A look at the project ahead

Imagine that you have a classification task, and you want to solve it by using a transformer-based neural network model. Here are your options:

Train a model from scratch: One approach is to train a new model entirely from scratch. However, this method might not be the most effective. When you start from scratch, you miss out on the opportunity to benefit from transfer learning.
Fine-tune a pretrained model: Transfer learning involves repurposing a pretrained model that was initially trained for a different task. By fine-tuning this pretrained model, you can adapt it to your specific classification task.

In this hands-on project, you gain a comprehensive understanding of the entire end-to-end pipeline for fine-tuning a transformer-based neural network.

Learning objectives

Upon completion of this project, you have the ability to:

Define and pretrain a transformer-based neural network using PyTorch for a classification task.
Fully fine-tune the pretrained model for a different classification task.
Compare results by fine-tuning only the last layer of the pretrained model.

What you'll need

For this project, you need an intermediate level of proficiency in Python, PyTorch, and deep learning. Additionally, the only equipment that you need is a computer equipped with a modern browser, such as the latest versions of Chrome, Edge, Firefox, or Safari.

Fine-tune a transformer-based neural network with PyTorch

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