def main():
parser = ArgumentParser()
parser.add_argument('--data_name', default='albert', type=str)
parser.add_argument(
"--file_num",
type=int,
default=10,
help="Number of dynamic masking to pregenerate (with different masks)"
) # TODO: --file-num 이름이 어울리지 않음
parser.add_argument(
"--reduce_memory",
action="store_true",
help=
"Store training data as on-disc memmaps to massively reduce memory usage"
)
parser.add_argument("--epochs",
type=int,
default=4,
help="Number of epochs to train for")
parser.add_argument('--share_type',
default='all',
type=str,
choices=['all', 'attention', 'ffn', 'None'])
parser.add_argument('--num_eval_steps', default=100)
parser.add_argument('--num_save_steps', default=200)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument("--train_batch_size",
default=4,
type=int,
help="Total batch size for training.")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Linear warmup over warmup_steps.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument('--max_grad_norm', default=1.0, type=float)
parser.add_argument("--learning_rate",
default=0.00176,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--fp16_opt_level',
type=str,
default='O2',
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
args = parser.parse_args()
pregenerated_data = config['data_dir'] / "corpus/train"
assert pregenerated_data.is_dir(), \
"--pregenerated_data should point to the folder of files made by prepare_lm_data_mask.py!"
samples_per_epoch = 0
for i in range(args.file_num):
data_file = pregenerated_data / f"{args.data_name}_file_{i}.json"
metrics_file = pregenerated_data / f"{args.data_name}_file_{i}_metrics.json" # TODO: 어디에 있지?
if data_file.is_file() and metrics_file.is_file():
metrics = json.loads(metrics_file.read_text())
samples_per_epoch += metrics['num_training_examples']
else:
if i == 0:
exit("No training data was found!")
print(
f"Warning! There are fewer epochs of pregenerated data ({i}) than training epochs ({args.epochs})."
)
print(
"This script will loop over the available data, but training diversity may be negatively impacted."
)
break
logger.info(f"samples_per_epoch: {samples_per_epoch}")
if args.local_rank == -1 or args.no_cuda:
device = torch.device(f"cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
args.n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
f"device: {device} , distributed training: {bool(args.local_rank != -1)}, 16-bits training: {args.fp16}, "
f"share_type: {args.share_type}")
if args.gradient_accumulation_steps < 1:
raise ValueError(
f"Invalid gradient_accumulation_steps parameter: {args.gradient_accumulation_steps}, should be >= 1"
)
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
seed_everything(args.seed)
tokenizer = BertTokenizer(vocab_file=config['albert_vocab_path'])
total_train_examples = samples_per_epoch * args.epochs
num_train_optimization_steps = int(total_train_examples /
args.train_batch_size /
args.gradient_accumulation_steps)
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
args.warmup_steps = int(num_train_optimization_steps *
args.warmup_proportion)
bert_config = BertConfig.from_pretrained(str(config['albert_config_path']),
share_type=args.share_type)
model = BertForPreTraining(config=bert_config)
# model = BertForMaskedLM.from_pretrained(config['checkpoint_dir'] / 'checkpoint-580000')
model.to(device)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
# optimizer = Lamb(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
lr_scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=args.warmup_steps,
t_total=num_train_optimization_steps)
if args.fp16:
try:
from apex import amp
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank], output_device=args.local_rank)
global_step = 0
mask_metric = LMAccuracy()
sop_metric = LMAccuracy()
tr_mask_acc = AverageMeter()
tr_sop_acc = AverageMeter()
tr_loss = AverageMeter()
tr_mask_loss = AverageMeter()
tr_sop_loss = AverageMeter()
loss_fct = CrossEntropyLoss(ignore_index=-1)
train_logs = {}
logger.info("***** Running training *****")
logger.info(f" Num examples = {total_train_examples}")
logger.info(f" Batch size = {args.train_batch_size}")
logger.info(f" Num steps = {num_train_optimization_steps}")
