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d2321bf1ed1207ee9afba00880809436b43fc0cc |
# Dataset Card for `msmarco-passage/eval`
The `msmarco-passage/eval` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/msmarco-passage#msmarco-passage/eval).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,092
- For `docs`, use [`irds/msmarco-passage`](https://huggingface.co/datasets/irds/msmarco-passage)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/msmarco-passage_eval', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
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},
booktitle={InCoCo@NIPS},
year={2016}
}
```
| irds/msmarco-passage_eval | [
"task_categories:text-retrieval",
"source_datasets:irds/msmarco-passage",
"region:us"
]
| 2023-01-05T03:17:13+00:00 | {"source_datasets": ["irds/msmarco-passage"], "task_categories": ["text-retrieval"], "pretty_name": "`msmarco-passage/eval`", "viewer": false} | 2023-01-05T03:17:19+00:00 |
de45da64bde97a5f1f91e59ebb0f1afc64a4c988 |
# Dataset Card for `msmarco-passage/train/triples-small`
The `msmarco-passage/train/triples-small` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/msmarco-passage#msmarco-passage/train/triples-small).
# Data
This dataset provides:
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/msmarco-passage`](https://huggingface.co/datasets/irds/msmarco-passage)
## Usage
```python
from datasets import load_dataset
docpairs = load_dataset('irds/msmarco-passage_train_triples-small', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
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},
booktitle={InCoCo@NIPS},
year={2016}
}
```
| irds/msmarco-passage_train_triples-small | [
"task_categories:text-retrieval",
"source_datasets:irds/msmarco-passage",
"region:us"
]
| 2023-01-05T03:17:25+00:00 | {"source_datasets": ["irds/msmarco-passage"], "task_categories": ["text-retrieval"], "pretty_name": "`msmarco-passage/train/triples-small`", "viewer": false} | 2023-01-05T03:17:31+00:00 |
7ffed33f9c1e1f94dd4d8e816ff253f4dc335ef9 |
# Dataset Card for `msmarco-passage/train/triples-v2`
The `msmarco-passage/train/triples-v2` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/msmarco-passage#msmarco-passage/train/triples-v2).
# Data
This dataset provides:
- `docpairs`; count=397,768,673
- For `docs`, use [`irds/msmarco-passage`](https://huggingface.co/datasets/irds/msmarco-passage)
## Usage
```python
from datasets import load_dataset
docpairs = load_dataset('irds/msmarco-passage_train_triples-v2', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
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},
booktitle={InCoCo@NIPS},
year={2016}
}
```
| irds/msmarco-passage_train_triples-v2 | [
"task_categories:text-retrieval",
"source_datasets:irds/msmarco-passage",
"region:us"
]
| 2023-01-05T03:17:36+00:00 | {"source_datasets": ["irds/msmarco-passage"], "task_categories": ["text-retrieval"], "pretty_name": "`msmarco-passage/train/triples-v2`", "viewer": false} | 2023-01-05T03:17:42+00:00 |
b1129fe39c9a242ff4f864aa9b8dc6d2d23bec25 |
# Dataset Card for `msmarco-passage/trec-dl-hard`
The `msmarco-passage/trec-dl-hard` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/msmarco-passage#msmarco-passage/trec-dl-hard).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=4,256
- For `docs`, use [`irds/msmarco-passage`](https://huggingface.co/datasets/irds/msmarco-passage)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/msmarco-passage_trec-dl-hard', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/msmarco-passage_trec-dl-hard', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Mackie2021DlHard,
title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset},
author={Iain Mackie and Jeffrey Dalton and Andrew Yates},
journal={ArXiv},
year={2021},
volume={abs/2105.07975}
}
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
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},
booktitle={InCoCo@NIPS},
year={2016}
}
```
| irds/msmarco-passage_trec-dl-hard | [
"task_categories:text-retrieval",
"source_datasets:irds/msmarco-passage",
"region:us"
]
| 2023-01-05T03:17:47+00:00 | {"source_datasets": ["irds/msmarco-passage"], "task_categories": ["text-retrieval"], "pretty_name": "`msmarco-passage/trec-dl-hard`", "viewer": false} | 2023-01-05T03:17:54+00:00 |
0d1c5543ae8c9d7c9a844ca272c1b9794216f7ea |
# Dataset Card for `msmarco-passage/trec-dl-hard/fold1`
The `msmarco-passage/trec-dl-hard/fold1` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/msmarco-passage#msmarco-passage/trec-dl-hard/fold1).
# Data
This dataset provides:
- `queries` (i.e., topics); count=10
- `qrels`: (relevance assessments); count=1,072
- For `docs`, use [`irds/msmarco-passage`](https://huggingface.co/datasets/irds/msmarco-passage)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/msmarco-passage_trec-dl-hard_fold1', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/msmarco-passage_trec-dl-hard_fold1', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Mackie2021DlHard,
title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset},
author={Iain Mackie and Jeffrey Dalton and Andrew Yates},
journal={ArXiv},
year={2021},
volume={abs/2105.07975}
}
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
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},
booktitle={InCoCo@NIPS},
year={2016}
}
```
| irds/msmarco-passage_trec-dl-hard_fold1 | [
"task_categories:text-retrieval",
"source_datasets:irds/msmarco-passage",
"region:us"
]
| 2023-01-05T03:17:59+00:00 | {"source_datasets": ["irds/msmarco-passage"], "task_categories": ["text-retrieval"], "pretty_name": "`msmarco-passage/trec-dl-hard/fold1`", "viewer": false} | 2023-01-05T03:18:05+00:00 |
28f16fc5228f9fb890b1e089ab6c77317c20aef2 |
# Dataset Card for `msmarco-passage/trec-dl-hard/fold2`
The `msmarco-passage/trec-dl-hard/fold2` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/msmarco-passage#msmarco-passage/trec-dl-hard/fold2).
# Data
This dataset provides:
- `queries` (i.e., topics); count=10
- `qrels`: (relevance assessments); count=898
- For `docs`, use [`irds/msmarco-passage`](https://huggingface.co/datasets/irds/msmarco-passage)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/msmarco-passage_trec-dl-hard_fold2', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/msmarco-passage_trec-dl-hard_fold2', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Mackie2021DlHard,
title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset},
author={Iain Mackie and Jeffrey Dalton and Andrew Yates},
journal={ArXiv},
year={2021},
volume={abs/2105.07975}
}
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
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},
booktitle={InCoCo@NIPS},
year={2016}
}
```
| irds/msmarco-passage_trec-dl-hard_fold2 | [
"task_categories:text-retrieval",
"source_datasets:irds/msmarco-passage",
"region:us"
]
| 2023-01-05T03:18:10+00:00 | {"source_datasets": ["irds/msmarco-passage"], "task_categories": ["text-retrieval"], "pretty_name": "`msmarco-passage/trec-dl-hard/fold2`", "viewer": false} | 2023-01-05T03:18:16+00:00 |
9328cd2381826e6c4f4720f1b045ba75a4124f61 |
# Dataset Card for `msmarco-passage/trec-dl-hard/fold3`
The `msmarco-passage/trec-dl-hard/fold3` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/msmarco-passage#msmarco-passage/trec-dl-hard/fold3).
# Data
This dataset provides:
- `queries` (i.e., topics); count=10
- `qrels`: (relevance assessments); count=444
- For `docs`, use [`irds/msmarco-passage`](https://huggingface.co/datasets/irds/msmarco-passage)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/msmarco-passage_trec-dl-hard_fold3', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/msmarco-passage_trec-dl-hard_fold3', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Mackie2021DlHard,
title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset},
author={Iain Mackie and Jeffrey Dalton and Andrew Yates},
journal={ArXiv},
year={2021},
volume={abs/2105.07975}
}
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
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},
booktitle={InCoCo@NIPS},
year={2016}
}
```
| irds/msmarco-passage_trec-dl-hard_fold3 | [
"task_categories:text-retrieval",
"source_datasets:irds/msmarco-passage",
"region:us"
]
| 2023-01-05T03:18:22+00:00 | {"source_datasets": ["irds/msmarco-passage"], "task_categories": ["text-retrieval"], "pretty_name": "`msmarco-passage/trec-dl-hard/fold3`", "viewer": false} | 2023-01-05T03:18:28+00:00 |
e05f315864a95239e4d0b39ce54fdc79b25dffb2 |
# Dataset Card for `msmarco-passage/trec-dl-hard/fold4`
The `msmarco-passage/trec-dl-hard/fold4` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/msmarco-passage#msmarco-passage/trec-dl-hard/fold4).
