Instructions to use tdopierre/ProtAugment-ParaphraseGenerator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tdopierre/ProtAugment-ParaphraseGenerator with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tdopierre/ProtAugment-ParaphraseGenerator") model = AutoModelForSeq2SeqLM.from_pretrained("tdopierre/ProtAugment-ParaphraseGenerator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: "en" | |
| tags: | |
| - Paraphase Generation | |
| - Data Augmentation | |
| datasets: | |
| - Quora | |
| - MSR | |
| - Google-PAWS | |
| [](https://arxiv.org/abs/2105.12995) | |
| This model is used to generate paraphrases. It has been trained on a mix of 3 different paraphrase detection datasets: MSR, Quora, Google-PAWS. | |
| We use this model in our ACL'21 Paper ["PROTAUGMENT: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning"](https://arxiv.org/abs/2105.12995) | |
| Jointly used with generation constraints, this model allows to generate diverse paraphrases. We use those paraphrases as a data augmentation technique to further boosts a classification model's generalization capability. Feel free to play with the [code](https://github.com/tdopierre/ProtAugment)! | |
| If you use this model, please consider citing our paper. | |
| ``` | |
| @article{Dopierre2021ProtAugmentUD, | |
| title={ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning}, | |
| author={Thomas Dopierre and C. Gravier and Wilfried Logerais}, | |
| journal={ArXiv}, | |
| year={2021}, | |
| volume={abs/2105.12995} | |
| } | |
| ``` | |