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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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**U**niversal **S**entence **E**ncoder for **R**ussian (USER) is a sentence-transformer model for extracting embeddings exclusively for Russian language. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model is initialized from `deepvk/deberta-v1-base` and trained to work exclusively with the Russian language. Its quality on other languages was not evaluated. Each input text should start with "query: " or "passage: ". For tasks other than retrieval, you can simply use the "query: " prefix.

翻訳待ち・原文を表示

**U**niversal **S**entence **E**ncoder for **R**ussian (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-m3_en_ru` 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:

翻訳待ち・原文を表示

**USER2** is a new generation of the **U**niversal **S**entence **E**ncoder for **R**ussian, designed for sentence representation with long-context support of up to 8,192 tokens. The models are built on top of the `RuModernBERT` encoders and are fine-tuned for retrieval and semantic tasks. They also support Matryoshka Representation Learning (MRL) — a technique that enables reducing embedding size with minimal loss in representation quality. To evaluate the model, we measure quality on the `MTEB-rus` benchmark. Additionally, to measure long-context retrieval, we run Russian subset of MultiLongDocRetrieval (MLDR) task.

翻訳待ち・原文を表示

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。