# Data
This dataset provides:
- `queries` (i.e., topics); count=10
- `qrels`: (relevance assessments); count=716
- For `docs`, use [`irds/msmarco-passage`](https://huggingface.co/datasets/irds/msmarco-passage)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/msmarco-passage_trec-dl-hard_fold4', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/msmarco-passage_trec-dl-hard_fold4', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Mackie2021DlHard,
title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset},
author={Iain Mackie and Jeffrey Dalton and Andrew Yates},
journal={ArXiv},
year={2021},
volume={abs/2105.07975}
}
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
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},
booktitle={InCoCo@NIPS},
year={2016}
}
```
| irds/msmarco-passage_trec-dl-hard_fold4 | [
"task_categories:text-retrieval",
"source_datasets:irds/msmarco-passage",
"region:us"
]
| 2023-01-05T03:18:33+00:00 | {"source_datasets": ["irds/msmarco-passage"], "task_categories": ["text-retrieval"], "pretty_name": "`msmarco-passage/trec-dl-hard/fold4`", "viewer": false} | 2023-01-05T03:18:39+00:00 |
7f96a0dfde501b9cb37db2482b6efbf357a317db |
# Dataset Card for `msmarco-passage/trec-dl-hard/fold5`
The `msmarco-passage/trec-dl-hard/fold5` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/msmarco-passage#msmarco-passage/trec-dl-hard/fold5).
# Data
This dataset provides:
- `queries` (i.e., topics); count=10
- `qrels`: (relevance assessments); count=1,126
- For `docs`, use [`irds/msmarco-passage`](https://huggingface.co/datasets/irds/msmarco-passage)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/msmarco-passage_trec-dl-hard_fold5', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/msmarco-passage_trec-dl-hard_fold5', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Mackie2021DlHard,
title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset},
author={Iain Mackie and Jeffrey Dalton and Andrew Yates},
journal={ArXiv},
year={2021},
volume={abs/2105.07975}
}
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
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},
booktitle={InCoCo@NIPS},
year={2016}
}
```
| irds/msmarco-passage_trec-dl-hard_fold5 | [
"task_categories:text-retrieval",
"source_datasets:irds/msmarco-passage",
"region:us"
]
| 2023-01-05T03:18:45+00:00 | {"source_datasets": ["irds/msmarco-passage"], "task_categories": ["text-retrieval"], "pretty_name": "`msmarco-passage/trec-dl-hard/fold5`", "viewer": false} | 2023-01-05T03:18:51+00:00 |
913b4b74161e8b0da4594bfd2b35d311f93bc461 |
# Dataset Card for `mmarco/de`
The `mmarco/de` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/de).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_de_dev`](https://huggingface.co/datasets/irds/mmarco_de_dev), [`mmarco_de_train`](https://huggingface.co/datasets/irds/mmarco_de_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_de', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_de | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:18:56+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/de`", "viewer": false} | 2023-01-05T03:19:02+00:00 |
5318697117a4dd1f182ab0f84c8169d9f77fd8f4 |
# Dataset Card for `mmarco/de/dev`
The `mmarco/de/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/de/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_de`](https://huggingface.co/datasets/irds/mmarco_de)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_de_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_de_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_de_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_de",
"region:us"
]
| 2023-01-05T03:19:07+00:00 | {"source_datasets": ["irds/mmarco_de"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/de/dev`", "viewer": false} | 2023-01-05T03:19:14+00:00 |
39560409ee079e73867791aa2836b00d7b105121 |
# Dataset Card for `mmarco/de/train`
The `mmarco/de/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/de/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_de`](https://huggingface.co/datasets/irds/mmarco_de)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_de_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_de_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_de_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_de_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_de",
"region:us"
]
| 2023-01-05T03:19:19+00:00 | {"source_datasets": ["irds/mmarco_de"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/de/train`", "viewer": false} | 2023-01-05T03:19:25+00:00 |
3e18dee28ad468246a5280b641c1a7f59d2286ac |
# Dataset Card for `mmarco/es`
The `mmarco/es` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/es).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_es_dev`](https://huggingface.co/datasets/irds/mmarco_es_dev), [`mmarco_es_train`](https://huggingface.co/datasets/irds/mmarco_es_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_es', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_es | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:19:31+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/es`", "viewer": false} | 2023-01-05T03:19:37+00:00 |
d2ae92f49e7c11f60a7551ad2e4733343daa21c5 |
# Dataset Card for `mmarco/es/dev`
The `mmarco/es/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/es/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,092
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_es`](https://huggingface.co/datasets/irds/mmarco_es)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_es_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_es_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_es_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_es",
"region:us"
]
| 2023-01-05T03:19:42+00:00 | {"source_datasets": ["irds/mmarco_es"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/es/dev`", "viewer": false} | 2023-01-05T03:19:48+00:00 |
224ef56d6b405ccf0d1d0b16d72b6cabd078c93d |
# Dataset Card for `mmarco/es/train`
The `mmarco/es/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/es/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_es`](https://huggingface.co/datasets/irds/mmarco_es)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_es_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_es_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_es_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_es_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_es",
"region:us"
]
| 2023-01-05T03:19:54+00:00 | {"source_datasets": ["irds/mmarco_es"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/es/train`", "viewer": false} | 2023-01-05T03:19:59+00:00 |
9be96a7ba7c9b3c875234cc6502e3265e3fca1c4 |
# Dataset Card for `mmarco/fr`
The `mmarco/fr` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/fr).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_fr_dev`](https://huggingface.co/datasets/irds/mmarco_fr_dev), [`mmarco_fr_train`](https://huggingface.co/datasets/irds/mmarco_fr_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_fr', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_fr | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:20:05+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/fr`", "viewer": false} | 2023-01-05T03:20:11+00:00 |
8ce34210d36d56a3699310cb6c46887a695dd2bf |
# Dataset Card for `mmarco/fr/dev`
The `mmarco/fr/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/fr/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_fr`](https://huggingface.co/datasets/irds/mmarco_fr)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_fr_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_fr_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_fr_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_fr",
"region:us"
]
| 2023-01-05T03:20:16+00:00 | {"source_datasets": ["irds/mmarco_fr"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/fr/dev`", "viewer": false} | 2023-01-05T03:20:22+00:00 |
e1ebd14d7bb66bad1f0c37581ecb3fbc74c89c42 |
# Dataset Card for `mmarco/fr/train`
The `mmarco/fr/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/fr/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_fr`](https://huggingface.co/datasets/irds/mmarco_fr)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_fr_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_fr_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_fr_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_fr_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_fr",
"region:us"
]
| 2023-01-05T03:20:28+00:00 | {"source_datasets": ["irds/mmarco_fr"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/fr/train`", "viewer": false} | 2023-01-05T03:20:35+00:00 |
121d66064395028f03b0c2eb1c2e78a71d8d5772 |
# Dataset Card for `mmarco/id`
The `mmarco/id` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/id).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_id_dev`](https://huggingface.co/datasets/irds/mmarco_id_dev), [`mmarco_id_train`](https://huggingface.co/datasets/irds/mmarco_id_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_id', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_id | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:20:40+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/id`", "viewer": false} | 2023-01-05T03:20:46+00:00 |
a024cb2ae75bbcd0fa6c3cac7fb14ebcf1f5e206 |
# Dataset Card for `mmarco/id/dev`
The `mmarco/id/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/id/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_id`](https://huggingface.co/datasets/irds/mmarco_id)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_id_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_id_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_id_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_id",
"region:us"
]
| 2023-01-05T03:20:51+00:00 | {"source_datasets": ["irds/mmarco_id"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/id/dev`", "viewer": false} | 2023-01-05T03:20:57+00:00 |
8ed13a03e20df3f825e3819af6924db5ffdeb207 |
# Dataset Card for `mmarco/id/train`
The `mmarco/id/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/id/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_id`](https://huggingface.co/datasets/irds/mmarco_id)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_id_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_id_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_id_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_id_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_id",
"region:us"
]
