mteb/CQADupstackProgrammersRetrieval-Fa

1---2annotations_creators:3- derived4language:5- fas6license: unknown7multilinguality: monolingual8source_datasets:9- mteb/cqadupstack-programmers10task_categories:11- text-retrieval12task_ids:13- multiple-choice-qa14dataset_info:15- config_name: corpus16  features:17  - name: _id18    dtype: string19  - name: text20    dtype: string21  - name: title22    dtype: string23  splits:24  - name: test25    num_bytes: 5358234426    num_examples: 3217627  download_size: 2597534828  dataset_size: 5358234429- config_name: qrels30  features:31  - name: query-id32    dtype: string33  - name: corpus-id34    dtype: string35  - name: score36    dtype: int6437  splits:38  - name: test39    num_bytes: 4545240    num_examples: 167541  download_size: 2211042  dataset_size: 4545243- config_name: queries44  features:45  - name: _id46    dtype: string47  - name: text48    dtype: string49  splits:50  - name: test51    num_bytes: 9389652    num_examples: 87653  download_size: 5152254  dataset_size: 9389655configs:56- config_name: corpus57  data_files:58  - split: test59    path: corpus/test-*60- config_name: qrels61  data_files:62  - split: test63    path: qrels/test-*64- config_name: queries65  data_files:66  - split: test67    path: queries/test-*68tags:69- mteb70- text71---72<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->73 74<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;">75  <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">CQADupstackProgrammersRetrieval-Fa</h1>76  <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>77  <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>78</div>79 80CQADupstackProgrammersRetrieval-Fa81 82|               |                                             |83|---------------|---------------------------------------------|84| Task category | t2t                              |85| Domains       | Web                               |86| Reference     | https://huggingface.co/datasets/MCINext/cqadupstack-programmers-fa |87 88 89## How to evaluate on this task90 91You can evaluate an embedding model on this dataset using the following code:92 93```python94import mteb95 96task = mteb.get_tasks(["CQADupstackProgrammersRetrieval-Fa"])97evaluator = mteb.MTEB(task)98 99model = mteb.get_model(YOUR_MODEL)100evaluator.run(model)101```102 103<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->104To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). 105 106## Citation107 108If 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).109 110```bibtex111 112 113@article{enevoldsen2025mmtebmassivemultilingualtext,114  title={MMTEB: Massive Multilingual Text Embedding Benchmark},115  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},116  publisher = {arXiv},117  journal={arXiv preprint arXiv:2502.13595},118  year={2025},119  url={https://arxiv.org/abs/2502.13595},120  doi = {10.48550/arXiv.2502.13595},121}122 123@article{muennighoff2022mteb,124  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},125  title = {MTEB: Massive Text Embedding Benchmark},126  publisher = {arXiv},127  journal={arXiv preprint arXiv:2210.07316},128  year = {2022}129  url = {https://arxiv.org/abs/2210.07316},130  doi = {10.48550/ARXIV.2210.07316},131}132```133 134# Dataset Statistics135<details>136  <summary> Dataset Statistics</summary>137 138The following code contains the descriptive statistics from the task. These can also be obtained using:139 140```python141import mteb142 143task = mteb.get_task("CQADupstackProgrammersRetrieval-Fa")144 145desc_stats = task.metadata.descriptive_stats146```147 148```json149{150    "test": {151        "num_samples": 33052,152        "number_of_characters": 31246627,153        "num_documents": 32176,154        "min_document_length": 13,155        "average_document_length": 969.6830246146196,156        "max_document_length": 5175,157        "unique_documents": 32176,158        "num_queries": 876,159        "min_query_length": 11,160        "average_query_length": 52.6324200913242,161        "max_query_length": 132,162        "unique_queries": 876,163        "none_queries": 0,164        "num_relevant_docs": 1675,165        "min_relevant_docs_per_query": 1,166        "average_relevant_docs_per_query": 1.9121004566210045,167        "max_relevant_docs_per_query": 149,168        "unique_relevant_docs": 1675,169        "num_instructions": null,170        "min_instruction_length": null,171        "average_instruction_length": null,172        "max_instruction_length": null,173        "unique_instructions": null,174        "num_top_ranked": null,175        "min_top_ranked_per_query": null,176        "average_top_ranked_per_query": null,177        "max_top_ranked_per_query": null178    }179}180```181 182</details>183 184---185*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*