Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
1M - 10M
ArXiv:
License:
File size: 5,581 Bytes
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# coding=utf-8
# 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 csv
import datasets
_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 = """\
This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge, covering 57 tasks including elementary mathematics, US history, computer science, law, and more.
"""
_HOMEPAGE = "https://github.com/hendrycks/test"
_URL = "data.tar"
_SUBJECTS = [
"all",
"abstract_algebra",
"anatomy",
"astronomy",
"business_ethics",
"clinical_knowledge",
"college_biology",
"college_chemistry",
"college_computer_science",
"college_mathematics",
"college_medicine",
"college_physics",
"computer_security",
"conceptual_physics",
"econometrics",
"electrical_engineering",
"elementary_mathematics",
"formal_logic",
"global_facts",
"high_school_biology",
"high_school_chemistry",
"high_school_computer_science",
"high_school_european_history",
"high_school_geography",
"high_school_government_and_politics",
"high_school_macroeconomics",
"high_school_mathematics",
"high_school_microeconomics",
"high_school_physics",
"high_school_psychology",
"high_school_statistics",
"high_school_us_history",
"high_school_world_history",
"human_aging",
"human_sexuality",
"international_law",
"jurisprudence",
"logical_fallacies",
"machine_learning",
"management",
"marketing",
"medical_genetics",
"miscellaneous",
"moral_disputes",
"moral_scenarios",
"nutrition",
"philosophy",
"prehistory",
"professional_accounting",
"professional_law",
"professional_medicine",
"professional_psychology",
"public_relations",
"security_studies",
"sociology",
"us_foreign_policy",
"virology",
"world_religions",
]
class Mmlu(datasets.GeneratorBasedBuilder):
"""Measuring Massive Multitask Language Understanding, consisting of 57 tasks"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=sub, version=datasets.Version("1.0.0"), description=f"MMLU Subject {sub}"
)
for sub in _SUBJECTS
]
def _info(self):
features = datasets.Features(
{
"question": datasets.Value("string"),
"choices": datasets.features.Sequence(datasets.Value("string")),
"answer": datasets.features.ClassLabel(num_classes=4, names=["A", "B", "C", "D"]),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
archive = dl_manager.download(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split("auxiliary_train"),
gen_kwargs={
"iter_archive": dl_manager.iter_archive(archive),
"split": "auxiliary_train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"iter_archive": dl_manager.iter_archive(archive), "split": "test"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"iter_archive": dl_manager.iter_archive(archive),
"split": "val",
},
),
datasets.SplitGenerator(
name=datasets.Split("dev"),
gen_kwargs={
"iter_archive": dl_manager.iter_archive(archive),
"split": "dev",
},
),
]
def _generate_examples(self, iter_archive, split):
"""Yields examples as (key, example) tuples."""
n_yielded_files = 0
for id_file, (path, file) in enumerate(iter_archive):
if f"/{split}/" in path:
if split == "auxiliary_train" or (self.config.name in path or self.config.name == "all"):
n_yielded_files += 1
lines = (line.decode("utf-8") for line in file)
reader = csv.reader(lines)
for id_line, data in enumerate(reader):
yield f"{id_file}_{id_line}", {"question": data[0], "choices": data[1:5], "answer": data[5]}
#else:
#print("KO", path)
#else:
#print("KO2", split, path)
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