MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation
We introduce MrBERT, a family of 150M-300M parameter encoders built on the ModernBERT architecture and pre-trained on 35 languages and code. Through targeted adaptation, this model family achieves state-of-the-art results on Catalan- and Spanish-specific tasks, while establishing robust performance across specialized biomedical and legal domains. To bridge the gap between research and production, we incorporate Matryoshka Representation Learning (MRL), enabling flexible vector sizing that significantly reduces inference and storage costs. Ultimately, the MrBERT family demonstrates that modern encoder architectures can be optimized for both localized linguistic excellence and efficient, hi...
| Authors | Daniel Tamayo, Iñaki Lacunza, Paula Rivera-Hidalgo et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 February 2026 |
| Last updated at source | 7 March 2026 |
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