| 2023-01-05T03:21:03+00:00 | {"source_datasets": ["irds/mmarco_id"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/id/train`", "viewer": false} | 2023-01-05T03:21:09+00:00 |
6911b71cabc9c941677f0b6f2ff11d36c0cc897b |
# Dataset Card for `mmarco/it`
The `mmarco/it` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/it).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_it_dev`](https://huggingface.co/datasets/irds/mmarco_it_dev), [`mmarco_it_train`](https://huggingface.co/datasets/irds/mmarco_it_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_it', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_it | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:21:14+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/it`", "viewer": false} | 2023-01-05T03:21:20+00:00 |
9041a86a6faf53429176be84cb19064891334ed5 |
# Dataset Card for `mmarco/it/dev`
The `mmarco/it/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/it/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_it`](https://huggingface.co/datasets/irds/mmarco_it)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_it_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_it_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_it_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_it",
"region:us"
]
| 2023-01-05T03:21:25+00:00 | {"source_datasets": ["irds/mmarco_it"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/it/dev`", "viewer": false} | 2023-01-05T03:21:31+00:00 |
ed816a1fc65c82f46a2cd1eb029e7432d4032c13 |
# Dataset Card for `mmarco/it/train`
The `mmarco/it/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/it/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_it`](https://huggingface.co/datasets/irds/mmarco_it)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_it_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_it_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_it_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_it_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_it",
"region:us"
]
| 2023-01-05T03:21:37+00:00 | {"source_datasets": ["irds/mmarco_it"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/it/train`", "viewer": false} | 2023-01-05T03:21:42+00:00 |
0d5b98409b96ddb651dd383b19a955fd285ba41c |
# Dataset Card for `mmarco/pt`
The `mmarco/pt` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/pt).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_pt_dev`](https://huggingface.co/datasets/irds/mmarco_pt_dev), [`mmarco_pt_dev_small`](https://huggingface.co/datasets/irds/mmarco_pt_dev_small), [`mmarco_pt_dev_v1.1`](https://huggingface.co/datasets/irds/mmarco_pt_dev_v1.1), [`mmarco_pt_train`](https://huggingface.co/datasets/irds/mmarco_pt_train), [`mmarco_pt_train_v1.1`](https://huggingface.co/datasets/irds/mmarco_pt_train_v1.1)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_pt', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_pt | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:21:48+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/pt`", "viewer": false} | 2023-01-05T03:21:53+00:00 |
fd0b5ecc91d7554363318d22452441ace17ef00a |
# Dataset Card for `mmarco/pt/dev`
The `mmarco/pt/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/pt/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,619
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_pt`](https://huggingface.co/datasets/irds/mmarco_pt)
This dataset is used by: [`mmarco_pt_dev_v1.1`](https://huggingface.co/datasets/irds/mmarco_pt_dev_v1.1)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_pt_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_pt_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_pt_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_pt",
"region:us"
]
| 2023-01-05T03:21:59+00:00 | {"source_datasets": ["irds/mmarco_pt"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/pt/dev`", "viewer": false} | 2023-01-05T03:22:05+00:00 |
dc6503ba07a3359edd9d8f8721cb2ea7b4513a6d |
# Dataset Card for `mmarco/pt/dev/small`
The `mmarco/pt/dev/small` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/pt/dev/small).
# Data
This dataset provides:
- `queries` (i.e., topics); count=7,000
- `qrels`: (relevance assessments); count=7,437
- For `docs`, use [`irds/mmarco_pt`](https://huggingface.co/datasets/irds/mmarco_pt)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_pt_dev_small', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_pt_dev_small', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_pt_dev_small | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_pt",
"region:us"
]
| 2023-01-05T03:22:10+00:00 | {"source_datasets": ["irds/mmarco_pt"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/pt/dev/small`", "viewer": false} | 2023-01-05T03:22:16+00:00 |
da1c91489c6eb056422972f1f5e8c07f022c25ea |
# Dataset Card for `mmarco/pt/dev/v1.1`
The `mmarco/pt/dev/v1.1` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/pt/dev/v1.1).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- For `docs`, use [`irds/mmarco_pt`](https://huggingface.co/datasets/irds/mmarco_pt)
- For `qrels`, use [`irds/mmarco_pt_dev`](https://huggingface.co/datasets/irds/mmarco_pt_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_pt_dev_v1.1', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_pt_dev_v1.1 | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_pt",
"source_datasets:irds/mmarco_pt_dev",
"region:us"
]
| 2023-01-05T03:22:21+00:00 | {"source_datasets": ["irds/mmarco_pt", "irds/mmarco_pt_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/pt/dev/v1.1`", "viewer": false} | 2023-01-05T03:22:27+00:00 |
5d4cf64899999da4ed32d43b226395439548d865 |
# Dataset Card for `mmarco/pt/train`
The `mmarco/pt/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/pt/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=811,690
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_pt`](https://huggingface.co/datasets/irds/mmarco_pt)
This dataset is used by: [`mmarco_pt_train_v1.1`](https://huggingface.co/datasets/irds/mmarco_pt_train_v1.1)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_pt_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_pt_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_pt_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_pt_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_pt",
"region:us"
]
| 2023-01-05T03:22:32+00:00 | {"source_datasets": ["irds/mmarco_pt"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/pt/train`", "viewer": false} | 2023-01-05T03:22:38+00:00 |
00857b925e6581b887e42c57add9bb865666ff79 |
# Dataset Card for `mmarco/pt/train/v1.1`
The `mmarco/pt/train/v1.1` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/pt/train/v1.1).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- For `docs`, use [`irds/mmarco_pt`](https://huggingface.co/datasets/irds/mmarco_pt)
- For `qrels`, use [`irds/mmarco_pt_train`](https://huggingface.co/datasets/irds/mmarco_pt_train)
- For `docpairs`, use [`irds/mmarco_pt_train`](https://huggingface.co/datasets/irds/mmarco_pt_train)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_pt_train_v1.1', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_pt_train_v1.1 | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_pt",
"source_datasets:irds/mmarco_pt_train",
"region:us"
]
| 2023-01-05T03:22:43+00:00 | {"source_datasets": ["irds/mmarco_pt", "irds/mmarco_pt_train"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/pt/train/v1.1`", "viewer": false} | 2023-01-05T03:22:49+00:00 |
302ec30bd28f6e934b2be22f26c6469902df3f18 |
# Dataset Card for `mmarco/ru`
The `mmarco/ru` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/ru).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_ru_dev`](https://huggingface.co/datasets/irds/mmarco_ru_dev), [`mmarco_ru_train`](https://huggingface.co/datasets/irds/mmarco_ru_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_ru', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_ru | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:22:54+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/ru`", "viewer": false} | 2023-01-05T03:23:00+00:00 |
396f5d096f7d354cfa20f28225cf3f1077a9e72d |
# Dataset Card for `mmarco/ru/dev`
The `mmarco/ru/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/ru/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_ru`](https://huggingface.co/datasets/irds/mmarco_ru)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_ru_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_ru_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_ru_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_ru",
"region:us"
]
| 2023-01-05T03:23:06+00:00 | {"source_datasets": ["irds/mmarco_ru"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/ru/dev`", "viewer": false} | 2023-01-05T03:23:11+00:00 |
99dbbec8730c9bc682b9f0831b1ab146fe87787b |
# Dataset Card for `mmarco/ru/train`
The `mmarco/ru/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/ru/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_ru`](https://huggingface.co/datasets/irds/mmarco_ru)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_ru_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_ru_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_ru_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_ru_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_ru",
"region:us"
]
| 2023-01-05T03:23:17+00:00 | {"source_datasets": ["irds/mmarco_ru"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/ru/train`", "viewer": false} | 2023-01-05T03:23:22+00:00 |
9ba54124147f5d9e6ec7297445e3f96c0a20244b |
# Dataset Card for `mmarco/v2/ar`
The `mmarco/v2/ar` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/ar).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_ar_dev`](https://huggingface.co/datasets/irds/mmarco_v2_ar_dev), [`mmarco_v2_ar_train`](https://huggingface.co/datasets/irds/mmarco_v2_ar_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_ar', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_ar | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:23:28+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/ar`", "viewer": false} | 2023-01-05T03:23:34+00:00 |
55b93d3088b4564bba7b51e4a1a9973c89cc137d |
# Dataset Card for `mmarco/v2/ar/dev`
The `mmarco/v2/ar/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/ar/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_ar`](https://huggingface.co/datasets/irds/mmarco_v2_ar)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_ar_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_ar_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_ar_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_ar",
"region:us"
]
