Biogpt

🚀 BioGPT

BioGPT是一個特定領域的生成式Transformer語言模型，它在大規模生物醫學文獻上進行了預訓練。該模型在多個生物醫學自然語言處理任務中表現出色，能為生物醫學術語生成流暢的描述，拓寬了預訓練語言模型在生物醫學領域的應用範圍。

🚀 快速開始

你可以直接使用文本生成管道來使用這個模型。由於生成過程依賴於一定的隨機性，為了保證結果的可重複性，我們設置了一個隨機種子：

>>> 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.'}]

✨ 主要特性

受預訓練語言模型在通用自然語言領域的巨大成功啟發，其在生物醫學領域也受到了越來越多的關注。在通用語言領域的預訓練語言模型的兩個主要分支，即BERT（及其變體）和GPT（及其變體）中，前者在生物醫學領域得到了廣泛研究，如BioBERT和PubMedBERT。雖然它們在各種判別式下游生物醫學任務中取得了巨大成功，但缺乏生成能力限制了它們的應用範圍。

BioGPT是一個特定領域的生成式Transformer語言模型，在大規模生物醫學文獻上進行了預訓練。在六個生物醫學自然語言處理任務中對BioGPT進行了評估，結果表明該模型在大多數任務上優於以前的模型。特別是，在BC5CDR、KD - DTI和DDI端到端關係提取任務中，分別獲得了44.98%、38.42%和40.76%的F1分數，在PubMedQA上獲得了78.2%的準確率，創造了新的記錄。文本生成的案例研究進一步證明了BioGPT在生物醫學文獻方面的優勢，能夠為生物醫學術語生成流暢的描述。

💻 使用示例

基礎用法

以下是如何在PyTorch中使用該模型獲取給定文本特徵的示例：

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)

高級用法

以下是使用束搜索解碼的示例：

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.'

📚 詳細文檔

如果您在研究中發現BioGPT很有用，請引用以下論文：

@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},
}

📄 許可證

本項目採用MIT許可證。