Update README.md

README.md

@@ -29,7 +29,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["汪汪队立大功第1季动画动画冒险剧情本领高强的狗狗巡逻队精通科技的10岁男孩", "超人总动员2喜剧动作动画冒险家庭亲情超级英雄励志超能先生变奶爸超人家族时隔14年强势回归"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('YangsHao/RecBERT')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -49,11 +49,11 @@ def cls_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ['汪汪队立大功第1季动画动画冒险剧情本领高强的狗狗巡逻队精通科技的10岁男孩', '超人总动员2喜剧动作动画冒险家庭亲情超级英雄励志超能先生变奶爸超人家族时隔14年强势回归']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('YangsHao/RecBERT')
+model = AutoModel.from_pretrained('YangsHao/RecBERT')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')