diff --git a/llmfoundry/eval/metrics/nlp.py b/llmfoundry/eval/metrics/nlp.py
index 71077a78a9..03efbcc404 100644
--- a/llmfoundry/eval/metrics/nlp.py
+++ b/llmfoundry/eval/metrics/nlp.py
@@ -37,21 +37,12 @@ def update(self, batch: dict, output_logits: torch.Tensor, labels: torch.Tensor)
             cont_tok_logits = output_logits[batch_idx].index_select(dim=0, index=cont_idx - 1)
             # labels have been shifted left by one index, so the cont_idx needs to be shifted as well.
             cont_tok_targ = labels[batch_idx].index_select(dim=0, index=cont_idx - 1)
-<<<<<<< HEAD
             mean_logit_of_targ_tok = cont_tok_logits.index_select(dim=1, index=cont_tok_targ).diagonal().mean()
             probabilites.append(torch.exp(-mean_logit_of_targ_tok)) # undo negative log prob to get unnormalized probability
 
         for (start, end), gold_idx in zip(batch['choice_groupings'], batch['gold_indices']):
             subset = probabilites[start:end]
             subset = torch.tensor(subset) / torch.tensor(subset).sum() # normalize probability
-=======
-            probability = cont_tok_logits.index_select(dim=1, index=cont_tok_targ).diagonal().mean()
-            probabilites.append(torch.exp(-probability))
-
-        for (start, end), gold_idx in zip(batch['choice_groupings'], batch['gold_indices']):
-            subset = probabilites[start:end]
-            subset = torch.tensor(subset) / torch.tensor(subset).sum()
->>>>>>> 1e65cdcaf6b33e791632c31186dbe72ee24df9dc
             tgt_prob = torch.zeros_like(subset)
             tgt_prob[gold_idx] = 1.0
             self.brier_score_sum += torch.nn.functional.mse_loss(subset, tgt_prob)