def train_one(config: Config,
train_insts: List[Instance],
dev_insts: List[Instance],
model_name: str,
test_insts: List[Instance] = None,
config_name: str = None,
result_filename: str = None) -> NNCRF:
train_batches = batching_list_instances(config, train_insts)
dev_batches = batching_list_instances(config, dev_insts)
if test_insts:
test_batches = simple_batching(config, test_insts)
else:
test_batches = None
model = NNCRF(config)
model.train()
optimizer = get_optimizer(config, model)
epoch = config.num_epochs
best_dev_f1 = -1
saved_test_metrics = None
for i in range(1, epoch + 1):
epoch_loss = 0
start_time = time.time()
model.zero_grad()
if config.optimizer.lower() == "sgd":
optimizer = lr_decay(config, optimizer, i)
for index in np.random.permutation(len(train_batches)):
model.train()
loss = model(*train_batches[index])
epoch_loss += loss.item()
loss.backward()
optimizer.step()
model.zero_grad()
end_time = time.time()
print("Epoch %d: %.5f, Time is %.2fs" %
(i, epoch_loss, end_time - start_time),
flush=True)
model.eval()
# metric is [precision, recall, f_score]
dev_metrics = evaluate_model(config, model, "dev", dev_insts)
if test_insts is not None:
test_metrics = evaluate_model(config, model, "test", test_insts)
if dev_metrics[2] > best_dev_f1:
print("saving the best model...")
best_dev_f1 = dev_metrics[2]
if test_insts is not None:
saved_test_metrics = test_metrics
torch.save(model.state_dict(), model_name)
# # Save the corresponding config as well.
if config_name:
f = open(config_name, 'wb')
pickle.dump(config, f)
f.close()
if result_filename:
write_results(result_filename, test_insts)
model.zero_grad()
if test_insts is not None:
print(f"The best dev F1: {best_dev_f1}")
print(f"The corresponding test: {saved_test_metrics}")
return model
def evaluate_on_test(config: Config, all_train_insts: List[Instance],
dev_insts: List[Instance], test_insts: List[Instance]):
print("[Training Info] Training the final model")
model_folder = config.model_folder
res_folder = config.result_folder
model_name = model_folder + "/final_lstm_crf.m"
config_name = model_folder + "/config.conf"
res_name = res_folder + "/lstm_crf.results".format()
model = train_one(config=config,
train_insts=all_train_insts,
dev_insts=dev_insts,
model_name=model_name,
config_name=config_name,
test_insts=test_insts,
result_filename=res_name)
print("Archiving the best Model...")
with tarfile.open(model_folder + "/" + model_folder + ".tar.gz",
"w:gz") as tar:
tar.add(model_folder, arcname=os.path.basename(model_folder))
# print("The best dev: %.2f" % (best_dev[0]))
# print("The corresponding test: %.2f" % (best_test[0]))
# print("Final testing.")
model.load_state_dict(torch.load(model_name))
model.eval()
evaluate_model(config, model, "test", test_insts)
write_results(res_name, test_insts)
def train_model(config: Config, epoch: int, train_insts: List[Instance],
dev_insts: List[Instance], test_insts: List[Instance]):
### Data Processing Info
train_num = len(train_insts)
print("number of instances: %d" % (train_num))
print(colored("[Shuffled] Shuffle the training instance ids", "red"))
random.shuffle(train_insts)
batched_data = batching_list_instances(config, train_insts)
dev_batches = batching_list_instances(config, dev_insts)
test_batches = batching_list_instances(config, test_insts)
if config.embedder_type == "normal":
model = NNCRF(config)
optimizer = get_optimizer(config, model)
scheduler = None
else:
print(
colored(
f"[Model Info]: Working with transformers package from huggingface with {config.embedder_type}",
'red'))
print(
colored(
f"[Optimizer Info]: You should be aware that you are using the optimizer from huggingface.",
'red'))
print(
colored(
f"[Optimizer Info]: Change the optimier in transformers_util.py if you want to make some modifications.",
'red'))
model = TransformersCRF(config)
optimizer, scheduler = get_huggingface_optimizer_and_scheduler(
config,
model,
num_training_steps=len(batched_data) * epoch,
weight_decay=0.0,
eps=1e-8,
warmup_step=0)
print(
colored(f"[Optimizer Info] Modify the optimizer info as you need.",
'red'))
print(optimizer)
model.to(config.device)
best_dev = [-1, 0]
best_test = [-1, 0]
model_folder = config.model_folder
res_folder = "results"
if os.path.exists("model_files/" + model_folder):
raise FileExistsError(
f"The folder model_files/{model_folder} exists. Please either delete it or create a new one "
f"to avoid override.")
