diff --git a/04_multilingual-ner.ipynb b/04_multilingual-ner.ipynb
index 203ec10..e763888 100644
--- a/04_multilingual-ner.ipynb
+++ b/04_multilingual-ner.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -23,8 +23,7 @@
      "output_type": "stream",
      "text": [
       "Using transformers v4.11.3\n",
-      "Using datasets v1.13.0\n",
-      "Using accelerate v0.5.1\n"
+      "Using datasets v1.16.1\n"
      ]
     }
    ],
@@ -50,13 +49,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Tokens</th>\n",
@@ -85,14 +113,18 @@
        "      <td>B-LOC</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
-       "</table>"
+       "</table>\n",
+       "</div>"
       ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "            0      1   2  3         4          5   6       7   8           9\n",
+       "Tokens   Jeff   Dean  is  a  computer  scientist  at  Google  in  California\n",
+       "Tags    B-PER  I-PER   O  O         O          O   O   B-ORG   O       B-LOC"
       ]
      },
+     "execution_count": 3,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -108,9 +140,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05e77c3023bb420a8f56f5f08b7850fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/9.04k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1619822fd444fccb40d8f20f3c3ef2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/23.1k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -128,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -137,7 +197,7 @@
        "['PAN-X.af', 'PAN-X.ar', 'PAN-X.bg']"
       ]
      },
-     "execution_count": null,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -149,13 +209,83 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading and preparing dataset xtreme/PAN-X.de (download: 223.17 MiB, generated: 9.08 MiB, post-processed: Unknown size, total: 232.25 MiB) to /root/.cache/huggingface/datasets/xtreme/PAN-X.de/1.0.0/2fc6b63c5326cc0d1f73060649612889b3a7ed8a6605c91cecdbd228a7158b17...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42ffcdb1e2a240eeb423f965692f1412",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/234M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbfda93d7a3e497285f3ea902b3e772e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d01423687b8447a1b59eb8c02b6cdd51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb1bd46311eb4b4e9d324901adc4b450",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset xtreme downloaded and prepared to /root/.cache/huggingface/datasets/xtreme/PAN-X.de/1.0.0/2fc6b63c5326cc0d1f73060649612889b3a7ed8a6605c91cecdbd228a7158b17. Subsequent calls will reuse this data.\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e36aea61c9bc4620961a8a553452c716",
+       "model_id": "399c9e3044fa4c33b8c87341b9cfaa62",
        "version_major": 2,
        "version_minor": 0
       },
@@ -185,7 +315,7 @@
        "})"
       ]
      },
-     "execution_count": null,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -199,13 +329,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e72fc7257d6a4b72b0d75edc220ace3c",
+       "model_id": "1d4a02565c0f4dcb8bc5eede7d5d9fa7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -220,13 +350,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading and preparing dataset xtreme/PAN-X.fr (download: 223.17 MiB, generated: 6.37 MiB, post-processed: Unknown size, total: 229.65 MiB) to /Users/lewtun/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/fb182342ff5c7a211ebf678cde070463acd29524b30b87f8f38c617948c2826a...\n"
+      "Downloading and preparing dataset xtreme/PAN-X.fr (download: 223.17 MiB, generated: 6.37 MiB, post-processed: Unknown size, total: 229.53 MiB) to /root/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/2fc6b63c5326cc0d1f73060649612889b3a7ed8a6605c91cecdbd228a7158b17...\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
+       "model_id": "a64341694ef5443f932bb38fe2bfab91",
        "version_major": 2,
        "version_minor": 0
       },
@@ -240,7 +370,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
+       "model_id": "14528528a1684fd3928d570b78a610ab",
        "version_major": 2,
        "version_minor": 0
       },
@@ -254,7 +384,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
+       "model_id": "ef0a6881f29b439ebce6d175ae757fdf",
        "version_major": 2,
        "version_minor": 0
       },
@@ -269,13 +399,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dataset xtreme downloaded and prepared to /Users/lewtun/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/fb182342ff5c7a211ebf678cde070463acd29524b30b87f8f38c617948c2826a. Subsequent calls will reuse this data.\n"
