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  1. README.md +56 -0
  2. msmarco-passage_trec-dl-hard.py +43 -0
README.md ADDED
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+ ---
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+ pretty_name: '`msmarco-passage/trec-dl-hard`'
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+ viewer: false
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+ source_datasets: ['irds/msmarco-passage']
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+ task_categories:
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+ - text-retrieval
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+ ---
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+
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+ # Dataset Card for `msmarco-passage/trec-dl-hard`
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+
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+ The `msmarco-passage/trec-dl-hard` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
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+ For more information about the dataset, see the [documentation](https://ir-datasets.com/msmarco-passage#msmarco-passage/trec-dl-hard).
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+
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+ # Data
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+
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+ This dataset provides:
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+ - `queries` (i.e., topics); count=50
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+ - `qrels`: (relevance assessments); count=4,256
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+
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+ - For `docs`, use [`irds/msmarco-passage`](https://huggingface.co/datasets/irds/msmarco-passage)
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+
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+ ## Usage
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ queries = load_dataset('irds/msmarco-passage_trec-dl-hard', 'queries')
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+ for record in queries:
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+ record # {'query_id': ..., 'text': ...}
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+
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+ qrels = load_dataset('irds/msmarco-passage_trec-dl-hard', 'qrels')
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+ for record in qrels:
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+ record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
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+
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+ ```
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+
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+ Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
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+ data in 🤗 Dataset format.
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+
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+ ## Citation Information
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+
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+ ```
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+ @article{Mackie2021DlHard,
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+ title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset},
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+ author={Iain Mackie and Jeffrey Dalton and Andrew Yates},
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+ journal={ArXiv},
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+ year={2021},
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+ volume={abs/2105.07975}
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+ }
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+ @inproceedings{Bajaj2016Msmarco,
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+ title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
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+ author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang},
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+ booktitle={InCoCo@NIPS},
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+ year={2016}
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+ }
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+ ```
msmarco-passage_trec-dl-hard.py ADDED
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+
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+ """
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+ """ # TODO
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+ try:
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+ import ir_datasets
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+ except ImportError as e:
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+ raise ImportError('ir-datasets package missing; `pip install ir-datasets`')
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+ import datasets
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+
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+ IRDS_ID = 'msmarco-passage/trec-dl-hard'
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+ IRDS_ENTITY_TYPES = {'queries': {'query_id': 'string', 'text': 'string'}, 'qrels': {'query_id': 'string', 'doc_id': 'string', 'relevance': 'int64', 'iteration': 'string'}}
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+
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+ _CITATION = '@article{Mackie2021DlHard,\n title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset},\n author={Iain Mackie and Jeffrey Dalton and Andrew Yates},\n journal={ArXiv},\n year={2021},\n volume={abs/2105.07975}\n}\n@inproceedings{Bajaj2016Msmarco,\n title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},\n author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang},\n booktitle={InCoCo@NIPS},\n year={2016}\n}'
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+
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+ _DESCRIPTION = "" # TODO
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+
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+ class msmarco_passage_trec_dl_hard(datasets.GeneratorBasedBuilder):
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+ BUILDER_CONFIGS = [datasets.BuilderConfig(name=e) for e in IRDS_ENTITY_TYPES]
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features({k: datasets.Value(v) for k, v in IRDS_ENTITY_TYPES[self.config.name].items()}),
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+ homepage=f"https://ir-datasets.com/msmarco-passage#msmarco-passage/trec-dl-hard",
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ return [datasets.SplitGenerator(name=self.config.name)]
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+
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+ def _generate_examples(self):
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+ dataset = ir_datasets.load(IRDS_ID)
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+ for i, item in enumerate(getattr(dataset, self.config.name)):
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+ key = i
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+ if self.config.name == 'docs':
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+ key = item.doc_id
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+ elif self.config.name == 'queries':
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+ key = item.query_id
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+ yield key, item._asdict()
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+
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+ def as_dataset(self, split=None, *args, **kwargs):
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+ split = self.config.name # always return split corresponding with this config to avid returning a redundant DatasetDict layer
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+ return super().as_dataset(split, *args, **kwargs)