book

Mastering Transformers. - Second Edition

Name: Mastering Transformers. - Second Edition
ISBN: 9781837633784

by Savaş Yıldırım, Meysam Asgari- Chenaghlu

June 2024

Intermediate to advanced

462 pages

10h 56m

English

Packt Publishing

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Mastering Transformers
ContributorsAbout the authorsAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your Thoughts
Part 1: Recent Developments in the Field, Installations, and Hello World Applications
Chapter 1: From Bag-of-Words to the Transformers
Evolution of NLP approachesRecalling traditional NLP approachesLanguage modeling and generationLeveraging DLConsidering the word order with RNN modelsLSTMs and gated recurrent unitsContextual word embeddings and TLOverview of the Transformer architectureAttention mechanismMulti-head attention mechanismsUsing TL with TransformersMultimodal learningSummaryReferences
Chapter 2: A Hands-On Introduction to the Subject
Technical requirementsInstalling transformer with AnacondaInstallation on LinuxInstallation on WindowsInstallation on macOSInstalling TensorFlow, PyTorch, and TransformerInstalling and using Google ColabWorking with language models and tokenizersWorking with community-provided modelsWorking with multimodal transformersWorking with benchmarks and datasetsImportant benchmarksGLUE benchmarkSuperGLUE benchmarkXTREME benchmarkXGLUE benchmarkSQuAD benchmarkAccessing the datasets with an application programming interfaceData manipulation using the datasets librarySorting, indexing, and shufflingCaching and reusabilityDataset filter and map functionProcessing data with the map functionWorking with local filesPreparing a dataset for model trainingBenchmarking for speed and memorySummary
Part 2: Transformer Models: From Autoencoders to Autoregressive Models
Chapter 3: Autoencoding Language Models
Technical requirementsBERT – one of the autoencoding language modelsBERT language model pretraining tasksA deeper look into the BERT language modelAutoencoding language model training for any languageSharing models with the communityOther autoencoding modelsIntroducing ALBERTRoBERTaELECTRADeBERTaWorking with tokenization algorithmsBPEWordPiece tokenizationSentence piece tokenizationThe tokenizers librarySummary
Chapter 4: From Generative Models to Large Language Models
Technical requirementsAn introduction to GLMsWorking with GLMsGPT model familyTransformer-XLXLNetWorking with text-to-text modelsMulti-task learning with T5Zero-Shot Text Generalization with T0Another Denoising-Based Seq2Seq Model – BARTGLM trainingNLG using AR modelsSummaryReferences
Chapter 5: Fine-Tuning Language Models for Text Classification
Technical requirementsIntroduction to text classificationFine-tuning a BERT model for single-sentence binary classificationTraining a classification model with native PyTorchFine-tuning BERT for multi-class classification with custom datasetsFine-tuning the BERT model for sentence-pair regressionMultilabel text classificationUtilizing run_glue.py to fine-tune the modelsSummaryReferences
Chapter 6: Fine-Tuning Language Models for Token Classification
Technical requirementsIntroduction to token classificationUnderstanding NERUnderstanding POS taggingUnderstanding QAFine-tuning language models for NERQuestion answering using token classificationQuestion answering for many tasksSummary

