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DISC FinLLM

Developed by Go4miii
DISC-FinLLM is a large financial domain model developed based on Baichuan-13B-Chat, providing financial consulting, text analysis, calculation, and knowledge retrieval Q&A services.
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Release Time : 10/23/2023

Model Overview

DISC-FinLLM is a financial large model developed by the Data Intelligence and Social Computing Laboratory (Fudan-DISC) at Fudan University. It includes four major modules: financial consulting, financial text analysis, financial calculation, and financial knowledge retrieval Q&A, offering professional, intelligent, and comprehensive services for the financial sector.

Model Features

Multi-Module Financial Expert System
Integrates four major modules—financial consulting, text analysis, calculation, and knowledge retrieval—to cover diverse needs in the financial domain.
Professional Financial Knowledge
Trained on the high-quality financial dataset DISC-Fin-SFT, containing approximately 250,000 financial domain data entries.
Complex Financial Calculation Capabilities
Supports professional financial model calculations such as the Black-Scholes option pricing model and EDF expected default probability model.

Model Capabilities

Financial Consulting
Financial Text Information Extraction
Financial Sentiment Analysis
Financial Text Classification
Financial Text Generation
Financial Mathematical Calculation
Financial Statistical Analysis
Financial Model Calculation
Financial Knowledge Retrieval
Investment Advice
Current Affairs Analysis
Policy Interpretation

Use Cases

Financial Services
Professional Financial Consulting
Explains financial professional knowledge to users, such as the concept of non-performing bank assets.
Engages in multi-turn dialogues with users on financial topics within the Chinese financial context.
Investment Analysis
Provides investment advice to users based on financial news and research reports.
Offers investment analysis and recommendations based on professional financial knowledge.
Financial NLP
Financial Text Analysis
Performs NLP tasks such as information extraction and sentiment analysis on financial texts.
Demonstrates significant advantages in financial NLP task evaluations.
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