Abusive Content Detection with LangChain Integration Using Fine-Tuned RoBERTa

The proliferation of abusive content on social media platforms necessitates the development of automated detection systems. This research focuses on integrating a fine-tuned RoBERTa model with LangChain to classify text into hateful, offensive, or neither categories. The study leverages a pretrained RoBERTa model for high-accuracy classification and employs LangChain to generate meaningful explanations for predictions. Results demonstrate an overall accuracy of 91%, highlighting the model's efficacy in detecting abusive content while providing transparent reasoning for classifications.