I carefully read your paper and so impressed with it. So I was trying to train my own discriminator on a generic dataset. But it seems that we can only choose the original GPT-2 when we launch the run_pplm_discrim_train.py.
Intuitively we have two strategies:
PlanA: Train the discriminator on top of the original GPT-2 -> plug it in our fine-tuned GPT-2 -> train together -> generate text
PlanB: Train the discriminator on top of the fine-tuned GPT-2 -> plug the former in the latter -> train together -> generate text.
May I know which one is correct? If PlanB is, how can we do that? (I tried to replace the model with a fine-tuned one but got the errors as in the following pic
Hi!
Thanks for your cool works!
I carefully read your paper and so impressed with it. So I was trying to train my own discriminator on a generic dataset. But it seems that we can only choose the original GPT-2 when we launch the run_pplm_discrim_train.py.
Intuitively we have two strategies: PlanA: Train the discriminator on top of the original GPT-2 -> plug it in our fine-tuned GPT-2 -> train together -> generate text PlanB: Train the discriminator on top of the fine-tuned GPT-2 -> plug the former in the latter -> train together -> generate text.
May I know which one is correct? If PlanB is, how can we do that? (I tried to replace the model with a fine-tuned one but got the errors as in the following pic
Many thanks and best regards!