Create README.md

README.md

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+---
+license: mit
+task_categories:
+- fill-mask
+tags:
+- pretraining
+- encoder
+- multilingual
+---
+
+# mmBERT Mid-training 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 2 of 3**: High-quality mid-training data mixture (600B tokens) with context extension to 8192 tokens.
+
+This dataset contains the mid-training phase data used to train all [mmBERT encoder models](https://huggingface.co/collections/jhu-clsp/mmbert-a-modern-multilingual-encoder-68b725831d7c6e3acc435ed4). This phase focuses on higher quality data sources and extends the context length from 1024 to 8192 tokens. The data is provided in **MDS format** ready for use with [Composer](https://github.com/mosaicml/composer) and the [ModernBERT training repository](https://github.com/answerdotai/ModernBERT).
+
+## 📊 Data Composition
+
+| Data Source | Tokens (B) | Percentage | Description |
+|:------------|:-----------|:-----------|:------------|
+| FineWeb2 | 506.7 | 84.3% | High-quality multilingual web crawl data |
+| DCLM (Dolmino) | 40.0 | 6.7% | Filtered high-quality English web data |
+| Starcoder | 17.2 | 2.9% | Code repositories and files |
+| Arxiv | 5.4 | 0.9% | Academic preprints |
+| Dolmino Math | 4.3 | 0.7% | Mathematical content |
+| Books | 3.9 | 0.7% | Literature and reference books |
+| PeS2o | 3.2 | 0.5% | Scientific papers |
+| Tulu Flan | 3.1 | 0.5% | Instruction-following data |
+| StackExchange | 3.0 | 0.5% | Q&A forums |
+| StackExchange (Dolmino) | 2.8 | 0.5% | Curated Q&A content |
+| Wikipedia (MegaWika) | 1.2 | 0.2% | Encyclopedia articles |
+| **Total** | **600.8** | **100.0%** | High-quality data for context extension |
+
+## 🌍 Language Coverage
+
+This phase covers **110 languages** plus code, with inverse temperature sampling at τ=0.5. Expands from the initial 60 languages to include:
+- **Additional mid-resource languages**: Uzbek, Bosnian, Catalan, Albanian, and 46 others
+- **Enhanced quality**: Uses filtered FineWeb2-HQ and higher quality DCLM
+- **Longer contexts**: Optimized for 8192 token sequences
+
+## ⚙️ Key Features
+
+- **Context Extension**: RoPE base frequency adjusted to 160k for 8192 token support
+- **Quality Upgrade**: Switches to filtered, higher-quality versions of datasets
+- **Reduced Masking**: Mask rate lowered to 15% (from 30% in pre-training)
+- **Language Expansion**: Adds 50 new languages while maintaining data quality
+
+## 🚀 Usage
+
+For mid-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-midtraining',
+    local='/tmp/mmbert-midtraining-data',
+    shuffle=True
+)
+
+# Access samples
+for sample in dataset:
+    text = sample['text']
+    # Process your data...
+```
+
+## 🔗 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 3**: [Decay Phase Data](https://huggingface.co/datasets/jhu-clsp/mmbert-decay) (100B 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}, 
+}
+```