diff --git a/notebooks/01_model_results.ipynb b/notebooks/01_model_results.ipynb
index 62277d4..3152bc5 100644
--- a/notebooks/01_model_results.ipynb
+++ b/notebooks/01_model_results.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -40,14 +31,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 9/9 [00:00<00:00, 3965.62it/s]\n"
+      "100%|██████████| 9/9 [00:00<00:00, 3711.04it/s]\n"
      ]
     }
    ],
@@ -69,249 +60,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "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>dataset_name</th>\n",
-       "      <th>subsample_frac</th>\n",
-       "      <th>seed</th>\n",
-       "      <th>save_dir</th>\n",
-       "      <th>model_name</th>\n",
-       "      <th>alpha</th>\n",
-       "      <th>max_depth</th>\n",
-       "      <th>use_cache</th>\n",
-       "      <th>save_dir_unique</th>\n",
-       "      <th>accuracy_train</th>\n",
-       "      <th>accuracy_cv</th>\n",
-       "      <th>accuracy_test</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>rotten_tomatoes</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/results</td>\n",
-       "      <td>decision_tree</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/resul...</td>\n",
-       "      <td>0.565879</td>\n",
-       "      <td>0.563766</td>\n",
-       "      <td>0.551595</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>rotten_tomatoes</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/results</td>\n",
-       "      <td>decision_tree</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/resul...</td>\n",
-       "      <td>0.566054</td>\n",
-       "      <td>0.554529</td>\n",
-       "      <td>0.558161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>rotten_tomatoes</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/results</td>\n",
-       "      <td>ridge</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/resul...</td>\n",
-       "      <td>1.000000</td>\n",
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-       "      <td>0.656660</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>rotten_tomatoes</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/results</td>\n",
-       "      <td>ridge</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/resul...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.675311</td>\n",
-       "      <td>0.684803</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>rotten_tomatoes</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/results</td>\n",
-       "      <td>ridge</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/resul...</td>\n",
-       "      <td>0.998250</td>\n",
-       "      <td>0.725044</td>\n",
-       "      <td>0.712946</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>rotten_tomatoes</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/results</td>\n",
