Can you provide a more detailed guide to fine-tuning operations?

[chenzebiaohub]

I want to fine-tune a mt5 Chinese model on my own dataset！ Extremely grateful.

[asahi417]

Hey, do you have your dataset on HuggingFace?

On Wed, 8 May 2024 at 13:37, 小陈 @.***> wrote:

@asahi417 https://github.com/asahi417

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[chenzebiaohub]

hello! thanks your reply! I have the dataset organized ~ not uploaded to huggingface at the moment, would like to load the dataset locally for mT5 fine-tuning

[asahi417]

Cool. Are you gonna load the dataset locally via HuggingFace dataset? If it uses the HuggingFace dataset, it should be pretty straightforward, but otherwise can be a bit tricky.

On Wed, 8 May 2024 at 14:08, 小陈 @.***> wrote:

hello! thanks your reply! I have the dataset organized ~ not uploaded to huggingface at the moment, would like to load the dataset locally for mT5 fine-tuning

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[chenzebiaohub]

Very excited to see your feedback! I will upload the dataset to huggingface, how do I need to fine tune it after that?

[chenzebiaohub]

@asahi417

[asahi417]

Try the following command.

lmqg-train-search -c "tmp" -d "{your-hf-dataset-alias}" -m "mt5-small" -b 64 --epoch-partial 5 -e 15 --language "zh" --n-max-config 1 -g 2 4 --lr 1e-04 5e-04 1e-03 --label-smoothing 0 0.15

That will launch the fine-tuning with hyperparameter gridsearch. you might need to play around with the parameter. See the full description at lmqg-train-search -h.

[asahi417]

For example, following command is used to train https://huggingface.co/lmqg/mt5-base-zhquad-qag.

LA='zh'
MODEL="google/mt5-base"
MODEL_SHORT='mt5-base'
lmqg-train-search --use-auth-token -d "lmqg/qag_${LA}quad" -m "${MODEL}" -b 8 -g 8 16 -c "lmqg_output/${MODEL_SHORT}-${LA}quad-qag" -i 'paragraph' -o 'questions_answers' --n-max-config 2 --epoch-partial 5 -e 15 --max-length-output-eval 256 --max-length-output 256 --lr 1e-04 5e-04 1e-03

[asahi417]

See more example here

https://github.com/asahi417/lm-question-generation/blob/master/misc/2023_acl_qag/model_finetuning.end2end.sh

[chenzebiaohub]

Very excited to see your feedback! THANKS