Book description
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
Publisher resources
Table of contents
- Foreword
- Preface
- 1. Hello Transformers
- 2. Text Classification
- 3. Transformer Anatomy
-
4. Multilingual Named Entity Recognition
- The Dataset
- Multilingual Transformers
- A Closer Look at Tokenization
- Transformers for Named Entity Recognition
- The Anatomy of the Transformers Model Class
- Tokenizing Texts for NER
- Performance Measures
- Fine-Tuning XLM-RoBERTa
- Error Analysis
- Cross-Lingual Transfer
- Interacting with Model Widgets
- Conclusion
- 5. Text Generation
- 6. Summarization
- 7. Question Answering
- 8. Making Transformers Efficient in Production
- 9. Dealing with Few to No Labels
- 10. Training Transformers from Scratch
- 11. Future Directions
- Index
- About the Authors
Product information
- Title: Natural Language Processing with Transformers, Revised Edition
- Author(s):
- Release date: May 2022
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781098136796
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