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import os
import datasets
import pandas as pd
_CITATION = """
"""
_DESCRIPTION = """
"""
_HOMEPAGE = "https://github.com/OpenLMLab/GAOKAO-Bench"
_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License"
_URL = r"https://huggingface.co/datasets/AsakusaRinne/gaokao_bench/resolve/main/gaokao_bench.zip"
task_list = [
'2010-2013_English_MCQs',
'2010-2022_Biology_MCQs',
'2010-2022_Chemistry_MCQs',
'2010-2022_Chinese_Lang_and_Usage_MCQs',
'2010-2022_Chinese_Modern_Lit',
'2010-2022_English_Fill_in_Blanks',
'2010-2022_English_Reading_Comp',
'2010-2022_Geography_MCQs',
'2010-2022_History_MCQs',
'2010-2022_Math_I_MCQs',
'2010-2022_Math_II_MCQs',
'2010-2022_Physics_MCQs',
'2010-2022_Political_Science_MCQs',
'2012-2022_English_Cloze_Test',
]
class GaokaoBenchConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super().__init__(version=datasets.Version("1.0.0"), **kwargs)
class GaokaoBench(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
GaokaoBenchConfig(
name=task_name,
)
for task_name in task_list
]
def _info(self):
features = datasets.Features(
{
"index":datasets.Value("int32"),
"question": datasets.Value("string"),
"year": datasets.Value("int32"),
"category": datasets.Value("string"),
"score": datasets.Value("int32"),
"answer": datasets.Value("string"),
"analysis":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, "test", f"{task_name}_test.csv"
# ),
# },
# ),
# datasets.SplitGenerator(
# name=datasets.Split("val"),
# gen_kwargs={
# "filepath": os.path.join(
# data_dir, "val", f"{task_name}_val.csv"
# ),
# },
# ),
datasets.SplitGenerator(
name=datasets.Split("dev"),
gen_kwargs={
"filepath": os.path.join(
data_dir, 'gaokao_bench/Multiple-choice_Questions', f'{task_name}.csv'
)
},
),
]
def _generate_examples(self, filepath):
df = pd.read_csv(filepath,encoding="utf-8")
for i, instance in enumerate(df.to_dict(orient="records")):
if "answer" not in instance.keys():
instance["answer"]=""
if "analysis" not in instance.keys():
instance["analysis"]=""
yield i, instance |