deberta-v1-base
deepvk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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kazRush-kk-ru
deepvk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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View details ↗RuModernBERT-base
deepvkThe Russian version of the modernized bidirectional encoder-only Transformer model, ModernBERT. RuModernBERT was pre-trained on approximately 2 trillion tokens of Russian, English, and code data with a context length of up to 8,192 tokens, using data from the internet, books, scientific sources, and social media. We observed that several Russian lowercase letters were split into multiple subword tokens. This can be problematic for tasks like Named Entity Recognition (NER), where it is important that the first token of a word is a semantically meaningful unit. To address this, we release a patched revision of the tokenizer with minimal but targeted change. Six common Russian lowercase letters *(а, е, и, н, о, т)* are now encoded as single tokens. These tokens were assigned to [unusedX] slots in the vocabulary. Corresponding BPE merges were added to ensure proper single-token encoding during inference. To preserve compatibility with the pretrained model each new token was initialized with the embedding of its uppercase counterpart both in tok_embedding and lm_head. To prevent duplicate vectors and maintain robustness, a small amount of Gaussian noise was added during initialization with gamma 1e-3.
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View details ↗RuModernBERT-small
deepvk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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View details ↗USER-base
deepvk**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. Using this model becomes easy when you have `sentence-transformers` installed:
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View details ↗USER-bge-m3
deepvk**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:
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View details ↗USER2-base
deepvk**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.
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View details ↗USER2-small
deepvk**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.
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