diff --git a/ark_nlp/dataset/global_pointer_named_entity_recognition_dataset.py b/ark_nlp/dataset/global_pointer_named_entity_recognition_dataset.py
index f18d8e1..d713615 100644
--- a/ark_nlp/dataset/global_pointer_named_entity_recognition_dataset.py
+++ b/ark_nlp/dataset/global_pointer_named_entity_recognition_dataset.py
@@ -66,7 +66,7 @@ def _convert_to_transfomer_ids(self, bert_tokenizer):
                         continue
                     global_label[self.cat2id[info_['type']], start_idx+1, end_idx+1] = 1
 
-            global_label = torch.tensor(global_label).to_sparse()
+            global_label = global_label.to_sparse()
 
             features.append({
                 'input_ids': input_ids,
diff --git a/ark_nlp/model/ner/crf_bert/__init__.py b/ark_nlp/model/ner/crf_bert/__init__.py
index 35ef16c..62226e4 100644
--- a/ark_nlp/model/ner/crf_bert/__init__.py
+++ b/ark_nlp/model/ner/crf_bert/__init__.py
@@ -13,8 +13,8 @@
 from ark_nlp.factory.optimizer import get_default_crf_bert_optimizer as get_default_model_optimizer
 from ark_nlp.factory.optimizer import get_default_crf_bert_optimizer as get_default_crf_bert_optimizer
 
-from ark_nlp.factory.task import BIONERTask as Task
-from ark_nlp.factory.task import BIONERTask as CrfBertNERTask
+from ark_nlp.factory.task import CRFNERTask as Task
+from ark_nlp.factory.task import CRFNERTask as CrfBertNERTask
 
-from ark_nlp.factory.predictor import BIONERPredictor as Predictor
-from ark_nlp.factory.predictor import BIONERPredictor as CrfBertNERPredictor
\ No newline at end of file
+from ark_nlp.factory.predictor import CRFNERPredictor as Predictor
+from ark_nlp.factory.predictor import CRFNERPredictor as CrfBertNERPredictor
\ No newline at end of file
diff --git a/ark_nlp/processor/tokenizer/transfomer.py b/ark_nlp/processor/tokenizer/transfomer.py
index 75b3e8f..37aeabe 100644
--- a/ark_nlp/processor/tokenizer/transfomer.py
+++ b/ark_nlp/processor/tokenizer/transfomer.py
@@ -229,8 +229,13 @@ class TokenTokenizer(TransfomerTokenizer):
 
     def tokenize(self, text, **kwargs):
         tokens = []
-        text = ' '.join([token_ for token_ in text])
-        tokens = self.vocab.tokenize(text)
+        for token_ in text:
+            tokenized_token_ = self.vocab.tokenize(token_)
+            if tokenized_token_ == []:
+                tokens.extend([token_])
+            else:
+                tokens.extend(tokenized_token_)
+            
         return tokens
 
     def sequence_to_ids(self, sequence, **kwargs):