{"id":6436,"date":"2023-06-19T19:13:11","date_gmt":"2023-06-20T03:13:11","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=6436"},"modified":"2025-04-24T17:15:20","modified_gmt":"2025-04-24T17:15:20","slug":"how-to-build-a-text-classification-model-using-huggingface-transformers-and-comet","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/how-to-build-a-text-classification-model-using-huggingface-transformers-and-comet\/","title":{"rendered":"How to Build a Text Classification Model Using HuggingFace Transformers and Comet"},"content":{"rendered":"\n
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Text classification is an interesting part of machine learning and natural language processing that is used in business and everyday life to determine the sentiment of a text. For example, a company can use machine learning to determine whether customer reviews are positive or not. Machine learning models may now be developed in a variety of methods to classify text. You can create one from scratch or use a pre-trained model.<\/p>\n

This article will show you how to build your text classification model using transformers (which includes a state-of-the-art pre-trained model) and how to utilize Comet to keep track of your model\u2019s experiments. Without further ado, let\u2019s get started!<\/p>\n

What are Transformers?<\/h2>\n

Hugging face transformers <\/a>deliver state-of-the-art (cutting-edge) pre-trained models that let you perform tasks on many aspects of data such as text, audio, or other input. They use a machine learning approach known as transfer learning, in which a model has already been trained, so you don\u2019t have to worry about developing a model from scratch. This can save resources such as processing power, data sourcing, and so on. All you need to do is fine-tune the model so that it works effectively for your application.<\/p>\n

For example, we may utilize transformers to build a text classification model, which saves time and money because we don\u2019t have to train the model from scratch or obtain a large amount of data.<\/p>\n

Transformers are supported by three of the most prominent deep learning libraries \u2014 Jax, PyTorch, and TensorFlow \u2014 with seamless integration.<\/p>\n

What is Comet?<\/h2>\n

Comet<\/a> is a machine-learning platform that allows you to track the artifacts of your machine-learning experiments such as model metrics (e.g., accuracy score, confusion matrix), hyperparameters, model metadata, and so on.<\/p>\n

We will use Comet to keep track of the metrics, hyperparameters, and so on of the transformer model, we will build for text classification.<\/p>\n

Now enough talking, let\u2019s get to business!<\/p>\n

To get the most out of this tutorial click here<\/a> to download the Colab notebook so you can easily follow along.<\/p><\/blockquote>\n

Dataset<\/h2>\n

The dataset we will be utilizing is an IMDb dataset that contains movie reviews with labels indicating whether the review is positive or negative. The dataset is a public dataset from HuggingFace and can be accessed by installing the dataset library. You can install the dataset library using pip<\/code>.<\/p>\n

Libraries, tools, and environment<\/h2>\n

These are the libraries we will be using.<\/p>\n