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A groundbreaking architecture in deep learning, introduced in 2017 by Vaswani et al. It has revolutionized natural language processing and has found applications in various domains beyond text processing.
At its core, the Transformer relies on attention mechanism to process input data. Unlike previous models that processed sequences step by step, Transformers can look at an entire sequence at once. This allows them to capture long-range dependencies more effectively.
The model consists of two main parts: an encoder and a decoder. The encoder processes the input sequence, while the decoder generates the output. Transformers have several advantages over previous architectures:
- They can handle long sequences more effectively.
- They allow for more parallelization, making training faster.
- They have shown superior performance on many language tasks.
Despite their power, Transformers also have limitations. They can be computationally expensive, especially for very long sequences and are data hungry.