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 configurion 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)")
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.")
group.add_argument('--endpoints',
default=None,
help="EndpointConfig defining the server from which "
"pull 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():
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('-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
def create_argument_parser():
import argparse
parser = argparse.ArgumentParser(
description='evaluate a Rasa NLU pipeline with cross '
'validation or on external data')
utils.add_logging_option_arguments(parser, default=logging.INFO)
_add_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')
utils.add_logging_option_arguments(parser, default=logging.INFO)
_add_arguments(parser)
return parser
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():
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.")
group.add_argument('--endpoints',
default=None,
help="EndpointConfig defining the server from which "
"pull 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('--report', required=False, nargs='?',
const="reports", default=False,
help="output path to save the intent/entity"
"metrics report")
parser.add_argument('--successes', required=False, nargs='?',
const="successes.json", default=False,
help="output path to save successful predictions")
parser.add_argument('--errors', required=False, default="errors.json",
help="output path to save model errors")
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.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('--endpoints',
help='Configuration file for the model server as a yaml file')
parser.add_argument('--wait_time_between_pulls',
type=int,
default=10,
help='Wait time in seconds between NLU model server'
'queries.')
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
def create_argument_parser():
parser = argparse.ArgumentParser(description='parse incoming text')
cli.add_server_arguments(parser)
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
parser = argparse.ArgumentParser(description='parse incoming text')
cli.add_server_arguments(parser)
utils.add_logging_option_arguments(parser)
return parser