Optimized NLP Model with Caching, Parallel Processing and Streamlined I/O

This research explores optimization strategies employed within Quesgen.ai, an advanced question generation system driven by natural language processing (NLP) and machine learning (ML) algorithms. The study delves into three pivotal areas of optimization: caching, parallel processing, and streamlining I/O operations. By implementing caching mechanisms using Python’s functool.lru_cache, parallelization via concurrent.futures and multiprocessing modules, and streamlining I/O operations through efficient batching and buffering techniques, significant enhancements in time and space complexity are realized. The effectiveness of these optimization approaches is empirically evaluated, showcasing their profound impact on the efficiency and scalability of the Quesgen.ai platform. This research contributes to the advancement of question generation systems, providing valuable insights into effective optimization strategies for NLP applications, such as question generation, semantic analysis, and language modeling.