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Args.py
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Args.py
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import argparse
class Parser(object):
def getParser(self):
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--data_dir',
type=str,
default='./bin/predicate_classifiction/classification_data',
help='The input data dir. Should contain the data files.')
parser.add_argument('--model_dir',
type=str,
default='./model',
help='The config json file and model file corresponding to the pre-trained BERT model.')
parser.add_argument('--task_name',
type=str,
default='zy',
help='The name of the task to train.')
parser.add_argument('--vocab_file',
type=str,
default='./model/vocab.txt',
help='The vocabulary file that the BERT model was trained on.')
parser.add_argument('--output_dir',
type=str,
default='./output/predicate_classifiction_model',
help="The output directory where the model checkpoints will be written.")
# Other parameters
parser.add_argument('--do_lower_case',
type=bool,
default=True,
help="Whether to lower case the input text."
"Should be True for uncased models and False for cased models.")
parser.add_argument('--max_seq_length',
type=int,
default=128,
help="The maximum total input sequence length after WordPiece tokenization."
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded.")
parser.add_argument('--do_train',
type=bool,
default=False,
help="Whether to run training.")
parser.add_argument('--do_eval',
type=bool,
default=False,
help="Whether to run eval on dev set.")
parser.add_argument('--do_predict',
type=bool,
default=True,
help="Whether to run the model in inference mode on the test set.")
parser.add_argument('--train_batch_size',
type=int,
default=32,
help="Total batch size for training.")
parser.add_argument('--eval_batch_size',
type=int,
default=8,
help="Total batch size for eval.")
parser.add_argument('--predict_batch_size',
type=int,
default=8,
help="Total batch size for predict.")
parser.add_argument('--learning_rate',
type=float,
default=2e-5,
help="The initial learning rate for Adam.")
parser.add_argument('--num_train_epochs',
type=float,
default=6.0,
help="Total number of training epochs to perform.")
parser.add_argument('--warmup_proportion',
type=float,
default=0.1,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10% of training.")
return parser