indonesia-bert-sentiment-classification
| Property | Value |
|---|---|
| Base Model | IndoBERT Base Model (phase1 - uncased) |
| Task | Sentiment Analysis |
| Language | Indonesian |
| Model Hub | Hugging Face |
What is indonesia-bert-sentiment-classification?
The indonesia-bert-sentiment-classification is a specialized NLP model designed for sentiment analysis of Indonesian text. Built upon the IndoBERT Base Model, it has been fine-tuned using the Prosa sentiment dataset to accurately classify text into three sentiment categories: positive, neutral, and negative. This model represents a significant advancement in Indonesian language processing capabilities.
Implementation Details
The model leverages the Transformers library and can be easily implemented using the pipeline architecture. It uses AutoModelForSequenceClassification and AutoTokenizer for processing input text, making it straightforward to integrate into existing NLP workflows.
- Built on IndoBERT Base Model (phase1 - uncased)
- Implements 3-class sentiment classification
- Uses Hugging Face's Transformers library
- Provides confidence scores for predictions
Core Capabilities
- Accurate sentiment classification for Indonesian text
- Real-time text analysis with confidence scores
- Support for batch processing
- Easy integration with Python applications
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically trained for Indonesian language sentiment analysis, utilizing the robust IndoBERT architecture and Prosa dataset. It provides accurate three-way classification with confidence scores, making it particularly valuable for Indonesian text analysis tasks.
Q: What are the recommended use cases?
The model is ideal for analyzing Indonesian social media content, customer feedback, reviews, and any text-based sentiment analysis requirements in Indonesian language. It can be used in customer service applications, social media monitoring, and market research focusing on Indonesian language content.
