prediksi-emosi-indobert
| Property | Value |
|---|---|
| Author | azizp128 |
| Downloads | 43,039 |
| Framework | PyTorch |
| Language | Indonesian |
What is prediksi-emosi-indobert?
prediksi-emosi-indobert is a specialized emotion classification model designed for Indonesian text analysis. Built on the IndoBERT architecture, this model can identify six distinct emotions: anger, sadness, happiness, love, fear, and disgust in Indonesian sentences or paragraphs. The model has been specifically trained on Indonesian tweets to ensure cultural and linguistic accuracy.
Implementation Details
The model leverages the IndoBERT Base Model P1 as its foundation and implements a text classification pipeline. It's deployed as a web application accessible through Heroku, allowing users to input text and receive emotion predictions in percentage format for each emotional category.
- Built on IndoBERT architecture optimized for Indonesian language
- Implements transformer-based text classification
- Provides percentage-based emotion probability distribution
- Deployed as a user-friendly web application
Core Capabilities
- Multi-class emotion classification across 6 categories
- Real-time text analysis and prediction
- Support for both single sentences and paragraphs
- Specialized handling of Indonesian language nuances
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on Indonesian language emotion detection, utilizing the powerful IndoBERT architecture while being specifically trained on Indonesian social media content for better cultural context understanding.
Q: What are the recommended use cases?
The model is ideal for sentiment analysis in Indonesian social media monitoring, customer feedback analysis, and general emotion detection in Indonesian text content. It's particularly useful for businesses and researchers working with Indonesian language data.
