How can I use masked_lm_labels correctly?
# ❓ Questions & Help

<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
     new models and benchmarks, and migration questions. For all other questions,
     we direct you to Stack Overflow (SO) where a whole community of PyTorch and
     Tensorflow enthusiast can help you out. Make sure to tag your question with the
     right deep learning framework as well as the huggingface-transformers tag: 
     https://stackoverflow.com/questions/tagged/huggingface-transformers 
     
     If your question wasn't answered after a period of time on Stack Overflow, you
     can always open a question on GitHub. You should then link to the SO question 
     that you posted.
     -->

## Details
<!-- Description of your issue -->
Hi, I have a dataset like :

From Monday to Friday most people are busy working or studying, but in the evenings and weekends they are free and _ themselves.

And there are four candidates for the missing blank area:

["love", "work", "enjoy", "play"], here "enjoy" is the correct answer, it is a cloze-style task, and it looks like the maskLM in the BERT.

I want to train the model so that it can work better. I notice that there is a parameter called masked_lm_labels, and it can computing the masked language modeling loss. What should I do to train the BertForMaskedLM model with it.

Do you have any Example? Or can you teach me how to do that?
Thanks!
<!-- You should first ask your question on SO, and only if
     you didn't get an answer ask it here on GitHub. -->
**A link to original question on Stack Overflow**: 
Can any one help me
QAQ<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
