def hyperargs(): # type: () -> {}
"""
Builds different sets of arguments for the classifier. Must be the same for
training and predicting.
:return: the labeled arguments
:rtype: {}
"""
retdict = {}
for curwindow in [128, 64, 32, 256]:
for curstride in [0.7, 0.8, 0.9]:
accargs = ClassificationArgs()
accargs.num_train_epochs = 5
accargs.fp16 = False
accargs.overwrite_output_dir = True
accargs.evaluate_during_training = False
accargs.sliding_window = True
accargs.max_seq_length = curwindow
accargs.stride = curstride
accargs.labels_list = [1, 0]
accargs.save_eval_checkpoints = False
accargs.save_model_every_epoch = False
accargs.silent = True
accargs.manual_seed = 18
retdict['basic5epochs' + str(curwindow) + 'win' +
str(int(curstride * 10.0)) + 'stride'] = accargs
return retdict
def buildbertargs(): # type: () -> ClassificationArgs
"""
Builds arguments for the classifier. Must be the same for
training and predicting.
:return: the arguments
:rtype: ClassificationArgs
"""
accargs = ClassificationArgs()
accargs.num_train_epochs = 5
accargs.fp16 = False
accargs.overwrite_output_dir = True
accargs.evaluate_during_training = False
accargs.sliding_window = True
accargs.max_seq_length = 256
accargs.stride = 0.9
accargs.labels_list = [1, 0]
accargs.save_model_every_epoch = False
accargs.silent = True
accargs.manual_seed = 18
return accargs