SkMTEB
Collection
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This repository is publicly accessible, but you have to accept the conditions to access its files and content.
Access Request Terms:
By requesting access to this Slovak-Pharmacy dataset, you confirm that you:
- will use the materials solely for research and non-commercial purposes;
- will cite the SkMTEB paper and respect the CC-BY-NC-ND-4.0 License;
- will not attempt to extract, infer, or reconstruct data from the dataset;
- will not share, redistribute, or republish the dataset or any portion of it;
- confirm that the intended use described in your access request is accurate and complete;
- acknowledge that the data has been anonymized, but will immediately notify dpo@kinit.sk if you identify any personal data that may have been inadvertently included;
- will ensure that your downstream use complies with applicable laws, regulations, and ethical AI principles.
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A reranking dataset created from Q&A content collected from DrMax pharmacy website. The dataset consists of questions about medications, health conditions, and pharmaceutical advice, with answers provided by qualified pharmacists. This dataset is designed to evaluate models' ability to rank relevant pharmaceutical information and expert responses.
| Task category | t2t |
| Domains | Medical, Web |
| Reference | https://huggingface.co/datasets/slovak-nlp/slovak-pharmacy-drmax-reranking |
Source datasets:
Access & Usage
This dataset is released under the CC BY-NC-ND 4.0 license and is intended for research purposes only. Commercial use and derivative works are not permitted.
The data collected from the pharmacy website has been anonymized prior to release. Any personally identifiable information has been removed or obfuscated.
Access to this dataset is gated. By requesting access, you agree to the dataset terms, including proper citation of the SkMTEB paper and compliance with applicable data protection regulations. If you believe your data is present in this dataset and wish to have it removed, please contact our Data Protection Officer at dpo@kinit.sk.
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("SlovakPharmacyDrMaxReranking")
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 repository.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.
@inproceedings{suppa-etal-2026-skmteb,
title = {{SkMTEB}: {S}lovak Massive Text Embedding Benchmark and Model Adaptation},
author = {{\v{S}}uppa, Marek and Ridzik, Andrej and Hl{\'a}dek, Daniel and Kn{\v{a}}{\v{z}}ekov{\'a}, Nat{\'a}lia and Ondrejov{\'a}, Vikt{\'o}ria},
booktitle = {Proceedings of ACL},
year = {2026},
}
@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, Loïc 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
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("SlovakPharmacyDrMaxReranking")
desc_stats = task.metadata.descriptive_stats
{}
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