When doing Rasa init and visiting the config.yml, our pipeline looks like this.
pipeline: # # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model. # # If you'd like to customize it, uncomment and adjust the pipeline. # # See https://rasa.com/docs/rasa/tuning-your-model for more information. - name: WhitespaceTokenizer - name: RegexFeaturizer - name: LexicalSyntacticFeaturizer - name: CountVectorsFeaturizer - name: CountVectorsFeaturizer analyzer: char_wb min_ngram: 1 max_ngram: 4 - name: DIETClassifier epochs: 100 constrain_similarities: true - name: EntitySynonymMapper - name: ResponseSelector epochs: 100
Is there a reason why we need two mentions of CountVectorFeaturizer in our pipeline?