Transformer Model is a neural network architecture introduced in the 2017 paper “Attention is All You Need.” It serves as the foundation for most modern AI systems. Unlike older recurrent models, transformers process input in parallel. They use self-attention to identify relationships between tokens, regardless of position.
Key Characteristics of Transformer Models
Self-Attention Mechanism: Assigns importance to each word based on its context.
Parallelization: Processes all input tokens at once, which speeds up training and inference.
Layered Architecture: Includes encoder and decoder stacks with attention heads and feedforward layers.
Scalability: Performs well even when scaled to billions of parameters.
Pretraining and Fine-Tuning: Learns from general data first, then adapts to specific tasks.
Applications of Transformer Models
Natural Language Processing: Powers BERT, GPT, and T5 for tasks like summarization and translation.
Computer Vision: Enables classification and object detection using Vision Transformers.
Multimodal AI: Combines text, image, and audio for integrated understanding.
Code Generation: Assists with writing and completing code in developer tools.
Healthcare AI: Helps interpret clinical notes and medical data using fine-tuned models.
Why Transformer Models Matter
Transformer models reshaped deep learning. They enable top performance in many domains. Because of their flexibility and speed, they drive most advanced AI today. Consequently, as AI continues to expand, transformers will remain central to innovation.
