Datasets:
Tasks:
Text Retrieval
Source Datasets:
irds/trec-spanish
| """ | |
| """ # TODO | |
| try: | |
| import ir_datasets | |
| except ImportError as e: | |
| raise ImportError('ir-datasets package missing; `pip install ir-datasets`') | |
| import datasets | |
| IRDS_ID = 'trec-spanish/trec4' | |
| IRDS_ENTITY_TYPES = {'queries': {'query_id': 'string', 'description_es1': 'string', 'description_en1': 'string', 'description_es2': 'string', 'description_en2': 'string'}, 'qrels': {'query_id': 'string', 'doc_id': 'string', 'relevance': 'int64', 'iteration': 'string'}} | |
| _CITATION = '@inproceedings{Harman1995Trec4,\n title={Overview of the Fourth Text REtrieval Conference (TREC-4)},\n author={Donna Harman},\n booktitle={TREC},\n year={1995}\n}\n@misc{Rogers2000Spanish,\n title={TREC Spanish LDC2000T51},\n author={Rogers, Willie},\n year={2000},\n url={https://catalog.ldc.upenn.edu/LDC2000T51},\n publisher={Linguistic Data Consortium}\n}' | |
| _DESCRIPTION = "" # TODO | |
| class trec_spanish_trec4(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/trec-spanish#trec-spanish/trec4", | |
| 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) | |