def create_argument_parser():
"""Parse all the command line arguments for the visualisation script."""
parser = argparse.ArgumentParser(
description='Visualize the stories in a dialogue training file')
parser.add_argument('-s', '--stories',
required=True,
type=str,
help="story file or folder containing the stories"
"to visualize")
parser.add_argument('-d', '--domain',
required=True,
type=str,
help="domain file")
parser.add_argument('-o', '--output',
required=True,
type=str,
help="filename of the output path, e.g. 'graph.png")
parser.add_argument('-m', '--max_history',
default=2,
type=int,
help="max history to consider when merging "
"paths in the output graph")
parser.add_argument('-nlu', '--nlu_data',
default=None,
type=str,
help="path of the Rasa NLU training data, "
"used to insert example messages into the graph")
utils.add_logging_option_arguments(parser)
return parser
def create_argparser():
parser = argparse.ArgumentParser(description='starts the bot')
parser.add_argument('-d',
'--core',
required=True,
type=str,
help="core model to run")
parser.add_argument('-u', '--nlu', type=str, help="nlu model to run")
parser.add_argument('-p',
'--port',
default=5002,
type=int,
help="port to run the server at")
parser.add_argument('-o',
'--log_file',
type=str,
default="rasa_core.log",
help="store log file in specified file")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the visualisation script."""
parser = argparse.ArgumentParser(
description='Visualize the stories in a dialogue training file')
parser.add_argument('-o',
'--output',
required=True,
type=str,
help="filename of the output path, e.g. 'graph.html")
parser.add_argument('-m',
'--max_history',
default=2,
type=int,
help="max history to consider when merging "
"paths in the output graph")
parser.add_argument('-nlu',
'--nlu_data',
default=None,
type=str,
help="path of the Rasa NLU training data, "
"used to insert example messages into the graph")
utils.add_logging_option_arguments(parser)
cli.arguments.add_config_arg(parser, nargs=1)
cli.arguments.add_domain_arg(parser)
cli.arguments.add_model_and_story_group(parser,
allow_pretrained_model=False)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the server script."""
parser = argparse.ArgumentParser(
description='starts server to serve an agent')
parser.add_argument(
'-e', '--environment',
type=str,
help="environment config file to run with the server")
parser.add_argument(
'--cors',
nargs='*',
type=str,
help="enable CORS for the passed origin. "
"Use * to whitelist all origins")
parser.add_argument(
'--auth_token',
type=str,
help="Enable token based authentication. Requests need to provide "
"the token to be accepted.")
parser.add_argument(
'-o', '--log_file',
type=str,
default="rasa_core.log",
help="store log file in specified file")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the restore script."""
parser = argparse.ArgumentParser(
description='starts the bot')
parser.add_argument(
'-d', '--core',
required=True,
type=str,
help="core model to run")
parser.add_argument(
'-u', '--nlu',
type=str,
help="nlu model to run")
parser.add_argument(
'tracker_dump',
type=str,
help="file that contains a dumped tracker state in json format")
parser.add_argument(
'--enable_api',
action="store_true",
help="Start the web server api in addition to the input channel")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Create argument parser for the evaluate script."""
parser = argparse.ArgumentParser(
description='evaluates a dialogue model')
parser.add_argument(
'-s', '--stories',
type=str,
required=True,
help="file that contains the stories to evaluate on")
parser.add_argument(
'-m', '--max_stories',
type=int,
default=None,
help="maximum number of stories to test on")
parser.add_argument(
'-d', '--core',
required=True,
type=str,
help="core model to run with the server")
parser.add_argument(
'-u', '--nlu',
type=str,
help="nlu model to run with the server. None for regex interpreter")
parser.add_argument(
'-o', '--output',
type=str,
default="story_confmat.pdf",
help="output path for the created evaluation plot")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the visualisation script."""
parser = argparse.ArgumentParser(
description='Visualize the stories in a dialogue training file')
parser.add_argument('-s', '--stories',
required=True,
type=str,
help="story file or folder containing the stories"
"to visualize")
parser.add_argument('-d', '--domain',
required=True,
type=str,
help="domain file")
parser.add_argument('-o', '--output',
required=True,
type=str,
help="filename of the output path, e.g. 'graph.png")
parser.add_argument('-m', '--max_history',
default=2,
type=int,
help="max history to consider when merging "
"paths in the output graph")
parser.add_argument('-nlu', '--nlu_data',
default=None,
type=str,
help="path of the Rasa NLU training data, "
"used to insert example messages into the graph")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the run script."""
