Text classification is one of the most common tasks in NLP. It is applied in a wide variety of applications, including sentiment analysis, spam filtering, news categorization, etc. Here, we show you how you can detect fake news (classifying an article as REAL or FAKE) using the state-of-the-art models, a tutorial that can be extended to really any text classification task.

The Transformer is the basic building block of most current state-of-the-art architectures of NLP. Its primary advantage is its multi-head attention mechanisms which allow for an increase in performance and significantly more parallelization than previous competing models such as recurrent neural networks. In this tutorial, we will use pre-trained BERT, one of the most popular transformer models, and fine-tune it on fake news detection.

The main source code of this article is available in this Google Colab Notebook.

The preprocessing code is also available in this Google Colab Notebook.

#python #text-classification #pytorch #deep-learning #nlp