| # Plug and Play Language Models: a Simple Approach to Controlled Text Generation | |
| Authors: [Sumanth Dathathri](https://dathath.github.io/), [Andrea Madotto](https://andreamad8.github.io/), Janice Lan, Jane Hung, Eric Frank, [Piero Molino](https://w4nderlu.st/), [Jason Yosinski](http://yosinski.com/), and [Rosanne Liu](http://www.rosanneliu.com/) | |
| This folder contains the original code used to run the Plug and Play Language Model (PPLM). | |
| Paper link: https://arxiv.org/abs/1912.02164 | |
| Blog link: https://eng.uber.com/pplm | |
| Please check out the repo under uber-research for more information: https://github.com/uber-research/PPLM | |
| # Note | |
| ⚠️ This project should be run with pytorch-lightning==1.0.4 which has a potential security vulnerability | |
| ## Setup | |
| ```bash | |
| git clone https://github.com/huggingface/transformers && cd transformers | |
| pip install . | |
| pip install nltk torchtext # additional requirements. | |
| cd examples/research_projects/pplm | |
| ``` | |
| ## PPLM-BoW | |
| ### Example command for bag-of-words control | |
| ```bash | |
| python run_pplm.py -B military --cond_text "The potato" --length 50 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.03 --window_length 5 --kl_scale 0.01 --gm_scale 0.99 --colorama --sample | |
| ``` | |
| ### Tuning hyperparameters for bag-of-words control | |
| 1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model. | |
| 2. If the language being generated is repetitive (For e.g. "science science experiment experiment"), there are several options to consider: </br> | |
| a) Reduce the `--stepsize` </br> | |
| b) Increase `--kl_scale` (the KL-loss coefficient) or decrease `--gm_scale` (the gm-scaling term) </br> | |
| c) Add `--grad-length xx` where xx is an (integer <= length, e.g. `--grad-length 30`).</br> | |
| ## PPLM-Discrim | |
| ### Example command for discriminator based sentiment control | |
| ```bash | |
| python run_pplm.py -D sentiment --class_label 2 --cond_text "My dog died" --length 50 --gamma 1.0 --num_iterations 10 --num_samples 10 --stepsize 0.04 --kl_scale 0.01 --gm_scale 0.95 --sample | |
| ``` | |
| ### Tuning hyperparameters for discriminator control | |
| 1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model. | |
| 2. Use `--class_label 3` for negative, and `--class_label 2` for positive | |