def __call__(self, config):
print("Preprocess the data")
train = Corpus.load(config.ftrain)
dev = Corpus.load(config.fdev)
test = Corpus.load(config.ftest)
if os.path.exists(config.vocab):
vocab = torch.load(config.vocab)
else:
vocab = Vocab.from_corpus(corpus=train, min_freq=2)
vocab.read_embeddings(Embedding.load(config.fembed, config.unk))
torch.save(vocab, config.vocab)
config.update({
'n_words': vocab.n_train_words,
'n_tags': vocab.n_tags,
'n_rels': vocab.n_rels,
'pad_index': vocab.pad_index,
'unk_index': vocab.unk_index
})
print(vocab)
print("Load the dataset")
trainset = TextDataset(vocab.numericalize(train))
devset = TextDataset(vocab.numericalize(dev))
testset = TextDataset(vocab.numericalize(test))
# set the data loaders
train_loader = batchify(dataset=trainset,
batch_size=config.batch_size,
n_buckets=config.buckets,
shuffle=True)
dev_loader = batchify(dataset=devset,
batch_size=config.batch_size,
n_buckets=config.buckets)
test_loader = batchify(dataset=testset,
batch_size=config.batch_size,
n_buckets=config.buckets)
print(f"{'train:':6} {len(trainset):5} sentences in total, "
f"{len(train_loader):3} batches provided")
print(f"{'dev:':6} {len(devset):5} sentences in total, "
f"{len(dev_loader):3} batches provided")
print(f"{'test:':6} {len(testset):5} sentences in total, "
f"{len(test_loader):3} batches provided")
print("Create the model")
parser = BiaffineParser(config, vocab.embeddings)
if torch.cuda.is_available():
parser = parser.cuda()
print(f"{parser}\n")
model = Model(vocab, parser)
total_time = timedelta()
best_e, best_metric = 1, Metric()
model.optimizer = Adam(model.parser.parameters(),
config.lr,
(config.beta_1, config.beta_2),
config.epsilon)
model.scheduler = ExponentialLR(model.optimizer,
config.decay ** (1 / config.steps))
for epoch in range(1, config.epochs + 1):
start = datetime.now()
# train one epoch and update the parameters
model.train(train_loader)
print(f"Epoch {epoch} / {config.epochs}:")
loss, train_metric = model.evaluate(train_loader, config.punct)
print(f"{'train:':6} Loss: {loss:.4f} {train_metric}")
loss, dev_metric = model.evaluate(dev_loader, config.punct)
print(f"{'dev:':6} Loss: {loss:.4f} {dev_metric}")
loss, test_metric = model.evaluate(test_loader, config.punct)
print(f"{'test:':6} Loss: {loss:.4f} {test_metric}")
t = datetime.now() - start
# save the model if it is the best so far
if dev_metric > best_metric and epoch > config.patience:
best_e, best_metric = epoch, dev_metric
model.parser.save(config.model + f".{best_e}")
print(f"{t}s elapsed (saved)\n")
else:
print(f"{t}s elapsed\n")
total_time += t
if epoch - best_e >= config.patience:
break
model.parser = BiaffineParser.load(config.model + f".{best_e}")
loss, metric = model.evaluate(test_loader, config.punct)
print(f"max score of dev is {best_metric.score:.2%} at epoch {best_e}")
print(f"the score of test at epoch {best_e} is {metric.score:.2%}")
print(f"average time of each epoch is {total_time / epoch}s")
print(f"{total_time}s elapsed")
def __call__(self, config):
print("Preprocess the data")
if config.input_type == "conllu":
train = UniversalDependenciesDatasetReader()
train.load(config.ftrain)
dev = UniversalDependenciesDatasetReader()
dev.load(config.fdev)
test = UniversalDependenciesDatasetReader()
test.load(config.ftest)
else:
train = Corpus.load(config.ftrain)
dev = Corpus.load(config.fdev)
test = Corpus.load(config.ftest)
if config.use_predicted:
if config.input_type == "conllu":
train_predicted = UniversalDependenciesDatasetReader()
train_predicted.load(config.fpredicted_train)
dev_predicted = UniversalDependenciesDatasetReader()
dev_predicted.load(config.fpredicted_dev)
test_predicted = UniversalDependenciesDatasetReader()
test_predicted.load(config.fpredicted_test)
