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The successor to BERT (Bidirectional Encoder Representations from Transformers), is a next-generation family of encoder-only transformer models designed for a wide range of natural language processing (NLP) tasks. As a drop-in replacement, ModernBERT improves both performance and accuracy while addressing some of BERT's limitations ¡ª notably including support for dramatically longer context lengths thanks to Alternating Attention. Teams with NLP needs should consider ModernBERT before defaulting to a general-purpose generative model.