import ast from pathlib import Path from typing import Dict, List, Tuple import datasets import pandas as pd from nusacrowd.nusa_datasets.facqa.utils.facqa_utils import (getAnswerString, listToString) from nusacrowd.utils import schemas from nusacrowd.utils.configs import NusantaraConfig from nusacrowd.utils.constants import Tasks _CITATION = """ @inproceedings{purwarianti2007machine, title={A Machine Learning Approach for Indonesian Question Answering System}, author={Ayu Purwarianti, Masatoshi Tsuchiya, and Seiichi Nakagawa}, booktitle={Proceedings of Artificial Intelligence and Applications }, pages={573--578}, year={2007} } """ _LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data) _LOCAL = False _DATASETNAME = "facqa" _DESCRIPTION = """ FacQA: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article. Each row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the corresponding short passage. There are six categories of questions: date, location, name, organization, person, and quantitative. """ _HOMEPAGE = "https://github.com/IndoNLP/indonlu" _LICENSE = "CC-BY-SA 4.0" _URLS = { _DATASETNAME: { "test": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/facqa_qa-factoid-itb/test_preprocess.csv", "train": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/facqa_qa-factoid-itb/train_preprocess.csv", "validation": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/facqa_qa-factoid-itb/valid_preprocess.csv", } } _SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING] _SOURCE_VERSION = "1.0.0" _NUSANTARA_VERSION = "1.0.0" class FacqaDataset(datasets.GeneratorBasedBuilder): """FacQA dataset is a labeled dataset for indonesian question answering task""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION) BUILDER_CONFIGS = [ NusantaraConfig( name="facqa_source", version=SOURCE_VERSION, description="FacQA source schema", schema="source", subset_id="facqa", ), NusantaraConfig( name="facqa_nusantara_qa", version=NUSANTARA_VERSION, description="FacQA Nusantara schema", schema="nusantara_qa", subset_id="facqa", ), ] DEFAULT_CONFIG_NAME = "facqa_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "index": datasets.Value("int64"), "question": [datasets.Value("string")], "passage": [datasets.Value("string")], "seq_label": [datasets.Value("string")], } ) elif self.config.schema == "nusantara_qa": features = schemas.qa_features 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] train_csv_path = Path(dl_manager.download_and_extract(urls["train"])) validation_csv_path = Path(dl_manager.download_and_extract(urls["validation"])) test_csv_path = Path(dl_manager.download_and_extract(urls["test"])) data_files = { "train": train_csv_path, "validation": validation_csv_path, "test": test_csv_path, } return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_files["train"], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": data_files["test"], "split": "test", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": data_files["validation"], "split": "dev", }, ), ] def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" df = pd.read_csv(filepath, sep=",", header="infer").reset_index() if self.config.schema == "source": for row in df.itertuples(): entry = {"index": row.index, "question": ast.literal_eval(row.question), "passage": ast.literal_eval(row.passage), "seq_label": ast.literal_eval(row.seq_label)} yield row.index, entry elif self.config.schema == "nusantara_qa": for row in df.itertuples(): entry = { "id": str(row.index), "question_id": str(row.index), "document_id": str(row.index), "question": listToString(ast.literal_eval(row.question)), "type": "extractive", "choices": [], "context": listToString(ast.literal_eval(row.passage)), "answer": [getAnswerString(ast.literal_eval(row.passage), ast.literal_eval(row.seq_label))], } yield row.index, entry