LLaMA-Factory/evaluation/mmlu/mmlu.py

162 lines
4.7 KiB
Python

# 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{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
}
"""
_DESCRIPTION = """\
Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).
"""
_HOMEPAGE = "https://github.com/hendrycks/test"
_LICENSE = "MIT"
_URL = "mmlu.zip"
task_list = [
"high_school_european_history",
"business_ethics",
"clinical_knowledge",
"medical_genetics",
"high_school_us_history",
"high_school_physics",
"high_school_world_history",
"virology",
"high_school_microeconomics",
"econometrics",
"college_computer_science",
"high_school_biology",
"abstract_algebra",
"professional_accounting",
"philosophy",
"professional_medicine",
"nutrition",
"global_facts",
"machine_learning",
"security_studies",
"public_relations",
"professional_psychology",
"prehistory",
"anatomy",
"human_sexuality",
"college_medicine",
"high_school_government_and_politics",
"college_chemistry",
"logical_fallacies",
"high_school_geography",
"elementary_mathematics",
"human_aging",
"college_mathematics",
"high_school_psychology",
"formal_logic",
"high_school_statistics",
"international_law",
"high_school_mathematics",
"high_school_computer_science",
"conceptual_physics",
"miscellaneous",
"high_school_chemistry",
"marketing",
"professional_law",
"management",
"college_physics",
"jurisprudence",
"world_religions",
"sociology",
"us_foreign_policy",
"high_school_macroeconomics",
"computer_security",
"moral_scenarios",
"moral_disputes",
"electrical_engineering",
"astronomy",
"college_biology",
]
class MMLUConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super().__init__(version=datasets.Version("1.0.0"), **kwargs)
class MMLU(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
MMLUConfig(
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, "data", "test", f"{task_name}_test.csv"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "data", "val", f"{task_name}_val.csv"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "data", "dev", f"{task_name}_dev.csv"),
},
),
]
def _generate_examples(self, filepath):
df = pd.read_csv(filepath)
df.columns = ["question", "A", "B", "C", "D", "answer"]
for i, instance in enumerate(df.to_dict(orient="records")):
yield i, instance