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
Big Bird: Transformers for Longer Sequences

BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the c

Google Research 457 Dec 23, 2022
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

CRNN paper:An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition 1. create your ow

Tsukinousag1 3 Apr 02, 2022
Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification"

PTR Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification" If you use the code, please cite the following paper: @art

THUNLP 118 Dec 30, 2022
precise iris segmentation

PI-DECODER Introduction PI-DECODER, a decoder structure designed for Precise Iris Segmentation and Location. The decoder structure is shown below: Ple

8 Aug 08, 2022
Neural network models for joint POS tagging and dependency parsing (CoNLL 2017-2018)

Neural Network Models for Joint POS Tagging and Dependency Parsing Implementations of joint models for POS tagging and dependency parsing, as describe

Dat Quoc Nguyen 152 Sep 02, 2022
Stanford CoreNLP provides a set of natural language analysis tools written in Java

Stanford CoreNLP Stanford CoreNLP provides a set of natural language analysis tools written in Java. It can take raw human language text input and giv

Stanford NLP 8.8k Jan 07, 2023
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
Connectionist Temporal Classification (CTC) decoding algorithms: best path, beam search, lexicon search, prefix search, and token passing. Implemented in Python.

CTC Decoding Algorithms Update 2021: installable Python package Python implementation of some common Connectionist Temporal Classification (CTC) decod

Harald Scheidl 736 Jan 03, 2023
DeBERTa: Decoding-enhanced BERT with Disentangled Attention

DeBERTa: Decoding-enhanced BERT with Disentangled Attention This repository is the official implementation of DeBERTa: Decoding-enhanced BERT with Dis

Microsoft 1.2k Jan 03, 2023
Test finetuning of XLSR (multilingual wav2vec 2.0) for other speech classification tasks

wav2vec_finetune Test finetuning of XLSR (multilingual wav2vec 2.0) for other speech classification tasks Initial test: gender recognition on this dat

8 Aug 11, 2022
Practical Machine Learning with Python

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

Dipanjan (DJ) Sarkar 2k Jan 08, 2023
profile tools for pytorch nn models

nnprof Introduction nnprof is a profile tool for pytorch neural networks. Features multi profile mode: nnprof support 4 profile mode: Layer level, Ope

Feng Wang 42 Jul 09, 2022
Snowball compiler and stemming algorithms

Snowball is a small string processing language for creating stemming algorithms for use in Information Retrieval, plus a collection of stemming algori

Snowball Stemming language and algorithms 613 Jan 07, 2023
Graphical user interface for Argos Translate

Argos Translate GUI Website | GitHub | PyPI Graphical user interface for Argos Translate. Install pip3 install argostranslategui

Argos Open Tech 16 Dec 07, 2022
Converts python code into c++ by using OpenAI CODEX.

🦾 codex_py2cpp 🤖 OpenAI Codex Python to C++ Code Generator Your Python Code is too slow? 🐌 You want to speed it up but forgot how to code in C++? ⌨

Alexander 423 Jan 01, 2023
🤗 The largest hub of ready-to-use NLP datasets for ML models with fast, easy-to-use and efficient data manipulation tools

🤗 The largest hub of ready-to-use NLP datasets for ML models with fast, easy-to-use and efficient data manipulation tools

Hugging Face 15k Jan 02, 2023
:house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.

(Framework for Adapting Representation Models) What is it? FARM makes Transfer Learning with BERT & Co simple, fast and enterprise-ready. It's built u

deepset 1.6k Dec 27, 2022
Ptorch NLU, a Chinese text classification and sequence annotation toolkit, supports multi class and multi label classification tasks of Chinese long text and short text, and supports sequence annotation tasks such as Chinese named entity recognition, part of speech tagging and word segmentation.

Pytorch-NLU,一个中文文本分类、序列标注工具包,支持中文长文本、短文本的多类、多标签分类任务,支持中文命名实体识别、词性标注、分词等序列标注任务。 Ptorch NLU, a Chinese text classification and sequence annotation toolkit, supports multi class and multi label classifi

186 Dec 24, 2022
Sequence model architectures from scratch in PyTorch

This repository implements a variety of sequence model architectures from scratch in PyTorch. Effort has been put to make the code well structured so that it can serve as learning material. The train

Brando Koch 11 Mar 28, 2022
This repository contains the code, data, and models of the paper titled "CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summarization for 1500+ Language Pairs".

CrossSum This repository contains the code, data, and models of the paper titled "CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summ

BUET CSE NLP Group 29 Nov 19, 2022