Getting model.save_pretrained() to save the best iteration when fine tuning a model with keras (using callbacks)?
Hi all, I’m just getting started with fine tuning HF models in keras. I’ve been using the following code:model.compile(optimizer=Adam(learning_rate=learning_rate),metrics = [‘accuracy’])history = model.fit(train_dataset,epochs = epochs,batch_size = batch_size,validation_data = test_dataset,callbacks = [LearningRateScheduler(lr_scheduler),EarlyStopping(monitor=‘val_loss’, patience=5)],verbose = 1)and then runningmodel.save_pretrained(model_path)and …from_pretrained(model_path).I’m using a learning rate scheduler and often the best model occurs several epochs before the end of training. When I was training my own models I would be usingModelCheckpoint(filepath=model_path, monitor=‘val_loss’, mode=‘min’, save_best_only=True),which lets me load the best model.But when using model.save_pretrained() and …from_pretrained() for a pretrained HF model how can I get it to load the best model instead of the last one?Thanks for your help!