else:
train_predicted = Corpus.load(config.fpredicted_train)
dev_predicted = Corpus.load(config.fpredicted_dev)
test_predicted = Corpus.load(config.fpredicted_test)
if path.exists(config.main_path + "/exp") != True:
os.mkdir(config.main_path + "/exp")
if path.exists(config.main_path + "/model") != True:
os.mkdir(config.main_path + "/model")
if path.exists(config.main_path + config.model + config.modelname) != True:
os.mkdir(config.main_path + config.model + config.modelname)
vocab = Vocab.from_corpus(config=config, corpus=train, min_freq=2)
torch.save(vocab, config.main_path + config.vocab + config.modelname + "/vocab.tag")
config.update({
'n_words': vocab.n_train_words,
'n_tags': vocab.n_tags,
'n_rels': vocab.n_rels,
'pad_index': vocab.pad_index,
'unk_index': vocab.unk_index
})
print("Load the dataset")
if config.use_predicted:
trainset = TextDataset(vocab.numericalize(train, train_predicted))
devset = TextDataset(vocab.numericalize(dev, dev_predicted))
testset = TextDataset(vocab.numericalize(test, test_predicted))
else:
trainset = TextDataset(vocab.numericalize(train))
devset = TextDataset(vocab.numericalize(dev))
testset = TextDataset(vocab.numericalize(test))
# set the data loaders
train_loader, _ = batchify(dataset=trainset,
batch_size=config.batch_size,
n_buckets=config.buckets,
shuffle=True)
dev_loader, _ = batchify(dataset=devset,
batch_size=config.batch_size,
n_buckets=config.buckets)
test_loader, _ = batchify(dataset=testset,
batch_size=config.batch_size,
n_buckets=config.buckets)
print(f"{'train:':6} {len(trainset):5} sentences in total, "
f"{len(train_loader):3} batches provided")
print(f"{'dev:':6} {len(devset):5} sentences in total, "
f"{len(dev_loader):3} batches provided")
print(f"{'test:':6} {len(testset):5} sentences in total, "
f"{len(test_loader):3} batches provided")
print("Create the model")
parser = BiaffineParser(config, vocab.n_rels, vocab.bertmodel)
print("number of pars:{}".format(sum(p.numel() for p in parser.parameters()
if p.requires_grad)))
if torch.cuda.is_available():
print('device:cuda')
device = torch.device('cuda')
parser = parser.to(device)
# print(f"{parser}\n")
model = Model(vocab, parser, config, vocab.n_rels)
total_time = timedelta()
best_e, best_metric = 1, Metric()
num_train_optimization_steps = int(config.num_iter_encoder * config.epochs * len(train_loader))
warmup_steps = int(config.warmupproportion * num_train_optimization_steps)
if config.use_two_opts:
model_nonbert = []
model_bert = []
layernorm_params = ['layernorm_key_layer', 'layernorm_value_layer', 'dp_relation_k', 'dp_relation_v']
for name, param in parser.named_parameters():
if 'bert' in name and not any(nd in name for nd in layernorm_params):
model_bert.append((name, param))
else:
model_nonbert.append((name, param))
# Prepare optimizer and schedule (linear warmup and decay) for Non-bert parameters
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters_nonbert = [
{'params': [p for n, p in model_nonbert if not any(nd in n for nd in no_decay)],
'weight_decay': config.weight_decay},
{'params': [p for n, p in model_nonbert if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
model.optimizer_nonbert = AdamW(optimizer_grouped_parameters_nonbert, lr=config.lr2)
model.scheduler_nonbert = get_linear_schedule_with_warmup(model.optimizer_nonbert,
num_warmup_steps=warmup_steps,
num_training_steps=num_train_optimization_steps)
# Prepare optimizer and schedule (linear warmup and decay) for Bert parameters
optimizer_grouped_parameters_bert = [
{'params': [p for n, p in model_bert if not any(nd in n for nd in no_decay)],
'weight_decay': config.weight_decay},
{'params': [p for n, p in model_bert if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
