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This is documentation for Rasa Open Source Documentation v2.2.x, which is no longer actively maintained.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 importerdomain
- domain used for loadingremove_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.