diff --git a/rpunct/punctuate.py b/rpunct/punctuate.py
index c0143c08..e5f61eb8 100644
--- a/rpunct/punctuate.py
+++ b/rpunct/punctuate.py
@@ -10,14 +10,14 @@
 
 
 class RestorePuncts:
-    def __init__(self, wrds_per_pred=250):
+    def __init__(self, model='felflare/bert-restore-punctuation', wrds_per_pred=250, use_cuda=False, silent=False):
         self.wrds_per_pred = wrds_per_pred
         self.overlap_wrds = 30
         self.valid_labels = ['OU', 'OO', '.O', '!O', ',O', '.U', '!U', ',U', ':O', ';O', ':U', "'O", '-O', '?O', '?U']
-        self.model = NERModel("bert", "felflare/bert-restore-punctuation", labels=self.valid_labels,
-                              args={"silent": True, "max_seq_length": 512})
+        self.model = NERModel("bert", f"{model}", labels=self.valid_labels, use_cuda=use_cuda,
+                              args={"silent": silent, "max_seq_length": 512})
 
-    def punctuate(self, text: str, lang:str=''):
+    def punctuate(self, text: str, lang: str = ''):
         """
         Performs punctuation restoration on arbitrarily large text.
         Detects if input is not English, if non-English was detected terminates predictions.
@@ -38,8 +38,10 @@ def punctuate(self, text: str, lang:str=''):
         splits = self.split_on_toks(text, self.wrds_per_pred, self.overlap_wrds)
         # predict slices
         # full_preds_lst contains tuple of labels and logits
+        print(f'predicting {len(splits)} slices')
         full_preds_lst = [self.predict(i['text']) for i in splits]
         # extract predictions, and discard logits
+        print(f'combining predictions')
         preds_lst = [i[0][0] for i in full_preds_lst]
         # join text slices
         combined_preds = self.combine_results(text, preds_lst)