Transformers
Neural networks that process sequences using attention mechanisms
Overview
Transformers are a class of neural network architectures that handle sequence data (e.g., text) using attention mechanisms. By focusing on how each part of a sequence relates to other parts, Transformers can capture contextual dependencies in a more flexible manner than some earlier architectures.
What Are Transformers?
Transformers were introduced to address limitations in processing sequential data, particularly in tasks like language translation. Unlike recurrent networks that process input step by step, Transformers can leverage attention mechanisms to consider all positions in a sequence simultaneously.
Significance
- Parallel Processing
Because attention allows processing all positions at once, Transformers can often train faster on modern hardware. - Context Awareness
Self-attention layers enable the model to identify relationships between tokens or elements at different positions.
How Do They Work?
- Input Embedding
Each item in a sequence (e.g., a word token) is converted into a numerical representation. - Self-Attention
The model calculates attention scores that emphasize important elements in the sequence relative to each token. - Feed-Forward Layers
Nonlinear transformations are applied to combine information learned through attention. - Encoder-Decoder (Optional)
For tasks like machine translation, a separate encoder and decoder may be used, each containing multiple attention layers.
Common Applications
- Natural Language Processing
Language translation, text summarization, sentiment analysis, and more. - Large Language Models
Generating text, answering questions, or performing other language-oriented tasks. - Multimodal Tasks
Integrating data such as text and images in a unified model.