diff --git a/bert_score/scorer.py b/bert_score/scorer.py
index 48e7054..f4f08c9 100644
--- a/bert_score/scorer.py
+++ b/bert_score/scorer.py
@@ -1,6 +1,5 @@
 import os
-import pathlib
-import sys
+
 import time
 import warnings
 from collections import defaultdict
@@ -44,7 +43,6 @@ def __init__(
                       `model_type` or `lang`
             - :param: `num_layers` (int): the layer of representation to use.
                       default using the number of layer tuned on WMT16 correlation data
-            - :param: `verbose` (bool): turn on intermediate status update
             - :param: `idf` (bool): a booling to specify whether to use idf or not (this should be True even if `idf_sents` is given)
             - :param: `idf_sents` (List of str): list of sentences used to compute the idf weights
             - :param: `device` (str): on which the contextual embedding model will be allocated on.
@@ -54,7 +52,6 @@ def __init__(
             - :param: `lang` (str): language of the sentences; has to specify
                       at least one of `model_type` or `lang`. `lang` needs to be
                       specified when `rescale_with_baseline` is True.
-            - :param: `return_hash` (bool): return hash code of the setting
             - :param: `rescale_with_baseline` (bool): rescale bertscore with pre-computed baseline
             - :param: `baseline_path` (str): customized baseline file
             - :param: `use_fast_tokenizer` (bool): `use_fast` parameter passed to HF tokenizer
@@ -179,7 +176,7 @@ def compute_idf(self, sents):
 
         self._idf_dict = get_idf_dict(sents, self._tokenizer, nthreads=self.nthreads)
 
-    def score(self, cands, refs, verbose=False, batch_size=64, return_hash=False):
+    def score(self, cands, refs, verbose=False, batch_size=None, return_hash=False):
         """
         Args:
             - :param: `cands` (list of str): candidate sentences
@@ -217,6 +214,9 @@ def score(self, cands, refs, verbose=False, batch_size=64, return_hash=False):
             idf_dict[self._tokenizer.sep_token_id] = 0
             idf_dict[self._tokenizer.cls_token_id] = 0
 
+        if batch_size is None:
+            batch_size = self.batch_size
+
         all_preds = bert_cos_score_idf(
             self._model,
             refs,