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655000b7048ffb4cf5ab507b64880b680a153c38 |
# Dataset Card for `clueweb12/b13/ntcir-www-3`
The `clueweb12/b13/ntcir-www-3` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/clueweb12#clueweb12/b13/ntcir-www-3).
# Data
This dataset provides:
- `queries` (i.e., topics); count=160
- For `docs`, use [`irds/clueweb12_b13`](https://huggingface.co/datasets/irds/clueweb12_b13)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/clueweb12_b13_ntcir-www-3', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ...}
```
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 π€ Dataset format.
| irds/clueweb12_b13_ntcir-www-3 | [
"task_categories:text-retrieval",
"source_datasets:irds/clueweb12_b13",
"region:us"
]
| 2023-01-05T02:58:41+00:00 | {"source_datasets": ["irds/clueweb12_b13"], "task_categories": ["text-retrieval"], "pretty_name": "`clueweb12/b13/ntcir-www-3`", "viewer": false} | 2023-01-05T02:58:47+00:00 |
332f7264493aebd076d614cefb8de1abe392dad6 |
# Dataset Card for `clueweb12/b13/trec-misinfo-2019`
The `clueweb12/b13/trec-misinfo-2019` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/clueweb12#clueweb12/b13/trec-misinfo-2019).
# Data
This dataset provides:
- `queries` (i.e., topics); count=51
- `qrels`: (relevance assessments); count=22,859
- For `docs`, use [`irds/clueweb12_b13`](https://huggingface.co/datasets/irds/clueweb12_b13)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/clueweb12_b13_trec-misinfo-2019', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'cochranedoi': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/clueweb12_b13_trec-misinfo-2019', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'effectiveness': ..., 'redibility': ...}
```
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 π€ Dataset format.
## Citation Information
```
@inproceedings{Abualsaud2019TrecDecision,
title={Overview of the TREC 2019 Decision Track},
author={Mustafa Abualsaud and Christina Lioma and Maria Maistro and Mark D. Smucker and Guido Zuccon},
booktitle={TREC},
year={2019}
}
```
| irds/clueweb12_b13_trec-misinfo-2019 | [
"task_categories:text-retrieval",
"source_datasets:irds/clueweb12_b13",
"region:us"
]
| 2023-01-05T02:58:52+00:00 | {"source_datasets": ["irds/clueweb12_b13"], "task_categories": ["text-retrieval"], "pretty_name": "`clueweb12/b13/trec-misinfo-2019`", "viewer": false} | 2023-01-05T02:58:58+00:00 |
ec0c54e364d446fb49fc4c1655639138f883aee2 |
# Dataset Card for `codec`
The `codec` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/codec#codec).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=729,824
- `queries` (i.e., topics); count=42
- `qrels`: (relevance assessments); count=6,186
This dataset is used by: [`codec_economics`](https://huggingface.co/datasets/irds/codec_economics), [`codec_history`](https://huggingface.co/datasets/irds/codec_history), [`codec_politics`](https://huggingface.co/datasets/irds/codec_politics)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/codec', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'text': ..., 'url': ...}
queries = load_dataset('irds/codec', 'queries')
for record in queries:
record # {'query_id': ..., 'query': ..., 'domain': ..., 'guidelines': ...}
qrels = load_dataset('irds/codec', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{mackie2022codec,
title={CODEC: Complex Document and Entity Collection},
author={Mackie, Iain and Owoicho, Paul and Gemmell, Carlos and Fischer, Sophie and MacAvaney, Sean and Dalton, Jeffery},
booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year={2022}
}
```
| irds/codec | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T02:59:04+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`codec`", "viewer": false} | 2023-01-05T02:59:09+00:00 |
a03e874ae88baddeb147cbebe9e7bdf368a68dba |
# Dataset Card for `codec/economics`
The `codec/economics` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/codec#codec/economics).
# Data
This dataset provides:
- `queries` (i.e., topics); count=14
- `qrels`: (relevance assessments); count=1,970
- For `docs`, use [`irds/codec`](https://huggingface.co/datasets/irds/codec)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/codec_economics', 'queries')
for record in queries:
record # {'query_id': ..., 'query': ..., 'domain': ..., 'guidelines': ...}
qrels = load_dataset('irds/codec_economics', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{mackie2022codec,
title={CODEC: Complex Document and Entity Collection},
author={Mackie, Iain and Owoicho, Paul and Gemmell, Carlos and Fischer, Sophie and MacAvaney, Sean and Dalton, Jeffery},
booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year={2022}
}
```
| irds/codec_economics | [
"task_categories:text-retrieval",
"source_datasets:irds/codec",
"region:us"
]
| 2023-01-05T02:59:15+00:00 | {"source_datasets": ["irds/codec"], "task_categories": ["text-retrieval"], "pretty_name": "`codec/economics`", "viewer": false} | 2023-01-05T02:59:20+00:00 |
e6fa05fded809259ab7ecba70180075ef753ff69 |
# Dataset Card for `codec/history`
The `codec/history` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/codec#codec/history).
# Data
This dataset provides:
- `queries` (i.e., topics); count=14
- `qrels`: (relevance assessments); count=2,024
- For `docs`, use [`irds/codec`](https://huggingface.co/datasets/irds/codec)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/codec_history', 'queries')
for record in queries:
record # {'query_id': ..., 'query': ..., 'domain': ..., 'guidelines': ...}
qrels = load_dataset('irds/codec_history', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{mackie2022codec,
title={CODEC: Complex Document and Entity Collection},
author={Mackie, Iain and Owoicho, Paul and Gemmell, Carlos and Fischer, Sophie and MacAvaney, Sean and Dalton, Jeffery},
booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year={2022}
}
```
| irds/codec_history | [
"task_categories:text-retrieval",
"source_datasets:irds/codec",
"region:us"
]
| 2023-01-05T02:59:26+00:00 | {"source_datasets": ["irds/codec"], "task_categories": ["text-retrieval"], "pretty_name": "`codec/history`", "viewer": false} | 2023-01-05T02:59:31+00:00 |
9b586aef35f669e67c9a6a125bf79cf4fda5a6a0 |
# Dataset Card for `codec/politics`
The `codec/politics` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/codec#codec/politics).
# Data
This dataset provides:
- `queries` (i.e., topics); count=14
- `qrels`: (relevance assessments); count=2,192
- For `docs`, use [`irds/codec`](https://huggingface.co/datasets/irds/codec)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/codec_politics', 'queries')
for record in queries:
record # {'query_id': ..., 'query': ..., 'domain': ..., 'guidelines': ...}
qrels = load_dataset('irds/codec_politics', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{mackie2022codec,
title={CODEC: Complex Document and Entity Collection},
author={Mackie, Iain and Owoicho, Paul and Gemmell, Carlos and Fischer, Sophie and MacAvaney, Sean and Dalton, Jeffery},
booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year={2022}
}
```
| irds/codec_politics | [
"task_categories:text-retrieval",
"source_datasets:irds/codec",
"region:us"
]
| 2023-01-05T02:59:37+00:00 | {"source_datasets": ["irds/codec"], "task_categories": ["text-retrieval"], "pretty_name": "`codec/politics`", "viewer": false} | 2023-01-05T02:59:43+00:00 |
fa7626cf818659caf8e9d0aed32b919c5a012b5a |
# Dataset Card for `cord19`
The `cord19` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/cord19#cord19).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=192,509
This dataset is used by: [`cord19_trec-covid`](https://huggingface.co/datasets/irds/cord19_trec-covid), [`cord19_trec-covid_round5`](https://huggingface.co/datasets/irds/cord19_trec-covid_round5)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/cord19', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'doi': ..., 'date': ..., 'abstract': ...}
```
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 π€ Dataset format.
## Citation Information
```
@article{Wang2020Cord19,
title={CORD-19: The Covid-19 Open Research Dataset},
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},
journal={ArXiv},
year={2020}
}
```
| irds/cord19 | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T02:59:48+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`cord19`", "viewer": false} | 2023-01-05T02:59:54+00:00 |
2f3c1421303b0dc01c2f2b449b306939919179d4 |
# Dataset Card for `cord19/fulltext/trec-covid`
The `cord19/fulltext/trec-covid` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/cord19#cord19/fulltext/trec-covid).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=69,318
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/cord19_fulltext_trec-covid', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/cord19_fulltext_trec-covid', '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 π€ Dataset format.
## Citation Information
```
@article{Voorhees2020TrecCovid,
title={TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection},
author={E. Voorhees and Tasmeer Alam and Steven Bedrick and Dina Demner-Fushman and W. Hersh and Kyle Lo and Kirk Roberts and I. Soboroff and Lucy Lu Wang},
journal={ArXiv},
year={2020},
volume={abs/2005.04474}
}
@article{Wang2020Cord19,
title={CORD-19: The Covid-19 Open Research Dataset},
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},
journal={ArXiv},
year={2020}
}
```
| irds/cord19_fulltext_trec-covid | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T02:59:59+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`cord19/fulltext/trec-covid`", "viewer": false} | 2023-01-05T03:00:05+00:00 |
82b3b2bd9156a8cd8ff4470ee3c1ac4071f1207b |
# Dataset Card for `cord19/trec-covid`
The `cord19/trec-covid` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/cord19#cord19/trec-covid).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=69,318
- For `docs`, use [`irds/cord19`](https://huggingface.co/datasets/irds/cord19)
This dataset is used by: [`cord19_trec-covid_round5`](https://huggingface.co/datasets/irds/cord19_trec-covid_round5)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/cord19_trec-covid', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/cord19_trec-covid', '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 π€ Dataset format.
## Citation Information
```
@article{Voorhees2020TrecCovid,
title={TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection},
author={E. Voorhees and Tasmeer Alam and Steven Bedrick and Dina Demner-Fushman and W. Hersh and Kyle Lo and Kirk Roberts and I. Soboroff and Lucy Lu Wang},
journal={ArXiv},
year={2020},
volume={abs/2005.04474}
}
@article{Wang2020Cord19,
title={CORD-19: The Covid-19 Open Research Dataset},
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},
journal={ArXiv},
year={2020}
}
```
| irds/cord19_trec-covid | [
"task_categories:text-retrieval",
"source_datasets:irds/cord19",
"region:us"
]
| 2023-01-05T03:00:10+00:00 | {"source_datasets": ["irds/cord19"], "task_categories": ["text-retrieval"], "pretty_name": "`cord19/trec-covid`", "viewer": false} | 2023-01-05T03:00:16+00:00 |
98237ea0a13cd99c51f1be77986f5a9948bd1a07 |
# Dataset Card for `cord19/trec-covid/round1`
The `cord19/trec-covid/round1` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/cord19#cord19/trec-covid/round1).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=51,078
- `queries` (i.e., topics); count=30
- `qrels`: (relevance assessments); count=8,691
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/cord19_trec-covid_round1', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'doi': ..., 'date': ..., 'abstract': ...}
queries = load_dataset('irds/cord19_trec-covid_round1', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/cord19_trec-covid_round1', '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 π€ Dataset format.
## Citation Information
```
@article{Voorhees2020TrecCovid,
title={TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection},
author={E. Voorhees and Tasmeer Alam and Steven Bedrick and Dina Demner-Fushman and W. Hersh and Kyle Lo and Kirk Roberts and I. Soboroff and Lucy Lu Wang},
journal={ArXiv},
year={2020},
volume={abs/2005.04474}
}
@article{Wang2020Cord19,
title={CORD-19: The Covid-19 Open Research Dataset},
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},
journal={ArXiv},
year={2020}
}
```
| irds/cord19_trec-covid_round1 | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:00:22+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`cord19/trec-covid/round1`", "viewer": false} | 2023-01-05T03:00:27+00:00 |
bf5a71315325ecbaac8c532e8aa4c5e6f086ebb9 |
# Dataset Card for `cord19/trec-covid/round2`
The `cord19/trec-covid/round2` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/cord19#cord19/trec-covid/round2).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=59,887
- `queries` (i.e., topics); count=35
- `qrels`: (relevance assessments); count=12,037
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/cord19_trec-covid_round2', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'doi': ..., 'date': ..., 'abstract': ...}
queries = load_dataset('irds/cord19_trec-covid_round2', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/cord19_trec-covid_round2', '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 π€ Dataset format.