model_path = f"model_files/{model_folder}/lstm_crf.m"
config_path = f"model_files/{model_folder}/config.conf"
res_path = f"{res_folder}/{model_folder}.results"
print("[Info] The model will be saved to: %s.tar.gz" % (model_folder))
os.makedirs(f"model_files/{model_folder}",
exist_ok=True) ## create model files. not raise error if exist
os.makedirs(res_folder, exist_ok=True)
no_incre_dev = 0
print(
colored(
f"[Train Info] Start training, you have set to stop if performace not increase for {config.max_no_incre} epochs",
'red'))
for i in tqdm(range(1, epoch + 1), desc="Epoch"):
epoch_loss = 0
start_time = time.time()
model.zero_grad()
if config.optimizer.lower() == "sgd":
optimizer = lr_decay(config, optimizer, i)
for index in tqdm(np.random.permutation(len(batched_data)),
desc="--training batch",
total=len(batched_data)):
model.train()
loss = model(**batched_data[index])
epoch_loss += loss.item()
loss.backward()
if config.max_grad_norm > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(),
config.max_grad_norm)
optimizer.step()
optimizer.zero_grad()
model.zero_grad()
if scheduler is not None:
scheduler.step()
end_time = time.time()
print("Epoch %d: %.5f, Time is %.2fs" %
(i, epoch_loss, end_time - start_time),
flush=True)
model.eval()
dev_metrics = evaluate_model(config, model, dev_batches, "dev",
dev_insts)
test_metrics = evaluate_model(config, model, test_batches, "test",
test_insts)
if dev_metrics[2] > best_dev[0]:
print("saving the best model...")
no_incre_dev = 0
best_dev[0] = dev_metrics[2]
best_dev[1] = i
best_test[0] = test_metrics[2]
best_test[1] = i
torch.save(model.state_dict(), model_path)
# Save the corresponding config as well.
f = open(config_path, 'wb')
pickle.dump(config, f)
f.close()
write_results(res_path, test_insts)
else:
no_incre_dev += 1
model.zero_grad()
if no_incre_dev >= config.max_no_incre:
print(
"early stop because there are %d epochs not increasing f1 on dev"
% no_incre_dev)
break
print("Archiving the best Model...")
with tarfile.open(f"model_files/{model_folder}/{model_folder}.tar.gz",
"w:gz") as tar:
tar.add(f"model_files/{model_folder}",
arcname=os.path.basename(model_folder))
print("Finished archiving the models")
print("The best dev: %.2f" % (best_dev[0]))
print("The corresponding test: %.2f" % (best_test[0]))
print("Final testing.")
model.load_state_dict(torch.load(model_path))
model.eval()
evaluate_model(config, model, test_batches, "test", test_insts)
write_results(res_path, test_insts)
def train_model(config: Config, epoch: int, train_insts: List[Instance],
dev_insts: List[Instance], test_insts: List[Instance]):
model = NNCRF(config)
optimizer = get_optimizer(config, model)
train_num = len(train_insts)
print("number of instances: %d" % (train_num))
print(colored("[Shuffled] Shuffle the training instance ids", "red"))
random.shuffle(train_insts)
batched_data = batching_list_instances(config, train_insts)
dev_batches = batching_list_instances(config, dev_insts)
test_batches = batching_list_instances(config, test_insts)
best_dev = [-1, 0]
best_test = [-1, 0]
model_folder = config.model_folder
res_folder = "results"
if os.path.exists(model_folder):
raise FileExistsError(
f"The folder {model_folder} exists. Please either delete it or create a new one "
f"to avoid override.")
model_name = model_folder + "/lstm_crf.m".format()
config_name = model_folder + "/config.conf"
res_name = res_folder + "/lstm_crf.results".format()
print("[Info] The model will be saved to: %s.tar.gz" % (model_folder))
if not os.path.exists(model_folder):
os.makedirs(model_folder)
if not os.path.exists(res_folder):
os.makedirs(res_folder)
for i in range(1, epoch + 1):
epoch_loss = 0
start_time = time.time()
model.zero_grad()
if config.optimizer.lower() == "sgd":
optimizer = lr_decay(config, optimizer, i)
for index in np.random.permutation(len(batched_data)):
model.train()
loss = model(*batched_data[index])
epoch_loss += loss.item()
loss.backward()
optimizer.step()
model.zero_grad()
loss.detach()
end_time = time.time()
print("Epoch %d: %.5f, Time is %.2fs" %
(i, epoch_loss, end_time - start_time),
flush=True)
model.eval()
dev_metrics = evaluate_model(config, model, dev_batches, "dev",
dev_insts)
test_metrics = evaluate_model(config, model, test_batches, "test",
test_insts)
if test_metrics[1][2] > best_test[0]:
print("saving the best model...")