+      "Dataset xtreme downloaded and prepared to /root/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/2fc6b63c5326cc0d1f73060649612889b3a7ed8a6605c91cecdbd228a7158b17. Subsequent calls will reuse this data.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cbdd4421d79e4645804ee0e293bcd27c",
+       "model_id": "716e1b199e074dad8e12e23ce357264c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -290,13 +420,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading and preparing dataset xtreme/PAN-X.it (download: 223.17 MiB, generated: 7.35 MiB, post-processed: Unknown size, total: 230.63 MiB) to /Users/lewtun/.cache/huggingface/datasets/xtreme/PAN-X.it/1.0.0/fb182342ff5c7a211ebf678cde070463acd29524b30b87f8f38c617948c2826a...\n"
+      "Downloading and preparing dataset xtreme/PAN-X.it (download: 223.17 MiB, generated: 7.35 MiB, post-processed: Unknown size, total: 230.52 MiB) to /root/.cache/huggingface/datasets/xtreme/PAN-X.it/1.0.0/2fc6b63c5326cc0d1f73060649612889b3a7ed8a6605c91cecdbd228a7158b17...\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
+       "model_id": "dd120e61a92b4d7a98517b3ad559d3e9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -310,7 +440,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
+       "model_id": "d589ccbc8ccd4d4a861600325ea0a3f4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -324,7 +454,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
+       "model_id": "b7d8098aa7434705835be33b0bc6d958",
        "version_major": 2,
        "version_minor": 0
       },
@@ -339,13 +469,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dataset xtreme downloaded and prepared to /Users/lewtun/.cache/huggingface/datasets/xtreme/PAN-X.it/1.0.0/fb182342ff5c7a211ebf678cde070463acd29524b30b87f8f38c617948c2826a. Subsequent calls will reuse this data.\n"
+      "Dataset xtreme downloaded and prepared to /root/.cache/huggingface/datasets/xtreme/PAN-X.it/1.0.0/2fc6b63c5326cc0d1f73060649612889b3a7ed8a6605c91cecdbd228a7158b17. Subsequent calls will reuse this data.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "df69d9802e33407587c275a6594a66b6",
+       "model_id": "547453dd36924596893dde54961d5498",
        "version_major": 2,
        "version_minor": 0
       },
@@ -360,13 +490,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading and preparing dataset xtreme/PAN-X.en (download: 223.17 MiB, generated: 7.30 MiB, post-processed: Unknown size, total: 230.59 MiB) to /Users/lewtun/.cache/huggingface/datasets/xtreme/PAN-X.en/1.0.0/fb182342ff5c7a211ebf678cde070463acd29524b30b87f8f38c617948c2826a...\n"
+      "Downloading and preparing dataset xtreme/PAN-X.en (download: 223.17 MiB, generated: 7.30 MiB, post-processed: Unknown size, total: 230.47 MiB) to /root/.cache/huggingface/datasets/xtreme/PAN-X.en/1.0.0/2fc6b63c5326cc0d1f73060649612889b3a7ed8a6605c91cecdbd228a7158b17...\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
+       "model_id": "c587f0d64ea947019891f705403540c1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -380,7 +510,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
+       "model_id": "998cfc75ead24210b2943ea582fd3ee1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -394,7 +524,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
+       "model_id": "f69b64da972f43cea20b25865d744a89",
        "version_major": 2,
        "version_minor": 0
       },
@@ -409,13 +539,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dataset xtreme downloaded and prepared to /Users/lewtun/.cache/huggingface/datasets/xtreme/PAN-X.en/1.0.0/fb182342ff5c7a211ebf678cde070463acd29524b30b87f8f38c617948c2826a. Subsequent calls will reuse this data.\n"
+      "Dataset xtreme downloaded and prepared to /root/.cache/huggingface/datasets/xtreme/PAN-X.en/1.0.0/2fc6b63c5326cc0d1f73060649612889b3a7ed8a6605c91cecdbd228a7158b17. Subsequent calls will reuse this data.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c6a47e1860db4c2ea599939049a63141",
+       "model_id": "1e753bfadd264501a4b0e88ac7af3af7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -450,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -497,7 +627,7 @@
        "Number of training examples  12580  4580  1680  1180"
       ]
      },
-     "execution_count": null,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -511,17 +641,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "langs: ['de', 'de', 'de', 'de', 'de', 'de', 'de', 'de', 'de', 'de', 'de', 'de']\n",
-      "ner_tags: [0, 0, 0, 0, 5, 6, 0, 0, 5, 5, 6, 0]\n",
       "tokens: ['2.000', 'Einwohnern', 'an', 'der', 'Danziger', 'Bucht', 'in', 'der',\n",
-      "'polnischen', 'Woiwodschaft', 'Pommern', '.']\n"
+      "'polnischen', 'Woiwodschaft', 'Pommern', '.']\n",
