(df.to_dict(orient="records")):
2023-09-28 06:39:16 +00:00
question = instance.pop("Question", "")
answer = instance.pop("Answer", "")
instance["question"] = question
instance["answer"] = answer
2023-09-23 13:10:17 +00:00
yield i, instance