bert-finetuned-japanese-sentiment

What is bert-finetuned-japanese-sentiment?

This is a specialized BERT model fine-tuned for Japanese sentiment analysis, based on the cl-tohoku/bert-base-japanese-v2 architecture. The model is designed to classify Japanese text into positive, neutral, or negative sentiments, making it particularly useful for analyzing customer feedback and reviews. Trained on 20,000 Amazon review sentences, it achieved an impressive accuracy of 81.32% with strong precision (71.24%) and recall (75.60%) metrics.

Implementation Details

The model was trained for 6 epochs using the Adam optimizer with a learning rate of 2e-05. Training was conducted with a batch size of 16, achieving a final training loss of 0.0876 and validation loss of 1.0289. The implementation uses Transformers 4.27.4 and Pytorch 2.0.0+cu118.

• F1 Score: 0.728455
• Precision: 0.712440
• Recall: 0.756031
• Linear learning rate scheduler

Core Capabilities

• Three-class sentiment classification (positive, neutral, negative)
• Specialized for Japanese text analysis
• Optimized for product review sentiment analysis
• Production-ready with inference endpoints support