Kikai gakushū enjinia no tame no Transformers: saisentan no shizen gengo shori raiburari ni yoru moderu kaihatsu / = Natural language processing with transformers :
機械学習エンジニアのためのTransformers : 最先端の自然言語処理ライブラリによるモデル開発 /
"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 trai...
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| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Electronic eBook |
| Language: | Japanese |
| Published: |
Tōkyō-to Shinjuku-ku
Orairī Japan
2022
|
| Edition: | Shohan. |
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9784873119953/?ar |
| Summary: | "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." -- |
| Item Description: | Includes bibiographical references |
| Physical Description: | 1 Online-Ressource (424 Seiten) color illustrations.. |
| ISBN: | 9784873119953 4873119952 |
Staff View
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| spelling | Tunstall, Lewis VerfasserIn aut Natural language processing with transformers 880-01 Kikai gakushū enjinia no tame no Transformers saisentan no shizen gengo shori raiburari ni yoru moderu kaihatsu / = Natural language processing with transformers : Lewis Tunstall, Leandro von Werra, Thomas Wolf cho ; Nakayama Hiroki yaku = Natural language processing with transformers : building language applications with Hugging Face / Lewis Tunstall, Leandro von Werra and Thomas Wolf Natural language processing with transformers : 880-02 Shohan. 880-03 Tōkyō-to Shinjuku-ku Orairī Japan 2022 1 Online-Ressource (424 Seiten) color illustrations.. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibiographical references "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." -- In Japanese. Natural language processing (Computer science) Python (Computer program language) Deep learning (Machine learning) Traitement automatique des langues naturelles Python (Langage de programmation) Apprentissage profond Werra, Leandro von VerfasserIn aut Wolf, Thomas VerfasserIn aut Nakayama, Hiroki ÜbersetzerIn trl 245-01/Chin 機械学習エンジニアのためのTransformers : 最先端の自然言語処理ライブラリによるモデル開発 / Lewis Tunstall, Leandro von Werra, Thomas Wolf著 ; 中山光樹訳 = Natural language processing with transformers : building language applications with Hugging Face / Lewis Tunstall, Leandro von Werra and Thomas Wolf. 250-02/Chin 初版. 264-03/Chin 東京都新宿区 オライリー・ジャパン 2022 |
| spellingShingle | Tunstall, Lewis Werra, Leandro von Wolf, Thomas Kikai gakushū enjinia no tame no Transformers saisentan no shizen gengo shori raiburari ni yoru moderu kaihatsu / = Natural language processing with transformers : Natural language processing (Computer science) Python (Computer program language) Deep learning (Machine learning) Traitement automatique des langues naturelles Python (Langage de programmation) Apprentissage profond |
| title | Kikai gakushū enjinia no tame no Transformers saisentan no shizen gengo shori raiburari ni yoru moderu kaihatsu / = Natural language processing with transformers : |
| title_alt | Natural language processing with transformers Natural language processing with transformers : |
| title_auth | Kikai gakushū enjinia no tame no Transformers saisentan no shizen gengo shori raiburari ni yoru moderu kaihatsu / = Natural language processing with transformers : |
| title_exact_search | Kikai gakushū enjinia no tame no Transformers saisentan no shizen gengo shori raiburari ni yoru moderu kaihatsu / = Natural language processing with transformers : |
| title_full | Kikai gakushū enjinia no tame no Transformers saisentan no shizen gengo shori raiburari ni yoru moderu kaihatsu / = Natural language processing with transformers : Lewis Tunstall, Leandro von Werra, Thomas Wolf cho ; Nakayama Hiroki yaku = Natural language processing with transformers : building language applications with Hugging Face / Lewis Tunstall, Leandro von Werra and Thomas Wolf |
| title_fullStr | Kikai gakushū enjinia no tame no Transformers saisentan no shizen gengo shori raiburari ni yoru moderu kaihatsu / = Natural language processing with transformers : Lewis Tunstall, Leandro von Werra, Thomas Wolf cho ; Nakayama Hiroki yaku = Natural language processing with transformers : building language applications with Hugging Face / Lewis Tunstall, Leandro von Werra and Thomas Wolf |
| title_full_unstemmed | Kikai gakushū enjinia no tame no Transformers saisentan no shizen gengo shori raiburari ni yoru moderu kaihatsu / = Natural language processing with transformers : Lewis Tunstall, Leandro von Werra, Thomas Wolf cho ; Nakayama Hiroki yaku = Natural language processing with transformers : building language applications with Hugging Face / Lewis Tunstall, Leandro von Werra and Thomas Wolf |
| title_short | Kikai gakushū enjinia no tame no Transformers |
| title_sort | kikai gakushu enjinia no tame no transformers saisentan no shizen gengo shori raiburari ni yoru moderu kaihatsu natural language processing with transformers |
| title_sub | saisentan no shizen gengo shori raiburari ni yoru moderu kaihatsu / = Natural language processing with transformers : |
| topic | Natural language processing (Computer science) Python (Computer program language) Deep learning (Machine learning) Traitement automatique des langues naturelles Python (Langage de programmation) Apprentissage profond |
| topic_facet | Natural language processing (Computer science) Python (Computer program language) Deep learning (Machine learning) Traitement automatique des langues naturelles Python (Langage de programmation) Apprentissage profond |
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