model.optimizer_bert = AdamW(optimizer_grouped_parameters_bert, lr=config.lr1)
model.scheduler_bert = get_linear_schedule_with_warmup(
model.optimizer_bert, num_warmup_steps=warmup_steps, num_training_steps=num_train_optimization_steps
)
else:
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in parser.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': config.weight_decay},
{'params': [p for n, p in parser.named_parameters() if any(nd in n for nd in no_decay)],
'weight_decay': 0.0}
]
model.optimizer = AdamW(optimizer_grouped_parameters, lr=config.lr1)
model.scheduler = get_linear_schedule_with_warmup(
model.optimizer, num_warmup_steps=warmup_steps, num_training_steps=num_train_optimization_steps
)
for epoch in range(1, config.epochs + 1):
start = datetime.now()
# train one epoch and update the parameters
if config.use_predicted:
model.train_predicted(train_loader)
else:
model.train(train_loader)
print(f"Epoch {epoch} / {config.epochs}:")
if config.use_predicted:
loss, dev_metric = model.evaluate_predicted(dev_loader, config.punct)
else:
loss, dev_metric = model.evaluate(dev_loader, config.punct)
print(f"{'dev:':6} Loss: {loss:.4f} {dev_metric}")
if config.use_predicted:
loss, test_metric = model.evaluate_predicted(test_loader, config.punct)
else:
loss, test_metric = model.evaluate(test_loader, config.punct)
print(f"{'test:':6} Loss: {loss:.4f} {test_metric}")
t = datetime.now() - start
# save the model if it is the best so far
if dev_metric > best_metric:
best_e, best_metric = epoch, dev_metric
print(config.model + config.modelname + "/model_weights")
model.parser.save(config.main_path + config.model + config.modelname + "/model_weights")
print(f"{t}s elapsed (saved)\n")
else:
print(f"{t}s elapsed\n")
total_time += t
if epoch - best_e >= config.patience:
break
model.parser = BiaffineParser.load(config.main_path + config.model + config.modelname + "/model_weights")
if config.use_predicted:
loss, metric = model.evaluate_predicted(test_loader, config.punct)
else:
loss, metric = model.evaluate(test_loader, config.punct)
print(metric)
print(f"max score of dev is {best_metric.score:.2%} at epoch {best_e}")
print(f"the score of test at epoch {best_e} is {metric.score:.2%}")
print(f"average time of each epoch is {total_time / epoch}s")
print(f"{total_time}s elapsed")
def __call__(self, config):
print("Preprocess the data")
train = Corpus.load(config.ftrain)
dev = Corpus.load(config.fdev)
test = Corpus.load(config.ftest)
if path.exists(config.model) != True:
os.mkdir(config.model)
if path.exists("model/") != True:
os.mkdir("model/")
if path.exists(config.model + config.modelname) != True:
os.mkdir(config.model + config.modelname)
if config.checkpoint:
vocab = torch.load(config.main_path + config.vocab +
config.modelname + "/vocab.tag")
else:
vocab = Vocab.from_corpus(config=config,
corpus=train,
corpus_dev=dev,
corpus_test=test,
min_freq=0)
train_seq = read_seq(config.ftrain_seq, vocab)
total_act = 0
for x in train_seq:
total_act += len(x)
print("number of transitions:{}".format(total_act))
torch.save(vocab, config.vocab + config.modelname + "/vocab.tag")
config.update({
'n_words': vocab.n_train_words,
'n_tags': vocab.n_tags,
'n_rels': vocab.n_rels,
'n_trans': vocab.n_trans,
'pad_index': vocab.pad_index,
'unk_index': vocab.unk_index
})
print("Load the dataset")
trainset = TextDataset(vocab.numericalize(train, train_seq))
devset = TextDataset(vocab.numericalize(dev))
testset = TextDataset(vocab.numericalize(test))
# set the data loaders
train_loader, _ = batchify(dataset=trainset,
batch_size=config.batch_size,
n_buckets=config.buckets,
shuffle=True)
dev_loader, _ = batchify(dataset=devset,
batch_size=config.batch_size,
n_buckets=config.buckets)
