license: mit
language:
- en
library_name: transformers
tags:
- text-classification
- yelp-reviews
- gpt-2
- bert
Model Description
This model predicts the star rating (1 - 5) of a Yelp review based on its text content. It was trained using GPT-2 and BERT, with BERT achieving the best performance at 75% validation accuracy. The model addresses class imbalance using weighted loss and optimizes hyperparameters to enhance generalization.
Training Details
Dataset: Yelp Reviews dataset (100,000 samples used)
Preprocessing:
- GPT-2 Tokenizer with Byte-Pair Encoding (BPE) for rare words
- Truncation (128 tokens) and padding for uniform input size
Models Trained:
GPT-2: Fine-tuned with a custom classification head, achieving 67% validation accuracy
BERT: Fine-tuned with bidirectional attention, achieving 75% validation accuracy
Loss Function: Weighted Cross-Entropy Loss to counteract class imbalance
Limitations
Performance may degrade on highly informal or extremely short reviews
Class imbalance still affects predictions for underrepresented ratings
Model was trained on English-language reviews only