How to fine-tune BERT for specific use cases?

The world of machine learning is evolving rapidly, with new open-source models being released almost daily. Among these, BERT holds a special place, although it’s fairly old it has consistently proven its utility for a wide range of tasks, including classification.

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In this tutorial, I’ll guide you through the steps to fine-tune the BERT model for detecting whether a website is a phishing site or not. By the end of this guide, you’ll have a fully trained phishing classifier using BERT.

Important Links

Here are the links to a few resources you’ll need for this project:

• Dataset: I used this dataset for fine-tuning the model Dataset.
• Code: The code for this project can be found in my GitHub repository.
• Model: The model is available for download on HuggingFace.

Well, let’s get it over with!

0. Setting up the environment

Before everything, go ahead and create a virtual environment. I used Anaconda to create a virtual environment. Use this command to create one:

conda create -n YourEnvName python=3.10