spaCy NLP library with pipelines. Use for text processing.
Scanned 9/2/2026
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---
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).
## References
- [spaCy Documentation](https://spacy.io/)
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