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Finance Sentiment Zh Fast
A model based on distiluse, used to analyze the sentiment tendency of Chinese financial news and output three labels: positive, negative, and neutral.
Downloads 47
Release Time : 7/12/2023
Model Overview
This model is specifically designed for sentiment analysis of Chinese financial news and can identify positive, negative, and neutral emotions in the text. It is trained on a translated version of the Financial PhraseBank dataset and is suitable for text analysis in the financial field.
Model Features
Multilingual support
Developed based on the multilingual model distiluse, supporting the analysis of Chinese financial text
Efficient inference
It can process 264.6 samples per second on an RTX 3090 graphics card
Optimized for the financial field
Specifically trained and optimized for financial news and financial text
Model Capabilities
Sentiment analysis of Chinese text
Financial text classification
Emotional tendency recognition
Use Cases
Financial analysis
Sentiment analysis of financial reports
Analyze the sentiment tendency in company financial reports
Accurately identify positive or negative statements in financial reports
Market news monitoring
Monitor the sentiment changes in financial news in real - time
Help investors understand the market sentiment trend
Investment decision - making support
Analysis of stock reviews
Analyze the sentiment tendency of stock forums and reviews
Provide sentiment indicators for investment decision - making
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