def __init__(self, model_weight_filename=None):
"""
Load an instance of BERT model for dimension classification.
"""
self.num_labels = len(DimensionDataset.label2idx)
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
logging.info('*** Instantiate model ***')
if model_weight_filename:
config = BertConfig(vocab_size_or_config_json_file=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072)
self.model = BertForTokenClassification(config, self.num_labels)
logging.info('*** Loading model weights ***')
self.model.load_state_dict(
torch.load(model_weight_filename, map_location=self.device))
else:
# load bert pretrained with empty token classification top layers
self.model = BertForTokenClassification.from_pretrained(
"bert-base-uncased", num_labels=self.num_labels)
logging.info('*** Loading tokenizer ***')
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
def load_pretrained_model_tokenizer(model_type="BertForSequenceClassification",
base_model=None,
base_tokenizer=None,
device="cuda",
chinese=False,
num_labels=2):
# Load pre-trained model (weights)
if base_model is None:
if chinese:
base_model = "bert-base-chinese"
else:
base_model = "bert-base-uncased"
if model_type == "BertForSequenceClassification":
model = BertForSequenceClassification.from_pretrained(
base_model, num_labels=num_labels)
# Load pre-trained model tokenizer (vocabulary)
elif model_type == "BertForNextSentencePrediction":
model = BertForNextSentencePrediction.from_pretrained(base_model)
elif model_type == "BertForTokenClassification":
model = BertForTokenClassification.from_pretrained(
base_model, num_labels=num_labels)
elif model_type == "BertMSE":
model = BertMSE()
else:
print("[Error]: unsupported model type")
return None, None
if base_tokenizer is None:
# Download from huggingface
tokenizer = BertTokenizer.from_pretrained(base_model)
else:
# Load local file
tokenizer = BertTokenizer.from_pretrained(base_tokenizer)
model.to(device)
return model, tokenizer
def predict(name, lang='eng', path='learn', model_dir='models'):
path, model_dir = Path(path), Path(model_dir)
print('Loading model...')
device = 'cpu'
state = torch.load(path / model_dir / f'{name}.pth', map_location=device)
bert_model = 'bert-base-cased' if lang == 'eng' else 'bert-base-multilingual-cased'
print(f'Lang: {lang}\nModel: {bert_model}\nRun: {name}')
model = BertForTokenClassification.from_pretrained(bert_model,
num_labels=len(VOCAB),
cache_dir='bertm')
model.load_state_dict(state['model'], strict=True)
print('Done')
try:
while True:
# get sentence
sent = input('Enter sentence: ')
words = sent.split()
x, mask = to_feature(words, bert_model)
with torch.no_grad():
# predict named entities
out = model(x)
pred = out.argmax(-1).view(-1)
print(pred)
active_pred = pred[mask == 1]
print('Named Entities')
active_pred = active_pred.tolist()
for w, l in zip(words, active_pred[1:-1]):
print(f'{w} {idx2label[l]}')
except Exception as e:
print('See ya')
def test(config):
print('-' * 50)
print('Loading core model......')
load_model_name = config['test_model']
if os.path.exists(config['test_model']):
pass
else:
print('the test model ' + config['test_model'] +
' is not exist pls check it in the config.txt')
print('Core model name is : ' + load_model_name)
model = BertForTokenClassification.from_pretrained(
config['model_name'], num_labels=config['tagset_size'] + 1) # load ptm
print('-' * 50)
print('Deploying the test data......')
config['shuffle'] = False
test_sents, test_data_loader = prepare_data(config)
print('Test data loaded done!')
model.to(config['device'])
checkpoint = torch.load(load_model_name)
model.load_state_dict(checkpoint['net'])
model.eval()
print('-' * 50)
print('core model loaded done! Start predicting......')
with torch.no_grad():
results = predict(model, test_data_loader, config)
results = restore_result(test_sents, results)
if config['output_file']:
write(results, config['output_file'])
else:
print('The output file is not pointed. pls check the config.txt')
sys.exit(1)
print('Test process done!')
def __init__(self, num_labels):
super(NER1, self).__init__()
self.bert = BertForTokenClassification.from_pretrained('bert-base-chinese', num_labels=num_labels)
for param in self.bert.parameters():
param.requires_grad = True
self.dp1 = nn.Dropout(0.1)
def __init__(self, args, params, device):
super(Net, self).__init__()
self.bert = BertForTokenClassification.from_pretrained(
args.bert_model_dir, num_labels=len(params.tag2idx))
self.bilstm = nn.LSTM(bidirectional=True,
num_layers=2,
input_size=768,
hidden_size=768 // 2,
batch_first=True)
self.fc = nn.Linear(768, len(params.tag2idx))
self.num_labels = 2
self.device = device
def main():
slot2Id = getSlot2Id()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = BertForTokenClassification.from_pretrained(
config.pretrained_model_name_or_path, num_labels=len(slot2Id))
model.to(device)
x, y = processData()
train_dataloader, val_dataloader = getDataLoader(x, y)
train(model, device, train_dataloader, val_dataloader, config.epochs,
config.max_grad_norm)
def __init__(self, dir_path, max_seq_length=30):
self.max_seq_length = max_seq_length
self.processor = NerProcessor.load(
os.path.join(dir_path, PROCESSOR_NAME))
self.tokenizer = BertTokenizer.from_pretrained(dir_path)
self.classifier = BertForTokenClassification.from_pretrained(
dir_path, len(self.processor.labels))
self.classifier.eval()
self.id2label = {
i: label
for i, label in enumerate(self.processor.labels)
}
global debug_message
debug_message = False
def __init__(self, opt):
super(BERT_REL, self).__init__()
self.opt = opt
self.bertForToken = BertForTokenClassification.from_pretrained(self.opt.bert_model_dir, num_labels=self.opt.tag_nums)
# tag分类
self.num_labels = self.opt.tag_nums
# 关系分类
self.rel_bert = BertModel.from_pretrained(self.opt.bert_model_dir)
self.rel_fc = nn.Sequential(nn.Linear(768, 1024), nn.ReLU(), nn.Linear(1024, self.opt.rel_nums))
self.id2tag = json.loads(open(opt.id2tag_dir, 'r').readline())
self.type2types = json.loads(open(opt.type2types_dir, 'r').readline())
self.sep1 = torch.LongTensor([1]).to("cuda")
self.sep2 = torch.LongTensor([2]).to("cuda")
self.init_weights()
def BuildModel(config, weight=None):
# change the forward method: do not consider 'X' when computing loss
def new_forward(self,
input_ids,
token_type_ids=None,
attention_mask=None,
add_masks=None,
labels=None,
weight=weight):
sequence_output, _ = self.bert(input_ids,
token_type_ids,
attention_mask,
output_all_encoded_layers=False)
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
if labels is not None:
if weight is not None:
weight = weight.to(torch.float).to(config['device'])
loss_fct = nn.CrossEntropyLoss(weight=weight,
ignore_index=self.num_labels - 1)
# Only keep active parts of the loss
if attention_mask is not None or add_masks is not None:
if add_masks is None:
add_masks = 1
if attention_mask is None:
attention_mask = 1
active_loss = (attention_mask.view(-1)
== 1) * (add_masks.view(-1) == 1)
active_logits = logits.view(-1, self.num_labels)[active_loss]
active_labels = labels.view(-1)[active_loss]
loss = loss_fct(active_logits, active_labels)
else:
loss = loss_fct(logits.view(-1, self.num_labels),
labels.view(-1))
return loss
else:
return logits
BertForTokenClassification.forward = new_forward
model = BertForTokenClassification.from_pretrained(
config['name'], num_labels=config['num_labels'])
model.to(config['device'])
return model
def __init__(self, opt):
super(BERT_CNN_CRF, self).__init__()
self.opt = opt
self.bertForToken = BertForTokenClassification.from_pretrained(self.opt.bert_model_dir, num_labels=self.opt.tag_nums)
