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") - Notebooks
- Google Colab
- Kaggle
metadata
language: en
tags:
- Paraphase Generation
- Data Augmentation
datasets:
- Quora
- MSR
- Google-PAWS
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"
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!
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}
}