In Rasa 2.X, we are able to load the trained nlu model with following code to parse data. from rasa.nlu.model import Interpreter interpreter = Interpreter.load('models/nlu-xyz/nlu') result = interpreter.parse('hello world',only_output_properties=False) embeds = result.get("text_sparse_features") However, I’m getting error due to Interpreter is removed in Rasa 3.0 ImportError: cannot import name 'Interpreter' from 'rasa.nlu.model' May I know any other approach in Rasa 3.0 can achieve the same thing?

Hi, Yes you can do it without command line or server, with both Rasa 2 and 3, try: from rasa.core.agent import Agent agent = Agent.load(model_path='/path/to/my/model.tar.gz') # For Rasa 2 (I tried it with 2.8.8) # agent.parse_message_using_nlu_interpreter( # message_data='Hello there') # For Rasa 3 agent.parse_message( message_data='Hello there') Beware, the output is async, so you might need to await it or something like that: # Not a great solution, but shows the idea import asyncio asyncio.run(agent.parse_message_using_nlu_interpreter( message_data='Hello there')) My output (for Rasa 2, but it should be the same for Rasa 3): In [5]: asyncio.run(agent.parse_message_using_nlu_interpreter(message_data="Hello there")) Out[5]: {'text': 'Hello there', 'intent': {'id': -5370814688626815577, 'name': 'greet', 'confidence': 0.9921479821205139}, 'entities': [], 'intent_ranking': [{'id': -5370814688626815577, 'name': 'greet', 'confidence': 0.9921479821205139}, #... More intent ranking ], 'response_selector': {'all_retrieval_intents': [], 'default': {'response': {'id': None, 'responses': None, 'response_templates': None, 'confidence': 0.0, 'intent_response_key': None, 'utter_action': 'utter_None', 'template_name': 'utter_None'}, 'ranking': []}}} Hope that helps!

Using nlu model with Interpreter.load in Rasa 3.0

Rasa Open Source

isgaal (isgaal) January 25, 2022, 11:19am 8

Okay, thanks a lot for your help

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Using nlu model with Interpreter.load in Rasa 3.0

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