In this video, we will be showing you how to train a text classifier using Hugging Face's Transformers library. We'll be covering everything from loading and preprocessing the data, to fine-tuning the model and making predictions. Whether you're a beginner or an experienced data scientist, this video will give you a comprehensive understanding of how to use Hugging Face's Transformers library to train a text classifier. We'll start by loading and preprocessing the data using the built-in functions provided by the library. Next, we'll be using one of the pre-trained models such as BERT or GPT-2, which have achieved state-of-the-art results on a wide range of NLP tasks, as a starting point for our text classifier. We'll then fine-tune the model on our specific task and dataset, and evaluate its performance. Throughout the video, we'll be providing tips and best practices for training text classifiers with Hugging Face's Transformers library. We'll also show you how to use the library's built-in functions to make predictions on new data. By the end of the video, you'll have a solid understanding of how to use Hugging Face's Transformers library to train text classifiers, and you'll be able to apply this knowledge to your own projects. So, if you're ready to dive into the world of text classification with Hugging Face, hit play and let's get started! Code (colab Notebook): https://colab.research.google.com/drive/1nvIRwKoel8BsLWRdEXlQKKdkeatgTtjr?usp=sharing Dataset: https://drive.google.com/file/d/1vEP9WsNWJx7ipTDOIXB3RiVE7dn0sCzj/view?usp=sharing Video sections: 00:00 Intro 00:17 Introduction to Hugging Face 00:36 Introduction to Transformer models 01:01 The five Steps for training a text classifier 01:45 Code 04:58 Outro #machinelearning #nlp #ai #huggingface #datascience