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indommlu.py

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+import csv
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """
+@inproceedings{koto-etal-2023-large,
+    title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}",
+    author = "Koto, Fajri  and
+      Aisyah, Nurul  and
+      Li, Haonan  and
+      Baldwin, Timothy",
+    editor = "Bouamor, Houda  and
+      Pino, Juan  and
+      Bali, Kalika",
+    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.emnlp-main.760",
+    doi = "10.18653/v1/2023.emnlp-main.760",
+    pages = "12359--12374",
+}
+"""
+
+_DATASETNAME = "indommlu"
+
+_DESCRIPTION = """
+IndoMMLU is the first multi-task language understanding benchmark for Indonesian culture and languages, which consists
+of questions from primary school to university entrance exams in Indonesia. By employing professional teachers, we
+obtain 14,906 questions across 63 tasks and education levels, with 46% of the questions focusing on assessing
+proficiency in the Indonesian language and knowledge of nine local languages and cultures in Indonesia.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/indolem/IndoMMLU"
+
+_LANGUAGES = ["ind", "ban", "mad", "nij", "sun", "jav", "mak", "bjn", "abl"]
+
+_LICENSE = Licenses.CC_BY_NC_SA_4_0.value
+
+_LOCAL = False
+
+_URLS = {_DATASETNAME: {"test": "https://huggingface.co/datasets/indolem/IndoMMLU/resolve/main/IndoMMLU.csv"}}
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+lang2subject = {"ind": "Bahasa Indonesia", "ban": "Bahasa Bali", "mad": "Bahasa Madura", "nij": "Bahasa Dayak Ngaju", "sun": "Bahasa Sunda", "jav": "Bahasa Jawa", "mak": "Bahasa Makassar", "bjn": "Bahasa Banjar", "abl": "Bahasa Lampung"}
+
+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",  # SMA --> high school level"
+    "Seleksi PTN": "University entrance test",
+    "SD": "SD",  # SD --> elementary school level
+    "SMP": "SMP",  # SMP --> junior high school level
+    "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):
+    # Fixing Level
+    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]
+
+    # Fixing class
+    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()))
+
+    # sanity check over the level and kelas
+    return fixed_level, fixed_kelas
+
+
+def pass_schema_filter(schema, row):
+    if schema == "source":
+        return True
+    lang = schema.split("_")[1]
+    if lang not in _LANGUAGES:  # seacrowd_qa
+        return True
+    if lang == "ind":  # contains "Bahasa Indonesia" and all other non-language subjects
+        return (lang2subject[lang] == row["subject"]) or (row["subject"] not in lang2subject.values())
+    return lang2subject[lang] == row["subject"]
+
+
+class IndoMMLUDataset(datasets.GeneratorBasedBuilder):
+    """IndoMMLU is the first multitask language understanding benchmark for Indonesian culture and languages."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_qa",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_qa",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+    for lang in _LANGUAGES:
+        lang_config = SEACrowdConfig(
+            name=f"{_DATASETNAME}_{lang}_seacrowd_qa",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} {lang} SEACrowd schema",
+            schema=f"seacrowd_qa",
+            subset_id=_DATASETNAME,
+        )
+        BUILDER_CONFIGS.append(lang_config)
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "subject": datasets.Value("string"),
+                    "group": datasets.Value("string"),
+                    "level": datasets.Value("string"),
+                    "class": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "options": datasets.Value("string"),
+                    "answer": datasets.Value("string"),
+                    "is_for_fewshot": datasets.Value("string"),
+                }
+            )
+
+        else:
+            features = schemas.qa_features
+            features["meta"] = {
+                "subject": datasets.Value("string"),
+                "group": datasets.Value("string"),
+                "level": datasets.Value("string"),
+                "class": datasets.Value("string"),
+                "is_for_fewshot": datasets.Value("string"),
+            }
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_dir, "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        data = csv.DictReader(open(filepath[split], newline=""))
+        print(self.config.schema)
+        for i, row in enumerate(data):
+            if pass_schema_filter(self.config.schema, row):
+                fixed_level, fixed_kelas = fix_level(row["level"], row["kelas"])
+                # The choices are in the format of ["A. xxx", "B. xxx", ...], but answer is only with ["A"], replacing both with only the answer content
+                choices = row["jawaban"].split("\n")
+                answer_choice = row["kunci"]
+                # Find the corresponding choice in the choices.
+                # Skip the 2 datapoint (i = 4223, 14150) with invalid answer_choice.
+                corresponding_choice = next((choice for choice in choices if choice.startswith(answer_choice)), None)
+                if corresponding_choice is None:
+                    continue
+                else:
+                    if self.config.schema == "source":
+                        yield i, {
+                            "subject": subject2english[row["subject"]],
+                            "group": subject2group[row["subject"]],
+                            "level": fixed_level,
+                            "class": fixed_kelas,
+                            "question": row["soal"],
+                            "options": [opt[2:].strip() for opt in choices],  # remove A., B., ... in the options,
+                            "answer": corresponding_choice[2:].strip(),  # remove A., B., ... in the answer
+                            "is_for_fewshot": row["is_for_fewshot"],
+                        }
+                    else:
+                        yield i, {
+                            "id": str(i),
+                            "question_id": str(i),
+                            "document_id": str(i),
+                            "question": row["soal"],
+                            "type": "multiple_choice",
+                            "choices": [opt[2:].strip() for opt in choices],  # remove A., B., ... in the options
+                            "context": "",
+                            "answer": [corresponding_choice[2:].strip()],  # remove A., B., ... in the answer,
+                            "meta": {"subject": subject2english[row["subject"]], "group": subject2group[row["subject"]], "level": fixed_level, "class": fixed_kelas, "is_for_fewshot": row["is_for_fewshot"]},
+                        }