diff --git a/examples/15_AUC_multiclass.ipynb b/examples/15_AUC_multiclass.ipynb
index 02cb8ea7d9bab08971aeea7ac4f607954d718a88..f0af0e1a7c3d8d57590b250adc5b156903b0306a 100644
--- a/examples/15_AUC_multiclass.ipynb
+++ b/examples/15_AUC_multiclass.ipynb
@@ -26,8 +26,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-12-10 21:26:26.784170: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
-      "2021-12-10 21:26:26.784221: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
+      "2021-12-11 16:34:55.734357: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2021-12-11 16:34:55.734404: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
      ]
     }
    ],
@@ -39,7 +39,7 @@
     "from pytorch_widedeep import Trainer\n",
     "from pytorch_widedeep.preprocessing import TabPreprocessor\n",
     "from pytorch_widedeep.models import TabMlp, WideDeep\n",
-    "from torchmetrics import AUC\n",
+    "from torchmetrics import AUC, AUROC\n",
     "from pytorch_widedeep.initializers import XavierNormal\n",
     "from pytorch_widedeep.datasets import load_ecoli\n",
     "from pytorch_widedeep.utils import LabelEncoder\n",
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 2,
    "id": "07c75f0c",
    "metadata": {},
    "outputs": [
@@ -163,7 +163,7 @@
        "4    ADI_ECOLI  0.23  0.32  0.48  0.5  0.55  0.25  0.35    cp"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 3,
    "id": "1e3f8efc",
    "metadata": {},
    "outputs": [
@@ -193,7 +193,7 @@
        "Name: class, dtype: int64"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -205,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 4,
    "id": "e4db0d6d",
    "metadata": {},
    "outputs": [],
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 5,
    "id": "005531a3",
    "metadata": {},
    "outputs": [],
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 6,
    "id": "214b3071",
    "metadata": {},
    "outputs": [],
@@ -239,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 7,
    "id": "168c81f1",
    "metadata": {},
    "outputs": [],
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 8,
    "id": "3a7b246b",
    "metadata": {},
    "outputs": [],
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 9,
    "id": "7a2dac24",
    "metadata": {},
    "outputs": [],
@@ -298,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 10,
    "id": "511198d4",
    "metadata": {},
    "outputs": [
@@ -331,7 +331,7 @@
        ")"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 11,
    "id": "a5359b0f",
    "metadata": {},
    "outputs": [
@@ -356,49 +356,40 @@
      "output_type": "stream",
      "text": [
       "/home/palo/miniconda3/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
+      "  warnings.warn(*args, **kwargs)\n",
+      "/home/palo/miniconda3/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
       "  warnings.warn(*args, **kwargs)\n"
      ]
     }
    ],
    "source": [
-    "auc = AUC()"
+    "auc = AUC(reorder=True)\n",
+    "auc.num_classes = df_enc[\"class\"].nunique()\n",
+    "auroc = AUROC(num_classes=df_enc[\"class\"].nunique())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 12,
    "id": "34a18ac0",
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "'AUC' object has no attribute 'num_classes'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_1193/1577559620.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m )\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 ] = alias\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 ] = alias\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 ] = alias\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 ] = alias\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 ] = alias\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 ] = alias\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 ] = alias\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 ] = alias\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 ] = alias\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 ] = alias\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 ] = alias\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 ] = alias\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 ] = alias\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/training/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X_wide, X_tab, X_text, X_img, X_train, X_val, val_split, target, n_epochs, validation_freq, batch_size, custom_dataloader, finetune, finetune_epochs, finetune_max_lr, finetune_deeptabular_gradual, finetune_deeptabular_max_lr, finetune_deeptabular_layers, finetune_deeptext_gradual, finetune_deeptext_max_lr, finetune_deeptext_layers, finetune_deepimage_gradual, finetune_deepimage_max_lr, finetune_deepimage_layers, finetune_routine, stop_after_finetuning, **kwargs)\u001b[0m\n\u001b[1;32m    615\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargett\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    616\u001b[0m                     \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_description\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"epoch %i\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 617\u001b[0;31m                     \u001b[0mtrain_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargett\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    618\u001b[0m                     \u001b[0mprint_loss_and_metric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_container\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/training/trainer.py\u001b[0m in \u001b[0;36m_train_step\u001b[0;34m(self, data, target, batch_idx)\u001b[0m\n\u001b[1;32m   1168\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1169\u001b[0m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1170\u001b[0;31m             \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1171\u001b[0m         \u001b[0;31m# TODO raise exception if the loss is exploding with non scaled target values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1172\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/training/trainer.py\u001b[0m in \u001b[0;36m_get_score\u001b[0;34m(self, y_pred, y)\u001b[0m\n\u001b[1;32m   1212\u001b[0m                 \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1213\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1214\u001b[0;31m                 \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1215\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1216\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/metrics.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, y_pred, y_true)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mlogs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprefix\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTorchMetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m                 \u001b[0;32mif\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_classes\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m                     \u001b[0mmetric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_classes\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# type: ignore[operator]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1128\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1129\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1130\u001b[0;31m         raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0m\u001b[1;32m   1131\u001b[0m             type(self).__name__, name))\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'AUC' object has no attribute 'num_classes'"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "epoch 1: 100%|██████████| 6/6 [00:00<00:00, 79.20it/s, loss=0.1, metrics={'AUC': 8.5, 'AUROC': 0.427}]\n",
+      "valid: 100%|██████████| 1/1 [00:00<00:00,  6.06it/s, loss=0.0961, metrics={'AUC': 6.5, 'AUROC': 0.419}]\n",
+      "epoch 2: 100%|██████████| 6/6 [00:00<00:00, 82.52it/s, loss=0.095, metrics={'AUC': 4.5, 'AUROC': 0.4418}]\n",
+      "valid: 100%|██████████| 1/1 [00:00<00:00,  5.69it/s, loss=0.0917, metrics={'AUC': 6.5, 'AUROC': 0.4351}]\n",
+      "epoch 3: 100%|██████████| 6/6 [00:00<00:00, 103.30it/s, loss=0.0908, metrics={'AUC': 5.5, 'AUROC': 0.4715}]\n",
+      "valid: 100%|██████████| 1/1 [00:00<00:00,  5.35it/s, loss=0.0875, metrics={'AUC': 6.5, 'AUROC': 0.4633}]\n",
+      "epoch 4: 100%|██████████| 6/6 [00:00<00:00, 90.88it/s, loss=0.0872, metrics={'AUC': 7.0, 'AUROC': 0.4767}]\n",
+      "valid: 100%|██████████| 1/1 [00:00<00:00,  5.44it/s, loss=0.0874, metrics={'AUC': 6.5, 'AUROC': 0.4652}]\n",
+      "epoch 5: 100%|██████████| 6/6 [00:00<00:00, 88.87it/s, loss=0.0866, metrics={'AUC': 6.0, 'AUROC': 0.4775}]\n",
+      "valid: 100%|██████████| 1/1 [00:00<00:00,  5.37it/s, loss=0.087, metrics={'AUC': 6.5, 'AUROC': 0.4524}]\n"
      ]
     }
    ],
@@ -414,11 +405,10 @@
     "    lr_schedulers={\"deeptabular\": deep_sch},\n",
     "    initializers={\"deeptabular\": XavierNormal},\n",
     "    optimizers={\"deeptabular\": deep_opt},\n",
-    "    metrics=[auc],\n",
-    "    verbose=0,\n",
+    "    metrics=[auc, auroc],\n",
     ")\n",
     "\n",
-    "trainer.fit(X_train=X_train, X_val=X_val, n_epochs=5, batch_size=10)"
+    "trainer.fit(X_train=X_train, X_val=X_val, n_epochs=5, batch_size=50)"
    ]
   }
  ],
diff --git a/pytorch_widedeep/metrics.py b/pytorch_widedeep/metrics.py
index 2486eb13ab8fa562becdc0c930deffa66a63a6ea..e939ed8011dd7f32a92f28c732cfb11a103ac2ed 100644
--- a/pytorch_widedeep/metrics.py
+++ b/pytorch_widedeep/metrics.py
@@ -1,6 +1,7 @@
 import numpy as np
 import torch
 from torchmetrics import Metric as TorchMetric
+from torchmetrics import AUC
 
 from .wdtypes import *  # noqa: F403
 
@@ -38,10 +39,23 @@ class MultipleMetrics(object):
             if isinstance(metric, Metric):
                 logs[self.prefix + metric._name] = metric(y_pred, y_true)
             if isinstance(metric, TorchMetric):
+                if not hasattr(metric, "num_classes"):
+                    raise ValueError(
+                        """TorchMetric does not have num_classes attribute.