logger.info(f" warmup_steps = {args.warmup_steps}")
start_time = time.time()
seed_everything(args.seed) # Added here for reproducibility
for epoch in range(args.epochs):
for idx in range(args.file_num):
epoch_dataset = PregeneratedDataset(
file_id=idx,
training_path=pregenerated_data,
tokenizer=tokenizer,
reduce_memory=args.reduce_memory,
data_name=args.data_name)
if args.local_rank == -1:
train_sampler = RandomSampler(epoch_dataset)
else:
train_sampler = DistributedSampler(epoch_dataset)
train_dataloader = DataLoader(epoch_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size)
model.train()
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(train_dataloader):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, lm_label_ids, is_next = batch
outputs = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask)
prediction_scores = outputs[0]
seq_relationship_score = outputs[1]
masked_lm_loss = loss_fct(
prediction_scores.view(-1, bert_config.vocab_size),
lm_label_ids.view(-1))
next_sentence_loss = loss_fct(
seq_relationship_score.view(-1, 2), is_next.view(-1))
loss = masked_lm_loss + next_sentence_loss
mask_metric(logits=prediction_scores.view(
-1, bert_config.vocab_size),
target=lm_label_ids.view(-1))
sop_metric(logits=seq_relationship_score.view(-1, 2),
target=is_next.view(-1))
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
nb_tr_steps += 1
tr_mask_acc.update(mask_metric.value(), n=input_ids.size(0))
tr_sop_acc.update(sop_metric.value(), n=input_ids.size(0))
tr_loss.update(loss.item(), n=1)
tr_mask_loss.update(masked_lm_loss.item(), n=1)
tr_sop_loss.update(next_sentence_loss.item(), n=1)
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
lr_scheduler.step()
optimizer.step()
optimizer.zero_grad()
global_step += 1
if global_step % args.num_eval_steps == 0:
now = time.time()
eta = now - start_time
if eta > 3600:
eta_format = ('%d:%02d:%02d' %
(eta // 3600,
(eta % 3600) // 60, eta % 60))
elif eta > 60:
eta_format = '%d:%02d' % (eta // 60, eta % 60)
else:
eta_format = '%ds' % eta
train_logs['loss'] = tr_loss.avg
train_logs['mask_acc'] = tr_mask_acc.avg
train_logs['sop_acc'] = tr_sop_acc.avg
train_logs['mask_loss'] = tr_mask_loss.avg
train_logs['sop_loss'] = tr_sop_loss.avg
show_info = f'[Training]:[{epoch}/{args.epochs}]{global_step}/{num_train_optimization_steps} ' \
f'- ETA: {eta_format}' + "-".join(
[f' {key}: {value:.4f} ' for key, value in train_logs.items()])
logger.info(show_info)
tr_mask_acc.reset()
tr_sop_acc.reset()
tr_loss.reset()
tr_mask_loss.reset()
tr_sop_loss.reset()
start_time = now
if global_step % args.num_save_steps == 0:
if args.local_rank in [-1, 0] and args.num_save_steps > 0:
# Save model checkpoint
output_dir = config[
'checkpoint_dir'] / f'lm-checkpoint-{global_step}'
if not output_dir.exists():
output_dir.mkdir()
# save model
model_to_save = model.module if hasattr(
model, 'module'
) else model # Take care of distributed/parallel training
model_to_save.save_pretrained(str(output_dir))
torch.save(args, str(output_dir / 'training_args.bin'))
logger.info("Saving model checkpoint to %s",
output_dir)
# save config
output_config_file = output_dir / CONFIG_NAME
with open(str(output_config_file), 'w') as f:
f.write(model_to_save.config.to_json_string())
# save vocab
tokenizer.save_vocabulary(output_dir)
def main(args):
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
print("device: {}, n_gpu: {}, 16-bits training: {}".format(
device, n_gpu, args.fp16))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if args.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
args.train_batch_size = \
args.train_batch_size // args.gradient_accumulation_steps
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
if args.do_train:
assert (args.train_file is not None) and (args.dev_file is not None)
if args.eval_test:
assert args.test_file is not None
else:
assert args.dev_file is not None
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
if args.do_train:
logger.addHandler(logging.FileHandler(os.path.join(args.output_dir, "train.log"), 'w'))
else:
logger.addHandler(logging.FileHandler(os.path.join(args.output_dir, "eval.log"), 'w'))
print(args)
# tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
tokenizer = BertTokenizer(args.model + "vocab.txt", lowercase=False) # BertWordPieceTokenizer , lowercase=True
