# coding=utf-8 import json import datasets from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks _DATASETNAME = "iapp_squad" _CITATION = """\ @dataset { kobkrit_viriyayudhakorn_2021_4539916, author = {Kobkrit Viriyayudhakorn and Charin Polpanumas}, title = {iapp_wiki_qa_squad}, month = feb, year = 2021, publisher = {Zenodo}, version = 1, doi = {10.5281/zenodo.4539916}, url = {https://doi.org/10.5281/zenodo.4539916} } """ _DESCRIPTION = """ `iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles. It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset) to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in 5761/742/739 questions from 1529/191/192 articles. """ _HOMEPAGE = "https://github.com/iapp-technology/iapp-wiki-qa-dataset" _LICENSE = Licenses.MIT.value _HF_URL = " https://huggingface.co/datasets/iapp_wiki_qa_squad" _SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING] _LOCAL = False _LANGUAGES = ["tha"] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" _URLS = { "train": "https://raw.githubusercontent.com/iapp-technology/iapp-wiki-qa-dataset/main/squad_format/data/train.jsonl", "validation": "https://raw.githubusercontent.com/iapp-technology/iapp-wiki-qa-dataset/main/squad_format/data/valid.jsonl", "test": "https://raw.githubusercontent.com/iapp-technology/iapp-wiki-qa-dataset/main/squad_format/data/test.jsonl", } class IappWikiQASquadDataset(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ SEACrowdConfig(name=f"{_DATASETNAME}_source", version=datasets.Version(_SOURCE_VERSION), description=_DESCRIPTION, subset_id=f"{_DATASETNAME}", schema="source"), SEACrowdConfig(name=f"{_DATASETNAME}_seacrowd_qa", version=datasets.Version(_SEACROWD_VERSION), description=_DESCRIPTION, subset_id=f"{_DATASETNAME}", schema="seacrowd_qa"), ] DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" def _info(self): if self.config.schema == "source": features = datasets.Features( { "question_id": datasets.Value("string"), "article_id": datasets.Value("string"), "title": datasets.Value("string"), "context": datasets.Value("string"), "question": datasets.Value("string"), "answers": datasets.features.Sequence( { "text": datasets.Value("string"), "answer_start": datasets.Value("int32"), "answer_end": datasets.Value("int32"), } ), } ) elif self.config.schema == "seacrowd_qa": features = schemas.qa_features features["meta"] = { "answer_start": datasets.Value("int32"), "answer_end": datasets.Value("int32"), } return datasets.DatasetInfo(description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, citation=_CITATION, license=_LICENSE) def _split_generators(self, dl_manager): file_paths = dl_manager.download_and_extract(_URLS) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": file_paths["train"]}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"filepath": file_paths["validation"]}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": file_paths["test"]}, ), ] def _generate_examples(self, filepath): """Yields examples.""" with open(filepath, encoding="utf-8") as f: for id_, row in enumerate(f): data = json.loads(row) if self.config.schema == "source": yield id_, { "question_id": data["question_id"], "article_id": data["article_id"], "title": data["title"], "context": data["context"], "question": data["question"], "answers": { "text": data["answers"]["text"], "answer_start": data["answers"]["answer_start"], "answer_end": data["answers"]["answer_end"], }, } elif self.config.schema == "seacrowd_qa": yield id_, { "id": id_, "question_id": data["question_id"], "document_id": data["article_id"], "question": data["question"], "type": "abstractive", "choices": [], "context": data["context"], "answer": data["answers"]["text"], "meta": {"answer_start": data["answers"]["answer_start"][0], "answer_end": data["answers"]["answer_end"][0]}, }