Datasets:

mteb
/

SpanishPassageRetrievalS2S

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:

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.

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{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

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("SpanishPassageRetrievalS2S")

desc_stats = task.metadata.descriptive_stats

{}

This dataset card was automatically generated using MTEB