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  • 1
    UID:
    (DE-602)kobvindex_ZLB35106117
    Format: 432 S.
    ISBN: 9783960107132
    Content: Leistungsfähige State-of-the-Art-Sprachanwendungen mit vortrainierten Transformer-Modellen Transformer haben die NLP-Welt im Sturm erobert Von den Gründern von Hugging Face, der Plattform für vortrainierte Transformer-Modelle für TensorFlow und PyTorch Bietet einen fundierten und praxisnahen Überblick über die wichtigsten Methoden und Anwendungen im aktuellen NLP Hands-On: Jeder Programmierschritt kann in Jupyter Notebooks nachvollzogen werden Transformer haben sich seit ihrer Einführung nahezu über Nacht zur vorherrschenden Architektur im Natural Language Processing entwickelt. Sie liefern die besten Ergebnisse für eine Vielzahl von Aufgaben bei der maschinellen Sprachverarbeitung. Wenn Sie Data Scientist oder Programmierer sind, zeigt Ihnen dieses praktische Buch, wie Sie NLP-Modelle mit Hugging Face Transformers, einer Python-basierten Deep-Learning-Bibliothek, trainieren und skalieren können. Transformer kommen beispielsweise beim maschinellen Schreiben von Nachrichtenartikeln zum Einsatz, bei der Verbesserung von Google-Suchanfragen oder bei Chatbots. In diesem Handbuch zeigen Ihnen Lewis Tunstall, Leandro von Werra und Thomas Wolf, die auch die Transformers-Bibliothek von Hugging Face mitentwickelt haben, anhand eines praktischen Ansatzes, wie Transformer-basierte Modelle funktionieren und wie Sie sie in Ihre Anwendungen integrieren können. Sie werden schnell eine Vielzahl von Aufgaben wie Textklassifikation, Named Entity Recognition oder Question Answering kennenlernen, die Sie mit ihnen lösen können.
    Note: Lewis Tunstall ist Machine Learning Engineer bei Hugging Face. Der Schwerpunkt seiner Arbeit liegt derzeit auf der Entwicklung von Tools für die NLP-Community und darauf, Menschen zu schulen, diese effektiv zu nutzen. Leandro von Werra ist Machine Learning Engineer im Open-Source-Team von Hugging Face. Er konzentriert sich hauptsächlich auf Modelle, die Code generieren können, und auf die Zusammenarbeit mit der Community. Thomas Wolf ist Chief Science Officer und Mitgründer von Hugging Face. Sein Team hat sich der Aufgabe verschrieben, die KI-Forschung voranzutreiben und sie weiter zu demokratisieren.
    Language: German
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  • 2
    UID:
    (DE-604)BV047840675
    Format: 1 online resource (409 pages) , Illustrationen, Diagramme
    Edition: 1st edition
    ISBN: 9781098103217
    Content: 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 HuggingFace 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 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
    Note: Online resource; Title from title page (viewed March 25, 2022) , Mode of access: World Wide Web
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-0981-0324-8
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-0981-0324-8
    Language: English
    Subjects: Computer Science , Engineering
    RVK:
    RVK:
    Keywords: Natürliche Sprache ; Sprachverarbeitung ; Textverarbeitung ; Maschinelles Lernen ; Deep learning
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  • 3
    UID:
    (DE-627)1817900919
    Format: 1 Online-Ressource (xxii, 383 Seiten)
    Edition: Revised Edition
    ISBN: 9781098136765
    Additional Edition: 9781098136796
    Additional Edition: Erscheint auch als Druck-Ausgabe Tunstall, Lewis Natural language processing with transformers Beijing : O'Reilly, 2022 9781098136796
    Additional Edition: 1098136799
    Language: English
    Subjects: Engineering , Computer Science
    RVK:
    RVK:
    Keywords: Natürliche Sprache ; Sprachverarbeitung ; Automatische Sprachanalyse ; Deep learning ; Python
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  • 4
    UID:
    (DE-101)1259545636
    Format: 430 Seiten , Illustrationen , 24 cm
    Edition: 1. Auflage
    ISBN: 9783960092025 , 3960092024
    Uniform Title: Natural language processing with transformers
    Note: "Deutsche Ausgabe" - Umschlag
    Additional Edition: Parallele Sprachausgabe
    Additional Edition: Erscheint auch als Online-Ausgabe Natural Language Processing mit Transformern Heidelberg : O'Reilly, 2023
    Language: German
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  • 5
    UID:
    (DE-603)49640461X
    Edition: Revised edition
    ISBN: 1098136764 , 9781098136765
    Content: 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 -now revised in full color- 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.
    Language: English
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  • 6
    UID:
    (DE-627)1810987334
    Format: 1 online resource
    Edition: Revised edition.
    ISBN: 1098136764 , 9781098136765
    Content: 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 -now revised in full color- 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.
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 7
    UID:
    (DE-603)490578624
    Format: xxii, 383 Seiten , Illustrationen, Diagramme
    ISBN: 9781098103248
    Language: English
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  • 8
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Sebastopol, CA : O'Reilly Media Inc.
    UID:
    (DE-603)479538573
    Format: 1 Online-Ressource (82 pages)
    Edition: 1st edition
    ISBN: 9781098103231
    Content: 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 HuggingFace 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 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...
    Language: English
    Keywords: Electronic books
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  • 9
    Online Resource
    Online Resource
    Sebastopol, CA : O'Reilly Media, Inc | [Ann Arbor, Michigan] : [ProQuest]
    UID:
    (DE-603)491574401
    Format: 1 online resource (409 pages) , Illustrationen, Diagramme
    Edition: 1st edition
    ISBN: 9781098103217
    Note: Online resource; Title from title page (viewed March 25, 2022) , Mode of access: World Wide Web
    Additional Edition: Erscheint auch als Druck-Ausgabe
    Language: English
    Subjects: Computer Science , Engineering
    RVK:
    RVK:
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  • 10
    UID:
    (DE-602)kobvindex_ZLB34998754
    ISBN: 9781098136765 , 9781098136758
    Content: " 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 -now revised in full color- 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 "
    Content: Biographisches: " Lewis Tunstall is a data scientist at Swisscom, focused on building machine learning powered applications in the domains of natural language processing and time series. A former theoretical physicist, he has over 10 years experience translating complex subject matter to lay audiences and has taught machine learning to university students at both the graduate and undergraduate levels. " Biographisches: " Leandro von Werra is a data scientist at Swiss Mobiliar where he leads the company's natural language processing efforts to streamline and simplify processes for customers and employees. He has experience working across the whole machine learning stack, and is the creator of a popular Python library that combines Transformers with reinforcement learning. He also teaches data science and visualisation at the Bern University of Applied Sciences. " Biographisches: " Thomas Wolf is Chief Science Officer and co-founder of HuggingFace. His team is on a mission to catalyze and democratize NLP research. Prior to HuggingFace, Thomas gained a Ph.D. in physics, and later a law degree. He worked as a physics researcher and a European Patent Attorney. "
    Language: English
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