MultiLabel Text Classification using BERT Transformers

最新推荐文章于 2025-05-27 23:41:37 发布

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最新推荐文章于 2025-05-27 23:41:37 发布

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分类专栏： Python实战文章标签：自然语言处理人工智能语言模型编程实践开发语言架构设计

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本文介绍了如何利用预训练的BERT模型进行多标签文本分类。首先将文本转换为数值特征向量，然后在数据集上训练模型，添加新的线性层进行概率预测，最后使用二元交叉熵损失和Adam优化器进行优化。

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作者：禅与计算机程序设计艺术

1.简介

Multi-label text classification is a challenging task where the goal is to classify texts into one or more predefined categories/labels from a given list of labels. Traditionally, multi-label text classification has been achieved by exploiting both classical machine learning algorithms such as Naive Bayes and Support Vector Machines (SVM), and deep neural networks such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). However, with the rapid advancement of natural language processing techniques, it becomes possible for humans to label texts accurately, leading to the emergence of several techniques such as crowdsourcing platforms, weakly supervised learning methods, etc., which can provide valuable insigh

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