Fine-tuning bert-for-tf2 on a Q&A task about bert-for-tf2 HOT 3 OPEN

@vinnytwice - yes, sure. I guess, the 3 "\n" are how the run_squad.py script chooses to represent the data - text file with the context text and a question (separated by 3 \n), so that it is easy to parse. It would then use different segment_ids - 1 for the tokens belonging to the context text, and 0 for the question, when feeding the input into BERT.

from bert-for-tf2.

this could be quite of a task (although there is a lot of information on the net), depending on how you represent your data.
What should be easily accessible is trying to reproduce the BERT results on SQuAD. Check the BERT paper for how exactly BERT was applied on the SQuAD task.
And I'll check if I could add an example on SQuAD here.

from bert-for-tf2.

Hi Kpe thanks for answering..
Say I just present the dataset as sequence of paragraph separated by 3 "\n" as I saw in a repo extending BERT
https://github.com/Nagakiran1/Extending-Google-BERT-as-Question-and-Answering-model-and-Chatbot

I than should be able to do it with bert-for-tf2 right?

from bert-for-tf2.