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--- |
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dataset_info: |
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- config_name: corpus-en |
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features: |
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- name: _id |
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dtype: string |
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- name: title |
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dtype: string |
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- name: text |
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dtype: string |
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splits: |
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- name: corpus |
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num_bytes: 85781507 |
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num_examples: 90000 |
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download_size: 48916377 |
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dataset_size: 85781507 |
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- config_name: corpus-ru |
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features: |
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- name: _id |
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dtype: string |
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|
- name: title |
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dtype: string |
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- name: text |
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dtype: string |
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splits: |
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- name: corpus |
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num_bytes: 150466041 |
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num_examples: 90000 |
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download_size: 71713875 |
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dataset_size: 150466041 |
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- config_name: en |
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features: |
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- name: query-id |
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dtype: string |
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- name: corpus-id |
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dtype: string |
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- name: score |
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dtype: int64 |
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splits: |
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- name: test |
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num_bytes: 479912 |
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num_examples: 15000 |
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download_size: 190544 |
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dataset_size: 479912 |
|
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- config_name: queries-en |
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features: |
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- name: _id |
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dtype: string |
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- name: text |
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dtype: string |
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splits: |
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- name: queries |
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num_bytes: 3124999 |
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num_examples: 3000 |
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download_size: 1758575 |
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dataset_size: 3124999 |
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- config_name: queries-ru |
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features: |
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- name: _id |
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dtype: string |
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- name: text |
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dtype: string |
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splits: |
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- name: queries |
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num_bytes: 5550462 |
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num_examples: 3000 |
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download_size: 2606302 |
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dataset_size: 5550462 |
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- config_name: ru |
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features: |
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- name: query-id |
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dtype: string |
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- name: corpus-id |
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dtype: string |
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|
- name: score |
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dtype: int64 |
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splits: |
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- name: test |
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num_bytes: 479912 |
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num_examples: 15000 |
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download_size: 190544 |
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dataset_size: 479912 |
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configs: |
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- config_name: corpus-en |
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data_files: |
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- split: corpus |
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path: corpus-en/corpus-* |
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- config_name: corpus-ru |
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data_files: |
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- split: corpus |
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path: corpus-ru/corpus-* |
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- config_name: en |
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data_files: |
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- split: test |
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path: en/test-* |
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- config_name: queries-en |
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data_files: |
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- split: queries |
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path: queries-en/queries-* |
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- config_name: queries-ru |
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data_files: |
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- split: queries |
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path: queries-ru/queries-* |
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- config_name: ru |
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data_files: |
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- split: test |
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path: ru/test-* |
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language: |
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- ru |
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- en |
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tags: |
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- benchmark |
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- mteb |
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- retrieval |
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--- |
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# RuSciBench Dataset Collection |
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This repository contains the datasets for the **RuSciBench** benchmark, designed for evaluating semantic vector representations of scientific texts in Russian and English. |
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## Dataset Description |
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**RuSciBench** is the first benchmark specifically targeting scientific documents in the Russian language, alongside their English counterparts (abstracts and titles). The data is sourced from [eLibrary.ru](https://www.elibrary.ru), the largest Russian electronic library of scientific publications, integrated with the Russian Science Citation Index (RSCI). |
