I have trained pegasus for condition generation on a custom dataset. And have been successfully using it as well. My project aim is text summarization deployed on an heroku app. My model weights are over 2.5GB and heroku can’t support the same.
I trained pegasus on seq2seq.py in legacy dir in transformers. I was thinking if pruning would help in reducing the model capacity significantly but still keeping the accuracy and performance intact.
I couldn’t try keras pruning due to the complexity of the trainer present in seq2seq.py and I do not have any idea on how that works.
Any way you guys could help would be much appreciated.
Thanks