test_loader, _ = batchify(dataset=testset,
batch_size=config.batch_size,
n_buckets=config.buckets)
print(f"{'train:':6} {len(trainset):5} sentences in total, "
f"{len(train_loader):3} batches provided")
print(f"{'dev:':6} {len(devset):5} sentences in total, "
f"{len(dev_loader):3} batches provided")
print(f"{'test:':6} {len(testset):5} sentences in total, "
f"{len(test_loader):3} batches provided")
print("Create the model")
if config.checkpoint:
parser = Parser.load(config.main_path + config.model +
config.modelname + "/parser-checkpoint")
else:
parser = Parser(config, vocab.bertmodel)
print("number of parameters:{}".format(
sum(p.numel() for p in parser.parameters() if p.requires_grad)))
if torch.cuda.is_available():
print('Train/Evaluate on GPU')
device = torch.device('cuda')
parser = parser.to(device)
model = Model(vocab, parser, config, vocab.n_rels)
total_time = timedelta()
best_e, best_metric = 1, Metric()
## prepare optimisers
num_train_optimization_steps = int(config.epochs * len(train_loader))
warmup_steps = int(config.warmupproportion *
num_train_optimization_steps)
## one for parsing parameters, one for BERT parameters
if config.use_two_opts:
model_nonbert = []
model_bert = []
layernorm_params = [
'layernorm_key_layer', 'layernorm_value_layer',
'dp_relation_k', 'dp_relation_v'
]
for name, param in parser.named_parameters():
if 'bert' in name and not any(nd in name
for nd in layernorm_params):
model_bert.append((name, param))
else:
model_nonbert.append((name, param))
# Prepare optimizer and schedule (linear warmup and decay) for Non-bert parameters
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters_nonbert = [{
'params': [
p for n, p in model_nonbert
if not any(nd in n for nd in no_decay)
],
'weight_decay':
config.weight_decay
}, {
'params': [
p for n, p in model_nonbert
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
model.optimizer_nonbert = AdamW(
optimizer_grouped_parameters_nonbert, lr=config.lr2)
model.scheduler_nonbert = get_linear_schedule_with_warmup(
model.optimizer_nonbert,
num_warmup_steps=warmup_steps,
num_training_steps=num_train_optimization_steps)
# Prepare optimizer and schedule (linear warmup and decay) for Bert parameters
optimizer_grouped_parameters_bert = [{
'params': [
p for n, p in model_bert
if not any(nd in n for nd in no_decay)
],
'weight_decay':
config.weight_decay
}, {
'params':
[p for n, p in model_bert if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
model.optimizer_bert = AdamW(optimizer_grouped_parameters_bert,
lr=config.lr)
model.scheduler_bert = get_linear_schedule_with_warmup(
model.optimizer_bert,
num_warmup_steps=warmup_steps,
num_training_steps=num_train_optimization_steps)
else:
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in parser.named_parameters()
if not any(nd in n for nd in no_decay)
],
'weight_decay':
config.weight_decay
}, {
'params': [
p for n, p in parser.named_parameters()
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
model.optimizer = AdamW(optimizer_grouped_parameters, lr=config.lr)
model.scheduler = get_linear_schedule_with_warmup(
model.optimizer,
num_warmup_steps=warmup_steps,
num_training_steps=num_train_optimization_steps)
start_epoch = 1
## load model, optimiser, and other parameters from a checkpoint
if config.checkpoint:
check_load = torch.load(config.main_path + config.model +
config.modelname + "/checkpoint")
if config.use_two_opts:
model.optimizer_bert.load_state_dict(
check_load['optimizer_bert'])
model.optimizer_nonbert.load_state_dict(
check_load['optimizer_nonbert'])
model.scheduler_bert.load_state_dict(
check_load['lr_schedule_bert'])
model.scheduler_nonbert.load_state_dict(
check_load['lr_schedule_nonbert'])
start_epoch = check_load['epoch'] + 1
best_e = check_load['best_e']
