Hi,
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.
Hi Zack, hope you are doing well !!
Thank you for your reply.
Actually it is not generating random tokens, but it is misspelling them.
For e.g a word “productive” in input text is spelled as “priductive”