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| import os | |
| import json | |
| import datasets | |
| from datasets import BuilderConfig, Features, Value, Sequence | |
| _DESCRIPTION = """ | |
| # νκ΅μ΄ μ§μνμ΅ λ°μ΄ν°μ | |
| - quail λ°μ΄ν°μ μ νκ΅μ΄λ‘ λ³μν λ°μ΄ν°μ | |
| """ | |
| _CITATION = """ | |
| @inproceedings{KITD, | |
| title={μΈμ΄ λ²μ λͺ¨λΈμ ν΅ν νκ΅μ΄ μ§μ νμ΅ λ°μ΄ν° μΈνΈ ꡬμΆ}, | |
| author={μμμ, μΆνμ°½, κΉμ°, μ₯μ§μ, μ λ―Όμ, μ μ¬μ}, | |
| booktitle={μ 35ν νκΈ λ° νκ΅μ΄ μ 보μ²λ¦¬ νμ λν}, | |
| pages={591--595}, | |
| year={2023} | |
| } | |
| @inproceedings{KITD, | |
| title={Korean Instruction Tuning Dataset}, | |
| author={Yeongseo Lim, HyeonChang Chu, San Kim, Jin Yea Jang, Minyoung Jung, Saim Shin}, | |
| booktitle={Proceedings of the 35th Annual Conference on Human and Cognitive Language Technology}, | |
| pages={591--595}, | |
| year={2023} | |
| } | |
| """ | |
| def _list(data_list): | |
| result = list() | |
| for data in data_list: | |
| result.append(data) | |
| return result | |
| # quail | |
| _QUAIL_FEATURES = Features({ | |
| "data_index_by_user": Value(dtype="int32"), | |
| "id": Value(dtype="string"), | |
| "context_id": Value(dtype="string"), | |
| "question_id": Value(dtype="string"), | |
| "domain": Value(dtype="string"), | |
| "metadata": { | |
| "author": Value(dtype="string"), | |
| "title": Value(dtype="string"), | |
| "url": Value(dtype="string"), | |
| }, | |
| "context": Value(dtype="string"), | |
| "question": Value(dtype="string"), | |
| "question_type": Value(dtype="string"), | |
| "answers": Sequence(Value(dtype="string")), | |
| "correct_answer_id": Value(dtype="int32"), | |
| }) | |
| def _parsing_quail(file_path): | |
| with open(file_path, mode="r") as f: | |
| dataset = json.load(f) | |
| for _i, data in enumerate(dataset): | |
| _data_index_by_user = data["data_index_by_user"] | |
| _id = data["id"] | |
| _context_id = data["context_id"] | |
| _question_id = data["question_id"] | |
| _domain = data["domain"] | |
| _metadata = { | |
| "author": data["metadata"]["author"], | |
| "title": data["metadata"]["title"], | |
| "url": data["metadata"]["url"] | |
| } | |
| _context = data["context"] | |
| _question = data["question"] | |
| _question_type = data["question_type"] | |
| _answers = _list(data["_answers"]) | |
| _correct_answer_id = data["correct_answer_id"] | |
| yield _i, { | |
| "data_index_by_user": _data_index_by_user, | |
| "id": _id, | |
| "context_id": _context_id, | |
| "question_id": _question_id, | |
| "domain": _domain, | |
| "metadata": _metadata, | |
| "context": _context, | |
| "question": _question, | |
| "question_type": _question_type, | |
| "answers": _answers, | |
| "correct_answer_id": _correct_answer_id, | |
| } | |
| class QuailConfig(BuilderConfig): | |
| def __init__(self, name, feature, reading_fn, parsing_fn, citation, **kwargs): | |
| super(QuailConfig, self).__init__( | |
| name = name, | |
| version=datasets.Version("1.0.0"), | |
| **kwargs) | |
| self.feature = feature | |
| self.reading_fn = reading_fn | |
| self.parsing_fn = parsing_fn | |
| self.citation = citation | |
| class QUAIL(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| QuailConfig( | |
| name = "base", | |
| data_dir = "./quail", | |
| feature = _QUAIL_FEATURES, | |
| reading_fn = _parsing_quail, | |
| parsing_fn = lambda x:x, | |
| citation = _CITATION, | |
| ), | |
| ] | |
| def _info(self) -> datasets.DatasetInfo: | |
| """Returns the dataset metadata.""" | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=_QUAIL_FEATURES, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager: datasets.DownloadManager): | |
| """Returns SplitGenerators""" | |
| path_kv = { | |
| datasets.Split.TRAIN:[ | |
| os.path.join(dl_manager.manual_dir, f"train.json") | |
| ], | |
| datasets.Split.VALIDATION:[ | |
| os.path.join(dl_manager.manual_dir, f"validation.json") | |
| ], | |
| "challenge":[ | |
| os.path.join(dl_manager.manual_dir, f"challenge.json") | |
| ], | |
| } | |
| return [ | |
| datasets.SplitGenerator(name=k, gen_kwargs={"path_list": v}) | |
| for k, v in path_kv.items() | |
| ] | |
| def _generate_examples(self, path_list): | |
| """Yields examples.""" | |
| for path in path_list: | |
| try: | |
| for example in iter(self.config.reading_fn(path)): | |
| yield self.config.parsing_fn(example) | |
| except Exception as e: | |
| print(e) |