## Citation Information
```
@article{Voorhees2020TrecCovid,
title={TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection},
author={E. Voorhees and Tasmeer Alam and Steven Bedrick and Dina Demner-Fushman and W. Hersh and Kyle Lo and Kirk Roberts and I. Soboroff and Lucy Lu Wang},
journal={ArXiv},
year={2020},
volume={abs/2005.04474}
}
@article{Wang2020Cord19,
title={CORD-19: The Covid-19 Open Research Dataset},
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},
journal={ArXiv},
year={2020}
}
```
| irds/cord19_trec-covid_round2 | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:00:33+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`cord19/trec-covid/round2`", "viewer": false} | 2023-01-05T03:00:39+00:00 |
605c214bddf10bdd557ace09150fa1b1bedd0416 |
# Dataset Card for `cord19/trec-covid/round3`
The `cord19/trec-covid/round3` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/cord19#cord19/trec-covid/round3).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=128,492
- `queries` (i.e., topics); count=40
- `qrels`: (relevance assessments); count=12,713
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/cord19_trec-covid_round3', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'doi': ..., 'date': ..., 'abstract': ...}
queries = load_dataset('irds/cord19_trec-covid_round3', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/cord19_trec-covid_round3', '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 π€ Dataset format.
## Citation Information
```
@article{Voorhees2020TrecCovid,
title={TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection},
author={E. Voorhees and Tasmeer Alam and Steven Bedrick and Dina Demner-Fushman and W. Hersh and Kyle Lo and Kirk Roberts and I. Soboroff and Lucy Lu Wang},
journal={ArXiv},
year={2020},
volume={abs/2005.04474}
}
@article{Wang2020Cord19,
title={CORD-19: The Covid-19 Open Research Dataset},
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},
journal={ArXiv},
year={2020}
}
```
| irds/cord19_trec-covid_round3 | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:00:44+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`cord19/trec-covid/round3`", "viewer": false} | 2023-01-05T03:00:50+00:00 |
0cc515cf99545227460067a0cde9920c66ff47e7 |
# Dataset Card for `cord19/trec-covid/round4`
The `cord19/trec-covid/round4` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/cord19#cord19/trec-covid/round4).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=158,274
- `queries` (i.e., topics); count=45
- `qrels`: (relevance assessments); count=13,262
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/cord19_trec-covid_round4', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'doi': ..., 'date': ..., 'abstract': ...}
queries = load_dataset('irds/cord19_trec-covid_round4', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/cord19_trec-covid_round4', '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 π€ Dataset format.
## Citation Information
```
@article{Voorhees2020TrecCovid,
title={TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection},
author={E. Voorhees and Tasmeer Alam and Steven Bedrick and Dina Demner-Fushman and W. Hersh and Kyle Lo and Kirk Roberts and I. Soboroff and Lucy Lu Wang},
journal={ArXiv},
year={2020},
volume={abs/2005.04474}
}
@article{Wang2020Cord19,
title={CORD-19: The Covid-19 Open Research Dataset},
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},
journal={ArXiv},
year={2020}
}
```
| irds/cord19_trec-covid_round4 | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:00:55+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`cord19/trec-covid/round4`", "viewer": false} | 2023-01-05T03:01:01+00:00 |
99e35e9abc3b2b3d7a9bb88f3ecb2156add4efdd |
# Dataset Card for `cord19/trec-covid/round5`
The `cord19/trec-covid/round5` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/cord19#cord19/trec-covid/round5).
# Data
This dataset provides:
- `qrels`: (relevance assessments); count=23,151
- For `docs`, use [`irds/cord19`](https://huggingface.co/datasets/irds/cord19)
- For `queries`, use [`irds/cord19_trec-covid`](https://huggingface.co/datasets/irds/cord19_trec-covid)
## Usage
```python
from datasets import load_dataset
qrels = load_dataset('irds/cord19_trec-covid_round5', '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 π€ Dataset format.
## Citation Information
```
@article{Voorhees2020TrecCovid,
title={TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection},
author={E. Voorhees and Tasmeer Alam and Steven Bedrick and Dina Demner-Fushman and W. Hersh and Kyle Lo and Kirk Roberts and I. Soboroff and Lucy Lu Wang},
journal={ArXiv},
year={2020},
volume={abs/2005.04474}
}
@article{Wang2020Cord19,
title={CORD-19: The Covid-19 Open Research Dataset},
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},
journal={ArXiv},
year={2020}
}
```
| irds/cord19_trec-covid_round5 | [
"task_categories:text-retrieval",
"source_datasets:irds/cord19",
"source_datasets:irds/cord19_trec-covid",
"region:us"
]
| 2023-01-05T03:01:06+00:00 | {"source_datasets": ["irds/cord19", "irds/cord19_trec-covid"], "task_categories": ["text-retrieval"], "pretty_name": "`cord19/trec-covid/round5`", "viewer": false} | 2023-01-05T03:01:12+00:00 |
e631ffec40e83200210806b7e9c565b26a02f97b |
# Dataset Card for `cranfield`
The `cranfield` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/cranfield#cranfield).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=1,400
- `queries` (i.e., topics); count=225
- `qrels`: (relevance assessments); count=1,837
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/cranfield', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'text': ..., 'author': ..., 'bib': ...}
queries = load_dataset('irds/cranfield', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/cranfield', '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 π€ Dataset format.
| irds/cranfield | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:01:17+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`cranfield`", "viewer": false} | 2023-01-05T03:01:23+00:00 |
52c67d945ef9511d18477d413607ae97b9c88d8e |
# Dataset Card for `disks45/nocr`
The `disks45/nocr` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/disks45#disks45/nocr).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=528,155
This dataset is used by: [`disks45_nocr_trec-robust-2004`](https://huggingface.co/datasets/irds/disks45_nocr_trec-robust-2004), [`disks45_nocr_trec-robust-2004_fold1`](https://huggingface.co/datasets/irds/disks45_nocr_trec-robust-2004_fold1), [`disks45_nocr_trec-robust-2004_fold2`](https://huggingface.co/datasets/irds/disks45_nocr_trec-robust-2004_fold2), [`disks45_nocr_trec-robust-2004_fold3`](https://huggingface.co/datasets/irds/disks45_nocr_trec-robust-2004_fold3), [`disks45_nocr_trec-robust-2004_fold4`](https://huggingface.co/datasets/irds/disks45_nocr_trec-robust-2004_fold4), [`disks45_nocr_trec-robust-2004_fold5`](https://huggingface.co/datasets/irds/disks45_nocr_trec-robust-2004_fold5), [`disks45_nocr_trec7`](https://huggingface.co/datasets/irds/disks45_nocr_trec7), [`disks45_nocr_trec8`](https://huggingface.co/datasets/irds/disks45_nocr_trec8)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/disks45_nocr', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'body': ..., 'marked_up_doc': ...}
```
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 π€ Dataset format.
## Citation Information
```
@misc{Voorhees1996Disks45,
title = {NIST TREC Disks 4 and 5: Retrieval Test Collections Document Set},
author = {Ellen M. Voorhees},
doi = {10.18434/t47g6m},
year = {1996},
publisher = {National Institute of Standards and Technology}
}
```
| irds/disks45_nocr | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:01:29+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`disks45/nocr`", "viewer": false} | 2023-01-05T03:01:34+00:00 |
82515c250db47eba1d7cfa0fb32e1f81c1c6336d |
# Dataset Card for `disks45/nocr/trec-robust-2004`
The `disks45/nocr/trec-robust-2004` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/disks45#disks45/nocr/trec-robust-2004).
# Data
This dataset provides:
- `queries` (i.e., topics); count=250
- `qrels`: (relevance assessments); count=311,410
- For `docs`, use [`irds/disks45_nocr`](https://huggingface.co/datasets/irds/disks45_nocr)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/disks45_nocr_trec-robust-2004', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/disks45_nocr_trec-robust-2004', '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 π€ Dataset format.
## Citation Information
```
@misc{Voorhees1996Disks45,
title = {NIST TREC Disks 4 and 5: Retrieval Test Collections Document Set},
author = {Ellen M. Voorhees},
doi = {10.18434/t47g6m},
year = {1996},
publisher = {National Institute of Standards and Technology}
}
@inproceedings{Voorhees2004Robust,
title={Overview of the TREC 2004 Robust Retrieval Track},
author={Ellen Voorhees},
booktitle={TREC},
year={2004}
}
@inproceedings{Huston2014ACO,
title={A Comparison of Retrieval Models using Term Dependencies},
author={Samuel Huston and W. Bruce Croft},
booktitle={CIKM},
year={2014}
}
```
| irds/disks45_nocr_trec-robust-2004 | [
"task_categories:text-retrieval",
"source_datasets:irds/disks45_nocr",
"region:us"
]
| 2023-01-05T03:01:40+00:00 | {"source_datasets": ["irds/disks45_nocr"], "task_categories": ["text-retrieval"], "pretty_name": "`disks45/nocr/trec-robust-2004`", "viewer": false} | 2023-01-05T03:01:45+00:00 |
7a3dffc831270a0ac3f9004f5116e8624cbc339f |
# Dataset Card for `disks45/nocr/trec-robust-2004/fold1`
The `disks45/nocr/trec-robust-2004/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/disks45#disks45/nocr/trec-robust-2004/fold1).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=62,789
- For `docs`, use [`irds/disks45_nocr`](https://huggingface.co/datasets/irds/disks45_nocr)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/disks45_nocr_trec-robust-2004_fold1', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/disks45_nocr_trec-robust-2004_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 π€ Dataset format.
## Citation Information
```
@misc{Voorhees1996Disks45,
title = {NIST TREC Disks 4 and 5: Retrieval Test Collections Document Set},
author = {Ellen M. Voorhees},
doi = {10.18434/t47g6m},
year = {1996},
publisher = {National Institute of Standards and Technology}
}
@inproceedings{Voorhees2004Robust,
title={Overview of the TREC 2004 Robust Retrieval Track},
author={Ellen Voorhees},
booktitle={TREC},
year={2004}
}
@inproceedings{Huston2014ACO,
title={A Comparison of Retrieval Models using Term Dependencies},
author={Samuel Huston and W. Bruce Croft},
booktitle={CIKM},
year={2014}
}
```
| irds/disks45_nocr_trec-robust-2004_fold1 | [
"task_categories:text-retrieval",
"source_datasets:irds/disks45_nocr",
"region:us"
]
| 2023-01-05T03:01:51+00:00 | {"source_datasets": ["irds/disks45_nocr"], "task_categories": ["text-retrieval"], "pretty_name": "`disks45/nocr/trec-robust-2004/fold1`", "viewer": false} | 2023-01-05T03:01:57+00:00 |
2e65f37fb30c2fb303fcdb94de14dee46f659ab1 |
# Dataset Card for `disks45/nocr/trec-robust-2004/fold2`
The `disks45/nocr/trec-robust-2004/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/disks45#disks45/nocr/trec-robust-2004/fold2).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=63,917
- For `docs`, use [`irds/disks45_nocr`](https://huggingface.co/datasets/irds/disks45_nocr)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/disks45_nocr_trec-robust-2004_fold2', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/disks45_nocr_trec-robust-2004_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 π€ Dataset format.
