diff --git a/finetune/finetune.py b/finetune/finetune.py
index b975f24..ae2eed7 100644
--- a/finetune/finetune.py
+++ b/finetune/finetune.py
@@ -260,13 +260,13 @@ def process_batch(
         images = []
         
         conv[0]['image'] = Image.open(conv[len(conv)-2]['image']).convert("RGB")
-        conv[len(conv)-2]['image'] = None
+        # conv[len(conv)-2]['image'] = None
 
         for message in conv:
             loss_mask_val = (
                 False if message["role"] in ("system", "user", "observation") else True
             )
-            
+            print(message)
             new_input_ids_all = tokenizer.apply_chat_template(
                 [message], tokenize=True, return_dict=True, padding=True
             )
@@ -342,16 +342,19 @@ def process_batch_eval(
     batched_images = []
 
     for conv in batched_conv:
-       
+        print(conv)
+        print("----------------divide--------------------")
         idx = len(conv) - 2
         conv[0]['image'] = Image.open(conv[idx]["image"]).convert("RGB")
-        conv[idx]['image'] = None
+        # conv[idx]['image'] = None
 
         new_input_ids_all = tokenizer.apply_chat_template(
             conv, tokenize=True, return_dict=True, padding=True
         )
         
         input_ids = new_input_ids_all["input_ids"][0]
+        print("input_ids")
+        print(input_ids)
         attention_mask = new_input_ids_all["attention_mask"][0]
         position_ids = list(range(len(input_ids)))
 
@@ -371,7 +374,6 @@ def process_batch_eval(
             output_segment = input_ids[
                 dialogue_parts[end_idx - 1] : dialogue_parts[end_idx]
             ]
-
             output_segment.append(151336)  # Add EOS token
 
             batched_input_ids.append(input_segment[:max_input_length])
@@ -379,6 +381,7 @@ def process_batch_eval(
             batched_position_ids.append(position_segment[:max_input_length])
             batched_output_ids.append(output_segment[:max_output_length])
             batched_images.append(new_input_ids_all["images"][0])
+        
 
     del (
         batched_conv,
@@ -389,7 +392,6 @@ def process_batch_eval(
         output_segment,
     )
     torch.cuda.empty_cache()
-
     return {
         "input_ids": batched_input_ids,
         "attention_mask": batched_attention_mask,
@@ -430,12 +432,24 @@ def compute_metrics(eval_preds: EvalPrediction, tokenizer):
     batched_pred_ids[batched_pred_ids == -100] = tokenizer.pad_token_id
     batched_label_ids[batched_label_ids == -100] = tokenizer.pad_token_id
     metrics_dct = {"rouge-1": [], "rouge-2": [], "rouge-l": [], "bleu-4": []}
-    for pred_ids, label_ids in zip(batched_pred_ids, batched_label_ids):
+    # 选择 batched_pred_ids 中索引为偶数的元素和 batched_label_ids 中索引为奇数的元素
+    even_pred_ids = [batched_pred_ids[i] for i in range(len(batched_pred_ids)) if i % 2 == 0]
+    odd_label_ids = [batched_label_ids[i] for i in range(len(batched_label_ids)) if i % 2 == 1]
+    for pred_ids, label_ids in zip(even_pred_ids, odd_label_ids):
+        if (hasattr(pred_ids, 'size') and pred_ids.size == 0) or (not hasattr(pred_ids, 'size') and len(pred_ids) == 0):
+            print("Error: Empty prediction!")
+            continue
         pred_txt = tokenizer.decode(pred_ids, skip_special_tokens=True).strip()
         label_txt = tokenizer.decode(label_ids, skip_special_tokens=True).strip()
         pred_tokens = list(jieba.cut(pred_txt))
         label_tokens = list(jieba.cut(label_txt))
         rouge = Rouge()
+        if not pred_tokens or not label_tokens:
+            print(f"Warning: Empty tokens. Prediction: {pred_tokens}, Label: {label_tokens}")
+            # 如果为空，可能是分词失败或数据问题，采取合适的措施（例如跳过，填充默认值等）
+            pred_tokens = ["[EMPTY]"]  # 或者填充一个默认值
+            label_tokens = ["[EMPTY]"]
+            # continue  # 跳过当前样本或处理为空的情况
         scores = rouge.get_scores(" ".join(pred_tokens), " ".join(label_tokens))
         for k, v in scores[0].items():
             metrics_dct[k].append(round(v["f"] * 100, 4))