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modeling_internlm2.py

@@ -1494,6 +1494,7 @@ class InternLM2ForCausalLM_score(InternLM2ForCausalLM):
         logits = logits.float()
 
         scores = self.lm_regression_head(hidden_states)
+        scores = F.sigmoid(scores)
 
         if input_ids is not None:
             batch_size = input_ids.shape[0]

modeling_internvl_chat.py

@@ -28,8 +28,6 @@ weight_tensor = torch.Tensor([5.,4.,3.,2.,1.])
 
 def get_special_token(tokenizer):
     preferential_ids_ = [id_[-1] for id_ in tokenizer(special_words)["input_ids"]]
-    print(preferential_ids_)
-    print(tokenizer.batch_decode(preferential_ids_))
     return preferential_ids_