LatentOps [WIP]

Source code of paper: Composable Text Controls in Latent Space with ODEs

Code is coming soon...

Preparation

Recommended Environment

We recommend to create a new conda enviroment (named latentops) by:

conda create -n latentops python==3.9.1 pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch

Then activate latentops and install the required packages by running:

conda activate latentops
bash build_envs.sh

Prepare Datasets

Download and process the datasets by running the script:

bash download_datasets.sh

Pretrained Models

Download and process the pretrained model by running the script:

bash download_pretrained_models.sh

Prepare Classifiers

Download and process the external classifiers by running the script:

bash download_classifiers.sh

Conditional Generation

You can do conditional generation (default Yelp) by running:

cd code
bash conditional_generation.sh $1 $2

$1 represents operators (1 for sentiment, 4 for tense, 33 for formality). $2 represents desired labels:

sentiment: 0-negative, 1-positive
tense: 0-past, 1-present, 2-future
formality: 0-informal, 1-formal

For examples, you can run:

# for positive sentences
bash conditional_generation.sh 1 1
# for past sentences
bash conditional_generation.sh 4 0
# for positive & future sentences
bash conditional_generation.sh '1,4' '1,2'
# for positive & future & informal
bash conditional_generation.sh '1,4,33' '1,2,0'
# for positive & future & informal and negative & future & informal
bash conditional_generation.sh '1,4,33' '1,2,0;0,2,0'

The generated files can be found in ../ckpts/model/sample/ (default: ../ckpts/large_yelp/sample/sampling*.txt)

Outputs

To facilitate comparison, we provide the output files of text editing with single attribute (text style transfer) in ./outputs folder.

Cite

@article{liu2022composable,
    title={Composable Text Control Operations in Latent Space with Ordinary Differential Equations},
    author={Liu, Guangyi and Feng, Zeyu and Gao, Yuan and Yang, Zichao and Liang, Xiaodan and Bao, Junwei and He, Xiaodong and Cui, Shuguang and Li, Zhen and Hu, Zhiting},
    journal={arXiv preprint arXiv:2208.00638},
    year={2022}
}

RealDuxy / LatentOps