Transformers have revolutionized Natural Language Processing by enabling deep contextual understanding and parallel processing of text. Unlike RNNs, transformers rely on attention mechanisms, allowing them to focus on relevant parts of the input sequence regardless of position. This post explores the core principles behind transformers and how they outperform traditional sequence models. Stay tuned for an in-depth explanation and practical code examples.