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Add model card

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+ ---
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+ license: apache-2.0
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+ language: en
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+ ---
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+
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+ # BART (base-sized model)
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+
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+ 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).
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+
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+ 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.
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+
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+ ## Model description
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+
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+ BART is a transformer encoder-encoder (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.
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+ 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).
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+
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+ ## Intended uses & limitations
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+
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+ 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.
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+
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+ ### How to use
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+
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+ Here is how to use this model in PyTorch:
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+
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+ ```python
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+ from transformers import BartTokenizer, BartModel
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+
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+ tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
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+ model = BartModel.from_pretrained('facebook/bart-large')
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+
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+ inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
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+ outputs = model(**inputs)
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+
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+ last_hidden_states = outputs.last_hidden_state
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+ ```
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+
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+ ### BibTeX entry and citation info
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+
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+ ```bibtex
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+ @article{DBLP:journals/corr/abs-1910-13461,
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+ author = {Mike Lewis and
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+ Yinhan Liu and
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+ Naman Goyal and
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+ Marjan Ghazvininejad and
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+ Abdelrahman Mohamed and
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+ Omer Levy and
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+ Veselin Stoyanov and
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+ Luke Zettlemoyer},
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+ title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
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+ Generation, Translation, and Comprehension},
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+ journal = {CoRR},
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+ volume = {abs/1910.13461},
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+ year = {2019},
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+ url = {http://arxiv.org/abs/1910.13461},
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+ eprinttype = {arXiv},
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+ eprint = {1910.13461},
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+ timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
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+ biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
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+ bibsource = {dblp computer science bibliography, https://dblp.org}
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+ }
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+ ```