| import json | |
| import datasets | |
| from datasets import Features, Sequence, Array2D, Value | |
| from datasets.info import DatasetInfo | |
| _DESCRIPTION = """\ | |
| GQA is a dataset containing 58K questions about subgraphs extracted from Wikidata. | |
| The data are made from Lc-QuAD 2.0 and MCWQ datasets. | |
| """ | |
| _URLS = { | |
| "train": "train.jsonl", | |
| "test": "test.jsonl", | |
| } | |
| class GQAConfig(datasets.BuilderConfig): | |
| """BuilderConfig for GQA.""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for GQA. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(GQAConfig, self).__init__(**kwargs) | |
| class GQA(datasets.GeneratorBasedBuilder): | |
| """GQA: A graph question answering dataset.""" | |
| def _info(self) -> DatasetInfo: | |
| return DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=Features( | |
| { | |
| "id": Value("string"), | |
| "question": Value("string"), | |
| "answers": Sequence(Value("string")), | |
| "sparql": Value("string"), | |
| "subgraph": | |
| { | |
| "entities": Sequence(Value("string")), | |
| "relations": Sequence(Value("string")), | |
| "adjacency": Array2D(shape=(None, 3), dtype='int64'), | |
| "entity_labels": Sequence(datasets.Value("string")), | |
| "relation_labels": Sequence(Value("string")), | |
| } | |
| } | |
| ) | |
| ) | |
| def _split_generators(self, dl_manager: datasets.DownloadManager): | |
| downloaded_files = dl_manager.download_and_extract(_URLS) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| with open(filepath, encoding="utf-8") as f: | |
| for row in f: | |
| sample = json.loads(row) | |
| id_ = sample["id"] | |
| yield id_, sample | |