code/train-classifier.py [61:88]:
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                    help="Encoder optimizer (SGD / RMSprop / Adam, etc.)")
parser.add_argument("--clip_grad_norm", type=float, default=5,
                    help="Clip gradients norm (0 to disable)")
parser.add_argument("--epoch_size", type=int, default=100000,
                    help="Epoch size / evaluation frequency")
parser.add_argument("--max_epoch", type=int, default=100000,
                    help="Maximum epoch size")
parser.add_argument("--stop_crit", type=str, default="",
                    help="Stopping criterion, and number of non-increase before stopping the experiment")
parser.add_argument("--metrics", type=str, default="",
                    help="Validation metrics")
# reload models
parser.add_argument("--reload_model", type=str, default="",
                    help="Reload a pretrained model")
# evaluation
parser.add_argument("--n_eval_sentences", type=int, default=-1,
                    help="Number of experiments to consider for evaluation (-1 for everything)")
parser.add_argument("--eval_only", type=int, default=0,
                    help="Only run evaluations")
params = parser.parse_args()


if __name__ == '__main__':

    # check parameters
    params.name = params.exp_name
    assert len(params.name.strip()) > 0
    check_mono_data_params(params)
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code/train-lm.py [63:90]:
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                    help="Encoder optimizer (SGD / RMSprop / Adam, etc.)")
parser.add_argument("--clip_grad_norm", type=float, default=5,
                    help="Clip gradients norm (0 to disable)")
parser.add_argument("--epoch_size", type=int, default=100000,
                    help="Epoch size / evaluation frequency")
parser.add_argument("--max_epoch", type=int, default=100000,
                    help="Maximum epoch size")
parser.add_argument("--stop_crit", type=str, default="",
                    help="Stopping criterion, and number of non-increase before stopping the experiment")
parser.add_argument("--metrics", type=str, default="",
                    help="Validation metrics")
# reload models
parser.add_argument("--reload_model", type=str, default="",
                    help="Reload a pretrained model")
# evaluation
parser.add_argument("--n_eval_sentences", type=int, default=-1,
                    help="Number of experiments to consider for evaluation (-1 for everything)")
parser.add_argument("--eval_only", type=int, default=0,
                    help="Only run evaluations")
params = parser.parse_args()


if __name__ == '__main__':

    # check parameters
    params.name = params.exp_name
    assert len(params.name.strip()) > 0
    check_mono_data_params(params)
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