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Rasa nlu integration

Skill a5c-ai/babysitter/library/specializations/ai-agents-conversational/skills/rasa-nlu-integration

Rasa NLU pipeline configuration and training for intent and entity extractionFrom its SKILL.md

Install
npx -y skills add a5c-ai/babysitter --skill rasa-nlu-integration

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

1.6 KB, 239 tokens by cl100k_base, as published. Nobody here has run it

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

What ships with it: 1 file

485 B alongside SKILL.md

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