Efficient Document Search with ModernBERT 🤩

Are you interested in building an efficient semantic search system for your internal documents ?

In this blog post, we’ll share a new (well, but also classic) embedding model that’s essential for your document search system. Let’s dive in!

Understanding ModernBERT: The New BERT

ModernBERT, recently released by Answer.ai, represents a significant leap forward in the BERT (Bidirectional Encoder Representations from Transformers) family of models. While the original BERT model, released in 2018, marked the beginning of the transformer era in AI, ModernBERT builds upon its success with several key improvements:

1. Extended Context Window: ModernBERT can handle up to 8,000 words, a substantial increase from BERT’s 512-word limit. This allows for better understanding of longer documents.
2. Improved Efficiency: Despite its enhanced capabilities, ModernBERT remains a relatively small model, making it fast and efficient for various NLP tasks.
3. Architectural Makeover: The model incorporates both local and global attention mechanisms, leading to improved performance across a range of tasks.
4. Versatility: ModernBERT can be used for multiple NLP tasks, including classification, sentiment analysis, and semantic search.