Update app.py

app.py

@@ -50,9 +50,12 @@ def get_sim(x):
 #tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
 
 
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")
 
-tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-model = GPT2LMHeadModel.from_pretrained('gpt2')
+
+#tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+#model = GPT2LMHeadModel.from_pretrained('gpt2')
 
 
 
@@ -60,22 +63,19 @@ def sentence_prob_mean(text):
     # Tokenize the input text and add special tokens
     input_ids = tokenizer.encode(text, return_tensors='pt')
 
-    # Obtain model outputs
     with torch.no_grad():
         outputs = model(input_ids, labels=input_ids)
         logits = outputs.logits  # logits are the model outputs before applying softmax
 
-    # Shift logits and labels so that tokens are aligned:
+
     shift_logits = logits[..., :-1, :].contiguous()
     shift_labels = input_ids[..., 1:].contiguous()
 
-    # Calculate the softmax probabilities
+
     probs = softmax(shift_logits, dim=-1)
 
-    # Gather the probabilities of the actual token IDs
     gathered_probs = torch.gather(probs, 2, shift_labels.unsqueeze(-1)).squeeze(-1)
 
-    # Compute the mean probability across the tokens
     mean_prob = torch.mean(gathered_probs).item()
 
     return mean_prob