[Rasa] 'rasa train core" returns GraphicsSchemaValidationException error

Getting Started with Rasa
1

raise GraphSchemaValidationException( rasa.engine.exceptions.GraphSchemaValidationException: Your model uses a component ‘DIETClassifier’ which needs the param ‘training_data’ to be provided to its method ‘train’. Please make sure that you registered your component correctly and that your model configuration is valid.See Custom Graph Components for more information.

This command was working fine with the previous config.yml file. While the testing model in an Interactive mode, accidentally the working config.yml was overwritten by the system and it stopped working.

Here is the config.yml that needs to be reviewed and corrected:

The config recipe.

Model Configuration

recipe: default.v1

Configuration for 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 Tuning Your NLU Model for more information.

  • name: WhitespaceTokenizer
  • name: RegexFeaturizer
  • name: LexicalSyntacticFeaturizer
  • name: CountVectorsFeaturizer
  • name: CountVectorsFeaturizer analyzer: char_wb min_ngram: 1 max_ngram: 4

#- name: your.custom.Policy

#- name: “SpacyNLP”

# language model to load

model: “en_core_web_md”

  • name: DIETClassifier5 epochs: 100 train: training_data constrain_similarities: true

  • name: EntitySynonymMapper

  • name: ResponseSelector epochs: 100 constrain_similarities: true

  • name: FallbackClassifier threshold: 0.3 ambiguity_threshold: 0.1

Configuration for 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 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

=============================================================================

  • name: RulePolicy

    Confidence threshold for the core_fallback_action_name to apply.

    The action will apply if no other action was predicted with

    a confidence >= core_fallback_threshold

    core_fallback_threshold: 0.4 core_fallback_action_name: “action_default_fallback” enable_fallback_prediction: True

=============================================================================

  • name: DIETClassifier random_seed: 42 loss_type: cross_entropy model_confidence: softmax constrain_similarities: True intent_classification: True entity_recognition: False use_masked_language_model: True epochs: 80 number_of_transformer_layers: 4 transformer_size: 256 drop_rate: 0.2 batch_size: [64, 256] embedding_dimension: 50 train: TrainingData hidden_layers_sizes: text: [512, 128] label: [] dense_dimension: text: 128