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
]
train_df = pd.DataFrame(train_data)
train_df.columns = ["text", "labels"]
# Preparing eval data
eval_data = [
["Theoden was the king of Rohan", "true"],
["Merry was the king of Rohan", "false"],
]
eval_df = pd.DataFrame(eval_data)
eval_df.columns = ["text", "labels"]
model_args = ClassificationArgs()
model_args.reprocess_input_data = True
model_args.overwrite_output_dir = True
model_args.evaluate_during_training = True
model_args.manual_seed = 4
model_args.use_multiprocessing = True
model_args.train_batch_size = 16
model_args.eval_batch_size = 8
model_args.labels_list = ["true", "false"]
model_args.wandb_project = "Simple Sweep"
def train():
# Initialize a new wandb run
wandb.init()
# Create a TransformerModel
model = ClassificationModel("roberta", "roberta-base", use_cuda=True, args=model_args, sweep_config=wandb.config,)