cqadupstack-unix
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下载链接：
https://modelscope.cn/datasets/MTEB/cqadupstack-unix
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资源简介：
<!-- 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;">CQADupstackUnixRetrieval</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, Programming |
| 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(["CQADupstackUnixRetrieval"])
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("CQADupstackUnixRetrieval")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 48454,
"number_of_characters": 47711333,
"num_documents": 47382,
"min_document_length": 56,
"average_document_length": 1005.8120383267908,
"max_document_length": 32623,
"unique_documents": 47382,
"num_queries": 1072,
"min_query_length": 15,
"average_query_length": 50.32369402985075,
"max_query_length": 124,
"unique_queries": 1072,
"none_queries": 0,
"num_relevant_docs": 1693,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.5792910447761195,
"max_relevant_docs_per_query": 22,
"unique_relevant_docs": 1693,
"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;">CQADupstackUnixRetrieval</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">一款MTEB（Massive Text Embedding Benchmark）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 | 文本到文本（t2t） |
| 领域 | 书面文本、网络文本、编程领域 |
| 参考链接 | http://nlp.cis.unimelb.edu.au/resources/cqadupstack/ |
## 如何在该任务上开展模型评估
您可以通过如下代码在该数据集上评估嵌入模型：
python
import mteb
task = mteb.get_tasks(["CQADupstackUnixRetrieval"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
<!-- 数据集自述文件中需要添加arXiv链接以自动关联数据集与论文 -->
> 若需了解如何在MTEB任务上运行模型，请参阅其[GitHub仓库](https://github.com/embeddings-benchmark/mteb)。
## 引用说明
若您使用本数据集，请同时引用该数据集与MTEB，因本数据集作为[MMTEB贡献项目](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>
下述代码展示了该任务的描述性统计结果。您也可以通过如下代码获取相关统计信息：
python
import mteb
task = mteb.get_task("CQADupstackUnixRetrieval")
desc_stats = task.metadata.descriptive_stats
json
{
"test": {
"num_samples": 48454,
"number_of_characters": 47711333,
"num_documents": 47382,
"min_document_length": 56,
"average_document_length": 1005.8120383267908,
"max_document_length": 32623,
"unique_documents": 47382,
"num_queries": 1072,
"min_query_length": 15,
"average_query_length": 50.32369402985075,
"max_query_length": 124,
"unique_queries": 1072,
"none_queries": 0,
"num_relevant_docs": 1693,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.5792910447761195,
"max_relevant_docs_per_query": 22,
"unique_relevant_docs": 1693,
"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[MTEB](https://github.com/embeddings-benchmark/mteb)自动生成*
提供机构：
maas
创建时间：
2024-09-06
