text-classification bool 2 classes | text stringlengths 0 664k |
|---|---|
false |
# Dataset Card for `lotte/writing/dev`
The `lotte/writing/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/lotte#lotte/writing/dev).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=277,072
This dataset is used by: [`lotte_writing_dev_forum`](https://huggingface.co/datasets/irds/lotte_writing_dev_forum), [`lotte_writing_dev_search`](https://huggingface.co/datasets/irds/lotte_writing_dev_search)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/lotte_writing_dev', '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{Santhanam2021ColBERTv2,
title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction",
author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia",
journal= "arXiv preprint arXiv:2112.01488",
year = "2021",
url = "https://arxiv.org/abs/2112.01488"
}
```
|
false |
# Dataset Card for `lotte/writing/dev/forum`
The `lotte/writing/dev/forum` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/writing/dev/forum).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,003
- `qrels`: (relevance assessments); count=15,098
- For `docs`, use [`irds/lotte_writing_dev`](https://huggingface.co/datasets/irds/lotte_writing_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_writing_dev_forum', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_writing_dev_forum', '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{Santhanam2021ColBERTv2,
title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction",
author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia",
journal= "arXiv preprint arXiv:2112.01488",
year = "2021",
url = "https://arxiv.org/abs/2112.01488"
}
```
|
false |
# Dataset Card for `lotte/writing/dev/search`
The `lotte/writing/dev/search` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/writing/dev/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=497
- `qrels`: (relevance assessments); count=1,287
- For `docs`, use [`irds/lotte_writing_dev`](https://huggingface.co/datasets/irds/lotte_writing_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_writing_dev_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_writing_dev_search', '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{Santhanam2021ColBERTv2,
title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction",
author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia",
journal= "arXiv preprint arXiv:2112.01488",
year = "2021",
url = "https://arxiv.org/abs/2112.01488"
}
```
|
false |
# Dataset Card for `lotte/writing/test`
The `lotte/writing/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/lotte#lotte/writing/test).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=199,994
This dataset is used by: [`lotte_writing_test_forum`](https://huggingface.co/datasets/irds/lotte_writing_test_forum), [`lotte_writing_test_search`](https://huggingface.co/datasets/irds/lotte_writing_test_search)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/lotte_writing_test', '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{Santhanam2021ColBERTv2,
title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction",
author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia",
journal= "arXiv preprint arXiv:2112.01488",
year = "2021",
url = "https://arxiv.org/abs/2112.01488"
}
```
|
false |
# Dataset Card for `lotte/writing/test/forum`
The `lotte/writing/test/forum` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/writing/test/forum).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,000
- `qrels`: (relevance assessments); count=12,906
- For `docs`, use [`irds/lotte_writing_test`](https://huggingface.co/datasets/irds/lotte_writing_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_writing_test_forum', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_writing_test_forum', '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{Santhanam2021ColBERTv2,
title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction",
author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia",
journal= "arXiv preprint arXiv:2112.01488",
year = "2021",
url = "https://arxiv.org/abs/2112.01488"
}
```
|
false |
# Dataset Card for `lotte/writing/test/search`
The `lotte/writing/test/search` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/writing/test/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=1,071
- `qrels`: (relevance assessments); count=3,546
- For `docs`, use [`irds/lotte_writing_test`](https://huggingface.co/datasets/irds/lotte_writing_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_writing_test_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_writing_test_search', '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{Santhanam2021ColBERTv2,
title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction",
author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia",
journal= "arXiv preprint arXiv:2112.01488",
year = "2021",
url = "https://arxiv.org/abs/2112.01488"
}
```
|
false |
# Dataset Card for `msmarco-passage`
The `msmarco-passage` 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).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`msmarco-passage_dev`](https://huggingface.co/datasets/irds/msmarco-passage_dev), [`msmarco-passage_dev_judged`](https://huggingface.co/datasets/irds/msmarco-passage_dev_judged), [`msmarco-passage_eval`](https://huggingface.co/datasets/irds/msmarco-passage_eval), [`msmarco-passage_train_triples-small`](https://huggingface.co/datasets/irds/msmarco-passage_train_triples-small), [`msmarco-passage_train_triples-v2`](https://huggingface.co/datasets/irds/msmarco-passage_train_triples-v2), [`msmarco-passage_trec-dl-hard`](https://huggingface.co/datasets/irds/msmarco-passage_trec-dl-hard), [`msmarco-passage_trec-dl-hard_fold1`](https://huggingface.co/datasets/irds/msmarco-passage_trec-dl-hard_fold1), [`msmarco-passage_trec-dl-hard_fold2`](https://huggingface.co/datasets/irds/msmarco-passage_trec-dl-hard_fold2), [`msmarco-passage_trec-dl-hard_fold3`](https://huggingface.co/datasets/irds/msmarco-passage_trec-dl-hard_fold3), [`msmarco-passage_trec-dl-hard_fold4`](https://huggingface.co/datasets/irds/msmarco-passage_trec-dl-hard_fold4), [`msmarco-passage_trec-dl-hard_fold5`](https://huggingface.co/datasets/irds/msmarco-passage_trec-dl-hard_fold5)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/msmarco-passage', '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
```
@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}
}
```
|
false |
# Dataset Card for `msmarco-passage/dev`
The `msmarco-passage/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/msmarco-passage#msmarco-passage/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/msmarco-passage`](https://huggingface.co/datasets/irds/msmarco-passage)
This dataset is used by: [`msmarco-passage_dev_judged`](https://huggingface.co/datasets/irds/msmarco-passage_dev_judged)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/msmarco-passage_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/msmarco-passage_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
```
@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}
}
```
|
false |
# Dataset Card for `msmarco-passage/dev/judged`
The `msmarco-passage/dev/judged` 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/dev/judged).
# Data
This dataset provides:
- `queries` (i.e., topics); count=55,578
- For `docs`, use [`irds/msmarco-passage`](https://huggingface.co/datasets/irds/msmarco-passage)
- For `qrels`, use [`irds/msmarco-passage_dev`](https://huggingface.co/datasets/irds/msmarco-passage_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/msmarco-passage_dev_judged', '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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# 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}
}
```
|
false |
# Dataset Card for `mr-tydi/ja/train`
The `mr-tydi/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/mr-tydi#mr-tydi/ja/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=3,697
- `qrels`: (relevance assessments); count=3,697
- 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_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_ja_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}
}
```
|
false |
# Dataset Card for `mr-tydi/ko`
The `mr-tydi/ko` 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/ko).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=1,496,126
- `queries` (i.e., topics); count=2,019
- `qrels`: (relevance assessments); count=2,116
This dataset is used by: [`mr-tydi_ko_dev`](https://huggingface.co/datasets/irds/mr-tydi_ko_dev), [`mr-tydi_ko_test`](https://huggingface.co/datasets/irds/mr-tydi_ko_test), [`mr-tydi_ko_train`](https://huggingface.co/datasets/irds/mr-tydi_ko_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mr-tydi_ko', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
queries = load_dataset('irds/mr-tydi_ko', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_ko', '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}
}
```
|
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