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.gitignore

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# Secret & local files
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secret/
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run_rasa*
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# DVC
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.dvc/

config.yml

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version: "3.1"
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language: en
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pipeline:
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- name: WhitespaceTokenizer
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- name: RegexFeaturizer
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- name: LexicalSyntacticFeaturizer
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- name: CountVectorsFeaturizer
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- name: CountVectorsFeaturizer
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analyzer: "char_wb"
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min_ngram: 1
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max_ngram: 4
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- name: DIETClassifier
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epochs: 100
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- name: FallbackClassifier
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threshold: 0.7
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- name: DucklingEntityExtractor
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url: http://localhost:8000
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dimensions:
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- amount-of-money
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- time
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- number
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- name: SpacyNLP
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model: "en_core_web_md"
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case_sensitive: false
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- name: "SpacyEntityExtractor"
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- name: WhitespaceTokenizer
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- name: RegexFeaturizer
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- name: LexicalSyntacticFeaturizer
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- name: CountVectorsFeaturizer
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- name: CountVectorsFeaturizer
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analyzer: "char_wb"
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min_ngram: 1
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max_ngram: 4
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- name: DIETClassifier
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epochs: 100
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- name: FallbackClassifier
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threshold: 0.7
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- name: DucklingEntityExtractor
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url: http://localhost:8000
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dimensions:
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- amount-of-money
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- time
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- number
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- name: SpacyNLP
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model: "en_core_web_md"
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case_sensitive: false
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- name: "SpacyEntityExtractor"
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# Note: It is not possible to use the SpacyTokenizer + SpacyFeaturizer in
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# combination with the WhitespaceTokenizer, and as a result the
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# PERSON extraction by Spacy is not very robust.
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# Because of this, the nlu training data is annotated as well, and the
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# DIETClassifier will also extract PERSON entities .
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dimensions: ["PERSON"]
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- name: EntitySynonymMapper
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dimensions: ["PERSON"]
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- name: EntitySynonymMapper
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policies:
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- name: AugmentedMemoizationPolicy
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- name: TEDPolicy
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epochs: 40
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- name: RulePolicy
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core_fallback_threshold: 0.4
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core_fallback_action_name: "action_default_fallback"
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enable_fallback_prediction: True
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enable_fallback_prediction: true
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assistant_id: 20231006-075245-hoary-gallery

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