/BioGPT

Primary LanguagePythonMIT LicenseMIT

BioGPT

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.

News!

  • 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.

Requirements and Installation

  • 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.

Getting Started

Pre-trained models

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

Model Description URL
BioGPT Pre-trained BioGPT model checkpoint link
BioGPT-QA-PubMedQA-BioGPT Fine-tuned BioGPT for question answering task on PubMedQA link
BioGPT-QA-PubMEDQA-BioGPT-Large Fine-tuned BioGPT-Large for question answering task on PubMedQA link
BioGPT-RE-BC5CDR Fine-tuned BioGPT for relation extraction task on BC5CDR link
BioGPT-RE-DDI Fine-tuned BioGPT for relation extraction task on DDI link
BioGPT-RE-DTI Fine-tuned BioGPT for relation extraction task on KD-DTI link
BioGPT-DC-HoC Fine-tuned BioGPT for document classification task on HoC link

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

Example Usage

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.

Downstream tasks

See corresponding folder in examples:

License

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

Contributing

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.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.