cqadupstack-gaming

Name: cqadupstack-gaming
Creator: maas
Published: 2025-11-12 16:16:56
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魔搭社区2025-11-12 更新2025-05-10 收录

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<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;">CQADupstackGamingRetrieval</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 | Web, Written | | 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(["CQADupstackGamingRetrieval"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  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("CQADupstackGamingRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 46896, "number_of_characters": 22263573, "num_documents": 45301, "min_document_length": 46, "average_document_length": 489.74152888457206, "max_document_length": 28835, "unique_documents": 45301, "num_queries": 1595, "min_query_length": 15, "average_query_length": 48.772413793103446, "max_query_length": 149, "unique_queries": 1595, "none_queries": 0, "num_relevant_docs": 2263, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.418808777429467, "max_relevant_docs_per_query": 30, "unique_relevant_docs": 2263, "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)*

<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;">CQADupstackGamingRetrieval</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测试（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测试（Massive Text Embedding Benchmark）</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（text-to-text，简称t2t） | | 领域 | 网络、书面文本 | | 参考文献 | http://nlp.cis.unimelb.edu.au/resources/cqadupstack/ | ## 任务评估方法您可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(["CQADupstackGamingRetrieval"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model)  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)。 ## 引用规范若您使用本数据集，请同时引用该数据集与[MTEB](https://github.com/embeddings-benchmark/mteb)；由于本数据集已作为[多语言大规模文本嵌入基准测试（Massive Multilingual Text Embedding Benchmark，简称MMTEB）贡献项](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb)的一部分经过额外处理，故需同时引用相关成果。 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 Lo{"i"c} 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> 以下代码展示了该任务的描述性统计数据。您也可通过如下代码获取相关统计信息： python import mteb task = mteb.get_task("CQADupstackGamingRetrieval") desc_stats = task.metadata.descriptive_stats json { "test": { "num_samples": 46896, "number_of_characters": 22263573, "num_documents": 45301, "min_document_length": 46, "average_document_length": 489.74152888457206, "max_document_length": 28835, "unique_documents": 45301, "num_queries": 1595, "min_query_length": 15, "average_query_length": 48.772413793103446, "max_query_length": 149, "unique_queries": 1595, "none_queries": 0, "num_relevant_docs": 2263, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.418808777429467, "max_relevant_docs_per_query": 30, "unique_relevant_docs": 2263, "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

搜集汇总

数据集介绍