The Annotated Transformer
By Sasha Rush, Austin Huang, Suraj Subramanian, Jonathan Sum, Khalid Almubarak, Stella Biderman
Free
Added 10 months ago
Description
Annotated version of a transformer with comments adds explanation to the model architecture.
Summary
This post by Sasha Rush and others presents a thorough, annotated version of the transformer model, detailing its architecture and applications in sequences.
Key Insights
💡 The annotation demonstrates how transformers handle dependencies within sequences differently than RNNs. 💡 Layer normalization and dropout significantly contribute to the robustness of the transformer model. 💡 Positional encodings are a unique strategy employed by transformers to make sense of sequence order without conventional recurrent structures.