+      "ner_tags: [0, 0, 0, 0, 5, 6, 0, 0, 5, 5, 6, 0]\n",
+      "langs: ['de', 'de', 'de', 'de', 'de', 'de', 'de', 'de', 'de', 'de', 'de', 'de']\n"
      ]
     }
    ],
@@ -533,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -555,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -574,9 +704,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0f02081d74341a5acaffa4294306c29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/6290 [00:00<?, ?ex/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ac36d7f55a74c2fb0197fa4b768de1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/6290 [00:00<?, ?ex/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01dfbacea0f6453ea226d9e60305034b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/12580 [00:00<?, ?ex/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# hide_output\n",
     "def create_tag_names(batch):\n",
@@ -587,7 +760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -670,7 +843,7 @@
        "Tags           B-LOC    I-LOC  O  "
       ]
      },
-     "execution_count": null,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -684,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -743,7 +916,7 @@
        "train       5366  6186  5810"
       ]
      },
-     "execution_count": null,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -777,13 +950,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-26 13:50:43.827680: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2022-11-26 13:50:44.585803: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
+      "2022-11-26 13:50:44.585880: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
+      "2022-11-26 13:50:44.585886: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1324109509bb4056b1d2ab8e9af0f9e4",
+       "model_id": "3b291ffaf4174f0fa084f43cc2553b09",
        "version_major": 2,
        "version_minor": 0
       },
@@ -797,7 +981,7 @@
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-       "model_id": "70e765f2ded04031b1b09e6f6bdb8fd5",
+       "model_id": "dd94673143234452b1c420d524aa3c5d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -811,7 +995,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2b04031c091e458cbe33613a4a71f26b",
+       "model_id": "78e1e4b82be54e66929312eb7fc683d1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -825,7 +1009,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cfc04444ed064ef183309691d7300553",
+       "model_id": "6fabfb9b570c4f22b78838aed4ef914f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -839,12 +1023,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "acd2b13e44d641a4bef1d839a6106376",
+       "model_id": "2badebcc26cb4974a9636bafad95c167",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Downloading:   0%|          | 0.00/512 [00:00<?, ?B/s]"
+       "Downloading:   0%|          | 0.00/615 [00:00<?, ?B/s]"
       ]
      },
      "metadata": {},
@@ -853,7 +1037,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2fed8c75130c48159d6d015af9840fe0",
+       "model_id": "b5b64c9f316941e8b03c24be6a436491",
        "version_major": 2,
        "version_minor": 0
       },
@@ -867,7 +1051,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dce3206a657a43e3a66429554a870996",
+       "model_id": "94f91cbe70e64624804c2066679832e4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -891,7 +1075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -902,13 +1086,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>BERT</th>\n",
@@ -937,14 +1150,18 @@
        "      <td>&lt;/s&gt;</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
-       "</table>"
+       "</table>\n",
+       "</div>"
       ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "           0      1      2       3      4    5     6      7      8     9\n",
+       "BERT   [CLS]   Jack    Spa  ##rrow  loves  New  York      !  [SEP]  None\n",
+       "XLM-R    <s>  ▁Jack  ▁Spar     row  ▁love    s  ▁New  ▁York      !  </s>"
       ]
      },
+     "execution_count": 17,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -976,7 +1193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -985,7 +1202,7 @@
        "'<s> Jack Sparrow loves New York!</s>'"
       ]
      },
-     "execution_count": null,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -994,13 +1211,6 @@
     "\"\".join(xlmr_tokens).replace(u\"\\u2581\", \" \")"
    ]
   },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "[[train_ner_tagger]]"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1052,7 +1262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1104,7 +1314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1114,7 +1324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1128,9 +1338,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a20a180b17da4a8882bdb04cd3795c50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/1.04G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# hide_output\n",