if args.do_train or (not args.eval_test):
with open(args.dev_file) as f:
dataset_json = json.load(f)
eval_dataset = dataset_json['data']
eval_examples = read_squad_examples(
input_file=args.dev_file, is_training=False,
version_2_with_negative=args.version_2_with_negative)
eval_features = convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=False)
print("***** Dev *****")
print(" Num orig examples = %d", len(eval_examples))
print(" Num split examples = %d", len(eval_features))
print(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
eval_dataloader = DataLoader(eval_data, batch_size=args.eval_batch_size)
if args.do_train:
train_examples = read_squad_examples(
input_file=args.train_file, is_training=True, version_2_with_negative=args.version_2_with_negative)
train_features = convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=True)
if args.train_mode == 'sorted' or args.train_mode == 'random_sorted':
train_features = sorted(train_features, key=lambda f: np.sum(f.input_mask))
else:
random.shuffle(train_features)
all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
all_start_positions = torch.tensor([f.start_position for f in train_features], dtype=torch.long)
all_end_positions = torch.tensor([f.end_position for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_start_positions, all_end_positions)
train_dataloader = DataLoader(train_data, batch_size=args.train_batch_size)
train_batches = [batch for batch in train_dataloader]
num_train_optimization_steps = \
len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
print("***** Train *****")
print(" Num orig examples = %d", len(train_examples))
print(" Num split examples = %d", len(train_features))
print(" Batch size = %d", args.train_batch_size)
print(" Num steps = %d", num_train_optimization_steps)
eval_step = max(1, len(train_batches) // args.eval_per_epoch)
best_result = None
lrs = [args.learning_rate] if args.learning_rate else \
[1e-6, 2e-6, 3e-6, 5e-6, 1e-5, 2e-5, 3e-5, 5e-5]
for lr in lrs:
model = BertForQuestionAnswering.from_pretrained(
args.model)
if args.fp16:
model.half()
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
param_optimizer = list(model.named_parameters())
param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]]
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer
if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex"
"to use distributed and fp16 training.")
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=lr,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
else:
optimizer = AdamW(optimizer_grouped_parameters,
lr=lr)
tr_loss = 0
nb_tr_examples = 0
nb_tr_steps = 0
global_step = 0
start_time = time.time()
for epoch in range(int(args.num_train_epochs)):
model.train()
print("Start epoch #{} (lr = {})...".format(epoch, lr))
if args.train_mode == 'random' or args.train_mode == 'random_sorted':
random.shuffle(train_batches)
for step, batch in enumerate(train_batches):
if n_gpu == 1:
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, start_positions, end_positions = batch
# print('TENSORS', input_ids.size(), segment_ids.size(), input_mask.size(), start_positions.size(), end_positions.size())
outputs = model(input_ids=input_ids,
attention_mask=input_mask,
token_type_ids=segment_ids,
start_positions=start_positions,
end_positions=end_positions, return_dict=False)
loss = outputs[0]
if n_gpu > 1:
loss = loss.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
lr_this_step = lr * \
warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
if (step + 1) % eval_step == 0:
print('Epoch: {}, Step: {} / {}, used_time = {:.2f}s, loss = {:.6f}'.format(
epoch, step + 1, len(train_batches), time.time() - start_time, tr_loss / nb_tr_steps))
save_model = False
if args.do_eval:
result, _, _ = \
evaluate(args, model, device, eval_dataset,
eval_dataloader, eval_examples, eval_features)
model.train()
result['global_step'] = global_step
result['epoch'] = epoch
result['learning_rate'] = lr
result['batch_size'] = args.train_batch_size
if (best_result is None) or (result[args.eval_metric] > best_result[args.eval_metric]):
best_result = result
save_model = True
print("!!! Best dev %s (lr=%s, epoch=%d): %.2f" %
