config.yml

recipe: default.v1
assistant_id: 20230325-021950-weighted-cosine
language: en 
# pipeline:
#   - name: "WhitespaceTokenizer"
#   - name: "RegexFeaturizer"
#   - name: "LexicalSyntacticFeaturizer"
#   - name: "CountVectorsFeaturizer"
#   - name: "CountVectorsFeaturizer"
#     analyzer: "char_wb"
#     min_ngram: 1
#     max_ngram: 4
#   - name: "DIETClassifier"
#   - name: "EntitySynonymMapper"


# pipeline:
#   - name: SpacyNLP
#     model: "en_core_web_md"
#   - name: SpacyTokenizer # Tokenizer
#   - name: RegexFeaturizer ## Featuriser For regex based entities
#   - name: SpacyFeaturizer # Spacy Featuriser
#   # - name: SpacyEntityExtractor # can only extract entities that pretrained Spacy model recognizes
#   - name: DIETClassifier # Diet Classifier
#     epochs: 100
#     # entity_recognition: False # when using both spacy entity extractor and diet entity extractor, it provides entity from both

pipeline:
  - name: WhitespaceTokenizer
  - name: LanguageModelFeaturizer # Bert can be used with any tokenizer
    model_name: "bert"
    model_weights: "rasa/LaBSE"
  - name: DIETClassifier
    epochs: 200


policies: null
# # No configuration for policies was provided. The following default policies were used to train your model.
# # If you'd like to customize them, uncomment and adjust the policies.
# # See https://rasa.com/docs/rasa/policies for more information.
#   - name: MemoizationPolicy
#   - name: RulePolicy
#   - name: UnexpecTEDIntentPolicy
#     max_history: 5
#     epochs: 100
#   - name: TEDPolicy
#     max_history: 5
#     epochs: 100
#     constrain_similarities: true