diff --git a/ulmfit/__main__.py b/ulmfit/__main__.py
index 25b7bf8..951cf9a 100644
--- a/ulmfit/__main__.py
+++ b/ulmfit/__main__.py
@@ -141,7 +141,7 @@ class ULMFiT:
 
     def eval(self, glob="data/mldoc/*-1/models/sp30k/lstm_nl4.m", dataset_template='${ds_name}', name=None,
              num_lm_epochs=0, train=True, to_csv=None, return_df=False, label_smoothing_eps=0.0,
-             lmseed=None, ftseed=None, clsweightseed=None, clstrainseed=None,
+             lmseed=None, ftseed=None, clsweightseed=None, clstrainseed=None, save_name="cls_best",
              skip_on_error=True, **trn_params):
         results = []
         model_args = {}
@@ -169,8 +169,8 @@ class ULMFiT:
                     last_model_dir = params.model_dir.relative_to(data_dir.parent)
                     if (params.model_dir/"cls_best.pth").exists():
                         print("Evaluating previously trained model")
-                        d_tst = params.validate_cls(label_smoothing_eps=label_smoothing_eps, use_cache=True, mode="test")
-                        d_val = params.validate_cls(label_smoothing_eps=label_smoothing_eps, use_cache=True, mode="valid")
+                        d_tst = params.validate_cls(save_name=save_name, label_smoothing_eps=label_smoothing_eps, use_cache=True, mode="test")
+                        d_val = params.validate_cls(save_name=save_name, label_smoothing_eps=label_smoothing_eps, use_cache=True, mode="valid")
                         d={}
                         d.update(d_val)
                         d.update(d_tst)
diff --git a/ulmfit/train_clas.py b/ulmfit/train_clas.py
index 7dfed85..9ee3dd5 100644
--- a/ulmfit/train_clas.py
+++ b/ulmfit/train_clas.py
@@ -155,7 +155,7 @@ class CLSHyperParams(LMHyperParams):
 
     def validate_cls(self, save_name='cls_best', bs=40, data_tst=None, learn=None,
                      dump_preds=None, mode="test", label_smoothing_eps=None, use_cache=False):
-        cache_file = (self.model_dir / f'results_{mode}.json')
+        cache_file = (self.model_dir / f'results_{mode+("" if save_name == "cls_best" else save_name)}.json')
         if use_cache and cache_file.exists():
             with cache_file.open("r") as fp:
                 return json.load(fp)