     "import torch\n",
@@ -1143,7 +1368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1216,7 +1441,7 @@
        "Input IDs    0  21763  37456  15555   5161  7  2356   5753  38     2"
       ]
      },
-     "execution_count": null,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1229,7 +1454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1250,7 +1475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1302,28 +1527,28 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Tags</th>\n",
-       "      <td>O</td>\n",
-       "      <td>I-LOC</td>\n",
+       "      <td>B-ORG</td>\n",
+       "      <td>B-LOC</td>\n",
        "      <td>B-LOC</td>\n",
        "      <td>B-LOC</td>\n",
-       "      <td>O</td>\n",
-       "      <td>I-LOC</td>\n",
-       "      <td>O</td>\n",
-       "      <td>O</td>\n",
-       "      <td>I-LOC</td>\n",
        "      <td>B-LOC</td>\n",
+       "      <td>B-LOC</td>\n",
+       "      <td>B-LOC</td>\n",
+       "      <td>B-LOC</td>\n",
+       "      <td>B-ORG</td>\n",
+       "      <td>B-ORG</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          0      1      2      3      4      5     6      7      8      9\n",
-       "Tokens  <s>  ▁Jack  ▁Spar    row  ▁love      s  ▁New  ▁York      !   </s>\n",
-       "Tags      O  I-LOC  B-LOC  B-LOC      O  I-LOC     O      O  I-LOC  B-LOC"
+       "            0      1      2      3      4      5      6      7      8      9\n",
+       "Tokens    <s>  ▁Jack  ▁Spar    row  ▁love      s   ▁New  ▁York      !   </s>\n",
+       "Tags    B-ORG  B-LOC  B-LOC  B-LOC  B-LOC  B-LOC  B-LOC  B-LOC  B-ORG  B-ORG"
       ]
      },
-     "execution_count": null,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1335,7 +1560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1363,7 +1588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1372,7 +1597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1382,7 +1607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1469,7 +1694,7 @@
        "[1 rows x 25 columns]"
       ]
      },
-     "execution_count": null,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1481,7 +1706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1594,7 +1819,7 @@
        "[2 rows x 25 columns]"
       ]
      },
-     "execution_count": null,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1607,7 +1832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1778,7 +2003,7 @@
        "[4 rows x 25 columns]"
       ]
      },
-     "execution_count": null,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1803,7 +2028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1828,7 +2053,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1839,9 +2064,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f87f31ec9d44e358d4ce3d6cbb2a8e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b793a780c5894846a442308144d6eed1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a74065b4fdab4b1c90c2a39df75ec095",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/13 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# hide_output\n",
     "panx_de_encoded = encode_panx_dataset(panx_ch[\"de\"])"
@@ -1856,7 +2124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -1887,7 +2155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1921,7 +2189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1942,24 +2210,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "96bce6906b5d4713a064d40b8808c234",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=\"<center>\\n<img src=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#hide_output\n",
     "from huggingface_hub import notebook_login\n",
@@ -1969,7 +2222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1983,7 +2236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1994,7 +2247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2006,7 +2259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -2024,20 +2277,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "CODECARBON : No CPU tracking mode found. Falling back on CPU constant mode.\n",
-      "CODECARBON : Failed to match CPU TDP constant. Falling back on a global constant.\n",
-      "/home/lewtun/miniconda3/envs/book/lib/python3.7/site-packages/apscheduler/util.py:95: PytzUsageWarning: The zone attribute is specific to pytz's interface; please migrate to a new time zone provider. For more details on how to do so, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
-      "  if obj.zone == 'local':\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# hide_output\n",
