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bank-complaints-classification-based-in-bert-and-lora's Introduction

bank-complaints-classification-based-in-BERT

Main work:

• 对数据进行清洗,包括去除HTML、URL、emoji,以及处理重复文本和无效标注数据。运用欠采样、数据增强等方法改善数据类别不平衡的影响。

• 分别采用全量参数微调和LoRA微调技术对BERT模型在数据集上进行微调,并通过精确度,召回率,F1 score评估模型的分类效果。利用LoRA的方法,在仅使用全量参数微调75%的时间内,达到了全量微调97%的效果。

• 采用balanced cross entropy和focal loss对损失函数进行优化,将难分类样本的召回率提高4%。

• 搭建了一个自动化的投诉文本归类pipeline,为银行客户投诉处理提供了高效解决方案。

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