Include a reference on in-domain LM pre-training for BERT
From https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/lm_finetuning#introduction

> As such, it's hard to predict what effect this step will have on final model performance, but it's reasonable to conjecture that this approach can improve the final classification performance, especially when a large unlabelled corpus from the target domain is available, labelled data is limited, or the target domain is very unusual and different from 'normal' English text.
> If you are aware of any literature on this subject, please feel free to add it in here, or open an issue and tag me (@Rocketknight1) and I'll include it.

Hi @Rocketknight1 this paper https://arxiv.org/pdf/1905.05583.pdf studies within-task and within-domain pre-training for BERT in section 5.4 and they achieve a good boost from it.
Ah, thank you very much for this! I'll read over the paper and include it as a reference soon.<|||||>Finally read it once I had some free time at the weekend and added PR #715. Thank you!<|||||>Thank you @Rocketknight1 !<|||||>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.
