IndoBERT-Sentiment-Analysis
| Property | Value |
|---|---|
| Base Model | indobenchmark/indobert-base-p1 |
| Task | Sentiment Analysis |
| Accuracy | 94.52% |
| F1 Score | 94.51% |
| HuggingFace | Model Repository |
What is IndoBERT-Sentiment-Analysis?
IndoBERT-Sentiment-Analysis is a specialized NLP model fine-tuned for sentiment analysis tasks in the Indonesian language. Built upon the indobert-base-p1 architecture, this model has been optimized through careful training to achieve exceptional performance in understanding and classifying Indonesian text sentiments.
Implementation Details
The model was trained using a carefully crafted training procedure with the following specifications: Learning rate of 2e-05, Adam optimizer with betas=(0.9,0.999), linear learning rate scheduler, and batch sizes of 6 for both training and evaluation. The training process spanned 5 epochs, showing consistent improvement in performance.
- Training batch size: 6
- Evaluation batch size: 6
- Optimizer: Adam
- Learning rate: 2e-05
- Number of epochs: 5
Core Capabilities
- High accuracy (94.52%) in sentiment classification
- Robust F1 score of 94.51%
- Specialized for Indonesian language processing
- Efficient training convergence with minimal loss (final validation loss: 0.4227)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional performance in Indonesian sentiment analysis, achieving over 94% accuracy while being specifically optimized for Indonesian language nuances. The careful fine-tuning process and stable training metrics make it particularly reliable for production environments.
Q: What are the recommended use cases?
The model is ideal for sentiment analysis tasks involving Indonesian text, including social media monitoring, customer feedback analysis, and automated content classification. Its high accuracy makes it suitable for both research and commercial applications requiring Indonesian language sentiment understanding.
