Fine-tune DeBERTa v3 language model, worthwhile endeavour?

[shensmobile]

Hey everyone, I've been using RoBERTa for the past year or so but have been looking into DeBERTa as well. My typical workflow with RoBERTa is to fine-tune the MLM using ~3mil medical reports to domain adapt before training on down-stream tasks. I've found that this greatly improved performance of the downstream models.

With DeBERTa, I presume that I can't use my existing code for fine-tuning the MLM since DeBERTa doesn't use MLM, it uses RTD. The pre-training scripts here seem to be for training a model from scratch (which I don't think I have good enough data or compute power/time to do efficiently).

I presume that if I wanted to fine-tune the RTD language model, I would use the "deberta-v3-X-continue" option in rtd.sh? If so, do you guys think this would be worth my time? Or should I just fine tune my downstream tasks on the supplied pre-trained models?

[StephennFernandes]

given that you have a significantly good amount of training data, i believe this could be a really good endevour as the DebERTa-v3 architecture and training procedure is insanely great. good h-param search and a nice continual pretraining should give great results. do let me know how it goes.

[shensmobile]

Would I use the deberta-v3-X-continue in rtd.sh or pretrain a model from scratch using my dataset?

[StephennFernandes]

do continual pretraining, i mean use the deberta-v3-X-continue. all medical domain LM are a result of continual pretraining

[priamai]

Hi all, I am in the exact same boat here. What is that rtd.sh is mentioned? I mean I know is a bash file but where is it ? Would be nice to see a python script that shows how the domain adaptation should be run and how to save the model.

[fmobrj]

do continual pretraining, i mean use the deberta-v3-X-continue. all medical domain LM are a result of continual pretraining

Hi, @StephennFernandes. How are you doing? Have you managed to sucessfully pretrain or continue pretraining a deberta V3 model in another language? Back when we were talking, my discriminator couldnt get better.

Best regards, Fabio.

[zkalson]

Hey all, I'm going down a similar path of continual pre-training. Any insight on how to make the model compatible with the huggingface transformers library?

[neavo]

Hey everyone, I've been using RoBERTa for the past year or so but have been looking into DeBERTa as well. My typical workflow with RoBERTa is to fine-tune the MLM using ~3mil medical reports to domain adapt before training on down-stream tasks. I've found that this greatly improved performance of the downstream models.

I attempted to continue pre-training mDeberta v3 using the MLM task in a manner similar to other BERT-like models. From the results, it seems that the MLM task can achieve effects similar to those of other BERT-like models. Therefore, regardless of whether further pre-training with the RTD task can yield better outcomes, the MLM task might be a safe choice to fall back on. Give it a try!