+                        Use metric in this library or extend the metric by num_classes attribute,
+                        see `examples <https://github.com/jrzaurin/pytorch-widedeep/tree/master/examples>`
+                        """
+                    )
                 if metric.num_classes == 2:
-                    metric.update(torch.round(y_pred).int(), y_true.int())
+                    if isinstance(metric, AUC):
+                        metric.update(torch.round(y_pred).int(), y_true.int())
+                    else:
+                        metric.update(y_pred, y_true.int())
                 if metric.num_classes > 2:  # type: ignore[operator]
-                    metric.update(torch.max(y_pred, dim=1).indices, y_true.int())  # type: ignore[attr-defined]
+                    if isinstance(metric, AUC):
+                        metric.update(torch.max(y_pred, dim=1).indices, y_true.int())  # type: ignore[attr-defined]
+                    else:
+                        metric.update(y_pred, y_true.int())  # type: ignore[attr-defined]
                 logs[self.prefix + type(metric).__name__] = (
                     metric.compute().detach().cpu().numpy()
                 )
@@ -396,3 +410,62 @@ class R2Score(Metric):
         y_true_avg = self.y_true_sum / self.num_examples
         self.denominator += ((y_true - y_true_avg) ** 2).sum().item()
         return np.array((1 - (self.numerator / self.denominator)))
+
+
+class Accuracy(Metric):
+    r"""Class to calculate the accuracy for both binary and categorical problems
+
+    Parameters
+    ----------
+    top_k: int, default = 1
+        Accuracy will be computed using the top k most likely classes in
+        multiclass problems
+
+    Examples
+    --------
+    >>> import torch
+    >>>
+    >>> from pytorch_widedeep.metrics import Accuracy
+    >>>
+    >>> acc = Accuracy()
+    >>> y_true = torch.tensor([0, 1, 0, 1]).view(-1, 1)
+    >>> y_pred = torch.tensor([[0.3, 0.2, 0.6, 0.7]]).view(-1, 1)
+    >>> acc(y_pred, y_true)
+    array(0.5)
+    >>>
+    >>> acc = Accuracy(top_k=2)
+    >>> y_true = torch.tensor([0, 1, 2])
+    >>> y_pred = torch.tensor([[0.3, 0.5, 0.2], [0.1, 0.1, 0.8], [0.1, 0.5, 0.4]])
+    >>> acc(y_pred, y_true)
+    array(0.66666667)
+    """
+
+    def __init__(self, top_k: int = 1):
+        super(Accuracy, self).__init__()
+
+        self.top_k = top_k
+        self.correct_count = 0
+        self.total_count = 0
+        self._name = "acc"
+
+    def reset(self):
+        """
+        resets counters to 0
+        """
+        self.correct_count = 0
+        self.total_count = 0
+
+    def __call__(self, y_pred: Tensor, y_true: Tensor) -> np.ndarray:
+        num_classes = y_pred.size(1)
+
+        if num_classes == 1:
+            y_pred = y_pred.round()
+            y_true = y_true
+        elif num_classes > 1:
+            y_pred = y_pred.topk(self.top_k, 1)[1]
+            y_true = y_true.view(-1, 1).expand_as(y_pred)
+
+        self.correct_count += y_pred.eq(y_true).sum().item()  # type: ignore[assignment]
+        self.total_count += len(y_pred)
+        accuracy = float(self.correct_count) / float(self.total_count)
+        return np.array(accuracy)
diff --git a/pytorch_widedeep/training/trainer.py b/pytorch_widedeep/training/trainer.py
index 4d32c829cff0c7a45fa961941306ad1b38c8f116..db9004a0cc6053d6da48745356f4e59c8aa6648d 100644
--- a/pytorch_widedeep/training/trainer.py
+++ b/pytorch_widedeep/training/trainer.py
@@ -147,10 +147,14 @@ class Trainer:
           <https://github.com/jrzaurin/pytorch-widedeep/tree/master/examples>`__
           folder in the repo
         - List of objects of type :obj:`torchmetrics.Metric`. This can be any
-          metric from torchmetrics library `Examples
+          metric from torchmetrics library that has attribute num_classes `Examples
           <https://torchmetrics.readthedocs.io/en/latest/references/modules.html#
-          classification-metrics>`_. This can also be a custom metric as
-          long as it is an object of type :obj:`Metric`. See `the instructions
+          classification-metrics>`_.
