xiangwenkai / fapm Goto Github PK

1. (Optional) Creating conda environment

conda create -n lavis python=3.8
conda activate lavis

2. for development, you may build from source

git clone https://github.com/salesforce/LAVIS.git
cd LAVIS
pip install -e .

Raw data are avaliable at https://ftp.uniprot.org/pub/databases/uniprot/previous_releases/release-2023_04/knowledgebase/, this file is very large and need to be processed to get its name, sequence, GO label, function description and prompt.
The domain level protein dataset we used are avaliable at https://ftp.ebi.ac.uk/pub/databases/interpro/releases/95.0/protein2ipr.dat.gz
In this respository, We provide the experimental train/val/test sets of Swiss-Prot, which are avaliable at data/swissprot_exp

ESM2 embeddings generation code: https://github.com/facebookresearch/esm
The generation command:

git clone https://github.com/facebookresearch/esm.git
python scripts/extract.py esm2_t33_650M_UR50D you_path/protein.fasta you_path_to_save_embedding_files --repr_layers 33 --truncation_seq_length 1024 --include per_tok

The default path to save embedding files in this respository is data/emb_esm2_3b

Source: https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B

data config: lavis/configs/datasets/protein/GO_defaults_cap.yaml
stage1 config: lavis/projects/blip2/train/protein_pretrain_stage1.yaml
stage1 training command: run_scripts/blip2/train/protein_pretrain_domain_stage1.sh
stage2 config: lavis/projects/blip2/train/protein_pretrain_stage2.yaml
stage2 training/finetuning command: run_scripts/blip2/train/protein_pretrain_domain_stage2.sh

You can download our trained models from drive: https://drive.google.com/drive/folders/1aA0eSYxNw3DvrU5GU1Cu-4q2kIxxAGSE?usp=drive_link

config: lavis/projects/blip2/eval/caption_protein_eval.yaml
command: run_scripts/blip2/eval/eval_cap_protein.sh

We provide an example in FAPM_inference.py. You can change the example protein to you custom case