(args.eval_metric, str(lr), epoch, result[args.eval_metric]))
else:
save_model = True
if save_model:
model_to_save = model.module if hasattr(model, 'module') else model
output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
output_config_file = os.path.join(args.output_dir, "config.json")
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
if best_result:
with open(os.path.join(args.output_dir, "eval_results.txt"), "w") as writer:
for key in sorted(best_result.keys()):
writer.write("%s = %s\n" % (key, str(best_result[key])))
if args.do_eval:
if args.eval_test:
with open(args.test_file) as f:
dataset_json = json.load(f)
eval_dataset = dataset_json['data']
eval_examples = read_squad_examples(
input_file=args.test_file, is_training=False,
version_2_with_negative=args.version_2_with_negative)
eval_features = convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=False)
print("***** Test *****")
print(" Num orig examples = %d", len(eval_examples))
print(" Num split examples = %d", len(eval_features))
print(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
eval_dataloader = DataLoader(eval_data, batch_size=args.eval_batch_size)
model = BertForQuestionAnswering.from_pretrained(args.output_dir)
if args.fp16:
model.half()
model.to(device)
na_prob_thresh = 1.0
if args.version_2_with_negative:
eval_result_file = os.path.join(args.output_dir, "eval_results.txt")
if os.path.isfile(eval_result_file):
with open(eval_result_file) as f:
for line in f.readlines():
if line.startswith('best_f1_thresh'):
na_prob_thresh = float(line.strip().split()[-1])
print("na_prob_thresh = %.6f" % na_prob_thresh)
result, preds, _ = \
evaluate(args, model, device, eval_dataset,
eval_dataloader, eval_examples, eval_features,
na_prob_thresh=na_prob_thresh,
pred_only=args.eval_test)
with open(os.path.join(args.output_dir, "predictions.json"), "w") as writer:
writer.write(json.dumps(preds, indent=4) + "\n")
print(s)
tokenizer = BertTokenizer("data/atis/token.vocab",
bos_token="<BOS>",
eos_token="<EOS>",
model_max_len=50)
tokenizer.prepare_for_model(tokenizer.encode(y), return_tensors="pt")
tokenizer.SPECIAL_TOKENS_ATTRIBUTES
tokenizer.encode(y)
tokenizer.encode_plus(y)
y = "<BOS> embedding what is the flight number <EOS>"
ids = tokenizer.encode_plus
tokenizer.decode(tokenizer.encode(y))
tokenizer.save_pretrained("data/atis/save")
tokenizer.save_vocabulary("data/atis/save/saved")
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased",
bos_token="<BOS>",
eos_token="<EOS>")
tokenizer.tokenize("i like tea")
special_tokens = {"bos_token": "<BOS>", "eos_token": "<EOS>"}
tokenizer.add_special_tokens(special_tokens)
tokenizer.bos_token_id
tokenizer.eos_token_id
tokenizer.all_special_ids
tokenizer.special_tokens_map
tokenizer.additional_special_tokens
y = "<BOS> I like embeddings <EOS> [SEP] i like tea"
- clinical BERT is used for the WordPiece vocabulary
"""
with open('data/vocab.pk', 'rb') as fd:
vocab = pickle.load(fd)
split_pt = min(vocab.section_start_vocab_id, vocab.category_start_vocab_id)
metadata_tokens = vocab.i2w[split_pt:] + ['digitparsed']
with open('data/clinic_bert_vocab.txt', 'r') as fd:
clinic_bert_tokens = fd.readlines()
clinic_bert_tokens = list(set(list(map(_standardize, clinic_bert_tokens))))
vocab_fn = 'data/clinic_bert_plus_metadata_vocab.txt'
with open(vocab_fn, 'w') as fd:
fd.write('\n'.join(clinic_bert_tokens + metadata_tokens))
tokenizer = BertTokenizer(vocab_fn, never_split=metadata_tokens, do_basic_tokenize=False)
# Add metadata as `additional_special_tokens` so that they do not get subdivided into word pieces
special_tokens_dict = {'cls_token': '[CLS]', 'sep_token': '[SEP]', 'unk_token': '[UNK]', 'bos_token': '[BOS]',
'eos_token': '[EOS]', 'pad_token': '[PAD]', 'mask_token': '[MASK]',
'additional_special_tokens': metadata_tokens}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print('We have added', num_added_toks, 'tokens')
example_sentence = 'header=HPI example clinical text with tricky autoimmunihistory word'
print('Tokenization of sentence: {}'.format(example_sentence))
out_fn = 'data/bert_tokenizer_vocab.pth'
print('Generated BERT tokenizer vocabulary and saving to {}'.format(out_fn))
tokenizer.save_vocabulary(out_fn)
