How does the finetune on transformer (t5) work?

Question

I am using pytorch lightning to finetune t5 transformer on a specific task. However, I was not able to understand how the finetuning works. I always see this code :

tokenizer = AutoTokenizer.from_pretrained(hparams.model_name_or_path) model = AutoModelForSeq2SeqLM.from_pretrained(hparams.model_name_or_path)

I don't get how the finetuning is done, are they freezing the whole model and training the head only, (if so how can I change the head) or are they using the pre-trained model as a weight initializing? I have been looking for an answer for couple days already. Any links or help are appreciated.

Answer 1

If you are using PyTorch Lightning, then it won't freeze the head until you specify it do so. Lightning has a callback which you can use to freeze your backbone and training only the head module. See Backbone Finetuning

Also checkout Ligthning-Flash, it allows you to quickly build model for various text tasks and uses Transformers library for backbone. You can use the Trainer to specify which kind of finetuning you want to apply for your training.

Thanks