conda create -n esmtherm python=3.10 --file=environment.yml
conda activate esmtherm
pip install -e .
# To download the model checkpoints
gdown https://drive.google.com/drive/u/1/folders/1z3_IbeD8oORmLndqCCiqYRCxmbKNbWXb -O output_dir/checkpoint-best --folder
Download the supplementary materials from Tsuboyama et al. (2023),
extract K50_dG_Dataset1_Dataset2.csv and place it under data
directory.
# analyze and filter dataset
python prebuild_dataset.py
# create training dataset
python build_dataset.py \
--dataset_dir datasets/dataset \
--csv datasets/analysis/filtered_data.csv \
--split_csv datasets/wildtype_split.csv
# training
python train.py \
--dataset_dir datasets/dataset \
--output_dir output_dir \
--model_name facebook/esm2_t12_35M_UR50D
# evaluation
python evaluate.py \
--model_name_or_path output_dir/checkpoint-best \
--input_csv _your_input_csv_ \
--output_csv __your_output_csv_
More instructions can be found with --help
flag.