Transformer Model

The transformer model revolutionized natural language processing by introducing a mechanism known as 'attention', which allows it to weigh the significance of different parts of input sequences differently. This contrasts with recurrent neural networks (RNNs) and convolutional neural networks (CNNs), which process sequences through recurrence and convolution respectively. The self-attention mechanism in transformers enables parallelization, speeding up training times and improving performance on tasks such as translation, summarization, and other NLP tasks. The BERT and GPT series of models build upon this architecture, showcasing its flexibility and power.