def create_argument_parser(): import argparse parser = argparse.ArgumentParser( description='run a Rasa NLU model locally on the command line ' 'for manual testing') parser.add_argument('-m', '--model', required=True, help="path to model") utils.add_logging_option_arguments(parser, default=logging.INFO) return parser
def create_argument_parser(): parser = argparse.ArgumentParser( description='train a custom language parser') parser.add_argument('-o', '--path', default="models/nlu/", help="Path where model files will be saved") group = parser.add_mutually_exclusive_group(required=True) group.add_argument('-d', '--data', default=None, help="Location of the training data. For JSON and " "markdown data, this can either be a single file " "or a directory containing multiple training " "data files.") group.add_argument('-u', '--url', default=None, help="URL from which to retrieve training data.") parser.add_argument('-c', '--config', required=True, help="Rasa NLU configuration file") parser.add_argument('-t', '--num_threads', default=1, type=int, help="Number of threads to use during model training") parser.add_argument('--project', default=None, help="Project this model belongs to.") parser.add_argument('--fixed_model_name', help="If present, a model will always be persisted " "in the specified directory instead of creating " "a folder like 'model_20171020-160213'") parser.add_argument( '--storage', help='Set the remote location where models are stored. ' 'E.g. on AWS. If nothing is configured, the ' 'server will only serve the models that are ' 'on disk in the configured `path`.') utils.add_logging_option_arguments(parser) return parser
def create_argument_parser(): import argparse parser = argparse.ArgumentParser( description='evaluate a Rasa NLU pipeline with cross ' 'validation or on external data') parser.add_argument('-d', '--data', required=True, help="file containing training/evaluation data") parser.add_argument('--mode', default="evaluation", help="evaluation|crossvalidation (evaluate " "pretrained model or train model " "by crossvalidation)") # todo: make the two different modes two subparsers parser.add_argument('-c', '--config', help="model configuration file (crossvalidation only)") parser.add_argument('-m', '--model', required=False, help="path to model (evaluation only)") parser.add_argument('-f', '--folds', required=False, default=10, help="number of CV folds (crossvalidation only)") parser.add_argument('--errors', required=False, default="errors.json", help="output path for the json with wrong predictions") parser.add_argument('--histogram', required=False, default="hist.png", help="output path for the confidence histogram") parser.add_argument('--confmat', required=False, default="confmat.png", help="output path for the confusion matrix plot") utils.add_logging_option_arguments(parser, default=logging.INFO) return parser
def create_argument_parser(): parser = argparse.ArgumentParser(description='parse incoming text') parser.add_argument('-e', '--emulate', choices=['wit', 'luis', 'dialogflow'], help='which service to emulate (default: None i.e. use' ' simple built in format)') parser.add_argument('-P', '--port', type=int, default=5000, help='port on which to run server') parser.add_argument( '--pre_load', nargs='+', default=[], help='Preload models into memory before starting the ' 'server. \nIf given `all` as input all the models ' 'will be loaded.\nElse you can specify a list of ' 'specific project names.\nEg: python -m ' 'rasa_nlu_gao.server --pre_load project1 --path projects ' '-c config.yaml') parser.add_argument('-t', '--token', help="auth token. If set, reject requests which don't " "provide this token as a query parameter") parser.add_argument('-w', '--write', help='file where logs will be saved') parser.add_argument('--path', required=True, help="working directory of the server. Models are" "loaded from this directory and trained models " "will be saved here.") parser.add_argument('--cors', nargs="*", help='List of domain patterns from where CORS ' '(cross-origin resource sharing) calls are ' 'allowed. The default value is `[]` which ' 'forbids all CORS requests.') parser.add_argument('--max_training_processes', type=int, default=1, help='Number of processes used to handle training ' 'requests. Increasing this value will have a ' 'great impact on memory usage. It is ' 'recommended to keep the default value.') parser.add_argument('--num_threads', type=int, default=1, help='Number of parallel threads to use for ' 'handling parse requests.') parser.add_argument('--response_log', help='Directory where logs will be saved ' '(containing queries and responses).' 'If set to ``null`` logging will be disabled.') parser.add_argument( '--storage', help='Set the remote location where models are stored. ' 'E.g. on AWS. If nothing is configured, the ' 'server will only serve the models that are ' 'on disk in the configured `path`.') parser.add_argument('-c', '--config', help="Default model configuration file used for " "training.") utils.add_logging_option_arguments(parser) return parser