Chapter 7: Text Representation
Technical requirementsIntroduction to sentence embeddingsCross-encoder versus bi-encoderBenchmarking sentence similarity modelsUsing BART for zero-shot learningSemantic similarity experiment with FLAIRAverage word embeddingsRNN-based document embeddingsTransformer-based BERT embeddingsSBERT embeddingsText clustering with Sentence-BERTTopic modeling with BERTopicSemantic search with SBERTInstruction fine-tuned embedding modelsSummaryFurther reading
Chapter 8: Boosting Model Performance
Technical requirementsImproving performance with data augmentationCharacter-level augmentationWord-level augmentationSentence-level augmentationBoosting IMDB text classification with augmentationAdapting the model to the domainOptimizing the parameters with HPOSummary
Chapter 9: Parameter Efficient Fine-Tuning
Technical requirementsIntroduction to PEFTUnderstanding Types of PEFTAdditive methodsSelective methodsLow-rank fine-tuningHands-on PEFT experimentsFine-tuning a BERT checkpoint with adapter tuningEfficiently fine-tune FLAN-T5 for an NLI task with LoraTuning with QLoRASummaryReferences
Part 3: Advanced Topics
Chapter 10: Large Language Models
Technical requirementsWhy large language models?Importance of reward functionThe instruction-following ability of LLMsFine-tuning large language modelsSummary
Chapter 11: Explainable AI (XAI) in NLP
Technical requirementsInterpreting attention headsVisualizing attention heads with exBERTMultiscale visualization of attention heads with BertVizUnderstanding the inner parts of BERT with probing classifiersExplain the model decisionInterpret Transformers’ decision with LIMEInterpret Transformers’ decision with SHAPSummary
Chapter 12: Working with Efficient Transformers
Technical requirementsIntroduction to efficient, light, and fast transformersImplementation for model size reductionWorking with DistilBERT for knowledge distillationPruning transformersQuantizationWorking with efficient self-attentionSparse attention with fixed patternsLearnable patternsLow-rank factorization, kernel methods, and other approachesEasier quantization using bitsandbytesSummaryReferences
Chapter 13: Cross-Lingual and Multilingual Language Modeling
Technical requirementsTranslation language modeling and cross-lingual knowledge sharingXLM and mBERTmBERTXLMCross-lingual similarity tasksCross-lingual text similarityVisualizing cross-lingual textual similarityCross-lingual classificationCross-lingual zero-shot learningMassive multilingual translationFine-tuning the performance of multilingual modelsSummaryReferences
Chapter 14: Serving Transformer Models
Technical requirementsFastAPI Transformer model servingDockerizing APIsFaster Transformer model serving using TFXLoad testing using LocustFaster inference using ONNXSageMaker inferenceSummaryFurther reading
Chapter 15: Model Tracking and Monitoring
Technical requirementsTracking model metricsTracking model training with TensorBoardTracking model training live with W&BSummaryFurther reading
Part 4: Transformers beyond NLP
Chapter 16: Vision Transformers
Technical requirementsVision transformersImage classification using transformersSemantic segmentation and object detection using transformersVisual prompt modelsSummary
Chapter 17: Multimodal Generative Transformers
Technical requirementsMultimodal learningGenerative multimodal AIStable Diffusion for text-to-image generationStable Diffusion in actionMusic generation using MusicGenText-to-speech generation using transformersSummary
Chapter 18: Revisiting Transformers Architecture for Time Series
Technical requirementsUnderstanding time series conceptsTransformers and time series modelingSummary
Index
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Overview

"Mastering Transformers" is your comprehensive guide to understanding and implementing the transformative potential of transformer-based models in natural language processing (NLP) and beyond. This book thoroughly covers modern architectures like BERT, GPT, and Vision Transformers, equipping you with the skills to develop cutting-edge applications for real-world challenges.

What this Book will help me do

Learn to build and fine-tune NLP models using Python and transformer libraries.
Gain insights into solving text classification and token classification problems.
Explore the implementation of transformer-based solutions for computer vision tasks.
Understand state-of-the-art methods in multimodal and generative AI applications.
Develop strategies for model efficiency, performance optimization, and monitoring.

Author(s)

Savaş Yıldırım and Meysam Asgari-Chenaghlu are experts in advanced deep learning techniques with extensive experience in machine learning applications. They have successfully contributed to various projects in the realm of artificial intelligence and authored educational resources. Their goal is to make complex concepts accessible to developers.

Who is it for?

This book caters to data scientists, ML/NLP engineers, and AI enthusiasts who wish to master transformer architectures. It's ideal for learners familiar with Python, having basic knowledge of machine learning and programming, seeking to develop practical skills in NLP and multimodal tasks. Advanced beginners aiming to take their AI expertise to the next level will find this insightful.

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