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---
license: mit
task_categories:
- fill-mask
tags:
- pretraining
- encoder
- multilingual
---
# mmBERT Training Data (Ready-to-Use)
[](https://opensource.org/licenses/MIT)
[](https://arxiv.org/abs/2509.06888)
[](https://huggingface.co/collections/jhu-clsp/mmbert-a-modern-multilingual-encoder-68b725831d7c6e3acc435ed4)
[](https://github.com/jhu-clsp/mmBERT)
> **Complete Training Dataset**: Pre-randomized and ready-to-use multilingual training data (3T tokens) for encoder model pre-training.
This dataset is part of the complete, pre-shuffled training data used to train the [mmBERT encoder models](https://huggingface.co/collections/jhu-clsp/mmbert-a-modern-multilingual-encoder-68b725831d7c6e3acc435ed4). Unlike the individual phase datasets, this version is ready for immediate use but **the mixture cannot be modified easily**. The data is provided in **decompressed MDS format** ready for use with [ModernBERT's Composer](https://github.com/mosaicml/composer) and the [ModernBERT training repository](https://github.com/answerdotai/ModernBERT).
## Licensing & Attribution
This dataset aggregates multiple open-source datasets under permissive licenses. See individual source datasets for specific attribution requirements.
## Related Resources
- **Models**: [mmBERT Model Suite](https://huggingface.co/collections/jhu-clsp/mmbert-a-modern-multilingual-encoder-68b725831d7c6e3acc435ed4)
- **Individual Phases**: [Pre-training](https://huggingface.co/datasets/jhu-clsp/mmbert-pretrain-p1-fineweb2-langs) | [Mid-training](https://huggingface.co/datasets/jhu-clsp/mmbert-midtraining) | [Decay](https://huggingface.co/datasets/jhu-clsp/mmbert-decay)
- **Checkpoints**: [Training Checkpoints](https://huggingface.co/datasets/jhu-clsp/mmbert-checkpoints)
- **Paper**: [Arxiv link](https://arxiv.org/abs/2509.06888)
- **Code**: [GitHub Repository](https://github.com/jhu-clsp/mmBERT)
## Citation
```bibtex
@misc{marone2025mmbertmodernmultilingualencoder,
title={mmBERT: A Modern Multilingual Encoder with Annealed Language Learning},
author={Marc Marone and Orion Weller and William Fleshman and Eugene Yang and Dawn Lawrie and Benjamin Van Durme},
year={2025},
eprint={2509.06888},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2509.06888},
}
``` |