Describe the role and implementation of padding masks in the Transformer model.

Padding masks in the Transformer model play the crucial role of preventing the model from attending to padding tokens during self-attention calculations. Padding tokens are added to sequences to make them all the same length within a batch, as neural networks typically process data in batches of fixed size. However, these padding tokens do not contain any meaningful information and should not influence the attention weights. Without padding masks, the self-attention mechanism would treat padding tokens as if they were real words, potentially leading to inaccurate attention weights and degraded performance. The implementation of padding masks typically involves creating a boolean mask that indicates which tokens are padding tokens and which are real words. This mask is a matrix with the same dimensions as the input sequence. Typically, a value of `True` or `1` in the mask indicates a padding token, while `False` or `0` indicates a real word. This mask is then used to modify the attention scores before the softmax function is applied. Specifically, the attention scores corresponding to padding tokens are set to a very large negative value (e.g., -infinity). This ensures that after the softmax function is applied, the attention weights for padding tokens are effectively zero, preventing them from contributing to the weighted sum of value vectors. In essence, padding masks ensure that the model only attends to the meaningful words in the input sequence and ignores the irrelevant padding tokens, leading to more accurate and efficient learning.