Preparing Your Dataset for Fine-Tuning
Explore how to prepare datasets for fine-tuning OpenAI models by acquiring relevant data, cleaning and formatting it, managing token limits, and splitting into training, validation, and test sets to optimize model performance.
Once we are ready to fine-tune using the OpenAI API, we'll need to acquire and prepare the data we will use for the fine-tuning.
Acquiring our dataset
Before we start fine-tuning a model with the OpenAI API, it's important to get a suitable dataset and have a good understanding of it. The dataset we choose should align well with the goals of our project. For instance, if we aim to fine-tune a model to generate medical text, a dataset filled with medical journals or articles would be needed. The right dataset forms the foundation upon which the fine-tuning process is built, making its selection a critical step.
The quality of the data we acquire is as important as the quantity. A high-quality dataset is one that is rich in relevant information, well-organized, and free from errors or inconsistencies. On the other hand, the quantity refers to the size of the dataset, which should be substantial enough to cover a wide range of scenarios and examples within ...