python毕业设计-基于pytorch + bert的多标签文本分类(源码+文档).zip

上传者: 59708493 | 上传时间: 2025-05-14 21:39:20 | 文件大小: 665KB | 文件类型: ZIP
项目概述 项目目标:实现一个多标签文本分类模型,使用PyTorch框架和预训练的BERT模型。 技术要点:使用BERT模型进行文本特征提取,然后结合全连接层进行多标签分类。 数据集:准备一个适合的多标签文本分类数据集,可以考虑使用开源的数据集或者自己构建数据集。 项目步骤 数据预处理:加载数据集,进行数据清洗、分词和标记化。 模型构建:使用PyTorch加载预训练的BERT模型,添加全连接层进行多标签分类任务。 模型训练:定义损失函数和优化器,对模型进行训练。 模型评估:评估模型性能,可以使用准确率、召回率、F1值等指标。 模型部署:将训练好的模型部署到应用中,接收用户输入的文本并进行多标签分类。 源码+文档 源码:将代码结构化,包含数据处理、模型构建、训练、评估和部署等部分。 文档:编写项目报告,包含项目背景、目的、方法、实现、结果分析等内容,以及使用说明和参考文献。 其他建议 学习资料:深入学习PyTorch和BERT模型的相关知识,可以参考官方文档、教程和论文。 调参优化:尝试不同的超参数设置、模型结构和优化策略,优化模型性能。 团队协作:如果可能,可以与同学或导师合作,共同

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