-       "      <td>ridge</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/resul...</td>\n",
-       "      <td>0.998250</td>\n",
-       "      <td>0.727176</td>\n",
-       "      <td>0.725141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>rotten_tomatoes</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>results</td>\n",
-       "      <td>decision_tree</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>results/df9fad423b673921d74f03314189b00c5f44a6...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>rotten_tomatoes</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/results</td>\n",
-       "      <td>decision_tree</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/resul...</td>\n",
-       "      <td>0.572003</td>\n",
-       "      <td>0.569805</td>\n",
-       "      <td>0.557223</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>rotten_tomatoes</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/results</td>\n",
-       "      <td>decision_tree</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/resul...</td>\n",
-       "      <td>0.574803</td>\n",
-       "      <td>0.564121</td>\n",
-       "      <td>0.557223</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      dataset_name  subsample_frac  seed  \\\n",
-       "0  rotten_tomatoes             1.0     2   \n",
-       "1  rotten_tomatoes             1.0     1   \n",
-       "2  rotten_tomatoes             1.0     2   \n",
-       "3  rotten_tomatoes             1.0     1   \n",
-       "4  rotten_tomatoes             1.0     2   \n",
-       "5  rotten_tomatoes             1.0     1   \n",
-       "6  rotten_tomatoes             0.1     1   \n",
-       "7  rotten_tomatoes             1.0     2   \n",
-       "8  rotten_tomatoes             1.0     1   \n",
-       "\n",
-       "                                           save_dir     model_name  alpha  \\\n",
-       "0  /home/chansingh/cookiecutter-ml-research/results  decision_tree    1.0   \n",
-       "1  /home/chansingh/cookiecutter-ml-research/results  decision_tree    1.0   \n",
-       "2  /home/chansingh/cookiecutter-ml-research/results          ridge    0.1   \n",
-       "3  /home/chansingh/cookiecutter-ml-research/results          ridge    0.1   \n",
-       "4  /home/chansingh/cookiecutter-ml-research/results          ridge    1.0   \n",
-       "5  /home/chansingh/cookiecutter-ml-research/results          ridge    1.0   \n",
-       "6                                           results  decision_tree    1.0   \n",
-       "7  /home/chansingh/cookiecutter-ml-research/results  decision_tree    1.0   \n",
-       "8  /home/chansingh/cookiecutter-ml-research/results  decision_tree    1.0   \n",
-       "\n",
-       "   max_depth  use_cache                                    save_dir_unique  \\\n",
-       "0          2          0  /home/chansingh/cookiecutter-ml-research/resul...   \n",
