RuSciBenchCociteRetrieval

An MTEB dataset

Massive Text Embedding Benchmark

This task focuses on Co-citation Prediction for scientific papers from eLibrary, Russia's largest electronic library of scientific publications. Given a query paper (title and abstract), the goal is to retrieve other papers that are co-cited with it. Two papers are considered co-cited if they are both cited by at least 5 of the same other papers. Similar to the Direct Citation task, this task employs a retrieval setup: for a given query paper, all other papers in the corpus that are not co-cited with it are considered negative examples. The task is available for both Russian and English scientific texts.

Task category	t2t
Domains	Academic, Non-fiction, Written
Reference	https://github.com/mlsa-iai-msu-lab/ru_sci_bench_mteb

Source datasets:

• mlsa-iai-msu-lab/ru_sci_bench_cocite_retrieval

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("RuSciBenchCociteRetrieval")
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.

@article{vatolin2024ruscibench,
  author = {Vatolin, A. and Gerasimenko, N. and Ianina, A. and Vorontsov, K.},
  doi = {10.1134/S1064562424602191},
  issn = {1531-8362},
  journal = {Doklady Mathematics},
  month = {12},
  number = {1},
  pages = {S251--S260},
  title = {RuSciBench: Open Benchmark for Russian and English Scientific Document Representations},
  url = {https://doi.org/10.1134/S1064562424602191},
  volume = {110},
  year = {2024},
}


@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("RuSciBenchCociteRetrieval")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 186000,
        "number_of_characters": 170246071,
        "documents_text_statistics": {
            "total_text_length": 164202113,
            "min_text_length": 9,
            "average_text_length": 912.2339611111111,
            "max_text_length": 14007,
            "unique_texts": 179965
        },
        "documents_image_statistics": null,
        "queries_text_statistics": {
            "total_text_length": 6043958,
            "min_text_length": 72,
            "average_text_length": 1007.3263333333333,
            "max_text_length": 8248,
            "unique_texts": 6000
        },
        "queries_image_statistics": null,
        "relevant_docs_statistics": {
            "num_relevant_docs": 30000,
            "min_relevant_docs_per_query": 5,
            "average_relevant_docs_per_query": 5.0,
            "max_relevant_docs_per_query": 5,
            "unique_relevant_docs": 30000
        },
        "top_ranked_statistics": null,
        "hf_subset_descriptive_stats": {
            "ru": {
                "num_samples": 93000,
                "number_of_characters": 83311888,
                "documents_text_statistics": {
                    "total_text_length": 80339089,
                    "min_text_length": 18,
                    "average_text_length": 892.6565444444444,
                    "max_text_length": 14007,
                    "unique_texts": 89990
                },
                "documents_image_statistics": null,
                "queries_text_statistics": {
                    "total_text_length": 2972799,
                    "min_text_length": 90,
                    "average_text_length": 990.933,
                    "max_text_length": 5011,
                    "unique_texts": 3000
                },
                "queries_image_statistics": null,
                "relevant_docs_statistics": {
                    "num_relevant_docs": 15000,
                    "min_relevant_docs_per_query": 5,
                    "average_relevant_docs_per_query": 5.0,
                    "max_relevant_docs_per_query": 5,
                    "unique_relevant_docs": 15000
                },
                "top_ranked_statistics": null
            },
            "en": {
                "num_samples": 93000,
                "number_of_characters": 86934183,
                "documents_text_statistics": {
                    "total_text_length": 83863024,
                    "min_text_length": 9,
                    "average_text_length": 931.8113777777778,
                    "max_text_length": 8168,
                    "unique_texts": 89984
                },
                "documents_image_statistics": null,
                "queries_text_statistics": {
                    "total_text_length": 3071159,
                    "min_text_length": 72,
                    "average_text_length": 1023.7196666666666,
                    "max_text_length": 8248,
                    "unique_texts": 3000
                },
                "queries_image_statistics": null,
                "relevant_docs_statistics": {
                    "num_relevant_docs": 15000,
                    "min_relevant_docs_per_query": 5,
                    "average_relevant_docs_per_query": 5.0,
                    "max_relevant_docs_per_query": 5,
                    "unique_relevant_docs": 15000
                },
                "top_ranked_statistics": null
            }
        }
    }
}

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This dataset card was automatically generated using MTEB