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