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).

References

• spaCy Documentation