)
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 0)
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 2)
            End of stream token id.
        ngram (`int`, *optional*, defaults to 2)
            Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first
            token.
        num_buckets (`int`, *optional*, defaults to 32)
            The number of buckets to use for each attention layer. This is for relative position calculation. See the
            [T5 paper](see https://arxiv.org/abs/1910.10683) for more details.
        relative_max_distance (`int`, *optional*, defaults to 128)
            Relative distances greater than this number will be put into the last same bucket. This is for relative
            position calculation. See the [T5 paper](see https://arxiv.org/abs/1910.10683) for more details.
        disable_ngram_loss (`bool`, *optional*, defaults to `False`):
            Whether be trained predicting only the next first token.
        eps (`float`, *optional*, defaults to 0.0):
            Controls the `epsilon` parameter value for label smoothing in the loss calculation. If set to 0, no label
            smoothing is performed.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
    zxlm-prophetnetZpast_key_valuesZ