ntains most of the main methods. Users should refer to
this superclass for more information regarding those methods.

Args:
    vocab_file (`str`):
        [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
        contains the vocabulary necessary to instantiate a tokenizer.
    bos_token (`str`, *optional*, defaults to `"<s>"`):
        The begin of sequence token.
    eos_token (`str`, *optional*, defaults to `"</s>"`):
        The end of sequence token.
    unk_token (`str`, *optional*, defaults to `"<unk>"`):
        The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
        token instead.
    pad_token (`str`, *optional*, defaults to `"<pad>"`):
        The token used for padding, for example when batching sequences of different lengths.
    normalize (`bool`, *optional*, defaults to `False`):
        Whether to convert numeric quantities in the text to their spelt-out english counterparts.
    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).
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