mteb/norquad_retrieval

NorQuadRetrieval

An MTEB dataset

Massive Text Embedding Benchmark

Human-created question for Norwegian wikipedia passages.

Task category	t2t
Domains	Encyclopaedic, Non-fiction, Written
Reference	https://aclanthology.org/2023.nodalida-1.17/

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_tasks(["NorQuadRetrieval"])
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 repitory.

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{ivanova-etal-2023-norquad,
  abstract = {In this paper we present NorQuAD: the first Norwegian question answering dataset for machine reading comprehension. The dataset consists of 4,752 manually created question-answer pairs. We here detail the data collection procedure and present statistics of the dataset. We also benchmark several multilingual and Norwegian monolingual language models on the dataset and compare them against human performance. The dataset will be made freely available.},
  address = {T{\'o}rshavn, Faroe Islands},
  author = {Ivanova, Sardana  and
Andreassen, Fredrik  and
Jentoft, Matias  and
Wold, Sondre  and
{\O}vrelid, Lilja},
  booktitle = {Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)},
  editor = {Alum{\"a}e, Tanel  and
Fishel, Mark},
  month = may,
  pages = {159--168},
  publisher = {University of Tartu Library},
  title = {{N}or{Q}u{AD}: {N}orwegian Question Answering Dataset},
  url = {https://aclanthology.org/2023.nodalida-1.17},
  year = {2023},
}


@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, Lo{\"\i}c 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("NorQuadRetrieval")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 2072,
        "number_of_characters": 273854,
        "num_documents": 1048,
        "min_document_length": 1,
        "average_document_length": 214.5114503816794,
        "max_document_length": 2606,
        "unique_documents": 1048,
        "num_queries": 1024,
        "min_query_length": 11,
        "average_query_length": 47.896484375,
        "max_query_length": 100,
        "unique_queries": 1024,
        "none_queries": 0,
        "num_relevant_docs": 2048,
        "min_relevant_docs_per_query": 2,
        "average_relevant_docs_per_query": 2.0,
        "max_relevant_docs_per_query": 2,
        "unique_relevant_docs": 1328,
        "num_instructions": null,
        "min_instruction_length": null,
        "average_instruction_length": null,
        "max_instruction_length": null,
        "unique_instructions": null,
        "num_top_ranked": null,
        "min_top_ranked_per_query": null,
        "average_top_ranked_per_query": null,
        "max_top_ranked_per_query": null
    }
}

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This dataset card was automatically generated using MTEB