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args.py
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"""Command-line arguments for setup.py, train.py, test.py.
Author:
Chris Chute ([email protected])
"""
import argparse
def get_setup_args():
"""Get arguments needed in setup.py."""
parser = argparse.ArgumentParser('Download and pre-process SQuAD')
add_common_args(parser)
parser.add_argument('--train_url',
type=str,
default='https://github.com/chrischute/squad/data/train-v2.0.json')
parser.add_argument('--dev_url',
type=str,
default='https://github.com/chrischute/squad/data/dev-v2.0.json')
parser.add_argument('--test_url',
type=str,
default='https://github.com/chrischute/squad/data/test-v2.0.json')
parser.add_argument('--glove_url',
type=str,
default='http://nlp.stanford.edu/data/glove.840B.300d.zip')
parser.add_argument('--cove_url',
type=str,
default='https://s3.amazonaws.com/research.metamind.io/cove/wmtlstm-b142a7f2.pth')
parser.add_argument('--train_meta_file',
type=str,
default='./data/train_meta.json')
parser.add_argument('--dev_meta_file',
type=str,
default='./data/dev_meta.json')
parser.add_argument('--test_meta_file',
type=str,
default='./data/test_meta.json')
parser.add_argument('--word2idx_file',
type=str,
default='./data/word2idx.json')
parser.add_argument('--char2idx_file',
type=str,
default='./data/char2idx.json')
parser.add_argument('--answer_file',
type=str,
default='./data/answer.json')
parser.add_argument('--para_limit',
type=int,
default=400,
help='Max number of words in a paragraph')
parser.add_argument('--ques_limit',
type=int,
default=50,
help='Max number of words to keep from a question')
parser.add_argument('--test_para_limit',
type=int,
default=1000,
help='Max number of words in a paragraph at test time')
parser.add_argument('--test_ques_limit',
type=int,
default=100,
help='Max number of words in a question at test time')
parser.add_argument('--char_dim',
type=int,
default=64,
help='Size of char vectors (char-level embeddings)')
parser.add_argument('--glove_dim',
type=int,
default=300,
help='Size of GloVe word vectors to use')
parser.add_argument('--glove_num_vecs',
type=int,
default=2196017,
help='Number of GloVe vectors')
parser.add_argument('--num_features',
type=int,
default=3,
help='Size of extra features')
parser.add_argument('--ans_limit',
type=int,
default=30,
help='Max number of words in a training example answer')
parser.add_argument('--char_limit',
type=int,
default=16,
help='Max number of chars to keep from a word')
parser.add_argument('--include_test_examples',
type=lambda s: s.lower().startswith('t'),
default=True,
help='Process examples from the test set')
args = parser.parse_args()
return args
def get_train_args():
"""Get arguments needed in train.py."""
parser = argparse.ArgumentParser('Train a model on SQuAD')
add_common_args(parser)
add_train_test_args(parser)
parser.add_argument('--eval_steps',
type=int,
default=50000,
help='Number of steps between successive evaluations.')
parser.add_argument('--lr',
type=float,
default=0.5,
help='Learning rate.')
parser.add_argument('--l2_wd',
type=float,
default=0,
help='L2 weight decay.')
parser.add_argument('--num_epochs',
type=int,
default=30,
help='Number of epochs for which to train. Negative means forever.')
parser.add_argument('--metric_name',
type=str,
default='F1',
choices=('NLL', 'EM', 'F1'),
help='Name of dev metric to determine best checkpoint.')
parser.add_argument('--optimizer',
type=str,
default='Adadelta',
choices=('Adadelta', 'Adamax'),
help='Name of the optimizer to be used.')
parser.add_argument('--max_checkpoints',
type=int,
default=5,
help='Maximum number of checkpoints to keep on disk.')
parser.add_argument('--max_grad_norm',
type=float,
default=5.0,
help='Maximum gradient norm for gradient clipping.')
parser.add_argument('--seed',
type=int,
default=224,
help='Random seed for reproducibility.')
parser.add_argument('--use_ema',
type=lambda s: s.lower().startswith('t'),
default=True,
help='Use exponential moving average of parameters.')
parser.add_argument('--ema_decay',
type=float,
default=0.999,
help='Decay rate for exponential moving average of parameters.')
