Refactor(Fine-Tuning): Improve fine-tuning script modularity, options

[justinthelaw]

The fine_tuning.ipynb scripts need to be refactored for more modularity and fine-grained control:

• All blocks in the Jupyter Notebook that have named methods and classes need to be split-out into separate Python files

In addition to this, extra modules and options should be added. These modules and options should have the capability to be hot-swapped and/or turned on/off depending on the preferences of the user. After some research, the following are possible ways to improve the fine_tuning.ipynb through the introduction of more parameters and methods:

• Fine-Tuning:

  • Data Augmentation: Augment your training data by adding noise, paraphrasing, or using techniques like back-translation. This can help the model generalize better to unseen examples.
  • Curriculum Learning: Start training with easier examples and gradually increase the difficulty. This can make the optimization landscape smoother and help with convergence.

• Regularization:

  • Dropout: If it's not already being used, introducing dropout layers can help prevent overfitting.
  • Weight Decay (L2 Regularization): Add L2 regularization to the model's weights. This can often be done directly through the optimizer (e.g., the weight_decay parameter in Adam).
  • LayerNorm: Ensure that normalization layers like LayerNorm are used, especially if they are part of the original T5 model.

• Optimization:

  • Alternative Optimizers: Consider trying optimizers like AdamW, RAdam, or LAMB.
  • Learning Rate Schedule: Adjust the learning rate schedule. Experiment with other schedules like cosine annealing or cyclic learning rates.
  • Gradient Clipping: The provided code uses a gradient clipping value of 1.0. You can experiment with different values to see if they yield better results.

• Loss Functions:

  • Label Smoothing: Instead of using a hard one-hot encoded target, you can soften the targets which can prevent the model from becoming overly confident in its predictions.
  • Focal Loss: This can be used to handle extreme class imbalance or when you want the model to focus more on harder examples.

• Custom Penalty:

  • Refine Format Penalty: The custom format penalty can be refined based on what works best empirically. Consider using different regular expressions or adjusting the penalty strength.
  • Additional Penalties: Introduce penalties based on other criteria specific to your use-case.

• Batching:

  • Dynamic Padding: Instead of padding all sequences to a fixed length, pad sequences in each batch to the maximum length in that batch. This can save computation and sometimes improve results.
  • Gradient Accumulation: If memory constraints are not allowing you to increase batch size, consider accumulating gradients over several forward/backward passes before performing an optimization step.

• External Knowledge:

  • Knowledge Distillation: If you have a larger, more accurate model, you can use its predictions to guide the training of the smaller T5 model. The smaller model learns to imitate the larger model's behavior.

• Evaluation & Feedback:

  • Early Stopping: Monitor the model's performance on a validation set and stop training once the performance plateaus or starts degrading to avoid overfitting.
  • Model Checkpointing: Regularly save model checkpoints. This allows you to revert to the best version if later versions start to overfit.