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adbf823 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | import argparse
def args_parser():
parser = argparse.ArgumentParser(description='LLM-Prop')
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=200)
parser.add_argument('--bs',
help='Batch size',
type=int,
default=64)
parser.add_argument('--lr',
help='Learning rate',
type=float,
default=0.001)
parser.add_argument('--max_len',
help='Max input sequence length',
type=int,
default=888)
parser.add_argument('--dr',
help='Drop rate',
type=float,
default=0.2)
parser.add_argument('--warmup_steps',
help='Warmpup steps',
type=int,
default=30000)
parser.add_argument('--preprocessing_strategy',
help='Data preprocessing technique: "none", "bond_lengths_replaced_with_num", "bond_angles_replaced_with_ang", "no_stopwords", or "no_stopwords_and_lengths_and_angles_replaced"',
type=str,
choices=["none", "bond_lengths_replaced_with_num", "bond_angles_replaced_with_ang", "no_stopwords", "no_stopwords_and_lengths_and_angles_replaced"],
default="no_stopwords_and_lengths_and_angles_replaced")
parser.add_argument('--tokenizer',
help='Tokenizer name: "t5_tokenizer" or "modified"',
type=str,
choices=["t5_tokenizer", "modified"],
default="modified")
parser.add_argument('--pooling',
help='Pooling method. "cls" or "mean"',
type=str,
choices=["cls", "mean"],
default="cls")
parser.add_argument('--normalizer',
help='Labels scaling technique. "z_norm", "mm_norm", or "ls_norm"',
type=str,
choices=["z_norm", "mm_norm", "ls_norm", "no_norm"],
default="z_norm")
parser.add_argument('--scheduler',
help='Learning rate scheduling technique. "linear", "onecycle", "step", or "lambda" (no scheduling))',
type=str,
choices=["linear", "onecycle", "step", "lambda"],
default="onecycle")
parser.add_argument('--property_name', '--property',
help='The name of the property to predict. "band_gap", "volume", "is_gap_direct", "energy_per_atom", "formation_energy_per_atom", or "e_above_hull". "epa" is accepted as an alias for "energy_per_atom".',
type=str,
choices=["band_gap", "volume", "is_gap_direct", "energy_per_atom", "formation_energy_per_atom", "e_above_hull", "epa"],
default="is_gap_direct")
parser.add_argument('--optimizer',
help='Optimizer type. "adamw" or "sgd"',
type=str,
choices=["adamw", "sgd"],
default="adamw")
parser.add_argument('--task_name',
help='the name of the task: "regression" if property_name is band_gap or volume or "classification" if property_name is is_gap_direct',
type=str,
choices=["regression", "classification"],
default="classification")
parser.add_argument('--train_data_path',
help="the path to the training data",
type=str,
default="data/samples/train_data.csv")
parser.add_argument('--valid_data_path',
help="the path to the valid data",
type=str,
default="data/samples/validation_data.csv")
parser.add_argument('--test_data_path',
help="the path to the test data",
type=str,
default="data/samples/test_data.csv")
parser.add_argument('--checkpoint',
help="the path to the the best checkpoint for evaluation",
type=str,
default="")
args = parser.parse_args()
return args
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