args = parser.parse_args()
if args.metric_name == 'NLL':
# Best checkpoint is the one that minimizes negative log-likelihood
args.maximize_metric = False
elif args.metric_name in ('EM', 'F1'):
# Best checkpoint is the one that maximizes EM or F1
args.maximize_metric = True
else:
raise ValueError(f'Unrecognized metric name: "{args.metric_name}"')
if not args.optimizer:
raise argparse.ArgumentError('Missing required argument --optimizer')
return args
def get_test_args():
"""Get arguments needed in test.py."""
parser = argparse.ArgumentParser('Test a trained model on SQuAD')
add_common_args(parser)
add_train_test_args(parser)
parser.add_argument('--split',
type=str,
default='dev',
choices=('train', 'dev', 'test'),
help='Split to use for testing.')
parser.add_argument('--sub_file',
type=str,
default='submission.csv',
help='Name for submission file.')
parser.add_argument('--use_ensemble',
type=lambda s: s.lower().startswith('t'),
default=False,
help='Whether to use an ensemble of models.')
parser.add_argument('--ensemble_models',
type=str,
default='best.pth.tar',
help='Whether to use an ensemble of models.')
# Require load_path for test.py
args = parser.parse_args()
if not args.load_path:
raise argparse.ArgumentError('Missing required argument --load_path')
return args
def add_common_args(parser):
"""Add arguments common to all 3 scripts: setup.py, train.py, test.py"""
parser.add_argument('--train_record_file',
type=str,
default='./data/train.npz')
parser.add_argument('--dev_record_file',
type=str,
default='./data/dev.npz')
parser.add_argument('--test_record_file',
type=str,
default='./data/test.npz')
parser.add_argument('--word_emb_file',
type=str,
default='./data/word_emb.json')
parser.add_argument('--char_emb_file',
type=str,
default='./data/char_emb.json')
parser.add_argument('--cove_emb_file',
type=str,
default='./data/wmtlstm-b142a7f2.pth')
parser.add_argument('--train_eval_file',
type=str,
default='./data/train_eval.json')
parser.add_argument('--dev_eval_file',
type=str,
default='./data/dev_eval.json')
parser.add_argument('--test_eval_file',
type=str,
default='./data/test_eval.json')
parser.add_argument('--model',
type=str,
choices=['bidaf', 'bidafextra', 'fusionnet'],
default='bidaf')
def add_train_test_args(parser):
"""Add arguments common to train.py and test.py"""
parser.add_argument('--name',
'-n',
type=str,
required=True,
help='Name to identify training or test run.')
parser.add_argument('--max_ans_len',
type=int,
default=15,
help='Maximum length of a predicted answer.')
parser.add_argument('--num_workers',
type=int,
default=4,
help='Number of sub-processes to use per data loader.')
parser.add_argument('--save_dir',
type=str,
default='./save/',
help='Base directory for saving information.')
parser.add_argument('--batch_size',
type=int,
default=64,
help='Batch size per GPU. Scales automatically when \
multiple GPUs are available.')
parser.add_argument('--drop_prob',
type=float,
default=0.3,
help='Probability of zeroing an activation in dropout layers.')
parser.add_argument('--use_squad_v2',
type=lambda s: s.lower().startswith('t'),
default=True,
help='Whether to use SQuAD 2.0 (unanswerable) questions.')
parser.add_argument('--hidden_size',
type=int,
default=100,
help='Number of features in encoder hidden layers.')
parser.add_argument('--number_of_class',
type=int,
default=3)
parser.add_argument('--enc_rnn_layers',
type=int,
default=2,
help="Encoding RNN layers")
parser.add_argument('--inf_rnn_layers',
type=int,
default=2,
help="Inference RNN layers")
parser.add_argument('--glove_dim',
type=int,
default=300,
help='Size of GloVe word vectors to use')
parser.add_argument('--cove_dim',
type=int,
default=600,
help='Size of CoVe word vectors to use')
parser.add_argument('--concepts_size',
type=int,
default=125,
help='Size of CoVe word vectors to use')
parser.add_argument('--num_features',
type=int,
default=3,
help='Size of extra features')
parser.add_argument('--pos_size',
type=int,
default=56+1,
help='How many kinds of POS tags.')
parser.add_argument('--pos_dim',
type=int,
default=12,
help='The embedding dimension for POS tags.')
parser.add_argument('--ner_size',
type=int,
default=19+1,
help='How many kinds of named entity tags.')
parser.add_argument('--ner_dim',
type=int,
default=8,
help='The embedding dimension for named entity tags.')
parser.add_argument('--num_visuals',
type=int,
default=10,
help='Number of examples to visualize in TensorBoard.')
parser.add_argument('--load_path',
type=str,
default=None,
help='Path to load as a model checkpoint.')