Update README.md

README.md

@@ -105,4 +105,113 @@ configs:
   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). ([Dataset Link](https://huggingface.co/datasets/mlsa-iai-msu-lab/ru_sci_bench_cite_retrieval))
+    *   `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.
+    *   `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}
+}
+```