## Citation Information
```
@misc{Voorhees1996Disks45,
title = {NIST TREC Disks 4 and 5: Retrieval Test Collections Document Set},
author = {Ellen M. Voorhees},
doi = {10.18434/t47g6m},
year = {1996},
publisher = {National Institute of Standards and Technology}
}
@inproceedings{Voorhees2004Robust,
title={Overview of the TREC 2004 Robust Retrieval Track},
author={Ellen Voorhees},
booktitle={TREC},
year={2004}
}
@inproceedings{Huston2014ACO,
title={A Comparison of Retrieval Models using Term Dependencies},
author={Samuel Huston and W. Bruce Croft},
booktitle={CIKM},
year={2014}
}
```
| irds/disks45_nocr_trec-robust-2004_fold2 | [
"task_categories:text-retrieval",
"source_datasets:irds/disks45_nocr",
"region:us"
]
| 2023-01-05T03:02:02+00:00 | {"source_datasets": ["irds/disks45_nocr"], "task_categories": ["text-retrieval"], "pretty_name": "`disks45/nocr/trec-robust-2004/fold2`", "viewer": false} | 2023-01-05T03:02:08+00:00 |
95802b20acedd71aa9e5d18f027d436e300e9768 |
# Dataset Card for `disks45/nocr/trec-robust-2004/fold3`
The `disks45/nocr/trec-robust-2004/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/disks45#disks45/nocr/trec-robust-2004/fold3).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=62,901
- For `docs`, use [`irds/disks45_nocr`](https://huggingface.co/datasets/irds/disks45_nocr)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/disks45_nocr_trec-robust-2004_fold3', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/disks45_nocr_trec-robust-2004_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 π€ Dataset format.
## Citation Information
```
@misc{Voorhees1996Disks45,
title = {NIST TREC Disks 4 and 5: Retrieval Test Collections Document Set},
author = {Ellen M. Voorhees},
doi = {10.18434/t47g6m},
year = {1996},
publisher = {National Institute of Standards and Technology}
}
@inproceedings{Voorhees2004Robust,
title={Overview of the TREC 2004 Robust Retrieval Track},
author={Ellen Voorhees},
booktitle={TREC},
year={2004}
}
@inproceedings{Huston2014ACO,
title={A Comparison of Retrieval Models using Term Dependencies},
author={Samuel Huston and W. Bruce Croft},
booktitle={CIKM},
year={2014}
}
```
| irds/disks45_nocr_trec-robust-2004_fold3 | [
"task_categories:text-retrieval",
"source_datasets:irds/disks45_nocr",
"region:us"
]
| 2023-01-05T03:02:13+00:00 | {"source_datasets": ["irds/disks45_nocr"], "task_categories": ["text-retrieval"], "pretty_name": "`disks45/nocr/trec-robust-2004/fold3`", "viewer": false} | 2023-01-05T03:02:19+00:00 |
17af47a69cde35eeccbf0a620b9bc463852db686 |
# Dataset Card for `disks45/nocr/trec-robust-2004/fold4`
The `disks45/nocr/trec-robust-2004/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/disks45#disks45/nocr/trec-robust-2004/fold4).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=57,962
- For `docs`, use [`irds/disks45_nocr`](https://huggingface.co/datasets/irds/disks45_nocr)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/disks45_nocr_trec-robust-2004_fold4', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/disks45_nocr_trec-robust-2004_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 π€ Dataset format.
## Citation Information
```
@misc{Voorhees1996Disks45,
title = {NIST TREC Disks 4 and 5: Retrieval Test Collections Document Set},
author = {Ellen M. Voorhees},
doi = {10.18434/t47g6m},
year = {1996},
publisher = {National Institute of Standards and Technology}
}
@inproceedings{Voorhees2004Robust,
title={Overview of the TREC 2004 Robust Retrieval Track},
author={Ellen Voorhees},
booktitle={TREC},
year={2004}
}
@inproceedings{Huston2014ACO,
title={A Comparison of Retrieval Models using Term Dependencies},
author={Samuel Huston and W. Bruce Croft},
booktitle={CIKM},
year={2014}
}
```
| irds/disks45_nocr_trec-robust-2004_fold4 | [
"task_categories:text-retrieval",
"source_datasets:irds/disks45_nocr",
"region:us"
]
| 2023-01-05T03:02:24+00:00 | {"source_datasets": ["irds/disks45_nocr"], "task_categories": ["text-retrieval"], "pretty_name": "`disks45/nocr/trec-robust-2004/fold4`", "viewer": false} | 2023-01-05T03:02:30+00:00 |
8e5830b11ffd3fbadba98f540791125d6f15d0e0 |
# Dataset Card for `disks45/nocr/trec-robust-2004/fold5`
The `disks45/nocr/trec-robust-2004/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/disks45#disks45/nocr/trec-robust-2004/fold5).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=63,841
- For `docs`, use [`irds/disks45_nocr`](https://huggingface.co/datasets/irds/disks45_nocr)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/disks45_nocr_trec-robust-2004_fold5', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/disks45_nocr_trec-robust-2004_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 π€ Dataset format.
## Citation Information
```
@misc{Voorhees1996Disks45,
title = {NIST TREC Disks 4 and 5: Retrieval Test Collections Document Set},
author = {Ellen M. Voorhees},
doi = {10.18434/t47g6m},
year = {1996},
publisher = {National Institute of Standards and Technology}
}
@inproceedings{Voorhees2004Robust,
title={Overview of the TREC 2004 Robust Retrieval Track},
author={Ellen Voorhees},
booktitle={TREC},
year={2004}
}
@inproceedings{Huston2014ACO,
title={A Comparison of Retrieval Models using Term Dependencies},
author={Samuel Huston and W. Bruce Croft},
booktitle={CIKM},
year={2014}
}
```
| irds/disks45_nocr_trec-robust-2004_fold5 | [
"task_categories:text-retrieval",
"source_datasets:irds/disks45_nocr",
"region:us"
]
| 2023-01-05T03:02:35+00:00 | {"source_datasets": ["irds/disks45_nocr"], "task_categories": ["text-retrieval"], "pretty_name": "`disks45/nocr/trec-robust-2004/fold5`", "viewer": false} | 2023-01-05T03:02:41+00:00 |
47d1eb11b8cd84a3e894376f2ac32778ba3db2ee |
# Dataset Card for `disks45/nocr/trec7`
The `disks45/nocr/trec7` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/disks45#disks45/nocr/trec7).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=80,345
- For `docs`, use [`irds/disks45_nocr`](https://huggingface.co/datasets/irds/disks45_nocr)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/disks45_nocr_trec7', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/disks45_nocr_trec7', '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 π€ Dataset format.
## Citation Information
```
@misc{Voorhees1996Disks45,
title = {NIST TREC Disks 4 and 5: Retrieval Test Collections Document Set},
author = {Ellen M. Voorhees},
doi = {10.18434/t47g6m},
year = {1996},
publisher = {National Institute of Standards and Technology}
}
@inproceedings{Voorhees1998Trec7,
title = {Overview of the Seventh Text Retrieval Conference (TREC-7)},
author = {Ellen M. Voorhees and Donna Harman},
year = {1998},
booktitle = {TREC}
}
```
| irds/disks45_nocr_trec7 | [
"task_categories:text-retrieval",
"source_datasets:irds/disks45_nocr",
"region:us"
]
| 2023-01-05T03:02:46+00:00 | {"source_datasets": ["irds/disks45_nocr"], "task_categories": ["text-retrieval"], "pretty_name": "`disks45/nocr/trec7`", "viewer": false} | 2023-01-05T03:02:52+00:00 |
8dd6749f122fa62cac2d269410a61a62a2e52fc7 |
# Dataset Card for `disks45/nocr/trec8`
The `disks45/nocr/trec8` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/disks45#disks45/nocr/trec8).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=86,830
- For `docs`, use [`irds/disks45_nocr`](https://huggingface.co/datasets/irds/disks45_nocr)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/disks45_nocr_trec8', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/disks45_nocr_trec8', '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 π€ Dataset format.
## Citation Information
```
@misc{Voorhees1996Disks45,
title = {NIST TREC Disks 4 and 5: Retrieval Test Collections Document Set},
author = {Ellen M. Voorhees},
doi = {10.18434/t47g6m},
year = {1996},
publisher = {National Institute of Standards and Technology}
}
@inproceedings{Voorhees1999Trec8,
title = {Overview of the Eight Text Retrieval Conference (TREC-8)},
author = {Ellen M. Voorhees and Donna Harman},
year = {1999},
booktitle = {TREC}
}
```
| irds/disks45_nocr_trec8 | [
"task_categories:text-retrieval",
"source_datasets:irds/disks45_nocr",
"region:us"
]
| 2023-01-05T03:02:58+00:00 | {"source_datasets": ["irds/disks45_nocr"], "task_categories": ["text-retrieval"], "pretty_name": "`disks45/nocr/trec8`", "viewer": false} | 2023-01-05T03:03:03+00:00 |
d14722a3b1c5c2d1a2c81f7675c27563055daead |
# Dataset Card for `dpr-w100`
The `dpr-w100` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/dpr-w100#dpr-w100).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=21,015,324
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/dpr-w100', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ..., 'title': ...}
```
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 π€ Dataset format.
## Citation Information
```
@misc{Karpukhin2020Dpr,
title={Dense Passage Retrieval for Open-Domain Question Answering},
author={Vladimir Karpukhin and Barlas OΔuz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih},
year={2020},
eprint={2004.04906},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | irds/dpr-w100 | [
"task_categories:text-retrieval",
"language:en",
"arxiv:2004.04906",
"region:us"
]
| 2023-01-05T03:03:09+00:00 | {"language": ["en"], "source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`dpr-w100`", "viewer": false} | 2023-10-23T10:25:33+00:00 |
6695b56cd62bf515d7dfb782c4d484f5b5c9ecf2 |
# Dataset Card for `codesearchnet`
The `codesearchnet` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/codesearchnet#codesearchnet).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=2,070,536
This dataset is used by: [`codesearchnet_challenge`](https://huggingface.co/datasets/irds/codesearchnet_challenge), [`codesearchnet_test`](https://huggingface.co/datasets/irds/codesearchnet_test), [`codesearchnet_train`](https://huggingface.co/datasets/irds/codesearchnet_train), [`codesearchnet_valid`](https://huggingface.co/datasets/irds/codesearchnet_valid)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/codesearchnet', 'docs')
for record in docs:
record # {'doc_id': ..., 'repo': ..., 'path': ..., 'func_name': ..., 'code': ..., 'language': ...}
```
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 π€ Dataset format.
## Citation Information
```
@article{Husain2019CodeSearchNet,
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
author={Hamel Husain and Ho-Hsiang Wu and Tiferet Gazit and Miltiadis Allamanis and Marc Brockschmidt},
journal={ArXiv},
year={2019}
}
```
| irds/codesearchnet | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:03:20+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`codesearchnet`", "viewer": false} | 2023-01-05T03:03:26+00:00 |
2686b0d4a10f365b8e51c3dc4941937c95b07ca7 |
# Dataset Card for `codesearchnet/challenge`
The `codesearchnet/challenge` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/codesearchnet#codesearchnet/challenge).
# Data
This dataset provides:
- `queries` (i.e., topics); count=99
- `qrels`: (relevance assessments); count=4,006
- For `docs`, use [`irds/codesearchnet`](https://huggingface.co/datasets/irds/codesearchnet)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/codesearchnet_challenge', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/codesearchnet_challenge', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'note': ...}
```
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 π€ Dataset format.
