Four Strategies to Enhance Transformers in Sequential Event (Part Two)
In part two of our series, we explore whether foundation models like BERT can be further optimized to predict sequences of event-based data. Using user interaction logs from a quiz system as a test case, we demonstrate four advanced techniques to boost the performance of vanilla BERT.
- Distilled BERT: To prevent overfitting with limited data, we simplify the model using a teacher-student architecture, maintaining the same knowledge while reducing model capacity.
- Curriculum Learning: We apply curriculum learning by sorting the training data based on sequence length and complexity. Initially, the model is trained on shorter, simpler sequences, gradually introducing more complex sequences to build a strong foundation before tackling difficult examples.
- LoRA Fine-Tuning: LoRA is applied to the embedding layers for the new action-time tokens and the key/value projections in the Transformer model. This technique adapts the model to the specific nuances of the quiz interaction data efficiently, with minimal computational resources.
- Data Augmentation: We generate pseudo samples using contrastive loss to create different views of the data. Techniques include synonym replacement, random insertion, random swap, random deletion, back translation, time augmentation, subsequence extraction, and action shuffling.
Our training loop involves iterating over the dataset, computing loss, performing backpropagation, and updating model parameters, with validation steps to monitor performance. We also use WandB for experiment tracking and logging.
In this video, we break down each of these techniques step-by-step, explaining the thought process and implementation details. This is part two of our series, focusing on transforming BERT into a more robust model for sequential prediction tasks.
Ready to see how BERT can be enhanced for sequential event-based data? Watch the full video on our channel and explore the code on GitHub. Don’t forget to subscribe and hit the bell icon to stay updated with our latest videos!
GitHub link:
