What is transformer-based models?

Transformer-based models are a type of neural network architecture that rely on the self-attention mechanism to weigh the importance of different parts of the input data. They are particularly well-suited for handling sequential data, like text, and have achieved state-of-the-art results in many natural language processing (NLP) tasks such as machine translation, text summarization, and question answering. They are also used in computer vision and other domains. Key advantages include their ability to process data in parallel and capture long-range dependencies more effectively than recurrent neural networks (RNNs).