How to Utilize ModernBERT and Synthetic Data for Robust Text Classification

Learn how to fine-tune ModernBERT and create augmentations of text samples

In this article, I discuss how you can implement and fine-tune the new ModernBERT text model. Furthermore, I use the model on a classic text classification task and show you how you can utilize synthetic data to improve the model's performance.

In this article, I discuss how you can finetune ModernBERT for your classification task. Furthermore, I show you how you can leverage synthetic data to improve the performance of your text classification model. Image by ChatGPT.

Table of Contents

· Table of Contents
· Finding a dataset
· Implementing ModernBERT
· Detecting errors
· Synthesize data to improve model performance
· New results after augmentation
· My thoughts and future work
· Conclusion

Finding a dataset

First, we need to find a dataset to perform text classification on. To keep it simple, I found an open-source dataset on HuggingFace where you predict the sentiment of a given text. The sentiment can be predicted in the classes:

• Negative (id 0)
• Neutral (id 1)
• Positive (id 2)