# tag分类
self.num_labels = self.opt.tag_nums
self.crf = CRF(self.opt.tag_nums, batch_first=True)
# 关系分类
self.type_emb = nn.Embedding(3, self.opt.bert_hidden_size)
self.rel_cnns = Encoder(enc_method='cnn', filters_num=self.opt.filter_num, filters=self.opt.filters, f_dim=self.opt.bert_hidden_size)
self.classifier_rels = nn.Linear(len(self.opt.filters)*self.opt.filter_num, self.opt.rel_nums)
self.id2tag = json.loads(open(opt.id2tag_dir, 'r').readline())
self.type2types = json.loads(open(opt.type2types_dir, 'r').readline())
self.init_weights()
def keywordextract(sentence,
model_path='./pretrained/keyword_extraction_pretrained.pt'):
# returns a single keyword of given sentence
device = torch.device('cpu')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased',
do_lower_case=True)
model = BertForTokenClassification.from_pretrained("bert-base-uncased",
num_labels=3)
model.to(device)
text = sentence
tkns = tokenizer.tokenize(text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tkns)
segments_ids = [0] * len(tkns)
tokens_tensor = torch.tensor([indexed_tokens]).to(device)
segments_tensors = torch.tensor([segments_ids]).to(device)
model = torch.load(model_path, map_location=device)
model.eval()
prediction = []
logit = model(tokens_tensor,
token_type_ids=None,
attention_mask=segments_tensors)
#logit = model(tokens_tensor)
logit = logit.detach().cpu().numpy()
prediction.extend([list(p) for p in np.argmax(logit, axis=2)])
keyword = None
for k, j in enumerate(prediction[0]):
if j == 1 or j == 0:
# print(tokenizer.convert_ids_to_tokens(tokens_tensor[0].to('cpu').numpy())[k])
keyword = tokenizer.convert_ids_to_tokens(
tokens_tensor[0].to('cpu').numpy())[k]
if "#" in keyword:
keyword = keyword.replace("#", "")
for word in sentence.split():
if keyword in word:
keyword = word.lower()
return keyword
def train():
model_path = dir_path + '/models/'
print('your model would be saved at', model_path)
model = BertForTokenClassification.from_pretrained(
"bert-base-multilingual-cased", num_labels=len(bert_io.tag2idx))
model.to(device)
trn_data = bert_io.convert_to_bert_input(trn)
sampler = RandomSampler(trn_data)
trn_dataloader = DataLoader(trn_data,
sampler=sampler,
batch_size=batch_size)
# load optimizer
FULL_FINETUNING = True
if FULL_FINETUNING:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay_rate':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay_rate':
0.0
}]
else:
param_optimizer = list(model.classifier.named_parameters())
optimizer_grouped_parameters = [{
"params": [p for n, p in param_optimizer]
}]
optimizer = Adam(optimizer_grouped_parameters, lr=3e-5)
# train
epochs = 10
max_grad_norm = 1.0
num_of_epoch = 0
for _ in trange(epochs, desc="Epoch"):
# TRAIN loop
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(trn_dataloader):
# add batch to gpu
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_orig_tok_to_maps, b_input_args, b_input_masks = batch
# forward pass
loss = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_masks,
labels=b_input_args)
# backward pass
loss.backward()
# track train loss
tr_loss += loss.item()
nb_tr_examples += b_input_ids.size(0)
nb_tr_steps += 1
# gradient clipping
torch.nn.utils.clip_grad_norm_(parameters=model.parameters(),
max_norm=max_grad_norm)
# update parameters
optimizer.step()
model.zero_grad()
# break
# break
# print train loss per epoch
print("Train loss: {}".format(tr_loss / nb_tr_steps))
model_saved_path = model_path + 'ko-srl-epoch-' + str(
num_of_epoch) + '.pt'
torch.save(model, model_saved_path)
num_of_epoch += 1
print('...training is done')
def main():
"""Training pipeline"""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
data = pd.read_csv("train_m.txt", sep='\t', encoding="latin1").fillna(method="ffill")
getter = SentenceGetter(data)
sentences = [" ".join([s[0] for s in sent]) for sent in getter.sentences]
labels = [[s[1] for s in sent] for sent in getter.sentences]
tags_vals = list(set(data["tag"].values))
tag2idx = {t: i for i, t in enumerate(tags_vals)}
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
tokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]
input_ids = pad_sequences([tokenizer.convert_tokens_to_ids(txt) for txt in tokenized_texts],
maxlen=MAX_LEN, dtype="long", truncating="post", padding="post")
tags = pad_sequences([[tag2idx.get(l) for l in lab] for lab in labels],
maxlen=MAX_LEN, value=tag2idx["O"], padding="post",
dtype="long", truncating="post")
attention_masks = [[float(i>0) for i in ii] for ii in input_ids]
tr_inputs, val_inputs, tr_tags, val_tags = train_test_split(input_ids, tags,
random_state=2018, test_size=0.1)
tr_masks, val_masks, _, _ = train_test_split(attention_masks, input_ids,
random_state=2018, test_size=0.1)
tr_inputs = torch.tensor(tr_inputs)
val_inputs = torch.tensor(val_inputs)
tr_tags = torch.tensor(tr_tags)
val_tags = torch.tensor(val_tags)
tr_masks = torch.tensor(tr_masks)
val_masks = torch.tensor(val_masks)
train_data = TensorDataset(tr_inputs, tr_masks, tr_tags)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=bs)
valid_data = TensorDataset(val_inputs, val_masks, val_tags)
valid_sampler = SequentialSampler(valid_data)
valid_dataloader = DataLoader(valid_data, sampler=valid_sampler, batch_size=bs)
model = BertForTokenClassification.from_pretrained("bert-base-uncased", num_labels=len(tag2idx))
model.cuda();
FULL_FINETUNING = True
if FULL_FINETUNING:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay_rate': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay_rate': 0.0}
]
else:
param_optimizer = list(model.classifier.named_parameters())
optimizer_grouped_parameters = [{"params": [p for n, p in param_optimizer]}]
optimizer = Adam(optimizer_grouped_parameters, lr=3e-5)
epochs = 5
max_grad_norm = 1.0
for _ in trange(epochs, desc="Epoch"):
# TRAIN loop
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(train_dataloader):
# add batch to gpu
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_mask, b_labels = batch
# forward pass
loss = model(b_input_ids, token_type_ids=None,
attention_mask=b_input_mask, labels=b_labels)
# backward pass
loss.backward()
# track train loss
tr_loss += loss.item()
nb_tr_examples += b_input_ids.size(0)
nb_tr_steps += 1
# gradient clipping
torch.nn.utils.clip_grad_norm_(parameters=model.parameters(), max_norm=max_grad_norm)
# update parameters
optimizer.step()
model.zero_grad()
# print train loss per epoch
print("Train loss: {}".format(tr_loss/nb_tr_steps))
# VALIDATION on validation set
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
predictions , true_labels = [], []
for batch in valid_dataloader:
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_mask, b_labels = batch
with torch.no_grad():
tmp_eval_loss = model(b_input_ids, token_type_ids=None,
attention_mask=b_input_mask, labels=b_labels)
logits = model(b_input_ids, token_type_ids=None,
attention_mask=b_input_mask)
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
predictions.extend([list(p) for p in np.argmax(logits, axis=2)])
true_labels.append(label_ids)
tmp_eval_accuracy = flat_accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += b_input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss/nb_eval_steps
print("Validation loss: {}".format(eval_loss))
print("Validation Accuracy: {}".format(eval_accuracy/nb_eval_steps))
pred_tags = [tags_vals[p_i] for p in predictions for p_i in p]
valid_tags = [tags_vals[l_ii] for l in true_labels for l_i in l for l_ii in l_i]
print("F1-Score: {}".format(f1_score(pred_tags, valid_tags)))
print("Precision-Score: {}".format(precision_score(pred_tags, valid_tags)))
