Post

Benjamin Warner
@benjaminwarner.dev

This week we released ModernBERT, the first encoder to reach SOTA on most common benchmarks across language understanding, retrieval, and code, while running twice as fast as DeBERTaV3 on short context and three times faster than NomicBERT & GTE on long context.

Benjamin Warner
@benjaminwarner.dev

ModernBERT-base is the first encoder to beat DeBERTaV3-base on GLUE. ModernBERT is also competitive or top scoring on single vector retrieval, ColBERT retrieval, and programming benchmarks.

Benjamin Warner
@benjaminwarner.dev

ModernBERT was designed from the ground up for speed and memory efficiency. ModernBERT is both faster and more memory efficient than every major encoder released since the original BERT.

Benjamin Warner
@benjaminwarner.dev

How did we do it? First, we brought all the modern LLM architectural improvements to encoders, including alternating global & local attention, RoPE, and GeGLU layers, and added full model unpadding using Flash Attention for maximum performance (illustrated in the next post).

Benjamin Warner
@benjaminwarner.dev

Second, we carefully designed ModernBERT's architecture run to efficiently across most common GPUs. Many common older models don't consider the hardware they will run on and are slower than they should be. Not so with ModernBERT. (Full model sequence packing illustrated below)