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Organization

Center for Language and Speech Processing @ JHU

jhu-clsp

Models in Library1
Datasets in Library1
Models on Hugging Face53
Followers261

Models

mmBERT is a modern multilingual encoder that significantly outperforms previous generation models like XLM-R on classification, embedding, and retrieval tasks. Built on the ModernBERT architecture with novel multilingual training innovations, mmBERT demonstrates that low-resource languages can be effectively learned during the decay phase of training. It is also significantly faster than any previous multilingual encoder. mmBERT represents the first significant advancement over XLM-R for massively multilingual encoder models. Key features include: 1. Massive Language Coverage - Trained on over 1800 languages with progressive inclusion strategy 2. Modern Architecture - Built on ModernBERT…

Open weights mit 8,192 tokens transformers

Datasets

This dataset contains the pre-training phase data used to train all Ettin encoder and decoder models. The data is provided in MDS format ready for use with Composer and the ModernBERT training repository. For pre-training, see the ModernBERT repo: https://github.com/AnswerDotAI/ModernBERT Each folder contains one data source in MDS (Mosaic Data Shard) format: - arxiv/ - Academic papers from ArXiv - books/ - Literature and reference books - cchead/ - High-quality Common Crawl documents - ccnews/ - News articles from Common Crawl - dclm/ - DataComp-LM filtered web data - openwebmath/ - Mathematical web content - algebraicstackexchange/ - Math Q&A from StackExchange - pes2o/ - Scientific…

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