--- language: - spa multilinguality: monolingual source_datasets: - jinaai/spanish_passage_retrieval task_categories: - text-retrieval task_ids: [] dataset_info: - config_name: corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 124756 num_examples: 265 download_size: 61560 dataset_size: 124756 - config_name: qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 54482 num_examples: 1289 download_size: 7380 dataset_size: 54482 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 14727 num_examples: 167 download_size: 6348 dataset_size: 14727 configs: - config_name: corpus data_files: - split: test path: corpus/test-* - config_name: qrels data_files: - split: test path: qrels/test-* - config_name: queries data_files: - split: test path: queries/test-* tags: - mteb - text ---

SpanishPassageRetrievalS2S

An MTEB dataset
Massive Text Embedding Benchmark
Test collection for passage retrieval from health-related Web resources in Spanish. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | None | | Reference | https://mklab.iti.gr/results/spanish-passage-retrieval-dataset/ | ## 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_task("SpanishPassageRetrievalS2S") 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](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{10.1007/978-3-030-15719-7_19, abstract = {This paper describes a new test collection for passage retrieval from health-related Web resources in Spanish. The test collection contains 10,037 health-related documents in Spanish, 37 topics representing complex information needs formulated in a total of 167 natural language questions, and manual relevance assessments of text passages, pooled from multiple systems. This test collection is the first to combine search in a language beyond English, passage retrieval, and health-related resources and topics targeting the general public.}, address = {Cham}, author = {Kamateri, Eleni and Tsikrika, Theodora and Symeonidis, Spyridon and Vrochidis, Stefanos and Minker, Wolfgang and Kompatsiaris, Yiannis}, booktitle = {Advances in Information Retrieval}, editor = {Azzopardi, Leif and Stein, Benno and Fuhr, Norbert and Mayr, Philipp and Hauff, Claudia and Hiemstra, Djoerd}, isbn = {978-3-030-15719-7}, pages = {148--154}, publisher = {Springer International Publishing}, title = {A Test Collection for Passage Retrieval Evaluation of Spanish Health-Related Resources}, year = {2019}, } @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: ```python import mteb task = mteb.get_task("SpanishPassageRetrievalS2S") desc_stats = task.metadata.descriptive_stats ``` ```json {} ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*