def add_model_args()

in drqa/reader/config.py [0:0]

• 57 lines of code
• 1 McCabe index (conditional complexity)

def add_model_args(parser):
    parser.register('type', 'bool', str2bool)

    # Model architecture
    model = parser.add_argument_group('DrQA Reader Model Architecture')
    model.add_argument('--model-type', type=str, default='rnn',
                       help='Model architecture type')
    model.add_argument('--embedding-dim', type=int, default=300,
                       help='Embedding size if embedding_file is not given')
    model.add_argument('--hidden-size', type=int, default=128,
                       help='Hidden size of RNN units')
    model.add_argument('--doc-layers', type=int, default=3,
                       help='Number of encoding layers for document')
    model.add_argument('--question-layers', type=int, default=3,
                       help='Number of encoding layers for question')
    model.add_argument('--rnn-type', type=str, default='lstm',
                       help='RNN type: LSTM, GRU, or RNN')

    # Model specific details
    detail = parser.add_argument_group('DrQA Reader Model Details')
    detail.add_argument('--concat-rnn-layers', type='bool', default=True,
                        help='Combine hidden states from each encoding layer')
    detail.add_argument('--question-merge', type=str, default='self_attn',
                        help='The way of computing the question representation')
    detail.add_argument('--use-qemb', type='bool', default=True,
                        help='Whether to use weighted question embeddings')
    detail.add_argument('--use-in-question', type='bool', default=True,
                        help='Whether to use in_question_* features')
    detail.add_argument('--use-pos', type='bool', default=True,
                        help='Whether to use pos features')
    detail.add_argument('--use-ner', type='bool', default=True,
                        help='Whether to use ner features')
    detail.add_argument('--use-lemma', type='bool', default=True,
                        help='Whether to use lemma features')
    detail.add_argument('--use-tf', type='bool', default=True,
                        help='Whether to use term frequency features')

    # Optimization details
    optim = parser.add_argument_group('DrQA Reader Optimization')
    optim.add_argument('--dropout-emb', type=float, default=0.4,
                       help='Dropout rate for word embeddings')
    optim.add_argument('--dropout-rnn', type=float, default=0.4,
                       help='Dropout rate for RNN states')
    optim.add_argument('--dropout-rnn-output', type='bool', default=True,
                       help='Whether to dropout the RNN output')
    optim.add_argument('--optimizer', type=str, default='adamax',
                       help='Optimizer: sgd or adamax')
    optim.add_argument('--learning-rate', type=float, default=0.1,
                       help='Learning rate for SGD only')
    optim.add_argument('--grad-clipping', type=float, default=10,
                       help='Gradient clipping')
    optim.add_argument('--weight-decay', type=float, default=0,
                       help='Weight decay factor')
    optim.add_argument('--momentum', type=float, default=0,
                       help='Momentum factor')
    optim.add_argument('--fix-embeddings', type='bool', default=True,
                       help='Keep word embeddings fixed (use pretrained)')
    optim.add_argument('--tune-partial', type=int, default=0,
                       help='Backprop through only the top N question words')
    optim.add_argument('--rnn-padding', type='bool', default=False,
                       help='Explicitly account for padding in RNN encoding')
    optim.add_argument('--max-len', type=int, default=15,
                       help='The max span allowed during decoding')