diff --git a/README.md b/README.md
index a0f973e0..8a72ee2a 100644
--- a/README.md
+++ b/README.md
@@ -51,7 +51,7 @@ from fastNLP.transformers.torch import BertTokenizer
 #  该文件还存在，将自动读取缓存文件，而不再次运行预处理代码。
 @cache_results('caches/cache.pkl')
 def prepare_data():
-    # 会自动下载 SST2 数据，并且可以通过文档看到返回的 dataset 应该是包含"raw_words"和"target"两个field的
+    # 会自动下载数据，并且可以通过文档看到返回的 dataset 应该是包含"raw_words"和"target"两个field的
     data_bundle = ChnSentiCorpLoader().load()
     # 使用tokenizer对数据进行tokenize
     tokenizer = BertTokenizer.from_pretrained('hfl/chinese-bert-wwm')
@@ -130,7 +130,7 @@ evaluator.run()
 from fastNLP.io import ChnSentiCorpLoader
 from functools import partial
 
-# 会自动下载 SST2 数据，并且可以通过文档看到返回的 dataset 应该是包含"raw_words"和"target"两个field的
+# 会自动下载数据，并且可以通过文档看到返回的 dataset 应该是包含"raw_words"和"target"两个field的
 data_bundle = ChnSentiCorpLoader().load()
 
 # 使用tokenizer对数据进行tokenize
diff --git a/fastNLP/core/callbacks/topk_saver.py b/fastNLP/core/callbacks/topk_saver.py
index 1a4de8d5..21a8961f 100644
--- a/fastNLP/core/callbacks/topk_saver.py
+++ b/fastNLP/core/callbacks/topk_saver.py
@@ -50,6 +50,8 @@ class Saver:
         self.save_fn_name = 'save_checkpoint' if save_object == 'trainer' else 'save_model'
 
         self.timestamp_path = self.folder.joinpath(os.environ[FASTNLP_LAUNCH_TIME])
+        # 打印这次运行时 checkpoint 所保存在的文件夹，因为这个文件夹是根据时间实时生成的，因此需要打印出来防止用户混淆；
+        logger.info(f"The checkpoint will be saved in this folder for this time: {self.timestamp_path}.")
 
     def save(self, trainer, folder_name):
         """
diff --git a/fastNLP/core/drivers/torch_driver/torch_driver.py b/fastNLP/core/drivers/torch_driver/torch_driver.py
index 3307e3c9..db011403 100644
--- a/fastNLP/core/drivers/torch_driver/torch_driver.py
+++ b/fastNLP/core/drivers/torch_driver/torch_driver.py
@@ -199,7 +199,8 @@ class TorchDriver(Driver):
                                      f"`only_state_dict=False`")
         if not isinstance(res, dict):
             res = res.state_dict()
-        model.load_state_dict(res)
+        _strict = kwargs.get("strict", True)
+        model.load_state_dict(res, _strict)
 
     @rank_zero_call
     def save_checkpoint(self, folder: Path, states: Dict, dataloader, only_state_dict: bool = True, should_save_model: bool = True, **kwargs):