| --- |
| license: apache-2.0 |
| language: en |
| --- |
| |
| <span style="color:red; font-size:20px" ><b> |
| Attention! This is a proof-of-concept model deployed here just for research demonstration. |
| Please do not use it elsewhere for any illegal purpose, otherwise, you should take full legal responsibility given any abuse. |
| </b></span> |
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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. |
|
|
| ## Model description |
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| 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. |
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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. |
|
|
| ### How to use |
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| 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} |
| } |
| ``` |
