readme: ---
  license: apache-2.0
  language: en
  ---
  
  # BART (base-sized model) 
  
  BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/bart). 
  
  Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
  
  ## Model description
  
  BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
  
  BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).
  
  ## Intended uses & limitations
  
  You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=bart) to look for fine-tuned versions on a task that interests you.
  
  ### How to use
  
  Here is how to use this model in PyTorch:
  
  ```python
  from transformers import BartTokenizer, BartModel
  
  tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
  model = BartModel.from_pretrained('facebook/bart-base')
  
  inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
  outputs = model(**inputs)
  
  last_hidden_states = outputs.last_hidden_state
  ```
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{DBLP:journals/corr/abs-1910-13461,
    author    = {Mike Lewis and
                 Yinhan Liu and
                 Naman Goyal and
                 Marjan Ghazvininejad and
                 Abdelrahman Mohamed and
                 Omer Levy and
                 Veselin Stoyanov and
                 Luke Zettlemoyer},
    title     = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
                 Generation, Translation, and Comprehension},
    journal   = {CoRR},
    volume    = {abs/1910.13461},
    year      = {2019},
    url       = {http://arxiv.org/abs/1910.13461},
    eprinttype = {arXiv},
    eprint    = {1910.13461},
    timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
    biburl    = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  ```