Standard Transformer Models

- Overview

Standard transformer models are deep learning (DL) architectures based on self-attention mechanisms that process data sequences in parallel rather than sequentially. 

Introduced in the 2017 landmark paper “Attention Is All You Need” by Vaswani et al., the standard design relies on three primary variants grouped by their structural layout: 

1. Core Variants:

• Encoder-Decoder: The original baseline structure featuring both an encoder to read inputs and a decoder to generate outputs, ideal for tasks like machine translation. 
• Encoder-Only: Models that use only the encoder stack to deeply analyze and represent context, commonly applied in classification and text understanding tasks.
• Decoder-Only: Autoregressive models that use masked self-attention for text generation and conversational AI, which power modern large language models.

2. Key Architectural Components:

• Self-Attention: A deep learning (DL) architecture that processes sequences - text, images, audio - by computing weighted relationships between every element simultaneously rather than reading them left to right. 
• Positional Encoding: Signals added to token embeddings to give the model awareness of word or token order since inputs are processed in parallel.
• Feed-Forward Networks: Fully connected layers that project attention outputs into higher-dimensional spaces for deeper feature refinement.