By name: spacy description: spaCy NLP library with pipelines. Use for text processing.
spaCy
spaCy is "Industrial Strength" NLP. Unlike NLTK (academic), spaCy focuses on providing the best single algorithm for a task. v3.8 supports Python 3.13.
When to Use
- NER (Named Entity Recognition): Extracting person names, dates, orgs.
- Parsing: Dependency parsing to understand sentence structure.
- Speed: Cython-optimized pipelines.
Core Concepts
Pipeline
Tokenizer -> Tagger -> Parser -> NER.
Doc / Token / Span
The core data structures. Efficient memory usage.
Prodigy
The annotation tool (paid) from the same creators, tightly integrated.
Best Practices (2025)
Do:
- Use Transformer pipelines:
en_core_web_trf(Roberta-based) for high accuracy. - Use
nlp.pipe(): For batch processing huge texts.
Don't:
- Don't use for GenAI: spaCy is for structure extraction, not text generation (LLMs).