BERTBERT
Multi-tower deep learning model for restaurant review classification
Overview
Developed for TikTok TechJam 2025, BERTBERT is a multi-tower deep learning architecture designed to classify restaurant feedback from reviews. The model processes over 100K reviews while handling severe class imbalance in the training data.
Technical Highlights
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Multi-Tower Architecture: Designed a cross-attention architecture that processes text reviews alongside 20 metadata inputs (rating, time, location, user history, etc.) for more accurate classification.
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Custom Labelling Solution: Built a labelling pipeline that outperformed GPT-4o on domain-specific classification tasks, achieving higher accuracy on edge cases and ambiguous reviews.
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Class Imbalance Handling: Implemented specialized sampling and loss weighting strategies to handle the severe class imbalance inherent in review datasets where most reviews are positive.
Scale
Processed and trained on 100K+ restaurant reviews with real-world noise and inconsistencies.