Config File for Indic language

Rasa Open Source
1

We are working on a project in indic languages.We have around 20 intent with average 30 examples per intent.On testing the model with 100 sentences we are getting average accuracy.We are using the below config.

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: 3
- name: rasa_nlu_examples.featurizers.sparse.TfIdfFeaturizer
  min_ngram: 1
  max_ngram: 3
- name: DIETClassifier
  epochs: 450
  constrain_similarities: true
- name: EntitySynonymMapper
- name: ResponseSelector
  epochs: 450
  constrain_similarities: true
- name: FallbackClassifier
  threshold: 0.7
  ambiguity_threshold: 0.1

# Configuration for Rasa Core.
# https://rasa.com/docs/rasa/core/policies/
policies:
# # 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
  core_fallback_threshold: 0.4
  core_fallback_action_name: "action_default_fallback"
  enable_fallback_prediction: true

- name: UnexpecTEDIntentPolicy
  max_history: 5
  epochs: 450
- name: TEDPolicy
  max_history: 5
  epochs: 450
  constrain_similarities: true

We needed some suggestion how to increase the accuracy of the model.