def arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument("corpus",
choices=CORPUS_PATH.keys(),
help="Corpus identifier.")
parser.add_argument("run_id", help="Run identifier")
parser.add_argument("--train_with_dev",
help="Use dev set during training",
action='store_true')
parser.add_argument("--model_file",
help="Load existing model instead of training new.")
parser.add_argument(
"--pooled_contextual_embeddings",
help="Boolean flag whether to use pooled variant of FlairEmbeddings.",
action='store_true')
parser.add_argument("--contextual_forward_path",
help="Path to contextual string embeddings (forward)")
parser.add_argument("--contextual_backward_path",
help="Path to contextual string embeddings (backward)")
parser.add_argument("--embedding_lang",
choices=['en', 'nl'],
help="Specify language of embeddings.")
parser.add_argument(
"--fine_tune",
help="Fine tune an existing model (has to be passed with --model_file)",
action='store_true')
return parser.parse_args()
def arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument("corpus",
choices=CORPUS_PATH.keys(),
help="Corpus identifier.")
parser.add_argument("run_id", help="Run Identifier")
return parser.parse_args()
def arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument("corpus",
choices=CORPUS_PATH.keys(),
help="Corpus identifier.")
parser.add_argument("run_id", help="Run identifier")
parser.add_argument("feature_extractor",
choices=crf_util.FEATURE_EXTRACTOR.keys(),
help="Feature extractor.")
return parser.parse_args()
def arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument("corpus",
choices=CORPUS_PATH.keys(),
help="Corpus identifier.")
parser.add_argument("run_id", help="Run identifier")
parser.add_argument("--train_sample_frac",
help="Fraction of the training data to use.",
type=float,
default=0.1)
parser.add_argument("--random_seed",
help="Seed for the training set sampler.",
type=int,
default=42)
return parser.parse_args()
def arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument("corpus",
choices=CORPUS_PATH.keys(),
help="Corpus identifier.")
parser.add_argument("run_id", help="Run identifier")
parser.add_argument("feature_extractor",
choices=crf_util.FEATURE_EXTRACTOR.keys(),
help="Feature extractor.")
parser.add_argument("--n_iter",
help="Number of random search trials",
default=1,
type=int)
parser.add_argument("--n_jobs",
help="Number of concurrent jobs",
default=1,
type=int)
return parser.parse_args()
def arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument("corpus", choices=CORPUS_PATH.keys(), help="Corpus identifier.")
parser.add_argument("run_id", help="Run identifier")
parser.add_argument("--train_sample_frac",
help="Fraction of the training data to use.",
type=float,
default=0.1)
parser.add_argument("--random_seed",
help="Seed for the training set sampler.",
type=int,
default=42)
parser.add_argument("--save_final_model",
help="If passed, the final model is saved.",
action='store_true')
parser.add_argument("--embedding_lang",
choices = ['en','nl'],
help="Specify language of embeddings.")
return parser.parse_args()