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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- transformers |
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- bart |
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- paraphrase |
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- seq2seq |
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datasets: |
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- quora |
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- paws |
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--- |
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# BART Paraphrase Model (Large) |
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A large BART seq2seq (text2text generation) model fine-tuned on 3 paraphrase datasets. |
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## Model description |
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The BART model was proposed in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. (2019). |
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- Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT). |
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- The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token. |
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- BART is particularly effective when fine tuned for text generation. This model is fine-tuned on 3 paraphrase datasets (Quora, PAWS and MSR paraphrase corpus). |
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The original BART code is from this [repository](https://github.com/pytorch/fairseq/tree/master/examples/bart). |
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## Intended uses & limitations |
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You can use the pre-trained model for paraphrasing an input sentence. |
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### How to use |
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```python |
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import torch |
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from transformers import BartForConditionalGeneration, BartTokenizer |
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input_sentence = "They were there to enjoy us and they were there to pray for us." |
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model = BartForConditionalGeneration.from_pretrained('eugenesiow/bart-paraphrase') |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = model.to(device) |
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tokenizer = BartTokenizer.from_pretrained('eugenesiow/bart-paraphrase') |
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batch = tokenizer(input_sentence, return_tensors='pt') |
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generated_ids = model.generate(batch['input_ids']) |
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generated_sentence = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) |
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print(generated_sentence) |
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``` |
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### Output |
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``` |
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['They were there to enjoy us and to pray for us.'] |
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``` |
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## Training data |
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The model was fine-tuned on a pretrained [`facebook/bart-large`](https://huggingface.co/facebook/bart-large), using the [Quora](https://huggingface.co/datasets/quora), [PAWS](https://huggingface.co/datasets/paws) and [MSR paraphrase corpus](https://www.microsoft.com/en-us/download/details.aspx?id=52398). |
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## Training procedure |
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We follow the training procedure provided in the [simpletransformers](https://github.com/ThilinaRajapakse/simpletransformers) seq2seq [example](https://github.com/ThilinaRajapakse/simpletransformers/blob/master/examples/seq2seq/paraphrasing/train.py). |
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## BibTeX entry and citation info |
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```bibtex |
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@misc{lewis2019bart, |
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title={BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, |
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author={Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Ves Stoyanov and Luke Zettlemoyer}, |
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year={2019}, |
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eprint={1910.13461}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
