news_classification_summarization_QA_syatem

This project presents a robust system designed for the classification of Persian news articles. It employs a variety of methods, including classical TF-IDF document classification and transformer-based models, to accurately categorize the articles. In addition to classification, the system is capable of generating a question-answering system that can create relevant questions along with their answers. Furthermore, it includes a fine-tuned summarization component specifically designed for Persian news articles. This multi-faceted approach ensures a thorough analysis and understanding of the content, making it a valuable tool for any Persian language text analysis tasks.