-       "1          2          0  /home/chansingh/cookiecutter-ml-research/resul...   \n",
-       "2          2          0  /home/chansingh/cookiecutter-ml-research/resul...   \n",
-       "3          2          0  /home/chansingh/cookiecutter-ml-research/resul...   \n",
-       "4          2          0  /home/chansingh/cookiecutter-ml-research/resul...   \n",
-       "5          2          0  /home/chansingh/cookiecutter-ml-research/resul...   \n",
-       "6          2          0  results/df9fad423b673921d74f03314189b00c5f44a6...   \n",
-       "7          3          0  /home/chansingh/cookiecutter-ml-research/resul...   \n",
-       "8          3          0  /home/chansingh/cookiecutter-ml-research/resul...   \n",
-       "\n",
-       "   accuracy_train  accuracy_cv  accuracy_test  \n",
-       "0        0.565879     0.563766       0.551595  \n",
-       "1        0.566054     0.554529       0.558161  \n",
-       "2        1.000000     0.686679       0.656660  \n",
-       "3        1.000000     0.675311       0.684803  \n",
-       "4        0.998250     0.725044       0.712946  \n",
-       "5        0.998250     0.727176       0.725141  \n",
-       "6        1.000000     1.000000       0.500000  \n",
-       "7        0.572003     0.569805       0.557223  \n",
-       "8        0.574803     0.564121       0.557223  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "r"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_2393272/3066088654.py:4: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
+      "/tmp/ipykernel_3250460/3844074043.py:4: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
       "  r\n"
      ]
     },
@@ -336,10 +101,10 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>model_name</th>\n",
        "      <th>dataset_name</th>\n",
        "      <th>subsample_frac</th>\n",
        "      <th>save_dir</th>\n",
-       "      <th>model_name</th>\n",
        "      <th>alpha</th>\n",
        "      <th>max_depth</th>\n",
        "      <th>seed</th>\n",
@@ -352,38 +117,10 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>rotten_tomatoes</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>results</td>\n",
-       "      <td>decision_tree</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>rotten_tomatoes</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/results</td>\n",
        "      <td>decision_tree</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.565967</td>\n",
-       "      <td>0.559147</td>\n",
-       "      <td>0.554878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
        "      <td>rotten_tomatoes</td>\n",
        "      <td>1.0</td>\n",
        "      <td>/home/chansingh/cookiecutter-ml-research/results</td>\n",
-       "      <td>decision_tree</td>\n",
        "      <td>1.0</td>\n",
        "      <td>3</td>\n",
        "      <td>1.5</td>\n",
