Part 4 in the “LLMs from Scratch” series — a complete guide to understanding and building Large Language Models. If you are interested in learning more about how these models work I encourage you to…

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The post provides a complete guide to BERT, including its history, architecture, pre-training objectives, and fine-tuning for sentiment analysis. It discusses the key features of BERT, such as its encoder-only architecture, pre-training approach, model fine-tuning, and use of bidirectional context. The post also covers the tokenization process, creating train and validation data loaders, instantiating a BERT model, and setting up an optimizer, loss function, and scheduler for fine-tuning. The fine-tuning loop is explained, highlighting the steps taken for each epoch and within each batch.

A Complete Guide to BERT with Code

2 — Architecture and Pre-training Objectives

3 — Fine-Tuning BERT for Sentiment Analysis