arguana

<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->

<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">ArguAna</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>

NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval

| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Medical, Written |
| Reference | http://argumentation.bplaced.net/arguana/data |

## How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

```python
import mteb

task = mteb.get_tasks(["ArguAna"])
evaluator = mteb.MTEB(task)

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```

<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).

## Citation

If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).

```bibtex

@inproceedings{boteva2016,
author = {Boteva, Vera and Gholipour, Demian and Sokolov, Artem and Riezler, Stefan},
city = {Padova},
country = {Italy},
journal = {Proceedings of the 38th European Conference on Information Retrieval},
journal-abbrev = {ECIR},
title = {A Full-Text Learning to Rank Dataset for Medical Information Retrieval},
url = {http://www.cl.uni-heidelberg.de/~riezler/publications/papers/ECIR2016.pdf},
year = {2016},
}

@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```

# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>

The following code contains the descriptive statistics from the task. These can also be obtained using:

```python
import mteb

task = mteb.get_task("ArguAna")

desc_stats = task.metadata.descriptive_stats
```

```json
{
"test": {
"num_samples": 10080,
"number_of_characters": 10613204,
"num_documents": 8674,
"min_document_length": 3,
"average_document_length": 1030.2327645838136,
"max_document_length": 6674,
"unique_documents": 8674,
"num_queries": 1406,
"min_query_length": 251,
"average_query_length": 1192.7204836415362,
"max_query_length": 5500,
"unique_queries": 1406,
"none_queries": 0,
"num_relevant_docs": 1406,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 1406,
"num_instructions": null,
"min_instruction_length": null,
"average_instruction_length": null,
"max_instruction_length": null,
"unique_instructions": null,
"num_top_ranked": null,
"min_top_ranked_per_query": null,
"average_top_ranked_per_query": null,
"max_top_ranked_per_query": null
}
}
```

</details>

---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">ArguAna</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">一款<a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a>（Massive Text Embedding Benchmark，大规模文本嵌入基准）数据集</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>

NFCorpus：面向医学信息检索的全文学习排序数据集

| 任务类别 | t2t |
|---------|-----|
| 领域 | 医学、书面文本 |
| 参考链接 | http://argumentation.bplaced.net/arguana/data |

## 本任务评估方法

你可通过如下代码在本数据集上评估嵌入模型：

python
import mteb

task = mteb.get_tasks(["ArguAna"])
evaluator = mteb.MTEB(task)

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
欲了解如何在MTEB任务上运行模型，请参阅<a href="https://github.com/embeddings-benchmark/mteb">GitHub仓库</a>。

## 引用声明

若使用本数据集，请同时引用本数据集与<a href="https://github.com/embeddings-benchmark/mteb">MTEB</a>，因本数据集作为<a href="https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb">MMTEB贡献项</a>的一部分经过了额外处理。

bibtex

@inproceedings{boteva2016,
author = {Boteva, Vera and Gholipour, Demian and Sokolov, Artem and Riezler, Stefan},
city = {Padova},
country = {Italy},
journal = {Proceedings of the 38th European Conference on Information Retrieval},
journal-abbrev = {ECIR},
title = {A Full-Text Learning to Rank Dataset for Medical Information Retrieval},
url = {http://www.cl.uni-heidelberg.de/~riezler/publications/papers/ECIR2016.pdf},
year = {2016},
}

@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{"i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}

# 数据集统计信息
<details>
<summary> 数据集统计信息</summary>

下述代码包含本任务的描述性统计信息。你也可通过如下代码获取：

python
import mteb

task = mteb.get_task("ArguAna")

desc_stats = task.metadata.descriptive_stats

json
{
"test": {
"num_samples": 10080,
"number_of_characters": 10613204,
"num_documents": 8674,
"min_document_length": 3,
"average_document_length": 1030.2327645838136,
"max_document_length": 6674,
"unique_documents": 8674,
"num_queries": 1406,
"min_query_length": 251,
"average_query_length": 1192.7204836415362,
"max_query_length": 5500,
"unique_queries": 1406,
"none_queries": 0,
"num_relevant_docs": 1406,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 1406,
"num_instructions": null,
"min_instruction_length": null,
"average_instruction_length": null,
"max_instruction_length": null,
"unique_instructions": null,
"num_top_ranked": null,
"min_top_ranked_per_query": null,
"average_top_ranked_per_query": null,
"max_top_ranked_per_query": null
}
}

</details>

---
*本数据集卡片由<a href="https://github.com/embeddings-benchmark/mteb">MTEB</a>自动生成*

搜集汇总

背景与挑战

背景概述

Arguana是MTEB（大规模文本嵌入基准）中的一个数据集，专注于文本到文本的检索任务，应用于社交、网络和书面领域，源自ACL 2018年关于无先验主题知识的最佳反驳检索的研究。该数据集包含10080个测试样本，涉及约1060万字符，查询和文档的平均文本长度分别为1193和1030字符，每个查询平均对应1个相关文档。