🔮 Transformer Attention Visualizer

Understanding Transformer Attention

Transformers revolutionized NLP with the self-attention mechanism. Instead of processing sequences sequentially (like RNNs), transformers process all positions in parallel while learning which positions to pay attention to.

Self-Attention Mechanism

For each token in sequence:

• Queries (Q): "What am I looking for?"
• Keys (K): "What do I contain?"
• Values (V): "What should I output?"

Attention computed as:

• Attention(Q, K, V) = softmax(QK^T / √d_k) × V
• QK^T: Compatibility scores between all token pairs
• √d_k: Scaling factor (prevents large values)
• Softmax: Convert to probabilities
• × V: Weighted sum of values

Multi-Head Attention

Instead of single attention, use multiple heads in parallel:

• Each head learns different patterns
• Head 1 might focus on syntax
• Head 2 might focus on semantics
• Heads concatenated and projected
• Typical: 8-16 heads

Why Transformers Work

• Parallel Processing: All positions processed simultaneously
• Long-Range Dependencies: Any token can attend to any other
• No Vanishing Gradients: Direct connections between all positions
• Flexible: Works for various sequence lengths

Transformer Architecture

• Encoder:
  • Self-attention layer
  • Feed-forward layer
  • Layer normalization + residual connections
  • Stacked 6-12 layers
• Decoder:
  • Masked self-attention (autoregressive)
  • Cross-attention to encoder
  • Feed-forward layer
  • Layer normalization + residual
• Positional Encoding:
  • Injects position information
  • Sine/cosine functions
  • Learned embeddings (BERT)

Famous Transformer Models

• BERT (2018): Bidirectional encoder, masked language modeling
• GPT Series: Autoregressive decoder, massive scale
• T5: Text-to-text framework
• Vision Transformer (ViT): Transformers for images
• DALL-E: Text-to-image generation

Applications

• Machine translation (original application)
• Text generation (GPT)
• Question answering
• Summarization
• Image classification (ViT)
• Object detection (DETR)
• Protein structure prediction (AlphaFold)

Experiment with the Visualizer

Use the interactive tool above to:

• Enter sentences and see attention patterns
• Switch between different attention heads
• See which words attend to which
• Understand attention weight distributions
• Compare simple vs complex sentences

Transformers are the foundation of modern AI - GPT, BERT, and countless other breakthrough models. Understanding attention is key to understanding modern NLP!