Machine Learning

Find out how to Make the most of ModernBERT and Artificial Knowledge for Sturdy Textual content Classification | by Eivind Kjosbakken | Jan, 2025

January 23, 2025
roosho

Learn to fine-tune ModernBERT and create augmentations of textual content samples

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On this article, I focus on how one can implement and fine-tune the brand new ModernBERT textual content mannequin. Moreover, I exploit the mannequin on a basic textual content classification process and present you how one can make the most of artificial information to enhance the mannequin’s efficiency.

On this article, I focus on how one can finetune ModernBERT to your classification process. Moreover, I present you how one can leverage artificial information to enhance the efficiency of your textual content classification mannequin. Picture by ChatGPT.

· Desk of Contents
· Discovering a dataset
· Implementing ModernBERT
· Detecting errors
· Synthesize information to enhance mannequin efficiency
· New outcomes after augmentation
· My ideas 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 could be predicted within the courses:

  • Destructive (id 0)
  • Impartial (id 1)
  • Optimistic (id 2)