The Russian-Focused Embedders' Exploration: ruMTEB Benchmark and Russian Embedding Model Design

P. 236–254.

Снегирев А., Tikhonova M., Maksimova A., Fenogenova A., Abramov A.

Embedding models play a crucial role in Natural Language Processing (NLP) by creating text embeddings used in various tasks such as information retrieval and assessing semantic text similarity. This paper focuses on research related to embedding models in the Russian language. It introduces a new Russian-focused embedding model called ru-en-RoSBERTa and the ruMTEB benchmark, the Russian version extending the Massive Text Embedding Benchmark (MTEB). Our benchmark includes seven categories of tasks, such as semantic textual similarity, text classification, reranking, and retrieval. The research also assesses a representative set of Russian and multilingual models on the proposed benchmark. The findings indicate that the new model achieves results that are on par with state-of-the-art models in Russian. We release the model ru-en-RoSBERTa, and the ruMTEB framework comes with open-source code, integration into the original framework and a public leaderboard.

Keywords: Semantic textual similarity, Natural Language Processing, Embedding Models, MTEB

In book

Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies

Vol. 1: Volume 1: Long Papers. , Association for Computational Linguistics, 2025.