LightOn and Answer.ai Releases ModernBERT: A New Model Series that is a Pareto Improvement over BERT with both Speed and Accuracy

LightOn and Answer.ai Releases ModernBERT: A New Model Series that is a Pareto Improvement over BERT with both Speed and Accuracy

Introduction to ModernBERT

Since 2018, BERT has been a popular choice for natural language processing (NLP) due to its efficiency. However, it has limitations, especially with long texts, as it can only handle 512 tokens. Modern applications need more, and that’s where ModernBERT comes in.

Key Features of ModernBERT

Developed by a team from LightOn, Answer.ai, Johns Hopkins University, NVIDIA, and Hugging Face, ModernBERT is a new family of encoder-only models that addresses these challenges.

  • Extended Context Length: Handles up to 8,192 tokens for better performance on long-context tasks.
  • Enhanced Efficiency: Uses Flash Attention 2 and rotary positional embeddings (RoPE) for faster processing and better understanding of text positions.
  • Diverse Training: Trained on 2 trillion tokens from various domains, including coding, improving its versatility.
  • Multiple Configurations: Available in base (139M parameters) and large (395M parameters) to suit different needs.

Technical Advantages

ModernBERT incorporates several key enhancements:

  • Flash Attention: Improves memory and computational efficiency.
  • Global-Local Attention: Optimizes processing for long texts.
  • GeGLU Activation: Balances efficiency and capability.
  • Stable Training: Uses pre-normalization blocks and a specialized optimizer for better training stability.

Performance Insights

ModernBERT shows strong results across various benchmarks:

  • Outperforms existing models on the GLUE benchmark.
  • Achieves high scores in retrieval tasks like Dense Passage Retrieval (DPR).
  • Excels in long-context tasks and code-related applications.
  • Processes larger batch sizes efficiently for extensive applications.

Conclusion

ModernBERT is a significant upgrade over traditional encoder-only transformer models. Its improvements make it a powerful tool for various NLP applications, including semantic search and code retrieval. Released under the Apache 2.0 license, it is accessible for researchers and professionals alike.

Get Involved

Check out the Paper, Blog, and Model on Hugging Face. Follow us on Twitter, join our Telegram Channel, and connect on LinkedIn. Don’t forget to join our 60k+ ML SubReddit.

Transform Your Business with AI

To stay competitive, consider how ModernBERT and AI can enhance your operations:

  • Identify Automation Opportunities: Find key areas where AI can help.
  • Define KPIs: Measure the impact of your AI initiatives.
  • Select an AI Solution: Choose tools that fit your needs.
  • Implement Gradually: Start small, gather insights, and expand usage.

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