Every time i train my model my chatbot is giving different replys. some times i tried forcing the model train using --force and results are exceptionally good. I’m not sure why this is happening. But sometimes it is failing to extract intents and entities which were extracted correctly in previous model. Here is our config.yml language: en pipeline: - name: "lib.sana_custom_components.SanaSpellCheckerEN" - name: "lib.sana_custom_components.SanaTrueCaseConvertor" - name: SpacyNLP - name: SpacyTokenizer - name: RegexFeaturizer - name: LexicalSyntacticFeaturizer - name: CountVectorsFeaturizer - name: CountVectorsFeaturizer analyzer: "char_wb" min_ngram: 1 max_ngram: 4 - name: DIETClassifier epochs: 100 - name: EntitySynonymMapper - name: ResponseSelector epochs: 100 # Configuration for Rasa Core. # https://rasa.com/docs/rasa/core/policies/ policies: - name: MemoizationPolicy - name: MappingPolicy - name: TEDPolicy max_history: 5 epochs: 100 - name: FormPolicy - name: TwoStageFallbackPolicy nlu_threshold: 0.3 core_threshold: 0.3 fallback_core_action_name: "action_default_fallback" fallback_nlu_action_name: "action_default_fallback" deny_suggestion_intent_name: "out_of_scope"

Welcome Satya, What version of Rasa are you using?

Hi @SriSatyaLokesh to get reproducible training results, you should set a random_seed for some of the NLU components and policies. So your config would look like this: language: en pipeline: - name: "lib.sana_custom_components.SanaSpellCheckerEN" - name: "lib.sana_custom_components.SanaTrueCaseConvertor" - name: SpacyNLP - name: SpacyTokenizer - name: RegexFeaturizer - name: LexicalSyntacticFeaturizer - name: CountVectorsFeaturizer - name: CountVectorsFeaturizer analyzer: "char_wb" min_ngram: 1 max_ngram: 4 - name: DIETClassifier epochs: 100 random_seed: 42 - name: EntitySynonymMapper - name: ResponseSelector random_seed: 42 epochs: 100 policies: - name: MemoizationPolicy - name: MappingPolicy - name: TEDPolicy random_seed: 42 max_history: 5 epochs: 100 - name: FormPolicy - name: TwoStageFallbackPolicy nlu_threshold: 0.3 core_threshold: 0.3 fallback_core_action_name: "action_default_fallback" fallback_nlu_action_name: "action_default_fallback" deny_suggestion_intent_name: "out_of_scope" Please be aware that if you have drastically inconsistent results though, that may be an indicator that your training data needs some improvements. So it may be worth investing some time into adding more examples to your NLU data

Trained model is very inconsistent

Rasa Open Source

SriSatyaLokesh (Satya) May 19, 2020, 7:21am 3

rasa==1.9.6
rasa-sdk==1.9.0

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Trained model is very inconsistent

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