best_dev[0] = dev_metrics[1][2]
best_dev[1] = i
best_test[0] = test_metrics[1][2]
best_test[1] = i
torch.save(model.state_dict(), model_name)
# Save the corresponding config as well.
f = open(config_name, 'wb')
pickle.dump(config, f)
f.close()
print('Exact\n')
print_report(test_metrics[-2])
print('Overlap\n')
print_report(test_metrics[-1])
write_results(res_name, test_insts)
print("Archiving the best Model...")
with tarfile.open(model_folder + "/" + model_folder + ".tar.gz",
"w:gz") as tar:
tar.add(model_folder, arcname=os.path.basename(model_folder))
model.zero_grad()
print("Finished archiving the models")
print("The best dev: %.2f" % (best_dev[0]))
print("The corresponding test: %.2f" % (best_test[0]))
print("Final testing.")
model.load_state_dict(torch.load(model_name))
model.eval()
evaluate_model(config, model, test_batches, "test", test_insts)
write_results(res_name, test_insts)
def train_model(config: Config, epoch: int, train_insts: List[Instance],
dev_insts: List[Instance], test_insts: List[Instance]):
model = NNCRF(config)
optimizer = get_optimizer(config, model)
train_num = len(train_insts)
print("number of instances: %d" % (train_num))
print(colored("[Shuffled] Shuffle the training instance ids", "red"))
random.shuffle(train_insts)
batched_data = batching_list_instances(config, train_insts)
dev_batches = batching_list_instances(config, dev_insts)
test_batches = batching_list_instances(config, test_insts)
best_dev = [-1, 0]
best_test = [-1, 0]
model_folder = config.model_folder
res_folder = "results"
if os.path.exists("model_files/" + model_folder):
raise FileExistsError(
f"The folder model_files/{model_folder} exists. Please either delete it or create a new one "
f"to avoid override.")
model_path = f"model_files/{model_folder}/lstm_crf.m"
config_path = f"model_files/{model_folder}/config.conf"
res_path = f"{res_folder}/{model_folder}.results"
print("[Info] The model will be saved to: %s.tar.gz" % (model_folder))
os.makedirs(f"model_files/{model_folder}",
exist_ok=True) ## create model files. not raise error if exist
os.makedirs(res_folder, exist_ok=True)
no_incre_dev = 0
for i in tqdm(range(1, epoch + 1), desc="Epoch"):
epoch_loss = 0
start_time = time.time()
model.zero_grad()
if config.optimizer.lower() == "sgd":
optimizer = lr_decay(config, optimizer, i)
for index in tqdm(np.random.permutation(len(batched_data)),
desc="--training batch",
total=len(batched_data)):
model.train()
loss = model(*batched_data[index])
epoch_loss += loss.item()
loss.backward()
optimizer.step()
model.zero_grad()
end_time = time.time()
print("Epoch %d: %.5f, Time is %.2fs" %
(i, epoch_loss, end_time - start_time),
flush=True)
model.eval()
dev_metrics = evaluate_model(config, model, dev_batches, "dev",
dev_insts)
test_metrics = evaluate_model(config, model, test_batches, "test",
test_insts)
if dev_metrics[2] > best_dev[0]:
print("saving the best model...")
no_incre_dev = 0
best_dev[0] = dev_metrics[2]
best_dev[1] = i
best_test[0] = test_metrics[2]
best_test[1] = i
torch.save(model.state_dict(), model_path)
# Save the corresponding config as well.
f = open(config_path, 'wb')
pickle.dump(config, f)
f.close()
write_results(res_path, test_insts)
else:
no_incre_dev += 1
model.zero_grad()
if no_incre_dev >= config.max_no_incre:
print(
"early stop because there are %d epochs not increasing f1 on dev"
% no_incre_dev)
break
print("Archiving the best Model...")
