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args.py
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args.py
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import argparse
def _add_common_args(arg_parser):
arg_parser.add_argument('--config', type=str)
# # Input
# arg_parser.add_argument('--types_path', type=str, help="Path to type specifications")
#
# # Preprocessing
# arg_parser.add_argument('--tokenizer_path', type=str, help="Path to tokenizer")
# arg_parser.add_argument('--max_span_size', type=int, default=10, help="Maximum size of spans")
# arg_parser.add_argument('--lowercase', action='store_true', default=False,
# help="If true, input is lowercased during preprocessing")
# arg_parser.add_argument('--sampling_processes', type=int, default=4,
# help="Number of sampling processes. 0 = no multiprocessing for sampling")
#
# # Model / Training / Evaluation
# arg_parser.add_argument('--model_path', type=str, help="Path to directory that contains model checkpoints")
# arg_parser.add_argument('--model_type', type=str, default="spert", help="Type of model")
# arg_parser.add_argument('--cpu', action='store_true', default=False,
# help="If true, train/evaluate on CPU even if a CUDA device is available")
# arg_parser.add_argument('--eval_batch_size', type=int, default=1, help="Evaluation/Prediction batch size")
# arg_parser.add_argument('--max_pairs', type=int, default=1000,
# help="Maximum entity pairs to process during training/evaluation")
# arg_parser.add_argument('--rel_filter_threshold', type=float, default=0.4, help="Filter threshold for relations")
# arg_parser.add_argument('--size_embedding', type=int, default=25, help="Dimensionality of size embedding")
# arg_parser.add_argument('--prop_drop', type=float, default=0.1, help="Probability of dropout used in SpERT")
# arg_parser.add_argument('--freeze_transformer', action='store_true', default=True, help="Freeze BERT weights")
# arg_parser.add_argument('--no_overlapping', action='store_true', default=False,
# help="If true, do not evaluate on overlapping entities "
# "and relations with overlapping entities")
#
# # Misc
# arg_parser.add_argument('--seed', type=int, default=None, help="Seed")
# arg_parser.add_argument('--cache_path', type=str, default=None,
# help="Path to cache transformer models (for HuggingFace transformers library)")
# arg_parser.add_argument('--debug', action='store_true', default=False, help="Debugging mode on/off")
def _add_logging_args(arg_parser):
arg_parser.add_argument('--label', type=str, help="Label of run. Used as the directory name of logs/models")
arg_parser.add_argument('--log_path', type=str, help="Path do directory where training/evaluation logs are stored")
arg_parser.add_argument('--store_predictions', action='store_true', default=False,
help="If true, store predictions on disc (in log directory)")
arg_parser.add_argument('--store_examples', action='store_true', default=False,
help="If true, store evaluation examples on disc (in log directory)")
arg_parser.add_argument('--example_count', type=int, default=None,
help="Count of evaluation example to store (if store_examples == True)")
def train_argparser():
arg_parser = argparse.ArgumentParser()
arg_parser.add_argument('--train_path', type=str, help="Path to train dataset")
arg_parser.add_argument('--dev_path', type=str, help="Path to dev dataset")
arg_parser.add_argument('--test_path', type=str, help="Path to test dataset")
arg_parser.add_argument('--tokenizer_path', type=str, help="Path to tokenizer")
arg_parser.add_argument('--device', type=str, help="model device")
arg_parser.add_argument('--tokenizer_shape', type=int, help="bert output shape")
arg_parser.add_argument('--ways', type=int, help="few-shot ways")
arg_parser.add_argument('--shots', type=int, help="few-shot shots")
arg_parser.add_argument('--test_sample_num', type=int, help="test dataset sample num")
arg_parser.add_argument('--reduction_shape', type=int, help="shape after reduct")
arg_parser.add_argument('--reduction_lr', type=float, help="reduction model lr")
arg_parser.add_argument('--reduction_epoch', type=int, help="reduction model train epoch")
arg_parser.add_argument('--reduction_lr_warmup', type=float, help="reduction model train warmup rate")
arg_parser.add_argument('--calssify_lr', type=float, help="calssify model train lr")
arg_parser.add_argument('--outer_label_num', type=int, help="outer label sample num")
arg_parser.add_argument('--outer_sample_num', type=int, help="outer sample num")
arg_parser.add_argument('--randomseed', type=int, help="randomseed")
arg_parser.add_argument('--calssify_train_epoch', type=int, help="calssify train epoch")
arg_parser.add_argument('--weight_decay', type=float, help="weight decay")
arg_parser.add_argument('--test_classify', type=float, help="test classify train lr")
# # Logging
# arg_parser.add_argument('--save_path', type=str, help="Path to directory where model checkpoints are stored")
# arg_parser.add_argument('--init_eval', action='store_true', default=False,
# help="If true, evaluate validation set before training")
# arg_parser.add_argument('--save_optimizer', action='store_true', default=False,
# help="Save optimizer alongside model")
# arg_parser.add_argument('--train_log_iter', type=int, default=1, help="Log training process every x iterations")
# arg_parser.add_argument('--final_eval', action='store_true', default=False,
# help="Evaluate the model only after training, not at every epoch")
#
# # Model / Training
# arg_parser.add_argument('--train_batch_size', type=int, default=2, help="Training batch size")
# arg_parser.add_argument('--epochs', type=int, default=20, help="Number of epochs")
# arg_parser.add_argument('--neg_entity_count', type=int, default=100,
# help="Number of negative entity samples per document (sentence)")
# arg_parser.add_argument('--neg_relation_count', type=int, default=100,
# help="Number of negative relation samples per document (sentence)")
# arg_parser.add_argument('--lr', type=float, default=5e-5, help="Learning rate")
# arg_parser.add_argument('--lr_warmup', type=float, default=0.1,
# help="Proportion of total train iterations to warmup in linear increase/decrease schedule")
# arg_parser.add_argument('--weight_decay', type=float, default=0.01, help="Weight decay to apply")
# arg_parser.add_argument('--max_grad_norm', type=float, default=1.0, help="Maximum gradient norm")
_add_common_args(arg_parser)
# _add_logging_args(arg_parser)
return arg_parser
#
# def eval_argparser():
# arg_parser = argparse.ArgumentParser()
#
# # Input
# arg_parser.add_argument('--dataset_path', type=str, help="Path to dataset")
#
# _add_common_args(arg_parser)
# _add_logging_args(arg_parser)
#
# return arg_parser
#
#
# def predict_argparser():
# arg_parser = argparse.ArgumentParser()
#
# # Input
# arg_parser.add_argument('--dataset_path', type=str, help="Path to dataset")
# arg_parser.add_argument('--predictions_path', type=str, help="Path to store predictions")
# arg_parser.add_argument('--spacy_model', type=str, help="Label of SpaCy model (used for tokenization)")
#
# _add_common_args(arg_parser)
#
# return arg_parser