Datasets:
Tasks:
Text Retrieval
ArXiv:
File size: 2,658 Bytes
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"""
""" # TODO
try:
import ir_datasets
except ImportError as e:
raise ImportError('ir-datasets package missing; `pip install ir-datasets`')
import datasets
IRDS_ID = 'beir/hotpotqa'
IRDS_ENTITY_TYPES = {'docs': {'doc_id': 'string', 'text': 'string', 'title': 'string', 'url': 'string'}, 'queries': {'query_id': 'string', 'text': 'string'}}
_CITATION = '@inproceedings{Yang2018Hotpotqa,\n title = "{H}otpot{QA}: A Dataset for Diverse, Explainable Multi-hop Question Answering",\n author = "Yang, Zhilin and\n Qi, Peng and\n Zhang, Saizheng and\n Bengio, Yoshua and\n Cohen, William and\n Salakhutdinov, Ruslan and\n Manning, Christopher D.",\n booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",\n month = oct # "-" # nov,\n year = "2018",\n address = "Brussels, Belgium",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/D18-1259",\n doi = "10.18653/v1/D18-1259",\n pages = "2369--2380"\n}\n@article{Thakur2021Beir,\n title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models",\n author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna", \n journal= "arXiv preprint arXiv:2104.08663",\n month = "4",\n year = "2021",\n url = "https://arxiv.org/abs/2104.08663",\n}'
_DESCRIPTION = "" # TODO
class beir_hotpotqa(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [datasets.BuilderConfig(name=e) for e in IRDS_ENTITY_TYPES]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({k: datasets.Value(v) for k, v in IRDS_ENTITY_TYPES[self.config.name].items()}),
homepage=f"https://ir-datasets.com/beir#beir/hotpotqa",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
return [datasets.SplitGenerator(name=self.config.name)]
def _generate_examples(self):
dataset = ir_datasets.load(IRDS_ID)
for i, item in enumerate(getattr(dataset, self.config.name)):
key = i
if self.config.name == 'docs':
key = item.doc_id
elif self.config.name == 'queries':
key = item.query_id
yield key, item._asdict()
def as_dataset(self, split=None, *args, **kwargs):
split = self.config.name # always return split corresponding with this config to avid returning a redundant DatasetDict layer
return super().as_dataset(split, *args, **kwargs)
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