What is the Transformer Architecture? - Definition & Meaning

Definition

The Transformer architecture is a neural network architecture introduced in 2017 that uses self-attention to model relationships between all elements in a sequence at once. It forms the basis of GPT, BERT, Claude, and most modern language and multimodal models.

Technical explanation

Transformers replace recurrent layers (RNN, LSTM) with multi-head self-attention. Each element can communicate directly with every other element, enabling parallelization. A Transformer block contains attention, layer normalization, feed-forward networks, and residual connections. Encoder-only (BERT) learns bidirectional representations for classification and extraction; decoder-only (GPT) generates autoregressively. Encoder-decoder (T5) is used for translation and summarization. Positional encodings add order information. The scalability of Transformers has led to models with hundreds of billions of parameters.

How AVARC Solutions applies this

AVARC Solutions builds and integrates solutions on transformer models. From GPT and Claude for text generation to Vision Transformers for image analysis — we leverage the transformer architecture for chatbots, document processing, code assistance, and multimodal AI.

Practical examples

• GPT-4 using a transformer decoder to predict the next token based on all previous tokens in the context.
• BERT using a transformer encoder to learn meaningful representations for search and classification.
• A Vision Transformer (ViT) dividing images into patches and processing them as a sequence for image classification.

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