rasa-nlu-integration | babysitter | ClaudePluginHub

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rasa-nlu-integration

From babysitter

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Description

Rasa NLU pipeline configuration and training for intent and entity extraction

AI Summary

Configures and trains Rasa NLU pipelines for intent and entity extraction.

Install

1

Run in your terminal

$

npx claudepluginhub a5c-ai/babysitter

Tool Access

This skill is limited to using the following tools:

ReadWriteEditBashGlobGrep

Supporting Assets

View in Repository

README.md

Skill 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

Tokenizers: WhitespaceTokenizer, SpacyTokenizer
Featurizers: CountVectorsFeaturizer, SpacyFeaturizer
Classifiers: DIETClassifier, FallbackClassifier
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

Links

GitHub Stats

21 forks

Updated 1 month ago

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