language:

• multilingual
  license: mit
  task_categories:
• fill-mask
• feature-extraction
  tags:
• pretraining
• language-modeling
• encoder
• multilingual
• feature-extraction

mmBERT Pre-training Data P1

Phase 1 of 3: Diverse multilingual pre-training data mixture (trained for 2.3T tokens) used to train the mmBERT model suite.

NOTE: this is only P1 of the pre-training data due to HF limits, you need to download and combine all three into one folder

This dataset contains the pre-training phase data used to train all mmBERT encoder models. The data is provided in MDS format ready for use with Composer and the ModernBERT training repository.

📊 Data Composition

🌍 Language Coverage

This phase covers 60 languages plus code, with an inverse temperature sampling schedule starting at τ=0.7. Languages include:

• High-resource: English (34.5%), Russian (5.8%), German (4.4%), Spanish (4.5%), French (4.0%), Chinese (5.2%)
• Mid-resource: Italian, Portuguese, Japanese, Dutch, Polish, and 45 others
• Scripts: Latin, Cyrillic, Arabic, Chinese, Japanese, Thai, and many more

🚀 Usage

For pre-training, see the ModernBERT repo: https://github.com/AnswerDotAI/ModernBERT

Direct Access

Use the script at this link to load any section of the dataset on the fly. This will fail if you try to access too many samples though, due to HF rate-limiting. To download the full dataset, use HF Hub's Snapshot Download.

🔗 Related Resources

• Models: mmBERT Model Suite
• Phase 2: Mid-training Data (600B tokens)
• Phase 3: Decay Phase Data (100B tokens)
• Checkpoints: Training Checkpoints
• Paper: Arxiv link
• Hugging Face Paper: mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
• Code: GitHub Repository

Citation

@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}, 
}