Usage problem: I have created a custom NLU component following this . It detects language of the text, adds that language as an extra entity. I have not included this extra entity type under domain -> entity. But I have set a slot for this entity type, a slot of type unfeaturized. If I train NLU+core, with LanguageClassifier component and language slot both disabled, everything works as expected. But if I enable the LanguageClassifier component and language slot (type:unfeaturized) both, the core breaks up. It doesn’t proceed forward at all. The very first action is predicted as “custom_fallback” (with UserUtteranceReverted). So you can imagine the infinite loop. My guess is that the language slot of type unfeaturized is not working as it should. What do you guys think? How to solve this? Also, is there a better way to add this new 'language" property of text to message? Something like : message.set("language", language, True) Without affecting predictions by core? What all “message properties” does core use to predict next action? Gist of the component class: class LanguageClassifier(Component): requires: List = [ "entities", ] provides: List = [ "entities", ] # Component configurations. These values can be overwritten in the `config` file defaults: Dict = { "model_params": { "kernel": "linear", }, } # What language(s) this component can handle. Default - None : handles all languages language_list = None def __init__( self, component_config: Dict[Text, Any] = None): super(LanguageClassifier, self).__init__(component_config) def train(...): """ Train this component. """ def process(self, message, **kwargs): lang = "en" if some_condition: text = message.text text = text.lower() arr = self._featurize_text(text) lang = self.clf.predict([arr])[0] # I WANT TO DO THIS!! # message.set("language", lang, add_to_output=True) entity = self.convert_to_rasa(lang, 1.0) message.set( "entities", message.get("entities", []) + [entity], add_to_output=True ) @classmethod def load(...) -> "LanguageClassifier": pass def persist(...) -> Optional[Dict[Text, Any]]: pass def convert_to_rasa(self, value: Text, confidence: float) -> Dict: entity = { "value": value, "confidence": confidence, "entity": "language", "extractor": "LanguageClassifier", "start":0, "end":0 } return entity Command or request that led to error: Content of configuration file (config.yml) (if relevant): # Configuration for Rasa NLU. # https://rasa.com/docs/rasa/nlu/components/ language: en pipeline: - name : WhitespaceTokenizer - name : RegexFeaturizer - name : CRF…

How to properly use `unfeaturized` slot, so that it does not affect core predictions?

akelad (Akela Drissner) June 18, 2019, 1:50pm 2

what do your stories look like? And can you post the debug log of when this happens (from rasa shell --debug)

Topic		Replies	Views
I want to know the use case of unfeaturized slot Rasa Open Source	8	3218	May 3, 2021
Details about conversational-ai-workshop-18 Rasa Open Source	3	466	January 9, 2019
Confused about slot types and their impact on entity detection Rasa Open Source	2	420	July 24, 2020
How to prevent entities to effect Rasa Core prediction Rasa Open Source	1	532	March 9, 2020
Rasa NLU - intent classification and slots Rasa Open Source	1	462	January 29, 2020