Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application

TL;DR

This paper surveys the lifecycle of NLP from foundational theory to practical deployment, emphasizing preprocessing, tokenization, and the Hugging Face ecosystem. It presents a detailed inventory of techniques for data cleaning, multilingual processing, and domain-specific preprocessing, alongside practical workflows for dataset management, model fine-tuning, and deployment optimizations. The work highlights robust evaluation strategies, responsible AI considerations, and the interplay between tokenization choices and downstream performance across autoregressive, encoder-decoder, and decoder-only architectures. By detailing hands-on examples and pipelines, it provides a blueprint for building scalable, ethical, and production-ready NLP systems that leverage modern transformer-based models. The findings underscore the centrality of data quality, tokenization fidelity, and domain adaptation in delivering reliable, real-world NLP solutions.

Abstract

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.