print("Recall-Score: {}".format(recall_score(pred_tags, valid_tags)))
print(classification_report(pred_tags, valid_tags))
model.eval()
predictions = []
true_labels = []
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for batch in valid_dataloader:
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_mask, b_labels = batch
with torch.no_grad():
tmp_eval_loss = model(b_input_ids, token_type_ids=None,
attention_mask=b_input_mask, labels=b_labels)
logits = model(b_input_ids, token_type_ids=None,
attention_mask=b_input_mask)
logits = logits.detach().cpu().numpy()
predictions.extend([list(p) for p in np.argmax(logits, axis=2)])
label_ids = b_labels.to('cpu').numpy()
true_labels.append(label_ids)
tmp_eval_accuracy = flat_accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += b_input_ids.size(0)
nb_eval_steps += 1
pred_tags = [tags_vals[p_i] for p in predictions for p_i in p]
valid_tags = [tags_vals[l_ii] for l in true_labels for l_i in l for l_ii in l_i]
print("Validation loss: {}".format(eval_loss/nb_eval_steps))
print("Validation Accuracy: {}".format(eval_accuracy/nb_eval_steps))
print("Validation F1-Score: {}".format(f1_score(pred_tags, valid_tags)))
print("Precision-Score: {}".format(precision_score(pred_tags, valid_tags)))
print("Recall-Score: {}".format(recall_score(pred_tags, valid_tags)))
print(classification_report(pred_tags, valid_tags))
true_positives_O = 0
predicted_positives_O = 0
real_positives_O = 0
for pred, valid in zip(pred_tags, valid_tags):
if pred == 'I-claim' and valid == 'I-claim':
true_positives_O += 1
if pred == 'I-claim':
predicted_positives_O += 1
if valid == 'I-claim':
real_positives_O += 1
print("True positives I: {}".format(true_positives_O))
print("predicted positives I: {}".format(predicted_positives_O))
print("real positives I: {}".format(real_positives_O))
true_positives_B = 0
predicted_positives_B = 0
real_positives_B = 0
for pred, valid in zip(pred_tags, valid_tags):
if pred == 'B-claim' and valid == 'B-claim':
true_positives_B += 1
if pred == 'B-claim':
predicted_positives_B += 1
if valid == 'B-claim':
real_positives_B += 1
print("True positives B: {}".format(true_positives_B))
print("predicted positives B: {}".format(predicted_positives_B))
print("real positives B: {}".format(real_positives_B))
with open("resultados", 'w') as out:
out.write("Predictions:\n")
out.write("{}".format(list(zip(list(val_inputs), pred_tags, valid_tags))))
def run_ner(
lang: str = 'eng',
log_dir: str = 'logs',
task: str = NER,
batch_size: int = 1,
epochs: int = 1,
dataset: str = 'data/conll-2003/',
loss: str = 'cross',
max_seq_len: int = 128,
do_lower_case: bool = False,
warmup_proportion: float = 0.1,
rand_seed: int = None,
ds_size: int = None,
data_bunch_path: str = 'data/conll-2003/db',
tuned_learner: str = None,
do_train: str = False,
do_eval: str = False,
save: bool = False,
nameX: str = 'ner',
mask: tuple = ('s', 's'),
):
name = "_".join(
map(str, [
nameX, task, lang, mask[0], mask[1], loss, batch_size, max_seq_len,
do_train, do_eval
]))
log_dir = Path(log_dir)
log_dir.mkdir(parents=True, exist_ok=True)
init_logger(log_dir, name)
if rand_seed:
random.seed(rand_seed)
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(rand_seed)
trainset = dataset + lang + '/train.txt'
devset = dataset + lang + '/dev.txt'
testset = dataset + lang + '/test.txt'
bert_model = 'bert-base-cased' if lang == 'eng' else 'bert-base-multilingual-cased'
print(f'Lang: {lang}\nModel: {bert_model}\nRun: {name}')
model = BertForTokenClassification.from_pretrained(bert_model,
num_labels=len(VOCAB),
cache_dir='bertm')
if tuned_learner:
print('Loading pretrained learner: ', tuned_learner)
model.bert.load_state_dict(torch.load(tuned_learner))
model = torch.nn.DataParallel(model)
model_lr_group = bert_layer_list(model)
layers = len(model_lr_group)
kwargs = {'max_seq_len': max_seq_len, 'ds_size': ds_size, 'mask': mask}
train_dl = DataLoader(dataset=NerDataset(trainset,
bert_model,
train=True,
**kwargs),
batch_size=batch_size,
shuffle=True,
collate_fn=partial(pad, train=True))
dev_dl = DataLoader(dataset=NerDataset(devset, bert_model, **kwargs),
batch_size=batch_size,
shuffle=False,
collate_fn=pad)
test_dl = DataLoader(dataset=NerDataset(testset, bert_model, **kwargs),
batch_size=batch_size,
shuffle=False,
collate_fn=pad)
data = DataBunch(train_dl=train_dl,
valid_dl=dev_dl,
test_dl=test_dl,
collate_fn=pad,
path=Path(data_bunch_path))
train_opt_steps = int(len(train_dl.dataset) / batch_size) * epochs
optim = BertAdam(model.parameters(),
lr=0.01,
warmup=warmup_proportion,
t_total=train_opt_steps)
loss_fun = ner_loss_func if loss == 'cross' else partial(ner_loss_func,
zero=True)
metrics = [Conll_F1()]
learn = Learner(
data,
model,
BertAdam,
loss_func=loss_fun,
metrics=metrics,
true_wd=False,
layer_groups=model_lr_group,
path='learn' + nameX,
)
learn.opt = OptimWrapper(optim)
lrm = 1.6
# select set of starting lrs
lrs_eng = [0.01, 5e-4, 3e-4, 3e-4, 1e-5]
lrs_deu = [0.01, 5e-4, 5e-4, 3e-4, 2e-5]
startlr = lrs_eng if lang == 'eng' else lrs_deu
results = [['epoch', 'lr', 'f1', 'val_loss', 'train_loss', 'train_losses']]
if do_train:
learn.freeze()
learn.fit_one_cycle(1, startlr[0], moms=(0.8, 0.7))
learn.freeze_to(-3)
lrs = learn.lr_range(slice(startlr[1] / (1.6**15), startlr[1]))
learn.fit_one_cycle(1, lrs, moms=(0.8, 0.7))
learn.freeze_to(-6)
lrs = learn.lr_range(slice(startlr[2] / (1.6**15), startlr[2]))
learn.fit_one_cycle(1, lrs, moms=(0.8, 0.7))
learn.freeze_to(-12)
lrs = learn.lr_range(slice(startlr[3] / (1.6**15), startlr[3]))
learn.fit_one_cycle(1, lrs, moms=(0.8, 0.7))
learn.unfreeze()
lrs = learn.lr_range(slice(startlr[4] / (1.6**15), startlr[4]))
learn.fit_one_cycle(1, lrs, moms=(0.8, 0.7))
if do_eval:
res = learn.validate(test_dl, metrics=metrics)
met_res = [f'{m.__name__}: {r}' for m, r in zip(metrics, res[1:])]
print(f'Validation on TEST SET:\nloss {res[0]}, {met_res}')
results.append(['val', '-', res[1], res[0], '-', '-'])
with open(log_dir / (name + '.csv'), 'a') as resultFile:
wr = csv.writer(resultFile)
wr.writerows(results)
data_loader = DataLoader(args.data_dir,
args.bert_model_dir,
params,
token_pad_idx=0)
# Load training data and test data
train_data = data_loader.load_data('train')
val_data = data_loader.load_data('val')
# Specify the training and validation dataset sizes
params.train_size = train_data['size']
params.val_size = val_data['size']
# Prepare model
model = BertForTokenClassification.from_pretrained(args.bert_model_dir,
num_labels=len(
params.tag2idx))
model.to(params.device)
if args.fp16:
model.half()
if params.n_gpu > 1 and args.multi_gpu:
model = torch.nn.DataParallel(model)
# Prepare optimizer
if params.full_finetuning:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
# no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [{
'params': [
def __init__(self, opt):
super(BertNer, self).__init__()
self.opt = opt
self.num_labels = self.opt.tag_nums
self.bertForToken = BertForTokenClassification.from_pretrained(
self.opt.bert_model_dir, num_labels=self.opt.tag_nums)
help='path to save the final model')
args = parser.parse_args()
if args.lang == "en":
bert_model = "bert-base-uncased"
model_path = "/workdir/pretrain-model/bert-torch"
elif args.lang == "cn":
bert_model = "bert-base-chinese"
model_path = "/workdir/pretrain-model/bert-torch-cn"
bert_model = "bert-base-uncased"
# model_path = "D:/Github/BERT-Keyword-Extractor/model/en_model.pt"
tag2idx = {'B': 0, 'I': 1, 'O': 2}
tags_vals = ['B', 'I', 'O']
tokenizer = BertTokenizer.from_pretrained(bert_model, do_lower_case=True)
