Update README.md

README.md

@@ -61,31 +61,31 @@ base_model: meta-llama/Llama-2-7b-chat-hf
 
 ##### for brand name
 
-   def generate2(lista,keyword):
-    
-    prompt = f"""[INST] Extract the brand of the keyword from the given list if present.[/INST]
-
-        [KW] {keyword} [/KW]
-
-        [LIST] {lista} [/LIST]
-
-        
-        response ###"""
-    print("Prompt:")
-    print(prompt)
-    encoding = tokenizer(prompt, return_tensors="pt").to("cuda:0")
-    output = model.generate(input_ids=encoding.input_ids,
-                            attention_mask=encoding.attention_mask,
-                            max_new_tokens=200,
-                            do_sample=True,
-                            temperature=0.9,
-                            eos_token_id=tokenizer.eos_token_id,
-                            top_p=0.9,
-                           repetition_penalty=1.2)
-
-    print()
-    # Subtract the length of input_ids from output to get only the model's response
-    output_text = tokenizer.decode(output[0, len(encoding.input_ids[0]):], skip_special_tokens=False)
-    output_text = re.sub('\n+', '\n', output_text)  # remove excessive newline characters
-    print("Generated Assistant Response:")
-    return output_text   
+    def generate2(lista,keyword):
+      
+      prompt = f"""[INST] Extract the brand of the keyword from the given list if present.[/INST]
+  
+          [KW] {keyword} [/KW]
+  
+          [LIST] {lista} [/LIST]
+  
+          
+          response ###"""
+      print("Prompt:")
+      print(prompt)
+      encoding = tokenizer(prompt, return_tensors="pt").to("cuda:0")
+      output = model.generate(input_ids=encoding.input_ids,
+                              attention_mask=encoding.attention_mask,
+                              max_new_tokens=200,
+                              do_sample=True,
+                              temperature=0.9,
+                              eos_token_id=tokenizer.eos_token_id,
+                              top_p=0.9,
+                             repetition_penalty=1.2)
+  
+      print()
+      # Subtract the length of input_ids from output to get only the model's response
+      output_text = tokenizer.decode(output[0, len(encoding.input_ids[0]):], skip_special_tokens=False)
+      output_text = re.sub('\n+', '\n', output_text)  # remove excessive newline characters
+      print("Generated Assistant Response:")
+      return output_text