notice

This is documentation for Rasa Open Source Documentation v2.5.x, which is no longer actively maintained.
For up-to-date documentation, see the latest version (2.8.x).

Version: 2.5.x

rasa.model_training

TrainingResult Objects

class TrainingResult(NamedTuple)

Holds information about the results of training.

train_async

async train_async(domain: Union[Domain, Text], config: Text, training_files: Optional[Union[Text, List[Text]]], output: Text = DEFAULT_MODELS_PATH, dry_run: bool = False, force_training: bool = False, fixed_model_name: Optional[Text] = None, persist_nlu_training_data: bool = False, core_additional_arguments: Optional[Dict] = None, nlu_additional_arguments: Optional[Dict] = None, model_to_finetune: Optional[Text] = None, finetuning_epoch_fraction: float = 1.0) -> TrainingResult

Trains a Rasa model (Core and NLU).

Arguments:

  • domain - Path to the domain file.
  • config - Path to the config for Core and NLU.
  • training_files - Paths to the training data for Core and NLU.
  • output_path - Output path.
  • dry_run - If True then no training will be done, and the information about whether the training needs to be done will be printed.
  • force_training - If True retrain model even if data has not changed.
  • fixed_model_name - Name of model to be stored.
  • persist_nlu_training_data - True if the NLU training data should be persisted with the model.
  • core_additional_arguments - Additional training parameters for core training.
  • nlu_additional_arguments - Additional training parameters forwarded to training method of each NLU component.
  • model_to_finetune - Optional path to a model which should be finetuned or a directory in case the latest trained model should be used.
  • finetuning_epoch_fraction - The fraction currently specified training epochs in the model configuration which should be used for finetuning.

Returns:

An instance of TrainingResult.

handle_domain_if_not_exists

async handle_domain_if_not_exists(file_importer: TrainingDataImporter, output_path: Text, fixed_model_name: Optional[Text]) -> Text

Trains only the nlu model and prints a warning about missing domain.

dry_run_result

dry_run_result(fingerprint_comparison: FingerprintComparisonResult) -> Tuple[int, List[Text]]

Returns a dry run result.

Arguments:

  • fingerprint_comparison - A result of fingerprint comparison operation.

Returns:

A tuple where the first element is the result code and the second is the list of human-readable texts that need to be printed to the end user.

train_core_async

async train_core_async(domain: Union[Domain, Text], config: Text, stories: Text, output: Text, train_path: Optional[Text] = None, fixed_model_name: Optional[Text] = None, additional_arguments: Optional[Dict] = None, model_to_finetune: Optional[Text] = None, finetuning_epoch_fraction: float = 1.0) -> Optional[Text]

Trains a Core model.

Arguments:

  • domain - Path to the domain file.
  • config - Path to the config file for Core.
  • stories - Path to the Core training data.
  • output - Output path.
  • train_path - If None the model will be trained in a temporary directory, otherwise in the provided directory.
  • fixed_model_name - Name of model to be stored.
  • additional_arguments - Additional training parameters.
  • model_to_finetune - Optional path to a model which should be finetuned or a directory in case the latest trained model should be used.
  • finetuning_epoch_fraction - The fraction currently specified training epochs in the model configuration which should be used for finetuning.

Returns:

If train_path is given it returns the path to the model archive, otherwise the path to the directory with the trained model files.

train_nlu

train_nlu(config: Text, nlu_data: Text, output: Text, train_path: Optional[Text] = None, fixed_model_name: Optional[Text] = None, persist_nlu_training_data: bool = False, additional_arguments: Optional[Dict] = None, domain: Optional[Union[Domain, Text]] = None, model_to_finetune: Optional[Text] = None, finetuning_epoch_fraction: float = 1.0) -> Optional[Text]

Trains an NLU model.

Arguments:

  • config - Path to the config file for NLU.
  • nlu_data - Path to the NLU training data.
  • output - Output path.
  • train_path - If None the model will be trained in a temporary directory, otherwise in the provided directory.
  • fixed_model_name - Name of the model to be stored.
  • persist_nlu_training_data - True if the NLU training data should be persisted with the model.
  • additional_arguments - Additional training parameters which will be passed to the train method of each component.
  • domain - Path to the optional domain file/Domain object.
  • model_to_finetune - Optional path to a model which should be finetuned or a directory in case the latest trained model should be used.
  • finetuning_epoch_fraction - The fraction currently specified training epochs in the model configuration which should be used for finetuning.

Returns:

If train_path is given it returns the path to the model archive, otherwise the path to the directory with the trained model files.

train_nlu_async

async train_nlu_async(config: Text, nlu_data: Text, output: Text, train_path: Optional[Text] = None, fixed_model_name: Optional[Text] = None, persist_nlu_training_data: bool = False, additional_arguments: Optional[Dict] = None, domain: Optional[Union[Domain, Text]] = None, model_to_finetune: Optional[Text] = None, finetuning_epoch_fraction: float = 1.0) -> Optional[Text]

Trains an NLU model asynchronously.