deepvk/USER-bge-m3
sentence-similarity model by deepvk
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Model card & documentation
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Universal Sentence Encoder for Russian (USER) is a sentence-transformer model for extracting embeddings exclusively for Russian language. It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model is initialized from TatonkaHF/bge-m3enru which is shrinked version of baai/bge-m3 model and trained to work mainly with the Russian language. Its quality on other languages was not evaluated. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: However, you can use model directly with transformers Also, you can use native FlagEmbedding library for evaluation. Usage is described in bge-m3 model card. We follow the USER-base model training algorithm, with several changes as we use different backbone. Initialization: TatonkaHF/bge-m3enru – shrinked version of baai/bge-m3 to support only Russian and English tokens. Fine-tuning: Supervised fine-tuning two different models based on data…
Read the full model card ↗ · Preview checked 2026-10-31T14:00:21.088Z
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Model overview
deepvk/USER-bge-m3 is a sentence-similarity model repository published by deepvk on Hugging Face. The source reports the sentence-transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- sentence-similarity
- Library
- sentence-transformers
- Recent downloads (30 days)
- 295,662
- Cumulative likes
- 86
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 359,026,688
- Architecture
- XLMRobertaModel
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2024-07-05T16:48:46.000Z
- Last modified
- 2024-07-18T12:12:51.000Z
Compatibility and lineage
Reported languages: ru
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
Check license terms and access requirements first. Review the model card’s documented loading instructions, evaluations and safety limitations. Benchmark scores are not imported by this directory, and popularity is not an accuracy ranking.
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:31.802Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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