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Results
R
Results
Developed by shank250
该模型是在bert-base-uncased基础上进行微调的版本，专注于提升特定任务的召回率表现
Downloads 23
Release Time : 4/11/2025
Model Overview
基于BERT架构的文本处理模型，经过特定任务微调后展现出优秀的召回率性能
Model Features
高召回率表现
在评估集上达到0.9020的召回率，适合对召回率要求高的应用场景
基于BERT的强大基础
建立在bert-base-uncased基础上，继承了BERT模型的强大文本理解能力
高效微调
仅需3轮训练即可达到良好效果，训练损失从0.6766降至0.27
Model Capabilities
文本分类
自然语言理解
上下文特征提取
Use Cases
文本分析
敏感内容检测
识别文本中的敏感内容或特定类别信息
高召回率确保尽可能少的漏检
意图识别
分析用户输入的意图分类
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