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Modernbert Base Ft Financial News Sentiment Analysis
A ModernBERT-base fine-tuned model for financial news sentiment analysis, achieving an F1 score of 0.9765 on the evaluation set
Downloads 46
Release Time : 12/27/2024
Model Overview
This model is a BERT variant optimized for financial news sentiment analysis tasks, capable of accurately identifying emotional tendencies in financial news.
Model Features
High-Precision Sentiment Analysis
Achieves 97.65% F1 score on financial news datasets
Domain Adaptation
Specifically optimized for the financial news domain
Efficient Training
Completed training within 10 epochs using a linear learning rate scheduler
Model Capabilities
Financial text sentiment classification
News sentiment analysis
Text classification
Use Cases
Financial Analysis
Market Sentiment Monitoring
Analyze the sentiment tendencies of financial news towards specific stocks or markets
Provides real-time market sentiment indicators
Investment Decision Support
Evaluate the potential impact of news events on asset prices
Assists in quantitative investment strategies
Media Analysis
News Sentiment Tracking
Monitor the reporting tendencies of financial media towards specific companies or industries
Generates sentiment trend reports
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