Datasets:
Tasks:
Question Answering
Sub-tasks:
open-domain-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Source Datasets:
original
ArXiv:
License:
| """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" | |
| _URLS = { | |
| "train": "train.json", | |
| "val": "val.json", | |
| "test": "test.json" | |
| } | |
| _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 = dl_manager.download_and_extract(_URLS) | |
| 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 | |