We assume you are running soft-prompts on Linux with GPU available and anaconda3 installed. To set up the environment, please run
conda create -n soft-prompts python=3.7
conda activate soft-prompts
conda install -y pytorch==1.7.0 cudatoolkit=11.0 -c pytorch
pip install transformers==4.0.0 pyyaml tqdm
The prompts
folder contains the prompts we used for T-REx, Google-RE, and ConceptNet.
The db
folder contains the relations we used for experiments.
We further proprocessed T-REx to split it into train, dev, and test subsets.
To replicate our results on T-REx extended datasets with BERT-large-cased LM, run the following commands:
git clone git@github.com:hiaoxui/soft-prompts
cd soft-prompts
python3 -m soft_prompts.run.experiment config.yaml
You can read our paper on arXiv.
You can cite our paper by
@inproceedings{qin-eisner-2021-learning,
title = "Learning How to Ask: Querying {LM}s with Mixtures of Soft Prompts",
author = "Qin, Guanghui and
Eisner, Jason",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.naacl-main.410",
pages = "5203--5212",
}