TY - BOOK AU - Tunstall,Lewis AU - Werra,Leandro von AU - Wolf,Thomas TI - Natural language processing with transformers: building language applications with Hugging Face SN - 1098103246 AV - QA76.9.N38 T86 2022 U1 - 006.35 23 PY - 2022/// PY - 2022/// CY - Sebastopol, CA PB - O'Reilly Media KW - Natural language processing (Computer science) KW - Electronic transformers KW - Natural Language Processing KW - Machine learning KW - Traitement automatique des langues naturelles KW - Transformateurs électroniques KW - Python (Computer program language) KW - fast KW - nli KW - TM340 N1 - Includes bibliographical references and index; Hello transformers -- Text classification -- Transformer anatomy -- Multilingual named entity recognition -- Text generation -- Summarization -- Question answering -- Making transformers efficient in production -- Dealing with few to no labels -- Training transformers from scratch -- Future directions; The online book link; https://www.amazon.com/s?k=9781098103248&i=stripbooks-intl-ship&crid=30WBRN1TE95D4&sprefix=9781098103248%2Cstripbooks-intl-ship%2C474&ref=nb_sb_noss N2 - Since 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 ER -