Biomedical Ner All

🚀 生物医学命名实体识别模型

这是一个英文命名实体识别模型，可从给定文本语料（如病例报告）中识别生物医学实体（共107种实体），为生物医学领域的信息提取提供了有力支持。

🚀 快速开始

本模型可以通过从Hugging Face加载推理API，或者使用transformers库提供的pipeline对象这两种方式使用。

✨ 主要特性

• 实体识别能力：能够识别107种生物医学实体，适用于病例报告等文本语料。
• 模型基础：基于distilbert-base-uncased构建。
• 训练数据集：使用Maccrobat数据集进行训练，数据集链接：https://figshare.com/articles/dataset/MACCROBAT2018/9764942 。
• 碳排放：训练过程中的碳排放为0.0279399890043426千克。
• 训练时间：训练耗时30.16527分钟。
• 使用的GPU：1 x GeForce RTX 3060 Laptop GPU。

💻 使用示例

基础用法

from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")

pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""")

📚 详细文档

查看本模型及相应Python库的讲解教程视频：https://youtu.be/xpiDPdBpS18

📄 许可证

本模型采用Apache-2.0许可证。

👨‍🔬 作者信息

本模型是Deepak John Reji和Shaina Raza开展的“生物医学领域的人工智能”研究课题的一部分。如果您使用了本项目的代码、模型或数据集，请在以下仓库点星支持：

https://github.com/dreji18/Bio-Epidemiology-NER

💗 支持作者

如果您想支持作者，可以点击下面的按钮请作者喝咖啡：

测试样例

以下是一些测试用的文本样例：

• 示例1：

CASE: A 28-year-old previously healthy man presented with a 6-week history of palpitations.
The symptoms occurred during rest, 2–3 times per week, lasted up to 30 minutes at a time and were associated with dyspnea.
Except for a grade 2/6 holosystolic tricuspid regurgitation murmur (best heard at the left sternal border with inspiratory accentuation), physical examination yielded unremarkable findings.

• 示例2：

A 63-year-old woman with no known cardiac history presented with a sudden onset of dyspnea requiring intubation and ventilatory support out of hospital.
She denied preceding symptoms of chest discomfort, palpitations, syncope or infection.
The patient was afebrile and normotensive, with a sinus tachycardia of 140 beats/min.

• 示例3：

A 48 year-old female presented with vaginal bleeding and abnormal Pap smears.
Upon diagnosis of invasive non-keratinizing SCC of the cervix, she underwent a radical hysterectomy with salpingo-oophorectomy which demonstrated positive spread to the pelvic lymph nodes and the parametrium.
Pathological examination revealed that the tumour also extensively involved the lower uterine segment.