Update README.md

README.md

@@ -41,6 +41,9 @@ from transformers import (
     TrainingArguments,
     AutoConfig,
 )
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
 ```
 
 
@@ -77,7 +80,7 @@ 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 = PeftModel.from_pretrained(base_model_copy, "vahidthegreat/StanceAware-SBERT")
+lora_model = PeftModel.from_pretrained(lora_model, "vahidthegreat/StanceAware-SBERT")
 lora_model = lora_model.merge_and_unload()
 
 base_model.eval()
@@ -109,14 +112,39 @@ def two_sentence_similarity(model, tokenizer, text1, text2):
 text1 = "I love pineapple on pizza"
 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)
+
+print('\n\n')
+
+
+# Example sentences
+text1 = "I love pineapple on pizza"
+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)
 ```
+```output
+For Base Model sentences: 'I love pineapple on pizza' and 'I hate pineapple on pizza'
+Cosine Similarity: 0.8590984344482422
+
+For FineTuned Model sentences: 'I love pineapple on pizza' and 'I hate pineapple on pizza'
+Cosine Similarity: 0.5732507705688477
 
 
 
+For Base Model sentences: 'I love pineapple on pizza' and 'I like pineapple on pizza'
+Cosine Similarity: 0.9773550033569336
+
+For FineTuned Model sentences: 'I love pineapple on pizza' and 'I like pineapple on pizza'
+Cosine Similarity: 0.9712905883789062
+```
+
 ## Key Applications
 
 This stance-aware sentence transformer model can be applied to various fields within social computing and opinion analysis. Here are some key applications: