BERT fine tuning low epochs?

Hello - when running fine tuning (binary text classification) with Bert - is it normal that a low number of epochs are required before the loss starts to increase again? After 2-3 runs it seems to start overfitting. Have tried all of different batch sizes, classifier drop out. Task was running with 1000 sample training set.

Any thoufjts here ?

Yes, it’s not uncommon to observe overfitting after just a few epochs, especially with models like BERT and on small datasets.

BERT has millions of parameters and is pre-trained on a vast amount of data. When you fine-tune it on a relatively small dataset (like your 1000-sample training set), it can quickly adjust to the nuances (and even noise) of the dataset, leading to overfitting.

Some strategies to mitigate overfitting:

  1. Early Stopping: Monitor the performance on a validation set, and stop training as soon as the performance starts to degrade.

  2. Regularization: While you mentioned using dropout, there are other regularization methods like L1/L2 regularization you could experiment with.

  3. Data Augmentation: You can augment your text data by creating new samples using techniques like back translation, synonym replacement, and sentence shuffling.

  4. Reduce Model Size: Using a smaller version of BERT, like DistilBERT or TinyBERT, might be more suitable for a small dataset. These models have fewer parameters, making them less prone to overfitting.

  5. Increase Data: If possible, gather more data for your task or consider using external datasets to bolster your training set.

  6. Gradient Clipping: This involves setting a threshold value for the gradients to prevent them from getting too large, which can help in some overfitting scenarios.

  7. Learning Rate Schedule: Use schedules like the learning rate warm-up followed by decay. This can sometimes help in stabilizing training and reducing overfitting.

  8. Weight Decay: It’s another form of regularization that adds a penalty to the loss to discourage large weights.

  9. Use Ensembles: While this won’t prevent individual models from overfitting, ensembling multiple models can sometimes yield a more robust final model.

The best approach often depends on the specifics of the dataset and task. It may require some trial and error to figure out the best combination of techniques to use.