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 parameters is coming, currently available on PubMedQA task with SOTA performance 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:

bash git clone https://github.com/pytorch/fairseq cd fairseq git checkout v0.12.0 pip install . python setup.py build_ext --inplace cd .. * Moses bash git clone https://github.com/moses-smt/mosesdecoder.git export MOSES=${PWD}/mosesdecoder * fastBPE bash git clone https://github.com/glample/fastBPE.git export FASTBPE=${PWD}/fastBPE cd fastBPE g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast * sacremoses bash pip install sacremoses * sklearn bash 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 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: bash 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: python 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: python 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.

Downstream tasks

See corresponding folder in examples:

Relation Extraction on BC5CDR

Relation Extraction on KD-DTI

Relation Extraction on DDI

Document Classification on HoC

Question Answering on PubMedQA

Text Generation

Hugging Face 🤗 Usage

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:

python 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:

python 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:

```python import torch from transformers import BioGptTokenizer, BioGptForCausalLM, set_seed

tokenizer = BioGptTokenizer.frompretrained("microsoft/biogpt") model = BioGptForCausalLM.frompretrained("microsoft/biogpt")

sentence = "COVID-19 is" inputs = tokenizer(sentence, return_tensors="pt")

set_seed(42)

with torch.nograd(): beamoutput = model.generate(**inputs, minlength=100, maxlength=1024, numbeams=5, earlystopping=True ) tokenizer.decode(beamoutput[0], skipspecial_tokens=True) ```

For more information, please see the documentation on the Hugging Face website.

Demos

Check out these demos on Hugging Face Spaces: * Text Generation with BioGPT-Large * Question Answering with BioGPT-Large-PubMedQA

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.

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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.

Trademarks

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