Transformer models are a type of neural network architecture designed for processing sequential data, such as text. They use mechanisms called self-attention and positional encoding to weigh the importance of different words in a sentence, allowing them to capture context more effectively than previous models like RNNs (Recurrent Neural Networks). Introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017, transformer models have become the foundation for many advanced natural language processing tasks. They power popular applications like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers), enabling machines to understand and generate human-like text.