Positional encoding is a crucial feature in the transformer architecture. It helps represent the position of each token in a sentence through corresponding vectors. Unlike models like RNNs, which capture positional information through previous hidden states, transformers process each token in parallel and thus require explicit positional information. The positional encoding vector has the same dimension as the embedding vector, with each token having its own positional encoding vector depending on its position in the sentence.
Recurrent Neural Networks (RNNs):
Advantages:
- Captures positional information through hidden states. Disadvantages:
- Sequential processing, inefficient with large datasets.
- Prone to vanishing or exploding gradients. Evidence: Experiments with large datasets show RNNs struggle with processing long sequences.
Transformers:
Advantages:
- Parallel processing, efficient with large datasets.
- No issues with vanishing or exploding gradients due to attention mechanism. Disadvantages:
- Requires explicit positional information through positional encoding.
- Demands significant computational resources. Evidence: Experiments demonstrate that transformers outperform RNNs on large and long datasets.
- Understanding Positional Encoding in Transformers
- Insights into Transformer Architecture
- Comparison Between RNNs and Transformers
- Significance of Positional Encoding
- Advantages of Transformers Over RNNs
- Transformer Training and Inference
- Positional Encoding Functionalities
- Parallel Processing Benefits in Transformers
- Overview of Encoder in Transformer Model
- Understanding Decoder in Transformer Model
