--- dataset_info: - config_name: corpus-en features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 86543664 num_examples: 90000 download_size: 49348336 dataset_size: 86543664 - config_name: corpus-ru features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 151692193 num_examples: 90000 download_size: 72294562 dataset_size: 151692193 - config_name: en 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: 190933 dataset_size: 479970 - config_name: queries-en features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 4515069 num_examples: 3000 download_size: 2438842 dataset_size: 4515069 - config_name: queries-ru features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 7931539 num_examples: 3000 download_size: 3624950 dataset_size: 7931539 - config_name: ru 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: 190933 dataset_size: 479970 configs: - config_name: corpus-en data_files: - split: corpus path: corpus-en/corpus-* - config_name: corpus-ru data_files: - split: corpus path: corpus-ru/corpus-* - config_name: en data_files: - split: test path: en/test-* - config_name: queries-en data_files: - split: queries path: queries-en/queries-* - config_name: queries-ru data_files: - split: queries path: queries-ru/queries-* - config_name: ru data_files: - split: test path: ru/test-* language: - ru - en tags: - benchmark - mteb - retrieval --- # RuSciBench Dataset Collection This repository contains the datasets for the **RuSciBench** benchmark, designed for evaluating semantic vector representations of scientific texts in Russian and English. ## Dataset Description **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). The dataset comprises approximately 182,000 scientific paper abstracts and titles. All papers included in the benchmark have open licenses. ## Tasks 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. ### Classification Tasks ([Dataset Link](https://huggingface.co/datasets/mlsa-iai-msu-lab/ru_sci_bench_mteb)) 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). * `RuSciBenchOecdRuClassification` (subset `oecd_ru`) * `RuSciBenchOecdEnClassification` (subset `oecd_en`) 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). * `RuSciBenchGrntiRuClassification` (subset `grnti_ru`) * `RuSciBenchGrntiEnClassification` (subset `grnti_en`) 3. **Core RISC Affiliation:** Binary classification task to determine if a paper belongs to the Core of the Russian Index of Science Citation (RISC). * `RuSciBenchCoreRiscRuClassification` (subset `corerisc_ru`) * `RuSciBenchCoreRiscEnClassification` (subset `corerisc_en`) 4. **Publication Type Classification:** Classify documents into types like 'article', 'conference proceedings', 'survey', etc. (7 classes, balanced subset used). * `RuSciBenchPubTypesRuClassification` (subset `pub_type_ru`) * `RuSciBenchPubTypesEnClassification` (subset `pub_type_en`) ### Regression Tasks ([Dataset Link](https://huggingface.co/datasets/mlsa-iai-msu-lab/ru_sci_bench_mteb)) 1. **Year of Publication Prediction:** Predict the publication year of the paper. * `RuSciBenchYearPublRuRegression` (subset `yearpubl_ru`) * `RuSciBenchYearPublEnRegression` (subset `yearpubl_en`) 2. **Citation Count Prediction:** Predict the number of times a paper has been cited. * `RuSciBenchCitedCountRuRegression` (subset `cited_count_ru`) * `RuSciBenchCitedCountEnRegression` (subset `cited_count_en`) ### Retrieval Tasks 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). * `RuSciBenchCiteRuRetrieval` * `RuSciBenchCiteEnRetrieval` 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. ([Dataset Link](https://huggingface.co/datasets/mlsa-iai-msu-lab/ru_sci_bench_cocite_retrieval)) * `RuSciBenchCociteRuRetrieval` * `RuSciBenchCociteEnRetrieval` 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)) * `RuSciBenchTranslationSearchEnRetrieval` (Query: En, Corpus: Ru) * `RuSciBenchTranslationSearchRuRetrieval` (Query: Ru, Corpus: En) ## Usage These datasets are designed to be used with the MTEB library. **First, you need to install the MTEB fork containing the RuSciBench tasks:** ```bash pip install git+https://github.com/mlsa-iai-msu-lab/ru_sci_bench_mteb ``` Then you can evaluate sentence-transformer models easily: ```python from sentence_transformers import SentenceTransformer from mteb import MTEB # Example: Evaluate on Russian GRNTI classification model_name = "mlsa-iai-msu-lab/sci-rus-tiny3.1" # Or any other sentence transformer model = SentenceTransformer(model_name) evaluation = MTEB(tasks=["RuSciBenchGrntiRuClassification"]) # Select tasks results = evaluation.run(model, output_folder=f"results/{model_name.split('/')[-1]}") print(results) ``` For more details on the benchmark, tasks, and baseline model evaluations, please refer to the associated paper and code repository. * **Code Repository:** [https://github.com/mlsa-iai-msu-lab/ru_sci_bench_mteb](https://github.com/mlsa-iai-msu-lab/ru_sci_bench_mteb) * **Paper:** https://doi.org/10.1134/S1064562424602191 ## Citation If you use RuSciBench in your research, please cite the following paper: ```bibtex @article{Vatolin2024, author = {Vatolin, A. and Gerasimenko, N. and Ianina, A. and Vorontsov, K.}, title = {RuSciBench: Open Benchmark for Russian and English Scientific Document Representations}, journal = {Doklady Mathematics}, year = {2024}, volume = {110}, number = {1}, pages = {S251--S260}, month = dec, doi = {10.1134/S1064562424602191}, url = {https://doi.org/10.1134/S1064562424602191}, issn = {1531-8362} } ```