# Label the plot.
plt.title("Learning curve")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.show()
MAX_LEN = 40
bs = 8
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
print(torch.cuda.get_device_name(0))
config = BertConfig.from_json_file(
'./config/uncased_L-24_H-128_B-512_A-4_F-4_OPT.json')
#tokenizer = BertTokenizer.from_pretrained('./config/uncased_L-24_H-128_B-512_A-4_F-4_OPT.json', do_lower_case=True)
model = BertForTokenClassification(config)
model.cuda()
#BERT_FP = './config/uncased_L-24_H-1024_B-512_A-4.json'
#tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
#tokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]
#print(tokenized_texts[0])
#BERT_FP = './config/uncased_L-24_H-1024_B-512_A-4.json'
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased',
do_lower_case=True)
tokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]
print(tokenized_texts[0])
# Load data
test_data = data_loader.load_data('test')
# Specify the test set size
params.test_size = test_data['size']
params.eval_steps = params.test_size // params.batch_size
test_data_iterator = data_loader.data_iterator(test_data, shuffle=False)
logging.info("- done.")
# Define the model
#config_path = os.path.join(args.bert_model_dir, 'bert_config.json')
#config = BertConfig.from_json_file(config_path)
model = BertForTokenClassification("bert-base-uncased",
num_labels=len(params.tag2idx))
model = model.cuda()
model.to(params.device)
# Reload weights from the saved file
utils.load_checkpoint(
os.path.join(args.model_dir, args.restore_file + '.pth.tar'), model)
if args.fp16:
model.half()
if params.n_gpu > 1 and args.multi_gpu:
model = torch.nn.DataParallel(model)
logging.info("Starting evaluation...")
test_metrics = evaluate(model,
test_data_iterator,
params,
mark='Test',
verbose=True)
