The Annotated Transformer is an educational resource that provides annotated code implementations of the Transformer architecture for sequence-to-sequence tasks, built with PyTorch. It serves as a learning tool for understanding attention mechanisms, multi-head parallel attention, and scaled dot-product attention through executable examples that walk through each component of the model.
The project covers the full Transformer pipeline, including stacked encoder-decoder layers with residual connections and layer normalization, sinusoidal positional encoding for order-aware representation, and masked self-attention decoding for auto-regressive generation. It also demonstrates label smoothing regularization to reduce overconfidence during training, and provides a framework for neural machine translation that encodes input sequences and decodes output sequences using attention mechanisms.
The documentation includes annotated code that explains how multi-head self-attention works, how positional encoding injects sequence order information, and how the model processes sequences through stacked self-attention and feed-forward layers. The resource is designed to help learners implement and train Transformer-based sequence models for translation tasks, with explanations of each architectural component from the original paper.