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Calcite | Level 5


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:'train_data',
Input consists of ten columns, there is one word in each column in each row, also the labels(varchar type) are represented in the same way.
I tried to run it with diffrent learning rate, but every time I get the same error, any ideas how to fix that?
Community Manager

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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Community Manager

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).

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