| 2023-01-05T03:23:39+00:00 | {"source_datasets": ["irds/mmarco_v2_ar"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/ar/dev`", "viewer": false} | 2023-01-05T03:23:45+00:00 |
220bb4a55bb9f7ffe064ed30934761bc90e34b80 |
# Dataset Card for `mmarco/v2/ar/train`
The `mmarco/v2/ar/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/ar/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_v2_ar`](https://huggingface.co/datasets/irds/mmarco_v2_ar)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_ar_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_ar_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_v2_ar_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_ar_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_ar",
"region:us"
]
| 2023-01-05T03:23:50+00:00 | {"source_datasets": ["irds/mmarco_v2_ar"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/ar/train`", "viewer": false} | 2023-01-05T03:23:56+00:00 |
025cc71d4ecfe1f78fd4a92a271b365ca207d68c |
# Dataset Card for `mmarco/v2/de`
The `mmarco/v2/de` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/de).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_de_dev`](https://huggingface.co/datasets/irds/mmarco_v2_de_dev), [`mmarco_v2_de_train`](https://huggingface.co/datasets/irds/mmarco_v2_de_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_de', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_de | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:24:01+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/de`", "viewer": false} | 2023-01-05T03:24:07+00:00 |
775820735e29f81f97bd323f9e42da15d75a7399 |
# Dataset Card for `mmarco/v2/de/dev`
The `mmarco/v2/de/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/de/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_de`](https://huggingface.co/datasets/irds/mmarco_v2_de)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_de_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_de_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_de_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_de",
"region:us"
]
| 2023-01-05T03:24:12+00:00 | {"source_datasets": ["irds/mmarco_v2_de"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/de/dev`", "viewer": false} | 2023-01-05T03:24:18+00:00 |
06296b9d2a33c8eaa737bf43a0d0ad4ec6f989c6 |
# Dataset Card for `mmarco/v2/de/train`
The `mmarco/v2/de/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/de/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_v2_de`](https://huggingface.co/datasets/irds/mmarco_v2_de)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_de_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_de_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_v2_de_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_de_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_de",
"region:us"
]
| 2023-01-05T03:24:23+00:00 | {"source_datasets": ["irds/mmarco_v2_de"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/de/train`", "viewer": false} | 2023-01-05T03:24:29+00:00 |
8852c8ee2a3f9a4c186d0f3af18655f8715717a4 |
# Dataset Card for `mmarco/v2/dt`
The `mmarco/v2/dt` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/dt).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_dt_dev`](https://huggingface.co/datasets/irds/mmarco_v2_dt_dev), [`mmarco_v2_dt_train`](https://huggingface.co/datasets/irds/mmarco_v2_dt_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_dt', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_dt | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:24:34+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/dt`", "viewer": false} | 2023-01-05T03:24:40+00:00 |
5992a038543122ae908c19c2bf71dd9e1572bb76 |
# Dataset Card for `mmarco/v2/dt/dev`
The `mmarco/v2/dt/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/dt/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_dt`](https://huggingface.co/datasets/irds/mmarco_v2_dt)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_dt_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_dt_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_dt_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_dt",
"region:us"
]
| 2023-01-05T03:24:46+00:00 | {"source_datasets": ["irds/mmarco_v2_dt"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/dt/dev`", "viewer": false} | 2023-01-05T03:24:51+00:00 |
4dcdc7ea954e937a4f4fb21eff71ad33946caab3 |
# Dataset Card for `mmarco/v2/dt/train`
The `mmarco/v2/dt/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/dt/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_v2_dt`](https://huggingface.co/datasets/irds/mmarco_v2_dt)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_dt_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_dt_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_v2_dt_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_dt_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_dt",
"region:us"
]
| 2023-01-05T03:24:57+00:00 | {"source_datasets": ["irds/mmarco_v2_dt"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/dt/train`", "viewer": false} | 2023-01-05T03:25:03+00:00 |
1f59f33da3fdeaa1a99f599522cd5dada0bac269 |
# Dataset Card for `mmarco/v2/es`
The `mmarco/v2/es` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/es).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_es_dev`](https://huggingface.co/datasets/irds/mmarco_v2_es_dev), [`mmarco_v2_es_train`](https://huggingface.co/datasets/irds/mmarco_v2_es_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_es', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_es | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:25:08+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/es`", "viewer": false} | 2023-01-05T03:25:14+00:00 |
56f0cb61ca641e603ad81830a16b30870b9d50be |
# Dataset Card for `mmarco/v2/es/dev`
The `mmarco/v2/es/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/es/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_es`](https://huggingface.co/datasets/irds/mmarco_v2_es)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_es_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_es_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_es_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_es",
"region:us"
]
| 2023-01-05T03:25:19+00:00 | {"source_datasets": ["irds/mmarco_v2_es"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/es/dev`", "viewer": false} | 2023-01-05T03:25:25+00:00 |
651b9d0d9b949874c4cf759e3d6b69c0199da772 |
# Dataset Card for `mmarco/v2/es/train`
The `mmarco/v2/es/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/es/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_v2_es`](https://huggingface.co/datasets/irds/mmarco_v2_es)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_es_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_es_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_v2_es_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_es_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_es",
"region:us"
]
| 2023-01-05T03:25:30+00:00 | {"source_datasets": ["irds/mmarco_v2_es"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/es/train`", "viewer": false} | 2023-01-05T03:25:36+00:00 |
ce88693e3db6b50e721a57ea8ba9bab032288a7b |
# Dataset Card for `mmarco/v2/fr`
The `mmarco/v2/fr` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/fr).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_fr_dev`](https://huggingface.co/datasets/irds/mmarco_v2_fr_dev), [`mmarco_v2_fr_train`](https://huggingface.co/datasets/irds/mmarco_v2_fr_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_fr', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_fr | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:25:41+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/fr`", "viewer": false} | 2023-01-05T03:25:47+00:00 |
fdbe12aff10816aaf7155c18c41b789ffef690fc |
# Dataset Card for `mmarco/v2/fr/dev`
The `mmarco/v2/fr/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/fr/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_fr`](https://huggingface.co/datasets/irds/mmarco_v2_fr)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_fr_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_fr_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_fr_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_fr",
"region:us"
]
| 2023-01-05T03:25:52+00:00 | {"source_datasets": ["irds/mmarco_v2_fr"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/fr/dev`", "viewer": false} | 2023-01-05T03:25:58+00:00 |
963eb6d5d2b12cccc3f393c55f8a8c066d46fbd3 |
# Dataset Card for `mmarco/v2/fr/train`
The `mmarco/v2/fr/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/fr/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_v2_fr`](https://huggingface.co/datasets/irds/mmarco_v2_fr)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_fr_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_fr_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_v2_fr_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_fr_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_fr",
"region:us"
]
| 2023-01-05T03:26:03+00:00 | {"source_datasets": ["irds/mmarco_v2_fr"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/fr/train`", "viewer": false} | 2023-01-05T03:26:09+00:00 |
c36fafdb8684f28430b5013cece64fb14debb449 |
# Dataset Card for `mmarco/v2/hi`
The `mmarco/v2/hi` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/hi).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_hi_dev`](https://huggingface.co/datasets/irds/mmarco_v2_hi_dev), [`mmarco_v2_hi_train`](https://huggingface.co/datasets/irds/mmarco_v2_hi_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_hi', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_hi | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:26:14+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/hi`", "viewer": false} | 2023-01-05T03:26:20+00:00 |
a51c0764a63a7f644e3aa474442cf6bed41b91b7 |
# Dataset Card for `mmarco/v2/hi/dev`
The `mmarco/v2/hi/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/hi/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_hi`](https://huggingface.co/datasets/irds/mmarco_v2_hi)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_hi_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_hi_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_hi_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_hi",
"region:us"
]
| 2023-01-05T03:26:26+00:00 | {"source_datasets": ["irds/mmarco_v2_hi"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/hi/dev`", "viewer": false} | 2023-01-05T03:26:31+00:00 |
a63e5ec3dbf14df93dd249bb874a5eed2a74b1e5 |
