README.md

Replication study of paper "SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer"

This is a repository from the replication study of the work SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer, published in the proceedings of the ACL 2022 conference. The original implementation can be found here: google-research/prompt-tuning.

This replication study is a part of Replication Challenge organized by DisAI

Installation

Conda environment

conda init
conda create --name spot python 3.10.13

conda activate spot
pip install -r requirements-in.txt

Data preparation

CxC dataset

git clone https://github.com/google-research-datasets/Crisscrossed-Captions.git
cd Crisscrossed-Captions/data
mkdir sis sits sts
mv sis_test_raw.csv sis_val_raw.csv sis
mv sits_test_raw.csv sits_val_raw.csv sits
mv sts_test_raw.csv sts_val_raw.csv sts

curl https://cs.stanford.edu/people/karpathy/deepimagesent/coco.zip --output coco.zip
unzip coco.zip

Go to Prepare CxC.ipynb and run all blocks to get final data.

Other datasets

cd data
python dataset_downloader.py

How to run

First of all, it is necessary to create huggingface.conf and wandb.conf config files based on the example files with your own keys. These files are stores in config/ directory.

Firstly, activate conda environment:

conda activate spot

Run script.py with the desired settings, e.g:

cd src

python script.py --dataset squad --use_cpeft --wandb_project t5-squad-finetune --config_path ./configs/config-base.yaml

References

Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou’, and Daniel Cer. 2022. SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5039–5059, Dublin, Ireland. Association for Computational Linguistics.