Implement Chinese word segmentation

@@ -3,6 +3,7 @@ from transformers.models.bert import BertTokenizer, BertModel
 import torch
 import torch
 from tqdm import tqdm
 from tqdm import tqdm
 import numpy as np
 import numpy as np
+import jieba
 
 
 class BERT_CTM_Model:
 class BERT_CTM_Model:
     def __init__(self, bert_model_path):
     def __init__(self, bert_model_path):
@@ -19,9 +20,13 @@ class BERT_CTM_Model:
                 outputs = self.model(**inputs)
                 outputs = self.model(**inputs)
             embeddings.append(outputs.last_hidden_state.cpu().numpy())  # [batch_size, sequence_length, hidden_size]
             embeddings.append(outputs.last_hidden_state.cpu().numpy())  # [batch_size, sequence_length, hidden_size]
         return np.vstack(embeddings)
         return np.vstack(embeddings)
+    
+    def chinese_tokenize(self, text):
+        """使用jieba对中文文本进行分词"""
+        return " ".join(jieba.cut(text))
 
 
 if __name__ == "__main__":
 if __name__ == "__main__":
     model = BERT_CTM_Model('./bert_model')
     model = BERT_CTM_Model('./bert_model')
-    texts = ["这是第一个文本", "这是第二个文本"]
-    embeddings = model.get_bert_embeddings(texts)
-    print(embeddings.shape)
+    text = "这是一个测试文本"
+    tokenized_text = model.chinese_tokenize(text)
+    print(tokenized_text)