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Mastering Transformers

You're reading from  Mastering Transformers

Product type Book
Published in Sep 2021
Publisher Packt
ISBN-13 9781801077651
Pages 374 pages
Edition 1st Edition
Languages
Authors (2):
Savaş Yıldırım Savaş Yıldırım
Profile icon Savaş Yıldırım
Meysam Asgari- Chenaghlu Meysam Asgari- Chenaghlu
Profile icon Meysam Asgari- Chenaghlu
View More author details

Table of Contents (16) Chapters

Preface 1. Section 1: Introduction – Recent Developments in the Field, Installations, and Hello World Applications
2. Chapter 1: From Bag-of-Words to the Transformer 3. Chapter 2: A Hands-On Introduction to the Subject 4. Section 2: Transformer Models – From Autoencoding to Autoregressive Models
5. Chapter 3: Autoencoding Language Models 6. Chapter 4:Autoregressive and Other Language Models 7. Chapter 5: Fine-Tuning Language Models for Text Classification 8. Chapter 6: Fine-Tuning Language Models for Token Classification 9. Chapter 7: Text Representation 10. Section 3: Advanced Topics
11. Chapter 8: Working with Efficient Transformers 12. Chapter 9:Cross-Lingual and Multilingual Language Modeling 13. Chapter 10: Serving Transformer Models 14. Chapter 11: Attention Visualization and Experiment Tracking 15. Other Books You May Enjoy

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Sequences that are shorter than max_sen_len (maximum sentence length) are padded with a PAD value until they are max_sen_len in length."

A block of code is set as follows:

max_sen_len=max([len(s.split()) for s in sentences])
words = ["PAD"]+ list(set([w for s in sentences for w in s.split()]))
word2idx= {w:i for i,w in enumerate(words)}
max_words=max(word2idx.values())+1
idx2word= {i:w for i,w in enumerate(words)}
train=[list(map(lambda x:word2idx[x], s.split())) for s in sentences]

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

[default]
exten => s,1,Dial(Zap/1|30)
exten => s,2,Voicemail(u100)
exten => s,102,Voicemail(b100)
exten => i,1,Voicemail(s0)

Any command-line input or output is written as follows:

$ conda activate transformers
$ conda install -c conda-forge tensorflow

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "We must now take care of the computational cost of a particular model for a given environment (Random Access Memory (RAM), CPU, and GPU) in terms of memory usage and speed."

Tips or important notes

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