bert-base-multilingual-uncased-sentiment
| Property | Value |
|---|---|
| License | MIT |
| Downloads | 1.49M+ |
| Languages Supported | English, Dutch, German, French, Italian, Spanish |
| Author | nlptown |
What is bert-base-multilingual-uncased-sentiment?
This is a sophisticated sentiment analysis model built on BERT's multilingual architecture, specifically fine-tuned to analyze product reviews across six different languages. The model stands out for its ability to predict sentiment on a 5-star scale, making it particularly valuable for e-commerce and customer feedback analysis.
Implementation Details
The model was trained on an extensive dataset comprising over 629,000 product reviews across six languages, with English (150k), French (140k), and German (137k) having the largest training sets. The model demonstrates impressive accuracy, achieving 57-67% exact match accuracy and 93-95% accuracy when allowing for one-star variation across all supported languages.
- Based on bert-base-multilingual-uncased architecture
- Predicts ratings on a 1-5 star scale
- Optimized for product review sentiment analysis
Core Capabilities
- Multi-language support for six major European languages
- High accuracy rates (up to 67% exact match)
- Near-perfect accuracy when allowing one-star variation (93-95%)
- Direct applicability to e-commerce platforms
- Suitable for further fine-tuning on related tasks
Frequently Asked Questions
Q: What makes this model unique?
This model's unique strength lies in its ability to perform consistent sentiment analysis across six different languages while providing granular 5-star ratings instead of simple positive/negative classifications. Its high accuracy rates and extensive training data make it particularly reliable for real-world applications.
Q: What are the recommended use cases?
The model is ideal for: E-commerce platforms requiring multi-language review analysis, Customer feedback processing systems, Market research tools analyzing product sentiment across different regions, and Automated review classification systems. It can also serve as a base model for further fine-tuning on specific domains or languages.