# cache_dir='/workdir/pretrain-model/bert-torch-cn')
model = BertForTokenClassification.from_pretrained(bert_model,
num_labels=len(tag2idx))
# cache_dir='/workdir/pretrain-model/bert-torch-cn')
def casting(tkns, prediction):
# 把命中peace的值扩张到整个单词
for k, j in enumerate(prediction):
if j == 1 or j == 0:
# 中间是短词
if not tkns[k].find('##') == -1:
prediction[k] = 1
forwd = False
backward = False
for i in range(int(len(tkns) / 2)):
# forward
if k - i >= 0:
def train(args: Dict):
MAX_LEN = int(args['--max-len'])
bs = int(args['--batch-size'])
model_root = args['--model-root'] if args['--model-root'] else './models'
dataLoader= sentence.Sentence(args['--train-src'])
device = torch.device("cuda:0" if args['--cuda'] else "cpu")
print('use device: %s' % device, file=sys.stderr)
if args['--cuda']:
n_gpu = torch.cuda.device_count()
torch.cuda.get_device_name(0)
tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased', do_lower_case=False)
tokenized_texts = [tokenizer.tokenize(sent) for sent in dataLoader.sentences]
print(dataLoader.sentences[0])
print(tokenized_texts[0])
input_ids = pad_sequences([tokenizer.convert_tokens_to_ids(txt) for txt in tokenized_texts],
maxlen=MAX_LEN, dtype="long", truncating="post", padding="post")
tags = pad_sequences([[dataLoader.tag2idx.get(l) for l in lab] for lab in dataLoader.labels],
maxlen=MAX_LEN, value=dataLoader.tag2idx["O"], padding="post",
dtype="long", truncating="post")
attention_masks = [[float(i > 0) for i in ii] for ii in input_ids]
"""
The BERT Model requires us to have a [SEP] token at the end of each sentence as a part of its preprocessing. 102 is the index BERT recognizes as the index of [SEP]. Hence, I am adding it to the end of the sentence after padding/truncating
(as it might have been removed if the sequences were greater than 75 in length) to be compatible with BERT's requirement. I didn't have it in the beginning and I thought it would be the reason for the poor results but changing it didn't help and I chose to keep it anyways as it felt right. :)
"""
for i, inp in enumerate(input_ids):
if (102 not in inp):
inp[-1] = 102
tags[i][-1] = dataLoader.tag2idx.get("O")
tts = float(args['--train-test-split'])
tr_inputs, val_inputs, tr_tags, val_tags = train_test_split(input_ids, tags,
random_state=10, test_size=tts)
tr_masks, val_masks, _, _ = train_test_split(attention_masks, input_ids,
random_state=10, test_size=tts)
tr_inputs = torch.tensor(tr_inputs).to(torch.int64)
val_inputs = torch.tensor(val_inputs).to(torch.int64)
tr_tags = torch.tensor(tr_tags).to(torch.int64)
val_tags = torch.tensor(val_tags).to(torch.int64)
tr_masks = torch.tensor(tr_masks)
val_masks = torch.tensor(val_masks)
train_data = TensorDataset(tr_inputs, tr_masks, tr_tags)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=bs)
valid_data = TensorDataset(val_inputs, val_masks, val_tags)
valid_sampler = SequentialSampler(valid_data)
valid_dataloader = DataLoader(valid_data, sampler=valid_sampler, batch_size=bs)
model = BertForTokenClassification.from_pretrained(
"bert-base-multilingual-cased", num_labels=len(dataLoader.tag2idx))
if args['--cuda']:
model.cuda()
FULL_FINETUNING = True if args['--full-finetuning'] else False
if FULL_FINETUNING:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay_rate': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay_rate': 0.0}
]
else:
param_optimizer = list(model.classifier.named_parameters())
optimizer_grouped_parameters = [{"params": [p for n, p in param_optimizer]}]
optimizer = Adam(optimizer_grouped_parameters, lr=float(args['--lr']))
epochs = int(args['--max-epoch'])
max_grad_norm = 1.0
hist_valid_scores = []
for _ in trange(epochs, desc="Epoch"):
# TRAIN loop
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(train_dataloader):
# add batch to gpu
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_mask, b_labels = batch
# forward pass
loss = model(b_input_ids, token_type_ids=None,
attention_mask=b_input_mask, labels=b_labels)
# backward pass
loss.backward()
# track train loss
tr_loss += loss.item()
nb_tr_examples += b_input_ids.size(0)
nb_tr_steps += 1
# gradient clipping
torch.nn.utils.clip_grad_norm_(parameters=model.parameters(), max_norm=max_grad_norm)
# update parameters
optimizer.step()
model.zero_grad()
# print train loss per epoch
print("Train loss: {}".format(tr_loss / nb_tr_steps))
# VALIDATION on validation set
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
predictions, true_labels = [], []
for batch in valid_dataloader:
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_mask, b_labels = batch
with torch.no_grad():
tmp_eval_loss = model(b_input_ids, token_type_ids=None,
attention_mask=b_input_mask, labels=b_labels)
logits = model(b_input_ids, token_type_ids=None,
attention_mask=b_input_mask)
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
predictions.extend([list(p) for p in np.argmax(logits, axis=2)])
true_labels.append(label_ids)
tmp_eval_accuracy = flat_accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += b_input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
print("Validation loss: {}".format(eval_loss))
print("Validation Accuracy: {}".format(eval_accuracy / nb_eval_steps))
pred_tags = [dataLoader.tags_vals[p_i] for p in predictions for p_i in p]
valid_tags = [dataLoader.tags_vals[l_ii] for l in true_labels for l_i in l for l_ii in l_i]
f1=f1_score(valid_tags,pred_tags)
print("F1-Score: {}".format(f1))
is_better = len(hist_valid_scores) == 0 or f1 > max(hist_valid_scores)
hist_valid_scores.append(f1)
if is_better:
output_model_file = os.path.join(model_root, "model_file.bin")
output_config_file = os.path.join(model_root, "config_file.bin")
output_vocab_file = model_root
model_to_save = model.module if hasattr(model, 'module') else model
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(output_vocab_file)
print('reached maximum number of epochs!', file=sys.stderr)
exit(0)
device=device)
training_dataset = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
training_sampler = RandomSampler(training_dataset)
training_dataloader = DataLoader(training_dataset,
sampler=training_sampler,
batch_size=training_batch_size)
num_train_steps = int(
len(training_features) / training_batch_size /
gradient_accumulation_steps * num_train_epochs)
t_total = num_train_steps
if if_bert:
model = BertForTokenClassification.from_pretrained(
'../../model/bert-base-chinese.tar.gz', num_labels=3).to(device)
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
}]
def run_ner(
lang: str = 'eng',
log_dir: str = 'logs',
task: str = NER,
batch_size: int = 1,
lr: float = 5e-5,
epochs: int = 1,
dataset: str = 'data/conll-2003/',
loss: str = 'cross',
max_seq_len: int = 128,
do_lower_case: bool = False,
warmup_proportion: float = 0.1,
grad_acc_steps: int = 1,
rand_seed: int = None,
fp16: bool = False,
loss_scale: float = None,
ds_size: int = None,
data_bunch_path: str = 'data/conll-2003/db',
bertAdam: bool = False,
freez: bool = False,
one_cycle: bool = False,
discr: bool = False,
lrm: int = 2.6,
div: int = None,
tuned_learner: str = None,
do_train: str = False,
do_eval: str = False,
save: bool = False,
name: str = 'ner',
mask: tuple = ('s', 's'),
):
name = "_".join(
map(str, [
name, task, lang, mask[0], mask[1], loss, batch_size, lr,
max_seq_len, do_train, do_eval
]))
log_dir = Path(log_dir)
log_dir.mkdir(parents=True, exist_ok=True)
init_logger(log_dir, name)
if rand_seed:
random.seed(rand_seed)
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(rand_seed)
trainset = dataset + lang + '/train.txt'
devset = dataset + lang + '/dev.txt'
testset = dataset + lang + '/test.txt'
