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Distilbert Base Uncased Finetuned Ner
基于DistilBERT的轻量级命名实体识别模型，在conll2003数据集上微调
Downloads 15
Release Time : 3/2/2022
Model Overview
该模型是DistilBERT的轻量级版本，专门针对命名实体识别(NER)任务进行了微调。它在保持较高性能的同时，比原始BERT模型更小更快。
Model Features
轻量高效
基于DistilBERT架构，比标准BERT模型小40%，速度快60%，同时保留97%的性能
高精度NER
在conll2003数据集上达到93.14%的F1值，准确率达98.39%
快速微调
只需3轮训练即可达到高性能，训练损失从0.243降至0.0612
Model Capabilities
命名实体识别
文本标记分类
实体边界检测
实体类型分类
Use Cases
信息提取
新闻文章实体提取
从新闻文本中识别人名、地名、组织名等实体
可准确识别93%以上的实体
文档自动化处理
自动提取合同或法律文件中的关键实体信息
知识图谱构建
知识图谱实体链接
为知识图谱构建提供实体识别基础
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