parser = argparse.ArgumentParser(description='starts the bot')
parser.add_argument('-d',
'--core',
required=True,
type=str,
help="core model to run")
parser.add_argument('-u', '--nlu', type=str, help="nlu model to run")
parser.add_argument(
'-p',
'--port',
default=5002,
type=int,
help="port to run the server at (if a server is run "
"- depends on the chosen channel, e.g. facebook uses this)")
parser.add_argument('-o',
'--log_file',
type=str,
default="rasa_core.log",
help="store log file in specified file")
parser.add_argument(
'--credentials',
default=None,
help="authentication credentials for the connector as a yml file")
parser.add_argument('-c',
'--connector',
default="cmdline",
choices=["facebook", "slack", "telegram", "cmdline"],
help="service to connect to")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the training script."""
parser = argparse.ArgumentParser(description='trains a dialogue model')
parent_parser = argparse.ArgumentParser(add_help=False)
add_args_to_parser(parent_parser)
add_model_and_story_group(parent_parser)
utils.add_logging_option_arguments(parent_parser)
subparsers = parser.add_subparsers(help='mode', dest='mode')
subparsers.add_parser('default',
help='default mode: train a dialogue'
' model',
parents=[parent_parser])
compare_parser = subparsers.add_parser('compare',
help='compare mode: train multiple '
'dialogue models to compare '
'policies',
parents=[parent_parser])
interactive_parser = subparsers.add_parser('interactive',
help='teach the bot with '
'interactive learning',
parents=[parent_parser])
add_compare_args(compare_parser)
add_interactive_args(interactive_parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the training script."""
parser = argparse.ArgumentParser(
description='trains a dialogue model')
parser.add_argument(
'-s', '--stories',
type=str,
required=True,
help="file or folder containing the training stories")
parser.add_argument(
'-o', '--out',
type=str,
required=True,
help="directory to persist the trained model in")
parser.add_argument(
'-d', '--domain',
type=str,
required=True,
help="domain specification yaml file")
parser.add_argument(
'-u', '--nlu',
type=str,
default=None,
help="trained nlu model")
parser.add_argument(
'--history',
type=int,
default=3,
help="max history to use of a story")
parser.add_argument(
'--epochs',
type=int,
default=100,
help="number of epochs to train the model")
parser.add_argument(
'--validation_split',
type=float,
default=0.1,
help="Percentage of training samples used for validation, "
"0.1 by default")
parser.add_argument(
'--batch_size',
type=int,
default=20,
help="number of training samples to put into one training batch")
parser.add_argument(
'--online',
default=False,
action='store_true',
help="enable online training")
parser.add_argument(
'--augmentation',
type=int,
default=50,
help="how much data augmentation to use during training")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the run script."""
parser = argparse.ArgumentParser(
description='starts the bot')
parser.add_argument(
'-d', '--core',
required=True,
type=str,
help="core model to run")
parser.add_argument(
'-u', '--nlu',
type=str,
help="nlu model to run")
parser.add_argument(
'-p', '--port',
default=constants.DEFAULT_SERVER_PORT,
type=int,
help="port to run the server at")
parser.add_argument(
'--auth_token',
type=str,
help="Enable token based authentication. Requests need to provide "
"the token to be accepted.")
parser.add_argument(
'--cors',
nargs='*',
type=str,
help="enable CORS for the passed origin. "
"Use * to whitelist all origins")
parser.add_argument(
'-o', '--log_file',
type=str,
default="rasa_core.log",
help="store log file in specified file")
parser.add_argument(
'--credentials',
default=None,
help="authentication credentials for the connector as a yml file")
parser.add_argument(
'--endpoints',
default=None,
help="Configuration file for the connectors as a yml file")
parser.add_argument(
'-c', '--connector',
default="cmdline",
choices=["facebook", "slack", "telegram", "mattermost", "cmdline",
"twilio", "botframework", "rocketchat"],
help="service to connect to")
parser.add_argument(
'--enable_api',
action="store_true",
help="Start the web server api in addition to the input channel")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Create argument parser for the evaluate script."""
