Using NLU Only
User Guide
- Installation
- Rasa Tutorial
- Command Line Interface
- Architecture
- Messaging and Voice Channels
- Evaluating Models
- Validate Data
- Running the Server
- Running Rasa with Docker
- Cloud Storage
NLU
- About
- Using NLU Only
- Training Data Format
- Choosing a Pipeline
- Language Support
- Entity Extraction
- Components
Core
- About
- Stories
- Domains
- Responses
- Actions
- Policies
- Slots
- Forms
- Retrieval Actions
- Interactive Learning
- Fallback Actions
- Knowledge Base Actions
Conversation Design
API Reference
- Action Server
- HTTP API
- Jupyter Notebooks
- Agent
- Custom NLU Components
- Rasa SDK
- Events
- Tracker
- Tracker Stores
- Event Brokers
- Lock Stores
- Training Data Importers
- Featurization of Conversations
- Migration Guide
- Rasa OSS Change Log
Migrate from (beta)
Reference
Versions
viewing: 1.6.2
Warning: This document is for an old version of Rasa. The latest version is 1.10.26.
Using NLU Only
If you want to use Rasa only as an NLU component, you can!
Training NLU-only models
To train an NLU model only, run:
rasa train nlu
This will look for NLU training data files in the data/ directory and saves a trained model in the models/ directory. The name of the model will start with nlu-.
Testing your NLU model on the command line
To try out your NLU model on the command line, use the rasa shell nlu command:
rasa shell nlu
This will start the rasa shell and ask you to type in a message to test. You can keep typing in as many messages as you like.
Alternatively, you can leave out the nlu argument and pass in an nlu-only model directly:
rasa shell -m models/nlu-20190515-144445.tar.gz
Running an NLU server
To start a server with your NLU model, pass in the model name at runtime:
rasa run --enable-api -m models/nlu-20190515-144445.tar.gz
You can then request predictions from your model using the /model/parse endpoint. To do this, run:
curl localhost:5005/model/parse -d '{"text":"hello"}'
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