What Makes Transformers So Effective?
The Transformer architecture is a deep learning model introduced in the paper “Attention is All You Need” by Vaswani et al. in 2017. It revolutionized various natural language processing (NLP) tasks and has since been applied to many other domains.
Here are the key components of the Transformer architecture:
1. Self-Attention Mechanism: The core innovation of Transformers is the self-attention mechanism, which allows the model to weigh the importance of different input tokens when making predictions. It considers relationships between all tokens simultaneously, unlike previous models that relied on fixed-size context windows.
2. Multi-Head Attention: Transformers use multiple attention heads, each focusing on different parts of the input. This enables the model to learn different aspects of relationships between words, providing richer representations.
