Chargement en cours…
Kikai gakushū enjinia no tame no Transformers : saisentan no shizen gengo shori raiburari ni yoru moderu kaihatsu /
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...
| Cote: | Libro Electrónico |
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| Auteurs principaux: | , , |
| Autres auteurs: | |
| Format: | Électronique eBook |
| Langue: | Japonés Inglés |
| Publié: |
Tōkyō-to Shinjuku-ku :
Orairī Japan,
2022.
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| Édition: | Shohan. |
| Sujets: | |
| Accès en ligne: | Texto completo (Requiere registro previo con correo institucional) |
| Résumé: | "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." -- |
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| Description matérielle: | 1 online resource (424 pages) : color illustrations.. |
| Bibliographie: | Includes bibiographical references. |
| ISBN: | 9784873119953 4873119952 |