def preprocess_prediction_data(data, tokenizer, pad_token_id=0, max_ngram_filter_size=3):
    """
    It process the prediction data as the format used as training.
    Args:
        data (obj:`list[str]`): The prediction data whose each element is a tokenized text.
        tokenizer(obj: paddlenlp.data.JiebaTokenizer): It use jieba to cut the chinese string.
        pad_token_id(obj:`int`, optional, defaults to 0): The pad token index.
        max_ngram_filter_size (obj:`int`, optional, defaults to 3) Max n-gram size in TextCNN model.
            Users should refer to the ngram_filter_sizes setting in TextCNN, if ngram_filter_sizes=(1, 2, 3)
            then max_ngram_filter_size=3
    Returns:
        examples (obj:`list`): The processed data whose each element
            is a `list` object, which contains

            - word_ids(obj:`list[int]`): The list of word ids.
    """
    examples = []
    for text in data:
        ids = tokenizer.encode(text)
        seq_len = len(ids)
        # Sequence length should larger or equal than the maximum ngram_filter_size in TextCNN model
        if seq_len < max_ngram_filter_size:
            ids.extend([pad_token_id] * (max_ngram_filter_size - seq_len))
        examples.append(ids)
    return examples