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Test Ner
T
Test Ner
Developed by kSaluja
基于bert-base-uncased微调的命名实体识别模型，在评估集上F1值达95.91%
Downloads 15
Release Time : 5/6/2022
Model Overview
该模型是用于命名实体识别任务的BERT微调模型，能够识别文本中的特定实体类别
Model Features
高精度识别
在评估集上达到96.09%的精确率和95.74%的召回率
BERT基础架构
基于成熟的bert-base-uncased模型微调，具有强大的语义理解能力
线性学习率调度
采用线性学习率调度策略优化训练过程
Model Capabilities
文本实体识别
命名实体分类
序列标注
Use Cases
信息提取
新闻实体提取
从新闻文本中识别人物、地点、组织等实体
可准确标记文本中的各类命名实体
生物医学文本处理
识别医学文献中的专业术语和实体
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