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--- |
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license: mit |
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task_categories: |
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- fill-mask |
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tags: |
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- pretraining |
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- encoder |
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- multilingual |
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--- |
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# mmBERT Mid-training Data |
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[](https://opensource.org/licenses/MIT) |
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[](https://arxiv.org/abs/2509.06888) |
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[](https://huggingface.co/collections/jhu-clsp/mmbert-a-modern-multilingual-encoder-68b725831d7c6e3acc435ed4) |
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[](https://github.com/jhu-clsp/mmBERT) |
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> **Phase 2 of 3**: High-quality mid-training data mixture (600B tokens) with context extension to 8192 tokens. |
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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). |
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## π Data Composition |
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| Data Source | Tokens (B) | Percentage | Description | |
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|:------------|:-----------|:-----------|:------------| |
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| FineWeb2 | 506.7 | 84.3% | High-quality multilingual web crawl data | |
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| DCLM (Dolmino) | 40.0 | 6.7% | Filtered high-quality English web data | |
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| Starcoder | 17.2 | 2.9% | Code repositories and files | |
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| Arxiv | 5.4 | 0.9% | Academic preprints | |
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| Dolmino Math | 4.3 | 0.7% | Mathematical content | |
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| Books | 3.9 | 0.7% | Literature and reference books | |
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| PeS2o | 3.2 | 0.5% | Scientific papers | |
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| Tulu Flan | 3.1 | 0.5% | Instruction-following data | |
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| StackExchange | 3.0 | 0.5% | Q&A forums | |
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| StackExchange (Dolmino) | 2.8 | 0.5% | Curated Q&A content | |
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| Wikipedia (MegaWika) | 1.2 | 0.2% | Encyclopedia articles | |
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| **Total** | **600.8** | **100.0%** | High-quality data for context extension | |
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## π Language Coverage |
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This phase covers **110 languages** plus code, with inverse temperature sampling at Ο=0.5. Expands from the initial 60 languages to include: |
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- **Additional mid-resource languages**: Uzbek, Bosnian, Catalan, Albanian, and 46 others |
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- **Enhanced quality**: Uses filtered FineWeb2-HQ and higher quality DCLM |
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- **Longer contexts**: Optimized for 8192 token sequences |
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## βοΈ Key Features |
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- **Context Extension**: RoPE base frequency adjusted to 160k for 8192 token support |
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- **Quality Upgrade**: Switches to filtered, higher-quality versions of datasets |
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- **Reduced Masking**: Mask rate lowered to 15% (from 30% in pre-training) |
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- **Language Expansion**: Adds 50 new languages while maintaining data quality |
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## π Usage |
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For mid-training, see the ModernBERT repo: https://github.com/AnswerDotAI/ModernBERT |
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### Direct Access |
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```python |
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from streaming import StreamingDataset |
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# Load the streaming dataset |
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dataset = StreamingDataset( |
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remote='https://huggingface.co/datasets/jhu-clsp/mmbert-midtraining', |
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local='/tmp/mmbert-midtraining-data', |
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shuffle=True |
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) |
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# Access samples |
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for sample in dataset: |
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text = sample['text'] |
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# Process your data... |
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``` |
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## π Related Resources |
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- **Models**: [mmBERT Model Suite](https://huggingface.co/collections/jhu-clsp/mmbert-a-modern-multilingual-encoder-68b725831d7c6e3acc435ed4) |
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- **Phase 1**: [Pre-training Data](https://huggingface.co/datasets/jhu-clsp/mmbert-pretrain-p1-fineweb2-langs) (2.3T tokens) |
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- **Phase 3**: [Decay Phase Data](https://huggingface.co/datasets/jhu-clsp/mmbert-decay) (100B tokens) |
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- **Checkpoints**: [Training Checkpoints](https://huggingface.co/datasets/jhu-clsp/mmbert-checkpoints) |
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- **Paper**: [Arxiv link](https://arxiv.org/abs/2509.06888) |
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- **Code**: [GitHub Repository](https://github.com/jhu-clsp/mmBERT) |
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## Citation |
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```bibtex |
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@misc{marone2025mmbertmodernmultilingualencoder, |
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title={mmBERT: A Modern Multilingual Encoder with Annealed Language Learning}, |
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author={Marc Marone and Orion Weller and William Fleshman and Eugene Yang and Dawn Lawrie and Benjamin Van Durme}, |
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year={2025}, |
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eprint={2509.06888}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2509.06888}, |
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} |
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``` |
