BERT reasoning capabilities I'm working on a Twitter classification task and while analyzing the errors I found quite a few strange predictions. I'm searching for a tool (preferably open-source) similar to https://towardsdatascience.com/how-does-bert-reason-54feb363211 that is able to compute the highest positive/negative attribution given to the words (the reason why I'm not able to use the approach presented in the aforementioned article is the price). In this way, I hope that I'll be able to better understand (and possibly correct) these misclassifications. I tried looking at the attention heads but I don't feel that I'm able to fully understand and draw conclusions based on this information.
Any help would be greatly appreciated!
BERT is free. Any Sentiment Analysis model will achieve the task.