# Define the sentence encoder
sentence_encoder = opennre.encoder.BERTEncoder(
max_length=args.max_seq_len,
pretrain_path=os.path.join(root_path, 'pretrain/bert-base-uncased'),
mask_entity=False)
# Define the model
model = opennre.model.SoftmaxNN(sentence_encoder, len(rel2id), rel2id)
model.to(torch.device('cuda:0'))
# Define the whole training framework
framework = opennre.framework.SentenceRE(train_path=args.test_path,
val_path=args.test_path,
test_path=args.test_path,
model=model,
ckpt=args.model_path,
batch_size=args.batch_size,
max_epoch=1,
lr=2e-5,
opt='adamw')
framework.load_state_dict(torch.load(args.model_path)['state_dict'])
result = framework.eval_model(framework.test_loader)
# Print the result
print('Accuracy on test set: {}'.format(result['acc']))
print('Micro Precision: {}'.format(result['micro_p']))
print('Micro Recall: {}'.format(result['micro_r']))
print('Micro F1: {}'.format(result['micro_f1']))
# Define the sentence encoder
sentence_encoder = opennre.encoder.BERTEntityEncoder(
max_length=80,
pretrain_path=os.path.join(root_path, 'pretrain/bert-base-uncased'))
# Define the model
model = opennre.model.SoftmaxNN(sentence_encoder, len(rel2id), rel2id)
# Define the whole training framework
framework = opennre.framework.SentenceRE(
train_path=os.path.join(root_path, 'benchmark/wiki80/wiki80_train.txt'),
val_path=os.path.join(root_path, 'benchmark/wiki80/wiki80_val.txt'),
test_path=os.path.join(root_path, 'benchmark/wiki80/wiki80_val.txt'),
model=model,
ckpt=ckpt,
batch_size=64, # Modify the batch size w.r.t. your device
max_epoch=10,
lr=2e-5,
opt='adamw')
# Train the model
framework.train_model()
# Test the model
framework.load_state_dict(torch.load(ckpt)['state_dict'])
result = framework.eval_model(framework.test_loader)
# Print the result
print('Accuracy on test set: {}'.format(result['acc']))
def train(args):
# Some basic settings
# root_path = '.'
root_path = args.data_dir
sys.path.append(root_path)
if not os.path.exists('ckpt'):
os.mkdir('ckpt')
if len(args.ckpt) == 0:
args.ckpt = '{}_{}_{}'.format(args.dataset,
args.pretrain_path.split('/')[-1],
args.pooler)
ckpt = os.path.join(args.model_dir, 'ckpt/{}.pth.tar'.format(args.ckpt))
if args.dataset != 'none':
try:
opennre.download(args.dataset, root_path=root_path)
except:
pass
args.train_file = os.path.join(root_path, 'benchmark', args.dataset,
'{}_train.txt'.format(args.dataset))
args.val_file = os.path.join(root_path, 'benchmark', args.dataset,
'{}_val.txt'.format(args.dataset))
args.test_file = os.path.join(root_path, 'benchmark', args.dataset,
'{}_test.txt'.format(args.dataset))
if not os.path.exists(args.test_file):
logging.warn(
"Test file {} does not exist! Use val file instead".format(
args.test_file))
args.test_file = args.val_file
args.rel2id_file = os.path.join(root_path, 'benchmark', args.dataset,
'{}_rel2id.json'.format(args.dataset))
if args.dataset == 'wiki80':
args.metric = 'acc'
else:
args.metric = 'micro_f1'
else:
if not (os.path.exists(args.train_file) and os.path.exists(
args.val_file) and os.path.exists(args.test_file)
and os.path.exists(args.rel2id_file)):
raise Exception(
'--train_file, --val_file, --test_file and --rel2id_file are not specified or files do not exist. Or specify --dataset'
)
logging.info('Arguments:')
for arg in vars(args):
logging.info(' {}: {}'.format(arg, getattr(args, arg)))
# rel2id = json.load(open(args.rel2id_file))
rel2id = json.load(
open(
os.path.join(root_path, 'benchmark', args.dataset,
'finre_rel2id.json')))
# Define the sentence encoder
if args.pooler == 'entity':
sentence_encoder = opennre.encoder.BERTEntityEncoder(
max_length=args.max_length,
pretrain_path=args.pretrain_path,
mask_entity=args.mask_entity)
elif args.pooler == 'cls':
sentence_encoder = opennre.encoder.BERTEncoder(
max_length=args.max_length,
pretrain_path=args.pretrain_path,
mask_entity=args.mask_entity)
else:
raise NotImplementedError
# Define the model
model = opennre.model.SoftmaxNN(sentence_encoder, len(rel2id), rel2id)
# Define the whole training framework
framework = opennre.framework.SentenceRE(train_path=args.train_file,
val_path=args.val_file,
test_path=args.test_file,
model=model,
ckpt=ckpt,
batch_size=args.batch_size,
max_epoch=args.max_epoch,
lr=args.lr,
opt='adamw')
# Train the model
if not args.only_test:
framework.train_model('micro_f1')
# Test
framework.load_state_dict(torch.load(ckpt)['state_dict'])
result = framework.eval_model(framework.test_loader)
# Print the result
logging.info('Test set results:')
logging.info('Accuracy: {}'.format(result['acc']))
logging.info('Micro precision: {}'.format(result['micro_p']))
logging.info('Micro recall: {}'.format(result['micro_r']))
logging.info('Micro F1: {}'.format(result['micro_f1']))
