Fine-tune a transformer-based neural network with PyTorch
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Learn how to fine-tune transformer-based neural networks with PyTorch. Discover best practices and techniques for adapting pre-trained models to your specific tasks and improving performance.
At a Glance
Master the art of fine-tuning a transformer-based neural network using PyTorch. Discover the power of transfer learning as you meticulously fine-tune the entire neural network, comparing it to the more focused approach of fine-tuning just the final layer. Unlock this essential skill by immersing yourself in this end-to-end hands-on project today!
A look at the project ahead
- 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.
Learning objectives
- 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
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