Official code for GORetriever
python=3.8
sentence-transformers == 2.2.2
pyserini == 0.10.1.0
pygaggle == 0.0.3.1
faiss-cpu == 1.7.4
numpy == 1.23.5
nltk == 3.8.1
Download cross_encoder from https://drive.google.com/file/d/11W51FnM62Z79qGPkuZHRzAv6Bx_L1Mah/view?usp=sharing into folder cross_model
python predict.py \
--task [branch] \
--pro [k] \
--gpu [n] \
--file [file]
branch: bp, mf, cc
pro: number of retrieved proteins, set k = 3 for MFO and BPO and k = 2 for CCO
gpu: cuda number, default as 0
file: filename for test set, eg: ./data/test.txt
eg:
python predict.py --task bp --pro 3 --gpu 0
If you want to start your new experiment, delete the files in folders test, file, pro_index, go_index. Otherwise the program will reason based on these pre-existing files.
Rewrite:
- *_pro2go.npy - proteinid -> GO list dictionary in the training data
- pmid2text.npy - Pubmed ID -> title and abstract dictionary for the PubMed article you will use in the training and testing data
- proid2name.npy - proteinid -> protein name dictionary for the PubMed article you will use in the training and testing data
- go_index, pro_index - index built by pyserini
Delete (will be rebuilt after running predict.py):
- *_dev_t5_texts.npy - extracted sentences
- *_retrieval_all.npy
- *_retrieval_pro_3.npy - retrieval result
- *_t5_scores.npy - score cache
├── cross_model - Download
└────── *_PubMedBERT_epoch{}
└────── *_PubMedBERT_epoch1
├── data - training and testing data
└────── golden - evaluate files
└────── *_train.txt - train set
└────── test.txt - test set
├── file
└────── *_pro2go.npy - proteinid -> GO list
└────── pmid2text.npy - Pubmed ID -> title and abstract
└────── proid2name.npy - proteinid -> protein name
├── test
└────── *_dev_t5_texts.npy - extracted sentences
└────── *_retrieval_all.npy
└────── *_retrieval_pro_3.npy - retrieval result
└────── *_t5_scores.npy - score cache
├── go_index
├── pro_index
├── result - final result
├── predict.py
└── data_preprocess.py
