Trainingsstappen

Je kunt werken aan heel specifieke domeinen die spaCy-modellen tijdens hun training niet hebben gezien, zoals het medische domein. Met spaCy kun je bestaande modellen updaten met meer voorbeelden uit je eigen data om de modelprestaties op je eigen data te verbeteren.

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Natural Language Processing met spaCy

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Deze oefening maakt deel uit van de cursus

Natural Language Processing met spaCy

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

This chapter will introduce you to NLP, some of its use cases such as named-entity recognition and AI-powered chatbots. You’ll learn how to use the powerful spaCy library to perform various natural language processing tasks such as tokenization, sentence segmentation, POS tagging, and named entity recognition.

Exercise 1: Natural Language Processing (NLP) basics Exercise 2: Doc container in spaCy Exercise 3: NER use case Exercise 4: Tokenization with spaCy Exercise 5: spaCy basics Exercise 6: Running a spaCy pipeline Exercise 7: Lemmatization with spaCy Exercise 8: Sentence segmentation with spaCy Exercise 9: Linguistic features in spaCy Exercise 10: POS tagging with spaCy Exercise 11: NER with spaCy Exercise 12: Text processing with spaCy

Learn about linguistic features, word vectors, semantic similarity, analogies, and word vector operations. In this chapter you’ll discover how to use spaCy to extract word vectors, categorize texts that are relevant to a given topic and find semantically similar terms to given words from a corpus or from a spaCy model vocabulary.

Exercise 1: Linguistic features Exercise 2: Linguistic annotations in spaCy Exercise 3: Word-sense disambiguation with spaCy Exercise 4: Dependency parsing with spaCy Exercise 5: Introduction to word vectors Exercise 6: spaCy vocabulary Exercise 7: Word vectors in spaCy vocabulary Exercise 8: Word vectors and spaCy Exercise 9: Analogies and vector operations Exercise 10: Word vectors projection Exercise 11: Similar words in a vocabulary Exercise 12: Measuring semantic similarity with spaCy Exercise 13: Doc similarity with spaCy Exercise 14: Span similarity with spaCy Exercise 15: Semantic similarity for categorizing text

Get familiar with spaCy pipeline components, how to add a pipeline component, and analyze the NLP pipeline. You will also learn about multiple approaches for rule-based information extraction using EntityRuler, Matcher, and PhraseMatcher classes in spaCy and RegEx Python package.

Exercise 1: spaCy pipelines Exercise 2: Adding pipes in spaCy Exercise 3: Analyzing pipelines in spaCy Exercise 4: spaCy EntityRuler Exercise 5: EntityRuler with blank spaCy model Exercise 6: EntityRuler for NER Exercise 7: EntityRuler with multi-patterns in spaCy Exercise 8: RegEx with spaCy Exercise 9: RegEx in Python Exercise 10: RegEx with EntityRuler in spaCy Exercise 11: spaCy Matcher and PhraseMatcher Exercise 12: Matching a single term in spaCy Exercise 13: PhraseMatcher in spaCy Exercise 14: Matching with extended syntax in spaCy

Explore multiple real-world use cases where spaCy models may fail and learn how to train them further to improve model performance. You’ll be introduced to spaCy training steps and understand how to train an existing spaCy model or from scratch, and evaluate the model at the inference time.

Exercise 1: spaCy-modellen aanpassen Exercise 2: spaCy-modellen trainen Exercise 3: Modelprestaties op je eigen data Exercise 4: spaCy-trainingsdataformaat Exercise 5: Trainingsstappen

Huidige oefening

Exercise 6: Annoteren en trainingsdata voorbereiden Exercise 7: Compatibele trainingsdata Exercise 8: Trainen met spaCy Exercise 9: Stappen ter voorbereiding van training Exercise 10: Train een bestaand NER-model Exercise 11: Een spaCy-model vanaf nul trainen Exercise 12: Afronding