## Citation Information
```
@article{Husain2019CodeSearchNet,
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
author={Hamel Husain and Ho-Hsiang Wu and Tiferet Gazit and Miltiadis Allamanis and Marc Brockschmidt},
journal={ArXiv},
year={2019}
}
```
| irds/codesearchnet_challenge | [
"task_categories:text-retrieval",
"source_datasets:irds/codesearchnet",
"region:us"
]
| 2023-01-05T03:03:31+00:00 | {"source_datasets": ["irds/codesearchnet"], "task_categories": ["text-retrieval"], "pretty_name": "`codesearchnet/challenge`", "viewer": false} | 2023-01-05T03:03:37+00:00 |
853bf8f7923e1d3f916fecf38d4a437bb64ec18c |
# Dataset Card for `codesearchnet/test`
The `codesearchnet/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/codesearchnet#codesearchnet/test).
# Data
This dataset provides:
- `queries` (i.e., topics); count=100,529
- `qrels`: (relevance assessments); count=100,529
- For `docs`, use [`irds/codesearchnet`](https://huggingface.co/datasets/irds/codesearchnet)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/codesearchnet_test', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/codesearchnet_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 π€ Dataset format.
## Citation Information
```
@article{Husain2019CodeSearchNet,
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
author={Hamel Husain and Ho-Hsiang Wu and Tiferet Gazit and Miltiadis Allamanis and Marc Brockschmidt},
journal={ArXiv},
year={2019}
}
```
| irds/codesearchnet_test | [
"task_categories:text-retrieval",
"source_datasets:irds/codesearchnet",
"region:us"
]
| 2023-01-05T03:03:42+00:00 | {"source_datasets": ["irds/codesearchnet"], "task_categories": ["text-retrieval"], "pretty_name": "`codesearchnet/test`", "viewer": false} | 2023-01-05T03:03:48+00:00 |
2387d7238e065304190aa0b769c9e7256e29e6d9 |
# Dataset Card for `codesearchnet/train`
The `codesearchnet/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/codesearchnet#codesearchnet/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=1,880,853
- `qrels`: (relevance assessments); count=1,880,853
- For `docs`, use [`irds/codesearchnet`](https://huggingface.co/datasets/irds/codesearchnet)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/codesearchnet_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/codesearchnet_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 π€ Dataset format.
## Citation Information
```
@article{Husain2019CodeSearchNet,
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
author={Hamel Husain and Ho-Hsiang Wu and Tiferet Gazit and Miltiadis Allamanis and Marc Brockschmidt},
journal={ArXiv},
year={2019}
}
```
| irds/codesearchnet_train | [
"task_categories:text-retrieval",
"source_datasets:irds/codesearchnet",
"region:us"
]
| 2023-01-05T03:03:53+00:00 | {"source_datasets": ["irds/codesearchnet"], "task_categories": ["text-retrieval"], "pretty_name": "`codesearchnet/train`", "viewer": false} | 2023-01-05T03:03:59+00:00 |
20e11b36196452db968cf0e9aa62203728c7c9b1 |
# Dataset Card for `codesearchnet/valid`
The `codesearchnet/valid` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/codesearchnet#codesearchnet/valid).
# Data
This dataset provides:
- `queries` (i.e., topics); count=89,154
- `qrels`: (relevance assessments); count=89,154
- For `docs`, use [`irds/codesearchnet`](https://huggingface.co/datasets/irds/codesearchnet)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/codesearchnet_valid', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/codesearchnet_valid', '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 π€ Dataset format.
## Citation Information
```
@article{Husain2019CodeSearchNet,
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
author={Hamel Husain and Ho-Hsiang Wu and Tiferet Gazit and Miltiadis Allamanis and Marc Brockschmidt},
journal={ArXiv},
year={2019}
}
```
| irds/codesearchnet_valid | [
"task_categories:text-retrieval",
"source_datasets:irds/codesearchnet",
"region:us"
]
| 2023-01-05T03:04:05+00:00 | {"source_datasets": ["irds/codesearchnet"], "task_categories": ["text-retrieval"], "pretty_name": "`codesearchnet/valid`", "viewer": false} | 2023-01-05T03:04:10+00:00 |
f6b24444633ecf2c5790e7bef324c304ea186e6d |
# Dataset Card for `gov`
The `gov` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov#gov).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=1,247,753
This dataset is used by: [`gov_trec-web-2002`](https://huggingface.co/datasets/irds/gov_trec-web-2002), [`gov_trec-web-2002_named-page`](https://huggingface.co/datasets/irds/gov_trec-web-2002_named-page), [`gov_trec-web-2003`](https://huggingface.co/datasets/irds/gov_trec-web-2003), [`gov_trec-web-2003_named-page`](https://huggingface.co/datasets/irds/gov_trec-web-2003_named-page), [`gov_trec-web-2004`](https://huggingface.co/datasets/irds/gov_trec-web-2004)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/gov', 'docs')
for record in docs:
record # {'doc_id': ..., 'url': ..., 'http_headers': ..., 'body': ..., 'body_content_type': ...}
```
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 π€ Dataset format.
| irds/gov | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:04:16+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`gov`", "viewer": false} | 2023-01-05T03:04:22+00:00 |
503f29b4667d239494c65d0f5cd938780c471914 |
# Dataset Card for `gov/trec-web-2002`
The `gov/trec-web-2002` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov#gov/trec-web-2002).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=56,650
- For `docs`, use [`irds/gov`](https://huggingface.co/datasets/irds/gov)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov_trec-web-2002', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/gov_trec-web-2002', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Craswell2002TrecWeb,
title={Overview of the TREC-2002 Web Track},
author={Nick Craswell and David Hawking},
booktitle={TREC},
year={2002}
}
```
| irds/gov_trec-web-2002 | [
"task_categories:text-retrieval",
"source_datasets:irds/gov",
"region:us"
]
| 2023-01-05T03:04:27+00:00 | {"source_datasets": ["irds/gov"], "task_categories": ["text-retrieval"], "pretty_name": "`gov/trec-web-2002`", "viewer": false} | 2023-01-05T03:04:33+00:00 |
e78f05cb38dbfac1c3b0c50546cb475b49a229e6 |
# Dataset Card for `gov/trec-web-2002/named-page`
The `gov/trec-web-2002/named-page` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov#gov/trec-web-2002/named-page).
# Data
This dataset provides:
- `queries` (i.e., topics); count=150
- `qrels`: (relevance assessments); count=170
- For `docs`, use [`irds/gov`](https://huggingface.co/datasets/irds/gov)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov_trec-web-2002_named-page', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/gov_trec-web-2002_named-page', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Craswell2002TrecWeb,
title={Overview of the TREC-2002 Web Track},
author={Nick Craswell and David Hawking},
booktitle={TREC},
year={2002}
}
```
| irds/gov_trec-web-2002_named-page | [
"task_categories:text-retrieval",
"source_datasets:irds/gov",
"region:us"
]
| 2023-01-05T03:04:38+00:00 | {"source_datasets": ["irds/gov"], "task_categories": ["text-retrieval"], "pretty_name": "`gov/trec-web-2002/named-page`", "viewer": false} | 2023-01-05T03:04:44+00:00 |
35a75500ca891fa8f537320972c1cfa5936682ba |
# Dataset Card for `gov/trec-web-2003`
The `gov/trec-web-2003` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov#gov/trec-web-2003).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=51,062
- For `docs`, use [`irds/gov`](https://huggingface.co/datasets/irds/gov)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov_trec-web-2003', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ...}
qrels = load_dataset('irds/gov_trec-web-2003', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Craswell2003TrecWeb,
title={Overview of the TREC 2003 Web Track},
author={Nick Craswell and David Hawking and Ross Wilkinson and Mingfang Wu},
booktitle={TREC},
year={2003}
}
```
| irds/gov_trec-web-2003 | [
"task_categories:text-retrieval",
"source_datasets:irds/gov",
"region:us"
]
| 2023-01-05T03:04:49+00:00 | {"source_datasets": ["irds/gov"], "task_categories": ["text-retrieval"], "pretty_name": "`gov/trec-web-2003`", "viewer": false} | 2023-01-05T03:04:55+00:00 |
daf17aca06f2fbd03694fdbc0ad5d94d4fe53ff8 |
# Dataset Card for `gov/trec-web-2003/named-page`
The `gov/trec-web-2003/named-page` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov#gov/trec-web-2003/named-page).
# Data
This dataset provides:
- `queries` (i.e., topics); count=300
- `qrels`: (relevance assessments); count=352
- For `docs`, use [`irds/gov`](https://huggingface.co/datasets/irds/gov)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov_trec-web-2003_named-page', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/gov_trec-web-2003_named-page', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Craswell2003TrecWeb,
title={Overview of the TREC 2003 Web Track},
author={Nick Craswell and David Hawking and Ross Wilkinson and Mingfang Wu},
booktitle={TREC},
year={2003}
}
```
| irds/gov_trec-web-2003_named-page | [
"task_categories:text-retrieval",
"source_datasets:irds/gov",
"region:us"
]
| 2023-01-05T03:05:01+00:00 | {"source_datasets": ["irds/gov"], "task_categories": ["text-retrieval"], "pretty_name": "`gov/trec-web-2003/named-page`", "viewer": false} | 2023-01-05T03:05:06+00:00 |
d1352d159cb6c228181be05870570321b028abbe |
# Dataset Card for `gov/trec-web-2004`
The `gov/trec-web-2004` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov#gov/trec-web-2004).
# Data
This dataset provides:
- `queries` (i.e., topics); count=225
- `qrels`: (relevance assessments); count=88,566
- For `docs`, use [`irds/gov`](https://huggingface.co/datasets/irds/gov)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov_trec-web-2004', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/gov_trec-web-2004', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Craswell2004TrecWeb,
title={Overview of the TREC-2004 Web Track},
author={Nick Craswell and David Hawking},
booktitle={TREC},
year={2004}
}
```
| irds/gov_trec-web-2004 | [
"task_categories:text-retrieval",
"source_datasets:irds/gov",
"region:us"
]
| 2023-01-05T03:05:12+00:00 | {"source_datasets": ["irds/gov"], "task_categories": ["text-retrieval"], "pretty_name": "`gov/trec-web-2004`", "viewer": false} | 2023-01-05T03:05:17+00:00 |
a6e2d7fc839582490032cf2d4f3ae10161fd4beb |
# Dataset Card for `gov2`
The `gov2` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=25,205,179
This dataset is used by: [`gov2_trec-tb-2004`](https://huggingface.co/datasets/irds/gov2_trec-tb-2004), [`gov2_trec-tb-2005`](https://huggingface.co/datasets/irds/gov2_trec-tb-2005), [`gov2_trec-tb-2005_efficiency`](https://huggingface.co/datasets/irds/gov2_trec-tb-2005_efficiency), [`gov2_trec-tb-2005_named-page`](https://huggingface.co/datasets/irds/gov2_trec-tb-2005_named-page), [`gov2_trec-tb-2006`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006), [`gov2_trec-tb-2006_efficiency`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_efficiency), [`gov2_trec-tb-2006_efficiency_10k`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_efficiency_10k), [`gov2_trec-tb-2006_efficiency_stream1`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_efficiency_stream1), [`gov2_trec-tb-2006_efficiency_stream2`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_efficiency_stream2), [`gov2_trec-tb-2006_efficiency_stream3`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_efficiency_stream3), [`gov2_trec-tb-2006_efficiency_stream4`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_efficiency_stream4), [`gov2_trec-tb-2006_named-page`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_named-page)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/gov2', 'docs')
for record in docs:
record # {'doc_id': ..., 'url': ..., 'http_headers': ..., 'body': ..., 'body_content_type': ...}
```
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 π€ Dataset format.
