XLM-R Hyperparameters

[MaksymDel]

Hi @JunjieHu!

I am trying to reproduce results for XLM-R. The paper suggests that lr=3e-5 and effective bs=16 should be used for XLM-R. It would be very helpful if you could share some more details on hyperparameters (code in repo is configured only for mBert).

Specifically:

1. Are hparams the same for all the datasets?
2. Are they also the same for XLM and XLM-R-large?
3. Did you do warmup updates or/and learning rate scheduling?
4. Any other non-default specific decisions that you made that could impact training (dropout/grad clipping)?

Thanks in advance for your answers and thanks for this resource.

[JunjieHu]

Hi @maksym-del

Here are my answers to your questions.

1. Mostly we used the hyper-parameters provided in the scripts/*.sh. For tasks except the QA parts, I used lr=2e-5, effective batch_size=32, and max epoch=5, 10 or 20 depending on the size of the English training data. I did try to search among [2e-6, 2e-5, 3e-5, 5e-5], and 2e-5 generally worked well.
2. The same hparameters in the bash scripts are used for XLM/XLM-R-large/mBERT for most tasks. For the QA tasks, Sebastian might tune a little bit on different parameters.
3. For the fine-tuning, we didn't do warmup and learning rate scheduling in this work. In the other work, I tried Polynormials warmup w/ AdamW optimizer for the XNLI task, and it worked better than constant learning rate w/o warmup.
4. In my experience, the learning rate and optimizer could impact the performance greatly. In some cases, a smaller learning rate works better. As for optimizer, LAMB and SGD are slightly better than Adam, although we used Adam in this work without introducing more complexity to the code.

[MaksymDel]

Hi @JunjieHu

thanks for the clarification!

Regarding answer 3, I can see from the config file that you are indeed not using warmup steps, but from the code, it looks like a linear LR decay is still applied (just w/o warmup).

Please correct me if I'm wrong.