use_small = True
gpu = args.gpu
batch_size = 16
else:
use_small = False
gpu = args.gpu
batch_size = 64
dev_sql, dev_table, dev_db, test_sql, test_table, test_db = load_dataset(
use_small=use_small, mode='test')
#train_sql, train_table, train_db, dev_sql, dev_table, dev_db = load_dataset(use_small=use_small, mode='test')
model_bert, tokenizer, bert_config = get_bert(bert_path)
model = SQLNet(N_word=n_word,
use_ca=args.ca,
gpu=gpu,
trainable_emb=args.train_emb)
model_path = 'saved_model/best_model'
print("Loading from %s" % model_path)
model.load_state_dict(torch.load(model_path, map_location='cpu'))
print("Loaded model from %s" % model_path)
dev_acc = epoch_acc(model_bert, tokenizer, model, batch_size, dev_sql,
dev_table, dev_db, args.mode_type)
print('Dev Logic Form Accuracy: %.3f, Execution Accuracy: %.3f' %
(dev_acc[1], dev_acc[2]))
print("Start to predict test set")
predict_test(model_bert, tokenizer, model, batch_size, test_sql,
test_table, args.output_dir)
print("Output path of prediction result is %s" % args.output_dir)
'/media/yinshuai/d8644f6c-5a97-4e12-909b-b61d2271b61c/nlp-datasets/nlp2sql/char_embedding'
)
model = SQLNet(word_emb,
N_word=n_word,
use_ca=args.ca,
gpu=gpu,
trainable_emb=args.train_emb)
optimizer = torch.optim.Adam(model.parameters(),
lr=learning_rate,
weight_decay=0)
if args.restore:
model_path = 'saved_model/best_model_32'
print "Loading trained model from %s" % model_path
model.load_state_dict(torch.load(model_path))
# used to record best score of each sub-task
best_sn, best_sc, best_sa, best_wn, best_wc, best_wo, best_wv, best_wr = 0, 0, 0, 0, 0, 0, 0, 0
best_sn_idx, best_sc_idx, best_sa_idx, best_wn_idx, best_wc_idx, best_wo_idx, best_wv_idx, best_wr_idx = 0, 0, 0, 0, 0, 0, 0, 0
best_lf, best_lf_idx = 0.0, 0
best_ex, best_ex_idx = 0.0, 0
print "#" * 20 + " Star to Train " + "#" * 20
for i in range(args.epoch):
print 'Epoch %d' % (i + 1)
# train on the train dataset
# train_sql: a json list treated from train.json
# train_table: a dict treated from table.json
train_loss = epoch_train(model, optimizer, batch_size, train_sql,
train_table)