- You need to install all the dependencies from the
requirements.txt
. - You need to login to the huggingface-cli with a write token and that you have set up the correct paths for the train/finetune.py file either change the default values or give them as arguments.
- Use the bash script:
bash training/train.sh
from the root directory to train and save intermediate steps on huggingface. - To load the tensorboard use the following commands:
tensorboard --logdir <LOG_DIR> --port 6006 --bind_all
ssh -N -f -L localhost:16006:localhost:6006 <USER>@<SERVER>
The models for this project can be found at huggingface here (update path to new huggingface repo). For now there are a checkpoint for each 100 epochs.
- You can generate predictions with a trained model using the
prediction/predict.py
script.
- You can evaluate trained models with the two scripts
evaluation/evaluate_caption2smiles.py
andevaluation/evaluate_smiles2caption
based on predictions generated on the previous step.
The results from the evaluation step will be saved as a file under the evalauation
folder.