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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 = 'mmarco/v2/hi/train'
IRDS_ENTITY_TYPES = {'queries': {'query_id': 'string', 'text': 'string'}, 'qrels': {'query_id': 'string', 'doc_id': 'string', 'relevance': 'int64', 'iteration': 'string'}, 'docpairs': {'query_id': 'string', 'doc_id_a': 'string', 'doc_id_b': 'string'}}
_CITATION = '@article{Bonifacio2021MMarco,\n title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},\n author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},\n year={2021},\n journal={arXiv:2108.13897}\n}'
_DESCRIPTION = "" # TODO
class mmarco_v2_hi_train(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/mmarco#mmarco/v2/hi/train",
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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