spacy-ner

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)