gaokao_bench.py

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