beir_webis-touche2020_v2.py

"""
""" # TODO
try:
    import ir_datasets
except ImportError as e:
    raise ImportError('ir-datasets package missing; `pip install ir-datasets`')
import datasets

IRDS_ID = 'beir/webis-touche2020/v2'
IRDS_ENTITY_TYPES = {'docs': {'doc_id': 'string', 'text': 'string', 'title': 'string', 'stance': 'string', 'url': 'string'}, 'queries': {'query_id': 'string', 'text': 'string', 'description': 'string', 'narrative': 'string'}, 'qrels': {'query_id': 'string', 'doc_id': 'string', 'relevance': 'int64', 'iteration': 'string'}}

_CITATION = '@inproceedings{Bondarenko2020Tuche,\n  title={Overview of Touch{\\\'e} 2020: Argument Retrieval},\n  author={Alexander Bondarenko and Maik Fr{\\"o}be and Meriem Beloucif and Lukas Gienapp and Yamen Ajjour and Alexander Panchenko and Christian Biemann and Benno Stein and Henning Wachsmuth and Martin Potthast and Matthias Hagen},\n  booktitle={CLEF},\n  year={2020}\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_webis_touche2020_v2(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/webis-touche2020/v2",
            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)