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 parameters is coming, currently available on PubMedQA task with SOTA performance 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=${PWD}/mosesdecoder- fastBPE
git clone https://github.com/glample/fastBPE.git
export FASTBPE=${PWD}/fastBPE
cd fastBPE
g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast- sacremoses
pip install sacremoses- sklearn
pip install scikit-learnRemember 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 checkpoints along with fine-tuned checkpoints for downstream tasks, available both through URL download as well as through the Hugging Face 🤗 Hub.
| Model | Description | URL | 🤗 Hub |
|---|---|---|---|
| BioGPT | Pre-trained BioGPT model checkpoint | link | link |
| BioGPT-Large | Pre-trained BioGPT-Large model checkpoint | link | 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 | 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.tgzUse 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 src.transformer_lm_prompt import TransformerLanguageModelPrompt
m = TransformerLanguageModelPrompt.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 has also been integrated into the Hugging Face transformers library, and model checkpoints are available on the Hugging Face Hub.
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
from transformers import pipeline, set_seed
from transformers import BioGptTokenizer, BioGptForCausalLM
model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
set_seed(42)
generator("COVID-19 is", max_length=20, num_return_sequences=5, do_sample=True)Here is how to use this model to get the features of a given text in PyTorch:
from transformers import BioGptTokenizer, BioGptForCausalLM
tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)Beam-search decoding:
import torch
from transformers import BioGptTokenizer, BioGptForCausalLM, set_seed
tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
sentence = "COVID-19 is"
inputs = tokenizer(sentence, return_tensors="pt")
set_seed(42)
with torch.no_grad():
beam_output = model.generate(**inputs,
min_length=100,
max_length=1024,
num_beams=5,
early_stopping=True
)
tokenizer.decode(beam_output[0], skip_special_tokens=True)For more information, please see the documentation on the Hugging Face website.
Check out these demos on Hugging Face Spaces:
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 opencode@microsoft.com with any additional questions or comments.
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