mmBERT: ModernBERT goes Multilingual

TL;DR

This blog post introduces mmBERT, a state-of-the-art massively multilingual encoder model trained on 3T+ tokens of text in over 1800 languages. It shows significant performance and speed improvements over previous multilingual models, being the first to improve upon XLM-R, while also developing new strategies for effectively learning low-resource languages. mmBERT builds upon ModernBERT for a blazingly fast architecture, and adds novel components to enable efficient multilingual learning.

If you are interested in trying out the models yourself, some example boilerplate is available at the end of this blogpost!

Training Data

mmBERT was trained on a carefully curated multilingual dataset totaling over 3T tokens across three distinct training phases. The foundation of our training data consists of three primary open-source and high-quality web crawls that enable both multilingual coverage and data quality:

DCLM and Filtered DCLM provides the highest quality English content available, serving as the backbone for strong English performance (with the filtered data coming from Dolmino). This dataset represents state-of-the-art web filtering techniques and forms a crucial component. Due to the high quality of this data, we use a signficantly higher proportion of English than previous generation multilingual encoder models (up to 18%).

FineWeb2 delivers broad multilingual web content covering over 1,800 languages. This dataset enables our extensive multilingual coverage while maintaining reasonable quality standards across diverse language families and scripts.

FineWeb2-HQ consists of a filtered subset of FineWeb2 focusing on 20 high-resource languages. This filtered version provides higher-quality multilingual content that bridges the gap between English-only filtered data and broad multilingual coverage.

Training Recipe and Novel Components

mmBERT builds upon the ModernBERT architecture but introduces several key innovations for multilingual learning:

Click to see how to finetune this into a dense embedding model using Sentence Transformers Click to see how to finetune this into a multi-vector embedding model with PyLate Click to see how to finetune this into a sparse retrieval model using Sentence Transformers Click to see how to finetune this into a reranker model using Sentence Transformers