def __init__(
        self,
        tokenizer: PreTrainedTokenizer,
        max_length: int,
        padding: Union[bool, str, PaddingStrategy] = True,
        pad_to_multiple_of: int = 64,
        num_unique_labels: Optional[int] = None,
    ):
        """
        A Dataset class to work with HuggingFace-like datasets.

        Parameters
        ----------
        tokenizer: PreTrainedTokenizer
            A pretrained tokenizer
        max_length: int
            Maximum sequence length (equal for each batch).
            It should be se to max length accepted by the model or a smaller number.
        padding: Union[bool, str, PaddingStrategy]
            See `Padding and Truncation <https://huggingface.co/docs/transformers/pad_truncation>`_
            for more information (`truncation` is always True).
        pad_to_multiple_of: int
            Pad the sequence to a multiple of the provided value.
            This argument will be used whenever padding is not done to a fixed constant (e.g., max_length).
        num_unique_labels: Optional[int]
            Number of unique target labels in the task (classification only)
        """
        self.tokenizer = tokenizer
        self.max_length = max_length
        self.padding = padding
        self.pad_to_multiple_of = pad_to_multiple_of
        self.num_unique_labels = num_unique_labels
        self._data_collator = None

        A Dataset class to work with HuggingFace-like datasets.

        Parameters
        ----------
        tokenizer: PreTrainedTokenizer
            A pretrained tokenizer
        max_length: int
            Maximum sequence length (equal for each batch).
            It should be se to max length accepted by the model or a smaller number.
        padding: Union[bool, str, PaddingStrategy]
            See `Padding and Truncation <https://huggingface.co/docs/transformers/pad_truncation>`_
            for more information (`truncation` is always True).
        pad_to_multiple_of: int
            Pad the sequence to a multiple of the provided value.
            This argument will be used whenever padding is not done to a fixed constant (e.g., max_length).
        num_unique_labels: Optional[int]
            Number of unique target labels in the task (classification only)
        
fortuna/data/dataset/huggingface_datasets.py