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The dataset comprises approximately 182,000 scientific paper abstracts and titles. All papers included in the benchmark have open licenses. |
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## Tasks |
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The benchmark includes a variety of tasks grouped into Classification, Regression, and Retrieval categories, designed for both Russian and English texts based on paper abstracts. |
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### Classification Tasks |
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([Dataset Link](https://huggingface.co/datasets/mlsa-iai-msu-lab/ru_sci_bench_mteb)) |
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1. **Topic Classification (OECD):** Classify papers based on the first two levels of the Organization for Economic Co-operation and Development (OECD) rubricator (29 classes). |
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* `RuSciBenchOecdRuClassification` (subset `oecd_ru`) |
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* `RuSciBenchOecdEnClassification` (subset `oecd_en`) |
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2. **Topic Classification (GRNTI/SRSTI):** Classify papers based on the first level of the State Rubricator of Scientific and Technical Information (GRNTI/SRSTI) (29 classes). |
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* `RuSciBenchGrntiRuClassification` (subset `grnti_ru`) |
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* `RuSciBenchGrntiEnClassification` (subset `grnti_en`) |
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3. **Core RISC Affiliation:** Binary classification task to determine if a paper belongs to the Core of the Russian Index of Science Citation (RISC). |
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* `RuSciBenchCoreRiscRuClassification` (subset `corerisc_ru`) |
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* `RuSciBenchCoreRiscEnClassification` (subset `corerisc_en`) |
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4. **Publication Type Classification:** Classify documents into types like 'article', 'conference proceedings', 'survey', etc. (7 classes, balanced subset used). |
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* `RuSciBenchPubTypesRuClassification` (subset `pub_type_ru`) |
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* `RuSciBenchPubTypesEnClassification` (subset `pub_type_en`) |
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### Regression Tasks |
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([Dataset Link](https://huggingface.co/datasets/mlsa-iai-msu-lab/ru_sci_bench_mteb)) |
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1. **Year of Publication Prediction:** Predict the publication year of the paper. |
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* `RuSciBenchYearPublRuRegression` (subset `yearpubl_ru`) |
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* `RuSciBenchYearPublEnRegression` (subset `yearpubl_en`) |
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2. **Citation Count Prediction:** Predict the number of times a paper has been cited. |
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* `RuSciBenchCitedCountRuRegression` (subset `cited_count_ru`) |
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* `RuSciBenchCitedCountEnRegression` (subset `cited_count_en`) |
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### Retrieval Tasks |
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1. **Direct Citation Prediction:** Given a query paper abstract, retrieve abstracts of papers it directly cites from the corpus. Uses a retrieval setup (all non-positive documents are negative). ([Dataset Link](https://huggingface.co/datasets/mlsa-iai-msu-lab/ru_sci_bench_cite_retrieval)) |
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* `RuSciBenchCiteRuRetrieval` |
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* `RuSciBenchCiteEnRetrieval` |
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2. **Co-Citation Prediction:** Given a query paper abstract, retrieve abstracts of papers that are co-cited with it (cited by at least 5 common papers). Uses a retrieval setup. |
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* `RuSciBenchCociteRuRetrieval` |
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* `RuSciBenchCociteEnRetrieval` |
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3. **Translation Search:** Given an abstract in one language (e.g., Russian), retrieve its corresponding translation (e.g., English abstract of the same paper) from the corpus of abstracts in the target language. ([Dataset Link](https://huggingface.co/datasets/mlsa-iai-msu-lab/ru_sci_bench_translation_search)) |
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* `RuSciBenchTranslationSearchEnRetrieval` (Query: En, Corpus: Ru) |
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* `RuSciBenchTranslationSearchRuRetrieval` (Query: Ru, Corpus: En) |
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## Usage |
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These datasets are designed to be used with the MTEB library. **First, you need to install the MTEB fork containing the RuSciBench tasks:** |
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```bash |
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pip install git+https://github.com/mlsa-iai-msu-lab/ru_sci_bench_mteb |
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``` |
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Then you can evaluate sentence-transformer models easily: |
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```python |
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from sentence_transformers import SentenceTransformer |
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from mteb import MTEB |
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# Example: Evaluate on Russian GRNTI classification |
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model_name = "mlsa-iai-msu-lab/sci-rus-tiny3.1" # Or any other sentence transformer |
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model = SentenceTransformer(model_name) |
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evaluation = MTEB(tasks=["RuSciBenchGrntiRuClassification"]) # Select tasks |
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results = evaluation.run(model, output_folder=f"results/{model_name.split('/')[-1]}") |
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print(results) |
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``` |
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For more details on the benchmark, tasks, and baseline model evaluations, please refer to the associated paper and code repository. |
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* **Code Repository:** [https://github.com/mlsa-iai-msu-lab/ru_sci_bench_mteb](https://github.com/mlsa-iai-msu-lab/ru_sci_bench_mteb) |
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* **Paper:** https://doi.org/10.1134/S1064562424602191 |
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## Citation |
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If you use RuSciBench in your research, please cite the following paper: |
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```bibtex |
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@article{Vatolin2024, |
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author = {Vatolin, A. and Gerasimenko, N. and Ianina, A. and Vorontsov, K.}, |
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title = {RuSciBench: Open Benchmark for Russian and English Scientific Document Representations}, |
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journal = {Doklady Mathematics}, |
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year = {2024}, |
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volume = {110}, |
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number = {1}, |
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pages = {S251--S260}, |
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month = dec, |
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doi = {10.1134/S1064562424602191}, |
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url = {https://doi.org/10.1134/S1064562424602191}, |
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issn = {1531-8362} |
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|
} |
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|
``` |
