Hi,
I'm working at Sequnce Labaling model for NLP task (dlpy.applications.SequenceLabeling) and while I was trying to fit the data, I came across an error "ERROR: A floating-point overflow exception occurred, halting the analysis. This condition is usually caused by improperly scaled inputs, a large learning rate, or exploding gradients.".
This is my code:
Hi @Michal_S_00,
This question is being worked on by the developer through the issue created on the DLPy GitHub repository. I will update this thread once a solution is posted there.
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Here's a copy of the response from @lipcai on GitHub:
If your model generates floating-point exception errors, you can consider specifying values for gradient clipping parameters (clip_grad_min and clip_grad_max). https://github.com/sassoftware/python-dlpy/blob/af4874e00edc7a4b7c31646e76057a76d566481c/dlpy/tests/...
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