diff --git a/metrics/bleurt/README.md b/metrics/bleurt/README.md
index b14094e5..b8846d5b 100644
--- a/metrics/bleurt/README.md
+++ b/metrics/bleurt/README.md
@@ -38,7 +38,7 @@ This metric takes as input lists of predicted sentences and reference sentences:
 >>> predictions = ["hello there", "general kenobi"]
 >>> references = ["hello there", "general kenobi"]
 >>> bleurt = load("bleurt", module_type="metric")
->>> results = bleurt.compute(predictions=predictions, references=references)
+>>> results = bleurt.compute(predictions=predictions, references=references, batch_size=32)
 ```
 
 ### Inputs
@@ -76,7 +76,7 @@ Example with the default model (`"bleurt-base-128"`):
 >>> predictions = ["hello there", "general kenobi"]
 >>> references = ["hello there", "general kenobi"]
 >>> bleurt = load("bleurt", module_type="metric")
->>> results = bleurt.compute(predictions=predictions, references=references)
+>>> results = bleurt.compute(predictions=predictions, references=references, batch_size=32)
 >>> print(results)
 {'scores': [1.0295498371124268, 1.0445425510406494]}
 ```
@@ -86,7 +86,7 @@ Example with the full `"BLEURT-20"` model checkpoint:
 >>> predictions = ["hello there", "general kenobi"]
 >>> references = ["hello there", "general kenobi"]
 >>> bleurt = load("bleurt", module_type="metric", config_name="BLEURT-20")
->>> results = bleurt.compute(predictions=predictions, references=references)
+>>> results = bleurt.compute(predictions=predictions, references=references, batch_size=32)
 >>> print(results)
 {'scores': [1.015415906906128, 0.9985226988792419]}
 ```
diff --git a/metrics/bleurt/bleurt.py b/metrics/bleurt/bleurt.py
index 11ad20a7..c98cc6c8 100644
--- a/metrics/bleurt/bleurt.py
+++ b/metrics/bleurt/bleurt.py
@@ -120,6 +120,6 @@ def _download_and_prepare(self, dl_manager):
         model_path = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
         self.scorer = score.BleurtScorer(os.path.join(model_path, checkpoint_name))
 
-    def _compute(self, predictions, references):
-        scores = self.scorer.score(references=references, candidates=predictions)
+    def _compute(self, predictions, references, batch_size=None):
+        scores = self.scorer.score(references=references,candidates=predictions, batch_size=batch_size)
         return {"scores": scores}