NATURAL LANGUAGE PROCESSING WITH TRANSFORMERS

By:

TUNSTALL LEWIS

Contributor(s):

Material type:

TextPublication details: CALIFORNIA O'REILLY SPD PVT LTD 2023Edition: SECOND EDITIONDescription: XXII 386PISBN:

978-93-5542-032-9

Other title:

Building Language Applications with Hugging Face

Subject(s):

Summary: "Natural Language Processing with Transformers," authored by Lewis Tunstall, Leandro von Werra, and Thomas Wolf, serves as a comprehensive and practical guide for data scientists and machine learning engineers on applying state-of-the-art Transformer models to real-world Natural Language Processing tasks. Centered almost entirely on the powerful Hugging Face ecosystem—a library the authors helped build—the book moves beyond theory to provide hands-on instruction for the complete model lifecycle. It meticulously walks readers through loading data, tokenization, and fine-tuning powerful pre-trained models like BERT and GPT for a wide variety of applications, including text classification, named entity recognition (NER), question answering, and text summarization. Furthermore, it addresses the critical, real-world challenges of deploying models to production, covering essential optimization techniques such as knowledge distillation and quantization to make models smaller and faster.

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Holdings
Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Reference	Central Library, Aditya Institute of Technology and Management GF-03/L	Central Library, Aditya Institute of Technology and Management			006.35 TUN (Browse shelf(Opens below))					Not for Loan	NLP		54096

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NLP

"Natural Language Processing with Transformers," authored by Lewis Tunstall, Leandro von Werra, and Thomas Wolf, serves as a comprehensive and practical guide for data scientists and machine learning engineers on applying state-of-the-art Transformer models to real-world Natural Language Processing tasks. Centered almost entirely on the powerful Hugging Face ecosystem—a library the authors helped build—the book moves beyond theory to provide hands-on instruction for the complete model lifecycle. It meticulously walks readers through loading data, tokenization, and fine-tuning powerful pre-trained models like BERT and GPT for a wide variety of applications, including text classification, named entity recognition (NER), question answering, and text summarization. Furthermore, it addresses the critical, real-world challenges of deploying models to production, covering essential optimization techniques such as knowledge distillation and quantization to make models smaller and faster.

AIML

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