parser_model = ParserModel(dep_vocab, parser_config, dep_vec)
parser_model.load_state_dict(torch.load(parser_config.load_model_path, \
map_location=lambda storage, loc: storage))
vocab = creatVocab(config.train_file, config.min_occur_count)
vec = vocab.load_pretrained_embs(config.pretrained_embeddings_file)
pickle.dump(vocab, open(config.save_vocab_path, 'wb'))
config.use_cuda = False
gpu_id = -1
if gpu and args.gpu >= 0:
torch.cuda.set_device(args.gpu)
config.use_cuda = True
print("GPU ID: ", args.gpu)
gpu_id = args.gpu
model = BiLSTMModel(vocab, config, parser_config, vec)
if config.use_cuda:
#torch.backends.cudnn.enabled = True
model = model.cuda()
parser_model = parser_model.cuda()
classifier = SentenceClassifier(config, model, vocab, parser_model,
dep_vocab)
data = read_corpus(config.train_file)
dev_data = read_corpus(config.dev_file)
test_data = read_corpus(config.test_file)
train(data, dev_data, test_data, classifier, vocab, dep_vocab, config)
config.use_cuda = False
gpu_id = -1
if gpu and args.gpu != -1:
config.use_cuda = True
torch.cuda.set_device(args.gpu)
print('GPU ID:' + str(args.gpu))
gpu_id = args.gpu
print("\nGPU using status: ", config.use_cuda)
elmo = ElmoEmbedder(config.elmo_option_file, config.elmo_weight_file,
gpu_id)
elmo_layers = elmo.elmo_bilm.num_layers
elmo_dims = elmo.elmo_bilm.get_output_dim()
model = BiLSTMModel(vocab, config, parser_config, vec,
(elmo_layers, elmo_dims))
if config.use_cuda:
torch.backends.cudnn.enabled = False
model = model.cuda(args.gpu)
parser_extembed = parser_extembed.cuda(args.gpu)
parser_model = parser_model.cuda(args.gpu)
classifier = BiSententClassifier(model, elmo, vocab, parser_model,
parser_extembed)
data = read_corpus(config.train_file)
dev_data = read_corpus(config.dev_file)
test_data = read_corpus(config.test_file)
train(data, dev_data, test_data, classifier, vocab, dep_vocab, config)
