jhu-clsp/mmBERT-decay-data

MMBERT Decay Phase Data

Phase 3 of 3: Annealed language learning decay phase (100B tokens) with massive multilingual expansion to 1833 languages.

📊 Data Composition

NOTE: there are multiple decay data mixtures: this mixture described below is the Decay-Cont mixture. However, the data in this repository is the Decay-Eng. If you are interested in the others, please let me know so I can prioritize it.

Data Source	Tokens (B)	Percentage	Description
FineWeb2	78.5	76.0%	High-quality multilingual web crawl data
Wikipedia (MegaWika)	9.5	9.2%	Encyclopedia articles (1833 languages)
Arxiv	3.3	3.2%	Academic preprints
Textbooks (ProLong)	3.1	3.0%	Educational content
Code (ProLong)	2.8	2.7%	Code repositories and files
Books	2.2	2.1%	Literature and reference books
DCLM (Dolmino)	2.0	2.0%	High-quality English web data
Tulu Flan	1.0	1.0%	Instruction-following data
Starcoder	0.5	0.5%	Code repositories
Dolmino Math	0.5	0.5%	Mathematical content
Total	103.3	100.0%	Optimized for rapid language acquisition

🌍 Massive Language Coverage

This phase dramatically expands language coverage to 1833 languages, implementing the novel Cascading Annealed Language Learning (ALL) approach:

• Temperature Schedule: τ=0.3 (most uniform sampling)
• Low-resource Focus: Includes 1723 new languages with minimal data
• Rapid Learning: Demonstrates 68% performance improvement on Tigray and 26% on Faroese
• Script Diversity: Covers virtually all writing systems in FineWeb2

Key Innovation: Annealed Language Learning

Rather than training on all languages simultaneously, MMBERT uses a cascading approach:

1. Phase 1: 60 high-resource languages (τ=0.7)
2. Phase 2: 110 languages including mid-resource (τ=0.5)
3. Phase 3: 1833 languages with focus on low-resource (τ=0.3)

This enables rapid learning of new languages while maintaining performance on high-resource ones.

⚙️ Key Features

• Ultra-low Masking: 5% mask rate for optimal learning efficiency
• Model Merging: Three decay variants (English-focused, 110-lang, 1833-lang) merged using TIES. This is the English focused version.
• Quality Focus: Emphasizes highest-quality data sources

🚀 Usage

For decay phase training, see the ModernBERT repo: https://github.com/AnswerDotAI/ModernBERT

Direct Access

from streaming import StreamingDataset

# Load the streaming dataset
dataset = StreamingDataset(
    remote='https://huggingface.co/datasets/jhu-clsp/mmbert-decay',
    local='/tmp/mmbert-decay-data',
    shuffle=True
)

# Access samples
for sample in dataset:
    text = sample['text']
    # Process your data...

🎯 Performance Impact

The decay phase demonstrates remarkable efficiency in low-resource language learning:

• Tigray (TiQuAD): 68% improvement (12.1 F1 points) from including the language
• Faroese (FoQA): 26% improvement (15.4 F1 points)
• SOTA Performance: Can even outperforms GPT-4o, Gemini 2.5 Pro
• Rapid Acquisition: Significant gains with only 100B tokens of exposure

🔗 Related Resources

• Models: mmBERT Model Suite
• Phase 1: Pre-training Data (2.3T tokens)
• Phase 2: Mid-training Data (600B tokens)
• Checkpoints: Training Checkpoints
• Paper: Arxiv link
• 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}, 
}