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

The paper behind ModernBERT

Makes encoder models fast and powerful again with smart architecture choices.

ModernBERT introduces efficient encoder-only transformers with modern optimizations, achieving state-of-the-art performance while maintaining fast inference and low memory usage.

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https://arxiv.org/abs/2412.13663

🤔 Original Problem:

Encoder models like BERT, despite being widely used in production, haven't seen major improvements since release. They suffer from limited sequence lengths, suboptimal architecture, and inefficient training approaches.

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🔧 Solution in this Paper:

→ ModernBERT brings modern optimizations to encoder-only models with 8192 sequence length and 2 trillion training tokens

→ Uses alternating global-local attention mechanism - global attention every third layer, local sliding window attention for others

→ Implements advanced unpadding with Flash Attention for variable length sequences

→ Adopts GeGLU activation and RoPE positional embeddings for better performance

→ Removes bias terms in linear layers except final decoder for parameter efficiency

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🎯 Key Insights:

→ Encoder models can match decoder performance with proper modernization

→ Local-global attention mix provides optimal speed-quality tradeoff

→ Hardware-aware model design significantly improves inference efficiency

→ Modern tokenizers and large-scale training on diverse data is crucial

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📊 Results:

→ First encoder to beat DeBERTaV3-base on GLUE since 2021

→ Processes 8192-token sequences 2x faster than competitors

→ Best-in-class memory efficiency with superior performance

→ State-of-the-art results on code and long-context retrieval