# Financial Text Analysis

A Chinese financial sentiment analysis model fine-tuned based on bert-base-chinese, specifically designed for sentiment classification in analyst reports

KF-DeBERTa is a financial domain-specific language model jointly released by KakaoBank and FNGuid, built on the DeBERTa-v2 architecture, demonstrating excellent performance in both general and financial domain downstream tasks.

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.

A financial news sentiment analysis model fine-tuned based on ProsusAI/finbert, used to predict news sentiment tendencies (buy/neutral/sell)

A Korean financial domain named entity recognition model fine-tuned from klue/roberta-small, identifying specific entities in financial texts

A BERT-based Chinese financial news sentiment analysis model for determining the emotional tendency of financial news texts.

Fine-tuned RoBERTa model for FOMC hawkish-dovish-neutral classification tasks

Specific information about this model is unavailable. Please refer to the relevant documentation for details

Fine-tuned based on the BERTweet foundation model for detecting linguistic uncertainty (hedging) in social media texts.

A versatile large language model capable of handling various natural language tasks

SEC-BERT is a series of BERT models tailored for the financial domain, trained on 10-K annual reports from the U.S. Securities and Exchange Commission (SEC), supporting financial natural language processing research and fintech applications.

FReE is a model based on DistilBERT, specifically fine-tuned for financial relationship type detection and classification tasks.

A versatile language model based on Transformer architecture, supporting tasks such as text generation, Q&A, and code completion

FinBERT is a BERT model specifically optimized for the financial domain, designed to handle financial text-related natural language processing tasks.

This is a multi-class classification model trained with AutoNLP, specifically designed for bank transaction classification tasks.