Understanding Transformers

Transformers, introduced in 2017, revolutionized sequence transduction models by relying entirely on the attention mechanism and allowing for parallel processing, which significantly improved training efficiency and long-term dependency handling compared to previous models like RNNs, LSTMs, and CNNs. Key components of a transformer include tokenization, embedding, the attention mechanism, the encoder, and the decoder. GPT models, which stem from transformers, focus on generative tasks and omit the encoder stack, demonstrating high effectiveness in tasks like generating text after being pre-trained on large corpora of text.