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claude-codedevelopment

Babysitter rasa-nlu-integration

Rasa NLU pipeline configuration and training for intent and entity extraction

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/ai-agents-conversational/skills/rasa-nlu-integration" ~/.claude/skills/a5c-ai-babysitter-rasa-nlu-integration && rm -rf "$T"
manifest: library/specializations/ai-agents-conversational/skills/rasa-nlu-integration/SKILL.md
tags
#nlu-setup#rasa-pipeline#intent-classification#entity-extraction#training-data
source content

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