# Dataset Card for `mmarco/v2/hi/train`
The `mmarco/v2/hi/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/hi/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_v2_hi`](https://huggingface.co/datasets/irds/mmarco_v2_hi)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_hi_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_hi_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_v2_hi_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_hi_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_hi",
"region:us"
]
| 2023-01-05T03:26:37+00:00 | {"source_datasets": ["irds/mmarco_v2_hi"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/hi/train`", "viewer": false} | 2023-01-05T03:26:42+00:00 |
63a8e90a554c7673b908774dea617b7312e725d9 |
# Dataset Card for `mmarco/v2/id`
The `mmarco/v2/id` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/id).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_id_dev`](https://huggingface.co/datasets/irds/mmarco_v2_id_dev), [`mmarco_v2_id_train`](https://huggingface.co/datasets/irds/mmarco_v2_id_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_id', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_id | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:26:48+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/id`", "viewer": false} | 2023-01-05T03:26:53+00:00 |
feb53944fe08e3b192c908aa24ba935b2c372bca |
# Dataset Card for `mmarco/v2/id/dev`
The `mmarco/v2/id/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/id/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_id`](https://huggingface.co/datasets/irds/mmarco_v2_id)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_id_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_id_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_id_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_id",
"region:us"
]
| 2023-01-05T03:26:59+00:00 | {"source_datasets": ["irds/mmarco_v2_id"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/id/dev`", "viewer": false} | 2023-01-05T03:27:05+00:00 |
f893909ae46738fb4abc510ae09bd456942c1dd2 |
# Dataset Card for `mmarco/v2/id/train`
The `mmarco/v2/id/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/id/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_v2_id`](https://huggingface.co/datasets/irds/mmarco_v2_id)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_id_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_id_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_v2_id_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_id_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_id",
"region:us"
]
| 2023-01-05T03:27:10+00:00 | {"source_datasets": ["irds/mmarco_v2_id"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/id/train`", "viewer": false} | 2023-01-05T03:27:16+00:00 |
fea94875097d6e7e5981a0248b76d38bd7cf7927 |
# Dataset Card for `mmarco/v2/it`
The `mmarco/v2/it` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/it).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_it_dev`](https://huggingface.co/datasets/irds/mmarco_v2_it_dev), [`mmarco_v2_it_train`](https://huggingface.co/datasets/irds/mmarco_v2_it_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_it', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_it | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:27:21+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/it`", "viewer": false} | 2023-01-05T03:27:27+00:00 |
d4a4b740e65f57fb103c681260429c1f0a73c198 |
# Dataset Card for `mmarco/v2/it/dev`
The `mmarco/v2/it/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/it/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_it`](https://huggingface.co/datasets/irds/mmarco_v2_it)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_it_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_it_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_it_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_it",
"region:us"
]
| 2023-01-05T03:27:32+00:00 | {"source_datasets": ["irds/mmarco_v2_it"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/it/dev`", "viewer": false} | 2023-01-05T03:27:38+00:00 |
7ccf7e05af3e19adc9229c939a06e743f0f354db |
# Dataset Card for `mmarco/v2/it/train`
The `mmarco/v2/it/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/it/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_v2_it`](https://huggingface.co/datasets/irds/mmarco_v2_it)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_it_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_it_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_v2_it_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_it_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_it",
"region:us"
]
| 2023-01-05T03:27:44+00:00 | {"source_datasets": ["irds/mmarco_v2_it"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/it/train`", "viewer": false} | 2023-01-05T03:27:49+00:00 |
0de1fbfd7c9c175c5605cf3c74100e57335b7990 |
# Dataset Card for `mmarco/v2/ja`
The `mmarco/v2/ja` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/ja).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_ja_dev`](https://huggingface.co/datasets/irds/mmarco_v2_ja_dev), [`mmarco_v2_ja_train`](https://huggingface.co/datasets/irds/mmarco_v2_ja_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_ja', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_ja | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:27:55+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/ja`", "viewer": false} | 2023-01-05T03:28:00+00:00 |
81b384fff5bab1c0cf04303b48954ca35d5012d0 |
# Dataset Card for `mmarco/v2/ja/dev`
The `mmarco/v2/ja/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/ja/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_ja`](https://huggingface.co/datasets/irds/mmarco_v2_ja)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_ja_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_ja_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_ja_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_ja",
"region:us"
]
| 2023-01-05T03:28:06+00:00 | {"source_datasets": ["irds/mmarco_v2_ja"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/ja/dev`", "viewer": false} | 2023-01-05T03:28:11+00:00 |
019f1bc1bc08562ed1fc3dd95bd2750bb66bfb3f |
# Dataset Card for `mmarco/v2/ja/train`
The `mmarco/v2/ja/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/ja/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_v2_ja`](https://huggingface.co/datasets/irds/mmarco_v2_ja)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_ja_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_ja_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_v2_ja_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_ja_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_ja",
"region:us"
]
| 2023-01-05T03:28:18+00:00 | {"source_datasets": ["irds/mmarco_v2_ja"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/ja/train`", "viewer": false} | 2023-01-05T03:28:24+00:00 |
f657b5c613d6cf8b4c76c0c346cf788d095e0ab9 |
# Dataset Card for `mmarco/v2/pt`
The `mmarco/v2/pt` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/pt).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_pt_dev`](https://huggingface.co/datasets/irds/mmarco_v2_pt_dev), [`mmarco_v2_pt_train`](https://huggingface.co/datasets/irds/mmarco_v2_pt_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_pt', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_pt | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:28:29+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/pt`", "viewer": false} | 2023-01-05T03:28:35+00:00 |
80c6924eca6c275fb63f87f1edb7d1254f553197 |
# Dataset Card for `mmarco/v2/pt/dev`
The `mmarco/v2/pt/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/pt/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_pt`](https://huggingface.co/datasets/irds/mmarco_v2_pt)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_pt_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_pt_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_pt_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_pt",
"region:us"
]
| 2023-01-05T03:28:40+00:00 | {"source_datasets": ["irds/mmarco_v2_pt"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/pt/dev`", "viewer": false} | 2023-01-05T03:28:46+00:00 |
a182535672ccc70b49df8e95cdfcde91e75e6a8a |
# Dataset Card for `mmarco/v2/pt/train`
The `mmarco/v2/pt/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/pt/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_v2_pt`](https://huggingface.co/datasets/irds/mmarco_v2_pt)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_pt_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_pt_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_v2_pt_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_pt_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_pt",
"region:us"
]
| 2023-01-05T03:28:51+00:00 | {"source_datasets": ["irds/mmarco_v2_pt"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/pt/train`", "viewer": false} | 2023-01-05T03:28:57+00:00 |
9139938dfdff672f1e6041f2ae539d572102a7c7 |
# Dataset Card for `mmarco/v2/ru`
The `mmarco/v2/ru` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/ru).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_ru_dev`](https://huggingface.co/datasets/irds/mmarco_v2_ru_dev), [`mmarco_v2_ru_train`](https://huggingface.co/datasets/irds/mmarco_v2_ru_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_ru', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_ru | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:29:03+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/ru`", "viewer": false} | 2023-01-05T03:29:08+00:00 |
b2470571ef0729b660765abe4c46cd3cae23c178 |
# Dataset Card for `mmarco/v2/ru/dev`
The `mmarco/v2/ru/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/ru/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_ru`](https://huggingface.co/datasets/irds/mmarco_v2_ru)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_ru_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_ru_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_ru_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_ru",
"region:us"
]
| 2023-01-05T03:29:14+00:00 | {"source_datasets": ["irds/mmarco_v2_ru"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/ru/dev`", "viewer": false} | 2023-01-05T03:29:19+00:00 |
57c370fb695cbdbdcd461ac5174eb2b14cac6ce6 |
