This is documentation for Rasa Documentation v2.x, which is no longer actively maintained.
For up-to-date documentation, see the latest version (3.x).

Version: 2.x


RegexFeaturizer Objects

class RegexFeaturizer(SparseFeaturizer)


| __init__(component_config: Optional[Dict[Text, Any]] = None, known_patterns: Optional[List[Dict[Text, Text]]] = None, finetune_mode: bool = False) -> None

Constructs new features for regexes and lookup table using regex expressions.


  • component_config - Configuration for the component
  • known_patterns - Regex Patterns the component should pre-load itself with.
  • finetune_mode - Load component in finetune mode.


| train(training_data: TrainingData, config: Optional[RasaNLUModelConfig] = None, **kwargs: Any, ,) -> None

Trains the component with all patterns extracted from training data.


  • training_data - Training data consisting of training examples and patterns available.
  • config - NLU Pipeline config
  • **kwargs - Any other arguments


| @classmethod
| load(cls, meta: Dict[Text, Any], model_dir: Text, model_metadata: Optional[Metadata] = None, cached_component: Optional["RegexFeaturizer"] = None, should_finetune: bool = False, **kwargs: Any, ,) -> "RegexFeaturizer"

Loads a previously trained component.


  • meta - Configuration of trained component.
  • model_dir - Path where trained pipeline is stored.
  • model_metadata - Metadata for the trained pipeline.
  • cached_component - Previously cached component(if any).
  • should_finetune - Indicates whether to load the component for further finetuning.
  • **kwargs - Any other arguments.


| persist(file_name: Text, model_dir: Text) -> Optional[Dict[Text, Any]]

Persist this model into the passed directory.


  • file_name - Prefix to add to all files stored as part of this component.
  • model_dir - Path where files should be stored.


Metadata necessary to load the model again.