From the model card
Fields below are copied from the tags and counters on the HuggingFace repository ai-forever/ru-en-RoSBERTa at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- ai-forever
- Pipeline tag
- feature-extraction
- Library
- Sentence Transformers, Transformers
- Weight formats
- safetensors
- License tag
mit— read the license file in the repo before relying on it- Lineage
-
- base model ai-forever/ruRoberta-large
- fine-tune of ai-forever/ruRoberta-large
- Language tags
- Russian (ru), English (en)
- Papers cited
- arXiv:2408.12503
- Downloads (HF counter at last fetch)
- 511,825
- Likes (HF counter at last fetch)
- 82
- Model card
- https://huggingface.co/ai-forever/ru-en-RoSBERTa
Use cases
- Semantic search over Russian or bilingual document corpora
- Cross-lingual retrieval between Russian and English texts
- Clustering Russian-language documents by topic
- RAG pipelines requiring Russian-language query embedding
Pros
- Bilingual Russian-English coverage in a single model
- MIT license — unrestricted commercial use
- MTEB-trained — optimized for retrieval and similarity tasks
- text-embeddings-inference compatible for high-throughput serving
Cons
- Coverage drops sharply for languages beyond Russian and English
- Smaller embedding dimension than large multilingual models like E5
- Sentence transformer paradigm assumes passage-level inputs — poor for token-level tasks
- MTEB scores focus on English; Russian-only retrieval quality varies by domain
Tags
sentence-transformerssafetensorsrobertafeature-extractionmtebtransformersruenarxiv:2408.12503base_model:ai-forever/ruRoberta-largebase_model:finetune:ai-forever/ruRoberta-largelicense:mitmodel-indextext-embeddings-inferenceendpoints_compatibledeploy:azureregion:us