This repository provides the official implementation of Liger from the following paper:

Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision
Mayee F. Chen*, Daniel Y. Fu*, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, Christopher Ré

Paper: https://arxiv.org/abs/2203.13270

This repository depends on FlyingSquid. We recommend using conda to install FlyingSquid, and then you can install Liger:

git clone https://github.com/HazyResearch/flyingsquid.git
cd flyingsquid
conda env create -f environment.yml
conda activate flyingsquid
pip install -e .
cd ..
git clone https://github.com/HazyResearch/liger.git
cd liger
pip install -e .

1. Put the path to your pretrained embeddings in the respective dataset config file in configs. We provide the pretrained embeddings for the spam dataset to run our examples. If the directory of the L and Y matrices is different from the embeddings, put the path in the data_path field in yaml (refer to tennis.yaml).

2. We provide notebook example that you can directly run. We also provide a python script that you can run with the following command:

python run_liger.py -c configs/spam.yaml

To run for other datasets, change configs/spam.yaml to the dataset config instead.

3. You can run the python script and notebook for other datasets, but you need to put the pretrained embeddings path (and data path) in the config file. If you want to add more datasets, you can add you own dataset config.