Mastering Transformers, published by Packt

Overview

Mastering Transformers

Book Name

This is the code repository for Mastering Transformers, published by Packt.

Build state-of-the-art models from scratch with advanced natural language processing techniques

What is this book about?

Transformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm. With this book, you'll learn how to build various transformer-based NLP applications using the Python Transformers library.

This book covers the following exciting features:

  • Explore state-of-the-art NLP solutions with the Transformers library
  • Train a language model in any language with any transformer architecture
  • Fine-tune a pre-trained language model to perform several downstream tasks
  • Select the right framework for the training, evaluation, and production of an end-to-end solution
  • Get hands-on experience in using TensorBoard and Weights & Biases
  • Visualize the internal representation of transformer models for interpretability

If you feel this book is for you, get your copy today!

https://www.packtpub.com/

Instructions and Navigations

All of the code is organized into folders. For example, Chapter03.

The code will look like the following:

import pandas as pd
imdb_df = pd.read_csv("IMDB Dataset.csv")
reviews = imdb_df.review.to_string(index=None)
with open("corpus.txt", "w") as f:
      f.writelines(reviews)

Following is what you need for this book: This book is for deep learning researchers, hands-on NLP practitioners, as well as ML/NLP educators and students who want to start their journey with Transformers. Beginner-level machine learning knowledge and a good command of Python will help you get the best out of this book.

With the following software and hardware list you can run all code files present in the book (Chapter 1-11).

Software and Hardware List

Chapter Software required OS required
1-11 Python 3.6x, Transformers, Google Colaboratory, Jupyter Notebook, TensorFlow Windows, Mac OS X, and Linux (Any)
10 Docker, Locust.io Windows, Mac OS X, and Linux (Any)

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Code in Action

Click on the following link to see the Code in Action:

https://bit.ly/3i4vFzJ

Related products

Get to Know the Author

Savaş Yıldırım He graduated from the Istanbul Technical University Department of Computer Engineering and holds a Ph.D. degree in Natural Language Processing (NLP). Currently, he is an associate professor at the Istanbul Bilgi University, Turkey, and is a visiting researcher at the Ryerson University, Canada. He is a proactive lecturer and researcher with more than 20 years of experience teaching courses on machine learning, deep learning, and NLP.

Meysam Asgari-Chenaghlu He is an AI manager at Carbon Consulting and is also a Ph.D. candidate at the University of Tabriz. He has been a consultant for Turkey's leading telecommunication and banking companies. He has also worked on various projects, including natural language understanding and semantic search.

Owner
Packt
Providing books, eBooks, video tutorials, and articles for IT developers, administrators, and users.
Packt
A calibre plugin that generates Word Wise and X-Ray files then sends them to Kindle. Supports KFX, AZW3 and MOBI eBooks. X-Ray supports 18 languages.

WordDumb A calibre plugin that generates Word Wise and X-Ray files then sends them to Kindle. Supports KFX, AZW3 and MOBI eBooks. Languages X-Ray supp

172 Dec 29, 2022
Various Algorithms for Short Text Mining

Short Text Mining in Python Introduction This package shorttext is a Python package that facilitates supervised and unsupervised learning for short te

Kwan-Yuet 466 Dec 06, 2022
Text-to-Speech for Belarusian language

title emoji colorFrom colorTo sdk app_file pinned Belarusian TTS 🐸 green green gradio app.py false Belarusian TTS 📢 🤖 Belarusian TTS (text-to-speec

Yurii Paniv 1 Nov 27, 2021
Malware-Related Sentence Classification

Malware-Related Sentence Classification This repo contains the code for the ICTAI 2021 paper "Enrichment of Features for Malware-Related Sentence Clas

Chau Nguyen 1 Mar 26, 2022
SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch.