parser = argparse.ArgumentParser(description='evaluates a dialogue model')
parser.add_argument(
'-s',
'--stories',
type=str,
required=True,
help="file or folder containing stories to evaluate on")
parser.add_argument('-m',
'--max_stories',
type=int,
help="maximum number of stories to test on")
parser.add_argument('-d',
'--core',
required=True,
type=str,
help="core model to run with the server")
parser.add_argument(
'-u',
'--nlu',
type=str,
help="nlu model to run with the server. None for regex interpreter")
parser.add_argument(
'-o',
'--output',
type=str,
nargs="?",
const="story_confmat.pdf",
help="output path for the created evaluation plot. If not "
"specified, no plot will be generated.")
parser.add_argument(
'--e2e',
'--end-to-end',
action='store_true',
help="Run an end-to-end evaluation for combined action and "
"intent prediction. Requires a story file in end-to-end "
"format.")
parser.add_argument('--failed',
type=str,
default="failed_stories.md",
help="output path for the failed stories")
parser.add_argument(
'--endpoints',
default=None,
help="Configuration file for the connectors as a yml file")
parser.add_argument(
'--fail_on_prediction_errors',
action='store_true',
help="If a prediction error is encountered, an exception "
"is thrown. This can be used to validate stories during "
"tests, e.g. on travis.")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the run script."""
parser = argparse.ArgumentParser(
description='starts the bot')
parser.add_argument(
'-d', '--core',
required=True,
type=str,
help="core model to run")
parser.add_argument(
'-u', '--nlu',
type=str,
help="nlu model to run")
parser.add_argument(
'-p', '--port',
default=constants.DEFAULT_SERVER_PORT,
type=int,
help="port to run the server at")
parser.add_argument(
'--auth_token',
type=str,
help="Enable token based authentication. Requests need to provide "
"the token to be accepted.")
parser.add_argument(
'--cors',
nargs='*',
type=str,
help="enable CORS for the passed origin. "
"Use * to whitelist all origins")
parser.add_argument(
'-o', '--log_file',
type=str,
default="rasa_core.log",
help="store log file in specified file")
parser.add_argument(
'--credentials',
default=None,
help="authentication credentials for the connector as a yml file")
parser.add_argument(
'--endpoints',
default=None,
help="Configuration file for the connectors as a yml file")
parser.add_argument(
'-c', '--connector',
default="cmdline",
choices=["facebook", "slack", "telegram", "mattermost", "cmdline",
"twilio", "botframework", "rocketchat","bot"],
help="service to connect to")
parser.add_argument(
'--enable_api',
action="store_true",
help="Start the web server api in addition to the input channel")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the training script."""
parser = argparse.ArgumentParser(description='trains a dialogue model')
add_model_and_story_group(parser)
add_args_to_parser(parser)
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the training script."""
parser = argparse.ArgumentParser(description='trains a dialogue model')
# either the user can pass in a story file, or the data will get
# downloaded from a url
group = parser.add_mutually_exclusive_group(required=True)
group = add_args_to_group(group)
parser = add_args_to_parser(parser)
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
parser = argparse.ArgumentParser(description='Startet einen Chatbot')
parser.add_argument('-c',
'--core',
type=str,
required=True,
help="coreModel folder")
parser.add_argument('-n',
'--nlu',
type=str,
required=True,
help="nluModelFolder")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Create argument parser for the evaluate script."""
parser = argparse.ArgumentParser(description='evaluates a dialogue model')
parser.add_argument(
'-s',
'--stories',
type=str,
required=True,
help="file or folder containing stories to evaluate on")
parser.add_argument('-m',
'--max_stories',
type=int,
default=None,
help="maximum number of stories to test on")
parser.add_argument('-d',
'--core',
required=True,
type=str,
help="core model to run with the server")
parser.add_argument(
'-u',
'--nlu',
type=str,
help="nlu model to run with the server. None for regex interpreter")
parser.add_argument(
'-p',
'--message-preprocessor',
type=str,
help=
"The message pre-processor parameter, if None => no message pre-processor provided"
)
parser.add_argument(
'-i',
'--interpreter-class',
type=str,
help=
"The interpreter class parameter, if None => RASANluInterpreter provided"
)
parser.add_argument('-o',
'--output',
type=str,
default="story_confmat.pdf",
help="output path for the created evaluation plot")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Create argument parser for the evaluate script."""
parser = argparse.ArgumentParser(description='evaluates a dialogue model')
parent_parser = argparse.ArgumentParser(add_help=False)
add_args_to_parser(parent_parser)
utils.add_logging_option_arguments(parent_parser)
subparsers = parser.add_subparsers(help='mode', dest='mode')
subparsers.add_parser('default',
help='default mode: evaluate a dialogue'
' model',
parents=[parent_parser])
subparsers.add_parser('compare',
help='compare mode: evaluate multiple'
' dialogue models to compare '
'policies',
parents=[parent_parser])
return parser
def add_general_args(parser):
parser.add_argument(
'--augmentation',
type=int,
default=50,
help="how much data augmentation to use during training")
parser.add_argument('--dump_stories',
default=False,
action='store_true',
help="If enabled, save flattened stories to a file")
parser.add_argument(
'--debug_plots',
default=False,
action='store_true',
help="If enabled, will create plots showing checkpoints "
"and their connections between story blocks in a "
"file called `story_blocks_connections.html`.")
utils.add_logging_option_arguments(parser)
def create_argument_parser():
"""Parse all the command line arguments for the server script."""
parser = argparse.ArgumentParser(
description='starts server to serve an agent')
parser.add_argument('-d',
'--core',
required=True,
type=str,
help="core model to run with the server")
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('-u',
'--nlu',
type=str,
help="nlu model to run with the server")
parser.add_argument('-p',
'--port',
type=int,
default=5005,
help="port to run the server at")
parser.add_argument('--cors',
nargs='*',
type=str,
help="enable CORS for the passed origin. "
"Use * to whitelist all origins")
parser.add_argument(
'--auth_token',
type=str,
help="Enable token based authentication. Requests need to provide "
"the token to be accepted.")
parser.add_argument('-o',
'--log_file',
type=str,
default="rasa_core.log",
help="store log file in specified file")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the server script."""
parser = argparse.ArgumentParser(
description='starts server to serve an agent')
parser.add_argument(
'-d', '--core',
required=True,
type=str,
help="core model to run with the server")
parser.add_argument(
'-u', '--nlu',
type=str,
help="nlu model to run with the server")
parser.add_argument(
'-p', '--port',
type=int,
default=5005,
help="port to run the server at")
parser.add_argument(
'--cors',
nargs='*',
type=str,
help="enable CORS for the passed origin. "
"Use * to whitelist all origins")
parser.add_argument(
'--auth_token',
type=str,
help="Enable token based authentication. Requests need to provide "
"the token to be accepted.")
parser.add_argument(
'-o', '--log_file',
type=str,
default="rasa_core.log",
help="store log file in specified file")
parser.add_argument(
'--endpoints',
default=None,
help="Configuration file for the connectors as a yml file")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the run script."""
parser = argparse.ArgumentParser(
description='starts the bot')
parser.add_argument(
'-d', '--core',
required=True,
type=str,
help="core model to run")
parser.add_argument(
'-u', '--nlu',
type=str,
help="nlu model to run")
parser.add_argument(
'-p', '--port',
default=constants.DEFAULT_SERVER_PORT,
type=int,
help="port to run the server at (if a server is run "
"- depends on the chosen channel, e.g. facebook uses this)")
parser.add_argument(
'-o', '--log_file',
type=str,
default="rasa_core.log",
help="store log file in specified file")
parser.add_argument(
'--credentials',
default=None,
help="authentication credentials for the connector as a yml file")
parser.add_argument(
'--endpoints',
default=None,
help="Configuration file for the connectors as a yml file")
parser.add_argument(
'-c', '--connector',
default="cmdline",
choices=["facebook", "slack", "telegram", "mattermost", "cmdline",
"twilio"],
help="service to connect to")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the server script."""
