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Train deberta on goemotions with asymmetric loss - #2

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Train deberta on goemotions with asymmetric loss#2
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@d-ulker

@d-ulker d-ulker commented Sep 5, 2025

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Add unified pre/post-clip gradient norm logging across all trainers to ensure consistent and accurate monitoring.

Previously, gradient norm logging was inconsistent across different loss functions (BCE, Asymmetric, Combined), leading to misleadingly small or constant grad_norm values in the logs for some trainers. This PR introduces a BaseGradNormTrainer that uses Accelerate's clip_grad_norm_ to reliably compute and log both pre-clip and post-clip gradient norms for all training paths, enabling fair comparison and accurate monitoring.


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Co-authored-by: denizcan.uelker <denizcan.uelker@mercedes-benz.com>
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cursor Bot commented Sep 5, 2025

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Cursor Agent can help with this pull request. Just @cursor in comments and I'll start working on changes in this branch.
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Hi @uelkerd! 👋

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