Surgicberta

🚀 SurgicBERTa

SurgicBERTa是一個基於RoBERTa-base（Liu等人，2019）架構的語言模型。我們通過持續預訓練，使RoBERTa-base適應不同的外科手術教科書和學術論文。這些數據約有700萬個單詞和30萬條外科手術句子。在訓練中，我們使用了書籍和論文的全文，而非僅僅是摘要。自適應預訓練過程和評估任務的具體細節可在以下引用的論文中找到。

🚀 快速開始

暫未提供相關快速開始內容。

✨ 主要特性

• 基於RoBERTa-base架構，通過持續預訓練適應外科手術領域的教科書和學術論文。
• 訓練數據使用了書籍和論文的全文，而非僅摘要。

📄 許可證

本項目採用CC BY-NC-ND 4.0許可協議。

💻 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.

📚 詳細文檔

自適應預訓練過程和評估任務的具體細節可在以下引用的論文中找到。

📚 引用說明

引用SurgicBERTa模型

如果使用此模型，請引用以下論文：

@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},
}

引用語義角色標註相關

如果將此模型用於語義角色標註，請同時引用以下論文：

@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}
}