Binary Classification Given Large Number of Tags
I am currently running Zero Shot Learning usingpipeline("zero-shot-classification")with a large number of candidate tags. If enough tags have a high enough scores, that sequence sentence would be classified as matching. Otherwise, it would be classified as non matching.I am currently taking the mean of the scores of all candidate tags, and if they exceed a certain threshold, it would be classified as matching. However, this is not an accurate approach.Is there are more accurate “mathematical formula” that yields better results?Thank you,Stephen Wong