transformers

Originally designed for machine translation, transformers have become the foundation for many state-of-the-art NLP models like BERT, GPT, and others. Unlike recurrent neural networks (RNNs), which process sequential data step-by-step, transformers can process entire sequences in parallel. This is achieved through a mechanism called ‘self-attention,’ where each element in the sequence attends to all other elements, allowing the model to understand the relationships between them regardless of their distance. Transformers consist of an encoder and a decoder (though some models only use one). The encoder processes the input sequence, creating a contextualized representation. The decoder then uses this representation to generate an output sequence. Key components include multi-head attention (allowing the model to attend to different aspects of the data), positional encoding (to provide information about the order of elements since transformers don’t inherently process sequences sequentially), and feedforward neural networks for further processing. Their parallelization capabilities, ability to capture long-range dependencies, and scalability have made them dominant in NLP tasks such as text generation, question answering, sentiment analysis, and more.