book

Mastering Transformers

Name: Mastering Transformers
ISBN: 9781801077651

by Savaş Yıldırım, Meysam Asgari- Chenaghlu

September 2021

Beginner to intermediate

374 pages

7h 35m

English

Packt Publishing

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Mastering Transformers
ContributorsAbout the authorsAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesCode in ActionDownload the color imagesConventions usedGet in touchShare Your Thoughts
Section 1: Introduction – Recent Developments in the Field, Installations, and Hello World Applications
Chapter 1: From Bag-of-Words to the Transformer
Technical requirementsEvolution of NLP toward TransformersUnderstanding distributional semanticsBoW implementationOvercoming the dimensionality problemLanguage modeling and generationLeveraging DLLearning word embeddings A brief overview of RNNsLSTMs and gated recurrent unitsA brief overview of CNNsOverview of the Transformer architectureAttention mechanismMulti-head attention mechanismsUsing TL with TransformersSummaryReferences
Chapter 2: A Hands-On Introduction to the Subject
Technical requirementsInstalling Transformer with AnacondaInstallation on LinuxInstallation on WindowsInstallation on macOSInstalling TensorFlow, PyTorch, and TransformerInstalling using Google ColabWorking with language models and tokenizersWorking with community-provided modelsWorking with benchmarks and datasets Important benchmarksAccessing the datasets with an Application Programming InterfaceBenchmarking for speed and memorySummary
Section 2: Transformer Models – From Autoencoding 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 communityUnderstanding other autoencoding modelsIntroducing ALBERT RoBERTaELECTRAWorking with tokenization algorithmsByte pair encoding WordPiece tokenizationSentence piece tokenizationThe tokenizers librarySummary
Chapter 4:Autoregressive and Other Language Models
Technical requirementsWorking with AR language modelsIntroduction and training models with GPTTransformer-XLXLNetWorking with Seq2Seq modelsT5Introducing BARTAR language model trainingNLG using AR modelsSummarization and MT fine-tuning using simpletransformersSummaryReferences
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 PyTorch Fine-tuning BERT for multi-class classification with custom datasetsFine-tuning the BERT model for sentence-pair regression Utilizing run_glue.py to fine-tune the modelsSummary
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 classificationSummary

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 embeddingsSentence-BERT embeddingsText clustering with Sentence-BERTTopic modeling with BERTopicSemantic search with Sentence-BERTSummaryFurther reading
Section 3: Advanced Topics
Chapter 8: 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 approachesSummaryReferences
Chapter 9:Cross-Lingual and Multilingual Language Modeling
Technical requirementsTranslation language modeling and cross-lingual knowledge sharingXLM and mBERTmBERTXLM Cross-lingual similarity tasksCross-lingual text similarityVisualizing cross-lingual textual similarityCross-lingual classificationCross-lingual zero-shot learningFundamental limitations of multilingual modelsFine-tuning the performance of multilingual modelsSummaryReferences
Chapter 10: Serving Transformer Models
Technical requirementsfastAPI Transformer model servingDockerizing APIsFaster Transformer model serving using TFXLoad testing using LocustSummaryReferences
Chapter 11: Attention Visualization and Experiment Tracking
Technical requirementsInterpreting attention headsVisualizing attention heads with exBERTMultiscale visualization of attention heads with BertVizUnderstanding the inner parts of BERT with probing classifiersTracking model metricsTracking model training with TensorBoardTracking model training live with W&BSummaryReferences
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Content preview from Mastering Transformers

Chapter 7: Text Representation

So far, we have addressed classification and generation problems with the transformers library. Text representation is another crucial task in modern Natural Language Processing (NLP), especially for unsupervised tasks such as clustering, semantic search, and topic modeling. Representing sentences by using various models such as Universal Sentence Encoder (USE) and Siamese BERT (Sentence-BERT) with additional libraries such as sentence transformers will be explained here. Zero-shot learning using BART will also be explained, and you will learn how to utilize it. Few-shot learning methodologies and unsupervised use cases such as semantic text clustering and topic modeling will also be described. Finally, one-shot ...

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Mastering Transformers

by Savaş Yıldırım, Meysam Asgari- Chenaghlu

Chapter 7: Text Representation

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