This repository contains the implementation of BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining, by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu.

• BioGPT-Large model with 1.5B paramters is comming, currently available on PubMedQA task with SOTA perfromance of 81% accuracy. See Question Answering on PubMedQA for evaluation.

• PyTorch version == 1.12.0
• Python version == 3.10
• fairseq version == 0.12.0:

git clone https://github.com/pytorch/fairseq
cd fairseq
git checkout v0.12.0
pip install .
python setup.py build_ext --inplace
cd ..

• Moses

git clone https://github.com/moses-smt/mosesdecoder.git
export MOSES=${PWD}/mosesdecoder

• fastBPE

pip install fastBPE
git clone https://github.com/glample/fastBPE.git
export FASTBPE=${PWD}/fastBPE
cd fastBPE
g   -std=c  11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast

• sacremoses

pip install sacremoses

• sklearn

pip install scikit-learn

Remember to set the environment variables MOSES and FASTBPE to the path of Moses and fastBPE respetively, as they will be required later.

We provide our pre-trained BioGPT model checkpoint along with fine-tuned checkpoints for downstream tasks

Download them and extract them to the checkpoints folder of this project.

For example:

mkdir checkpoints
cd checkpoints
wget https://msramllasc.blob.core.windows.net/modelrelease/BioGPT/checkpoints/Pre-trained-BioGPT.tgz
tar -zxvf Pre-trained-BioGPT.tgz

Use pre-trained BioGPT model in your code:

import torch
from fairseq.models.transformer_lm import TransformerLanguageModel
m = TransformerLanguageModel.from_pretrained(
        "checkpoints/Pre-trained-BioGPT", 
        "checkpoint.pt", 
        "data",
        tokenizer='moses', 
        bpe='fastbpe', 
        bpe_codes="data/bpecodes",
        min_len=100,
        max_len_b=1024)
m.cuda()
src_tokens = m.encode("COVID-19 is")
generate = m.generate([src_tokens], beam=5)[0]
output = m.decode(generate[0]["tokens"])
print(output)

Use fine-tuned BioGPT model on KD-DTI for drug-target-interaction in your code:

import torch
from fairseq.models.transformer_lm import TransformerLanguageModel
m = TransformerLanguageModel.from_pretrained(
        "checkpoints/RE-DTI-BioGPT", 
        "checkpoint_avg.pt", 
        "data/KD-DTI/relis-bin",
        tokenizer='moses', 
        bpe='fastbpe', 
        bpe_codes="data/bpecodes",
        max_len_b=1024,
        beam=1)
m.cuda()
src_text="" # input text, e.g., a PubMed abstract
src_tokens = m.encode(src_text)
generate = m.generate([src_tokens], beam=args.beam)[0]
output = m.decode(generate[0]["tokens"])
print(output)

For more downstream tasks, please see below.

See corresponding folder in examples:

BioGPT is MIT-licensed. The license applies to the pre-trained models as well.

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

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