---
name: spacy-ner
description: spaCy NER model training and entity extraction for conversational AI
allowed-tools:
  - Read
  - Write
  - Edit
  - Bash
  - Glob
  - Grep
graph:
  domains: [domain:software-engineering]
  specializations: [specialization:ai-agents-conversational]
  skillAreas: [skill-area:natural-language-processing, skill-area:feature-engineering]
  roles: [role:ml-engineer, role:backend-engineer]
  workflows: [workflow:ml-model-lifecycle, workflow:feature-development]

---

# spaCy NER Skill

## Capabilities

- Train custom spaCy NER models
- Configure entity extraction pipelines
- Design annotation schemas
- Implement entity linking
- Set up model evaluation
- Deploy efficient NER inference

## Target Processes

- entity-extraction-slot-filling
- chatbot-design-implementation

## Implementation Details

### spaCy Components

1. **NER**: Named Entity Recognition
2. **EntityLinker**: Link to knowledge bases
3. **EntityRuler**: Rule-based matching
4. **SpanCategorizer**: Overlapping entities

### Training Configuration

- config.cfg setup
- Training data format (spaCy v3)
- Augmentation strategies
- Evaluation metrics

### Configuration Options

- Base model selection (en_core_web_*)
- Custom entity types
- Training parameters
- GPU acceleration
- Model packaging

### Best Practices

- Quality annotation data
- Balance entity types
- Use prodigy for annotation
- Regular model evaluation

### Dependencies

- spacy
- spacy-transformers (optional)