ptional*, defaults to `[1000, 1000, 1000, 1000]`):
            The bounding box to use for the special [SEP] token.
        pad_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
            The bounding box to use for the special [PAD] token.
        pad_token_label (`int`, *optional*, defaults to -100):
            The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
            CrossEntropyLoss.
        only_label_first_subword (`bool`, *optional*, defaults to `True`):
            Whether or not to only label the first subword, in case word labels are provided.
        additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
            Additional special tokens used by the tokenizer.
        sp_model_kwargs (`dict`, *optional*):
            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
            to set:

            - `enable_sampling`: Enable subword regularization.
            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.

              - `nbest_size = {0,1}`: No sampling is performed.
              - `nbest_size > 1`: samples from the nbest_size results.
              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
                using forward-filtering-and-backward-sampling algorithm.

            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
              BPE-dropout.

    Attributes:
        sp_model (`SentencePieceProcessor`):
            The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
    Ú	input_idsÚattention_maskú