# \[Rasa\] 'rasa train core" returns GraphicsSchemaValidationException error

**URL:** <https://forum.rasa.com/t/rasa-rasa-train-core-returns-graphicsschemavalidationexception-error/54151>\
**Category:** Getting Started with Rasa\
**Created:** [July 6, 2022, 9:08am UTC](https://forum.rasa.com/t/rasa-rasa-train-core-returns-graphicsschemavalidationexception-error/54151 "2022-07-06T09:08:37Z")\
**Posts on this page:** 1\
**Showing post:** 1

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**Author:** ![JayMaddireddy](https://avatars.discourse-cdn.com/v4/letter/j/ee59a6/32.png) [@JayMaddireddy](https://forum.rasa.com/u/JayMaddireddy)\
**Post date:** [July 6, 2022, 9:08am UTC](https://forum.rasa.com/t/rasa-rasa-train-core-returns-graphicsschemavalidationexception-error/54151/1 "2022-07-06T09:08:37Z")

</div>

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](https://rasa.com/docs/rasa/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.

# [Overview | Rasa Documentation](https://rasa.com/docs/rasa/model-configuration/)

recipe: default.v1

# Configuration for Rasa NLU.

# [https://rasa.com/docs/rasa/nlu/components/](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](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: 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.

# [https://rasa.com/docs/rasa/core/policies/](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 [Policy Overview | Rasa Documentation](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

# =============================================================================

- 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

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