with tarfile.open(f"model_files/{model_folder}/{model_folder}.tar.gz",
"w:gz") as tar:
tar.add(f"model_files/{model_folder}",
arcname=os.path.basename(model_folder))
print("Finished archiving the models")
print("The best dev: %.2f" % (best_dev[0]))
print("The corresponding test: %.2f" % (best_test[0]))
print("Final testing.")
model.load_state_dict(torch.load(model_path))
model.eval()
evaluate_model(config, model, test_batches, "test", test_insts)
write_results(res_path, test_insts)
def train_model(config: Config, train_insts: List[List[Instance]], dev_insts: List[Instance],
test_insts: List[Instance]):
train_num = sum([len(insts) for insts in train_insts])
print(f"[Training Info] number of instances: {train_num:d}")
dev_batches = batching_list_instances(config, dev_insts)
test_batches = batching_list_instances(config, test_insts)
best_dev = [-1, 0]
best_test = [-1, 0]
model_folder = config.model_folder
res_folder = "results"
# if os.path.exists(model_folder):
# raise FileExistsError(f"The folder {model_folder} exists. Please either delete it or create a new one "
# f"to avoid override.")
print(f"[Training Info] The model will be saved to: {model_folder}.tar.gz")
if not os.path.exists(model_folder):
os.makedirs(model_folder)
if not os.path.exists(res_folder):
os.makedirs(res_folder)
num_outer_iterations = config.num_outer_iterations
for iter in range(num_outer_iterations):
print(f"[Training Info] Running for {iter}th large iterations.")
model_names = [] # model names for each fold
train_batches = [batching_list_instances(config, insts) for insts in train_insts]
for fold_id, folded_train_insts in enumerate(train_insts):
print(f"[Training Info] Training fold {fold_id}.")
model_name = model_folder + f"/lstm_crf_{fold_id}.m"
model_names.append(model_name)
train_one(config=config, train_batches=train_batches[fold_id],
dev_insts=dev_insts, dev_batches=dev_batches, model_name=model_name)
# assign hard prediction to other folds
print("\n\n[Data Info] Assigning labels for the HARD approach")
for fold_id, folded_train_insts in enumerate(train_insts):
model = NNCRF(config)
model_name = model_names[fold_id]
model.load_state_dict(torch.load(model_name))
hard_constraint_predict(config=config, model=model,
fold_batches=train_batches[1 - fold_id],
folded_insts=train_insts[1 - fold_id]) # set a new label id
print("\n\n")
print("[Training Info] Training the final model")
all_train_insts = list(itertools.chain.from_iterable(train_insts))
model_name = model_folder + "/final_lstm_crf.m"
config_name = model_folder + "/config.conf"
res_name = res_folder + "/lstm_crf.results".format()
all_train_batches = batching_list_instances(config=config, insts=all_train_insts)
model = train_one(config=config, train_batches=all_train_batches, dev_insts=dev_insts, dev_batches=dev_batches,
model_name=model_name, config_name=config_name, test_insts=test_insts,
test_batches=test_batches, result_filename=res_name)
print("Archiving the best Model...")
with tarfile.open(model_folder + "/" + model_folder + ".tar.gz", "w:gz") as tar:
tar.add(model_folder, arcname=os.path.basename(model_folder))
