Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Transformers for Natural Language Processing
  • Table Of Contents Toc
Transformers for Natural Language Processing

Transformers for Natural Language Processing - Second Edition

By : Denis Rothman
3.8 (28)
close
close
Transformers for Natural Language Processing

Transformers for Natural Language Processing

3.8 (28)
By: Denis Rothman

Overview of this book

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.
Table of Contents (25 chapters)
close
close
18
Other Books You May Enjoy
19
Index
1
Appendix I — Terminology of Transformer Models

Semantic Role Labeling with BERT-Based Transformers

Transformers have made more progress in the past few years than NLP in the past generation. Standard NLU approaches first learn syntactical and lexical features to explain the structure of a sentence. The former NLP models would be trained to understand a language’s basic syntax before running Semantic Role Labeling (SRL).

Shi and Lin (2019) started their paper by asking if preliminary syntactic and lexical training can be skipped. Can a BERT-based model perform SRL without going through those classical training phases? The answer is yes!

Shi and Lin (2019) suggested that SRL can be considered sequence labeling and provide a standardized input format. Their BERT-based model produced surprisingly good results.

This chapter will use a pretrained BERT-based model provided by the Allen Institute for AI based on the Shi and Lin (2019) paper. Shi and Lin took SRL to the next level by dropping syntactic and lexical training...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Transformers for Natural Language Processing
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon