Hey guys, in the past I have always worked with Rasa X 0.41.1 and now updated to the newest version. I also needed to convert my files (domain, config, …). I did this with the automated migration. All the files look fine and also my mappings in the domain were converted to rules very well. After I built a model with the new files, the bot identifies every intent as an nlu_fallback with high probability – no matter what words I type. I guess there is some problem with my new config compared to the old one. My bot was mostly based on the mapping policy and the memoization policy. It looked like this:
language: de pipeline: supervised_embeddings policies: - name: MemoizationPolicy max_history: 2 - name: KerasPolicy - name: MappingPolicy - name: FallbackPolicy nlu_threshold: 0.35 core_threshold: 0.35 ambiguity_threshold: 0.05 fallback_action_name: action_default_fallback
The new transformed config is:
language: de pipeline: - name: SpacyNLP - name: SpacyTokenizer - name: SpacyFeaturizer - name: RegexFeaturizer - name: LexicalSyntacticFeaturizer - name: CountVectorsFeaturizer - name: CountVectorsFeaturizer analyzer: char_wb min_ngram: 1 max_ngram: 4 - name: DIETClassifier epochs: 1 - name: EntitySynonymMapper - name: ResponseSelector epochs: 1 - name: FallbackClassifier threshold: 0.35 ambiguity_threshold: 0.05 policies: - name: RulePolicy core_fallback_threshold: 0.35 core_fallback_action_name: action_default_fallback - name: MemoizationPolicy max_history: 2
Does someone has an idea about what could be the problem here and why the nlu always identifies the fallback intent?
Thanks in advance!