How does the Transformer process text?

The Transformer processes text through self-attention mechanisms rather than sequential recurrence. It encodes input text into context-rich representations by analyzing relationships between all words simultaneously.

Key mechanisms include: Input embeddings convert tokens to vectors. Positional encoding adds sequence order information. Multi-head self-attention computes weighted relationships across all tokens, focusing on relevance. Each attention head learns different relationship aspects. Layer outputs pass through position-wise feed-forward networks for transformation. Residual connections and layer normalization stabilize training.

This architecture enables highly parallel computation, excelling at capturing long-range dependencies. It forms the foundation for models like BERT and GPT, driving breakthroughs in machine translation, text summarization, and question answering by generating deep contextual understanding efficiently.