# Dataset Card for `mmarco/v2/ru/train`
The `mmarco/v2/ru/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/ru/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_v2_ru`](https://huggingface.co/datasets/irds/mmarco_v2_ru)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_ru_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_ru_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_v2_ru_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_ru_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_ru",
"region:us"
]
| 2023-01-05T03:29:25+00:00 | {"source_datasets": ["irds/mmarco_v2_ru"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/ru/train`", "viewer": false} | 2023-01-05T03:29:30+00:00 |
1e1c186e28642f5368e484e50e4763dc7771794a |
# Dataset Card for `mmarco/v2/vi`
The `mmarco/v2/vi` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/vi).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_vi_dev`](https://huggingface.co/datasets/irds/mmarco_v2_vi_dev), [`mmarco_v2_vi_train`](https://huggingface.co/datasets/irds/mmarco_v2_vi_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_vi', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_vi | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:29:36+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/vi`", "viewer": false} | 2023-01-05T03:29:42+00:00 |
a3a85f41d0fc827cf6ec0279324876b81d7e6f40 |
# Dataset Card for `mmarco/v2/vi/dev`
The `mmarco/v2/vi/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/vi/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_vi`](https://huggingface.co/datasets/irds/mmarco_v2_vi)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_vi_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_vi_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_vi_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_vi",
"region:us"
]
| 2023-01-05T03:29:47+00:00 | {"source_datasets": ["irds/mmarco_v2_vi"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/vi/dev`", "viewer": false} | 2023-01-05T03:29:53+00:00 |
118c5fc9012fdf927d1ddfe86fc1b8ea0e01bd39 |
# Dataset Card for `mmarco/v2/vi/train`
The `mmarco/v2/vi/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/vi/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_v2_vi`](https://huggingface.co/datasets/irds/mmarco_v2_vi)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_vi_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_vi_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_v2_vi_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_vi_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_vi",
"region:us"
]
| 2023-01-05T03:29:58+00:00 | {"source_datasets": ["irds/mmarco_v2_vi"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/vi/train`", "viewer": false} | 2023-01-05T03:30:04+00:00 |
9deac9ddcbad386ae7f5790bd31f60a82ca9350f |
# Dataset Card for `mmarco/v2/zh`
The `mmarco/v2/zh` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/zh).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_v2_zh_dev`](https://huggingface.co/datasets/irds/mmarco_v2_zh_dev), [`mmarco_v2_zh_train`](https://huggingface.co/datasets/irds/mmarco_v2_zh_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_v2_zh', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_zh | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:30:10+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/zh`", "viewer": false} | 2023-01-05T03:30:15+00:00 |
8dda454ab13ffc9292f910d94d54bea669940adb |
# Dataset Card for `mmarco/v2/zh/dev`
The `mmarco/v2/zh/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/zh/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_zh`](https://huggingface.co/datasets/irds/mmarco_v2_zh)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_zh_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_zh_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_zh_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_zh",
"region:us"
]
| 2023-01-05T03:30:21+00:00 | {"source_datasets": ["irds/mmarco_v2_zh"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/zh/dev`", "viewer": false} | 2023-01-05T03:30:26+00:00 |
d3ef087d03f6a09c9241aec41224881ab88bff95 |
# Dataset Card for `mmarco/v2/zh/train`
The `mmarco/v2/zh/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/zh/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_v2_zh`](https://huggingface.co/datasets/irds/mmarco_v2_zh)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_zh_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_zh_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_v2_zh_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_v2_zh_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_v2_zh",
"region:us"
]
| 2023-01-05T03:30:32+00:00 | {"source_datasets": ["irds/mmarco_v2_zh"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/v2/zh/train`", "viewer": false} | 2023-01-05T03:30:37+00:00 |
d8e4203011b1116de9d9738a8988a451861665a2 |
# Dataset Card for `mmarco/zh`
The `mmarco/zh` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/zh).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_zh_dev`](https://huggingface.co/datasets/irds/mmarco_zh_dev), [`mmarco_zh_dev_small`](https://huggingface.co/datasets/irds/mmarco_zh_dev_small), [`mmarco_zh_dev_v1.1`](https://huggingface.co/datasets/irds/mmarco_zh_dev_v1.1), [`mmarco_zh_train`](https://huggingface.co/datasets/irds/mmarco_zh_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_zh', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_zh | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:30:43+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/zh`", "viewer": false} | 2023-01-05T03:30:49+00:00 |
a8eeb0f8a9dee7c850da4151ca03edfdac83901e |
# Dataset Card for `mmarco/zh/dev`
The `mmarco/zh/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/zh/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_zh`](https://huggingface.co/datasets/irds/mmarco_zh)
This dataset is used by: [`mmarco_zh_dev_v1.1`](https://huggingface.co/datasets/irds/mmarco_zh_dev_v1.1)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_zh_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_zh_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_zh_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_zh",
"region:us"
]
| 2023-01-05T03:30:54+00:00 | {"source_datasets": ["irds/mmarco_zh"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/zh/dev`", "viewer": false} | 2023-01-05T03:31:00+00:00 |
c474a1fafa872b760cd9ab484f6c7423f7a546d2 |
# Dataset Card for `mmarco/zh/dev/small`
The `mmarco/zh/dev/small` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/zh/dev/small).
# Data
This dataset provides:
- `queries` (i.e., topics); count=6,980
- `qrels`: (relevance assessments); count=7,437
- For `docs`, use [`irds/mmarco_zh`](https://huggingface.co/datasets/irds/mmarco_zh)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_zh_dev_small', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_zh_dev_small', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_zh_dev_small | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_zh",
"region:us"
]
| 2023-01-05T03:31:05+00:00 | {"source_datasets": ["irds/mmarco_zh"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/zh/dev/small`", "viewer": false} | 2023-01-05T03:31:11+00:00 |
1fafbbeaf298d8e192277bca84e9d82bcf37e54d |
# Dataset Card for `mmarco/zh/dev/v1.1`
The `mmarco/zh/dev/v1.1` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/zh/dev/v1.1).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- For `docs`, use [`irds/mmarco_zh`](https://huggingface.co/datasets/irds/mmarco_zh)
- For `qrels`, use [`irds/mmarco_zh_dev`](https://huggingface.co/datasets/irds/mmarco_zh_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_zh_dev_v1.1', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_zh_dev_v1.1 | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_zh",
"source_datasets:irds/mmarco_zh_dev",
"region:us"
]
| 2023-01-05T03:31:16+00:00 | {"source_datasets": ["irds/mmarco_zh", "irds/mmarco_zh_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/zh/dev/v1.1`", "viewer": false} | 2023-01-05T03:31:22+00:00 |
9baf31ce58f4720ea71c70479f5dc2a6ff90942a |
# Dataset Card for `mmarco/zh/train`
The `mmarco/zh/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/zh/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_zh`](https://huggingface.co/datasets/irds/mmarco_zh)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_zh_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_zh_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_zh_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
| irds/mmarco_zh_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mmarco_zh",
"region:us"
]
| 2023-01-05T03:31:28+00:00 | {"source_datasets": ["irds/mmarco_zh"], "task_categories": ["text-retrieval"], "pretty_name": "`mmarco/zh/train`", "viewer": false} | 2023-01-05T03:31:33+00:00 |
2b33c9015daa0a100a121e5d364109bc7971b424 |
# Dataset Card for `mr-tydi/ar`
The `mr-tydi/ar` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/ar).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=2,106,586
- `queries` (i.e., topics); count=16,595
- `qrels`: (relevance assessments); count=16,749
This dataset is used by: [`mr-tydi_ar_dev`](https://huggingface.co/datasets/irds/mr-tydi_ar_dev), [`mr-tydi_ar_test`](https://huggingface.co/datasets/irds/mr-tydi_ar_test), [`mr-tydi_ar_train`](https://huggingface.co/datasets/irds/mr-tydi_ar_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mr-tydi_ar', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
queries = load_dataset('irds/mr-tydi_ar', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_ar', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_ar | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:31:39+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/ar`", "viewer": false} | 2023-01-05T03:31:44+00:00 |
116977c3b67189c26496ae6c113f160f8c012a89 |
# Dataset Card for `mr-tydi/ar/dev`
The `mr-tydi/ar/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/ar/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=3,115
- `qrels`: (relevance assessments); count=3,115
- For `docs`, use [`irds/mr-tydi_ar`](https://huggingface.co/datasets/irds/mr-tydi_ar)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_ar_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_ar_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_ar_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_ar",
"region:us"
]
| 2023-01-05T03:31:50+00:00 | {"source_datasets": ["irds/mr-tydi_ar"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/ar/dev`", "viewer": false} | 2023-01-05T03:31:55+00:00 |
ea56f4317930f205647148b0b2c582a74764ea4f |
# Dataset Card for `mr-tydi/ar/test`
The `mr-tydi/ar/test` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/ar/test).
