Transformers for Natural Language Processing
eBook - ePub

Transformers for Natural Language Processing

Denis Rothman

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  1. 564 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Transformers for Natural Language Processing

Denis Rothman

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About This Book

OpenAI's GPT-3, ChatGPT, GPT-4 and Hugging Face transformers for language tasks in one book. Get a taste of the future of transformers, including computer vision tasks and code writing and assistance.Purchase of the print or Kindle book includes a free eBook in PDF format

Key Features

  • Improve your productivity with OpenAI's ChatGPT and GPT-4 from prompt engineering to creating and analyzing machine learning models
  • Pretrain a BERT-based model from scratch using Hugging Face
  • Fine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your data

Book Description

Transformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs?Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses.You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model.If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides.The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details).You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using DALL-E 2, ChatGPT, and GPT-4.By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective.

What you will learn

  • Discover new techniques to investigate complex language problems
  • Compare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformers
  • Carry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3
  • Find out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-E
  • Learn the mechanics of advanced prompt engineering for ChatGPT and GPT-4

Who this book is for

If you want to learn about and apply transformers to your natural language (and image) data, this book is for you.You'll need a good understanding of Python and deep learning and a basic understanding of NLP to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters. And don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community to help guide you on your transformers journey!

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Information

Index

Symbols
345M-parameter GPT-2 model
downloading 475, 476
A
accuracy score 125
Allen Institute for AI
reference link 257
AllenNLP 343
URL 343
Amazon Web Services (AWS) 1, 12, 392
artificial intelligence, properties
computing power 6
data 5
model architecture 5
prompt engineering 6
attention heads 459
attention masks
creating 76
Automated Machine Learning (AutoML) 120
automatic question generation 303, 304
B
BERT-based transformer
architecture 258
basic samples 261-267
difficult samples 267-273
running 258
SRL experiments 259, 260
BERT-base multilingual model 324, 325
BERT model
architecture 62
attention masks, creating 76
batch size, selecting 77
BERT tokenizer, activating 75
BERT tokens, adding 75
configuration 78, 80
CUDA, specifying as device for torch 72
data, converting into torch tensors 77
data, processing 76
dataset, loading 73-75
data, splitting into training set 76
data, splitting into validation set 76
encoder stack 62-65
fine-tuning 68-70
hardware constraints 71
holdout dataset, used for evaluating 86, 87
holdout dataset, used for predicting 86, 87
Hugging Face BERT uncased base model, loading 80-82
Hugging Face PyTorch interface, installing 71
hyperparameters for training loop 83
iterator, creating 77
key features 68
label lists, creating 75
Matthews Correlation...

Table of contents