---
title: "EuroBERT: Scaling Multilingual Encoders for European Languages"
description: "The authors apply decoder-era techniques to encoders and build EuroBERT, a family of multilingual… 74 references, cited by 34 papers here."
authors: ["Nicolas Boizard", "Hippolyte Gisserot-Boukhlef", "Duarte M. Alves", "Andre Martins", "Ayoub Hammal", "Caio Corro", "Celine Hudelot", "Emmanuel Malherbe", "Etienne Malaboeuf", "Fanny Jourdan", "Gabriel Hautreux", "Joao Alves", "Kevin El Haddad", "Manuel Faysse", "Maxime Peyrard", "Nuno M. Guerreiro", "Patrick Fernandes", "Ricardo Rei", "Pierre Colombo"]
year: 2025
venue: "arXiv:2503.05500"
source: "https://arxiv.org/abs/2503.05500"
doi: "10.48550/arxiv.2503.05500"
arxiv: "2503.05500"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/boizard-2025-eurobert-scaling-multilingual-encoders-european/"
---
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# EuroBERT: Scaling Multilingual Encoders for European Languages

**Authors:** [Nicolas Boizard](https://md.research.elamur.ai/free/en/authors/nicolas-boizard/), Hippolyte Gisserot-Boukhlef, [Duarte M. Alves](https://md.research.elamur.ai/free/en/authors/duarte-m-alves/), [Andre Martins](https://md.research.elamur.ai/free/en/authors/andre-f-t-martins/), Ayoub Hammal, [Caio Corro](https://md.research.elamur.ai/free/en/authors/caio-corro/), [Celine Hudelot](https://md.research.elamur.ai/free/en/authors/celine-hudelot/), Emmanuel Malherbe, Etienne Malaboeuf, [Fanny Jourdan](https://md.research.elamur.ai/free/en/authors/fanny-jourdan/), Gabriel Hautreux, [Joao Alves](https://md.research.elamur.ai/free/en/authors/joao-alves/), Kevin El Haddad, [Manuel Faysse](https://md.research.elamur.ai/free/en/authors/manuel-faysse/), [Maxime Peyrard](https://md.research.elamur.ai/free/en/authors/maxime-peyrard/), [Nuno M. Guerreiro](https://md.research.elamur.ai/free/en/authors/nuno-m-guerreiro/), [Patrick Fernandes](https://md.research.elamur.ai/free/en/authors/patrick-fernandes/), [Ricardo Rei](https://md.research.elamur.ai/free/en/authors/ricardo-rei/) and [Pierre Colombo](https://md.research.elamur.ai/free/en/authors/pierre-colombo/) · **Year:** 2025 · **Venue:** arXiv:2503.05500 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2503.05500) · [PDF](https://arxiv.org/pdf/2503.05500) · [DOI 10.48550/arxiv.2503.05500](https://doi.org/10.48550/arxiv.2503.05500) · [arXiv 2503.05500](https://arxiv.org/abs/2503.05500) · [OpenAlex](https://openalex.org/W6948340691)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [34](https://md.research.elamur.ai/free/en/papers/boizard-2025-eurobert-scaling-multilingual-encoders-european/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Bidirectional encoders still power most retrieval, classification and regression systems, but recent modeling progress has mostly gone into decoder-only LLMs. As a result, practitioners rely on aging multilingual encoders such as XLM-RoBERTa. The authors apply decoder-era techniques to encoders and build EuroBERT, a family of multilingual models with a Llama 3-style architecture: no biases, grouped query attention, SwiGLU, RMSNorm and RoPE.

Training uses masked language modeling on about 5T tokens covering 15 European and major world languages, plus code and mathematics. It runs in two stages. A long pre-training stage is followed by a shorter annealing stage with a reweighted, quality-filtered data mix, a lower masking ratio and variable-length inputs up to 8,192 tokens.

Under a uniform fine-tuning protocol, EuroBERT matches or beats XLM-RoBERTa, mGTE-MLM and mDeBERTa-v3 on multilingual retrieval, classification and regression, and leads on code and math tasks. Ablations show that design choices trade retrieval against classification. The models, intermediate checkpoints and training framework are openly released, providing a modern long-context encoder baseline.

## Key points
- **Models and compute:** three sizes (210M, 610M, 2.1B parameters) with native 8,192-token context. They were trained on the Adastra supercomputer (MI250X / MI300A GPUs) for about 200k GPU hours in total.
- **Data:** 4.8T pre-training tokens plus 200B annealing tokens from FineWeb (English), CulturaX (multilingual), EuroLLM parallel data, The Stack v2 (38 programming languages) and Proof-Pile-2.
- **Languages:** the 15 covered languages include Russian and Polish, but Ukrainian is not among them, which matters for Ukrainian-language use.
- **Training recipe:** masking is 50% in pre-training and 10% in annealing. The final annealing mix is 26% English, 6% math, 4% code and 6% parallel data. RoPE theta is raised from 10,000 to 250,000 for long context.
- **Ablation findings:** higher masking helps retrieval, while lower masking helps classification (e.g., XNLI). Adding code and math improves multilingual retrieval (MIRACL) but slightly hurts classification. Keeping only the top educational-quality bucket degrades results, and mixing quality buckets 3 and 4 works best. Random cropping to variable lengths beats training on fixed lengths only.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Nicolas Boizard, Hippolyte Gisserot-Boukhlef, Duarte M. Alves, Andre Martins, Ayoub Hammal, Caio Corro, Celine Hudelot, Emmanuel Malherbe, Etienne Malaboeuf, Fanny Jourdan, Gabriel Hautreux, Joao Alves, Kevin El Haddad, Manuel Faysse, Maxime Peyrard, Nuno M. Guerreiro, Patrick Fernandes, Ricardo Rei and Pierre Colombo. 2025. EuroBERT: Scaling Multilingual Encoders for European Languages. arXiv:2503.05500.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{boizard2025eurobert,
  title = {{EuroBERT: Scaling Multilingual Encoders for European Languages}},
  author = {Nicolas Boizard, Hippolyte Gisserot-Boukhlef, Duarte M. Alves, Andre Martins, Ayoub Hammal, Caio Corro, Celine Hudelot, Emmanuel Malherbe, Etienne Malaboeuf, Fanny Jourdan, Gabriel Hautreux, Joao Alves, Kevin {El Haddad}, Manuel Faysse, Maxime Peyrard, Nuno M. Guerreiro, Patrick Fernandes, Ricardo Rei and Pierre Colombo},
  year = {2025},
  doi = {10.48550/arxiv.2503.05500},
  eprint = {2503.05500},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2503.05500}
}
```

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*Updated 2 November 2026*