parser = argparse.ArgumentParser(
description='starts server to serve an agent')
parser.add_argument('-d',
'--core',
required=True,
type=str,
help="core model to run with the server")
parser.add_argument('-u',
'--nlu',
type=str,
help="nlu model to run with the server")
parser.add_argument('-p',
'--port',
type=int,
default=50005,
help="port to run the server at")
parser.add_argument('--cors',
nargs='*',
type=str,
help="enable CORS for the passed origin. "
"Use * to whitelist all origins")
parser.add_argument(
'--auth_token',
type=str,
help="Enable token based authentication. Requests need to provide "
"the token to be accepted.")
parser.add_argument('-o',
'--log_file',
type=str,
default="rasa_core.log",
help="store log file in specified file")
parser.add_argument(
'--endpoints',
default=None,
help="Configuration file for the connectors as a yml file")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
parser = argparse.ArgumentParser(description='Startet Online-Training')
parser.add_argument('-s',
'--stories',
type=str,
required=True,
help="storiesFile")
parser.add_argument('-d',
'--domain',
type=str,
required=True,
help="domainFile")
parser.add_argument('-n',
'--nlu',
type=str,
required=True,
help="NLUModel")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Create argument parser for the evaluate script."""
parser = argparse.ArgumentParser(description='evaluates a dialogue model')
parser.add_argument(
'-s',
'--stories',
type=str,
required=True,
help="file or folder containing stories to evaluate on")
parser.add_argument('-m',
'--max_stories',
type=int,
default=100,
help="maximum number of stories to test on")
parser.add_argument('-d',
'--core',
required=True,
type=str,
help="core model to run with the server")
parser.add_argument(
'-u',
'--nlu',
type=str,
help="nlu model to run with the server. None for regex interpreter")
parser.add_argument(
'-o',
'--output',
type=str,
default="story_confmat.pdf",
help="output path for the created evaluation plot. If set to None"
"or an empty string, no plot will be generated.")
parser.add_argument('--failed',
type=str,
default="failed_stories.txt",
help="output path for the failed stories")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Create argument parser for the evaluate script."""
parser = argparse.ArgumentParser(
description='evaluates a dialogue model')
parser.add_argument(
'-s', '--stories',
type=str,
required=True,
help="file or folder containing stories to evaluate on")
parser.add_argument(
'-m', '--max_stories',
type=int,
default=100,
help="maximum number of stories to test on")
parser.add_argument(
'-d', '--core',
required=True,
type=str,
help="core model to run with the server")
parser.add_argument(
'-u', '--nlu',
type=str,
help="nlu model to run with the server. None for regex interpreter")
parser.add_argument(
'-o', '--output',
type=str,
default="story_confmat.pdf",
help="output path for the created evaluation plot. If set to None"
"or an empty string, no plot will be generated.")
parser.add_argument(
'--failed',
type=str,
default="failed_stories.txt",
help="output path for the failed stories")
utils.add_logging_option_arguments(parser)
return parser
def create_argparser():
parser = argparse.ArgumentParser(
description='starts the bot')
parser.add_argument(
'-u', '--nlu',
type=str,
default= "../models/nlu_models/default",
help="nlu model to use")
parser.add_argument(
'-m', '--model_output',
type=str,
default= "../models/dialogueModels/custom",
help="path of output dialogue model")
parser.add_argument(
'-td', '--training_data_file',
type=str,
default= "../data/stories/stories.md",
help="data to build model with")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the training script."""
parser = argparse.ArgumentParser(
description='trains a dialogue model')
parser.add_argument(
'-s', '--stories',
type=str,
required=True,
help="file or folder containing the training stories")
parser.add_argument(
'-o', '--out',
type=str,
required=True,
help="directory to persist the trained model in")
parser.add_argument(
'-d', '--domain',
type=str,
required=True,
help="domain specification yaml file")
parser.add_argument(
'-u', '--nlu',
type=str,
default=None,
help="trained nlu model")
parser.add_argument(
'--history',
type=int,
default=3,
help="max history to use of a story")
parser.add_argument(
'--epochs',
type=int,
default=100,
help="number of epochs to train the model")
parser.add_argument(
'--validation_split',
type=float,
default=0.1,
help="Percentage of training samples used for validation, "
"0.1 by default")
parser.add_argument(
'--batch_size',
type=int,
default=20,
help="number of training samples to put into one training batch")
parser.add_argument(
'--online',
default=False,
action='store_true',
help="enable online training")
parser.add_argument(
'--augmentation',
type=int,
default=50,
help="how much data augmentation to use during training")
parser.add_argument(
'--debug_plots',
default=False,
action='store_true',
help="If enabled, will create plots showing checkpoints "
"and their connections between story blocks in a "
"file called `story_blocks_connections.pdf`.")
