# High-Precision Text Analysis

This model is a fine-tuned BERT-uncased model for functional/non-functional requirement prediction, trained on AWS g5 instances using the PROMISE dataset and custom data, achieving an overall accuracy of 97%.

A text classification model based on TinyBERT, specifically designed to identify and classify climate skepticism viewpoints, addressing class imbalance issues through LLM data balancing technology.

A RoBERTa fine-tuned bias identification model for classifying 10 types of biases in text