Getting started with fine-tuning
In this section, we are going to cover all the steps needed to fine-tune an LLM with a full-code approach. We will be leveraging Hugging Face libraries, such as datasets (to load data from the Hugging Face datasets ecosystem) and tokenizers (to provide an implementation of the most popular tokenizers). The scenario we are going to address is a sentiment analysis task. Our goal is to fine-tune a model to make it an expert binary classifier of emotions, clustered into “positive” and “negative.”
Obtaining the dataset
The first ingredient that we need is the training dataset. For this purpose, I will leverage the datasets library available in Hugging Face to load a binary classification dataset called IMDB (you can find the dataset card at https://huggingface.co/datasets/imdb).
The dataset contains movie reviews, which are classified as positive or negative. More specifically, the dataset contains two columns:
...
