000			02173nam a22002657a 4500
005			20241126120748.0
008			241126b |||||||| |||| 00| 0 eng d
020			_a9789355420329
028			_a: Raghav Books Pvt. Ltd. _b: A-184, A Nand Gram, Ghaziabad, Ghaziabad _c: 1722 _d: 23/11/2024
082			_a006.35 TUN
100			_aTunstall, Lewis.
100			_a; Werra, Leondro von.
100			_a; Wolf, Thomas.
245			_aNatural Language Processing with Transformers _b: Building Language Applications with Hugging Face. _c/ Lewis Tunstall.
250			_a: Revised Edition
260			_aBeijing _b: O'Reilly, _c, 2022.
300			_axxii, 383p. _c: 24cm.
365			_b1593
520			_aSince their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments
650			_aNatural Language Processing
650			_aLanguage Applications
650			_a Hugging Face
700			_aLewis Tunstall
942			_cBOOK
999			_c62577 _d62577