Create README.md

README.md

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+---
+license: mit
+task_categories:
+- fill-mask
+tags:
+- pretraining
+- encoder
+- multilingual
+---
+
+# MMBERT Decay Phase Data
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![Paper](https://img.shields.io/badge/Paper-Arxiv-red)](https://arxiv.org/abs/2509.06888)
+[![Models](https://img.shields.io/badge/🤗%20Hugging%20Face-2%20Models-blue)](https://huggingface.co/collections/jhu-clsp/mmbert-a-modern-multilingual-encoder-68b725831d7c6e3acc435ed4)
+[![GitHub](https://img.shields.io/badge/GitHub-Code-black)](https://github.com/jhu-clsp/mmBERT)
+
+> **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
+
+```python
+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](https://huggingface.co/collections/jhu-clsp/mmbert-a-modern-multilingual-encoder-68b725831d7c6e3acc435ed4)
+- **Phase 1**: [Pre-training Data](https://huggingface.co/datasets/jhu-clsp/mmbert-pretrain-p1-fineweb2-langs) (2.3T tokens)
+- **Phase 2**: [Mid-training Data](https://huggingface.co/datasets/jhu-clsp/mmbert-midtraining) (600B tokens)
+- **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}, 
+}
+```