bert-movie-review-sentiment-analysis
| Property | Value |
|---|---|
| Model Type | BERT-based Sentiment Analysis |
| Accuracy | 92.5% |
| F1 Score | 91.8% |
| Framework | Hugging Face Transformers |
| Quantization | Float16 |
| Model URL | Hugging Face |
What is bert-movie-review-sentiment-analysis?
This is a specialized BERT model fine-tuned for sentiment analysis of movie reviews. Built by AventIQ-AI, it leverages the powerful BERT architecture to accurately classify movie reviews as either positive or negative. The model has been optimized through careful fine-tuning on the IMDb dataset and implements float16 quantization for improved efficiency.
Implementation Details
The model has been trained with specific configurations to ensure optimal performance: 10 epochs of training, a batch size of 32, and utilizing the AdamW optimizer with a 3e-5 learning rate. The implementation includes automatic handling of text padding and truncation up to 512 tokens, making it practical for real-world applications.
- Fine-tuned on 25,000 labeled IMDb movie reviews
- Implements float16 quantization for reduced latency and memory usage
- Includes confidence-based correction for ambiguous reviews
- Provides probability scores for classification decisions
Core Capabilities
- Binary sentiment classification (Positive/Negative)
- Handles variable-length movie reviews
- Provides confidence scores for predictions
- Optimized for production deployment with reduced model size
Frequently Asked Questions
Q: What makes this model unique?
This model combines high accuracy (92.5%) with practical optimizations like float16 quantization, making it both accurate and efficient for production use. It includes special handling for low-confidence predictions and explicit negation cases.
Q: What are the recommended use cases?
The model is ideal for automated sentiment analysis of movie reviews, content moderation systems, customer feedback analysis in the entertainment industry, and integration into recommendation systems. However, users should be aware of its limitations with sarcasm and nuanced expressions.
