merged_preds[i] = merge_regression(prediction_dict[i])
elif args.task_type == 'multiclass':
merged_preds[i] = avg_probs_multiclass(
np.array(prediction_dict[i]))
return merged_preds
for predictor_params in grid:
print(predictor_params, flush=True)
predictor = Classifier(**predictor_params).to(device)
if n_gpu > 1:
predictor = torch.nn.DataParallel(predictor)
if not(args.freeze_bert) and not(args.use_adversary):
param_optimizer = list(model.named_parameters()) + \
list(predictor.named_parameters())
elif args.freeze_bert and not(args.use_adversary):
param_optimizer = list(predictor.named_parameters())
elif args.freeze_bert and args.use_adversary:
raise Exception(
'No purpose in using an adversary if BERT layers are frozen')
else:
param_optimizer = list(model.named_parameters(
)) + list(predictor.named_parameters()) + list(discriminator.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(
nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(
nd in n for nd in no_decay)], 'weight_decay': 0.0}
merged_preds[i] = merge_regression(prediction_dict[i])
elif args.task_type == 'multiclass':
merged_preds[i] = avg_probs_multiclass(np.array(
prediction_dict[i]))
return merged_preds
for predictor_params in grid:
print(predictor_params, flush=True)
predictor = Classifier(**predictor_params).to(device)
if n_gpu > 1:
predictor = torch.nn.DataParallel(predictor)
if not (args.freeze_bert) and not (args.use_adversary):
param_optimizer = list(model.named_parameters()) + list(
predictor.named_parameters())
elif args.freeze_bert and not (args.use_adversary):
param_optimizer = list(predictor.named_parameters())
elif args.freeze_bert and args.use_adversary:
raise Exception(
'No purpose in using an adversary if BERT layers are frozen')
else:
param_optimizer = list(model.named_parameters()) + list(
predictor.named_parameters()) + list(
discriminator.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':