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Finbert Finetuned Github

Developed by Driisa
This model is a financial sentiment classification model fine-tuned on FinBERT, capable of categorizing financial texts into negative, neutral, and positive classes.
Downloads 4,622
Release Time : 2/14/2025

Model Overview

A financial sentiment analysis model fine-tuned on FinBERT, suitable for sentiment classification tasks in financial texts.

Model Features

Financial Domain Optimization
Fine-tuned for financial texts to improve the accuracy of sentiment analysis in the financial domain.
Multi-Dataset Training
Trained on a combination of financial phrase banks and GitHub-generated datasets to enhance model generalization.
Efficient Classification
Capable of quickly and accurately classifying financial texts as negative, neutral, or positive.

Model Capabilities

Financial Text Sentiment Analysis
Three-Class Sentiment Prediction
Financial Domain NLP Processing

Use Cases

Financial Analysis
Earnings Report Sentiment Analysis
Analyze the sentiment tendencies in company earnings reports to aid investment decisions.
Accuracy 95.21% (Financial Phrase Bank training set)
Stock Comment Sentiment Analysis
Evaluate market sentiment in stock comments.
Quantitative Trading
Market Sentiment Indicators
Provide market sentiment data for quantitative trading strategies.
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