What is the Transformer model
The Transformer is a deep learning architecture introduced in 2017, primarily designed for sequence-to-sequence tasks like machine translation. Its core innovation lies in using self-attention mechanisms instead of recurrent layers, enabling parallel processing of entire sequences.
Unlike previous recurrent models, it processes all input tokens simultaneously, eliminating sequential processing bottlenecks. Self-attention computes relationships between every pair of tokens in the input, weighting their importance. Positional encodings are added to provide sequence order information. The architecture comprises an encoder to process the input and a decoder to generate the output, with multi-head attention allowing focus on different representation subspaces.
The Transformer revolutionized natural language processing due to its superior parallelization and modeling capabilities. It forms the foundation for major Large Language Models (LLMs) like BERT, GPT, and T5, powering applications in machine translation, text summarization, question answering, and text generation by effectively capturing long-range dependencies and contextual information.
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