| irds/gov2 | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:05:23+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`gov2`", "viewer": false} | 2023-01-05T03:05:28+00:00 |
9afbbc7f7fe33a8317f1ffa590c7cb8822d47b43 |
# Dataset Card for `gov2/trec-tb-2004`
The `gov2/trec-tb-2004` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2004).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=58,077
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov2_trec-tb-2004', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/gov2_trec-tb-2004', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Clarke2004TrecTerabyte,
title={Overview of the TREC 2004 Terabyte Track},
author={Charles Clarke and Nick Craswell and Ian Soboroff},
booktitle={TREC},
year={2004}
}
```
| irds/gov2_trec-tb-2004 | [
"task_categories:text-retrieval",
"source_datasets:irds/gov2",
"region:us"
]
| 2023-01-05T03:05:34+00:00 | {"source_datasets": ["irds/gov2"], "task_categories": ["text-retrieval"], "pretty_name": "`gov2/trec-tb-2004`", "viewer": false} | 2023-01-05T03:05:40+00:00 |
ed007f5ae9e94b1ff6413897824f9f006648d38b |
# Dataset Card for `gov2/trec-tb-2005`
The `gov2/trec-tb-2005` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2005).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=45,291
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov2_trec-tb-2005', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/gov2_trec-tb-2005', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Clarke2005TrecTerabyte,
title={The TREC 2005 Terabyte Track},
author={Charles L. A. Clark and Falk Scholer and Ian Soboroff},
booktitle={TREC},
year={2005}
}
```
| irds/gov2_trec-tb-2005 | [
"task_categories:text-retrieval",
"source_datasets:irds/gov2",
"region:us"
]
| 2023-01-05T03:05:45+00:00 | {"source_datasets": ["irds/gov2"], "task_categories": ["text-retrieval"], "pretty_name": "`gov2/trec-tb-2005`", "viewer": false} | 2023-01-05T03:05:51+00:00 |
0c7faf9df24d33af823cfa8243e9315b67d9add6 |
# Dataset Card for `gov2/trec-tb-2005/efficiency`
The `gov2/trec-tb-2005/efficiency` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2005/efficiency).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50,000
- `qrels`: (relevance assessments); count=45,291
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov2_trec-tb-2005_efficiency', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/gov2_trec-tb-2005_efficiency', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Clarke2005TrecTerabyte,
title={The TREC 2005 Terabyte Track},
author={Charles L. A. Clark and Falk Scholer and Ian Soboroff},
booktitle={TREC},
year={2005}
}
```
| irds/gov2_trec-tb-2005_efficiency | [
"task_categories:text-retrieval",
"source_datasets:irds/gov2",
"region:us"
]
| 2023-01-05T03:05:56+00:00 | {"source_datasets": ["irds/gov2"], "task_categories": ["text-retrieval"], "pretty_name": "`gov2/trec-tb-2005/efficiency`", "viewer": false} | 2023-01-05T03:06:02+00:00 |
cb1e910defebf5653c29c7d2f99871e81b982399 |
# Dataset Card for `gov2/trec-tb-2005/named-page`
The `gov2/trec-tb-2005/named-page` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2005/named-page).
# Data
This dataset provides:
- `queries` (i.e., topics); count=252
- `qrels`: (relevance assessments); count=11,729
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov2_trec-tb-2005_named-page', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/gov2_trec-tb-2005_named-page', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Clarke2005TrecTerabyte,
title={The TREC 2005 Terabyte Track},
author={Charles L. A. Clark and Falk Scholer and Ian Soboroff},
booktitle={TREC},
year={2005}
}
```
| irds/gov2_trec-tb-2005_named-page | [
"task_categories:text-retrieval",
"source_datasets:irds/gov2",
"region:us"
]
| 2023-01-05T03:06:07+00:00 | {"source_datasets": ["irds/gov2"], "task_categories": ["text-retrieval"], "pretty_name": "`gov2/trec-tb-2005/named-page`", "viewer": false} | 2023-01-05T03:06:13+00:00 |
41b52f39f0d0c1bcc7ad6523e93099129fc19e8b |
# Dataset Card for `gov2/trec-tb-2006`
The `gov2/trec-tb-2006` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2006).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=31,984
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov2_trec-tb-2006', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/gov2_trec-tb-2006', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Buttcher2006TrecTerabyte,
title={The TREC 2006 Terabyte Track},
author={Stefan B\"uttcher and Charles L. A. Clarke and Ian Soboroff},
booktitle={TREC},
year={2006}
}
```
| irds/gov2_trec-tb-2006 | [
"task_categories:text-retrieval",
"source_datasets:irds/gov2",
"region:us"
]
| 2023-01-05T03:06:19+00:00 | {"source_datasets": ["irds/gov2"], "task_categories": ["text-retrieval"], "pretty_name": "`gov2/trec-tb-2006`", "viewer": false} | 2023-01-05T03:06:24+00:00 |
7f01c2772cc7296a065a539d116a4759cc4abfe7 | Someman/boudhastupa | [
"license:mit",
"region:us"
]
| 2023-01-05T03:06:25+00:00 | {"license": "mit"} | 2023-01-05T03:17:07+00:00 |
|
19243bade1eb835e274d190116b39e1530902d9d |
# Dataset Card for `gov2/trec-tb-2006/efficiency`
The `gov2/trec-tb-2006/efficiency` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2006/efficiency).
# Data
This dataset provides:
- `queries` (i.e., topics); count=100,000
- `qrels`: (relevance assessments); count=31,984
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov2_trec-tb-2006_efficiency', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/gov2_trec-tb-2006_efficiency', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Buttcher2006TrecTerabyte,
title={The TREC 2006 Terabyte Track},
author={Stefan B\"uttcher and Charles L. A. Clarke and Ian Soboroff},
booktitle={TREC},
year={2006}
}
```
| irds/gov2_trec-tb-2006_efficiency | [
"task_categories:text-retrieval",
"source_datasets:irds/gov2",
"region:us"
]
| 2023-01-05T03:06:30+00:00 | {"source_datasets": ["irds/gov2"], "task_categories": ["text-retrieval"], "pretty_name": "`gov2/trec-tb-2006/efficiency`", "viewer": false} | 2023-01-05T03:06:35+00:00 |
e3d756ae76572fe72ae3892800e5e0a52990cede |
# Dataset Card for `gov2/trec-tb-2006/efficiency/10k`
The `gov2/trec-tb-2006/efficiency/10k` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2006/efficiency/10k).
# Data
This dataset provides:
- `queries` (i.e., topics); count=10,000
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov2_trec-tb-2006_efficiency_10k', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Buttcher2006TrecTerabyte,
title={The TREC 2006 Terabyte Track},
author={Stefan B\"uttcher and Charles L. A. Clarke and Ian Soboroff},
booktitle={TREC},
year={2006}
}
```
| irds/gov2_trec-tb-2006_efficiency_10k | [
"task_categories:text-retrieval",
"source_datasets:irds/gov2",
"region:us"
]
| 2023-01-05T03:06:41+00:00 | {"source_datasets": ["irds/gov2"], "task_categories": ["text-retrieval"], "pretty_name": "`gov2/trec-tb-2006/efficiency/10k`", "viewer": false} | 2023-01-05T03:06:47+00:00 |
fd52750a3f6be643229b03bd1e639f17cbf4e9cc |
# Dataset Card for `gov2/trec-tb-2006/efficiency/stream1`
The `gov2/trec-tb-2006/efficiency/stream1` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2006/efficiency/stream1).
# Data
This dataset provides:
- `queries` (i.e., topics); count=25,000
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov2_trec-tb-2006_efficiency_stream1', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Buttcher2006TrecTerabyte,
title={The TREC 2006 Terabyte Track},
author={Stefan B\"uttcher and Charles L. A. Clarke and Ian Soboroff},
booktitle={TREC},
year={2006}
}
```
| irds/gov2_trec-tb-2006_efficiency_stream1 | [
"task_categories:text-retrieval",
"source_datasets:irds/gov2",
"region:us"
]
| 2023-01-05T03:06:52+00:00 | {"source_datasets": ["irds/gov2"], "task_categories": ["text-retrieval"], "pretty_name": "`gov2/trec-tb-2006/efficiency/stream1`", "viewer": false} | 2023-01-05T03:06:58+00:00 |
3e3164cff2535e08413aaf0176ef560864c138bf |
# Dataset Card for `gov2/trec-tb-2006/efficiency/stream2`
The `gov2/trec-tb-2006/efficiency/stream2` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2006/efficiency/stream2).
# Data
This dataset provides:
- `queries` (i.e., topics); count=25,000
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov2_trec-tb-2006_efficiency_stream2', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Buttcher2006TrecTerabyte,
title={The TREC 2006 Terabyte Track},
author={Stefan B\"uttcher and Charles L. A. Clarke and Ian Soboroff},
booktitle={TREC},
year={2006}
}
```
| irds/gov2_trec-tb-2006_efficiency_stream2 | [
"task_categories:text-retrieval",
"source_datasets:irds/gov2",
"region:us"
]
| 2023-01-05T03:07:03+00:00 | {"source_datasets": ["irds/gov2"], "task_categories": ["text-retrieval"], "pretty_name": "`gov2/trec-tb-2006/efficiency/stream2`", "viewer": false} | 2023-01-05T03:07:09+00:00 |
2abe54278e42abfb1eed006b2762636d4b9e50e4 |
# Dataset Card for `gov2/trec-tb-2006/efficiency/stream3`
The `gov2/trec-tb-2006/efficiency/stream3` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2006/efficiency/stream3).
# Data
This dataset provides:
- `queries` (i.e., topics); count=25,000
- `qrels`: (relevance assessments); count=31,984
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov2_trec-tb-2006_efficiency_stream3', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/gov2_trec-tb-2006_efficiency_stream3', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Buttcher2006TrecTerabyte,
title={The TREC 2006 Terabyte Track},
author={Stefan B\"uttcher and Charles L. A. Clarke and Ian Soboroff},
booktitle={TREC},
year={2006}
}
```
| irds/gov2_trec-tb-2006_efficiency_stream3 | [
"task_categories:text-retrieval",
"source_datasets:irds/gov2",
"region:us"
]
| 2023-01-05T03:07:14+00:00 | {"source_datasets": ["irds/gov2"], "task_categories": ["text-retrieval"], "pretty_name": "`gov2/trec-tb-2006/efficiency/stream3`", "viewer": false} | 2023-01-05T03:07:20+00:00 |
2fa71fc9278da58fb24782a6d2f45fcb08aca99a |
# Dataset Card for `gov2/trec-tb-2006/efficiency/stream4`
The `gov2/trec-tb-2006/efficiency/stream4` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2006/efficiency/stream4).
# Data
This dataset provides:
- `queries` (i.e., topics); count=25,000
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov2_trec-tb-2006_efficiency_stream4', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Buttcher2006TrecTerabyte,
title={The TREC 2006 Terabyte Track},
author={Stefan B\"uttcher and Charles L. A. Clarke and Ian Soboroff},
booktitle={TREC},
year={2006}
}
```
| irds/gov2_trec-tb-2006_efficiency_stream4 | [
"task_categories:text-retrieval",
"source_datasets:irds/gov2",
"region:us"
]
| 2023-01-05T03:07:26+00:00 | {"source_datasets": ["irds/gov2"], "task_categories": ["text-retrieval"], "pretty_name": "`gov2/trec-tb-2006/efficiency/stream4`", "viewer": false} | 2023-01-05T03:07:31+00:00 |
fe821b56dfd5271b0dfa32f13ed7336929e5a9cc |
# Dataset Card for `gov2/trec-tb-2006/named-page`
The `gov2/trec-tb-2006/named-page` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2006/named-page).
