This makes sense. However, I am referring to the case where I am running the pipeline as multi_class=True over the candidate labels. In this case, I notice the probability distribution is different compared to the case of individually running the model over the candidate labels and tweet (sequence) pairs. Is there something I’m missing or is this expected behavior?
Another thing I might need help with is addressing the following questions to get better results:


To make the process faster, is it advised to move to a smaller, less heavier model than bart-large-mnli or is there another way to make it faster to process 100K tweets while keeping the accuracy high by using bart-large-mnli?


It is interesting to note that the model is sensitive to case. Accordingly, for my case would it be better to run the model with preprocessed tweets as input (stopword removal, case folding, etc) or should the raw tweet work better?


Thanks @rsk97