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SSAF FinBert

Developed by likith123
SSAF-FinBert is a sentiment analysis model for stock market news, fine-tuned from FinBert, capable of classifying financial texts into positive, negative, and neutral sentiments.
Downloads 69
Release Time : 3/4/2024

Model Overview

This model is specifically designed for financial text sentiment analysis, fine-tuned on a Kaggle dataset with an accuracy rate of 81%-82%.

Model Features

Financial Domain Optimization
Fine-tuned specifically for stock market news articles, enhancing understanding of financial terminology.
Three-Class Sentiment Analysis
Accurately classifies text sentiment into positive, negative, and neutral categories.
High Accuracy
Achieves 81%-82% accuracy across different training platforms.

Model Capabilities

Financial Text Sentiment Classification
Stock News Sentiment Analysis
Three-Class Probability Prediction

Use Cases

Financial Analysis
Stock News Sentiment Monitoring
Analyzes the sentiment tendencies of stock-related news to assist in investment decisions.
Accuracy 81%-82%
Market Sentiment Analysis
Tracks overall market sentiment changes to predict market trends.
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