A transformer is a type of neural network architecture designed primarily for processing sequential data, particularly in natural language processing (NLP). Unlike traditional RNNs or LSTMs, transformers use self-attention mechanisms to process entire sequences of data in parallel, rather than step-by-step.
This self-attention mechanism allows the model to weigh the importance of different words in a sentence, regardless of their position. Transformers are highly effective for tasks like language translation, text generation, and sentiment analysis.
Transformer models like BERT, GPT, and T5 have revolutionized NLP by offering highly parallelizable, scalable architectures that deliver state-of-the-art performance in various language-based tasks.