|
|
| """ |
| """ |
| try: |
| import ir_datasets |
| except ImportError as e: |
| raise ImportError('ir-datasets package missing; `pip install ir-datasets`') |
| import datasets |
|
|
| IRDS_ID = 'beir/nq' |
| IRDS_ENTITY_TYPES = {'docs': {'doc_id': 'string', 'text': 'string', 'title': 'string'}, 'queries': {'query_id': 'string', 'text': 'string'}, 'qrels': {'query_id': 'string', 'doc_id': 'string', 'relevance': 'int64', 'iteration': 'string'}} |
|
|
| _CITATION = '@article{Kwiatkowski2019Nq,\n title = {Natural Questions: a Benchmark for Question Answering Research},\n author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov},\n year = {2019},\n journal = {TACL}\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 = "" |
|
|
| class beir_nq(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/nq", |
| 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 |
| return super().as_dataset(split, *args, **kwargs) |
|
|
