What is Transformer models?

Transformer models are a type of neural network architecture that rely on self-attention mechanisms to weigh the importance of different parts of the input data. They are particularly well-suited for handling sequential data, such as text, and have achieved state-of-the-art results in various natural language processing tasks like machine translation, text summarization, and question answering. Their ability to process entire input sequences in parallel makes them more efficient than recurrent neural networks for long sequences.