Datasets:
Tasks:
Text Retrieval
ArXiv:
File size: 2,620 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/fever'
IRDS_ENTITY_TYPES = {'docs': {'doc_id': 'string', 'text': 'string', 'title': 'string'}, 'queries': {'query_id': 'string', 'text': 'string'}}
_CITATION = '@inproceedings{Thorne2018Fever,\n title = "{FEVER}: a Large-scale Dataset for Fact Extraction and {VER}ification",\n author = "Thorne, James and\n Vlachos, Andreas and\n Christodoulopoulos, Christos and\n Mittal, Arpit",\n booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",\n month = jun,\n year = "2018",\n address = "New Orleans, Louisiana",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/N18-1074",\n doi = "10.18653/v1/N18-1074",\n pages = "809--819"\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_fever(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/fever",
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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