The goal is to create a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech technologies, including systems for speech recognition, speaker recognit

SpeechBrain 5.1k Jan 09, 2023
Google AI 2018 BERT pytorch implementation

BERT-pytorch Pytorch implementation of Google AI's 2018 BERT, with simple annotation BERT 2018 BERT: Pre-training of Deep Bidirectional Transformers f

Junseong Kim 5.3k Jan 07, 2023
Chinese NewsTitle Generation Project by GPT2.带有超级详细注释的中文GPT2新闻标题生成项目。

GPT2-NewsTitle 带有超详细注释的GPT2新闻标题生成项目 UpDate 01.02.2021 从网上收集数据,将清华新闻数据、搜狗新闻数据等新闻数据集,以及开源的一些摘要数据进行整理清洗,构建一个较完善的中文摘要数据集。 数据集清洗时,仅进行了简单地规则清洗。

logCong 785 Dec 29, 2022
Outreachy TFX custom component project

Schema Curation Custom Component Outreachy TFX custom component project This repo contains the code for Schema Curation Custom Component made as a par

Robert Crowe 5 Jul 16, 2021
硕士期间自学的NLP子任务,供学习参考

NLP_Chinese_down_stream_task 自学的NLP子任务,供学习参考 任务1 :短文本分类 (1).数据集:THUCNews中文文本数据集(10分类) (2).模型:BERT+FC/LSTM,Pytorch实现 (3).使用方法: 预训练模型使用的是中文BERT-WWM, 下载地

12 May 31, 2022
English loanwords in the world's languages

Wiktionary as CLDF Content cldf1 and cldf2 contain cldf-conform data sets with a total of 2 377 756 entries about the vocabulary of all 1403 languages

Viktor Martinović 3 Jan 14, 2022
🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy

spacy-transformers: Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy This package provides spaCy components and architectures to use tr

Explosion 1.2k Jan 08, 2023
My implementation of Safaricom Machine Learning Codility test. The code has bugs, logical I guess I made errors and any correction will be appreciated.

Safaricom_Codility Machine Learning 2022 The test entails two questions. Question 1 was on Machine Learning. Question 2 was on SQL I ran out of time.

Lawrence M. 1 Mar 03, 2022
DLO8012: Natural Language Processing & CSL804: Computational Lab - II

NATURAL-LANGUAGE-PROCESSING-AND-COMPUTATIONAL-LAB-II DLO8012: NLP & CSL804: CL-II [SEMESTER VIII] Syllabus NLP - Reference Books THE WALL MEGA SATISH

AMEY THAKUR 7 Apr 28, 2022
Pytorch implementation of Tacotron

Tacotron-pytorch A pytorch implementation of Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model. Requirements Install python 3 Install pytorc

soobin seo 203 Dec 02, 2022
This script just scrapes the most recent Nepali news from Kathmandu Post and notifies the user about current events at regular intervals.It sends out the most recent news at random!

Nepali-news-notifier This script just scrapes the most recent Nepali news from Kathmandu Post and notifies the user about current events at regular in

Sachit Yadav 1 Feb 11, 2022
Material for GW4SHM workshop, 16/03/2022.

GW4SHM Workshop Wednesday, 16th March 2022 (13:00 – 15:15 GMT): Presented by: Dr. Rhodri Nelson, Imperial College London Project website: https://www.

Devito Codes 1 Mar 16, 2022
Few-shot Natural Language Generation for Task-Oriented Dialog

Few-shot Natural Language Generation for Task-Oriented Dialog This repository contains the dataset, source code and trained model for the following pa

172 Dec 13, 2022
Machine learning models from Singapore's NLP research community

SG-NLP Machine learning models from Singapore's natural language processing (NLP) research community. sgnlp is a Python package that allows you to eas

AI Singapore | AI Makerspace 21 Dec 17, 2022
CCKS-Title-based-large-scale-commodity-entity-retrieval-top1

- 基于标题的大规模商品实体检索top1 一、任务介绍 CCKS 2020:基于标题的大规模商品实体检索,任务为对于给定的一个商品标题,参赛系统需要匹配到该标题在给定商品库中的对应商品实体。 输入:输入文件包括若干行商品标题。 输出:输出文本每一行包括此标题对应的商品实体,即给定知识库中商品 ID,

43 Nov 11, 2022
PyTorch Implementation of "Non-Autoregressive Neural Machine Translation"

Non-Autoregressive Transformer Code release for Non-Autoregressive Neural Machine Translation by Jiatao Gu, James Bradbury, Caiming Xiong, Victor O.K.

Salesforce 261 Nov 12, 2022