title: The Russian-focused embedders' exploration: ruMTEB benchmark and Russian
    embedding model design
summary: 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.
paper_authors._id: 66c832db840b9278c5b8fa91
paper_authors.name: Artem Snegirev
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-08-23T06:57:32.477Z
paper_authors._id: 5f8ed83d5d083370c711f642
paper_authors.avatarUrl: /avatars/d0cf3e587a64feed7c7ea7870f0deb5a.svg
paper_authors.fullname: Artem Snegirev
paper_authors.type: user
paper_authors.user: artemsnegirev
paper_authors._id: 66c832db840b9278c5b8fa92
paper_authors.name: Maria Tikhonova
paper_authors._id: 66c832db840b9278c5b8fa93
paper_authors.name: Anna Maksimova
paper_authors._id: 66c832db840b9278c5b8fa94
paper_authors.name: Alena Fenogenova
paper_authors._id: 66c832db840b9278c5b8fa95
paper_authors.name: Alexander Abramov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-26T07:15:12.748Z
paper_authors._id: 639bf7c0445b133a4e98544a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1671165672918-noauth.jpeg
paper_authors.fullname: Aleksandr Abramov
paper_authors.type: user
paper_authors.user: Andrilko
paper_title: The Russian-focused embedders' exploration: ruMTEB benchmark and Russian
    embedding model design
paper_summary: 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.
paper_ai_summary: A new Russian-focused embedding model, ru-en-RoSBERTa, and a Russian benchmark, ruMTEB, are introduced, demonstrating competitive performance in various NLP tasks compared to existing state-of-the-art models.
paper_ai_keywords: embedding models Natural Language Processing NLP ru-en-RoSBERTa ruMTEB Massive Text Embedding Benchmark MTEB semantic textual similarity text classification reranking retrieval