cqadupstack-webmasters

<!-- 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;">CQADupstackWebmastersRetrieval</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>

CQADupStack: A Benchmark Data Set for Community Question-Answering Research

| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Written, Web |
| Reference | http://nlp.cis.unimelb.edu.au/resources/cqadupstack/ |

## 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(["CQADupstackWebmastersRetrieval"])
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{hoogeveen2015,
acmid = {2838934},
address = {New York, NY, USA},
articleno = {3},
author = {Hoogeveen, Doris and Verspoor, Karin M. and Baldwin, Timothy},
booktitle = {Proceedings of the 20th Australasian Document Computing Symposium (ADCS)},
doi = {10.1145/2838931.2838934},
isbn = {978-1-4503-4040-3},
location = {Parramatta, NSW, Australia},
numpages = {8},
pages = {3:1--3:8},
publisher = {ACM},
series = {ADCS '15},
title = {CQADupStack: A Benchmark Data Set for Community Question-Answering Research},
url = {http://doi.acm.org/10.1145/2838931.2838934},
year = {2015},
}

@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("CQADupstackWebmastersRetrieval")

desc_stats = task.metadata.descriptive_stats
```

```json
{
"test": {
"num_samples": 17911,
"number_of_characters": 12355347,
"num_documents": 17405,
"min_document_length": 49,
"average_document_length": 708.3635736857225,
"max_document_length": 24968,
"unique_documents": 17405,
"num_queries": 506,
"min_query_length": 15,
"average_query_length": 51.93478260869565,
"max_query_length": 135,
"unique_queries": 506,
"none_queries": 0,
"num_relevant_docs": 1395,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 2.7569169960474307,
"max_relevant_docs_per_query": 207,
"unique_relevant_docs": 1395,
"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)*

<!-- 改编自：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;">CQADupstackWebmastersRetrieval</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'">大规模文本嵌入基准（Massive Text Embedding Benchmark，简称MTEB）</a>数据集</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">大规模文本嵌入基准</div>
</div>

# CQADupStack：面向社区问答研究的基准数据集

| | |
|---------------|---------------------------------------------|
| 任务类别 | t2t |
| 领域 | 书面文本、网络 |
| 参考来源 | http://nlp.cis.unimelb.edu.au/resources/cqadupstack/ |

## 本任务的评估方法

您可通过以下代码在该数据集上评估嵌入模型：

python
import mteb

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

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

<!-- 数据集README中需添加arXiv链接以自动关联数据集与对应论文 -->
若需了解如何在MTEB任务上运行模型，请访问[GitHub仓库](https://github.com/embeddings-benchmark/mteb)。

## 引用说明

若使用本数据集，请同时引用该数据集与[MTEB](https://github.com/embeddings-benchmark/mteb)，因为本数据集作为[大规模多语言文本嵌入基准（Massive Multilingual Text Embedding Benchmark，简称MMTEB）贡献项](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb)的一部分经过了额外处理。

bibtex

@inproceedings{hoogeveen2015,
acmid = {2838934},
address = {New York, NY, USA},
articleno = {3},
author = {Hoogeveen, Doris and Verspoor, Karin M. and Baldwin, Timothy},
booktitle = {Proceedings of the 20th Australasian Document Computing Symposium (ADCS)},
doi = {10.1145/2838931.2838934},
isbn = {978-1-4503-4040-3},
location = {Parramatta, NSW, Australia},
numpages = {8},
pages = {3:1--3:8},
publisher = {ACM},
series = {ADCS '15},
title = {CQADupStack: A Benchmark Data Set for Community Question-Answering Research},
url = {http://doi.acm.org/10.1145/2838931.2838934},
year = {2015},
}

@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>

以下为该任务的描述性统计数据。您也可通过以下代码获取：

python
import mteb

task = mteb.get_task("CQADupstackWebmastersRetrieval")

desc_stats = task.metadata.descriptive_stats

json
{
"test": {
"样本数量": 17911,
"字符总数": 12355347,
"文档数量": 17405,
"最小文档长度": 49,
"平均文档长度": 708.3635736857225,
"最大文档长度": 24968,
"唯一文档数": 17405,
"查询数量": 506,
"最小查询长度": 15,
"平均查询长度": 51.93478260869565,
"最大查询长度": 135,
"唯一查询数": 506,
"无效查询数": 0,
"相关文档总数": 1395,
"单查询最小相关文档数": 1,
"单查询平均相关文档数": 2.7569169960474307,
"单查询最大相关文档数": 207,
"唯一相关文档数": 1395,
"指令数量": null,
"最小指令长度": null,
"平均指令长度": null,
"最大指令长度": null,
"唯一指令数": null,
"Top排名样本数": null,
"单查询最小Top排名样本数": null,
"单查询平均Top排名样本数": null,
"单查询最大Top排名样本数": null
}
}

</details>

---
*本数据集卡片由[MTEB](https://github.com/embeddings-benchmark/mteb)自动生成*