# Data
This dataset provides:
- `queries` (i.e., topics); count=1,081
- `qrels`: (relevance assessments); count=1,257
- For `docs`, use [`irds/mr-tydi_ar`](https://huggingface.co/datasets/irds/mr-tydi_ar)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_ar_test', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_ar_test', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_ar_test | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_ar",
"region:us"
]
| 2023-01-05T03:32:01+00:00 | {"source_datasets": ["irds/mr-tydi_ar"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/ar/test`", "viewer": false} | 2023-01-05T03:32:07+00:00 |
82af0d4efac40a0c34be26552f1139519702e64a |
# Dataset Card for `mr-tydi/ar/train`
The `mr-tydi/ar/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/ar/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=12,377
- `qrels`: (relevance assessments); count=12,377
- For `docs`, use [`irds/mr-tydi_ar`](https://huggingface.co/datasets/irds/mr-tydi_ar)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_ar_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_ar_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_ar_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_ar",
"region:us"
]
| 2023-01-05T03:32:12+00:00 | {"source_datasets": ["irds/mr-tydi_ar"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/ar/train`", "viewer": false} | 2023-01-05T03:32:18+00:00 |
3d05169ef6d1fa01da83b6f5b80aa3d79dcb5792 |
# Dataset Card for `mr-tydi/bn`
The `mr-tydi/bn` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/bn).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=304,059
- `queries` (i.e., topics); count=2,264
- `qrels`: (relevance assessments); count=2,292
This dataset is used by: [`mr-tydi_bn_dev`](https://huggingface.co/datasets/irds/mr-tydi_bn_dev), [`mr-tydi_bn_test`](https://huggingface.co/datasets/irds/mr-tydi_bn_test), [`mr-tydi_bn_train`](https://huggingface.co/datasets/irds/mr-tydi_bn_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mr-tydi_bn', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
queries = load_dataset('irds/mr-tydi_bn', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_bn', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_bn | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:32:23+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/bn`", "viewer": false} | 2023-01-05T03:32:29+00:00 |
8c20e0907ac51fde8f7ba8de23137696c4406352 |
# Dataset Card for `mr-tydi/bn/dev`
The `mr-tydi/bn/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/bn/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=440
- `qrels`: (relevance assessments); count=443
- For `docs`, use [`irds/mr-tydi_bn`](https://huggingface.co/datasets/irds/mr-tydi_bn)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_bn_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_bn_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_bn_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_bn",
"region:us"
]
| 2023-01-05T03:32:34+00:00 | {"source_datasets": ["irds/mr-tydi_bn"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/bn/dev`", "viewer": false} | 2023-01-05T03:32:40+00:00 |
ef8865b143d065f164cee924e1b24fe612a32daa |
# Dataset Card for `mr-tydi/bn/test`
The `mr-tydi/bn/test` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/bn/test).
# Data
This dataset provides:
- `queries` (i.e., topics); count=111
- `qrels`: (relevance assessments); count=130
- For `docs`, use [`irds/mr-tydi_bn`](https://huggingface.co/datasets/irds/mr-tydi_bn)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_bn_test', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_bn_test', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_bn_test | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_bn",
"region:us"
]
| 2023-01-05T03:32:45+00:00 | {"source_datasets": ["irds/mr-tydi_bn"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/bn/test`", "viewer": false} | 2023-01-05T03:32:51+00:00 |
215a725e6ed16c8f82cfdbe4d3e93338517f60cb |
# Dataset Card for `mr-tydi/bn/train`
The `mr-tydi/bn/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/bn/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=1,713
- `qrels`: (relevance assessments); count=1,719
- For `docs`, use [`irds/mr-tydi_bn`](https://huggingface.co/datasets/irds/mr-tydi_bn)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_bn_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_bn_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_bn_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_bn",
"region:us"
]
| 2023-01-05T03:32:56+00:00 | {"source_datasets": ["irds/mr-tydi_bn"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/bn/train`", "viewer": false} | 2023-01-05T03:33:02+00:00 |
e97158d8f7045f844dcfcc9a623ed080400e190c |
# Dataset Card for `mr-tydi/en`
The `mr-tydi/en` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/en).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=32,907,100
- `queries` (i.e., topics); count=5,194
- `qrels`: (relevance assessments); count=5,360
This dataset is used by: [`mr-tydi_en_dev`](https://huggingface.co/datasets/irds/mr-tydi_en_dev), [`mr-tydi_en_test`](https://huggingface.co/datasets/irds/mr-tydi_en_test), [`mr-tydi_en_train`](https://huggingface.co/datasets/irds/mr-tydi_en_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mr-tydi_en', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
queries = load_dataset('irds/mr-tydi_en', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_en', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_en | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:33:08+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/en`", "viewer": false} | 2023-01-05T03:33:13+00:00 |
7810f17d9eae686e0a3777bebe55454484f4f579 |
# Dataset Card for `mr-tydi/en/dev`
The `mr-tydi/en/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/en/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=878
- `qrels`: (relevance assessments); count=878
- For `docs`, use [`irds/mr-tydi_en`](https://huggingface.co/datasets/irds/mr-tydi_en)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_en_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_en_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_en_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_en",
"region:us"
]
| 2023-01-05T03:33:19+00:00 | {"source_datasets": ["irds/mr-tydi_en"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/en/dev`", "viewer": false} | 2023-01-05T03:33:24+00:00 |
0c580e618d980b6d995940ee0d4d437531fd5fc7 |
# Dataset Card for `mr-tydi/en/test`
The `mr-tydi/en/test` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/en/test).
