Detecting multiple regexes as separate entities

Rasa Open Source
3

Here you go:

# Configuration for Rasa NLU.
# https://rasa.com/docs/rasa/nlu/components/
language: en

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: RegexEntityExtractor
  - name: LexicalSyntacticFeaturizer
  - name: CountVectorsFeaturizer
    analyzer: char_wb
    min_ngram: 1
    max_ngram: 4
  
  - name: DIETClassifier
    epochs: 100
  - name: EntitySynonymMapper
  - name: ResponseSelector
    epochs: 100
  - name: FallbackClassifier
    threshold: 0.3
    ambiguity_threshold: 0.1
    
  - name: "SpacyNLP"
    # language model to load
    model: "en_core_web_md"

    # when retrieving word vectors, this will decide if the casing
    # of the word is relevant. E.g. `hello` and `Hello` will
    # retrieve the same vector, if set to `False`. For some
    # applications and models it makes sense to differentiate
    # between these two words, therefore setting this to `True`.
    case_sensitive: False
  - name: "SpacyEntityExtractor"
  
    # dimensions to extract
    dimensions: ["PERSON", "LOC"]  
    
  - name: "DucklingEntityExtractor"
    url: "http://127.0.0.1



    dimensions: ["time", "amount-of-money", "distance", "amount-of-money", "phone-number", "url", "credit-card-number", "email"]
    locale: "en_GB"
    timezone: "Europe/London"
    timeout: 3