calc_metrics

calc_metrics.py

if accelerator.is_main_process:
    training_loss = total_loss.item() / len(train_dataloader)
    train_eval_data['train_loss_history'].append(training_loss)
    train_eval_data['valid_acc_history'].append(valid_acc)
    train_eval_data['valid_f1_history'].append(valid_f1)
accelerator.log(
    {
        "accuracy": valid_acc,
        "f1": valid_f1,
        "train_loss": total_loss.item() / len(train_dataloader),
        "epoch": epoch
    },
    step=epoch
)
if valid_f1 > best_f1:
    best_f1 = valid_f1
    curr_best_model = copy.deepcopy(model)
    best_acc = valid_acc