Indonesian RoBERTa Base Sentiment Classifier
| Property | Value |
|---|---|
| Parameter Count | 124M |
| License | MIT |
| Architecture | RoBERTa Base |
| Paper | RoBERTa Paper |
| Accuracy | 94.36% |
What is indonesian-roberta-base-sentiment-classifier?
This is a specialized sentiment analysis model built on RoBERTa architecture, specifically designed for Indonesian language text processing. The model is fine-tuned on the indonlu's SmSA dataset, containing Indonesian comments and reviews, achieving impressive accuracy metrics of 94.36% and an F1-macro score of 92.42%.
Implementation Details
The model leverages the Hugging Face Transformers library and was trained using PyTorch as the backend framework. It underwent 5 epochs of training, demonstrating consistent improvement in performance metrics throughout the training process.
- Built on pre-trained Indonesian RoBERTa Base model
- Fine-tuned on SmSA dataset for sentiment analysis
- Compatible with multiple frameworks
- Implements state-of-the-art transformer architecture
Core Capabilities
- High-accuracy sentiment analysis for Indonesian text
- Robust performance with 93.2% accuracy on benchmark test set
- Easy integration through Hugging Face's pipeline API
- Efficient processing of Indonesian comments and reviews
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Indonesian language sentiment analysis, combining the power of RoBERTa architecture with specialized training on Indonesian text data, making it particularly effective for local language processing tasks.
Q: What are the recommended use cases?
The model is ideal for analyzing Indonesian text sentiment in various contexts, including social media monitoring, customer feedback analysis, and general text sentiment classification tasks in Indonesian language contexts.
