# coding=utf-8 from typing import Dict, List, Tuple import datasets import pandas as pd from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks _CITATION = """\ @inproceedings{thai-etal-2022-uit, title = "{UIT}-{V}i{C}o{V}19{QA}: A Dataset for {COVID}-19 Community-based Question Answering on {V}ietnamese Language", author = "Thai, Triet and Thao-Ha, Ngan Chu and Vo, Anh and Luu, Son", editor = "Dita, Shirley and Trillanes, Arlene and Lucas, Rochelle Irene", booktitle = "Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation", month = oct, year = "2022", address = "Manila, Philippines", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.paclic-1.88", pages = "801--810", } """ _DATASETNAME = "uit_vicov19qa" _DESCRIPTION = """\ UIT-ViCoV19QA is the first Vietnamese community-based question answering dataset for developing question answering systems for COVID-19. The dataset comprises 4,500 question-answer pairs collected from trusted medical sources, with at least one answer and at most four unique paraphrased answers per question. This dataset contains 1800 questions that have at least two answers, 700 questions have at least three answers and half of them have a maximum of four paraphrased answers. """ _HOMEPAGE = "https://github.com/triet2397/UIT-ViCoV19QA" _LANGUAGES = ["vie"] _LICENSE = Licenses.UNKNOWN.value _PAPER_URL = "https://aclanthology.org/2022.paclic-1.88" _LOCAL = False _URLS = { "train": { "1_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/1_ans/UIT-ViCoV19QA_train.csv", "2_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/2_ans/UIT-ViCoV19QA_train.csv", "3_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/3_ans/UIT-ViCoV19QA_train.csv", "4_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/4_ans/UIT-ViCoV19QA_train.csv", }, "val": { "1_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/1_ans/UIT-ViCoV19QA_val.csv", "2_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/2_ans/UIT-ViCoV19QA_val.csv", "3_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/3_ans/UIT-ViCoV19QA_val.csv", "4_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/4_ans/UIT-ViCoV19QA_val.csv", }, "test": { "1_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/1_ans/UIT-ViCoV19QA_test.csv", "2_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/2_ans/UIT-ViCoV19QA_test.csv", "3_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/3_ans/UIT-ViCoV19QA_test.csv", "4_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/4_ans/UIT-ViCoV19QA_test.csv", }, } _SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class ViHealthQADataset(datasets.GeneratorBasedBuilder): """ This is a SeaCrowed dataloader for dataset uit_vicov19qa, The dataset comprises 4,500 question-answer pairs collected from trusted medical sources, with at least one answer and at most four unique paraphrased answers per question. """ subsets = ["1_ans", "2_ans", "3_ans", "4_ans"] BUILDER_CONFIGS = [ SEACrowdConfig( name=f"{_DATASETNAME}_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME} source schema", schema="source", subset_id=f"{_DATASETNAME}"), SEACrowdConfig( name=f"{_DATASETNAME}_seacrowd_qa", version=datasets.Version(_SEACROWD_VERSION), description=f"{_DATASETNAME} SEACrowd schema", schema="seacrowd_qa", subset_id=f"{_DATASETNAME}", ) ] DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "question": datasets.Value("string"), "answers": datasets.Value("string"), } ) elif self.config.schema == "seacrowd_qa": features = schemas.qa_features else: raise ValueError(f"No schema matched for {self.config.schema}") 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.""" data_dir = dl_manager.download_and_extract(_URLS) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_dir["train"], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": data_dir["val"], "split": "val", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": data_dir["test"], "split": "test", }, ), ] def _generate_examples(self, filepath: Dict, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" print(f"Generating examples for split {split}") sample_id = -1 for path in filepath.values(): raw_examples = pd.read_csv(path, na_filter=False, delimiter="|") for eid, exam in raw_examples.iterrows(): sample_id += 1 exam_id = exam[0] exam_quest = exam[1] exam_answers = exam[2:].values if self.config.schema == "source": yield sample_id, {"id": str(exam_id), "question": exam_quest, "answers": exam_answers } elif self.config.schema == "seacrowd_qa": yield sample_id, {"id": str(sample_id), "question_id": exam_id, "document_id": str(sample_id), "question": exam_quest, "type": None, "choices": [], "context": None, "answer": exam_answers, "meta": {} }