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 insights for multi-label text classification tasks. In this article, we will discuss how to perform multi-label text classification using the pre-trained transformer models called Bidirectional Encoder Repr