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.

翻译：本文聚焦于自然语言处理（NLP）及大语言模型（LLM）的作用，探讨了机器学习、深度学习与人工智能的交叉领域。随着人工智能持续从医疗保健到金融等各个领域引发变革，诸如分词、文本分类和实体识别等NLP技术对于处理和理解人类语言至关重要。本文讨论了先进的数据预处理技术以及使用Hugging Face等框架实现基于Transformer的模型。此外，文章还强调了处理多语言数据、减少偏见以及确保模型鲁棒性等挑战。通过探讨数据处理和模型微调的关键方面，本研究旨在为部署有效且符合伦理的人工智能解决方案提供见解。