# Data
This dataset provides:
- `queries` (i.e., topics); count=181
- `qrels`: (relevance assessments); count=2,361
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov2_trec-tb-2006_named-page', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/gov2_trec-tb-2006_named-page', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{Buttcher2006TrecTerabyte,
title={The TREC 2006 Terabyte Track},
author={Stefan B\"uttcher and Charles L. A. Clarke and Ian Soboroff},
booktitle={TREC},
year={2006}
}
```
| irds/gov2_trec-tb-2006_named-page | [
"task_categories:text-retrieval",
"source_datasets:irds/gov2",
"region:us"
]
| 2023-01-05T03:07:37+00:00 | {"source_datasets": ["irds/gov2"], "task_categories": ["text-retrieval"], "pretty_name": "`gov2/trec-tb-2006/named-page`", "viewer": false} | 2023-01-05T03:07:43+00:00 |
f6394b2a7d0fdf89462ab4fa26ca909543e89647 |
# Dataset Card for `istella22`
The `istella22` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/istella22#istella22).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,421,456
This dataset is used by: [`istella22_test`](https://huggingface.co/datasets/irds/istella22_test), [`istella22_test_fold1`](https://huggingface.co/datasets/irds/istella22_test_fold1), [`istella22_test_fold2`](https://huggingface.co/datasets/irds/istella22_test_fold2), [`istella22_test_fold3`](https://huggingface.co/datasets/irds/istella22_test_fold3), [`istella22_test_fold4`](https://huggingface.co/datasets/irds/istella22_test_fold4), [`istella22_test_fold5`](https://huggingface.co/datasets/irds/istella22_test_fold5)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/istella22', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'url': ..., 'text': ..., 'extra_text': ..., 'lang': ..., 'lang_pct': ...}
```
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 π€ Dataset format.
## Citation Information
```
@inproceedings{Dato2022Istella,
title={The Istella22 Dataset: Bridging Traditional and Neural Learning to Rank Evaluation},
author={Domenico Dato, Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto},
booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year={2022}
}
```
| irds/istella22 | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:07:48+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`istella22`", "viewer": false} | 2023-01-05T03:07:54+00:00 |
1d99a336dad14d72c13df9cb7d5e3c4ef410be80 |
# Dataset Card for `istella22/test`
The `istella22/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/istella22#istella22/test).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,198
- `qrels`: (relevance assessments); count=10,693
- For `docs`, use [`irds/istella22`](https://huggingface.co/datasets/irds/istella22)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/istella22_test', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/istella22_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 π€ Dataset format.
| irds/istella22_test | [
"task_categories:text-retrieval",
"source_datasets:irds/istella22",
"region:us"
]
| 2023-01-05T03:07:59+00:00 | {"source_datasets": ["irds/istella22"], "task_categories": ["text-retrieval"], "pretty_name": "`istella22/test`", "viewer": false} | 2023-01-05T03:08:05+00:00 |
9fc16f431ab78b78285726b01aec91953f01dbc3 |
# Dataset Card for `istella22/test/fold1`
The `istella22/test/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/istella22#istella22/test/fold1).
# Data
This dataset provides:
- `queries` (i.e., topics); count=440
- `qrels`: (relevance assessments); count=2,164
- For `docs`, use [`irds/istella22`](https://huggingface.co/datasets/irds/istella22)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/istella22_test_fold1', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/istella22_test_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 π€ Dataset format.
| irds/istella22_test_fold1 | [
"task_categories:text-retrieval",
"source_datasets:irds/istella22",
"region:us"
]
| 2023-01-05T03:08:10+00:00 | {"source_datasets": ["irds/istella22"], "task_categories": ["text-retrieval"], "pretty_name": "`istella22/test/fold1`", "viewer": false} | 2023-01-05T03:08:18+00:00 |
fbac99676c8676cf704f3bfb9f783ade1bb6b8ee |
# Dataset Card for `istella22/test/fold2`
The `istella22/test/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/istella22#istella22/test/fold2).
# Data
This dataset provides:
- `queries` (i.e., topics); count=440
- `qrels`: (relevance assessments); count=2,140
- For `docs`, use [`irds/istella22`](https://huggingface.co/datasets/irds/istella22)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/istella22_test_fold2', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/istella22_test_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 π€ Dataset format.
| irds/istella22_test_fold2 | [
"task_categories:text-retrieval",
"source_datasets:irds/istella22",
"region:us"
]
| 2023-01-05T03:08:23+00:00 | {"source_datasets": ["irds/istella22"], "task_categories": ["text-retrieval"], "pretty_name": "`istella22/test/fold2`", "viewer": false} | 2023-01-05T03:08:29+00:00 |
5d9c994c889eef5ccb629632d91a8559ae72bf8d |
# Dataset Card for `istella22/test/fold3`
The `istella22/test/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/istella22#istella22/test/fold3).
# Data
This dataset provides:
- `queries` (i.e., topics); count=440
- `qrels`: (relevance assessments); count=2,197
- For `docs`, use [`irds/istella22`](https://huggingface.co/datasets/irds/istella22)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/istella22_test_fold3', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/istella22_test_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 π€ Dataset format.
| irds/istella22_test_fold3 | [
"task_categories:text-retrieval",
"source_datasets:irds/istella22",
"region:us"
]
| 2023-01-05T03:08:34+00:00 | {"source_datasets": ["irds/istella22"], "task_categories": ["text-retrieval"], "pretty_name": "`istella22/test/fold3`", "viewer": false} | 2023-01-05T03:08:40+00:00 |
78bdf9e0e6c3326cd5275c28ce7a301bd969d566 |
# Dataset Card for `istella22/test/fold4`
The `istella22/test/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/istella22#istella22/test/fold4).
# Data
This dataset provides:
- `queries` (i.e., topics); count=439
- `qrels`: (relevance assessments); count=2,098
- For `docs`, use [`irds/istella22`](https://huggingface.co/datasets/irds/istella22)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/istella22_test_fold4', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/istella22_test_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 π€ Dataset format.
| irds/istella22_test_fold4 | [
"task_categories:text-retrieval",
"source_datasets:irds/istella22",
"region:us"
]
| 2023-01-05T03:08:45+00:00 | {"source_datasets": ["irds/istella22"], "task_categories": ["text-retrieval"], "pretty_name": "`istella22/test/fold4`", "viewer": false} | 2023-01-05T03:08:51+00:00 |
f19766cf5aaccbfd9ca38eb5f7914e0349fb8e76 |
# Dataset Card for `istella22/test/fold5`
The `istella22/test/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/istella22#istella22/test/fold5).
# Data
This dataset provides:
- `queries` (i.e., topics); count=439
- `qrels`: (relevance assessments); count=2,094
- For `docs`, use [`irds/istella22`](https://huggingface.co/datasets/irds/istella22)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/istella22_test_fold5', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/istella22_test_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 π€ Dataset format.
| irds/istella22_test_fold5 | [
"task_categories:text-retrieval",
"source_datasets:irds/istella22",
"region:us"
]
| 2023-01-05T03:08:57+00:00 | {"source_datasets": ["irds/istella22"], "task_categories": ["text-retrieval"], "pretty_name": "`istella22/test/fold5`", "viewer": false} | 2023-01-05T03:09:02+00:00 |
4b138571b1401911836b428a82441d66f95019f6 |
# Dataset Card for `kilt/codec`
The `kilt/codec` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/kilt#kilt/codec).
# Data
This dataset provides:
- `queries` (i.e., topics); count=42
- `qrels`: (relevance assessments); count=11,323
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/kilt_codec', 'queries')
for record in queries:
record # {'query_id': ..., 'query': ..., 'domain': ..., 'guidelines': ...}
qrels = load_dataset('irds/kilt_codec', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{mackie2022codec,
title={CODEC: Complex Document and Entity Collection},
author={Mackie, Iain and Owoicho, Paul and Gemmell, Carlos and Fischer, Sophie and MacAvaney, Sean and Dalton, Jeffery},
booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year={2022}
}
```
| irds/kilt_codec | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:09:08+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`kilt/codec`", "viewer": false} | 2023-01-05T03:09:13+00:00 |
c8bf6a2a6f8154011fafc511f01ebde3ddc1b175 |
# Dataset Card for `kilt/codec/economics`
The `kilt/codec/economics` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/kilt#kilt/codec/economics).
# Data
This dataset provides:
- `queries` (i.e., topics); count=14
- `qrels`: (relevance assessments); count=1,970
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/kilt_codec_economics', 'queries')
for record in queries:
record # {'query_id': ..., 'query': ..., 'domain': ..., 'guidelines': ...}
qrels = load_dataset('irds/kilt_codec_economics', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{mackie2022codec,
title={CODEC: Complex Document and Entity Collection},
author={Mackie, Iain and Owoicho, Paul and Gemmell, Carlos and Fischer, Sophie and MacAvaney, Sean and Dalton, Jeffery},
booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year={2022}
}
```
| irds/kilt_codec_economics | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:09:19+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`kilt/codec/economics`", "viewer": false} | 2023-01-05T03:09:25+00:00 |
6eb7b3780a1f1fa18250eab2203f49e0b42ae1f0 |
# Dataset Card for `kilt/codec/history`
The `kilt/codec/history` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/kilt#kilt/codec/history).
# Data
This dataset provides:
- `queries` (i.e., topics); count=14
- `qrels`: (relevance assessments); count=2,024
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/kilt_codec_history', 'queries')
for record in queries:
record # {'query_id': ..., 'query': ..., 'domain': ..., 'guidelines': ...}
qrels = load_dataset('irds/kilt_codec_history', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{mackie2022codec,
title={CODEC: Complex Document and Entity Collection},
author={Mackie, Iain and Owoicho, Paul and Gemmell, Carlos and Fischer, Sophie and MacAvaney, Sean and Dalton, Jeffery},
booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year={2022}
}
```
| irds/kilt_codec_history | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:09:30+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`kilt/codec/history`", "viewer": false} | 2023-01-05T03:09:36+00:00 |
148a3ea35e68f11b2e4e47c879cc4aad0fc4aa9e |
# Dataset Card for `kilt/codec/politics`
The `kilt/codec/politics` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/kilt#kilt/codec/politics).
# Data
This dataset provides:
- `queries` (i.e., topics); count=14
- `qrels`: (relevance assessments); count=2,192
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/kilt_codec_politics', 'queries')
for record in queries:
record # {'query_id': ..., 'query': ..., 'domain': ..., 'guidelines': ...}
qrels = load_dataset('irds/kilt_codec_politics', '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 π€ Dataset format.
