def training_epoch_end(self, training_step_outputs): """ save tokenizer and model on epoch end """ self.average_training_loss = np.round( torch.mean(torch.stack([x["loss"] for x in training_step_outputs])).item(), 4, ) path = f"{self.outputdir}/simplet5-epoch-{self.current_epoch}-train-loss-{str(self.average_training_loss)}-val-loss-{str(self.average_validation_loss)}"
Will be very helpful if you can allow the name customizable (note the 'path' assignment).
def training_epoch_end(self, training_step_outputs): """ save tokenizer and model on epoch end """ self.average_training_loss = np.round( torch.mean(torch.stack([x["loss"] for x in training_step_outputs])).item(), 4, ) path = f"{self.outputdir}/simplet5-epoch-{self.current_epoch}-train-loss-{str(self.average_training_loss)}-val-loss-{str(self.average_validation_loss)}"Will be very helpful if you can allow the name customizable (note the 'path' assignment).
Btw, SimpleT5 is simply cool!