# print("The best dev: %.2f" % (best_dev[0]))
# print("The corresponding test: %.2f" % (best_test[0]))
# print("Final testing.")
model.load_state_dict(torch.load(model_name))
model.eval()
evaluate_model(config, model, test_batches, "test", test_insts)
write_results(res_name, test_insts)
def train_one(config: Config,
train_batches: List[Tuple],
dev_insts: List[Instance],
dev_batches: List[Tuple],
model_name: str,
test_insts: List[Instance] = None,
test_batches: List[Tuple] = None,
config_name: str = None,
result_filename: str = None,
rate_schedule_neg=None,
rate_schedule_pos=None) -> NNCRF_sl:
model = NNCRF_sl(config)
model.train()
optimizer = get_optimizer(config, model)
epoch = config.num_epochs
best_dev_f1 = -1
saved_test_metrics = None
for i in range(1, epoch + 1):
ratios_sum = [0] * 6
forget_rate_neg = rate_schedule_neg[i - 1]
forget_rate_pos = rate_schedule_pos[i - 1]
epoch_loss = 0
epoch_loss_neg = 0
epoch_loss_pos = 0
start_time = time.time()
model.zero_grad()
if config.optimizer.lower() == "sgd":
optimizer = lr_decay(config, optimizer, i)
is_constrain = config.is_constrain
for index in np.random.permutation(len(train_batches)):
model.train()
tmp = tuple(
list(train_batches[index]) +
[forget_rate_neg, forget_rate_pos, is_constrain])
loss, ratios, loss_neg, loss_pos = model(*tmp)
ratios_sum = [
ratios_sum[i] + ratios[i] for i in range(len(ratios))
]
epoch_loss += loss.item()
epoch_loss_neg += loss_neg.item()
epoch_loss_pos += loss_pos.item()
loss.backward()
optimizer.step()
model.zero_grad()
end_time = time.time()
print("Epoch %d: %.5f, Time is %.2fs" %
(i, epoch_loss / epoch, end_time - start_time),
flush=True)
print('avg neg NLL: ' + str(epoch_loss_neg / epoch) +
' avg pos NLL: ' + str(epoch_loss_pos / epoch))
model.eval()
# metric is [precision, recall, f_score]
dev_metrics = evaluate_model(config, model, dev_batches, "dev",
dev_insts)
if test_insts is not None:
test_metrics = evaluate_model(config, model, test_batches, "test",
test_insts)
if dev_metrics[2] > best_dev_f1:
print("saving the best model..." + ' epoch' + str(i))
best_dev_f1 = dev_metrics[2]
if test_insts is not None:
saved_test_metrics = test_metrics
torch.save(model.state_dict(), model_name)
# # Save the corresponding config as well.
if config_name:
f = open(config_name, 'wb')
pickle.dump(config, f)
f.close()
if result_filename:
write_results(result_filename, test_insts)
model.zero_grad()
if test_insts is not None:
print(f"The best dev F1: {best_dev_f1}")
print(f"The corresponding test: {saved_test_metrics}")
return model
def train_model(config: Config, train_insts: List[List[Instance]],
dev_insts: List[Instance], test_insts: List[Instance]):
train_num = sum([len(insts) for insts in train_insts])
print("[Training Info] number of instances: %d" % (train_num))
dev_batches = batching_list_instances(config, dev_insts)
test_batches = batching_list_instances(config, test_insts)
best_dev = [-1, 0]
best_test = [-1, 0]
model_folder = config.model_folder
res_folder = config.res_folder
if os.path.exists(model_folder):
raise FileExistsError(
f"The folder {model_folder} exists. Please either delete it or create a new one "
f"to avoid override.")
print("[Training Info] The model will be saved to: %s.tar.gz" %
(model_folder))
if not os.path.exists(model_folder):
os.makedirs(model_folder)
if not os.path.exists(res_folder):
os.makedirs(res_folder)
num_outer_iterations = config.num_outer_iterations
SL_warmup = 2
for iter in range(num_outer_iterations):
print(f"[Training Info] Running for {iter}th large iterations.")
#change fold devision every two iter
if (iter > 0 and iter // 2 != (iter - 1) // 2):
train_insts = train_insts[0] + train_insts[1]
random.shuffle(train_insts)
num_insts_in_fold = math.ceil(len(train_insts) / config.num_folds)
train_insts = [
train_insts[i * num_insts_in_fold:(i + 1) * num_insts_in_fold]
for i in range(config.num_folds)
]
model_names = [] #model names for each fold
train_batches = [
batching_list_instances(config, insts) for insts in train_insts
]
neg_noise_rate_gold, pos_noise_rate_gold = ratio_estimation(
config, train_insts)
if (config.neg_noise_rate >= 0):
neg_noise_rate = config.neg_noise_rate
else:
neg_noise_rate = neg_noise_rate_gold
if (config.pos_noise_rate >= 0):
pos_noise_rate = config.pos_noise_rate
else:
pos_noise_rate = pos_noise_rate_gold
if (iter > 0):
neg_noise_rate = 0.005
pos_noise_rate = 0.15
print('negative noise rate: ' + str(neg_noise_rate))
print('positve noise rate: ' + str(pos_noise_rate))
if (config.warm_up_num == 0):
rate_schedule_neg, rate_schedule_pos = gen_forget_rate(
config.num_epochs, neg_noise_rate, pos_noise_rate,
config.num_gradual_neg, config.num_gradual_pos)
else:
rate_schedule_neg, rate_schedule_pos = gen_forget_rate_warmup(
config.num_epochs, neg_noise_rate, pos_noise_rate,
config.warm_up_num, config.num_gradual_neg,
config.num_gradual_pos)
for fold_id, folded_train_insts in enumerate(train_insts):
print(f"[Training Info] Training fold {fold_id}.")