parser.add_argument(
'--dump_stories',
default=False,
action='store_true',
help="If enabled, save flattened stories to a file")
parser.add_argument(
'--endpoints',
default=None,
help="Configuration file for the connectors as a yml file")
parser.add_argument(
'--nlu_threshold',
type=float,
default=DEFAULT_NLU_FALLBACK_THRESHOLD,
help="If NLU prediction confidence is below threshold, fallback "
"will get triggered.")
parser.add_argument(
'--core_threshold',
type=float,
default=DEFAULT_CORE_FALLBACK_THRESHOLD,
help="If Core action prediction confidence is below the threshold "
"a fallback action will get triggered")
parser.add_argument(
'--fallback_action_name',
type=str,
default=DEFAULT_FALLBACK_ACTION,
help="When a fallback is triggered (e.g. because the ML prediction "
"is of low confidence) this is the name of tje action that "
"will get triggered instead.")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the run script."""
parser = argparse.ArgumentParser(
description='starts the bot')
parser.add_argument(
'-d', '--core',
required=True,
type=str,
help="core model to run")
parser.add_argument(
'-u', '--nlu',
type=str,
help="nlu model to run")
parser.add_argument(
'-p', '--port',
default=constants.DEFAULT_SERVER_PORT,
type=int,
help="port to run the server at")
parser.add_argument(
'--auth_token',
type=str,
help="Enable token based authentication. Requests need to provide "
"the token to be accepted.")
parser.add_argument(
'--cors',
nargs='*',
type=str,
help="enable CORS for the passed origin. "
"Use * to whitelist all origins")
parser.add_argument(
'-o', '--log_file',
type=str,
default="rasa_core.log",
help="store log file in specified file")
parser.add_argument(
'--credentials',
default=None,
help="authentication credentials for the connector as a yml file")
parser.add_argument(
'--endpoints',
default=None,
help="Configuration file for the connectors as a yml file")
parser.add_argument(
'-c', '--connector',
type=str,
help="service to connect to")
parser.add_argument(
'--enable_api',
action="store_true",
help="Start the web server api in addition to the input channel")
jwt_auth = parser.add_argument_group('JWT Authentication')
jwt_auth.add_argument(
'--jwt_secret',
type=str,
help="Public key for asymmetric JWT methods or shared secret"
"for symmetric methods. Please also make sure to use "
"--jwt_method to select the method of the signature, "
"otherwise this argument will be ignored.")
jwt_auth.add_argument(
'--jwt_method',
type=str,
default="HS256",
help="Method used for the signature of the JWT authentication "
"payload.")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the training script."""
parser = argparse.ArgumentParser(description='trains a dialogue model')
# either the user can pass in a story file, or the data will get
# downloaded from a url
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('-s',
'--stories',
type=str,
help="file or folder containing the training stories")
group.add_argument(
'--url',
type=str,
help="If supplied, downloads a story file from a URL and "
"trains on it. Fetches the data by sending a GET request "
"to the supplied URL.")
group.add_argument(
'--core',
default=None,
help="path to load a pre-trained model instead of training (for "
"interactive mode only)")
parser.add_argument('-o',
'--out',
type=str,
required=False,
help="directory to persist the trained model in")
parser.add_argument('-d',
'--domain',
type=str,
required=False,
help="domain specification yaml file")
parser.add_argument('-u',
'--nlu',
type=str,
default=None,
help="trained nlu model")
parser.add_argument('--history',
type=int,
default=3,
help="max history to use of a story")
parser.add_argument('--epochs',
type=int,
default=100,
help="number of epochs to train the model")
parser.add_argument(
'--validation_split',
type=float,
default=0.1,
help="Percentage of training samples used for validation, "
"0.1 by default")
parser.add_argument(
'--batch_size',
type=int,
default=20,
help="number of training samples to put into one training batch")
parser.add_argument('--interactive',
default=False,
action='store_true',
help="enable interactive training")
parser.add_argument('--skip_visualization',
default=False,
action='store_true',
help="disables plotting the visualization during "
"interactive learning")
parser.add_argument(
'--finetune',
default=False,
action='store_true',
help="retrain the model immediately based on feedback.")
