  token.
For masked language modeling (e.g., [BertForMaskedLM]), the model expects a tensor of dimension
  (batch_size, seq_length) with each value corresponding to the expected label of each individual token: the
  labels being the token ID for the masked token, and values to be ignored for the rest (usually -100).
For sequence to sequence tasks,(e.g., [BartForConditionalGeneration],
  [MBartForConditionalGeneration]), the model expects a tensor of dimension (batch_size, tgt_seq_length) with each value corresponding to the target sequences associated with each input sequence. During
  training, both BART and T5 will make the appropriate decoder_input_ids and decoder attention masks internally.
  They usually do not need to be supplied. This does not apply to models leveraging the Encoder-Decoder framework. See
