mteb/CQADupstack-Wordpress-PL

1---2annotations_creators:3- derived4language:5- pol6license: unknown7multilinguality: translated8source_datasets:9- mteb/cqadupstack-wordpress10task_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: 5264314826    num_examples: 4860527  download_size: 3009941628  dataset_size: 5264314829- config_name: default30  features:31  - name: query-id32    dtype: string33  - name: corpus-id34    dtype: string35  - name: score36    dtype: int6437  splits:38  - name: test39    num_bytes: 1988540    num_examples: 74441  download_size: 1149042  dataset_size: 1988543- config_name: queries44  features:45  - name: _id46    dtype: string47  - name: text48    dtype: string49  splits:50  - name: test51    num_bytes: 3884852    num_examples: 54153  download_size: 2702654  dataset_size: 3884855configs:56- config_name: corpus57  data_files:58  - split: test59    path: corpus/test-*60- config_name: default61  data_files:62  - split: test63    path: data/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;">CQADupstack-Wordpress-PL</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 80CQADupStack: A Stack Exchange Question Duplicate Pairs Dataset81 82|               |                                             |83|---------------|---------------------------------------------|84| Task category | t2t                              |85| Domains       | Written, Web, Programming                               |86| Reference     | https://huggingface.co/datasets/clarin-knext/cqadupstack-wordpress-pl |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(["CQADupstack-Wordpress-PL"])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@misc{wojtasik2024beirpl,113  archiveprefix = {arXiv},114  author = {Konrad Wojtasik and Vadim Shishkin and Kacper Wołowiec and Arkadiusz Janz and Maciej Piasecki},115  eprint = {2305.19840},116  primaryclass = {cs.IR},117  title = {BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language},118  year = {2024},119}120 121 122@article{enevoldsen2025mmtebmassivemultilingualtext,123  title={MMTEB: Massive Multilingual Text Embedding Benchmark},124  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},125  publisher = {arXiv},126  journal={arXiv preprint arXiv:2502.13595},127  year={2025},128  url={https://arxiv.org/abs/2502.13595},129  doi = {10.48550/arXiv.2502.13595},130}131 132@article{muennighoff2022mteb,133  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},134  title = {MTEB: Massive Text Embedding Benchmark},135  publisher = {arXiv},136  journal={arXiv preprint arXiv:2210.07316},137  year = {2022}138  url = {https://arxiv.org/abs/2210.07316},139  doi = {10.48550/ARXIV.2210.07316},140}141```142 143# Dataset Statistics144<details>145  <summary> Dataset Statistics</summary>146 147The following code contains the descriptive statistics from the task. These can also be obtained using:148 149```python150import mteb151 152task = mteb.get_task("CQADupstack-Wordpress-PL")153 154desc_stats = task.metadata.descriptive_stats155```156 157```json158{159    "test": {160        "num_samples": 49146,161        "number_of_characters": 49898308,162        "num_documents": 48605,163        "min_document_length": 76,164        "average_document_length": 1025.9860508178172,165        "max_document_length": 28483,166        "unique_documents": 48605,167        "num_queries": 541,168        "min_query_length": 18,169        "average_query_length": 55.92606284658041,170        "max_query_length": 129,171        "unique_queries": 541,172        "none_queries": 0,173        "num_relevant_docs": 744,174        "min_relevant_docs_per_query": 1,175        "average_relevant_docs_per_query": 1.3752310536044363,176        "max_relevant_docs_per_query": 62,177        "unique_relevant_docs": 744,178        "num_instructions": null,179        "min_instruction_length": null,180        "average_instruction_length": null,181        "max_instruction_length": null,182        "unique_instructions": null,183        "num_top_ranked": null,184        "min_top_ranked_per_query": null,185        "average_top_ranked_per_query": null,186        "max_top_ranked_per_query": null187    }188}189```190 191</details>192 193---194*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*