GreenNode/cqadupstack-physics-vn

1---2dataset_info:3- config_name: corpus4  features:5  - name: _id6    dtype: string7  - name: title8    dtype: string9  - name: text10    dtype: string11  - name: og_title12    dtype: string13  - name: og_text14    dtype: string15  splits:16  - name: corpus17    num_bytes: 7264443818    num_examples: 3831619  download_size: 3961193920  dataset_size: 7264443821- config_name: default22  features:23  - name: query-id24    dtype: string25  - name: corpus-id26    dtype: string27  - name: score28    dtype: float6429  splits:30  - name: test31    num_bytes: 26232.47904811174532    num_examples: 99833  download_size: 1296234  dataset_size: 26232.47904811174535- config_name: queries36  features:37  - name: _id38    dtype: string39  - name: text40    dtype: string41  - name: og_text42    dtype: string43  splits:44  - name: train45    num_bytes: 7210746    num_examples: 48547  download_size: 4570148  dataset_size: 7210749configs:50- config_name: corpus51  data_files:52  - split: corpus53    path: corpus/corpus-*54- config_name: default55  data_files:56  - split: test57    path: qrels/test-*58- config_name: queries59  data_files:60  - split: train61    path: queries/train-*62tags:63- mteb64- text-retrieval65- vietnamese66license: cc-by-nc-sa-4.067task_categories:68- text-retrieval69multilinguality: translated70language: vi71source_datasets: mteb/cqadupstack-physics72---73## How to evaluate on this task74You can evaluate an embedding model on this dataset using the following code:75 76```python77import mteb78 79task = mteb.get_tasks(["CQADupstackPhysics-VN"])80evaluator = mteb.MTEB(task)81 82model = mteb.get_model(YOUR_MODEL)83evaluator.run(model)84```85 86<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->87To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). 88 89 90## Citation91If 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).92 93```bibtex94 95@misc{pham2025vnmtebvietnamesemassivetext,96    title={VN-MTEB: Vietnamese Massive Text Embedding Benchmark},97    author={Loc Pham and Tung Luu and Thu Vo and Minh Nguyen and Viet Hoang},98    year={2025},99    eprint={2507.21500},100    archivePrefix={arXiv},101    primaryClass={cs.CL},102    url={https://arxiv.org/abs/2507.21500}103}104 105@article{enevoldsen2025mmtebmassivemultilingualtext,106  title={MMTEB: Massive Multilingual Text Embedding Benchmark},107  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},108  publisher = {arXiv},109  journal={arXiv preprint arXiv:2502.13595},110  year={2025},111  url={https://arxiv.org/abs/2502.13595},112  doi = {10.48550/arXiv.2502.13595},113}114 115@article{muennighoff2022mteb,116  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{"\i}c and Reimers, Nils},117  title = {MTEB: Massive Text Embedding Benchmark},118  publisher = {arXiv},119  journal={arXiv preprint arXiv:2210.07316},120  year = {2022}121  url = {https://arxiv.org/abs/2210.07316},122  doi = {10.48550/ARXIV.2210.07316},123}124