Self-Attention Mechanism
The self-attention mechanism allows the model to weigh the importance of different words in a sentence dynamically. This is crucial for capturing long-range dependencies and contextual relationships.
Position Encoding
To handle sequential data, Transformers use position encoding to incorporate positional information into the model. This ensures the model understands the order of tokens in a sequence.
Multihead Attention
Multihead attention enables the model to focus on different parts of the input simultaneously by using multiple attention heads. This improves the model's ability to capture diverse patterns.
Transformer in Sequence Generation
Transformers excel at tasks like machine translation and text generation due to their parallel processing capabilities. The decoder layer uses self-attention and encoder-decoder attention to generate coherent outputs.
For a deeper dive into the fundamentals of Transformers, check out our Transformer Basics Tutorial.
Model Architecture
The Transformer architecture consists of encoder and decoder stacks, each containing multiple layers with self-attention and feed-forward networks.
Explore more advanced topics like BERT and its variants or Transformer optimization techniques to enhance your understanding.