Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908660862550016 |
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| author | Chen, Keyu Fei, Cheng Bi, Ziqian Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Xu, Jiawei Wang, Jinlang Yin, Caitlyn Heqi Zhang, Yichao Feng, Pohsun Wen, Yizhu Wang, Tianyang Li, Ming Ren, Jintao Niu, Qian Chen, Silin Hsieh, Weiche Yan, Lawrence K. Q. Liang, Chia Xin Xu, Han Tseng, Hong-Ming Song, Xinyuan Jiang, Zekun Liu, Ming |
| author_facet | Chen, Keyu Fei, Cheng Bi, Ziqian Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Xu, Jiawei Wang, Jinlang Yin, Caitlyn Heqi Zhang, Yichao Feng, Pohsun Wen, Yizhu Wang, Tianyang Li, Ming Ren, Jintao Niu, Qian Chen, Silin Hsieh, Weiche Yan, Lawrence K. Q. Liang, Chia Xin Xu, Han Tseng, Hong-Ming Song, Xinyuan Jiang, Zekun Liu, Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05026 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application Chen, Keyu Fei, Cheng Bi, Ziqian Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Xu, Jiawei Wang, Jinlang Yin, Caitlyn Heqi Zhang, Yichao Feng, Pohsun Wen, Yizhu Wang, Tianyang Li, Ming Ren, Jintao Niu, Qian Chen, Silin Hsieh, Weiche Yan, Lawrence K. Q. Liang, Chia Xin Xu, Han Tseng, Hong-Ming Song, Xinyuan Jiang, Zekun Liu, Ming Computation and Language Human-Computer Interaction 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. |
| title | Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application |
| topic | Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2411.05026 |