model_name = model_folder + f"/lstm_crf_{fold_id}.m"
model_names.append(model_name)
train_one(config=config,
train_batches=train_batches[fold_id],
dev_insts=dev_insts,
dev_batches=dev_batches,
model_name=model_name,
rate_schedule_neg=rate_schedule_neg,
rate_schedule_pos=rate_schedule_pos)
# assign hard prediction to other folds
print("\n\n[Data Info] Assigning labels for the HARD approach")
for fold_id, folded_train_insts in enumerate(train_insts):
model = NNCRF_sl(config)
model_name = model_names[fold_id]
model.load_state_dict(torch.load(model_name))
hard_constraint_predict(
config=config,
model=model,
fold_batches=train_batches[1 - fold_id],
folded_insts=train_insts[1 - fold_id]) ## set a new label id
print("\n\n")
print("[Training Info] Training the final model")
all_train_insts = list(itertools.chain.from_iterable(train_insts))
model_name = model_folder + "/num_outer_iterations_final_lstm_crf.m"
config_name = model_folder + "/num_outer_iterations_config.conf"
res_name = res_folder + "/num_outer_iterations_lstm_crf.results".format(
)
all_train_batches = batching_list_instances(config=config,
insts=all_train_insts)
neg_noise_rate, pos_noise_rate = ratio_estimation(config, train_insts)
rate_schedule_neg = np.zeros(config.num_epochs)
rate_schedule_pos = np.zeros(config.num_epochs)
model = train_one(config=config,
train_batches=all_train_batches,
dev_insts=dev_insts,
dev_batches=dev_batches,
model_name=model_name,
config_name=config_name,
test_insts=test_insts,
test_batches=test_batches,
result_filename=res_name,
rate_schedule_neg=rate_schedule_neg,
rate_schedule_pos=rate_schedule_pos)
print("Archiving the best Model...")
with tarfile.open(
model_folder + "/" + str(num_outer_iterations) + model_folder +
".tar.gz", "w:gz") as tar:
tar.add(model_folder, arcname=os.path.basename(model_folder))
model.load_state_dict(torch.load(model_name))
model.eval()
evaluate_model(config, model, test_batches, "test", test_insts)
write_results(res_name, test_insts)
def train_one(
config: Config,
train_batches: List[Tuple],
dev_insts: List[Instance],
dev_batches: List[Tuple],
model_name: str,
test_insts: List[Instance] = None,
test_batches: List[Tuple] = None,
config_name: str = None,
result_filename: str = None,
) -> NNCRF:
model = NNCRF(config)
model.train()
optimizer = get_optimizer(config, model)
lr_scheduler = SlantedTriangular(optimizer,
config.num_epochs,
num_steps_per_epoch=len(train_batches),
ratio=16)
epoch = config.num_epochs
best_dev_f1 = -1
saved_test_metrics = None
for i in range(1, epoch + 1):
epoch_loss = 0
start_time = time.time()
model.zero_grad()
# if config.optimizer.lower() == "sgd":
# optimizer = lr_decay(config, optimizer, i)
lr_scheduler.step(epoch=i)
for index in tqdm(np.random.permutation(len(train_batches)),
f"Training epoch {i}", len(train_batches)):
model.train()
loss = model(*train_batches[index])
epoch_loss += loss.item()
# print(f"Batch loss: {loss.item()}")
loss.backward()
optimizer.step()
model.zero_grad()
lr_scheduler.step_batch()
end_time = time.time()
print("Epoch %d: %.5f, Time is %.2fs" %
(i, epoch_loss / len(train_batches), end_time - start_time),
flush=True)
model.eval()
# metric is [precision, recall, f_score]
dev_metrics = evaluate_model(config, model, dev_batches, "dev",
dev_insts)
if test_insts is not None:
test_metrics = evaluate_model(config, model, test_batches, "test",
test_insts)
if dev_metrics[2] > best_dev_f1:
print("saving the best model...")
best_dev_f1 = dev_metrics[2]
if test_insts is not None:
saved_test_metrics = test_metrics
torch.save(model.state_dict(), model_name)
# # Save the corresponding config as well.
if config_name:
f = open(config_name, "wb")
pickle.dump(config, f)
f.close()
if result_filename:
write_results(result_filename, test_insts)
model.zero_grad()
if test_insts is not None:
print(f"The best dev F1: {best_dev_f1}")
print(f"The corresponding test: {saved_test_metrics}")
return model
