Biomednlp PubMedBERT Base Uncased Abstract Fulltext Pub Section

🚀 BiomedNLP - PubMedBERT-base-uncased-abstract-fulltext_pub_section

本项目是一个用于文档段落文本分类的模型，基于microsoft/BiomedNLP - PubMedBERT-base-uncased-abstract-fulltext进行微调。可对医学文档的不同段落进行分类，如背景、结论、方法、目标、结果等。

🚀 快速开始

安装依赖

根据需要安装transformers库：

pip install -U transformers

运行示例

运行以下代码，将示例文本替换为你的实际用例：

from transformers import pipeline

model_tag = "ml4pubmed/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section"
classifier = pipeline(
              'text-classification', 
              model=model_tag, 
            )
            
prompt = """
Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
"""

classifier(
    prompt,
) # classify the sentence

✨ 主要特性

• 适用数据集：pubmed、ml4pubmed/pubmed-classification-20k。
• 评估指标：f1。
• 支持任务：文本分类、文档段落分类、句子分类、文档分类等医学相关的文本分类任务。

📦 安装指南

若需要使用该模型，需安装transformers库，安装命令如下：

pip install -U transformers

💻 使用示例

基础用法

from transformers import pipeline

model_tag = "ml4pubmed/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section"
classifier = pipeline(
              'text-classification', 
              model=model_tag, 
            )
            
prompt = """
Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
"""

classifier(
    prompt,
) # classify the sentence

🔧 技术细节

训练指标

📄 许可证

本项目采用apache - 2.0许可证。