Introduction
Transformers have revolutionized natural language processing and beyond. Introduced in the "Attention is All You Need" paper, they've become the foundation for models like GPT, BERT, and countless others. Let's break down how they work with interactive visualizations.
Interactive Architecture Explorer
Click through the steps below to see how data flows through a transformer:
Input Embedding
Converts tokens to vectors
Self-Attention Mechanism
The key innovation of transformers is the self-attention mechanism. It allows the model to weigh the importance of different words in a sequence when processing each word.
Attention Formula
Q (Query): What information am I looking for?
K (Key): What information do I have?
V (Value): What is the actual information?
dk: Dimension of the key vectors (for scaling)
Multi-Head Attention
Instead of using a single attention function, transformers use multiple "attention heads" that can focus on different types of relationships:
Head 1: Syntax
Focuses on grammatical structure
Head 2: Semantics
Captures meaning relationships
Head 3: Dependencies
Tracks long-range dependencies
Positional Encoding
Since transformers process all positions in parallel, they need a way to understand word order. Positional encoding adds this information:
Sine and Cosine Encoding
This creates unique patterns for each position that the model can learn to interpret.
Why Transformers Work So Well
✅ Parallelization
Unlike RNNs, transformers can process all positions simultaneously, making training much faster.
✅ Long-range Dependencies
Direct connections between all positions help capture relationships regardless of distance.
✅ Transfer Learning
Pre-trained transformers can be fine-tuned for various tasks with minimal data.
Conclusion
Transformers have become the backbone of modern NLP and are expanding into computer vision, protein folding, and more. Understanding their architecture is key to leveraging their power and developing the next generation of AI models.