Biogpt

🚀 BioGPT

事前学習済み言語モデルは、一般的な自然言語分野での大成功に触発され、生物医学分野でもますます注目を集めています。一般言語分野の事前学習済み言語モデルの2つの主要な分岐、すなわちBERT（およびそのバリアント）とGPT（およびそのバリアント）のうち、前者はBioBERTやPubMedBERTなど、生物医学分野で広く研究されてきました。これらは様々な識別的な下流の生物医学タスクで大きな成功を収めていますが、生成能力の欠如がその適用範囲を制約しています。本論文では、大規模な生物医学文献で事前学習されたドメイン固有の生成型Transformer言語モデルであるBioGPTを提案します。BioGPTを6つの生物医学自然言語処理タスクで評価し、ほとんどのタスクで従来のモデルを上回ることを示します。特に、BC5CDR、KD - DTI、DDIのエンドツーエンド関係抽出タスクでそれぞれ44.98%、38.42%、40.76%のF1スコアを達成し、PubMedQAで78.2%の正解率を達成し、新記録を樹立しました。テキスト生成に関するケーススタディは、生物医学用語に対して流暢な記述を生成する際のBioGPTの生物医学文献における優位性をさらに実証しています。

🚀 クイックスタート

このモデルは、テキスト生成のパイプラインで直接使用できます。生成にはランダム性が含まれるため、再現性のためにシードを設定します。

基本的な使用法

>>> 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)
[{'generated_text': 'COVID-19 is a disease that spreads worldwide and is currently found in a growing proportion of the population'},
 {'generated_text': 'COVID-19 is one of the largest viral epidemics in the world.'},
 {'generated_text': 'COVID-19 is a common condition affecting an estimated 1.1 million people in the United States alone.'},
 {'generated_text': 'COVID-19 is a pandemic, the incidence has been increased in a manner similar to that in other'},
 {'generated_text': 'COVID-19 is transmitted via droplets, air-borne, or airborne transmission.'}]

高度な使用法

与えられたテキストの特徴を取得する方法：

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)

ビームサーチデコーディング：

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)
'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK), and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and more than 800,000 deaths.'

📚 ドキュメント

引用

もしあなたの研究でBioGPTが役立った場合、以下の論文を引用してください。

@article{10.1093/bib/bbac409,
    author = {Luo, Renqian and Sun, Liai and Xia, Yingce and Qin, Tao and Zhang, Sheng and Poon, Hoifung and Liu, Tie-Yan},
    title = "{BioGPT: generative pre-trained transformer for biomedical text generation and mining}",
    journal = {Briefings in Bioinformatics},
    volume = {23},
    number = {6},
    year = {2022},
    month = {09},
    abstract = "{Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98\%, 38.42\% and 40.76\% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2\% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.}",
    issn = {1477-4054},
    doi = {10.1093/bib/bbac409},
    url = {https://doi.org/10.1093/bib/bbac409},
    note = {bbac409},
    eprint = {https://academic.oup.com/bib/article-pdf/23/6/bbac409/47144271/bbac409.pdf},
}

📄 ライセンス

このプロジェクトはMITライセンスの下でライセンスされています。