notice

This is documentation for Rasa Open Source Documentation v2.2.x, which is no longer actively maintained.
For up-to-date documentation, see the latest version (2.3.x).

Version: 2.2.x

rasa.core.training

load_data

async load_data(resource_name: Union[Text, "TrainingDataImporter"], domain: "Domain", remove_duplicates: bool = True, unique_last_num_states: Optional[int] = None, augmentation_factor: int = 50, tracker_limit: Optional[int] = None, use_story_concatenation: bool = True, debug_plots: bool = False, exclusion_percentage: Optional[int] = None) -> List["TrackerWithCachedStates"]

Load training data from a resource.

Arguments:

  • resource_name - resource to load the data from. either a path or an importer
  • domain - domain used for loading
  • remove_duplicates - should duplicated training examples be removed?
  • unique_last_num_states - number of states in a conversation that make the a tracker unique (this is used to identify duplicates) augmentation_factor: by how much should the story training data be augmented tracker_limit: maximum number of trackers to generate during augmentation use_story_concatenation: should stories be concatenated when doing data augmentation debug_plots: generate debug plots during loading exclusion_percentage: how much data to exclude

Returns:

list of loaded trackers

persist_data

persist_data(trackers: List["DialogueStateTracker"], path: Text) -> None

Dump a list of dialogue trackers in the story format to disk.