In the realm of artificial intelligence, transformer models have emerged as a groundbreaking innovation. Introduced in the 2017 paper "Attention Is All You Need" by researchers at Google, transformers have fundamentally changed how machines process sequential data, such as language and images .

The Architecture of Transformers
At the core of transformer models lies the self-attention mechanism. This allows the model to weigh the relevance of different parts of the input data dynamically, enabling it to capture context more effectively than previous models like recurrent neural networks (RNNs) . Transformers consist of encoder and decoder components, which process input data in parallel, leading to faster and more efficient computations.
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