src/nli/training.py [818:865]:
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id2label = {
    0: 'e',
    1: 'n',
    2: 'c',
    -1: '-',
}


def count_acc(gt_list, pred_list):
    assert len(gt_list) == len(pred_list)
    gt_dict = list_dict_data_tool.list_to_dict(gt_list, 'uid')
    pred_list = list_dict_data_tool.list_to_dict(pred_list, 'uid')
    total_count = 0
    hit = 0
    for key, value in pred_list.items():
        if gt_dict[key]['label'] == value['predicted_label']:
            hit += 1
        total_count += 1
    return hit, total_count


def evaluation_dataset(args, eval_dataloader, eval_list, model, r_dict, eval_name):
    # r_dict = dict()
    pred_output_list = eval_model(model, eval_dataloader, args.global_rank, args)
    predictions = pred_output_list
    hit, total = count_acc(eval_list, pred_output_list)

    print(debug_node_info(args), f"{eval_name} Acc:", hit, total, hit / total)

    r_dict[f'{eval_name}'] = {
        'acc': hit / total,
        'correct_count': hit,
        'total_count': total,
        'predictions': predictions,
    }


def eval_model(model, dev_dataloader, device_num, args):
    model.eval()

    uid_list = []
    y_list = []
    pred_list = []
    logits_list = []

    with torch.no_grad():
        for i, batch in enumerate(dev_dataloader, 0):
            batch = move_to_device(batch, device_num)
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src/nli/training_extra.py [804:851]:
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id2label = {
    0: 'e',
    1: 'n',
    2: 'c',
    -1: '-',
}


def count_acc(gt_list, pred_list):
    assert len(gt_list) == len(pred_list)
    gt_dict = list_dict_data_tool.list_to_dict(gt_list, 'uid')
    pred_list = list_dict_data_tool.list_to_dict(pred_list, 'uid')
    total_count = 0
    hit = 0
    for key, value in pred_list.items():
        if gt_dict[key]['label'] == value['predicted_label']:
            hit += 1
        total_count += 1
    return hit, total_count


def evaluation_dataset(args, eval_dataloader, eval_list, model, r_dict, eval_name):
    # r_dict = dict()
    pred_output_list = eval_model(model, eval_dataloader, args.global_rank, args)
    predictions = pred_output_list
    hit, total = count_acc(eval_list, pred_output_list)

    print(debug_node_info(args), f"{eval_name} Acc:", hit, total, hit / total)

    r_dict[f'{eval_name}'] = {
        'acc': hit / total,
        'correct_count': hit,
        'total_count': total,
        'predictions': predictions,
    }


def eval_model(model, dev_dataloader, device_num, args):
    model.eval()

    uid_list = []
    y_list = []
    pred_list = []
    logits_list = []

    with torch.no_grad():
        for i, batch in enumerate(dev_dataloader, 0):
            batch = move_to_device(batch, device_num)
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