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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
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