## Citation Information
```
@inproceedings{mackie2022codec,
title={CODEC: Complex Document and Entity Collection},
author={Mackie, Iain and Owoicho, Paul and Gemmell, Carlos and Fischer, Sophie and MacAvaney, Sean and Dalton, Jeffery},
booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year={2022}
}
```
| irds/kilt_codec_politics | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:09:41+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`kilt/codec/politics`", "viewer": false} | 2023-01-05T03:09:47+00:00 |
8d45e5f830cd9fc636096112f75810cd36e98d2c |
# Dataset Card for `lotte/lifestyle/dev`
The `lotte/lifestyle/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/lifestyle/dev).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=268,893
This dataset is used by: [`lotte_lifestyle_dev_forum`](https://huggingface.co/datasets/irds/lotte_lifestyle_dev_forum), [`lotte_lifestyle_dev_search`](https://huggingface.co/datasets/irds/lotte_lifestyle_dev_search)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/lotte_lifestyle_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 π€ 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"
}
```
| irds/lotte_lifestyle_dev | [
"task_categories:text-retrieval",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:09:52+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/lifestyle/dev`", "viewer": false} | 2023-01-05T03:09:58+00:00 |
0bde24be728cf96f9f3fe015eff26bf55b4567bb |
# Dataset Card for `lotte/lifestyle/dev/forum`
The `lotte/lifestyle/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/lifestyle/dev/forum).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,076
- `qrels`: (relevance assessments); count=12,823
- For `docs`, use [`irds/lotte_lifestyle_dev`](https://huggingface.co/datasets/irds/lotte_lifestyle_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_lifestyle_dev_forum', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_lifestyle_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 π€ 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"
}
```
| irds/lotte_lifestyle_dev_forum | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_lifestyle_dev",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:10:04+00:00 | {"source_datasets": ["irds/lotte_lifestyle_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/lifestyle/dev/forum`", "viewer": false} | 2023-01-05T03:10:09+00:00 |
0d09b2703d15e805c03c5dce1a589b3329d1d9d4 |
# Dataset Card for `lotte/lifestyle/dev/search`
The `lotte/lifestyle/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/lifestyle/dev/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=417
- `qrels`: (relevance assessments); count=1,376
- For `docs`, use [`irds/lotte_lifestyle_dev`](https://huggingface.co/datasets/irds/lotte_lifestyle_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_lifestyle_dev_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_lifestyle_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 π€ 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"
}
```
| irds/lotte_lifestyle_dev_search | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_lifestyle_dev",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:10:15+00:00 | {"source_datasets": ["irds/lotte_lifestyle_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/lifestyle/dev/search`", "viewer": false} | 2023-01-05T03:10:21+00:00 |
b2c5c2e7b956f0d0af5eca72717f3375d8b5bbf8 |
# Dataset Card for `lotte/lifestyle/test`
The `lotte/lifestyle/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/lifestyle/test).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=119,461
This dataset is used by: [`lotte_lifestyle_test_forum`](https://huggingface.co/datasets/irds/lotte_lifestyle_test_forum), [`lotte_lifestyle_test_search`](https://huggingface.co/datasets/irds/lotte_lifestyle_test_search)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/lotte_lifestyle_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 π€ 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"
}
```
| irds/lotte_lifestyle_test | [
"task_categories:text-retrieval",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:10:26+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/lifestyle/test`", "viewer": false} | 2023-01-05T03:10:32+00:00 |
6fcb1bd0505f734da8f2b638297338b57bd1ad49 |
# Dataset Card for `lotte/lifestyle/test/forum`
The `lotte/lifestyle/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/lifestyle/test/forum).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,002
- `qrels`: (relevance assessments); count=10,278
- For `docs`, use [`irds/lotte_lifestyle_test`](https://huggingface.co/datasets/irds/lotte_lifestyle_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_lifestyle_test_forum', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_lifestyle_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 π€ 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"
}
```
| irds/lotte_lifestyle_test_forum | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_lifestyle_test",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:10:38+00:00 | {"source_datasets": ["irds/lotte_lifestyle_test"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/lifestyle/test/forum`", "viewer": false} | 2023-01-05T03:10:45+00:00 |
d4624d3500a39c861da3a99bc0e59ab332ec03da |
# Dataset Card for `lotte/lifestyle/test/search`
The `lotte/lifestyle/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/lifestyle/test/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=661
- `qrels`: (relevance assessments); count=1,804
- For `docs`, use [`irds/lotte_lifestyle_test`](https://huggingface.co/datasets/irds/lotte_lifestyle_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_lifestyle_test_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_lifestyle_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 π€ 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"
}
```
| irds/lotte_lifestyle_test_search | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_lifestyle_test",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:10:51+00:00 | {"source_datasets": ["irds/lotte_lifestyle_test"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/lifestyle/test/search`", "viewer": false} | 2023-01-05T03:10:56+00:00 |
386a3c998811da2c3e1be0a26eeccc4f8501b047 |
# Dataset Card for `lotte/pooled/dev`
The `lotte/pooled/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/pooled/dev).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=2,428,854
This dataset is used by: [`lotte_pooled_dev_forum`](https://huggingface.co/datasets/irds/lotte_pooled_dev_forum), [`lotte_pooled_dev_search`](https://huggingface.co/datasets/irds/lotte_pooled_dev_search)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/lotte_pooled_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 π€ 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"
}
```
| irds/lotte_pooled_dev | [
"task_categories:text-retrieval",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:11:02+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/pooled/dev`", "viewer": false} | 2023-01-05T03:11:08+00:00 |
127dfc62ed16c3b1333ab5da0acc1b8b549953db |
# Dataset Card for `lotte/pooled/dev/forum`
The `lotte/pooled/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/pooled/dev/forum).
# Data
This dataset provides:
- `queries` (i.e., topics); count=10,097
- `qrels`: (relevance assessments); count=68,685
- For `docs`, use [`irds/lotte_pooled_dev`](https://huggingface.co/datasets/irds/lotte_pooled_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_pooled_dev_forum', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_pooled_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 π€ 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"
}
```
| irds/lotte_pooled_dev_forum | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_pooled_dev",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:11:13+00:00 | {"source_datasets": ["irds/lotte_pooled_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/pooled/dev/forum`", "viewer": false} | 2023-01-05T03:11:19+00:00 |
cfbe6f21d7ddfb6d2ecae29a28492f01bf9be7e6 |
# Dataset Card for `lotte/pooled/dev/search`
The `lotte/pooled/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/pooled/dev/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,931
- `qrels`: (relevance assessments); count=8,573
- For `docs`, use [`irds/lotte_pooled_dev`](https://huggingface.co/datasets/irds/lotte_pooled_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_pooled_dev_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_pooled_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 π€ 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"
}
```
| irds/lotte_pooled_dev_search | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_pooled_dev",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:11:24+00:00 | {"source_datasets": ["irds/lotte_pooled_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/pooled/dev/search`", "viewer": false} | 2023-01-05T03:11:30+00:00 |
91603bc7ba09a81c628f3b2d9f1a782143f40ce3 |
# Dataset Card for `lotte/pooled/test`
The `lotte/pooled/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/pooled/test).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=2,819,103
This dataset is used by: [`lotte_pooled_test_forum`](https://huggingface.co/datasets/irds/lotte_pooled_test_forum), [`lotte_pooled_test_search`](https://huggingface.co/datasets/irds/lotte_pooled_test_search)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/lotte_pooled_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 π€ 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"
}
```
| irds/lotte_pooled_test | [
"task_categories:text-retrieval",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:11:35+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/pooled/test`", "viewer": false} | 2023-01-05T03:11:41+00:00 |
a0912b9914a6c53212691633b21328283b6cebfc |
# Dataset Card for `lotte/pooled/test/forum`
The `lotte/pooled/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/pooled/test/forum).
# Data
This dataset provides:
- `queries` (i.e., topics); count=10,025
- `qrels`: (relevance assessments); count=61,536
- For `docs`, use [`irds/lotte_pooled_test`](https://huggingface.co/datasets/irds/lotte_pooled_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_pooled_test_forum', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_pooled_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 π€ 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"
}
```
| irds/lotte_pooled_test_forum | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_pooled_test",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:11:47+00:00 | {"source_datasets": ["irds/lotte_pooled_test"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/pooled/test/forum`", "viewer": false} | 2023-01-05T03:11:52+00:00 |
5ee27e33ba7515cfdd0845c2792186d4f1ef1e65 |
# Dataset Card for `lotte/pooled/test/search`
The `lotte/pooled/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/pooled/test/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=3,869
- `qrels`: (relevance assessments); count=11,124
- For `docs`, use [`irds/lotte_pooled_test`](https://huggingface.co/datasets/irds/lotte_pooled_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_pooled_test_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_pooled_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 π€ 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"
}
```
| irds/lotte_pooled_test_search | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_pooled_test",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:11:58+00:00 | {"source_datasets": ["irds/lotte_pooled_test"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/pooled/test/search`", "viewer": false} | 2023-01-05T03:12:03+00:00 |
192ef68528f6dc82a600c33417e35201770b174d |
# Dataset Card for `lotte/recreation/dev`
The `lotte/recreation/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/recreation/dev).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=263,025
This dataset is used by: [`lotte_recreation_dev_forum`](https://huggingface.co/datasets/irds/lotte_recreation_dev_forum), [`lotte_recreation_dev_search`](https://huggingface.co/datasets/irds/lotte_recreation_dev_search)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/lotte_recreation_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 π€ 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"
}
```
| irds/lotte_recreation_dev | [
"task_categories:text-retrieval",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:12:09+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/recreation/dev`", "viewer": false} | 2023-01-05T03:12:15+00:00 |
7079c9382a7a969d3aeb1f0bdb30343b3a76c9ce |
# Dataset Card for `lotte/recreation/dev/forum`
The `lotte/recreation/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/recreation/dev/forum).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,002
- `qrels`: (relevance assessments); count=12,752
- For `docs`, use [`irds/lotte_recreation_dev`](https://huggingface.co/datasets/irds/lotte_recreation_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_recreation_dev_forum', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_recreation_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 π€ 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"
}
```
| irds/lotte_recreation_dev_forum | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_recreation_dev",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:12:20+00:00 | {"source_datasets": ["irds/lotte_recreation_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/recreation/dev/forum`", "viewer": false} | 2023-01-05T03:12:26+00:00 |
5fb2538e25d71328028783ff9fa647ef69a9a06c |
# Dataset Card for `lotte/recreation/dev/search`
The `lotte/recreation/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/recreation/dev/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=563
- `qrels`: (relevance assessments); count=1,754
- For `docs`, use [`irds/lotte_recreation_dev`](https://huggingface.co/datasets/irds/lotte_recreation_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_recreation_dev_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_recreation_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 π€ 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"
}
```
| irds/lotte_recreation_dev_search | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_recreation_dev",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:12:31+00:00 | {"source_datasets": ["irds/lotte_recreation_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/recreation/dev/search`", "viewer": false} | 2023-01-05T03:12:37+00:00 |
9bc24e76ed6b96b5cee6f99e09caccfa7dca1915 |
# Dataset Card for `lotte/recreation/test`
The `lotte/recreation/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/recreation/test).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=166,975
This dataset is used by: [`lotte_recreation_test_forum`](https://huggingface.co/datasets/irds/lotte_recreation_test_forum), [`lotte_recreation_test_search`](https://huggingface.co/datasets/irds/lotte_recreation_test_search)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/lotte_recreation_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 π€ 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"
}
```
| irds/lotte_recreation_test | [
"task_categories:text-retrieval",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:12:42+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/recreation/test`", "viewer": false} | 2023-01-05T03:12:48+00:00 |
6176527b4e32c49bb0b84e6940bd621753bf2000 |
# Dataset Card for `lotte/recreation/test/forum`
The `lotte/recreation/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/recreation/test/forum).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,002
- `qrels`: (relevance assessments); count=6,947
- For `docs`, use [`irds/lotte_recreation_test`](https://huggingface.co/datasets/irds/lotte_recreation_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_recreation_test_forum', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_recreation_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 π€ 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"
}
```
| irds/lotte_recreation_test_forum | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_recreation_test",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:12:53+00:00 | {"source_datasets": ["irds/lotte_recreation_test"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/recreation/test/forum`", "viewer": false} | 2023-01-05T03:12:59+00:00 |
e7ac4a98a196497f7c525868d34dc0dbd568802b |
# Dataset Card for `lotte/recreation/test/search`
The `lotte/recreation/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/recreation/test/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=924
