default_value=False,
help_str="Skip training and evaluate only"),
Option(
"eval_metrics",
default_value="bleu",
help_str="Comma-separated list of evaluation metrics (bleu/wer/cer)"
),
Option("run_for_epochs",
int,
help_str="How many epochs to run each test for"),
]
config_parser.add_task("experiment", experiment_options)
if args.generate_doc:
print(config_parser.generate_options_table())
exit(0)
if args.dynet_seed:
random.seed(args.dynet_seed)
np.random.seed(args.dynet_seed)
config = config_parser.args_from_config_file(args.experiments_file)
results = []
# Check ahead of time that all experiments exist, to avoid bad surprises
experiment_names = args.experiment_name or config.keys()
if args.experiment_name:
nonexistent = set(experiment_names).difference(config.keys())