best_metric = check_load['best_metric']
else:
model.optimizer.load_state_dict(check_load['optimizer'])
model.scheduler.load_state_dict(check_load['lr_schedule'])
start_epoch = check_load['epoch'] + 1
best_e = check_load['best_e']
best_metric = check_load['best_metric']
f1 = open(config.model + config.modelname + "/baseline.txt", "a")
f1.write("New Model:\n")
f1.close()
for epoch in range(start_epoch, config.epochs + 1):
start = datetime.now()
# train one epoch and update the parameters
model.train(train_loader)
print(f"Epoch {epoch} / {config.epochs}:")
f1 = open(config.model + config.modelname + "/baseline.txt", "a")
dev_metric = model.evaluate(dev_loader, config.punct)
f1.write(str(epoch) + "\n")
print(f"{'dev:':6} {dev_metric}")
f1.write(f"{'dev:':6} {dev_metric}")
f1.write("\n")
f1.close()
t = datetime.now() - start
# save the model if it is the best so far
if dev_metric > best_metric:
best_e, best_metric = epoch, dev_metric
print(config.model + config.modelname + "/model_weights")
model.parser.save(config.model + config.modelname +
"/model_weights")
print(f"{t}s elapsed (saved)\n")
else:
print(f"{t}s elapsed\n")
total_time += t
if epoch - best_e >= config.patience:
break
## save checkpoint
if config.use_two_opts:
checkpoint = {
"epoch": epoch,
"optimizer_bert": model.optimizer_bert.state_dict(),
"lr_schedule_bert": model.scheduler_bert.state_dict(),
"lr_schedule_nonbert":
model.scheduler_nonbert.state_dict(),
"optimizer_nonbert": model.optimizer_nonbert.state_dict(),
'best_metric': best_metric,
'best_e': best_e
}
torch.save(
checkpoint, config.main_path + config.model +
config.modelname + "/checkpoint")
parser.save(config.main_path + config.model +
config.modelname + "/parser-checkpoint")
else:
checkpoint = {
"epoch": epoch,
"optimizer": model.optimizer.state_dict(),
"lr_schedule": model.scheduler.state_dict(),
'best_metric': best_metric,
'best_e': best_e
}
torch.save(
checkpoint, config.main_path + config.model +
config.modelname + "/checkpoint")
parser.save(config.main_path + config.model +
config.modelname + "/parser-checkpoint")
model.parser = Parser.load(config.model + config.modelname +
"/model_weights")
metric = model.evaluate(test_loader, config.punct)
print(metric)
print(f"max score of dev is {best_metric.score:.2%} at epoch {best_e}")
print(f"the score of test at epoch {best_e} is {metric.score:.2%}")
print(f"average time of each epoch is {total_time / epoch}s")
print(f"{total_time}s elapsed")
def __call__(self, config):
if not os.path.exists(config.file):
os.mkdir(config.file)
if config.preprocess or not os.path.exists(config.vocab):
print("Preprocess the corpus")
pos_train = Corpus.load(config.fptrain, [1, 4], config.pos)
dep_train = Corpus.load(config.ftrain)
pos_dev = Corpus.load(config.fpdev, [1, 4])
dep_dev = Corpus.load(config.fdev)
pos_test = Corpus.load(config.fptest, [1, 4])
dep_test = Corpus.load(config.ftest)
print("Create the vocab")
vocab = Vocab.from_corpora(pos_train, dep_train, 2)
vocab.read_embeddings(Embedding.load(config.fembed))
print("Load the dataset")
pos_trainset = TextDataset(vocab.numericalize(pos_train, False),
config.buckets)
dep_trainset = TextDataset(vocab.numericalize(dep_train),
config.buckets)
pos_devset = TextDataset(vocab.numericalize(pos_dev, False),
config.buckets)
dep_devset = TextDataset(vocab.numericalize(dep_dev),
config.buckets)
pos_testset = TextDataset(vocab.numericalize(pos_test, False),
config.buckets)
dep_testset = TextDataset(vocab.numericalize(dep_test),
config.buckets)
torch.save(vocab, config.vocab)
torch.save(pos_trainset, os.path.join(config.file, 'pos_trainset'))
torch.save(dep_trainset, os.path.join(config.file, 'dep_trainset'))
torch.save(pos_devset, os.path.join(config.file, 'pos_devset'))
