Hi there is a library released by Pytorch for Model interpretability. It uses the internal embeddings and integrated gradient functions to give the saliency features and attributions of the input words. They have shown its applications for BERT model trained on SQuaD for Question Answering. Is it possible to do the same on these BART models? I am not really well versed with how the pipeline works, is it possible to run it on the distilbart zero-shot models? I am presuming I might have to replicate the zero-shot pipeline and pass the model through their interpretation functions.
  
      
      captum.ai
  
  
    

Captum · Model Interpretability for PyTorch 3

Model Interpretability for PyTorch