This is the supporting repository for the paper: RSTGen: Imbuing Fine-Grained Interpretable Control into Long-Form Text Generators
- Code is currently being refactored.
- To download the dataset, please refer to the Dataset section below.
- For understanding the features of the RSTGen framework as they were implemented, kindly refer to the files in
src/trainers
. - Code updates for proper functionality are planned over the next month.
-
Clone this repository:
git clone https://github.com/Rilwan-Adewoyin/RSTGen.git
-
Create and activate the conda environment:
cd ./RSTGen conda env create --file conda_environment.yml conda activate rstgen
I provided the post-processed dataset we created below. This downloads RST-Annotated texts across multiple sub-reddits:
-
To download the necessary data files, run the
download_data.py
script:python .data/download_data.py
This script will download the preprocessed files from the shared Google Drive link. The files, which are compressed with .tar.gz compression, will be decompressed and saved to the local directory
./data/data_files
.
- (This section is currently empty.)