book

Natural Language Processing with Flair

by Tadej Magajna

April 2022

Beginner to intermediate

200 pages

4h 17m

English

Packt Publishing

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ContributorsAbout the authorAbout the reviewers
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your Thoughts
Technical requirementsA brief introduction to NLPTokenizationText vectorizationNamed entity recognitionWord-sense disambiguationPart-of-speech taggingChunkingStemming and lemmatizationText classificationIntroducing FlairSetting up the development environmentCreating the virtual environmentInstalling a published version of Flair in a virtual environmentInstalling directly from the GitHub repository (optional)Running code that uses Flair Summary
Technical requirementsSentence and Token objectsUnderstanding the Sentence classUnderstanding the Token classTokenization in FlairSentence and Token object helper methodsUsing custom tokenizersUsing the TokenizerWrapper classUnderstanding the Corpus objectSummary
Technical requirementsUnderstanding word embeddingsCosine similarityThe "king – man ≈ queen – woman" analogyClassic word embeddings in FlairFlair embeddingsUnderstanding the contextuality of Flair embeddingsCharacter-level sequence modeling in Flair embeddingsPooled Flair embeddingsAvailable Flair embeddingsStacked embeddingsDocument embeddingsOther embeddings in FlairSummary
Technical requirementsUnderstanding sequence tagging in FlairNamed entity recognition in FlairTypes of NER taggers in FlairPart-of-speech tagging in FlairPart-of-speech tagging tag setsTypes of PoS taggers in FlairOther tagging tasks in FlairSequence labeling metricsMeasuring accuracy for sequence labeling tasksMeasuring the F1 score for sequence labeling tasksSummary
Technical requirementsThe motivation behind training custom modelsUnderstanding the hardware requirements for training modelsWorking with parameters for training and evaluationUnderstanding neural model trainingParameters and terminology used in Flair model trainingTraining custom sequence labeling modelsLoading a tagged corpusLoading the tag dictionaryBuilding the embedding stackInitializing the SequenceTagger objectTraining the modelUnderstanding training output filesLoading and using custom Flair modelsKnowing when to stop and try againMonitoring lossAssessing and comparing performance metricsSummary
Technical requirementsUnderstanding hyperparameter tuningOptimization methodsHyperparameter tuning in PythonHyperparameter tuning in Python hands-onHyperparameter optimization in FlairHyperparameter tuning in Flair hands-onSummary

Technical requirementsUnderstanding the how and why behind custom Flair embeddingsWhy training embeddings rarely ever means training embeddingsCorpora for Flair embeddingsDictionaries for training Flair embeddingsEvaluating word embeddingsTraining Flair embeddings on the world's smallest languagePreparing the dictionaryPreparing the corpusTraining the language modelUsing custom embeddings on downstream tasksPerforming intrinsic evaluation on custom Flair embeddingsSummary
Technical requirementsUnderstanding text classificationTraining text classifiersText classification in FlairUsing pre-trained Flair text classification modelsDocument embeddings in FlairText classifiers in FlairTraining a text classifier in FlairWorking with text classifiers that require little to no training dataZero-shot classification with TARSFew-shot classification with TARSSummary
Technical requirementsTechnical considerations for NLP models in productionClient-side versus server-side inferenceWhen to use managed services and when to self-serve server-side modelsManaged services for Flair modelsA model deployment decision-making flowchartSelf-serving flair modelsA proof-of-concept self-serve tool for FlairUsing managed services for deploying and using Flair modelsThe Hugging Face Models HubSummary
Technical requirements Understanding the trading strategyImplementing the Flair trading botA Flair coding cheat sheetSummary
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Overview

Natural Language Processing with Flair is your practical guide to mastering NLP using the innovative Flair framework. With hands-on exercises and detailed explanations, this book will empower you to solve real-world NLP problems such as text classification and sequence tagging effectively. By exploring Flair's capabilities, you will gain both confidence and competence in NLP.

What this Book will help me do

Understand core natural language processing concepts and terminology.
Develop expertise in the Flair NLP framework and leverage its features.
Train custom NLP models including sequence tagging and text classifiers.
Utilize advanced techniques like hyperparameter tuning and text classification with TARS.
Deploy high-performing NLP solutions built with Flair into production environments.

Author(s)

Natural Language Processing with Flair is authored by None Magajna, an experienced professional in natural language processing and related fields. With comprehensive knowledge of the Flair ecosystem, the author brings practical insights into solving NLP problems. None's writing focuses on combining deep expertise with accessible explanations to guide learners effectively.

Who is it for?

This book is ideal for software engineers, data scientists, and students seeking a practical introduction to NLP with Flair. Beginners in NLP will find it suited to their needs, while its coverage of hands-on projects ensures relevance for those aiming to implement real-world solutions. Knowledge of Python is expected to grasp the concepts effectively.