lgstd / Optimus

Optimus: the first large-scale pre-trained VAE language model

Optimus: the first pre-trained Big VAE language model

This repository contains source code necessary to reproduce the results presented in the paper Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space.

The network architecture of Optimus: encoder for representation learning and decoder for generation	Sentences are organized and manipulated in a pre-trained compact and smooth latent space

For more on this project, see the Microsoft Research Blog post.

[Code Cleaning in Progress, April 7, 2020]

Contents

There are four steps to use this codebase to reproduce the results in the paper.

1. Dependencies
2. Prepare datasets
3. Model training
   1. Pre-training on setences in Wikipedia
   2. Languange Modeling
   3. Guided Language Generation
   4. Low-resource Language Understanding
4. Collect and plot results

Dependencies

Pull docker from Docker Hub at: chunyl/pytorch-transformers:v2

Edit the project path to the absolute path on your computer by changing the "SCRIPTPATH" in "code/scripts/scripts_docker/run_docker.sh"

CD into the directory "code", and run docker

sh scripts/scripts_docker/run_docker.sh

Prepare Datasets

Please the data preparation at links:

Model Training

1. Pre-training on setences in Wikipedia

2. Languange Modeling

3. Guided Language Generation

4. Low-resource Language Understanding

Collect and Plot Results

Once the networks are trained and the results are saved, we extracted key results using Python script. The results can be plotted using the included IPython notebook plots/main_plots.ipynb. Start the IPython Notebook server:

$ cd plots
$ ipython notebook

Select the main_plots.ipynb notebook and execute the included code. Note that without modification, we have copyed our extracted results into the notebook, and script will output figures in the paper. If you've run your own training and wish to plot results, you'll have to organize your results in the same format instead.

Questions?

Please drop me (Chunyuan) a line if you have any questions.

@inproceedings{li2020_Optimus,
  title={Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space},
  author={Li, Chunyuan and Gao, Xiang and Li, Yuan and Li, Xiujun and Peng, Baolin and Zhang, Yizhe and Gao, Jianfeng},
  booktitle={arXiv},
  year={2020}
}