def __init__(
        self,
        file_split=TRAIN_FILE_SPLIT,
        cache_dir=CACHE_DIR,
        language=LANGUAGE_ENGLISH,
        to_lowercase=TO_LOWER_CASE,
        tok_language=TOK_ENGLISH,
        data_percent_used=DATA_PERCENT_USED,
    ):
        """
            Load the dataset here
        Args:
            file_split (str, optional):The subset to load.
                One of: {"train", "dev", "test"}
                Defaults to "train".
            cache_dir (str, optional):Path to store the data.
                Defaults to "./".
            language(str):Language required to load which xnli file (eg - "en", "zh")
            to_lowercase(bool):flag to convert samples in dataset to lowercase
            tok_language(Language, optional): language (Language, optional): The pretrained model's
                language. Defaults to Language.ENGLISH.
            data_percent_used(float, optional): Data used to create Torch Dataset.
                Defaults to "1.0" which is 100% data
        """
        if file_split not in VALID_FILE_SPLIT:
            raise ValueError("The file split is not part of ", VALID_FILE_SPLIT)

        self.file_split = file_split
        self.cache_dir = cache_dir
        self.language = language
        self.to_lowercase = to_lowercase
        self.tok_language = tok_language
        self.data_percent_used = data_percent_used

        df = _load_pandas_df(self.cache_dir, self.file_split, self.language, self.data_percent_used)

        self.df = df

        token_ids, input_mask, token_type_ids = _tokenize(
            tok_language, to_lowercase, cache_dir, self.df
        )

        self.token_ids = token_ids
        self.input_mask = input_mask
        self.token_type_ids = token_type_ids

        if file_split == TRAIN_FILE_SPLIT:
            label_encoder, train_labels = _fit_train_labels(self.df)
            self.label_encoder = label_encoder
            self.labels = train_labels
        else:
            # use the label_encoder passed when you create the test/validate dataset
            self.labels = self.df[LABEL_COL]

            Load the dataset here
        Args:
            file_split (str, optional):The subset to load.
                One of: {"train", "dev", "test"}
                Defaults to "train".
            cache_dir (str, optional):Path to store the data.
                Defaults to "./".
            language(str):Language required to load which xnli file (eg - "en", "zh")
            to_lowercase(bool):flag to convert samples in dataset to lowercase
            tok_language(Language, optional): language (Language, optional): The pretrained model's
                language. Defaults to Language.ENGLISH.
            data_percent_used(float, optional): Data used to create Torch Dataset.
                Defaults to "1.0" which is 100% data
        
utils_nlp/dataset/xnli_torch_dataset.py