https://github.com/da03/residual-ebm

Code for Residual Energy-Based Models for Text Generation in PyTorch.
https://github.com/da03/residual-ebm

machine-learning nlp

Last synced: over 1 year ago
JSON representation

Code for Residual Energy-Based Models for Text Generation in PyTorch.

• Host: GitHub
• URL: https://github.com/da03/residual-ebm
• Owner: da03
• License: mit
• Created: 2021-04-26T15:41:06.000Z (about 5 years ago)
• Default Branch: main
• Last Pushed: 2021-04-27T13:50:47.000Z (about 5 years ago)
• Last Synced: 2025-03-17T19:11:40.102Z (over 1 year ago)
• Topics: machine-learning, nlp
• Language: Jupyter Notebook
• Homepage:
• Size: 7.14 MB
• Stars: 23
• Watchers: 2
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Residual Energy-Based Models for Text Generation

This repo implements the Residual Energy-Based Model as described in [Residual Energy-Based Models for Text Generation](https://openreview.net/pdf?id=B1l4SgHKDH). Note that both data and model (generator+discriminator) are different from the original paper: the goal is just to show how the pipeline works, using much less computational resources.

The training and evaluation scripts can be found in [REBM.ipynb](REBM.ipynb). We suggest opening it using Google Colab with a GPU instance.

## Citation

```

@inproceedings{deng2019residual,

  title={Residual Energy-Based Models for Text Generation},

  author={Deng, Yuntian and Bakhtin, Anton and Ott, Myle and Szlam, Arthur and Ranzato, Marc'Aurelio},

  booktitle={International Conference on Learning Representations},

  year={2019}

}

@article{bakhtin2021residual,

  title={Residual Energy-Based Models for Text},

  author={Bakhtin, Anton and Deng, Yuntian and Gross, Sam and Ott, Myle and Ranzato, Marc'Aurelio and Szlam, Arthur},

  journal={Journal of Machine Learning Research},

  volume={22},

  number={40},

  pages={1--41},

  year={2021}

}

```