INNER CODE UNIT · Python

train_loss

mlwithme/BertWithPretrained · Tasks/TaskForChineseNER.py:146

        train_loss = losses / len(train_iter)
        logging.info(f"Epoch: [{epoch + 1}/{config.epochs}],"
                     f" Train loss: {train_loss:.3f}, Epoch time = {(end_time - start_time):.3f}s")
        if (epoch + 1) % config.model_val_per_epoch == 0:
            acc = evaluate(config, val_iter, model, data_loader)
            logging.info(f"Accuracy on val {acc:.3f}")
            config.writer.add_scalar('Testing/Acc', acc, global_steps)
            if acc > max_acc:
                max_acc = acc
                state_dict = deepcopy(model.state_dict())
                torch.save({'last_epoch': global_steps,
                            'model_state_dict': state_dict},
                           model_save_path)


def evaluate(config, val_iter, model, data_loader):
    model.eval()
    real_true, real_pred = [], []

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