def interactive_core(args: argparse.Namespace): args.finetune = False # Don't support finetuning zipped_model = train.train_core(args) perform_interactive_learning(args, zipped_model)
def interactive(args: argparse.Namespace) -> None: _set_not_required_args(args) file_importer = TrainingDataImporter.load_from_config( args.config, args.domain, args.data) if args.model is None: loop = asyncio.get_event_loop() story_graph = loop.run_until_complete(file_importer.get_stories()) if not story_graph or story_graph.is_empty(): rasa.shared.utils.cli.print_error_and_exit( "Could not run interactive learning without either core data or a model containing core data." ) zipped_model = train.train_core( args) if args.core_only else train.train(args) if not zipped_model: rasa.shared.utils.cli.print_error_and_exit( "Could not train an initial model. Either pass paths " "to the relevant training files (`--data`, `--config`, `--domain`), " "or use 'rasa train' to train a model.") else: zipped_model = get_provided_model(args.model) if not (zipped_model and os.path.exists(zipped_model)): rasa.shared.utils.cli.print_error_and_exit( f"Interactive learning process cannot be started as no initial model was " f"found at path '{args.model}'. Use 'rasa train' to train a model." ) if not args.skip_visualization: logger.info(f"Loading visualization data from {args.data}.") perform_interactive_learning(args, zipped_model, file_importer)
def interactive_core(args: argparse.Namespace): _set_not_required_args(args) if args.model is None: zipped_model = train.train_core(args) else: zipped_model = get_provided_model(args.model) perform_interactive_learning(args, zipped_model)
def interactive_core(args: argparse.Namespace): args.fixed_model_name = None args.store_uncompressed = False if args.model is None: zipped_model = train.train_core(args) else: zipped_model = get_provided_model(args.model) perform_interactive_learning(args, zipped_model)