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Browse files- README.md +56 -0
- msmarco-passage_trec-dl-hard.py +43 -0
README.md
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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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# Dataset Card for `msmarco-passage/trec-dl-hard`
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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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# Data
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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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- For `docs`, use [`irds/msmarco-passage`](https://huggingface.co/datasets/irds/msmarco-passage)
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## Usage
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```python
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from datasets import load_dataset
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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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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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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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## Citation Information
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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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```
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msmarco-passage_trec-dl-hard.py
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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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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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_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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_DESCRIPTION = "" # TODO
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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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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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def _split_generators(self, dl_manager):
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return [datasets.SplitGenerator(name=self.config.name)]
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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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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)
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