bert_model = 'bert-base-cased' if lang == 'eng' else 'bert-base-multilingual-cased'
print(f'Lang: {lang}\nModel: {bert_model}\nRun: {name}')
model = BertForTokenClassification.from_pretrained(bert_model,
num_labels=len(VOCAB),
cache_dir='bertm')
model = torch.nn.DataParallel(model)
model_lr_group = bert_layer_list(model)
layers = len(model_lr_group)
kwargs = {'max_seq_len': max_seq_len, 'ds_size': ds_size, 'mask': mask}
train_dl = DataLoader(dataset=NerDataset(trainset,
bert_model,
train=True,
**kwargs),
batch_size=batch_size,
shuffle=True,
collate_fn=partial(pad, train=True))
dev_dl = DataLoader(dataset=NerDataset(devset, bert_model, **kwargs),
batch_size=batch_size,
shuffle=False,
collate_fn=pad)
test_dl = DataLoader(dataset=NerDataset(testset, bert_model, **kwargs),
batch_size=batch_size,
shuffle=False,
collate_fn=pad)
data = DataBunch(train_dl=train_dl,
valid_dl=dev_dl,
test_dl=test_dl,
collate_fn=pad,
path=Path(data_bunch_path))
loss_fun = ner_loss_func if loss == 'cross' else partial(ner_loss_func,
zero=True)
metrics = [Conll_F1()]
learn = Learner(
data,
model,
BertAdam,
loss_func=loss_fun,
metrics=metrics,
true_wd=False,
layer_groups=None if not freez else model_lr_group,
path='learn',
)
# initialise bert adam optimiser
train_opt_steps = int(len(train_dl.dataset) / batch_size) * epochs
optim = BertAdam(model.parameters(),
lr=lr,
warmup=warmup_proportion,
t_total=train_opt_steps)
if bertAdam: learn.opt = OptimWrapper(optim)
else: print("No Bert Adam")
# load fine-tuned learner
if tuned_learner:
print('Loading pretrained learner: ', tuned_learner)
learn.load(tuned_learner)
# Uncomment to graph learning rate plot
# learn.lr_find()
# learn.recorder.plot(skip_end=15)
# set lr (discriminative learning rates)
if div: layers = div
lrs = lr if not discr else learn.lr_range(slice(lr / lrm**(layers), lr))
results = [['epoch', 'lr', 'f1', 'val_loss', 'train_loss', 'train_losses']]
if do_train:
for epoch in range(epochs):
if freez:
lay = (layers // (epochs - 1)) * epoch * -1
if lay == 0:
print('Freeze')
learn.freeze()
elif lay == layers:
print('unfreeze')
learn.unfreeze()
else:
print('freeze2')
learn.freeze_to(lay)
print('Freezing layers ', lay, ' off ', layers)
# Fit Learner - eg train model
if one_cycle: learn.fit_one_cycle(1, lrs, moms=(0.8, 0.7))
else: learn.fit(1, lrs)
results.append([
epoch,
lrs,
learn.recorder.metrics[0][0],
learn.recorder.val_losses[0],
np.array(learn.recorder.losses).mean(),
learn.recorder.losses,
])
if save:
m_path = learn.save(f"{lang}_{epoch}_model", return_path=True)
print(f'Saved model to {m_path}')
if save: learn.export(f'{lang}.pkl')
if do_eval:
res = learn.validate(test_dl, metrics=metrics)
met_res = [f'{m.__name__}: {r}' for m, r in zip(metrics, res[1:])]
print(f'Validation on TEST SET:\nloss {res[0]}, {met_res}')
results.append(['val', '-', res[1], res[0], '-', '-'])
with open(log_dir / (name + '.csv'), 'a') as resultFile:
wr = csv.writer(resultFile)
wr.writerows(results)
def build_model(full_fine_tunning=True, batch_size=32, epochs=3):
df = load_data(DATA_PATH)
df["word"] = df["word"].str.lower()
data = getter(df)
processed_texts = []
processed_tags = []
for item in data:
string, tags = process_terms(item)
processed_texts.append(string)
processed_tags.append(tags)
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased",
do_lower_case=True)
tokenized_sents = [
tokenizer.tokenize("[CLS] " + sent + " [SEP]")
for sent in processed_texts
]
tokenized_tags = label_tokenize(tokenized_sents, processed_tags)
bert_sents, bert_labels = remove_long_sent(tokenized_sents, tokenized_tags)
# indexing
input_ids = [tokenizer.convert_tokens_to_ids(x) for x in bert_sents]
label_ids = [[LABELS.get(l) for l in lab] for lab in bert_labels]
input_ids_pad = pad_sequences(
input_ids,
maxlen=BERT_INPUT_SEQUENCE_LENGTH,
dtype="long",
truncating="post",
padding="post",
)
labels_ids_pad = pad_sequences(
label_ids,
maxlen=BERT_INPUT_SEQUENCE_LENGTH,
value=LABELS["O"],
dtype="long",
truncating="post",
padding="post",
)
attention_masks = []
for seq in input_ids_pad:
mask = [float(i > 0) for i in seq]
attention_masks.append(mask)
train_data = TensorDataset(
torch.tensor(input_ids_pad),
torch.tensor(attention_masks),
torch.tensor(labels_ids_pad),
)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=batch_size)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
print("GPU: {}".format(device))
print("Number of GPUs: {}".format(n_gpu))
if device == torch.device("cuda"):
board = torch.cuda.get_device_name()
print("Board: {}".format(board))
model = BertForTokenClassification.from_pretrained("bert-base-uncased",
num_labels=len(LABELS))
if device == torch.device("cuda"):
model.cuda()
if full_fine_tunning:
param_optimizer = list(model.named_parameters())
no_decay = ["bias", "gamma", "beta"]
optimizer_grouped_parameters = [
{
"params": [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
"weight_decay_rate":
0.01,
},
{
"params": [
p for n, p in param_optimizer
if any(nd in n for nd in no_decay)
],
"weight_decay_rate":
0.0,
},
]
else:
param_optimizer = list(model.classifier.named_parameters())
optimizer_grouped_parameters = [{
"params": [p for n, p in param_optimizer]
}]
optimizer = BertAdam(optimizer_grouped_parameters, lr=5e-5, warmup=0.1)
tr_loss_set = []
for epoch in range(epochs):
# train
model.train()
tr_loss = 0
nb_tr_steps = 0
for step, batch in enumerate(train_dataloader):
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_masks, b_labels = batch
logits = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_masks)
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, len(LABELS)), b_labels.view(-1))
tr_loss_set.append(loss.item())
loss.backward()
# gradient clipping
torch.nn.utils.clip_grad_norm_(parameters=model.parameters(),
max_norm=1.0)
optimizer.step()
model.zero_grad()
tr_loss += loss.item()
nb_tr_steps += 1
print(f"# of EPOCH: {epoch}")
print("Train loss: {}".format(tr_loss / nb_tr_steps))
torch.save(model.state_dict(), str(MODEL_PATH))
model.config.to_json_file(MODEL_CONFIG_PATH)
def load_token_classifier(self, classifier_model_name, tag2idx):
self.model = BertForTokenClassification.from_pretrained(classifier_model_name, num_labels=len(tag2idx))
### CONFIG ###
MAX_LEN = 50
BATCH_SIZE = 32
TRAIN_URL = 'https://raw.githubusercontent.com/cicl-iscl/CyberWallE/master/data/train-improved-sentiwordnet-arguingfullindiv-pos.tsv?token=AD7GEDK3MI27HVJPQWOE74C6FBZHA'
DEV_URL = 'https://raw.githubusercontent.com/cicl-iscl/CyberWallE/master/data/dev-improved-sentiwordnet-arguingfullindiv-pos.tsv?token=AD7GEDM3LOMZM6MP4HZS4MS6FBZHK'
EPOCHS = 3
MAX_GRAD_NORM = 1.0
##############
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
print(torch.cuda.get_device_name(0))
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased',
do_lower_case=True)
model = BertForTokenClassification.from_pretrained('bert-base-uncased',
num_labels=2)
model.cuda()
def get_comments(filename, url=True):
if url:
comments = []
with urllib.request.urlopen(filename) as f:
for line in f:
if line.startswith(b'#'):
comments.append(line.decode("utf-8"))
else:
break
return comments
with open(filename, 'r', encoding='utf8') as f:
commentiter = takewhile(lambda s: s.startswith('#'), f)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default='/home/adzuser/user_achyuta/BERT_NER_Test/BERT-NER/NERdata/',
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default='NER',
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument(
"--output_dir",
default='ner_output',
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_test",
action='store_true',
help="Whether to run test on the test set.")
