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.
· 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
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)