--- a
+++ b/bert_mixup/late_mixup/utils.py
@@ -0,0 +1,24 @@
+import numpy as np
+from sklearn.metrics import roc_auc_score
+from torch import sigmoid
+from torch.nn.functional import softmax
+import torch
+
+
+def flat_accuracy(preds, labels):
+    """
+    Function to calculate the accuracy of our predictions vs labels
+    """
+    pred_flat = np.argmax(preds, axis=1).flatten()
+    labels_flat = labels.flatten()
+    return np.sum(pred_flat == labels_flat) / len(labels_flat)
+
+
+def flat_auroc_score(preds, labels):
+    """
+    Function to calculate the roc_auc_score of our predictions vs labels
+    """
+
+    pred_flat = softmax(torch.tensor(preds), dim=1)[:, 1]
+    # labels_flat = np.argmax(labels, axis=1)
+    return roc_auc_score(labels, pred_flat.detach().cpu().numpy())