Learn SPACY with Real Code Examples

Language class for language-specific models

Doc, Token, and Span objects for structured text representation

Pipeline components: tokenizer, tagger, parser, ner

Vectors and similarity computation modules

Integration hooks for custom components and ML models

Text is tokenized into Doc objects

Operations applied via pipeline components

Entities and dependencies stored in Doc/Token/Span

Vectors allow similarity computation

Batch and streaming pipelines optimize performance

Pipeline-centric architecture

Modular components (tokenizer, tagger, parser, NER)

Vector and ML model integration

Rule-based matching alongside ML

Support for custom extensions and components

Chatbots and conversational AI

Text analytics and information extraction

Document classification and sentiment analysis

Recommendation systems based on NLP

Multilingual NLP pipelines for global applications

High-performance industrial NLP

Python-native and efficient

Extensible pipelines

Seamless integration with ML/DL frameworks

Consistency and reproducibility

Use nlp.pipe for batch processing

Leverage GPU for vector computations

Optimize memory for large corpora

Parallelize preprocessing steps

Use cloud or distributed pipelines for heavy workloads

Upgrade via pip/conda

Check for deprecated APIs

Validate pipelines after upgrade

Update custom components if needed

Test model compatibility with new spaCy versions