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RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) is based on [DeepPavlov's RuBERT-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased). It is fine-tuned as a Siamese neural network to build effective token embeddings of 29 entity classes on Russian [1]. The fine-tuning procedure is the first stage of two-stage fine-tuning of a BERT-based language model for more robust named entity recognition [2].
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\[1\]: *Artemova*, *E.*, *Zmeev*, *M.*, *Loukachevitch*, *N.V.*, *Rozhkov*, *I.S.*, *Batura*, *T.*, *Ivanov*, *V.*, & *Tutubalina*, *E.* (2022). **RuNNE-2022 Shared Task: Recognizing Nested Named Entities**. Proceedings of the International Conference “Dialogue 2022”. https://www.dialog-21.ru/media/5747/artemovaelplusetal109.pdf
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\[2\]: *Bondarenko*, *I.* (2022). **Contrastive fine-tuning to improve generalization in deep NER**. Proceedings of the International Conference “Dialogue 2022”. https://www.dialog-21.ru/media/5751/bondarenkoi113.pdf
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RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) is based on [DeepPavlov's RuBERT-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased). It is fine-tuned as a Siamese neural network to build effective token embeddings of 29 entity classes on Russian [1]. The fine-tuning procedure is the first stage of two-stage fine-tuning of a BERT-based language model for more robust named entity recognition [2].
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\[1\]: *Artemova*, *E.*, *Zmeev*, *M.*, *Loukachevitch*, *N.V.*, *Rozhkov*, *I.S.*, *Batura*, *T.*, *Ivanov*, *V.*, & *Tutubalina*, *E.* (2022). **RuNNE-2022 Shared Task: Recognizing Nested Named Entities**. Proceedings of the International Conference “Dialogue 2022”. https://www.dialog-21.ru/media/5747/artemovaelplusetal109.pdf
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\[2\]: *Bondarenko*, *I.* (2022). **Contrastive fine-tuning to improve generalization in deep NER**. Proceedings of the International Conference “Dialogue 2022”. https://www.dialog-21.ru/media/5751/bondarenkoi113.pdf
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