GOOGLE TRANSFOMERS Google's Transformer model, introduced in the paper "Attention is All You Need," revolutionized natural language processing (NLP) by replacing the traditional recurrent and convolutional neural networks with an architecture based entirely on self-attention mechanisms. Here's a brief explanation of the key aspects of the Transformer model: 1. **Self-Attention Mechanism**: The core innovation of the Transformer is the self-attention mechanism, which allows the model to weigh the importance of different words in a sentence relative to each other. This enables the model to capture dependencies regardless of their distance in the input sequence. 2. **Parallelization**: Unlike RNNs, which process tokens sequentially, the Transformer processes the entire sequence simultaneously. This parallel processing significantly speeds up training and inference. 3. **Positional Encoding**: Since the Transformer d...