torch.save(dep_devset, os.path.join(config.file, 'dep_devset'))
torch.save(pos_testset, os.path.join(config.file, 'pos_testset'))
torch.save(dep_testset, os.path.join(config.file, 'dep_testset'))
else:
print("Load the vocab")
vocab = torch.load(config.vocab)
print("Load the datasets")
pos_trainset = torch.load(os.path.join(config.file,
'pos_trainset'))
dep_trainset = torch.load(os.path.join(config.file,
'dep_trainset'))
pos_devset = torch.load(os.path.join(config.file, 'pos_devset'))
dep_devset = torch.load(os.path.join(config.file, 'dep_devset'))
pos_testset = torch.load(os.path.join(config.file, 'pos_testset'))
dep_testset = torch.load(os.path.join(config.file, 'dep_testset'))
config.update({
'n_words': vocab.n_init,
'n_chars': vocab.n_chars,
'n_pos_tags': vocab.n_pos_tags,
'n_dep_tags': vocab.n_dep_tags,
'n_rels': vocab.n_rels,
'pad_index': vocab.pad_index,
'unk_index': vocab.unk_index
})
# set the data loaders
pos_train_loader = batchify(
pos_trainset, config.pos_batch_size // config.update_steps, True)
dep_train_loader = batchify(dep_trainset,
config.batch_size // config.update_steps,
True)
pos_dev_loader = batchify(pos_devset, config.pos_batch_size)
dep_dev_loader = batchify(dep_devset, config.batch_size)
pos_test_loader = batchify(pos_testset, config.pos_batch_size)
dep_test_loader = batchify(dep_testset, config.batch_size)
print(vocab)
print(f"{'pos_train:':10} {len(pos_trainset):7} sentences in total, "
f"{len(pos_train_loader):4} batches provided")
print(f"{'dep_train:':10} {len(dep_trainset):7} sentences in total, "
f"{len(dep_train_loader):4} batches provided")
print(f"{'pos_dev:':10} {len(pos_devset):7} sentences in total, "
f"{len(pos_dev_loader):4} batches provided")
print(f"{'dep_dev:':10} {len(dep_devset):7} sentences in total, "
f"{len(dep_dev_loader):4} batches provided")
print(f"{'pos_test:':10} {len(pos_testset):7} sentences in total, "
f"{len(pos_test_loader):4} batches provided")
print(f"{'dep_test:':10} {len(dep_testset):7} sentences in total, "
f"{len(dep_test_loader):4} batches provided")
print("Create the model")
parser = BiaffineParser(config, vocab.embed).to(config.device)
print(f"{parser}\n")
model = Model(config, vocab, parser)
total_time = timedelta()
best_e, best_metric = 1, AttachmentMethod()
model.optimizer = Adam(model.parser.parameters(), config.lr,
(config.mu, config.nu), config.epsilon)
model.scheduler = ExponentialLR(model.optimizer,
config.decay**(1 / config.decay_steps))
for epoch in range(1, config.epochs + 1):
start = datetime.now()
# train one epoch and update the parameters
model.train(pos_train_loader, dep_train_loader)
print(f"Epoch {epoch} / {config.epochs}:")
lp, ld, mp, mdt, mdp = model.evaluate(None, dep_train_loader)
print(f"{'train:':6} LP: {lp:.4f} LD: {ld:.4f} {mp} {mdt} {mdp}")
lp, ld, mp, mdt, dev_m = model.evaluate(pos_dev_loader,
dep_dev_loader)
print(f"{'dev:':6} LP: {lp:.4f} LD: {ld:.4f} {mp} {mdt} {dev_m}")
lp, ld, mp, mdt, mdp = model.evaluate(pos_test_loader,
dep_test_loader)
print(f"{'test:':6} LP: {lp:.4f} LD: {ld:.4f} {mp} {mdt} {mdp}")
t = datetime.now() - start
# save the model if it is the best so far
if dev_m > best_metric and epoch > config.patience:
best_e, best_metric = epoch, dev_m
model.parser.save(config.model)
print(f"{t}s elapsed (saved)\n")
else:
print(f"{t}s elapsed\n")
total_time += t
if epoch - best_e >= config.patience:
break
model.parser = BiaffineParser.load(config.model)
lp, ld, mp, mdt, mdp = model.evaluate(pos_test_loader, dep_test_loader)
print(f"max score of dev is {best_metric.score:.2%} at epoch {best_e}")
print(f"the score of test at epoch {best_e} is {mdp.score:.2%}")
print(f"average time of each epoch is {total_time / epoch}s")
print(f"{total_time}s elapsed")
