Rasa NLU Integration Skill

Capabilities

• Configure Rasa NLU pipelines
• Design training data in Rasa format
• Set up intent classification components
• Configure entity extraction (DIETClassifier)
• Implement pipeline optimization
• Set up model evaluation and testing

Target Processes

• intent-classification-system
• chatbot-design-implementation

Implementation Details

Pipeline Components

1. Tokenizers: WhitespaceTokenizer, SpacyTokenizer
2. Featurizers: CountVectorsFeaturizer, SpacyFeaturizer
3. Classifiers: DIETClassifier, FallbackClassifier
4. Entity Extractors: DIETClassifier, SpacyEntityExtractor

Configuration Files

• config.yml: Pipeline configuration
• nlu.yml: Training data
• domain.yml: Intents and entities

Configuration Options

• Pipeline component selection
• Featurizer settings
• Classifier parameters
• Entity extraction rules
• Fallback thresholds

Best Practices

• Start with recommended pipelines
• Tune based on domain
• Balance complexity vs performance
• Regular model retraining

Dependencies

• rasa