     "from transformers import Trainer\n",
@@ -2051,9 +2293,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='1575' max='1575' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [1575/1575 02:30, Epoch 3/3]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>Epoch</th>\n",
+       "      <th>Training Loss</th>\n",
+       "      <th>Validation Loss</th>\n",
+       "      <th>F1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.258100</td>\n",
+       "      <td>0.168955</td>\n",
+       "      <td>0.830334</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.130500</td>\n",
+       "      <td>0.135212</td>\n",
+       "      <td>0.848440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>0.083900</td>\n",
+       "      <td>0.133893</td>\n",
+       "      <td>0.866310</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrainOutput(global_step=1575, training_loss=0.15737919529279074, metrics={'train_runtime': 150.1186, 'train_samples_per_second': 251.401, 'train_steps_per_second': 10.492, 'total_flos': 863012377186080.0, 'train_loss': 0.15737919529279074, 'epoch': 3.0})"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#hide_input\n",
     "trainer.train()\n",
@@ -2062,15 +2362,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
        "      <th>Epoch</th>\n",
        "      <th>Training Loss</th>\n",
        "      <th>Validation Loss</th>\n",
@@ -2079,32 +2394,40 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th>0</th>\n",
        "      <td>1</td>\n",
-       "      <td>0.2652</td>\n",
-       "      <td>0.160244</td>\n",
-       "      <td>0.822974</td>\n",
+       "      <td>0.2581</td>\n",
+       "      <td>0.168955</td>\n",
+       "      <td>0.830334</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>2</th>\n",
        "      <td>2</td>\n",
-       "      <td>0.1314</td>\n",
-       "      <td>0.137195</td>\n",
-       "      <td>0.852747</td>\n",
+       "      <td>0.1305</td>\n",
+       "      <td>0.135212</td>\n",
+       "      <td>0.848440</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>4</th>\n",
        "      <td>3</td>\n",
-       "      <td>0.0806</td>\n",
-       "      <td>0.138774</td>\n",
-       "      <td>0.864591</td>\n",
+       "      <td>0.0839</td>\n",
+       "      <td>0.133893</td>\n",
+       "      <td>0.866310</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
-       "</table>"
+       "</table>\n",
+       "</div>"
       ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "   Epoch  Training Loss  Validation Loss        F1\n",
+       "0      1         0.2581         0.168955  0.830334\n",
+       "2      2         0.1305         0.135212  0.848440\n",
+       "4      3         0.0839         0.133893  0.866310"
       ]
      },
+     "execution_count": 48,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -2119,7 +2442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -2208,7 +2531,7 @@
        "Tags      O       B-LOC  I-LOC     O  "
       ]
      },
-     "execution_count": null,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2228,7 +2551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2259,13 +2582,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5695f889b4c54f0599afc6daa6dffd38",
+       "model_id": "d2c39105e2174903964de0a060de0fb8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2286,7 +2609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -2324,7 +2647,7 @@
        "      <td>[1, 1, 1, 1, 1, 1, 1]</td>\n",
        "      <td>[0, 10699, 11, 15, 16104, 1388, 2]</td>\n",
        "      <td>[IGN, B-ORG, IGN, I-ORG, I-ORG, I-ORG, IGN]</td>\n",
-       "      <td>[0.0, 0.014679872, 0.0, 0.009469474, 0.0103934...</td>\n",
+       "      <td>[0.0, 0.014765271, 0.0, 0.04603953, 0.04655944...</td>\n",
        "      <td>[I-ORG, B-ORG, I-ORG, I-ORG, I-ORG, I-ORG, I-ORG]</td>\n",
        "      <td>[&lt;s&gt;, ▁Ham, a, ▁(, ▁Unternehmen, ▁), &lt;/s&gt;]</td>\n",
        "    </tr>\n",
@@ -2340,7 +2663,7 @@
        "0  [IGN, B-ORG, IGN, I-ORG, I-ORG, I-ORG, IGN]   \n",
        "\n",
        "                                                loss  \\\n",
-       "0  [0.0, 0.014679872, 0.0, 0.009469474, 0.0103934...   \n",
+       "0  [0.0, 0.014765271, 0.0, 0.04603953, 0.04655944...   \n",
        "\n",
        "                                     predicted_label  \\\n",
        "0  [I-ORG, B-ORG, I-ORG, I-ORG, I-ORG, I-ORG, I-ORG]   \n",
@@ -2349,7 +2672,7 @@
        "0  [<s>, ▁Ham, a, ▁(, ▁Unternehmen, ▁), </s>]  "
       ]
      },
-     "execution_count": null,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2372,7 +2695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -2419,7 +2742,7 @@
