"""KQA Pro: A large-scale, diverse, challenging dataset of complex question answering over knowledge base.""" import json import os import datasets logger = datasets.logging.get_logger(__name__) _CITATION = """\ @inproceedings{KQAPro, title={{KQA P}ro: A Large Diagnostic Dataset for Complex Question Answering over Knowledge Base}, author={Cao, Shulin and Shi, Jiaxin and Pan, Liangming and Nie, Lunyiu and Xiang, Yutong and Hou, Lei and Li, Juanzi and He, Bin and Zhang, Hanwang}, booktitle={ACL'22}, year={2022} } """ _DESCRIPTION = """\ A large-scale, diverse, challenging dataset of complex question answering over knowledge base. """ _URL = "https://thukeg.gitee.io/kqa-pro/" _DOWNLOAD_URL = "https://cloud.tsinghua.edu.cn/f/df54ff66d1dc4ca7823e/?dl=1" _TRAIN_CONFIG_NAME = "train_val" _TEST_CONFIG_NAME = "test" class KQAProConfig(datasets.BuilderConfig): """BuilderConfig for KQA Pro.""" def __init__(self, **kwargs): """BuilderConfig for KQA Pro. Args: **kwargs: keyword arguments forwarded to super. """ super(KQAProConfig, self).__init__(**kwargs) class KQAPro(datasets.GeneratorBasedBuilder): """KQAPro: A large scale knowledge-based question answering dataset.""" BUILDER_CONFIGS = [ KQAProConfig( name=_TRAIN_CONFIG_NAME, description="KQA Pro" ), KQAProConfig( name=_TEST_CONFIG_NAME, description="KQA Pro" ), ] def _info(self): if self.config.name == _TEST_CONFIG_NAME: return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "question": datasets.Value("string"), "choices": datasets.features.Sequence(datasets.Value("string")), } ), supervised_keys=None, homepage=_URL, citation=_CITATION, ) return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "question": datasets.Value("string"), "sparql": datasets.Value("string"), "program": datasets.features.Sequence( { "function": datasets.Value("string"), "dependencies": datasets.features.Sequence(datasets.Value("int32")), "inputs": datasets.features.Sequence(datasets.Value("string")) } ), "choices": datasets.features.Sequence(datasets.Value("string")), "answer": datasets.Value("string") } ), # No default supervised_keys (as we have to pass both question # and context as input). supervised_keys=None, homepage=_URL, citation=_CITATION, ) def _split_generators(self, dl_manager): downloaded_files = { "train": "train.json", "val": "val.json", "test": "test.json" } if self.config.name == _TEST_CONFIG_NAME: return [ datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={ "filepath": downloaded_files["test"]}) ] return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={ "filepath": downloaded_files["train"]}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": downloaded_files["val"]}) ] def _generate_examples(self, filepath): """This function returns the examples in the raw (text) form.""" logger.info("generating examples from = %s", filepath) with open(filepath, encoding="utf-8") as f: kqa = json.load(f) for idx, sample in enumerate(kqa): yield idx, sample