max_grad_norm=args.max_grad_norm)
compute_loss_fct = MultipleChoiceLossCompute(criterion, criterion,
args.lm_coef, model_opt)
openAIModel = OpenAIModel()
openAIModel.load_openai_pretrained_model(dh_model.transformer,
n_ctx=n_ctx,
n_special=n_special)
dh_model.to(device)
dh_model = nn.DataParallel(dh_model)
n_updates = 0
n_epochs = 0
if dataset != 'stsb':
trYt = trY
if submit:
path = os.path.join(save_dir, desc, 'state_of_module')
torch.save(dh_model.state_dict(), make_path(path))
best_score = 0
for i in range(args.n_iter):
print("running epoch", i)
training_engine.run_epoch()
n_epochs += 1
training_engine.log(save_dir, desc)
if submit:
path = os.path.join(save_dir, desc, 'state_of_module')
dh_model.load_state_dict(torch.load(path))
training_engine.predict(dataset, args.submission_dir)
prediction = Prediction()
prediction.output_predictions(test_path, pred_path, out_path, topic)
dh_model.to(device)
dh_model = nn.DataParallel(dh_model)
n_updates = 0
n_epochs = 0
trYt = trY
best_score = 0
for i in range(args.n_iter):
print("running epoch", i)
run_epoch()
n_epochs += 1
# log(save_dir, desc)
torch.save(dh_model.state_dict(), 'model_state')
else:
n_vocab = len(voc)
max_len = 140
n_special = 2
n_ctx = max_len + 2
vocab = n_vocab + n_special + n_ctx
n_batch_train = args.n_batch * max(n_gpu, 1)
start_token = n_vocab
clf_token = n_vocab + 1
dh_model = DoubleHeadModel(args, clf_token, ('classification', 2), vocab, n_ctx)
dh_model.to(device)
dh_model = nn.DataParallel(dh_model)
dh_model.load_state_dict(torch.load('model_state'))