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# 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 = "https://arxiv.org/abs/2310.09550"
_DESCRIPTION = """\
The Ancient Chinese Language Understanding Evaluation (ACLUE) is an evaluation benchmark focused on ancient Chinese language comprehension. It aims to assess the performance of large-scale language models on understanding ancient Chinese.
"""
_HOMEPAGE = "https://github.com/isen-zhang/ACLUE"
_URL = r"https://huggingface.co/datasets/tyouisen/aclue/resolve/main/aclue_v1_0_0.zip"
task_list = ['polysemy_resolution',
'poetry_sentiment_analysis',
'named_entity_recognition',
'basic_ancient_chinese',
'poetry_context_prediction',
'sentence_segmentation',
'couplet_prediction',
'poetry_appreciate',
'ancient_chinese_culture',
'ancient_phonetics',
'homographic_character_resolution',
'ancient_literature',
'ancient_medical',
'poetry_quality_assessment',
'reading_comprehension']
class ACLUEConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super().__init__(version=datasets.Version("1.0.0"), **kwargs)
# V1.0.0 Init version
class ACLUE(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
ACLUEConfig(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,
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, encoding="utf-8")
for i, instance in enumerate(df.to_dict(orient="records")):
yield i, instance |