# Financial Text Processing

A ModernBERT-base fine-tuned model for financial news sentiment analysis, achieving an F1 score of 0.9765 on the evaluation set

A Chinese sentiment analysis model based on BERT architecture, specifically optimized for fund-related texts, supporting negative, positive, and neutral sentiment classification.

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.

This model is a named entity recognition model fine-tuned on a financial domain dataset, capable of accurately identifying named entities in financial texts.

Japanese sentiment analysis model trained on the chABSA dataset with 100% accuracy

This model is a financial text sentiment classification model fine-tuned on the financial_phrasebank dataset based on deepmind/language-perceiver.

FinBERT is a pre-trained natural language processing model specifically designed for financial text sentiment analysis