Building an finetune Model in PyTorch
Fine-tuning a model in PyTorch involves taking a pre-trained model (such as ResNet, BERT, or any other model trained on a large dataset) and adapting it to a specific task with a smaller, task-specific dataset. Fine-tuning is particularly useful for improving the model's performance on tasks similar to its original training dataset.
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