[Repo In Progress] Official implementation for AdaVAE, check the paper on arxiv https://arxiv.org/pdf/2205.05862.pdf.

make sure that you have installed:

transformers==3.1.0
torch
tensorboard
tqdm
apex [from https://github.com/NVIDIA/apex]
nltk

• Language Modeling: yelp, yahoo, snli, ptb from download_datasets.md in Optimus. Put them in the ./data/optimus_dataset folder.
• Low Resource Text Classification: yelp polarity dataset from Shen et. al., and put it to ./data folder directly. SST-2 and WNLI from GLUE, use download_glu_data.py to download them, and put both datasets in the ./glue_data folder.
• Controllable Text Generation: yelp polarity dataset from Shen et. al., and put it to ./data folder directly.
• Text Generation via Latent Manipulation: Any dataset mentioned above with pre-trained model weights in Language Modeling task.

Make sure that all data folders contain train.txt, test.txt, valid.txt files.

adavae
|____low_nlu
| |____run_cls.sh
| |____latent_classifier.py
| |____utils_glue.py
| |____...
|____controlgen
| |____oracle_cls.py
| |____run.sh
| |____run_vae_ctrl_gen.py
| |____...
|____README.md
|____dialogue
| |____run_spacefusion_gen.py
| |____...
|____data
|____src
| |____test.py
| |____adapters
| | |____vae.py
| | |____...
| |____adaVAE.py
| |____run_manipulation.sh
| |____run_lm.sh
| |____test.py
| |____...

Run language modeling task by bash src/run_lm.sh, change arguments accordingly.

Run classification task by bash low_nlu/run_cls.sh, change arguments accordingly.

Before conducting controllable text generation , you need to:

1. Pre-train an oracle classifier for controllability evaluation by python controlgen/oracle_cls.py.
2. Pre-train the AdaVAE model with Language Modeling task and load the weights.

Finally run controllable text generation task by bash controlgen/run.sh, change arguments accordingly.

Run manipulation or analogy generation by bash src/run_manipulation.sh, the default dataset for this task if yelp polarity dataset.

TBD.

TBD.

TBD. [Check src/test.py for more information]

Please email me or open an issue if you have any question.

if you find our work useful, please cite the paper :>

@article{tu2022adavae,
  title={AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling},
  author={Tu, Haoqin and Yang, Zhongliang and Yang, Jinshuai and Zhang, Siyu and Huang, Yongfeng},
  journal={arXiv preprint arXiv:2205.05862},
  year={2022}
}

We thank open sourced codes related to VAEs and parameter-efficient methods, which inspired our work !!