parser.add_argument("--do_pred",
action='store_true',
help="Whether to run pred on the pred set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument(
"--num_train_epochs",
default=4.0, #3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--clip',
type=float,
default=0.5,
help="gradient clipping")
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(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
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('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--text_a', type=str, default='', help="input text_a.")
parser.add_argument('--text_b', type=str, default='', help="input text_b.")
args = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
processors = {"ner": NerProcessor}
num_labels_task = {
"ner": 17 #6#12
}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
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
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 not args.do_train and not args.do_eval and not args.do_pred:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
num_labels = num_labels_task[task_name]
label_list = processor.get_labels()
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
#train_examples = train_examples[:1000]
print("train_examples :: ", len(list(train_examples)))
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(
args.local_rank))
#imodel = BertForSequenceClassification.from_pretrained(args.bert_model,
# cache_dir=cache_dir,
# num_labels = num_labels)
model = BertForTokenClassification.from_pretrained(args.bert_model,
cache_dir=cache_dir,
num_labels=num_labels)
if args.fp16:
model.half()
#model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
# 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
}]
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=args.learning_rate,
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 = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
if args.do_train:
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer)
#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_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
all_input_ids = [f.input_ids for f in train_features]
all_input_mask = [f.input_mask for f in train_features]
all_segment_ids = [f.segment_ids for f in train_features]
all_label_ids = [f.label_id for f in train_features]
# convert to cuda
#all_input_ids = all_input_ids.to(device)
#all_input_mask = all_input_mask.to(device)
#all_segment_ids = all_segment_ids.to(device)
#all_label_ids = all_label_ids.to(device)
#train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
#if args.local_rank == -1:
# train_sampler = RandomSampler(train_data)
#else:
# train_sampler = DistributedSampler(train_data)
#train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
# create model
#model.train()
model = KerasClassifier(build_fn=create_model(model), verbose=0)
# define the grid search parameters
batch_size = [10, 20, 40, 60, 80, 100]
epochs = [10, 50, 100]
param_grid = dict(batch_size=batch_size, epochs=epochs)
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1)
#keras_input = KerasInputFormatter([
# ('input_one', all_input_ids),
# ('input_two', all_segment_ids),
# ('input_three', all_input_mask),])
#input_one = Input(name = 'input_one', shape = (128,))
#input_two = Input(name = 'input_two', shape = (128,))
#input_three = Input(name = 'input_three', shape = (128,))
#input_four = Input(name = 'input_four', shape = (128,))
#output = y # define model here
#self.model = Model(inputs = [input_one, input_two, input_three], outputs = output)
grid_result = grid.fit(
[[all_input_ids, all_segment_ids, all_input_mask]],
[all_label_ids])
# summarize results
print("Best: %f using %s" %
(grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
'''
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
#print(input_ids.shape,input_mask.shape,segment_ids.shape,label_ids.shape)
#print(input_ids[0])
#print(label_ids[0])
#logits = model(input_ids, segment_ids, input_mask)
#import pdb;pdb.set_trace()
#print(logits.view(-1, num_labels).shape, label_ids.view(-1).shape)
loss = model(input_ids, segment_ids, input_mask, label_ids)
if 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:
optimizer.backward(loss)
else:
loss.backward()
# added clip
if args.clip is not None:
_ = torch.nn.utils.clip_grad_norm(model.parameters(), args.clip)
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used that handles this automatically
lr_this_step = args.learning_rate * 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 args.do_train:
# Save a trained model and the associated configuration
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
# Load a trained model and config that you have fine-tuned
config = BertConfig(output_config_file)
#model = BertForSequenceClassification(config, num_labels=num_labels)
model = BertForTokenClassification(config, num_labels=num_labels)
model.load_state_dict(torch.load(output_model_file))
else:
#model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=num_labels)
# Load a trained model and config that you have fine-tuned
print('for eval only......................')
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
config = BertConfig(output_config_file)
#model = BertForSequenceClassification(config, num_labels=num_labels)
model = BertForTokenClassification(config, num_labels=num_labels)
model.load_state_dict(torch.load(output_model_file))
model.to(device)
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
#import pdb;pdb.set_trace()
print("dev_eaxmples :: ", len(list(eval_examples)))
eval_features = convert_examples_to_features_pred(
eval_examples, label_list, args.max_seq_length, tokenizer)
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_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
predictions, true_labels = [], []
#predictions1 , true_labels1 = [], []
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
# get index till '[SEP]'
#print("label_list index SEP : ",label_list.index('[SEP]'))
pred_xx = [list(p) for p in np.argmax(logits, axis=2)]
pred_xx = [i[:i.index(label_list.index('[SEP]'))] for i in pred_xx]
label_ids_xx = [
i[:i.index(label_list.index('[SEP]'))]
for i in label_ids.tolist()
]
#print(label_ids_xx)
#print(pred_xx)
# new add
tmp_s = [
max(len(i), len(j)) for i, j in zip(label_ids_xx, pred_xx)
]
tmp_u = [(i + [31] * (k - len(i)) if len(i) != k else i,
j + [31] * (k - len(j)) if len(j) != k else j)
for i, j, k in zip(label_ids_xx, pred_xx, tmp_s)]
tmp_d1 = [h[0] for h in tmp_u]
tmp_d2 = [h[1] for h in tmp_u]
#print([list(p) for p in np.argmax(logits, axis=2)][:5])
#tmp_eval_accuracy = flat_accuracy(logits, label_ids)
tmp_eval_accuracy = flat_accc(pred_xx, label_ids_xx)
#tmp_eval_accuracy = flat_accc(tmp_d1, tmp_d2)
predictions.extend(tmp_d2)
true_labels.append(tmp_d1)
#predictions1.extend(pred_xx)
#true_labels1.append(label_ids_xx)
#print("tmp accuracy : ",tmp_eval_accuracy)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_steps
loss = tr_loss / nb_tr_steps if args.do_train else None
pred_tags = [[label_list[p_i] if p_i != 31 else 'XXX' for p_i in p]
for p in predictions]
valid_tags = [[
label_list[l_ii] if l_ii != 31 else 'YYY' for l_ii in l_i
] for l in true_labels for l_i in l]
print("valid_tags : ", valid_tags[:10])
print("pred_tags : ", pred_tags[:10])
print("Validation F1-Score: {}".format(f1_score(valid_tags,
pred_tags)))
print("Validation accuracy_score : {}".format(
accuracy_score(valid_tags, pred_tags)))
print("Validation classification_report : {}".format(
classification_report(valid_tags, pred_tags)))
#print("X Validation F1-Score: {}".format(f1_score(true_labels1, predictions1)))