parser.add_argument(
'--augmentation',
type=int,
default=50,
help="how much data augmentation to use during training")
parser.add_argument(
'--debug_plots',
default=False,
action='store_true',
help="If enabled, will create plots showing checkpoints "
"and their connections between story blocks in a "
"file called `story_blocks_connections.pdf`.")
parser.add_argument('--dump_stories',
default=False,
action='store_true',
help="If enabled, save flattened stories to a file")
parser.add_argument(
'--endpoints',
default=None,
help="Configuration file for the connectors as a yml file")
parser.add_argument(
'--nlu_threshold',
type=float,
default=DEFAULT_NLU_FALLBACK_THRESHOLD,
help="If NLU prediction confidence is below threshold, fallback "
"will get triggered.")
parser.add_argument(
'--core_threshold',
type=float,
default=DEFAULT_CORE_FALLBACK_THRESHOLD,
help="If Core action prediction confidence is below the threshold "
"a fallback action will get triggered")
parser.add_argument(
'--fallback_action_name',
type=str,
default=DEFAULT_FALLBACK_ACTION,
help="When a fallback is triggered (e.g. because the ML prediction "
"is of low confidence) this is the name of tje action that "
"will get triggered instead.")
utils.add_logging_option_arguments(parser)
return parser
def create_argument_parser():
"""Parse all the command line arguments for the training script."""
parser = argparse.ArgumentParser(
description='trains a dialogue model')
# either the user can pass in a story file, or the data will get
# downloaded from a url
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument(
'-s', '--stories',
type=str,
help="file or folder containing the training stories")
group.add_argument(
'--url',
type=str,
help="If supplied, downloads a story file from a URL and "
"trains on it. Fetches the data by sending a GET request "
"to the supplied URL.")
parser.add_argument(
'-o', '--out',
type=str,
required=True,
help="directory to persist the trained model in")
parser.add_argument(
'-d', '--domain',
type=str,
required=True,
help="domain specification yaml file")
parser.add_argument(
'-u', '--nlu',
type=str,
default=None,
help="trained nlu model")
parser.add_argument(
'--history',
type=int,
default=3,
help="max history to use of a story")
parser.add_argument(
'--epochs',
type=int,
default=100,
help="number of epochs to train the model")
parser.add_argument(
'--validation_split',
type=float,
default=0.1,
help="Percentage of training samples used for validation, "
"0.1 by default")
parser.add_argument(
'--batch_size',
type=int,
default=20,
help="number of training samples to put into one training batch")
parser.add_argument(
'--online',
default=False,
action='store_true',
help="enable online training")
parser.add_argument(
'--augmentation',
type=int,
default=50,
help="how much data augmentation to use during training")
parser.add_argument(
'--debug_plots',
default=False,
action='store_true',
help="If enabled, will create plots showing checkpoints "
"and their connections between story blocks in a "
"file called `story_blocks_connections.pdf`.")
parser.add_argument(
'--dump_stories',
default=False,
action='store_true',
help="If enabled, save flattened stories to a file")
parser.add_argument(
'--endpoints',
default=None,
help="Configuration file for the connectors as a yml file")
parser.add_argument(
'--nlu_threshold',
type=float,
default=DEFAULT_NLU_FALLBACK_THRESHOLD,
help="If NLU prediction confidence is below threshold, fallback "
"will get triggered.")
parser.add_argument(
'--core_threshold',
type=float,
default=DEFAULT_CORE_FALLBACK_THRESHOLD,
help="If Core action prediction confidence is below the threshold "
"a fallback action will get triggered")
parser.add_argument(
'--fallback_action_name',
type=str,
default=DEFAULT_FALLBACK_ACTION,
help="When a fallback is triggered (e.g. because the ML prediction "
"is of low confidence) this is the name of tje action that "
"will get triggered instead.")
utils.add_logging_option_arguments(parser)
return parser
