Surgicberta

🚀 SurgicBERTa

SurgicBERTa是一个基于RoBERTa-base（Liu等人，2019）架构的语言模型。我们通过持续预训练，使RoBERTa-base适应不同的外科手术教科书和学术论文。这些数据约有700万个单词和30万条外科手术句子。在训练中，我们使用了书籍和论文的全文，而非仅仅是摘要。自适应预训练过程和评估任务的具体细节可在以下引用的论文中找到。

🚀 快速开始

暂未提供相关快速开始内容。

✨ 主要特性

• 基于RoBERTa-base架构，通过持续预训练适应外科手术领域的教科书和学术论文。
• 训练数据使用了书籍和论文的全文，而非仅摘要。

📄 许可证

本项目采用CC BY-NC-ND 4.0许可协议。

💻 使用示例

示例1

hemithyroidectomy is the removal of half of the <mask> gland.

示例2

Li-Fraumeni <mask> is a hereditary tumor with autosomal dominant inheritance.

示例3

The fascia in front of the pancreas was cut to the spleen direction to <mask> the splenic artery and vein.

📚 详细文档

自适应预训练过程和评估任务的具体细节可在以下引用的论文中找到。

📚 引用说明

引用SurgicBERTa模型

如果使用此模型，请引用以下论文：

@article{bombieri_et_al_SurgicBERTa_2023,
title = {Surgicberta: a pre-trained language model for procedural surgical language},
journal = {International Journal of Data Science and Analytics},
year = {2023},
doi = { https://doi.org/10.1007/s41060-023-00433-5 },
url = { https://link.springer.com/article/10.1007/s41060-023-00433-5 },
author = {Marco Bombieri and Marco Rospocher and Simone Paolo Ponzetto and Paolo Fiorini},
}

引用语义角色标注相关

如果将此模型用于语义角色标注，请同时引用以下论文：

@article{bombieri_et_al_surgical_srl_2023,
title = {Machine understanding surgical actions from intervention procedure textbooks},
journal = {Computers in Biology and Medicine},
volume = {152},
pages = {106415},
year = {2023},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2022.106415},
url = {https://www.sciencedirect.com/science/article/pii/S0010482522011234},
author = {Marco Bombieri and Marco Rospocher and Simone Paolo Ponzetto and Paolo Fiorini},
keywords = {Semantic role labeling, Surgical data science, Procedural knowledge, Information extraction, Natural language processing}
}