CentralBankRoBERTa-sentiment-classifier
| Property | Value |
|---|---|
| License | MIT |
| Paper | View Paper |
| Performance Metrics | Accuracy: 88%, F1 Score: 0.88 |
| Downloads | 2.49M+ |
What is CentralBankRoBERTa-sentiment-classifier?
CentralBankRoBERTa-sentiment-classifier is a specialized language model designed for analyzing sentiment in central bank communications. Based on the RoBERTa architecture, it's specifically fine-tuned to detect positive or negative sentiments related to key economic agents including households, firms, the financial sector, and government.
Implementation Details
The model leverages the RoBERTa architecture and has been fine-tuned on a comprehensive dataset of central bank communications. It achieves impressive performance metrics with 88% accuracy across all evaluation metrics including precision and recall.
- Built on RoBERTa architecture for robust language understanding
- Specialized in financial and economic context analysis
- Implements binary sentiment classification (positive/negative)
- Easy integration with Hugging Face Transformers library
Core Capabilities
- Accurate sentiment analysis of central bank communications
- Specific focus on economic agent-related sentiments
- High-performance metrics (88% accuracy)
- Support for English language text analysis
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically designed for central bank communications analysis, combining economic agent classification with sentiment analysis capabilities. Its specialized training makes it particularly effective for financial and economic text analysis.
Q: What are the recommended use cases?
The model is ideal for analyzing central bank statements, monetary policy documents, and financial communications where understanding sentiment implications for different economic agents is crucial. It's particularly useful for researchers, economists, and financial analysts working with central bank communications.
