Transformer encoder sequence diagram maps the five-phase processing pipeline from raw input to contextualized embeddings. Client sends tokens to Encoder API, which orchestrates Embedding Layer lookup, Positional Encoder injection, multi-head self-attention in Attention Block, and Feed-Forward Layer normalization. This architecture demonstrates the canonical transformer encoder flow essential for NLP tasks like BERT, enabling practitioners to understand component interactions and data transformations. Fork this diagram on Diagrams.so to customize layer counts, add residual connections, or adapt for decoder architectures. Ideal reference for documenting model pipelines in research papers or production ML systems.