Update README.md

README.md

@@ -1,75 +1,77 @@
-## Introduction
-<p align="center">
-    <br>
-    <img src="assets/FAPM.png"/>
-    <br>
-<p>
-
-## Installation
-
-1. (Optional) Creating conda environment
-
-```bash
-conda create -n lavis python=3.8
-conda activate lavis
-```
- 
-2. for development, you may build from source
-
-```bash
-git clone https://github.com/xiangwenkai/FAPM.git
-cd FAPM
-pip install -e .
-
-pip install Biopython
-pip install fair-esm
-```
-
-### Datasets
-#### 1.raw dataset
-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  
-#### 2.ESM2 embeddings  
-Source code for ESM2 embeddings generation: *https://github.com/facebookresearch/esm*  
-The generation command:  
-```bash
-python esm_scripts/extract.py esm2_t33_3B_UR50D you_path/protein.fasta you_path_to_save_embedding_files --repr_layers 36 --truncation_seq_length 1024 --include per_tok
-```
-The default path to save embedding files in this respository is **data/emb_esm2_3b**
-
-## Pretraining language models  
-Source: *https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B*
-
-## Training
-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  
-
-## Trained models
-You can download our trained models from drive: *https://drive.google.com/drive/folders/1aA0eSYxNw3DvrU5GU1Cu-4q2kIxxAGSE?usp=drive_link*  
-
-## Testing
-config: lavis/projects/blip2/eval/caption_protein_eval.yaml  
-command: run_scripts/blip2/eval/eval_cap_protein.sh  
-
-## Inference example
-```
-python FAPM_inference.py \
---model_path model/checkpoint_mf2.pth \
---example_path data/emb_esm2_3b/P18281.pt \
---device cuda \
---prompt Acanthamoeba
-```  
-
-
-
-
-
-
-
-
-
-
+## Introduction
+<p align="center">
+    <br>
+    <img src="assets/FAPM.png"/>
+    <br>
+<p>
+Huggingface repo: *https://huggingface.co/wenkai/FAPM*  
+
+## Installation
+
+1. (Optional) Creating conda environment
+
+```bash
+conda create -n lavis python=3.8
+conda activate lavis
+```
+ 
+2. for development, you may build from source
+
+```bash
+git clone https://github.com/xiangwenkai/FAPM.git
+cd FAPM
+pip install -e .
+
+pip install Biopython
+pip install fair-esm
+```
+
+### Datasets
+#### 1.raw dataset
+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  
+#### 2.ESM2 embeddings  
+Source code for ESM2 embeddings generation: *https://github.com/facebookresearch/esm*  
+The generation command:  
+```bash
+python esm_scripts/extract.py esm2_t33_3B_UR50D you_path/protein.fasta you_path_to_save_embedding_files --repr_layers 36 --truncation_seq_length 1024 --include per_tok
+```
+The default path to save embedding files in this respository is **data/emb_esm2_3b**
+
+## Pretraining language models  
+Source: *https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B*
+
+## Training
+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  
+
+## Trained models  
+The models are avaliable at **https://huggingface.co/wenkai/FAPM/tree/main/model**  
+You can also download our trained models from google drive: *https://drive.google.com/drive/folders/1aA0eSYxNw3DvrU5GU1Cu-4q2kIxxAGSE?usp=drive_link*  
+
+## Testing
+config: lavis/projects/blip2/eval/caption_protein_eval.yaml  
+command: run_scripts/blip2/eval/eval_cap_protein.sh  
+
+## Inference example
+```
+python FAPM_inference.py \
+--model_path model/checkpoint_mf2.pth \
+--example_path data/emb_esm2_3b/P18281.pt \
+--device cuda \
+--prompt Acanthamoeba
+```  
+
+
+
+
+
+
+
+
+
+