Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference - Summary

ModernBERT is an enhanced version of the original BERT model, designed as an encoder-only transformer to significantly improve efficiency and performance in retrieval and classification tasks. By implementing state-of-the-art architectural advancements such as rotary positional embeddings, Gated Linear Units, alternating local and global attention mechanisms, and complete unpadding, ModernBERT achieves unprecedented speed and memory efficiency, allowing it to handle sequence lengths of up to 8192 tokens compared to BERT's 512. Trained on a dataset encompassing two trillion tokens, including code data, it excels in both text and code processing tasks, setting new benchmarks in natural language processing and outperforming other models on classification and long-context retrieval tasks like MLDR. The paper highlights ModernBERT's potential as a viable alternative to larger decoder-based models, emphasizing its compatibility with common GPUs and its promise for future NLP applications.