EuroBERT: Scaling Multilingual Encoders for European Languages
Authors: name: Nicolas Boizard, name: Hippolyte Gisserot-Boukhlef, name: Duarte M. Alves, name: André Martins, name: Ayoub Hammal, name: Caio Corro, name: Céline Hudelot, name: Emmanuel Malherbe, name: Etienne Malaboeuf, name: Fanny Jourdan, name: Gabriel Hautreux, name: João Alves, name: Kevin El-Haddad, name: Manuel Faysse, name: Maxime Peyrard, name: Nuno M. Guerreiro, name: Patrick Fernandes, name: Ricardo Rei, name: Pierre Colombo

General-purpose multilingual vector representations, used in retrieval,
regression and classification, are traditionally obtained from bidirectional
encoder models. Despite their wide applicability, encoders have been recently
overshadowed by advances in generative decoder-only models. However, many
innovations driving this progress are not inherently tied to decoders. In this
paper, we revisit the development of multilingual encoders through the lens of
these advances, and introduce EuroBERT, a family of multilingual encoders
covering European and widely spoken global languages. Our models outperform
existing alternatives across a diverse range of tasks, spanning multilingual
capabilities, mathematics, and coding, and natively supporting sequences of up
to 8,192 tokens. We also examine the design decisions behind EuroBERT, offering
insights into our dataset composition and training pipeline. We publicly
release the EuroBERT models, including intermediate training checkpoints,
together with our training framework.