Update train_single_dcu.py

train_single_dcu.py

@@ -149,10 +149,10 @@ if __name__=='__main__':
     beam_size = 4
     no_repeat_ngram_size = 2
 
-    train_data = LCSTS('/home/wanglch/projects/Umt5/data/lcsts_tsv/data1.tsv')
-    valid_data = LCSTS('/home/wanglch/projects/Umt5/data/lcsts_tsv/data2.tsv')
+    train_data = LCSTS('../Umt5/data/lcsts_tsv/data1.tsv')
+    valid_data = LCSTS('../Umt5/data/lcsts_tsv/data2.tsv')
 
-    model_checkpoint = "/home/wanglch/projects/Umt5/umt5_base"
+    model_checkpoint = "../Umt5/umt5_base"
     tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, trust_remote_code=True)
     model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, trust_remote_code=True)
     model = model.to(device)
@@ -180,7 +180,7 @@ if __name__=='__main__':
         if rouge_avg > best_avg_rouge:
             best_avg_rouge = rouge_avg
             print('saving new weights...\n')
-            weight_path = f'/home/wanglch/projects/saves/utm5/train_dtk_weights/epoch_{t+1}_valid_rouge_{rouge_avg:0.4f}_model_dtk_weights.bin'
+            weight_path = f'../saves/utm5/train_dtk_weights/epoch_{t+1}_valid_rouge_{rouge_avg:0.4f}_model_dtk_weights.bin'
             torch.save(model.state_dict(), weight_path)
             # 加载训练后的权重
             state_dict = torch.load(weight_path)
@@ -188,7 +188,7 @@ if __name__=='__main__':
             # 获取当前的日期和时间
             now = datetime.now()
             timestamp = now.strftime("%Y%m%d_%H%M%S")
-            new_model_path = f'/home/wanglch/projects/saves/utm5/train_dtk_weights/umt5_{timestamp}'
+            new_model_path = f'../saves/utm5/train_dtk_weights/umt5_{timestamp}'
             model.save_pretrained(new_model_path)
             tokenizer.save_pretrained(new_model_path)
     print("Done!")