Update README.md

README.md

@@ -108,8 +108,8 @@ class SiameseNetworkMPNet(nn.Module):
         self.normalize = normalize
         self.tokenizer = tokenizer
 
-    def apply_lora_weights(self, lora_model):
-        self.model = PeftModel.from_pretrained(self.model, lora_model)
+    def apply_lora_weights(self, finetuned_model):
+        self.model = PeftModel.from_pretrained(self.model, finetuned_model)
         self.model = self.model.merge_and_unload()
         return self
 
@@ -132,8 +132,8 @@ tokenizer = AutoTokenizer.from_pretrained(base_model_name)
 base_model = SiameseNetworkMPNet(model_name=base_model_name, tokenizer=tokenizer)
 
 # Load and apply LoRA weights
-lora_model = SiameseNetworkMPNet(model_name=base_model_name, tokenizer=tokenizer)
-lora_model.apply_lora_weights("vahidthegreat/StanceAware-SBERT")
+finetuned_model = SiameseNetworkMPNet(model_name=base_model_name, tokenizer=tokenizer)
+finetuned_model.apply_lora_weights("vahidthegreat/StanceAware-SBERT")
 ```
 
 #### Example Usage for Two-Sentence Similarity
@@ -163,7 +163,7 @@ text2 = "I hate pineapple on pizza"
 print(f"For Base Model sentences: '{text1}' and '{text2}'")
 two_sentence_similarity(base_model, tokenizer, text1, text2)
 print(f"\nFor FineTuned Model sentences: '{text1}' and '{text2}'")
-two_sentence_similarity(lora_model, tokenizer, text1, text2)
+two_sentence_similarity(finetuned_model, tokenizer, text1, text2)
 
 print('\n\n')
 
@@ -175,7 +175,7 @@ text2 = "I like pineapple on pizza"
 print(f"For Base Model sentences: '{text1}' and '{text2}'")
 two_sentence_similarity(base_model, tokenizer, text1, text2)
 print(f"\n\nFor FineTuned Model sentences: '{text1}' and '{text2}'")
-two_sentence_similarity(lora_model, tokenizer, text1, text2)
+two_sentence_similarity(finetuned_model, tokenizer, text1, text2)
 ```
 ```output
 For Base Model sentences: 'I love pineapple on pizza' and 'I hate pineapple on pizza'