@@ -393,25 +130,11 @@
        "      <td>0.557223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>rotten_tomatoes</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/results</td>\n",
+       "      <th>1</th>\n",
        "      <td>ridge</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.680995</td>\n",
-       "      <td>0.670732</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
        "      <td>rotten_tomatoes</td>\n",
        "      <td>1.0</td>\n",
        "      <td>/home/chansingh/cookiecutter-ml-research/results</td>\n",
-       "      <td>ridge</td>\n",
        "      <td>1.0</td>\n",
        "      <td>2</td>\n",
        "      <td>1.5</td>\n",
@@ -425,35 +148,26 @@
        "</div>"
       ],
       "text/plain": [
-       "      dataset_name  subsample_frac  \\\n",
-       "0  rotten_tomatoes             0.1   \n",
-       "1  rotten_tomatoes             1.0   \n",
-       "2  rotten_tomatoes             1.0   \n",
-       "3  rotten_tomatoes             1.0   \n",
-       "4  rotten_tomatoes             1.0   \n",
+       "      model_name     dataset_name  subsample_frac  \\\n",
+       "0  decision_tree  rotten_tomatoes             1.0   \n",
+       "1          ridge  rotten_tomatoes             1.0   \n",
        "\n",
-       "                                           save_dir     model_name  alpha  \\\n",
-       "0                                           results  decision_tree    1.0   \n",
-       "1  /home/chansingh/cookiecutter-ml-research/results  decision_tree    1.0   \n",
-       "2  /home/chansingh/cookiecutter-ml-research/results  decision_tree    1.0   \n",
-       "3  /home/chansingh/cookiecutter-ml-research/results          ridge    0.1   \n",
-       "4  /home/chansingh/cookiecutter-ml-research/results          ridge    1.0   \n",
+       "                                           save_dir  alpha  max_depth  seed  \\\n",
+       "0  /home/chansingh/cookiecutter-ml-research/results    1.0          3   1.5   \n",
+       "1  /home/chansingh/cookiecutter-ml-research/results    1.0          2   1.5   \n",
        "\n",
-       "   max_depth  seed  use_cache  accuracy_train  accuracy_cv  accuracy_test  \n",
-       "0          2   1.0        0.0        1.000000     1.000000       0.500000  \n",
-       "1          2   1.5        0.0        0.565967     0.559147       0.554878  \n",
-       "2          3   1.5        0.0        0.573403     0.566963       0.557223  \n",
-       "3          2   1.5        0.0        1.000000     0.680995       0.670732  \n",
-       "4          2   1.5        0.0        0.998250     0.726110       0.719043  "
+       "   use_cache  accuracy_train  accuracy_cv  accuracy_test  \n",
+       "0        0.0        0.573403     0.566963       0.557223  \n",
+       "1        0.0        0.998250     0.726110       0.719043  "
       ]
      },
