Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application
It addresses challenges like multilingual data and bias reduction for AI practitioners, but is incremental as it reviews existing methods without introducing new ones.
This paper discusses the application of natural language processing techniques, including tokenization and text classification, to process human language using transformer-based models, aiming to provide insights for deploying effective and ethically sound AI solutions.
With a focus on natural language processing (NLP) and the role of large language models (LLMs), we explore the intersection of machine learning, deep learning, and artificial intelligence. As artificial intelligence continues to revolutionize fields from healthcare to finance, NLP techniques such as tokenization, text classification, and entity recognition are essential for processing and understanding human language. This paper discusses advanced data preprocessing techniques and the use of frameworks like Hugging Face for implementing transformer-based models. Additionally, it highlights challenges such as handling multilingual data, reducing bias, and ensuring model robustness. By addressing key aspects of data processing and model fine-tuning, this work aims to provide insights into deploying effective and ethically sound AI solutions.