hi, I'm trying to understand your code and reproduce it. in the GIL implementation, I have a question:
your paper says q_{k} in concept store Q will be constantly updated.
(As the model M is finetuned for a downstream task, the representations qk are constantly updated)
however, after looking at the bash files and python files, I found that you build the concept store at the very beginning using the original XLNet checkpoints then save it as a static concept_store.pt file. During the training, it seems that you did not update the .pt file.
I'm a bit wonder here. did I miss any detail here? or maybe can you point out where is the function for updating embeddings in Q?
Thanks in advanced!
hi, I'm trying to understand your code and reproduce it. in the GIL implementation, I have a question:
your paper says q_{k} in concept store Q will be constantly updated.
(As the model M is finetuned for a downstream task, the representations qk are constantly updated)
however, after looking at the bash files and python files, I found that you build the concept store at the very beginning using the original XLNet checkpoints then save it as a static concept_store.pt file. During the training, it seems that you did not update the .pt file.
I'm a bit wonder here. did I miss any detail here? or maybe can you point out where is the function for updating embeddings in Q?
Thanks in advanced!