-     "execution_count": 25,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# group using these keys when averaging over random seeds\n",
+    "# group using these experiment hyperparams when averaging over random seeds\n",
     "group_keys = [k for k in notebook_helper.get_main_args_list(fname='01_train_model.py') if not k == 'seed']\n",
     "ravg = (\n",
     "    r\n",
@@ -461,131 +175,21 @@
     "    .mean()\n",
     "    .reset_index()\n",
     ")\n",
-    "ravg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "\n",
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-       "\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></th>\n",
-       "      <th>subsample_frac</th>\n",
-       "      <th>save_dir</th>\n",
-       "      <th>alpha</th>\n",
-       "      <th>max_depth</th>\n",
-       "      <th>seed</th>\n",
-       "      <th>use_cache</th>\n",
-       "      <th>accuracy_train</th>\n",
-       "      <th>accuracy_cv</th>\n",
-       "      <th>accuracy_test</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>model_name</th>\n",
-       "      <th>dataset_name</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>decision_tree</th>\n",
-       "      <th>rotten_tomatoes</th>\n",
-       "      <td>0.1</td>\n",
-       "      <td>results</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ridge</th>\n",
-       "      <th>rotten_tomatoes</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>/home/chansingh/cookiecutter-ml-research/results</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.99825</td>\n",
-       "      <td>0.72611</td>\n",
-       "      <td>0.719043</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               subsample_frac  \\\n",
-       "model_name    dataset_name                      \n",
-       "decision_tree rotten_tomatoes             0.1   \n",
-       "ridge         rotten_tomatoes             1.0   \n",
-       "\n",
-       "                                                                       save_dir  \\\n",
-       "model_name    dataset_name                                                        \n",
-       "decision_tree rotten_tomatoes                                           results   \n",
-       "ridge         rotten_tomatoes  /home/chansingh/cookiecutter-ml-research/results   \n",
-       "\n",
-       "                               alpha  max_depth  seed  use_cache  \\\n",
-       "model_name    dataset_name                                         \n",
-       "decision_tree rotten_tomatoes    1.0          2   1.0        0.0   \n",
-       "ridge         rotten_tomatoes    1.0          2   1.5        0.0   \n",
-       "\n",
-       "                               accuracy_train  accuracy_cv  accuracy_test  \n",
-       "model_name    dataset_name                                                 \n",
-       "decision_tree rotten_tomatoes         1.00000      1.00000       0.500000  \n",
