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In the training process, we save one model every 5000 iterations. Besides training loss, are there any criteria to help choose the best model? Because I think even if training loss is low, the performance is probably not good.
For example, I trained two models. One is model_400k iteration, the other is model_500k iteration. But the actual decoded content 400k's model is more reasonable than 500k's.
Thanks a lot!
The text was updated successfully, but these errors were encountered:
Hello,
In the training process, we save one model every 5000 iterations. Besides training loss, are there any criteria to help choose the best model? Because I think even if training loss is low, the performance is probably not good.
For example, I trained two models. One is model_400k iteration, the other is model_500k iteration. But the actual decoded content 400k's model is more reasonable than 500k's.
Thanks a lot!
The text was updated successfully, but these errors were encountered: