I am using T5-base model for abstractive summarization, results are good but I am getting newly generated spelling mistakes in the summary which were not actually present in input text.
Can anyone tell me why these spelling mistakes occuring and how can I solve this?
I think it’s due to your min_output size, for example if you have forced the model to generate results at minimum more than 50 sequence, and somehow the prediction length predicted only 40 sequences, I think it will start to generate random tokens just to reach the 50 seq.