        "      <td>1</td>\n",
        "      <td>15</td>\n",
        "      <td>I-ORG</td>\n",
-       "      <td>0.01</td>\n",
+       "      <td>0.05</td>\n",
        "      <td>I-ORG</td>\n",
        "      <td>▁(</td>\n",
        "    </tr>\n",
@@ -2428,7 +2751,7 @@
        "      <td>1</td>\n",
        "      <td>16104</td>\n",
        "      <td>I-ORG</td>\n",
-       "      <td>0.01</td>\n",
+       "      <td>0.05</td>\n",
        "      <td>I-ORG</td>\n",
        "      <td>▁Unternehmen</td>\n",
        "    </tr>\n",
@@ -2437,7 +2760,7 @@
        "      <td>1</td>\n",
        "      <td>1388</td>\n",
        "      <td>I-ORG</td>\n",
-       "      <td>0.01</td>\n",
+       "      <td>0.05</td>\n",
        "      <td>I-ORG</td>\n",
        "      <td>▁)</td>\n",
        "    </tr>\n",
@@ -2455,8 +2778,8 @@
        "      <td>1</td>\n",
        "      <td>83982</td>\n",
        "      <td>B-ORG</td>\n",
-       "      <td>0.34</td>\n",
-       "      <td>B-ORG</td>\n",
+       "      <td>1.02</td>\n",
+       "      <td>B-LOC</td>\n",
        "      <td>▁Luz</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2464,8 +2787,8 @@
        "      <td>1</td>\n",
        "      <td>10</td>\n",
        "      <td>I-ORG</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>I-ORG</td>\n",
+       "      <td>1.24</td>\n",
+       "      <td>I-LOC</td>\n",
        "      <td>▁a</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -2475,15 +2798,15 @@
       "text/plain": [
        "  attention_mask input_ids labels  loss predicted_label  input_tokens\n",
        "0              1     10699  B-ORG  0.01           B-ORG          ▁Ham\n",
-       "0              1        15  I-ORG  0.01           I-ORG            ▁(\n",
-       "0              1     16104  I-ORG  0.01           I-ORG  ▁Unternehmen\n",
-       "0              1      1388  I-ORG  0.01           I-ORG            ▁)\n",
+       "0              1        15  I-ORG  0.05           I-ORG            ▁(\n",
+       "0              1     16104  I-ORG  0.05           I-ORG  ▁Unternehmen\n",
+       "0              1      1388  I-ORG  0.05           I-ORG            ▁)\n",
        "1              1     56530      O  0.00               O           ▁WE\n",
-       "1              1     83982  B-ORG  0.34           B-ORG          ▁Luz\n",
-       "1              1        10  I-ORG  0.45           I-ORG            ▁a"
+       "1              1     83982  B-ORG  1.02           B-LOC          ▁Luz\n",
+       "1              1        10  I-ORG  1.24           I-LOC            ▁a"
       ]
      },
-     "execution_count": null,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2498,7 +2821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -2543,9 +2866,9 @@
        "      <td>▁von</td>\n",
        "      <td>▁/</td>\n",
        "      <td>▁und</td>\n",
+       "      <td>▁''</td>\n",
        "      <td>▁(</td>\n",
        "      <td>▁)</td>\n",
-       "      <td>▁''</td>\n",
        "      <td>▁A</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2556,56 +2879,56 @@
        "      <td>808</td>\n",
        "      <td>163</td>\n",
        "      <td>1171</td>\n",
+       "      <td>2898</td>\n",
        "      <td>246</td>\n",
        "      <td>246</td>\n",
-       "      <td>2898</td>\n",
        "      <td>125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>0.03</td>\n",
        "      <td>0.1</td>\n",
+       "      <td>0.13</td>\n",
        "      <td>0.14</td>\n",
-       "      <td>0.14</td>\n",
-       "      <td>0.64</td>\n",
+       "      <td>0.56</td>\n",
        "      <td>0.08</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.33</td>\n",
        "      <td>0.3</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>0.44</td>\n",
+       "      <td>0.41</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sum</th>\n",
-       "      <td>200.71</td>\n",
-       "      <td>138.05</td>\n",
-       "      <td>137.33</td>\n",
-       "      <td>114.92</td>\n",
-       "      <td>104.28</td>\n",
-       "      <td>99.15</td>\n",
-       "      <td>74.49</td>\n",
-       "      <td>72.35</td>\n",
-       "      <td>59.31</td>\n",
-       "      <td>54.48</td>\n",
+       "      <td>199.64</td>\n",
+       "      <td>144.8</td>\n",
+       "      <td>123.79</td>\n",
+       "      <td>114.14</td>\n",
+       "      <td>90.77</td>\n",
+       "      <td>88.87</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>81.06</td>\n",
+       "      <td>74.64</td>\n",
+       "      <td>51.69</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                   0       1       2       3       4      5      6      7  \\\n",
