diff --git a/modelscope/preprocessors/nlp.py b/modelscope/preprocessors/nlp.py
index c4b73fb8..97ebc6c9 100644
--- a/modelscope/preprocessors/nlp.py
+++ b/modelscope/preprocessors/nlp.py
@@ -379,9 +379,6 @@ class TokenClassificationPreprocessor(NLPTokenizerPreprocessorBase):
         kwargs['padding'] = kwargs.get(
             'padding', False if mode == ModeKeys.INFERENCE else 'max_length')
         kwargs['max_length'] = kwargs.pop('sequence_length', 128)
-        kwargs['is_split_into_words'] = kwargs.pop(
-            'is_split_into_words',
-            False if mode == ModeKeys.INFERENCE else True)
         self.label_all_tokens = kwargs.pop('label_all_tokens', False)
         super().__init__(model_dir, pair=False, mode=mode, **kwargs)
 
@@ -397,22 +394,6 @@ class TokenClassificationPreprocessor(NLPTokenizerPreprocessorBase):
             Dict[str, Any]: the preprocessed data
         """
 
-        # preprocess the data for the model input
-        # if isinstance(data, dict):
-        #     data = data[self.first_sequence]
-        # text = data.replace(' ', '').strip()
-        # tokens = []
-        # for token in text:
-        #     token = self.tokenizer.tokenize(token)
-        #     tokens.extend(token)
-        # input_ids = self.tokenizer.convert_tokens_to_ids(tokens)
-        # input_ids = self.tokenizer.build_inputs_with_special_tokens(input_ids)
-        # attention_mask = [1] * len(input_ids)
-        # token_type_ids = [0] * len(input_ids)
-
-        # new code to deal with labels
-        # tokenized_inputs = self.tokenizer(data, truncation=True, is_split_into_words=True)
-
         text_a = None
         labels_list = None
         if isinstance(data, str):
@@ -420,10 +401,14 @@ class TokenClassificationPreprocessor(NLPTokenizerPreprocessorBase):
         elif isinstance(data, dict):
             text_a = data.get(self.first_sequence)
             labels_list = data.get(self.label)
-        text_a = text_a.replace(' ', '').strip()
+
+        if isinstance(text_a, str):
+            text_a = text_a.replace(' ', '').strip()
+
         tokenized_inputs = self.tokenizer(
-            text_a,
+            [t for t in text_a],
             return_tensors='pt' if self._mode == ModeKeys.INFERENCE else None,
+            is_split_into_words=True,
             **self.tokenize_kwargs)
 
         if labels_list is not None:
diff --git a/tests/pipelines/test_word_segmentation.py b/tests/pipelines/test_word_segmentation.py
index 98fab808..c332d987 100644
--- a/tests/pipelines/test_word_segmentation.py
+++ b/tests/pipelines/test_word_segmentation.py
@@ -15,6 +15,7 @@ from modelscope.utils.test_utils import test_level
 class WordSegmentationTest(unittest.TestCase):
     model_id = 'damo/nlp_structbert_word-segmentation_chinese-base'
     sentence = '今天天气不错，适合出去游玩'
+    sentence_eng = 'I am a program.'
 
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_by_direct_model_download(self):
@@ -42,6 +43,7 @@ class WordSegmentationTest(unittest.TestCase):
         pipeline_ins = pipeline(
             task=Tasks.word_segmentation, model=self.model_id)
         print(pipeline_ins(input=self.sentence))
+        print(pipeline_ins(input=self.sentence_eng))
 
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_with_default_model(self):
diff --git a/tests/trainers/test_finetune_token_classificatin.py b/tests/trainers/test_finetune_token_classificatin.py
index 0348bef5..520d1a3c 100644
--- a/tests/trainers/test_finetune_token_classificatin.py
+++ b/tests/trainers/test_finetune_token_classificatin.py
@@ -45,83 +45,6 @@ class TestFinetuneTokenClassification(unittest.TestCase):
         for i in range(10):
             self.assertIn(f'epoch_{i+1}.pth', results_files)
 
-    @unittest.skip
-    def test_token_classification(self):
-        # WS task
-        os.system(
-            f'curl http://dingkun.oss-cn-hangzhou-zmf.aliyuncs.com/atemp/train.txt > {self.tmp_dir}/train.txt'
-        )
-        os.system(
-            f'curl http://dingkun.oss-cn-hangzhou-zmf.aliyuncs.com/atemp/dev.txt > {self.tmp_dir}/dev.txt'
-        )
-        from datasets import load_dataset
-        dataset = load_dataset(
-            'text',
-            data_files={
-                'train': f'{self.tmp_dir}/train.txt',
-                'test': f'{self.tmp_dir}/dev.txt'
-            })
-
-        def split_to_dict(examples):
-            text, label = examples['text'].split('\t')
-            return {
-                'first_sequence': text.split(' '),
-                'labels': label.split(' ')
-            }
-
-        dataset = dataset.map(split_to_dict, batched=False)
-
-        def reducer(x, y):
-            x = x.split(' ') if isinstance(x, str) else x
-            y = y.split(' ') if isinstance(y, str) else y
-            return x + y
-
-        label_enumerate_values = list(
-            set(reduce(reducer, dataset['train'][:1000]['labels'])))
-        label_enumerate_values.sort()
-
-        def cfg_modify_fn(cfg):
-            cfg.task = 'token-classification'
-            cfg['preprocessor'] = {'type': 'token-cls-tokenizer'}
-            cfg['dataset'] = {
-                'train': {
-                    'labels': label_enumerate_values,
-                    'first_sequence': 'first_sequence',
-                    'label': 'labels',
-                }
-            }
-            cfg.train.max_epochs = 3
-            cfg.train.lr_scheduler = {
-                'type': 'LinearLR',
-                'start_factor': 1.0,
-                'end_factor': 0.0,
-                'total_iters':
-                int(len(dataset['train']) / 32) * cfg.train.max_epochs,
-                'options': {
-                    'by_epoch': False
-                }
-            }
-            cfg.train.hooks = [{
-                'type': 'CheckpointHook',
-                'interval': 1
-            }, {
-                'type': 'TextLoggerHook',
-                'interval': 1
-            }, {
-                'type': 'IterTimerHook'
-            }, {
-                'type': 'EvaluationHook',
-                'by_epoch': False,
-                'interval': 300
-            }]
-            return cfg
-
-        self.finetune(
-            'damo/nlp_structbert_backbone_tiny_std',
-            dataset['train'],
-            dataset['test'],
-            cfg_modify_fn=cfg_modify_fn)
-
     @unittest.skip
     def test_word_segmentation(self):
         os.system(