The Annotated Transformer
17 days ago
- #Machine Learning
- #Transformer
- #Neural Networks
- The Transformer model has gained significant attention over the past five years.
- The post provides an annotated, line-by-line implementation of the Transformer paper, including reordered sections and added comments.
- The implementation includes code for multi-head attention, positional encoding, and the encoder-decoder architecture.
- Training details are provided, including the use of label smoothing and the Adam optimizer with a custom learning rate schedule.
- The model was trained on the WMT 2014 English-German dataset, achieving state-of-the-art results.
- Additional features like BPE/Word-piece, shared embeddings, beam search, and model averaging are mentioned but not covered in detail.
- Visualizations of attention mechanisms across different layers are provided to understand the model's inner workings.