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

• URL: http://arxiv.org/abs/2411.05026v1
• Date: Wed, 30 Oct 2024 09:35:35 GMT
• Title: Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application
• Authors: Keyu Chen, Cheng Fei, Ziqian Bi, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Caitlyn Heqi Yin, Yichao Zhang, Pohsun Feng, Yizhu Wen, Tianyang Wang, Ming Li, Jintao Ren, Qian Niu, Silin Chen, Weiche Hsieh, Lawrence K. Q. Yan, Chia Xin Liang, Han Xu, Hong-Ming Tseng, Xinyuan Song, Ming Liu,
• Abstract summary: We focus on natural language processing (NLP) and the role of large language models (LLMs) This paper discusses advanced data preprocessing techniques and the use of frameworks like Hugging Face for implementing transformer-based models. It highlights challenges such as handling multilingual data, reducing bias, and ensuring model robustness.
• Score: 17.367710635990083
• License:
• 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.

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