Transformers for Natural Language Processing
Being the first book in the market to dive deep into the Transformers, it is a step-by-step guide for data and AI practitioners to help enhance the performance of language understanding and gain expertise with hands-on implementation of transformers using PyTorch, TensorFlow, Hugging Face, Trax, and AllenNLP.
Offered by
Difficulty Level
Intermediate
Completion Time
12h48m
Language
English
About Book
Who Is This Book For?
Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers. Readers who can benefit the most from this book include experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data.
Transformers for Natural Language Processing
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 12h48m total length
Getting Started with the Model Architecture of the Transformer
Fine-Tuning BERT Models
Pretraining a RoBERTa Model from Scratch
Downstream NLP Tasks with Transformers
Machine Translation with the Transformer
Text Generation with OpenAI GPT-2 and GPT-3 Models
Applying Transformers to Legal and Financial Documents for AI Text Summarization
Matching Tokenizers and Datasets
Semantic Role Labeling with BERT-Based Transformers
Let Your Data Do the Talking: Story, Questions, and Answers
Detecting Customer Emotions to Make Predictions
Analyzing Fake News with Transformers
Appendix: Answers to the Questions
Related Resources
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