Why does my fine-tuned transformer model overfit despite a large dataset?

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Overfitting on a 100k dataset suggests that your model is likely memorizing noise rather than learning generalizable semantic features. In transformer architectures, this often happens if the "Weight Decay" is too low or if you are training for too many epochs. BERT models typically converge very quickly, often in just 2-4 epochs. Another culprit could be "Data Leakage"—check if similar sentences exist in both your training and validation sets. I recommend using a smaller learning rate (like $2 \times 10^{-5}$) and implementing "Early Stopping" based on validation loss to prevent the model from over-optimizing on the training distribution.

   Answered 2024-11-18 by Mary Higgins

Have you looked into the label distribution of your dataset? If it is heavily imbalanced, the model might be getting "lazy" by overfitting on the majority class.

   Answered 2024-11-22 by James Peterson

• James, I checked the distribution and it's actually fairly balanced (about 60/40). However, I noticed that many of my samples are very short, single-word reviews. I suspect the model is picking up on specific keywords rather than context. I’m going to try "Data Augmentation" by replacing synonyms to see if that forces the model to learn more robust embeddings. Do you think that would help more than just increasing the dropout further?

     Commented 2024-11-25 by David Miller

Try freezing the lower layers of the BERT model and only training the classification head. This prevents the pre-trained weights from being "distorted" by your specific niche data.

   Answered 2024-11-30 by Linda Foster

• Great point, Linda. Freezing layers is an excellent way to maintain the foundational linguistic knowledge while only adapting the model's final decision-making layer to the new task.

     Commented 2024-12-02 by Mary Higgins