Automatic Natural Language Processing (NLP) has become an essential aspect of research aimed at enabling machines to understand and analyze human languages. The integration of data augmentation and rebalancing techniques in the development of automatic NLP models represents a major step forward in enhancing the accuracy and robustness of these models. This study focuses on this crucial issue by highlighting the significant impact of these techniques on the performance of NLP models. We describe the collaborative process of developing an automatic NLP model incorporating data augmentation and rebalancing techniques, with a particular focus on improving classification models for the difficulty of Arabic texts. By using language models to generate new sentences and advanced techniques to rebalance datasets, our approach aims to improve the diversity and accuracy of classification models, thus contributing to more efficient and generalizable applications in NLP.