+          Objects of type :obj:`torchmetrics.Metric` can be extended with num_classes
+          attribute to be used with the Trainer object, see `examples
+          <https://github.com/jrzaurin/pytorch-widedeep/tree/master/examples>`.
+          This can also be a custom metric as long as it is an object of 
+          type :obj:`Metric`. See `the instructions
           <https://torchmetrics.readthedocs.io/en/latest/>`_.
     class_weight: float, List or Tuple. optional. default=None
         - float indicating the weight of the minority class in binary classification
diff --git a/tests/test_metrics/test_torchmetrics.py b/tests/test_metrics/test_torchmetrics.py
index a5bd69ae2aa375db8df16f493a4e21100799d978..c28495f45300928f670998202016ac21565cf934 100644
--- a/tests/test_metrics/test_torchmetrics.py
+++ b/tests/test_metrics/test_torchmetrics.py
@@ -1,13 +1,14 @@
 import numpy as np
 import torch
 import pytest
-from torchmetrics import F1, FBeta, Recall, Accuracy, Precision
+from torchmetrics import F1, FBeta, Recall, Accuracy, Precision, AUC
 from sklearn.metrics import (
     f1_score,
     fbeta_score,
     recall_score,
     accuracy_score,
     precision_score,
+    auc_score,
 )
 
 from pytorch_widedeep.metrics import MultipleMetrics
@@ -35,9 +36,12 @@ y_pred_bin_pt = torch.from_numpy(y_pred_bin_np)
         ("Recall", recall_score, Recall(num_classes=2, average="none")),
         ("F1", f1_score, F1(num_classes=2, average="none")),
         ("FBeta", f2_score_bin, FBeta(beta=2, num_classes=2, average="none")),
+        ("AUC", auc_score, AUC()),
     ],
 )
 def test_binary_metrics(metric_name, sklearn_metric, torch_metric):
+    if metric_name == "AUC":
+        torch_metric.num_classes=2
     sk_res = sklearn_metric(y_true_bin_np, y_pred_bin_np.round())
     wd_metric = MultipleMetrics(metrics=[torch_metric])
     wd_logs = wd_metric(y_pred_bin_pt, y_true_bin_pt)
@@ -82,11 +86,14 @@ def f2_score_multi(y_true, y_pred, average):
         ("Recall", recall_score, Recall(num_classes=3, average="macro")),
         ("F1", f1_score, F1(num_classes=3, average="macro")),
         ("FBeta", f2_score_multi, FBeta(beta=3, num_classes=3, average="macro")),
+        ("AUC", auc_score, AUC()),
     ],
 )
 def test_muticlass_metrics(metric_name, sklearn_metric, torch_metric):
     if metric_name == "Accuracy":
         sk_res = sklearn_metric(y_true_multi_np, y_pred_muli_np.argmax(axis=1))
+    elif metric_name == "AUC":
+        torch_metric.num_classes=3
     else:
         sk_res = sklearn_metric(
             y_true_multi_np, y_pred_muli_np.argmax(axis=1), average="macro"