#print("X Validation accuracy_score : {}".format(accuracy_score(true_labels1, predictions1)))
#print("X Validation classification_report : {}".format(classification_report(true_labels1, predictions1)))
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': loss
}
print(result)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
for key in sorted(result.keys()):
writer.write("%s = %s\n" % (key, str(result[key])))
if args.do_test and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_test_examples(args.data_dir)
print('test examples len : {}'.format(len(eval_examples)))
#import pdb;pdb.set_trace()
eval_features = convert_examples_to_features_pred(
eval_examples, label_list, args.max_seq_length, tokenizer)
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_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
model.eval()
test_loss, test_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
predictions, true_labels = [], []
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
# get index till '[SEP]'
#print("label_list index SEP : ",label_list.index('[SEP]'))
pred_xx = [list(p) for p in np.argmax(logits, axis=2)]
pred_xx = [i[:i.index(label_list.index('[SEP]'))] for i in pred_xx]
label_ids_xx = [
i[:i.index(label_list.index('[SEP]'))]
for i in label_ids.tolist()
]
#print(label_ids_xx)
#print(pred_xx)
# new add
tmp_s = [
max(len(i), len(j)) for i, j in zip(label_ids_xx, pred_xx)
]
tmp_u = [(i + [31] * (k - len(i)) if len(i) != k else i,
j + [31] * (k - len(j)) if len(j) != k else j)
for i, j, k in zip(label_ids_xx, pred_xx, tmp_s)]
tmp_d1 = [h[0] for h in tmp_u]
tmp_d2 = [h[1] for h in tmp_u]
#print([list(p) for p in np.argmax(logits, axis=2)][:5])
#tmp_eval_accuracy = flat_accuracy(logits, label_ids)
tmp_eval_accuracy = flat_accc(pred_xx, label_ids_xx)
#tmp_eval_accuracy = flat_accc(tmp_d1, tmp_d2)
predictions.extend(tmp_d2)
true_labels.append(tmp_d1)
#print("tmp accuracy : ",tmp_eval_accuracy)
test_loss += tmp_eval_loss.mean().item()
test_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
test_loss = test_loss / nb_eval_steps
test_accuracy = test_accuracy / nb_eval_steps
loss = tr_loss / nb_tr_steps if args.do_train else None
pred_tags = [[label_list[p_i] if p_i != 31 else 'XXX' for p_i in p]
for p in predictions]
valid_tags = [[
label_list[l_ii] if l_ii != 31 else 'YYY' for l_ii in l_i
] for l in true_labels for l_i in l]
print("valid_tags : ", valid_tags[:10])
print("pred_tags : ", pred_tags[:10])
print("Test F1-Score: {}".format(f1_score(valid_tags, pred_tags)))
print("Test accuracy_score : {}".format(
accuracy_score(valid_tags, pred_tags)))
print("Test classification_report : {}".format(
classification_report(valid_tags, pred_tags)))
#print("X Test F1-Score: {}".format(f1_score(true_labels, predictions)))
#print("X Test accuracy_score : {}".format(accuracy_score(true_labels, predictions)))
#print("X Test classification_report : {}".format(classification_report(true_labels, predictions)))
result = {
'test_loss': test_loss,
'test_accuracy': test_accuracy,
'global_step': global_step,
'loss': loss
}
print(result)
output_test_file = os.path.join(args.output_dir, "test_results.txt")
with open(output_test_file, "w") as writer:
for key in sorted(result.keys()):
writer.write("%s = %s\n" % (key, str(result[key])))
if args.do_pred and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
#eval_examples = processor.get_dev_examples(args.data_dir)
model.eval()
while True:
print(
'enter a text to get NER. otherwise press Ctrl+C to close session.'
)
text_a = input('>>>')
#"Japan began the defence of their Asian Cup title with a lucky 2-1 win against Syria in a Group C championship match on Friday . ."
eval_examples = {
'text_a': text_a,
'text_b':
"The foodservice pie business does not fit our long-term growth strategy .",
'label': '1',
'guid': '12345'
}
eval_features = convert_examples_to_features_test(
eval_examples, label_list, args.max_seq_length, tokenizer)
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_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
#model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
predictions, true_labels = [], []
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
pred_xx = [list(p) for p in np.argmax(logits, axis=2)]
pred_xx = [
i[:i.index(label_list.index('[SEP]'))] for i in pred_xx
]
print(pred_xx)
print([[label_list[p_i] if p_i != 31 else 'XXX' for p_i in p]
for p in pred_xx])
def do_07_bert(data: pd.DataFrame, cv=5):
# This has multiple issues, that we couldn't fix at this moment
# TODO: Try with cased model
# TODO: Try with data already tokenized by BERT tokenizer
# TODO: Fix Out uf memory error if possible
model_name = 'bert-base-uncased'
getter = SentenceGetter(data)
# we need actual sentences this time as bert provides a tagger we will re-use
sentences = [
" ".join([s[0] for s in sentence]) for sentence in getter.sentences
]
labels = [[s[2] for s in sent] for sent in getter.sentences]
tags_vals = list(set(data["Tag"].values))
tag2idx = {t: i for i, t in enumerate(tags_vals)}
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
bs = batch_size = 32
# use berts tokenizer
tokenizer = BertTokenizer.from_pretrained(model_name, do_lower_case=True)
tokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]
MAX_LEN = max(len(s) for s in tokenized_texts)
print("MAX_LEN: %d" % MAX_LEN)
# NOTE: The tutorial seems to assume that bert and our input are basically tokenized
# to the same units making the labels still applicable to the input
# Result of the below for the original texts is:
# Mean: 2.68, differing: 0.73
# for i in [4, 7, 58, 1200]:
# print(sentences[i])
# print(tokenized_texts[i])
# print("---")
# differences = [len(tokenized_texts[i]) - len(labels[i]) for i in range(len(sentences))]
# differences = [d * -1 if d < 0 else d for d in differences]
# mean = sum(differences) / len(differences)
# print("Mean: %.2f, differing: %.2f" % (mean, len([d for d in differences if d != 0]) / len(sentences)))
# Pad the inputs
input_ids = pad_sequences(
[tokenizer.convert_tokens_to_ids(txt) for txt in tokenized_texts],
maxlen=MAX_LEN,
dtype="long",
truncating="post",
padding="post")
tags = pad_sequences([[tag2idx.get(l) for l in lab] for lab in labels],
value=tag2idx["O"],
maxlen=MAX_LEN,
dtype="long",
padding="post",
truncating="post")
# Prepare test and training data
attention_masks = [[float(i > 0) for i in ii] for ii in input_ids]
tr_inputs, val_inputs, tr_tags, val_tags = train_test_split(
input_ids, tags, random_state=2018, test_size=0.1)
tr_masks, val_masks, _, _ = train_test_split(attention_masks,
input_ids,
random_state=2018,
test_size=0.1)
tr_inputs = torch.tensor(tr_inputs)
val_inputs = torch.tensor(val_inputs)
tr_tags = torch.tensor(tr_tags)
val_tags = torch.tensor(val_tags)
tr_masks = torch.tensor(tr_masks)
val_masks = torch.tensor(val_masks)
# training will be shuffled
train_data = TensorDataset(tr_inputs, tr_masks, tr_tags)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=bs)
# test data will be given sequentially
valid_data = TensorDataset(val_inputs, val_masks, val_tags)
valid_sampler = SequentialSampler(valid_data)
valid_dataloader = DataLoader(valid_data,
sampler=valid_sampler,
batch_size=bs)
# load the model and send params to gpu if available
model = BertForTokenClassification.from_pretrained("bert-base-uncased",
num_labels=len(tag2idx))
if device.type == "cuda":
model.cuda()
# Paramaters for finetuning
FULL_FINETUNING = True
if FULL_FINETUNING:
# "We also add some weight_decay as regularization to the main weight matrices."
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay_rate':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay_rate':
0.0
}]
else:
