Treating Punctuatio restoration as Seq2Seq task
Punctuation restoration has been tried to solve by treating it as a multi-class classification problem. i.e., fill in the punctuation between words {O, “,”,  “!”, “:”}.I tried seq2seq approach on for that and the trained BART model seems really good at it. To compare it to previous methods, I need to frame the results as a classification format like thishttps://github.com/nkrnrnk/BertPuncWhat could be best way to get Precision, Recall and F1 scores for seq2seq task?I tried to use tokenizer to separate the punctuations but it usually combines the punctuations and use another id.