DistilRoBERTa Financial Sentiment Analysis
| Property | Value |
|---|---|
| Parameter Count | 82.1M |
| License | Apache 2.0 |
| Accuracy | 98.23% |
| Training Dataset | Financial Phrasebank |
What is distilroberta-finetuned-financial-news-sentiment-analysis?
This model is a specialized version of DistilRoBERTa fine-tuned specifically for analyzing sentiment in financial news texts. It represents a significant advancement in financial text analysis, combining the efficiency of model distillation with high accuracy in sentiment classification. The model processes financial news sentences and classifies their sentiment with remarkable precision, achieving a 98.23% accuracy rate.
Implementation Details
Built on the DistilRoBERTa architecture, this model features 6 layers, 768-dimensional embeddings, and 12 attention heads. It was trained using the Adam optimizer with carefully tuned hyperparameters (learning rate: 2e-05) over 5 epochs. The model is trained on the Financial Phrasebank dataset, containing 4,840 annotated financial news sentences.
- Optimized architecture with 82.1M parameters (35% reduction from RoBERTa-base)
- 2x faster inference speed compared to RoBERTa-base
- Case-sensitive processing for enhanced accuracy
- Trained with batch size of 8 and linear learning rate scheduling
Core Capabilities
- High-accuracy financial sentiment classification
- Efficient processing of financial news and reports
- Robust performance on complex financial language
- Optimized for production deployment
Frequently Asked Questions
Q: What makes this model unique?
This model combines the efficiency of DistilRoBERTa with specialized financial sentiment analysis capabilities, achieving near-perfect accuracy while maintaining computational efficiency. Its distilled architecture makes it notably faster than full-size alternatives while maintaining high performance.
Q: What are the recommended use cases?
The model is ideal for analyzing financial news articles, earnings reports, market commentary, and other financial texts where sentiment analysis is crucial. It's particularly suitable for applications requiring real-time processing of financial information or large-scale analysis of financial documents.
