diff --git a/run_squad.py b/run_squad.py
index 8a69e057e5..50d450d85a 100644
--- a/run_squad.py
+++ b/run_squad.py
@@ -908,7 +908,7 @@ def main():
         model.eval()
         all_results = []
         logger.info("Start evaluating")
-        for input_ids, input_mask, segment_ids, example_index in tqdm(eval_dataloader, desc="Evaluating"):
+        for input_ids, input_mask, segment_ids, example_indices in tqdm(eval_dataloader, desc="Evaluating"):
             if len(all_results) % 1000 == 0:
                 logger.info("Processing example: %d" % (len(all_results)))
 
@@ -916,21 +916,18 @@ def main():
             input_mask = input_mask.to(device)
             segment_ids = segment_ids.to(device)
 
-            start_logits, end_logits = model(input_ids, segment_ids, input_mask)
+            with torch.no_grad():
+                batch_start_logits, batch_end_logits = model(input_ids, segment_ids, input_mask)
 
-            unique_id = [int(eval_features[e.item()].unique_id) for e in example_index]
-            start_logits = [x.view(-1).detach().cpu().numpy() for x in start_logits]
-            end_logits = [x.view(-1).detach().cpu().numpy() for x in end_logits]
-            for idx, i in enumerate(unique_id):
-                s = [float(x) for x in start_logits[idx]]
-                e = [float(x) for x in end_logits[idx]]
-                all_results.append(
-                    RawResult(
-                        unique_id=i,
-                        start_logits=s,
-                        end_logits=e
-                    )
-                )
+            for i, example_index in enumerate(example_indices):
+                start_logits = batch_start_logits[i].detach().cpu().tolist()
+                end_logits = batch_end_logits[i].detach().cpu().tolist()
+
+                eval_feature = eval_features[example_index.item()]
+                unique_id = int(eval_feature.unique_id)
+                all_results.append(RawResult(unique_id=unique_id,
+                                             start_logits=start_logits,
+                                             end_logits=end_logits))
 
         output_prediction_file = os.path.join(args.output_dir, "predictions.json")
         output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")