# Data
This dataset provides:
- `queries` (i.e., topics); count=744
- `qrels`: (relevance assessments); count=935
- For `docs`, use [`irds/mr-tydi_en`](https://huggingface.co/datasets/irds/mr-tydi_en)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_en_test', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_en_test', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_en_test | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_en",
"region:us"
]
| 2023-01-05T03:33:30+00:00 | {"source_datasets": ["irds/mr-tydi_en"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/en/test`", "viewer": false} | 2023-01-05T03:33:36+00:00 |
e7adc0010e9a25265ff0a34dbe4aa949601f35db |
# Dataset Card for `mr-tydi/en/train`
The `mr-tydi/en/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/en/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=3,547
- `qrels`: (relevance assessments); count=3,547
- For `docs`, use [`irds/mr-tydi_en`](https://huggingface.co/datasets/irds/mr-tydi_en)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_en_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_en_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_en_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_en",
"region:us"
]
| 2023-01-05T03:33:41+00:00 | {"source_datasets": ["irds/mr-tydi_en"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/en/train`", "viewer": false} | 2023-01-05T03:33:47+00:00 |
6fd3f05fad94fa1f76c8b200bf713b18e313ec2b |
# Dataset Card for `mr-tydi/fi`
The `mr-tydi/fi` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/fi).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=1,908,757
- `queries` (i.e., topics); count=9,572
- `qrels`: (relevance assessments); count=9,750
This dataset is used by: [`mr-tydi_fi_dev`](https://huggingface.co/datasets/irds/mr-tydi_fi_dev), [`mr-tydi_fi_test`](https://huggingface.co/datasets/irds/mr-tydi_fi_test), [`mr-tydi_fi_train`](https://huggingface.co/datasets/irds/mr-tydi_fi_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mr-tydi_fi', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
queries = load_dataset('irds/mr-tydi_fi', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_fi', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_fi | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:33:52+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/fi`", "viewer": false} | 2023-01-05T03:33:58+00:00 |
27aca1999a1a6338fd80316ca76567e21ea1b2ed |
# Dataset Card for `mr-tydi/fi/dev`
The `mr-tydi/fi/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/fi/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=1,738
- `qrels`: (relevance assessments); count=1,738
- For `docs`, use [`irds/mr-tydi_fi`](https://huggingface.co/datasets/irds/mr-tydi_fi)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_fi_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_fi_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_fi_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_fi",
"region:us"
]
| 2023-01-05T03:34:03+00:00 | {"source_datasets": ["irds/mr-tydi_fi"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/fi/dev`", "viewer": false} | 2023-01-05T03:34:09+00:00 |
d7a1fb0eb29da74a2131cf6fb729be32ad6fd13a |
# Dataset Card for `mr-tydi/fi/test`
The `mr-tydi/fi/test` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/fi/test).
# Data
This dataset provides:
- `queries` (i.e., topics); count=1,254
- `qrels`: (relevance assessments); count=1,451
- For `docs`, use [`irds/mr-tydi_fi`](https://huggingface.co/datasets/irds/mr-tydi_fi)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_fi_test', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_fi_test', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_fi_test | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_fi",
"region:us"
]
| 2023-01-05T03:34:15+00:00 | {"source_datasets": ["irds/mr-tydi_fi"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/fi/test`", "viewer": false} | 2023-01-05T03:34:20+00:00 |
da4363cbc02ab541845b6fe1765a416f1947e063 |
# Dataset Card for `mr-tydi/fi/train`
The `mr-tydi/fi/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/fi/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=6,561
- `qrels`: (relevance assessments); count=6,561
- For `docs`, use [`irds/mr-tydi_fi`](https://huggingface.co/datasets/irds/mr-tydi_fi)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_fi_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_fi_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_fi_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_fi",
"region:us"
]
| 2023-01-05T03:34:26+00:00 | {"source_datasets": ["irds/mr-tydi_fi"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/fi/train`", "viewer": false} | 2023-01-05T03:34:31+00:00 |
52fa3f74fba6e8df5ed50368faadf723b826b77e |
# Dataset Card for `mr-tydi/id`
The `mr-tydi/id` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/id).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=1,469,399
- `queries` (i.e., topics); count=6,977
- `qrels`: (relevance assessments); count=7,087
This dataset is used by: [`mr-tydi_id_dev`](https://huggingface.co/datasets/irds/mr-tydi_id_dev), [`mr-tydi_id_test`](https://huggingface.co/datasets/irds/mr-tydi_id_test), [`mr-tydi_id_train`](https://huggingface.co/datasets/irds/mr-tydi_id_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mr-tydi_id', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
queries = load_dataset('irds/mr-tydi_id', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_id', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_id | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:34:37+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/id`", "viewer": false} | 2023-01-05T03:34:43+00:00 |
0bc171fb2cf461c7bdc5cc09c206d5abdd319605 |
# Dataset Card for `mr-tydi/id/dev`
The `mr-tydi/id/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/id/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=1,224
- `qrels`: (relevance assessments); count=1,224
- For `docs`, use [`irds/mr-tydi_id`](https://huggingface.co/datasets/irds/mr-tydi_id)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_id_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_id_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_id_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_id",
"region:us"
]
| 2023-01-05T03:34:48+00:00 | {"source_datasets": ["irds/mr-tydi_id"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/id/dev`", "viewer": false} | 2023-01-05T03:34:54+00:00 |
6742bbe73521c9c48ddc8b9d20759a5adfe6f215 |
# Dataset Card for `mr-tydi/id/test`
The `mr-tydi/id/test` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/id/test).
# Data
This dataset provides:
- `queries` (i.e., topics); count=829
- `qrels`: (relevance assessments); count=961
- For `docs`, use [`irds/mr-tydi_id`](https://huggingface.co/datasets/irds/mr-tydi_id)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_id_test', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_id_test', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_id_test | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_id",
"region:us"
]
| 2023-01-05T03:34:59+00:00 | {"source_datasets": ["irds/mr-tydi_id"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/id/test`", "viewer": false} | 2023-01-05T03:35:05+00:00 |
a77f581e55790621d489d5efe98e850e12ce6e39 |
# Dataset Card for `mr-tydi/id/train`
The `mr-tydi/id/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/id/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=4,902
- `qrels`: (relevance assessments); count=4,902
- For `docs`, use [`irds/mr-tydi_id`](https://huggingface.co/datasets/irds/mr-tydi_id)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_id_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_id_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_id_train | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_id",
"region:us"
]
| 2023-01-05T03:35:10+00:00 | {"source_datasets": ["irds/mr-tydi_id"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/id/train`", "viewer": false} | 2023-01-05T03:35:16+00:00 |
fb0ed00b09554d1aac88efb7ee4274bbc4bd88e3 |
# Dataset Card for `mr-tydi/ja`
The `mr-tydi/ja` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/ja).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=7,000,027
- `queries` (i.e., topics); count=5,353
- `qrels`: (relevance assessments); count=5,548
This dataset is used by: [`mr-tydi_ja_dev`](https://huggingface.co/datasets/irds/mr-tydi_ja_dev), [`mr-tydi_ja_test`](https://huggingface.co/datasets/irds/mr-tydi_ja_test), [`mr-tydi_ja_train`](https://huggingface.co/datasets/irds/mr-tydi_ja_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mr-tydi_ja', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
queries = load_dataset('irds/mr-tydi_ja', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_ja', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_ja | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:35:22+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/ja`", "viewer": false} | 2023-01-05T03:35:27+00:00 |
bb8500a049bedc507eae40fa893bc6acdb97e1d2 |
# Dataset Card for `mr-tydi/ja/dev`
The `mr-tydi/ja/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/ja/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=928
- `qrels`: (relevance assessments); count=928
- For `docs`, use [`irds/mr-tydi_ja`](https://huggingface.co/datasets/irds/mr-tydi_ja)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_ja_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_ja_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_ja_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_ja",
"region:us"
]
| 2023-01-05T03:35:33+00:00 | {"source_datasets": ["irds/mr-tydi_ja"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/ja/dev`", "viewer": false} | 2023-01-05T03:35:38+00:00 |
81d197db47cb6d78960d14c6f731a1c9be52aedf |
# Dataset Card for `mr-tydi/ja/test`
The `mr-tydi/ja/test` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/ja/test).
# Data
This dataset provides:
- `queries` (i.e., topics); count=720
- `qrels`: (relevance assessments); count=923
- For `docs`, use [`irds/mr-tydi_ja`](https://huggingface.co/datasets/irds/mr-tydi_ja)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mr-tydi_ja_test', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_ja_test', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
| irds/mr-tydi_ja_test | [
"task_categories:text-retrieval",
"source_datasets:irds/mr-tydi_ja",
"region:us"
]
| 2023-01-05T03:35:44+00:00 | {"source_datasets": ["irds/mr-tydi_ja"], "task_categories": ["text-retrieval"], "pretty_name": "`mr-tydi/ja/test`", "viewer": false} | 2023-01-05T03:35:50+00:00 |
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