How to drop top layer /head of a finetuned transformers model

how to drop top layer or head from a pretrained/fine tuned transformers model.
for example i have fine tuned a deberta-v3-large for NER, and i want to use same model for multi class classification, as data is same so i have decided that i will use same finetuned model for classification task and add a new head in place of NER head and will train for one or 2 epochs, and is there any way to freeze previous layers and train only head on this task like we do in CNN models(like ResNet50).
I am new to pytorch, i am familiar how can we freeze initial layers of CNN in tensorflow, can we do same in pytorch.

You can iterate the parameters you want to freeze and set their .requires_grad attribute to False.
Depending on the model architecture, something like this could work:

for param in model.feature_extractor.parameters():
    param.requires_grad = False