Difference of performance when finetuning bert use the huggingface or the google official code
Hi all,I tend to finetune BERT with a simple text classification task.However, I got different results when using the huggingface library (torch 1.8.1+cu111) and google’s official code (Tensorflow v1.15).I wonder if there is any optimization in huggingface for fine-tuning bert?By the way, I believe that I use the same hyper-parameters.But I got the higher performance using the huggingface library.I have checked the details in a similar issue:Is Google's official BERT model and huggingface BERT model different or same?BeginnersGoogle’s Official BERTHuggingFace BERTIf they are same/equivalent, how are they created? 

Are the pretrained parameters just copied?
Or are they pretrained from scratch following exact same approach (objective/hyperparameters) and data mention in the paper?

I have been searching for this information for hours, but can’t find anywhere.However, it does not help me.