File size: 3,944 Bytes
316892a 5cd6898 316892a 5cd6898 316892a 410052b 316892a a51ecb1 316892a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
# 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 = """\
@misc{wei2024aceval,
title={AC-EVAL: Evaluating Ancient Chinese Language Understanding in Large Language Models},
author={Yuting Wei and Yuanxing Xu and Xinru Wei and Simin Yang and Yangfu Zhu and Yuqing Li and Di Liu and Bin Wu},
year={2024},
eprint={2403.06574},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """\
AC-EVAL presents a thorough evaluation suite for Large Language Models (LLMs) focusing on ancient Chinese, covering eras from the Pre-Qin period to the Qing dynasty. This suite includes 3245 multi-choice questions across 3 levels of difficulty and 13 diverse tasks.
"""
_HOMEPAGE = "https://github.com/yuting-wei/AC-EVAL"
_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License"
_URL = r"https://huggingface.co/datasets/yuting-wei/aceval/resolve/main/aceval.zip"
task_list = [
'historical_facts',
'geography',
'social_customs',
'art_and_cultural_heritage',
'philosophy_and_religion',
'lexical_pragmatics_analysis',
'allusions_and_idioms',
'word_sense_disambiguation',
'translation',
'event_extraction',
'sentence_pauses',
'summarization_and_analysis',
'poetry_appreciation'
]
class ACEVALConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super().__init__(version=datasets.Version("1.0.0"), **kwargs)
class ACEVAL(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
ACEVALConfig(
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"),
"Explanation":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("dev"),
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")):
if "Answer" not in instance.keys():
instance["Answer"]=""
if "Explanation" not in instance.keys():
instance["Explanation"]=""
yield i, instance
|