Transformer Models and NLP

Transformer-based models revolutionized natural language processing (NLP) by introducing self-attention mechanisms. Unlike recurrent neural networks (RNNs), transformers process entire text sequences in parallel, improving efficiency and coherence.

Key components include multi-head attention, which enables the model to consider multiple contextual relationships simultaneously, and positional encoding, ensuring word order comprehension.