- `qrels`: (relevance assessments); count=1,991
- For `docs`, use [`irds/lotte_recreation_test`](https://huggingface.co/datasets/irds/lotte_recreation_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_recreation_test_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_recreation_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 π€ 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"
}
```
| irds/lotte_recreation_test_search | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_recreation_test",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:13:04+00:00 | {"source_datasets": ["irds/lotte_recreation_test"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/recreation/test/search`", "viewer": false} | 2023-01-05T03:13:10+00:00 |
7e9d0fba97c5553fe1e84f07b681383b7078b662 |
# Dataset Card for `lotte/science/dev`
The `lotte/science/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/science/dev).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=343,642
This dataset is used by: [`lotte_science_dev_forum`](https://huggingface.co/datasets/irds/lotte_science_dev_forum), [`lotte_science_dev_search`](https://huggingface.co/datasets/irds/lotte_science_dev_search)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/lotte_science_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 π€ 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"
}
```
| irds/lotte_science_dev | [
"task_categories:text-retrieval",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:13:16+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/science/dev`", "viewer": false} | 2023-01-05T03:13:21+00:00 |
feb4f854a3b8fb64c0dd0ded0653b30c2e92d9df |
# Dataset Card for `lotte/science/dev/forum`
The `lotte/science/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/science/dev/forum).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,013
- `qrels`: (relevance assessments); count=12,271
- For `docs`, use [`irds/lotte_science_dev`](https://huggingface.co/datasets/irds/lotte_science_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_science_dev_forum', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_science_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 π€ 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"
}
```
| irds/lotte_science_dev_forum | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_science_dev",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:13:27+00:00 | {"source_datasets": ["irds/lotte_science_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/science/dev/forum`", "viewer": false} | 2023-01-05T03:13:32+00:00 |
53364075c982cbc9b3f3ad72eb7047977c8f3323 |
# Dataset Card for `lotte/science/dev/search`
The `lotte/science/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/science/dev/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=538
- `qrels`: (relevance assessments); count=1,480
- For `docs`, use [`irds/lotte_science_dev`](https://huggingface.co/datasets/irds/lotte_science_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_science_dev_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_science_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 π€ 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"
}
```
| irds/lotte_science_dev_search | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_science_dev",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:13:38+00:00 | {"source_datasets": ["irds/lotte_science_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/science/dev/search`", "viewer": false} | 2023-01-05T03:13:44+00:00 |
4059e65b15287127004aa66b766fc71f5409dd12 |
# Dataset Card for `lotte/science/test`
The `lotte/science/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/science/test).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=1,694,164
This dataset is used by: [`lotte_science_test_forum`](https://huggingface.co/datasets/irds/lotte_science_test_forum), [`lotte_science_test_search`](https://huggingface.co/datasets/irds/lotte_science_test_search)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/lotte_science_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 π€ 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"
}
```
| irds/lotte_science_test | [
"task_categories:text-retrieval",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:13:49+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/science/test`", "viewer": false} | 2023-01-05T03:13:55+00:00 |
72766e322efb20dfea29cc70907c5d1b517b8508 |
# Dataset Card for `lotte/science/test/forum`
The `lotte/science/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/science/test/forum).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,017
- `qrels`: (relevance assessments); count=15,515
- For `docs`, use [`irds/lotte_science_test`](https://huggingface.co/datasets/irds/lotte_science_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_science_test_forum', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_science_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 π€ 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"
}
```
| irds/lotte_science_test_forum | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_science_test",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:14:00+00:00 | {"source_datasets": ["irds/lotte_science_test"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/science/test/forum`", "viewer": false} | 2023-01-05T03:14:06+00:00 |
d48e8ba8cd88b2e05ef4e8c641d6048913630f13 |
# Dataset Card for `lotte/science/test/search`
The `lotte/science/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/science/test/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=617
- `qrels`: (relevance assessments); count=1,738
- For `docs`, use [`irds/lotte_science_test`](https://huggingface.co/datasets/irds/lotte_science_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_science_test_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_science_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 π€ 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"
}
```
| irds/lotte_science_test_search | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_science_test",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:14:11+00:00 | {"source_datasets": ["irds/lotte_science_test"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/science/test/search`", "viewer": false} | 2023-01-05T03:14:17+00:00 |
5633be42bae0d79cd1e695c9adba7c7db7765675 |
# Dataset Card for `lotte/technology/dev`
The `lotte/technology/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/technology/dev).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=1,276,222
This dataset is used by: [`lotte_technology_dev_forum`](https://huggingface.co/datasets/irds/lotte_technology_dev_forum), [`lotte_technology_dev_search`](https://huggingface.co/datasets/irds/lotte_technology_dev_search)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/lotte_technology_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 π€ 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"
}
```
| irds/lotte_technology_dev | [
"task_categories:text-retrieval",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:14:22+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/technology/dev`", "viewer": false} | 2023-01-05T03:14:28+00:00 |
a88b3bbcce8322c4164cafccb9e1f4e45c70b90f |
# Dataset Card for `lotte/technology/dev/forum`
The `lotte/technology/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/technology/dev/forum).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,003
- `qrels`: (relevance assessments); count=15,741
- For `docs`, use [`irds/lotte_technology_dev`](https://huggingface.co/datasets/irds/lotte_technology_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_technology_dev_forum', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_technology_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 π€ 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"
}
```
| irds/lotte_technology_dev_forum | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_technology_dev",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:14:34+00:00 | {"source_datasets": ["irds/lotte_technology_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/technology/dev/forum`", "viewer": false} | 2023-01-05T03:14:40+00:00 |
daae9c829591e515e2f214b6116f595c2bb99011 |
# Dataset Card for `lotte/technology/dev/search`
The `lotte/technology/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/technology/dev/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=916
- `qrels`: (relevance assessments); count=2,676
- For `docs`, use [`irds/lotte_technology_dev`](https://huggingface.co/datasets/irds/lotte_technology_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_technology_dev_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_technology_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 π€ 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"
}
```
| irds/lotte_technology_dev_search | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_technology_dev",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:14:45+00:00 | {"source_datasets": ["irds/lotte_technology_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/technology/dev/search`", "viewer": false} | 2023-01-05T03:14:51+00:00 |
bca4f7c1356530ab45196bfe991c138142961db5 |
# Dataset Card for `lotte/technology/test`
The `lotte/technology/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/technology/test).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=638,509
This dataset is used by: [`lotte_technology_test_forum`](https://huggingface.co/datasets/irds/lotte_technology_test_forum), [`lotte_technology_test_search`](https://huggingface.co/datasets/irds/lotte_technology_test_search)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/lotte_technology_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 π€ 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"
}
```
| irds/lotte_technology_test | [
"task_categories:text-retrieval",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:14:56+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/technology/test`", "viewer": false} | 2023-01-05T03:15:02+00:00 |
35ad62166a3d2daf366df949a86a3ab511c34deb |
# Dataset Card for `lotte/technology/test/forum`
The `lotte/technology/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/technology/test/forum).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,004
- `qrels`: (relevance assessments); count=15,890
- For `docs`, use [`irds/lotte_technology_test`](https://huggingface.co/datasets/irds/lotte_technology_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_technology_test_forum', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_technology_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 π€ 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"
}
```
| irds/lotte_technology_test_forum | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_technology_test",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:15:07+00:00 | {"source_datasets": ["irds/lotte_technology_test"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/technology/test/forum`", "viewer": false} | 2023-01-05T03:15:13+00:00 |
20dcea7394f3b955ada24c19e6cbe332592e9cc8 |
# Dataset Card for `lotte/technology/test/search`
The `lotte/technology/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/technology/test/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=596
- `qrels`: (relevance assessments); count=2,045
- For `docs`, use [`irds/lotte_technology_test`](https://huggingface.co/datasets/irds/lotte_technology_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_technology_test_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_technology_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 π€ 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"
}
```
| irds/lotte_technology_test_search | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_technology_test",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:15:18+00:00 | {"source_datasets": ["irds/lotte_technology_test"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/technology/test/search`", "viewer": false} | 2023-01-05T03:15:24+00:00 |
246bfbafd2636eb22716863a659ea11f6ca95319 |
# 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 π€ 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"
}
```
| irds/lotte_writing_dev | [
"task_categories:text-retrieval",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:15:29+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/writing/dev`", "viewer": false} | 2023-01-05T03:15:35+00:00 |
4a76341cc5d41f67ec0870886a6dac234bb22f91 |
# 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 π€ 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"
}
```
| irds/lotte_writing_dev_forum | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_writing_dev",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:15:41+00:00 | {"source_datasets": ["irds/lotte_writing_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/writing/dev/forum`", "viewer": false} | 2023-01-05T03:15:46+00:00 |
9d041d8753acfd4c26ae53cb609a544ce0ef76ad |
# 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 π€ 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"
}
```
| irds/lotte_writing_dev_search | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_writing_dev",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:15:52+00:00 | {"source_datasets": ["irds/lotte_writing_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/writing/dev/search`", "viewer": false} | 2023-01-05T03:15:57+00:00 |
de44627f761d60dd2769ad90e2c98ea8a9fcd028 |
# 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 π€ 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"
}
```
| irds/lotte_writing_test | [
"task_categories:text-retrieval",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:16:03+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/writing/test`", "viewer": false} | 2023-01-05T03:16:09+00:00 |
85c7c48d74c90c327785119dfcaf941809841631 |
# 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 π€ 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"
}
```
| irds/lotte_writing_test_forum | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_writing_test",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:16:14+00:00 | {"source_datasets": ["irds/lotte_writing_test"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/writing/test/forum`", "viewer": false} | 2023-01-05T03:16:21+00:00 |
8fdc4cc96dc63ab67aa866752b207e5f7038d15d |
# 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 π€ 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"
}
```
| irds/lotte_writing_test_search | [
"task_categories:text-retrieval",
"source_datasets:irds/lotte_writing_test",
"arxiv:2112.01488",
"region:us"
]
| 2023-01-05T03:16:27+00:00 | {"source_datasets": ["irds/lotte_writing_test"], "task_categories": ["text-retrieval"], "pretty_name": "`lotte/writing/test/search`", "viewer": false} | 2023-01-05T03:16:33+00:00 |
8945d071d1c2e1a986e9c4c8e23d413d1607456b |
# 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 π€ Dataset format.
## Citation Information
```
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang},
booktitle={InCoCo@NIPS},
year={2016}
}
```
| irds/msmarco-passage | [
"task_categories:text-retrieval",
"region:us"
]
| 2023-01-05T03:16:38+00:00 | {"source_datasets": [], "task_categories": ["text-retrieval"], "pretty_name": "`msmarco-passage`", "viewer": false} | 2023-01-05T03:16:44+00:00 |
404be20e8d8c396c2c82a101ea9a9ecbf8e2cfdb |
# 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 π€ Dataset format.
## Citation Information
```
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang},
booktitle={InCoCo@NIPS},
year={2016}
}
```
| irds/msmarco-passage_dev | [
"task_categories:text-retrieval",
"source_datasets:irds/msmarco-passage",
"region:us"
]
| 2023-01-05T03:16:49+00:00 | {"source_datasets": ["irds/msmarco-passage"], "task_categories": ["text-retrieval"], "pretty_name": "`msmarco-passage/dev`", "viewer": false} | 2023-01-05T03:16:55+00:00 |
8c074d85e7f17547f7b13a0c9e19ec7bc5b007b5 |
# 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 π€ Dataset format.
## Citation Information
```
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang},
booktitle={InCoCo@NIPS},
year={2016}
}
```
| irds/msmarco-passage_dev_judged | [
"task_categories:text-retrieval",
"source_datasets:irds/msmarco-passage",
"source_datasets:irds/msmarco-passage_dev",
"region:us"
]
| 2023-01-05T03:17:01+00:00 | {"source_datasets": ["irds/msmarco-passage", "irds/msmarco-passage_dev"], "task_categories": ["text-retrieval"], "pretty_name": "`msmarco-passage/dev/judged`", "viewer": false} | 2023-01-05T03:17:08+00:00 |
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