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import csv |
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import datasets |
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_CITATION = """\ |
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@inproceedings{koto-etal-2023-indommlu, |
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title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}", |
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author = "Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin", |
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booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)", |
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month = December, |
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year = "2023", |
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address = "Singapore", |
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publisher = "Association for Computational Linguistics", |
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}""" |
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subject2english = { |
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'Sejarah': 'History', |
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'Geografi': 'Geography', |
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'Bahasa Lampung': 'Lampungic', |
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'IPS': 'Social science', |
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'Bahasa Bali': 'Balinese', |
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'Bahasa Makassar': 'Makassarese', |
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'Bahasa Banjar': 'Banjarese', |
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'Kimia': 'Chemistry', |
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'Biologi': 'Biology', |
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'IPA': 'Science', |
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'Agama Kristen': 'Christian religion', |
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'Kesenian': 'Art', |
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'Agama Islam': 'Islam religion', |
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'Agama Hindu': 'Hindu religion', |
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'Bahasa Madura': 'Madurese', |
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'Penjaskes': 'Sport', |
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'Bahasa Indonesia': 'Indonesian language', |
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'Fisika': 'Physics', |
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'Budaya Alam Minangkabau': 'Minangkabau culture', |
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'Bahasa Dayak Ngaju': 'Dayak language', |
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'Sosiologi': 'Sociology', |
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'Ekonomi': 'Economy', |
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'Bahasa Sunda': 'Sundanese', |
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'Bahasa Jawa': 'Javanese', |
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'PPKN': 'Civic education', |
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} |
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subject2group = { |
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'Sejarah': 'Humanities', |
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'Geografi': 'Social science', |
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'Bahasa Lampung': 'Local languages and cultures', |
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'IPS': 'Social science', |
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'Bahasa Bali': 'Local languages and cultures', |
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'Bahasa Makassar': 'Local languages and cultures', |
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'Bahasa Banjar': 'Local languages and cultures', |
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'Kimia': 'STEM', |
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'Biologi': 'STEM', |
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'IPA': 'STEM', |
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'Agama Kristen': 'Humanities', |
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'Kesenian': 'Humanities', |
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'Agama Islam': 'Humanities', |
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'Agama Hindu': 'Humanities', |
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'Bahasa Madura': 'Local languages and cultures', |
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'Penjaskes': 'Humanities', |
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'Bahasa Indonesia': 'Indonesian language', |
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'Fisika': 'STEM', |
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'Budaya Alam Minangkabau': 'Local languages and cultures', |
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'Bahasa Dayak Ngaju': 'Local languages and cultures', |
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'Sosiologi': 'Social science', |
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'Ekonomi': 'Social science', |
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'Bahasa Sunda': 'Local languages and cultures', |
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'Bahasa Jawa': 'Local languages and cultures', |
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'PPKN': 'Social science', |
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} |
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special_case = ['SD-SMP-SMA', 'SD-SMP'] |
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level_mapper = { |
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'SMA': 'SMA', |
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'Seleksi PTN': 'University entrance test', |
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'SD': 'SD', |
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'SMP': 'SMP', |
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'Kelas I SD': 'SD', |
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'Kelas X SMA': 'SMA', |
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'Kelas XI SMA': 'SMA', |
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'Kelas XII SMA': 'SMA', |
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'V SD': 'SD', |
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'VI SD': 'SD', |
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'VII SMP': 'SMP', |
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'VIII SMP ': 'SMP', |
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'IX SMP': 'SMP', |
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'Kelas III SD':'SD', |
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'Kelas IV SD': 'SD', |
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'Kelas II SD': 'SD' |
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} |
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def fix_level(level, kelas): |
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if level in special_case: |
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kelas = float(kelas) |
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if kelas >=1 and kelas <= 6: |
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level = 'SD' |
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elif kelas >=7 and kelas <= 9: |
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level = 'SMP' |
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elif kelas >=10: |
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level = 'SMA' |
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else: |
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print(level) |
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fixed_level = level_mapper[level] |
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fixed_kelas = -1 |
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kelas = str(kelas) |
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if kelas.strip() in ['PTN', '2023-10-12 00:00:00']: |
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fixed_kelas = 13 |
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elif kelas == '4,5,6': |
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fixed_kelas = 6 |
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else: |
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fixed_kelas = int(float(kelas.strip())) |
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return fixed_level, fixed_kelas |
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_URL = { |
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'test': "https://huggingface.co/datasets/indolem/IndoMMLU/resolve/main/IndoMMLU.csv", |
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} |
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class IndoMMLUConfig(datasets.BuilderConfig): |
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"""IndoMMLUConfig for IndoMMLU""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for IndoStoryCloze. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__(version=datasets.Version("1.0.0"), **kwargs) |
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self.features = ['subject', 'group', 'level', 'class', 'question', 'options', 'answer', 'is_for_fewshot'] |
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class IndoMMLU(datasets.GeneratorBasedBuilder): |
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"""The IndoMMLU Datasets.""" |
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BUILDER_CONFIGS = [IndoMMLUConfig()] |
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def _info(self): |
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features = {feature: datasets.Value("string") for feature in self.config.features} |
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return datasets.DatasetInfo( |
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description='IndoMMLU', |
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features=datasets.Features(features), |
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homepage='https://github.com/fajri91/IndoMMLU', |
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citation=_CITATION |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_file = dl_manager.download_and_extract(_URL) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"data_file": downloaded_file['test']}), |
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] |
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def _generate_examples(self, data_file): |
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data = csv.DictReader(open(data_file, newline='')) |
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for i, row in enumerate(data): |
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fixed_level, fixed_kelas = fix_level(row['level'], row['kelas']) |
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yield i, { |
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"subject": subject2english[row['subject']], |
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"group": subject2group[row['subject']], |
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"level": fixed_level, |
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"class": fixed_kelas, |
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"question": row['soal'], |
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"options": row['jawaban'].split('\n'), |
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"answer": row['kunci'], |
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"is_for_fewshot": row['is_for_fewshot'] |
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} |
