Learn to fine-tune ModernBERT and create augmentations of textual content samples
On this article, I talk about how one can implement and fine-tune the brand new ModernBERT textual content mannequin. Moreover, I exploit the mannequin on a traditional textual content classification activity and present you how one can make the most of artificial information to enhance the mannequin’s efficiency.
· 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 have to discover a dataset to carry out textual content classification on. To maintain it easy, I discovered an open-source dataset on HuggingFace the place you are expecting the sentiment of a given textual content. The sentiment will be predicted within the courses:
- Unfavorable (id 0)
- Impartial (id 1)
- Constructive (id 2)