# "If you have limited resources, you can also try to just train the linear classifier on
# top of Bert and keep all other weights fixed. This will still give you a good performance."
param_optimizer = list(model.classifier.named_parameters())
optimizer_grouped_parameters = [{
"params": [p for n, p in param_optimizer]
}]
optimizer = Adam(optimizer_grouped_parameters, lr=3e-5)
# A function for finetuning
def flat_accuracy(preds, labels):
pred_flat = np.argmax(preds, axis=2).flatten()
labels_flat = labels.flatten()
return np.sum(pred_flat == labels_flat) / len(labels_flat)
# RUN FINE-TUNING
epochs = 5
max_grad_norm = 1.0
for _ in trange(epochs, desc="Epoch"):
# TRAIN loop
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(train_dataloader):
# add batch to gpu
print(batch)
batch = tuple(t.to(device) for t in batch)
print(batch)
b_input_ids, b_input_mask, b_labels = batch
# forward pass
loss = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
# backward pass
loss.backward()
# track train loss
tr_loss += loss.item()
nb_tr_examples += b_input_ids.size(0)
nb_tr_steps += 1
# gradient clipping
torch.nn.utils.clip_grad_norm_(parameters=model.parameters(),
max_norm=max_grad_norm)
# update parameters
optimizer.step()
model.zero_grad()
# print train loss per epoch
print("Train loss: {}".format(tr_loss / nb_tr_steps))
# VALIDATION on validation set
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
predictions, true_labels = [], []
for batch in valid_dataloader:
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_mask, b_labels = batch
with torch.no_grad():
tmp_eval_loss = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
logits = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask)
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
predictions.extend([list(p) for p in np.argmax(logits, axis=2)])
true_labels.append(label_ids)
tmp_eval_accuracy = flat_accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += b_input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
print("Validation loss: {}".format(eval_loss))
print("Validation Accuracy: {}".format(eval_accuracy / nb_eval_steps))
pred_tags = [tags_vals[p_i] for p in predictions for p_i in p]
valid_tags = [
tags_vals[l_ii] for l in true_labels for l_i in l for l_ii in l_i
]
print("F1-Score: {}".format(f1_score(pred_tags, valid_tags)))
# EVALUATION
model.eval()
predictions = []
true_labels = []
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for batch in valid_dataloader:
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_mask, b_labels = batch
with torch.no_grad():
tmp_eval_loss = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
logits = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask)
logits = logits.detach().cpu().numpy()
predictions.extend([list(p) for p in np.argmax(logits, axis=2)])
label_ids = b_labels.to('cpu').numpy()
true_labels.append(label_ids)
tmp_eval_accuracy = flat_accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += b_input_ids.size(0)
nb_eval_steps += 1
pred_tags = [[tags_vals[p_i] for p_i in p] for p in predictions]
valid_tags = [[tags_vals[l_ii] for l_ii in l_i] for l in true_labels
for l_i in l]
print("Validation loss: {}".format(eval_loss / nb_eval_steps))
print("Validation Accuracy: {}".format(eval_accuracy / nb_eval_steps))
print("Validation F1-Score: {}".format(f1_score(pred_tags, valid_tags)))
exit()
tr_inputs = torch.tensor(tr_inputs)
val_inputs = torch.tensor(val_inputs)
tr_tags = torch.tensor(tr_tags)
val_tags = torch.tensor(val_tags)
tr_masks = torch.tensor(tr_masks)
val_masks = torch.tensor(val_masks)
train_data = TensorDataset(tr_inputs, tr_masks, tr_tags)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=bs)
valid_data = TensorDataset(val_inputs, val_masks, val_tags)
valid_sampler = SequentialSampler(valid_data)
valid_dataloader = DataLoader(valid_data, sampler=valid_sampler, batch_size=bs)
model = BertForTokenClassification.from_pretrained("bert-base-uncased",
num_labels=len(tag2idx))
model.cuda()
FULL_FINETUNING = True
if FULL_FINETUNING:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay_rate':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay_rate':
def train(config):
print('-' * 50)
print('Loading pre-trained transfer model......')
model = BertForTokenClassification.from_pretrained(
config['model_name'], num_labels=config['tagset_size'] + 1)
if config['update_model']:
load_model_name = config['update_model']
print('load update model name is : ' + load_model_name)
checkpoint = torch.load(load_model_name)
model.load_state_dict(checkpoint['net'])
model.to(config['device'])
print('Load pre-trained transfer model done!')
print('-' * 50)
print('Deploying the training data......')
train_data_loader = prepare_data(config)
print('The training data is %d batches with %d batch sizes' %
(len(train_data_loader), config['batch_size']))
if os.path.isfile(config['valid_file']):
print('Dev process set ! Deploying the Dev data......')
config['mode'] = config['mode'].replace('train', 'valid')
valid_sents, valid_data_loader = prepare_data(config)
print('The validation data has loaded done!')
config['mode'] = config['mode'].replace('valid', 'train')
else:
print('the valid file ' + config['valid_file'] +
' is not exist pls check it in the config.txt')
print('-' * 50)
print('Train step! The model runs on ' + str(config['device']))
loss_list = dict()
# train set
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 = BertAdam(optimizer_grouped_parameters,
lr=config['lr'],
schedule=None)
loss_function = nn.NLLLoss(ignore_index=0)
best_f1 = 0.0
for epoch in range(config['epochs']):
model.train()
model.zero_grad()
total_loss = 0
batch_step = 0
for batch, data in enumerate(train_data_loader):
batch_step += 1
inputs, labels, seq_len = data
batch_size = inputs.size(0)
seql = inputs.size(1) - 2
segments_tensors = torch.zeros(inputs.size(0),
inputs.size(1),
dtype=torch.int64)
logits = model(input_ids=inputs.to(config['device']),
token_type_ids=segments_tensors.to(
config['device']),
attention_mask=None,
labels=None)
logits = logits[:, 1:-1, :]
logits = logits.reshape(batch_size * seql, -1)
logits = F.log_softmax(logits, 1)
labels = labels.to(config['device'])
loss = loss_function(logits, labels.view(batch_size * seql))
total_loss += float(loss)
loss.backward()
optimizer.step()
model.zero_grad()
print("\rEpoch: %d ! the process is in %d of %d ! " %
(epoch + 1, batch + 1, len(train_data_loader)),
end='')
loss_avg = total_loss / batch_step
loss_list[epoch] = loss_avg
print("The loss is %f ! " % (loss_avg))
# valid process
if os.path.isfile(config['valid_file']) and os.path.isfile(
config['gold_file']):
model.eval()
with torch.no_grad():
valid_results = predict(model, valid_data_loader, config)
valid_results = restore_result(valid_sents, valid_results)
tmp_filename = ''.join(
random.sample(string.ascii_letters + string.digits, 8))
write(valid_results, tmp_filename + '.txt')
# create an empty file
ftmp = open(tmp_filename + '_dict.txt', 'w', encoding='utf8')
ftmp.close()
res = score_shell(tmp_filename + '_dict.txt',
config['gold_file'], tmp_filename + '.txt',
tmp_filename + '_score.txt')
if res == 0:
get_score_cmd = 'grep \'F MEASURE\' ' + tmp_filename + '_score.txt'
f1 = os.popen(get_score_cmd).read().replace('\n', '')
print('The evaluation of epoch {} is {} !'.format(
str(epoch + 1), f1))
else:
print(
'The command of score failed, pls check or remove the validation step'
)
os.system('rm ' + tmp_filename + '_dict.txt')
os.system('rm ' + tmp_filename + '.txt')
os.system('rm ' + tmp_filename + '_score.txt')
# model save process
if config['model_path'] and (epoch + 1) % int(
config['save_model_epochs']) == 0:
state = {
'net': model.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch
}
if config['save_model_name']:
model_name = os.path.join(
config['model_path'],
config['save_model_name'] + '_' + str(epoch + 1) + '.pkl')
else:
model_name = os.path.join(config['model_path'],
str(epoch + 1) + '.pkl')
torch.save(state, model_name)
print('The epoch %d is saved successfully, named %s !' %
(epoch + 1, model_name))
print('train done!')
test_y_tensor = torch.tensor(test_output_ids).to(device)
test_mask_tensor = torch.tensor(test_attention_masks).to(device)
train_data = TensorDataset(train_x_tensor, train_mask_tensor,
train_y_tensor)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=bs)
test_data = TensorDataset(test_x_tensor, test_mask_tensor, test_y_tensor)
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data,
sampler=test_sampler,
batch_size=bs)
model = BertForTokenClassification.from_pretrained(
"bert-base-cased", num_labels=len(label2id)).to(device)
FULL_FINETUNING = False
if FULL_FINETUNING:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay_rate':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay_rate':
val_inputs = torch.tensor(val_inputs)
tr_tags = torch.tensor(tr_tags)
val_tags = torch.tensor(val_tags)
tr_masks = torch.tensor(tr_masks)
val_masks = torch.tensor(val_masks)
train_data = TensorDataset(tr_inputs, tr_masks, tr_tags)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=bs)
valid_data = TensorDataset(val_inputs, val_masks, val_tags)
valid_sampler = SequentialSampler(valid_data)
valid_dataloader = DataLoader(valid_data, sampler=valid_sampler, batch_size=bs)
model = BertForTokenClassification.from_pretrained(bert_model,
num_labels=len(tag2idx),
cache_dir=utiler.cache_dir)
model = model.cuda()
# model = torch.nn.DataParallel(model)
FULL_FINETUNING = True
if FULL_FINETUNING:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay_rate':
0.01
}, {
