Machine Translation by Jointly Learning to Align and Translate.
"""
import argparse
import logging
import pprint
import configurations
from machine_translation import main
from machine_translation.stream import get_tr_stream, get_dev_stream
logger = logging.getLogger(__name__)
# Get the arguments
parser = argparse.ArgumentParser()
parser.add_argument("--proto", default="get_config_en2zh",
help="Prototype config to use for config")
parser.add_argument("--bokeh", default=False, action="store_true",
help="Use bokeh server for plotting")
args = parser.parse_args()
if __name__ == "__main__":
# Get configurations for model
configuration = getattr(configurations, args.proto)()
logger.info("Model options:\n{}".format(pprint.pformat(configuration)))
# Get data streams and call main
main(configuration, get_tr_stream(**configuration),
get_dev_stream(**configuration), args.bokeh)
logger = logging.getLogger(__name__)
# Get the arguments
parser = argparse.ArgumentParser()
parser.add_argument("--proto",
default="get_config_cs2en",
help="Prototype config to use for config")
parser.add_argument("--bokeh",
default=False,
action="store_true",
help="Use bokeh server for plotting")
parser.add_argument(
"--mode",
choices=["train", "translate"],
default='train',
help="The mode to run. In the `train` mode a model is trained."
" In the `translate` mode a trained model is used to translate"
" an input file and generates tokenized translation.")
parser.add_argument("--test-file",
default='',
help="Input test file for `translate` mode")
args = parser.parse_args()
if __name__ == "__main__":
# Get configurations for model
configuration = getattr(configurations, args.proto)()
configuration['test_set'] = args.test_file
logger.info("Model options:\n{}".format(pprint.pformat(configuration)))
# Get data streams and call main
main(args.mode, configuration, args.bokeh)
from machine_translation import main
logger = logging.getLogger(__name__)
# Get the arguments
parser = argparse.ArgumentParser()
parser.add_argument(
"--proto", default="get_config_en2ch",
help="Prototype config to use for config")
parser.add_argument(
"--bokeh", default=False, action="store_true",
help="Use bokeh server for plotting")
parser.add_argument(
"--mode", choices=["train", "translate"], default='train',
help="The mode to run. In the `train` mode a model is trained."
" In the `translate` mode a trained model is used to translate"
" an input file and generates tokenized translation.")
parser.add_argument(
"--test-file", default='', help="Input test file for `translate` mode")
args = parser.parse_args()
if __name__ == "__main__":
# Get configurations for model
configuration = getattr(configurations, args.proto)()
# configuration['test_set'] = args.test_file
logger.info("Model options:\n{}".format(pprint.pformat(configuration)))
# Get data streams and call main
main(args.mode, configuration, args.bokeh)
import argparse
import logging
import pprint
import configurations
from machine_translation import main
from machine_translation.stream import get_tr_stream, get_dev_stream
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
logger = logging.getLogger(__name__)
# Get the arguments
parser = argparse.ArgumentParser()
parser.add_argument("--proto", default="get_config_de2en_os",
help="Prototype config to use for config")
parser.add_argument("--bokeh", default=True, action="store_true",
help="Use bokeh server for plotting")
args = parser.parse_args()
if __name__ == "__main__":
# Get configurations for model
configuration = getattr(configurations, args.proto)()
logger.info("Model options:\n{}".format(pprint.pformat(configuration)))
# Get data streams and call main
main(configuration, get_tr_stream(**configuration),
get_dev_stream(**configuration), args.bokeh)
