  matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new
  state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains
  of up to 6 ROUGE.
This model was contributed by sshleifer. The Authors' code can be found here.
Examples
Examples and scripts for fine-tuning BART and other models for sequence to sequence tasks can be found in
  examples/pytorch/summarization/.
An example of how to train [BartForConditionalGeneration] with a Hugging Face datasets
  object can be found in this forum discussion.
Distilled checkpoints are described in this paper.
Implementation Notes
Bart doesn't use token_type_ids for sequence classification. Use [BartTokenizer] or
  [~BartTokenizer.encode] to get the proper splitting.
