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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.
"""The CodeMMLU benchmark."""
import os
import json
import datasets
_CITATION = """\
@article{nguyen2024codemmlu,
title={CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding Capabilities},
author={Nguyen, Dung Manh and Phan, Thang Chau and Le, Nam Hai and Doan, Thong T. and Nguyen, Nam V. and Pham, Quang and Bui, Nghi D. Q.},
journal={arXiv preprint},
year={2024}
}
"""
_DESCRIPTION = """\
CodeMMLU is a comprehensive benchmark designed to evaluate the capabilities of large language models (LLMs) in coding and software knowledge
"""
_HOMEPAGE = "https://fsoft-ai4code.github.io/codemmlu/"
_URL = "data.tar"
_SUBJECTS = [
"software_principles", "dbms_sql", "others",
"programming_syntax", "api_frameworks",
"code_completion", "fill_in_the_middle", "code_repair", "defect_detection"
]
class CodeMMLU(datasets.GeneratorBasedBuilder):
"""CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding Capabilities"""
# Version history:
# 0.0.1: Initial release.
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=sub, version=datasets.Version("0.0.1"),
description="CodeMMLU test subject {}".format(sub)
)
for sub in _SUBJECTS
]
def _info(self):
features = datasets.Features(
{
"task_id": datasets.Value("string"),
"question": datasets.Value("string"),
"choices": datasets.features.Sequence(datasets.Value("string")),
}
)
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.TEST,
gen_kwargs={
"iter_archive": dl_manager.iter_archive(archive),
},
),
]
def _generate_examples(self, iter_archive):
"""This function returns the examples in the raw (text) form."""
for path, file in iter_archive:
if path.endswith(".jsonl"):
lines = (line.decode("utf-8") for line in file)
reader = [json.loads(line) for line in lines]
for data in reader:
id_ = data['task_id']
return_dict = {
"question": data['question'],
"choices": data['choices'],
}
if "fill_in_the_middle" in path:
return_dict['problem_description'] = data['problem_description']
yield id_, return_dict
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