Previously, on Day 36: Explained the concept and process of fine-tuning BERT on downstream NLP tasks, specifically illustrating how BERT can be adapted for text classification using labeled data and transfer learning principles.

Transformer models have changed how computers understand and generate language. At the heart of transformers are three main structures: encoder-only, decoder-only, and encoder-decoder. Understanding each helps you see how modern models like GPT actually work.

Transformer Architectures: Encoder, Decoder, and Encoder-Decoder

A transformer is a neural network designed to handle sequences—such as sentences or paragraphs. It builds up meaning using two key building blocks: attention layers and feedforward layers.

Encoder-only transformers process a whole input sequence at once. They build a detailed internal summary of the input. For example, with the sentence “Apples are red,” the encoder reads the entire thing together and produces a transformed version of each word.…