Thanks for sharing code!!!, I have a little question.
You can also define multiple verbalizations for a single label. For example, if you are unsure which words best represent the labels in a binary sentiment classification task, you could define your verbalizer as follows: VERBALIZER = {"+1": ["great", "good", "wonderful", "perfect"], "-1": ["bad", "terrible", "horrible"]}
If we want find the best represent verbalizations, Why the pml's output is sum of different verbalizations, is not the max of different verbalization?
cls_logits = cls_logits.sum(axis=1) / filler_len in line 229 of code
Thanks for sharing code!!!, I have a little question.
If we want find the best represent verbalizations, Why the pml's output is sum of different verbalizations, is not the max of different verbalization?
cls_logits = cls_logits.sum(axis=1) / filler_len
in line 229 of codeThank very much