def METHOD_NAME(args, verbalizer):
    """
    Load fewclue datasets and convert them to the standard format of PET.
    """
    split_id = args.split_id
    splits = [f"train_{split_id}", f"dev_{split_id}", "test_public", "test"]
    if args.task_name == "cluewsc":
        train_ds, dev_ds, public_test_ds, test_ds = load_dataset("fewclue", name=args.task_name, splits=splits)
        unlabeled_ds = None
    else:
        splits.append("unlabeled")
        train_ds, dev_ds, public_test_ds, test_ds, unlabeled_ds = load_dataset(
            "fewclue", name=args.task_name, splits=splits
        )
    data_ds = [train_ds, dev_ds, public_test_ds, test_ds, unlabeled_ds]
    # Preprocess data for EFL.
    if args.task_name == "chid":
        for index, sub_data_ds in enumerate(data_ds):
            data_ds[index] = convert_chid(sub_data_ds)
    elif args.task_name == "cluewsc":
        for index, sub_data_ds in enumerate(data_ds[:-1]):
            data_ds[index] = convert_cluewsc(sub_data_ds)
    orig_key = "label"
    if args.task_name == "tnews":
        orig_key = "label_desc"
    elif args.task_name == "iflytek":
        orig_key = "label_des"
    for index, sub_data_ds in enumerate(data_ds):
        is_train = index == 0
        if sub_data_ds is not None:
            data_ds[index] = convert_efl(sub_data_ds, args.label_words, orig_key, is_train)
    # Extend train dataset with pseudo-label data.
    data_ds[0] = extend_with_pseudo_data(data_ds[0], args.pseudo_data_path, verbalizer.labels_to_ids)
    return data_ds