-       "input_tokens       ▁    ▁der     ▁in    ▁von      ▁/   ▁und     ▁(     ▁)   \n",
-       "count           6066    1388     989     808     163   1171    246    246   \n",
-       "mean            0.03     0.1    0.14    0.14    0.64   0.08    0.3   0.29   \n",
-       "sum           200.71  138.05  137.33  114.92  104.28  99.15  74.49  72.35   \n",
+       "                   0      1       2       3      4      5     6      7      8  \\\n",
+       "input_tokens       ▁   ▁der     ▁in    ▁von     ▁/   ▁und   ▁''     ▁(     ▁)   \n",
+       "count           6066   1388     989     808    163   1171  2898    246    246   \n",
+       "mean            0.03    0.1    0.13    0.14   0.56   0.08  0.03   0.33    0.3   \n",
+       "sum           199.64  144.8  123.79  114.14  90.77  88.87  86.0  81.06  74.64   \n",
        "\n",
-       "                  8      9  \n",
-       "input_tokens    ▁''     ▁A  \n",
-       "count          2898    125  \n",
-       "mean           0.02   0.44  \n",
-       "sum           59.31  54.48  "
+       "                  9  \n",
+       "input_tokens     ▁A  \n",
+       "count           125  \n",
+       "mean           0.41  \n",
+       "sum           51.69  "
       ]
      },
-     "execution_count": null,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2625,7 +2948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -2681,37 +3004,37 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>0.66</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>0.48</td>\n",
-       "      <td>0.35</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>0.18</td>\n",
+       "      <td>0.62</td>\n",
+       "      <td>0.62</td>\n",
+       "      <td>0.51</td>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.17</td>\n",
        "      <td>0.03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sum</th>\n",
-       "      <td>1769.47</td>\n",
-       "      <td>930.94</td>\n",
-       "      <td>1850.39</td>\n",
-       "      <td>1111.03</td>\n",
-       "      <td>760.56</td>\n",
-       "      <td>750.91</td>\n",
-       "      <td>1354.46</td>\n",
+       "      <td>1673.46</td>\n",
+       "      <td>900.29</td>\n",
+       "      <td>1933.8</td>\n",
+       "      <td>1032.65</td>\n",
+       "      <td>730.53</td>\n",
+       "      <td>705.96</td>\n",
+       "      <td>1252.06</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "              0       1        2        3       4       5        6\n",
-       "labels    B-ORG   I-LOC    I-ORG    B-LOC   B-PER   I-PER        O\n",
-       "count      2683    1462     3820     3172    2893    4139    43648\n",
-       "mean       0.66    0.64     0.48     0.35    0.26    0.18     0.03\n",
-       "sum     1769.47  930.94  1850.39  1111.03  760.56  750.91  1354.46"
+       "              0       1       2        3       4       5        6\n",
+       "labels    B-ORG   I-LOC   I-ORG    B-LOC   B-PER   I-PER        O\n",
+       "count      2683    1462    3820     3172    2893    4139    43648\n",
+       "mean       0.62    0.62    0.51     0.33    0.25    0.17     0.03\n",
+       "sum     1673.46  900.29  1933.8  1032.65  730.53  705.96  1252.06"
       ]
      },
-     "execution_count": null,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2730,14 +3053,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
     "\n",
     "def plot_confusion_matrix(y_preds, y_true, labels):\n",
-    "    cm = confusion_matrix(y_true, y_preds, normalize=\"true\")\n",
+    "    cm = confusion_matrix(y_true.apply(lambda x: tag2index[x]),\n",
+    "                          y_preds.apply(lambda x: tag2index[x]),\n",
+    "                          normalize=\"true\")\n",
     "    fig, ax = plt.subplots(figsize=(6, 6))\n",
     "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
     "    disp.plot(cmap=\"Blues\", values_format=\".2f\", ax=ax, colorbar=False)\n",
@@ -2747,13 +3072,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "application/pdf": 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       "text/plain": [
        "<Figure size 432x432 with 1 Axes>"
       ]
@@ -2765,7 +3090,7 @@
     }
    ],
    "source": [
-    "plot_confusion_matrix(df_tokens[\"labels\"], df_tokens[\"predicted_label\"],\n",
+    "plot_confusion_matrix(df_tokens[\"predicted_label\"], df_tokens[\"labels\"],\n",
     "                      tags.names)"
    ]
   },
@@ -4153,9 +4478,26 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.7.13 ('base')",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
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+  "vscode": {
+   "interpreter": {
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+   }
   }
  },
  "nbformat": 4,