def train(args):
# load dataset
train_sentence_packs = json.load(open(args.prefix + args.dataset + '/train.json'))
random.shuffle(train_sentence_packs)
dev_sentence_packs = json.load(open(args.prefix + args.dataset + '/dev.json'))
instances_train = load_data_instances(train_sentence_packs, args)
instances_dev = load_data_instances(dev_sentence_packs, args)
random.shuffle(instances_train)
trainset = DataIterator(instances_train, args)
devset = DataIterator(instances_dev, args)
if not os.path.exists(args.model_dir):
os.makedirs(args.model_dir)
model = MultiInferBert(args).to(args.device)
optimizer = torch.optim.Adam([
{'params': model.bert.parameters(), 'lr': 5e-5},
{'params': model.cls_linear.parameters()}
], lr=5e-5)
best_joint_f1 = 0
best_joint_epoch = 0
for i in range(args.epochs):
print('Epoch:{}'.format(i))
for j in trange(trainset.batch_count):
_, tokens, lengths, masks, _, _, aspect_tags, tags = trainset.get_batch(j)
preds = model(tokens, masks)
preds_flatten = preds.reshape([-1, preds.shape[3]])
tags_flatten = tags.reshape([-1])
loss = F.cross_entropy(preds_flatten, tags_flatten, ignore_index=-1)
optimizer.zero_grad()
loss.backward()
optimizer.step()
joint_precision, joint_recall, joint_f1 = eval(model, devset, args)
if joint_f1 > best_joint_f1:
model_path = args.model_dir + 'bert' + args.task + '.pt'
torch.save(model, model_path)
best_joint_f1 = joint_f1
best_joint_epoch = i
print('best epoch: {}\tbest dev {} f1: {:.5f}\n\n'.format(best_joint_epoch, args.task, best_joint_f1))
def train(args):
# load double embedding
word2index = json.load(open(args.prefix + 'doubleembedding/word_idx.json'))
general_embedding = numpy.load(args.prefix + 'doubleembedding/gen.vec.npy')
general_embedding = torch.from_numpy(general_embedding)
domain_embedding = numpy.load(args.prefix + 'doubleembedding/' +
args.dataset + '_emb.vec.npy')
domain_embedding = torch.from_numpy(domain_embedding)
# load dataset
train_sentence_packs = json.load(
open(args.prefix + args.dataset + '/train.json'))
random.shuffle(train_sentence_packs)
dev_sentence_packs = json.load(
open(args.prefix + args.dataset + '/dev.json'))
instances_train = load_data_instances(train_sentence_packs, word2index,
args)
instances_dev = load_data_instances(dev_sentence_packs, word2index, args)
random.shuffle(instances_train)
trainset = DataIterator(instances_train, args)
devset = DataIterator(instances_dev, args)
if not os.path.exists(args.model_dir):
os.makedirs(args.model_dir)
# build model
if args.model == 'bilstm':
model = MultiInferRNNModel(general_embedding, domain_embedding,
args).to(args.device)
elif args.model == 'cnn':
model = MultiInferCNNModel(general_embedding, domain_embedding,
args).to(args.device)
parameters = list(model.parameters())
parameters = filter(lambda x: x.requires_grad, parameters)
optimizer = torch.optim.Adam(parameters, lr=args.lr)
# training
best_joint_f1 = 0
best_joint_epoch = 0
for i in range(args.epochs):
print('Epoch:{}'.format(i))
for j in trange(trainset.batch_count):
_, sentence_tokens, lengths, masks, aspect_tags, _, tags = trainset.get_batch(
j)
predictions = model(sentence_tokens, lengths, masks)
loss = 0.
tags_flatten = tags[:, :lengths[0], :lengths[0]].reshape([-1])
for k in range(len(predictions)):
prediction_flatten = predictions[k].reshape(
[-1, predictions[k].shape[3]])
loss = loss + F.cross_entropy(
prediction_flatten, tags_flatten, ignore_index=-1)
optimizer.zero_grad()
loss.backward()
optimizer.step()
joint_precision, joint_recall, joint_f1 = eval(model, devset, args)
if joint_f1 > best_joint_f1:
model_path = args.model_dir + args.model + args.task + '.pt'
torch.save(model, model_path)
best_joint_f1 = joint_f1
best_joint_epoch = i
print('best epoch: {}\tbest dev {} f1: {:.5f}\n\n'.format(
best_joint_epoch, args.task, best_joint_f1))
