Update README.md

README.md

@@ -45,7 +45,7 @@ pip install qa-metrics
 
 #### Method: `compute_score`
 **Parameters**
-- `reference_answer` (list of str): A list of gold (correct) answers to the question
+- `reference_answer` (str): gold (correct) answer to the question
 - `candidate_answer` (str): The answer provided by a candidate that needs to be evaluated
 
 **Returns**
@@ -61,6 +61,25 @@ rb.compute_score(reference_answer, candidate_answer)
 # (0.29113227128982544, 2.1645290851593018)
 ```
 
+
+#### Method: `compute_batch_scores`
+**Parameters**
+- `reference_answers` (list of str): A list of gold (correct) answers to the question
+- `candidate_answer` (list of str): A list of answers provided by a candidate that needs to be evaluated
+- `batch_size` (int): batch size to predict (default 1)
+
+**Returns**
+- `tuple`: A tuple of a list of normalized and raw scores.
+
+```python
+from qa_metrics.RewardBert import RewardBert
+
+rb = RewardBert(device='cuda')
+reference_answer = ["The Frog Prince"]
+candidate_answer = ["The movie \"The Princess and the Frog\" is loosely based off the Brother Grimm's \"Iron Henry\""]
+rb.compute_batch_scores(reference_answer, candidate_answer, batch_size=1)
+# ([0.29113227128982544], [2.1645290851593018])
+
 ## Acknowledgements
 
 We sincerely appreciate the contributions of the open-source community. The related projects are as follows: [R1-V](https://github.com/Deep-Agent/R1-V) , [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) , [Video-R1](https://github.com/tulerfeng/Video-R1), [Qwen-2.5-VL](https://arxiv.org/abs/2502.13923)