介绍 (Intro)
Welcome to this tutorial! This tutorial will teach you how to build a bidirectional LSTM for text classification in just a few minutes. If you haven’t already checked out my previous article on BERT Text Classification, this tutorial contains similar code with that one but contains some modifications to support LSTM. This article also gives explanations on how I preprocessed the dataset used in both articles, which is the REAL and FAKE News Dataset from Kaggle.
欢迎使用本教程！ 本教程将教您如何在短短几分钟内构建用于文本分类的双向LSTM 。 如果您还没有签出我以前关于BERT文本分类的文章，那么本教程将包含与该文章相似的代码，但会进行一些修改以支持LSTM。 本文还提供了有关如何预处理这两篇文章中使用的数据集的说明，这是来自Kaggle 的REAL和FAKE News数据集 。
First of all, what is an LSTM and why do we use it? LSTM stands for Long Short-Term Memory Network, which belongs to a larger category of neural networks called Recurrent Neural Network (RNN)
