README.md

---
annotations_creators:
- derived
language:
- eng
- rus
license: mit
multilinguality: multilingual
source_datasets:
- mlsa-iai-msu-lab/ru_sci_bench_cite_retrieval
task_categories:
- text-retrieval
- document-retrieval
task_ids:
- document-retrieval
dataset_info:
- config_name: en-corpus
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: title
    dtype: string
  splits:
  - name: test
    num_bytes: 86543664
    num_examples: 90000
  download_size: 48804097
  dataset_size: 86543664
- config_name: en-qrels
  features:
  - name: query-id
    dtype: string
  - name: corpus-id
    dtype: string
  - name: score
    dtype: int64
  splits:
  - name: test
    num_bytes: 479970
    num_examples: 15000
  download_size: 179821
  dataset_size: 479970
- config_name: en-queries
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: test
    num_bytes: 4515069
    num_examples: 3000
  download_size: 2441693
  dataset_size: 4515069
- config_name: ru-corpus
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: title
    dtype: string
  splits:
  - name: test
    num_bytes: 151692193
    num_examples: 90000
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- config_name: ru-qrels
  features:
  - name: query-id
    dtype: string
  - name: corpus-id
    dtype: string
  - name: score
    dtype: int64
  splits:
  - name: test
    num_bytes: 479970
    num_examples: 15000
  download_size: 179821
  dataset_size: 479970
- config_name: ru-queries
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: test
    num_bytes: 7931539
    num_examples: 3000
  download_size: 3592206
  dataset_size: 7931539
configs:
- config_name: en-corpus
  data_files:
  - split: test
    path: en-corpus/test-*
- config_name: en-qrels
  data_files:
  - split: test
    path: en-qrels/test-*
- config_name: en-queries
  data_files:
  - split: test
    path: en-queries/test-*
- config_name: ru-corpus
  data_files:
  - split: test
    path: ru-corpus/test-*
- config_name: ru-qrels
  data_files:
  - split: test
    path: ru-qrels/test-*
- config_name: ru-queries
  data_files:
  - split: test
    path: ru-queries/test-*
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->

<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
  <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">RuSciBenchCiteRetrieval</h1>
  <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
  <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>

This task is focused on Direct 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 papers that are directly cited by it from a larger corpus of papers.
        The dataset for this task consists of 3,000 query papers, 15,000 relevant (cited) papers,
        and 75,000 irrelevant papers. 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_cite_retrieval](https://huggingface.co/datasets/mlsa-iai-msu-lab/ru_sci_bench_cite_retrieval)


## 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("RuSciBenchCiteRetrieval")
evaluator = mteb.MTEB([task])

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```

<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
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

@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
<details>
  <summary> Dataset Statistics</summary>

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

```python
import mteb

task = mteb.get_task("RuSciBenchCiteRetrieval")

desc_stats = task.metadata.descriptive_stats
```

```json
{
    "test": {
        "num_samples": 186000,
        "number_of_characters": 174343293,
        "documents_text_statistics": {
            "total_text_length": 165626984,
            "min_text_length": 17,
            "average_text_length": 920.1499111111111,
            "max_text_length": 35721,
            "unique_texts": 179976
        },
        "documents_image_statistics": null,
        "queries_text_statistics": {
            "total_text_length": 8716309,
            "min_text_length": 92,
            "average_text_length": 1452.7181666666668,
            "max_text_length": 6357,
            "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": 85265983,
                "documents_text_statistics": {
                    "total_text_length": 81007919,
                    "min_text_length": 18,
                    "average_text_length": 900.0879888888888,
                    "max_text_length": 18100,
                    "unique_texts": 89994
                },
                "documents_image_statistics": null,
                "queries_text_statistics": {
                    "total_text_length": 4258064,
                    "min_text_length": 106,
                    "average_text_length": 1419.3546666666666,
                    "max_text_length": 4224,
                    "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": 89077310,
                "documents_text_statistics": {
                    "total_text_length": 84619065,
                    "min_text_length": 17,
                    "average_text_length": 940.2118333333333,
                    "max_text_length": 35721,
                    "unique_texts": 89991
                },
                "documents_image_statistics": null,
                "queries_text_statistics": {
                    "total_text_length": 4458245,
                    "min_text_length": 92,
                    "average_text_length": 1486.0816666666667,
                    "max_text_length": 6357,
                    "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
            }
        }
    }
}
```

</details>

---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*