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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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# BART (base-sized model) |
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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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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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## Model description |
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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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## Intended uses & limitations |
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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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### How to use |
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Here is how to use this model in PyTorch: |
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```python |
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from transformers import BartTokenizer, BartModel |
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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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inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") |
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outputs = model(**inputs) |
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last_hidden_states = outputs.last_hidden_state |
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``` |
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### BibTeX entry and citation info |
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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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``` |