-       "ridge         rotten_tomatoes         0.99825      0.72611       0.719043  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
+    "\n",
     "# apply cross validation\n",
-    "(\n",
+    "ravg_cv = (\n",
     "    ravg\n",
     "    .sort_values(by='accuracy_cv', ascending=False)\n",
     "    .groupby(by=['model_name', 'dataset_name'])\n",
     "    .first()\n",
-    ")"
+    "    .reset_index()\n",
+    ")\n",
+    "ravg_cv"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -598,7 +202,7 @@
     },
     {
      "data": {
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IaEJNAL7xLrjggkgkEmW+SJ8777wz5a/xggULanu0CvF7BQAAKM+DDz6Y8ueF119/vbZHi4jd5+cyAL65hJoAAAAAAABARhNqAgAAAAAAABlNqAkAADuQ6Y8RAwAAAPgmEGoCAAAAAAAAGU2oCQAAAAAAAGQ0oSYAAAAAAACQ0YSaAEC1u/POOyOZTJb56tSpU22PViEPPvhgyusAAAC44IILUv68cNxxx9X2aACwWxBqAgAAAAAAABlNqAkAAAAAAABkNKEmAAAAAAAAkNGEmgAAAAAAAEBGy6ntAQCgqtasWRP/+Mc/4pVXXolPPvkklixZEps2bYrGjRtH69at46CDDoo+ffrE6aefHvvuu2+Nz7dt27aYNGlSvPHGGzF16tSYN29eLF++PDZu3Bh169aNpk2bRseOHaN79+5x7LHHxg9+8INo3rx5jcy2cuXKeOmll+Ldd9+N6dOnxxdffBGrVq2KjRs3Rr169aJJkybRokWLOOCAA+LAAw+Mo446Ko4//vjYY489amS+nVmyZEmMHz8+Pvroo/j4449j6dKlsW7duigoKIi6detGo0aNonHjxtGuXbvo2LFjdOnSJQ477LD4zne+E/vss09tj5/SZ599Fi+88EK88847MXv27Fi0aFGsX78+kslkNG7cONq3bx9du3aNI488MgYOHBg9e/aslTnnz58fTz/9dLz++usxY8aMWLFiRWzatClatGgRbdq0ia5du8bAgQNj0KBBsddee9XKjLurZcuWxfPPPx+vvfZazJo1KxYuXBgFBQUREdGuXbs48MAD4/nnn69QzeLi4nj77bdj0qRJ8cEHH8TcuXMjPz8/NmzYEIlEIvbYY4/Ye++946CDDopjjjkmTj311Gjbtm11XF4Zy5cvjxdeeCHefvvtmDFjRixYsCC+/PLL2Lx5czRs2DBatWoVXbp0id69e8eAAQPi2GOPjTp1/NlNAKBqpkyZEi+++GK89957MWfOnFi+fHls2LAhGjduHJ06dYqLL744rr766hqdaePGjfHUU0/FSy+9FB9//HEsXLgwNmzYEA0bNoyWLVtGt27d4qijjopTTz01evToUaOzRURMmzYtnn766Xjrrbdi9uzZsWrVqti2bVs0adIk9tprr+jevXv0798/vv/970deXl6Z8+fNmxfbtm0rtdaoUaO0/uz25Zdfxosvvhj//Oc/45NPPon58+fH2rVrY9OmTVG/fv3Iy8sr+bnxhBNOiAEDBkRubm7a+gNQRUkAyFJr1qxJXn/99cmGDRsmI2KnX4lEInnqqacmZ8+eXarOj3/845THV9WSJUuSQ4YMSbZs2XKX5tv+Va9eveSFF16YnDdvXpVnKM/LL7+cPOmkk5J169at0GwRkczNzU32798/+fjjjye3bt26S/3uuOOOlLXmz59fqfknTpyY7NevX7JOnToVnn/7V+fOnZM333xzcvr06bvct7p+rySTyeRTTz2V7N27d4Wv4+CDD06OHTs2WVRUVKX+kyZNSln/b3/7W6nj5s6dm/zhD3+4y7/2OTk5ySuvvDK5fPnyKs1Xk8r7/VqVr44dO+6wZ6pzfvzjH5c6Zv78+clzzz03mZubu9N+u2rNmjXJYcOGJdu3b1+h66lbt27ytNNOS06bNq0Sv8K75vXXX08OGjSowv+e2muvvZL33ntvcsuWLdU2GwCQff72t7+l/G+HSZMmlTruqaeeSn7rW9/a6X9zfP2/1SrSo6I2btyYvPvuu5PNmzff5f8mOv7445OTJ08uVSfdP5dt989//jN59NFH7/JsjRs3Tv4//8//k1y3bl2pOh07dixz7LHHHlul2bb76KOPkmeffXayfv36Ffpvy5YtWybvvPPOMrMCUDv8EWYAstJbb70VhxxySNx7772xadOmXTonmUzGM888Ez179oy//vWv1TZbcXFx/O///m907do1fv3rX8eqVasqdP7WrVvjb3/7WxxyyCHxm9/8Jq2zffrppyV/2vTFF1+MoqKiCtfYtm1bTJgwIc4666w4//zz0zrfzqxbty7OPffcOOGEE+KVV16J4uLiSteaP39+jBgxIu644440TlhxCxYsiBNOOCFOP/30mDx5coXPnzFjRgwePDi+853vxMyZM6thwv/605/+FN27d48nnnhil3/tCwsL4/7774+DDjooPvzww2qdb3f25z//OQ466KB45JFHyvzp9coaM2ZMdO3aNX7+85/HkiVLKnRuUVFRPP3003HooYfGzTffHIWFhWmZKSJi8eLFceqpp8Zxxx0XL7zwQoX/PbVo0aK4/vrr41vf+lZMnTo1bXMBALu3goKCOP300+P000+Pjz/+uLbHKTFjxow47LDD4vbbb4+1a9fu8nmTJk2KI488MoYNG1ZtsxUVFcWNN94Yffr0ibfffnuXz9uwYUP89re/jYMPPjimTJlSbfNF/OfJThdffHEceuih8dhjj8WWLVsqdP6qVavizjvvjIMOOigmTJhQTVMCsKuEmgBknaeffjr69esXixcvrtT5mzZtip/85CdpDwwjIlavXh0DBw6MIUOGxIYNG6pUa9OmTXHjjTfGeeedl5bA4KGHHorDDjssXnvttSrX2q6iPxBWxZo1a+LEE0+MRx99tMZ6Vre33norDj/88LT8Pfnggw/i29/+djz33HNpmKysW2+9NS699NJd/kMEX7dy5co4/vjjKxXcftPdeeedcckll1T61/7rNm/eHD/60Y/iggsuiJUrV1apVlFRUYwcOTIGDBgQ69evr/JskyZNil69esWzzz5b5VqzZs2KPn36xFNPPVXlWgDA7m3t2rVxzDHHxNNPP13bo5TyzjvvxHe/+92YNWtWpc4vKiqK2267La699to0T/afP4x72mmnxW9+85tIJpOVqrFw4cI47rjj4s0330zzdP/xySefxOGHHx5/+ctfKj3jdosWLYpBgwbFfffdl6bpAKgM79QEIKu88cYb8T//8z+xdevWco+pU6dOtGnTJtq3bx8bN26MxYsXl7xv7quGDBmS1ndsrly5Mk444YT45JNPdnhcgwYNom3bttGyZcvYtm1bLFu2LJYtW1bu8Y888kgkEol46KGHKj3bb3/72/jpT3+60+Nyc3OjVatW0bp168jNzY3Vq1fHypUrU/761bTzzjsvPvjggx0e06BBg9h7771jjz32iPr160dBQUF8+eWXsXz58h3+nqkNb7/9dpx00kk7Db/z8vKiffv2kZOTE4sXL95hCLV+/fo444wz4sknn4xTTjklbbOOHDkyfvWrX6Xc23PPPaN169ZRv379WLFiRSxcuLDc/2Gwbt26OP/882PatGnRsGHDtM23O/vLX/4Sd911V8q9pk2bxp577hktWrSIjRs3xvz583caLG7atClOOeWUmDhx4g6Py83NLfn3VCKRiGXLlsXSpUvL/Xv72muvxcknnxyvvPJKpd85NH78+PjhD3+40z8s0aJFi2jdunU0a9Ys1qxZE0uXLi33ujdt2hRnn312PPvsszFo0KBKzQUA7N6KioritNNOS3l3Zp06daJ169bRpk2baNiwYaxatSoWLFhQqSfeVNSsWbPie9/73g7vzkwkEtGqVavo0KFDbNu2LRYvXpzy+FGjRsV+++2X1vkuuOCCnf6Byvr160e7du2idevWsXr16li6dGls3Lix1DEbNmyI008/fac/61XU5MmTY8CAATu9u7Vp06bRpk2baNGiRaxbty6WLl0aX375Zcpji4qK4pprron69evHJZdcktZ5Adg1Qk0AssaXX34Z55xzTrnhVLdu3eLaa6+Ns846K1q2bFlqb+rUqfHQQw/FH//4x5L/YZ5MJuPSSy+N3r17V3m2LVu2xCmnnFJuoNmsWbO47LLL4vvf/3707t27zP/0/+KLL+LZZ5+NkSNHxqJFi8qc//DDD8exxx5bqR+cxowZs8NAs2nTpvGTn/wkBg0aFMccc0zUq1evzDFz5syJDz74IF544YUYN25cjYecTzzxRLz44osp9w444IC48sorY+DAgdG1a9dIJBJljiksLIzZs2fHJ598EhMmTIjnn38+li9fXt1jl2vhwoVx2mmnlRtotm7dOq699to455xzygTvS5YsiUcffTRGjRoVn3/+eZlzt23bFuecc068//77cfDBB1d51kmTJpUJ1Hv27BnXXXddDBw4MNq1a1dqb/ny5fHkk0/G3XffHfn5+WXqzZkzJ+6+++5yQ9JMsM8++8TRRx9d8nn58uUxZ86cMscdcsgh0axZs12q+fVfp10xb968eOKJJ0qttWzZMq677rr4/ve/Hz179iy1l0wmY+rUqfGnP/0pZb1kMhnnn39+uYFmgwYN4sILL4zTTz89jj766P+/vfuOjqrO/z/+mhSSEFIIhBJ6k9BrKBGQDoJIFxSWZkMUcVXEwi6s6CqiC6IsigoqAoIrAi4gLF26dKnSW2KAFCCE9Pv7wx9+xbmTTEsyhOfjHM7R953P+74nk5kzN+/7+XysGs9xcXFasWKF3nnnHR09etRq/MaNGzVx4kS9+eabjjxNSdKOHTvUv39/mw3N+vXr6+mnn1aHDh2s3hNZWVnavn275s2bp08++cRqZntmZqaGDh2qAwcOOPU6AACAwm3KlCnasGHDbbGWLVtq5MiR6tatm0qWLHnbsRs3bmjlypUOL9/viMzMTA0YMMBmQ658+fIaM2aMHn74YZUrV+62Y0ePHtX8+fM1ffr025pz48aNU+/evd1S35w5c3JcQWfgwIEaOnSoOnbsKB+f//vzc3Z2tn788UfNnz9fn3322e/N4fj4eD366KMuz6a85dSpU+rWrZvNn1/VqlX1zDPPqHPnzqbXTHv37tWiRYs0ffp0qyasJI0ePVotWrRQvXr13FIvAMABBbabJwAADnriiScMSab/nn/+eSM1NTXXHAcPHjQaNGhgM88f/zli1KhRNvM8+eSTRlJSkl15UlJSbOYKDAw0fvnlF4fqOnDggFGkSBGbtT311FPG5cuXHcp58+ZN46uvvjLq1Klj9OzZ064xEyZMMD3/6dOn7Rrftm1b0/GvvvqqkZWV5VD9hmEYWVlZxrp164wHH3zQeOihh+weN3ToUJd/VwzDMDp27GjzNenTp49x6dKlXHNcv37deOyxx2zmqV+/vpGRkWF3TevXr8/1PVGkSBFj1qxZdv3Mk5KSjBYtWpjmCQkJMVJSUuyuraDNmTPH9HmsX7/ebeew5zPpL3/5i5GQkOD0Od555x2buXv37m38+uuvduXJyMgw3njjDcNisVjl8fLyMjZt2uRQXYmJiUZERIRpXaGhocbChQvtznXkyBGjVq1aprk6d+5sZGdnO1QbAAAoPGx9p/vjv4CAAGPu3LluP4ej3xv/+c9/2qxx0KBBdl1fnjt3zmjXrp1d3zPtvS4zDMP49ddfjeLFi5vmKVu2rLFmzRq78uzatcu45557cq3tvvvus7s2wzCM9PR0m9f8fn5+xowZM4zMzEy7cl24cMFo1aqVaa569erZ9TcIAIB70dQEANwRtm3bZvoHdEnG+PHjHcp15coVIzIyMteLJ3tt2rTJtDaLxWJ89tlnjj5VwzBsNwF79epld46srCyjcePGpnm8vb2NWbNmOVXbLdnZ2cahQ4fseqwrTc0rV64Y3t7eVmMHDhzoUv23XL9+3e7HuqOp+c0339j8nRswYIDDTdrRo0fbzDdt2jS78+TW1CxatKjDf4xJSkoyypYta5rvyy+/dChXQfKEpubo0aNdasgdP37c8Pf3N809YcIEp3La+rk0bNjQoTyPPvqoaZ7KlSsbJ0+edLiu+Ph4o1GjRqY5lyxZ4nA+AABQOOTW1AwMDDQ2bNiQJ+dw5Hvj6dOnjYCAANM8f/nLXxz6Tnjz5k2jTZs2uX7XdKSpOWzYMNMcJUqUMA4ePGh3HsMwjIsXLxpVq1bNsTZHm5qTJk0yzVO8eHFj165dDuUyjN9+hl27dnX5egsA4B5eAgDgDjB16lTTpWh69uypSZMmOZSrRIkSWrlypYoWLeqW2l566SXT2t544w2NGDHCqZwTJkww3f9t2bJlOn36tF05FixYoD179pge+/e//+3yHiAWi0W1a9d2KYc9jh49arpnzV//+le35C9WrJhb8tjL1u9r48aN9eWXX8rLy7GvZ++//766du1qemzy5Mm57k9or6lTp6pt27YOjQkJCbG5zOx3333nhqruDtHR0Xr//fdNl1a21/jx45WammoVf/zxxzVx4kSncg4bNkxPPfWUVXzfvn3auHGjXTkOHz6sOXPmWMVDQkK0fPlyVa1a1eG6wsLC9M033ygoKMjq2LRp0xzOBwAA7g7vvfee7rvvvoIuQzNnztTNmzet4k2aNNFnn33m0HdCf39/LV26VBEREW6pLT4+3uays1999ZXD219ERERoyZIlty1R64orV65o8uTJVnFfX18tXrxYTZo0cTinv7+/5s2bp/Lly1sd++CDD5Sdne1UrQAA59DUBAB4vMuXL2vJkiVWcX9/f02dOtWpnJUrV9bLL7/sYmXShg0btH37dqt4VFSUXnnlFafzWiwWzZw50+riLjs7Wx988IFdOd555x3TeI8ePfTEE084XVt+M9uXUfptP8M7zebNm033XbVYLJoxY4bpfqa5sVgs+uCDD0zHxsbGmr53HNWmTRunf2ceeughBQcHW8V3797tall3BS8vL3366acuNTRPnTqlRYsWWcUrVKig6dOnu1KepkyZouLFi1vF33//fbvGv/POO6Z/CHrzzTddummiWrVqpp/xGzZs0P79+53OCwAACqfo6GiPuEbKyMjQ559/bnps+vTp8vX1dThnaGio3n77bRcr+80XX3xhetNk7969bd5omZt69erpmWeecbU0SdKHH36o5ORkq/iYMWMcvkHzj8LCwkybpSdPntR///tfp/MCABxHUxMA4PHmzp2r9PR0q/jw4cNVpUoVp/O++OKLCgkJcaU0ffrpp6bxyZMnu9SEkKSKFSuqf//+VvHFixfnOnbnzp2mzTNvb299+OGHLtWV3zIzM03jZr8Tns7WXc09e/ZUixYtnM5bvXp1mzNvv/rqK6fz3vLiiy86PTYgIEAdO3a0ip87d07x8fGulHVX6NSpk2rVquVSjtmzZ5vOJv/HP/4hf39/l3IHBgZq5MiRVvEVK1bkOkv42rVr+uabb6zi1apV05NPPulSXZL01FNPmc7IZ5YwAAD4s9GjR7t8/eYO33//vS5dumQVv//++xUdHe103sGDB6tmzZqulCbpt2tzM67eMDx27FiHV6z5M8MwNHv2bKt4SEiIXnvtNZdyS7/drFmhQgWruD3X5wAA96GpCQDweOvWrTONDxo0yKW8AQEB6tu3r9PjMzMz9f3331vFK1eurHbt2rlS2u969uxpFTt79qxiY2NzHGdrdt6DDz6oihUruqO0fFOiRAnT+A8//JDPlbjOVs1Dhw51ObetHOvXr1dGRobTeUNDQ9W9e3enx0tS/fr1TeOnTp1yKe/dYNiwYS7nMPtDS9GiRTVgwACXc0vmn1NpaWm5zsb94YcflJKSYhUfPHiwW5YgK168uNq0aWMV37p1q8u5AQBA4RESEqLevXsXdBmS8u7a12KxuJzjxo0b+vnnn63i99xzj5o1a+ZS7oiICLVv396lHD/99JPOnz9vFe/Vq5dCQ0Ndyi1JPj4+ptdFfLcEgPxFUxMA4PF++uknq1hERIRLd6reYjYT0l7bt2/XtWvXrOLuvCBu1aqVaXzLli05jlu7dq1p3Nk9PguSrWVmx44da/f+op4gNjbWtIkXFBRkun+qo6KiolStWjWr+I0bN2zurWqP6Ohol++arl69umnc7P2D27m6r9OFCxd05MgRq3iXLl3ctq9w48aNFRAQYBXP7Q88q1evNo336dPHLXVJ5p+hO3bsYO8jAADwu+bNm8vPz6+gy5D024o7f+br66tevXq5nNuVa19J2rVrl7Kysqzi7riWkaQHHnjApfEF9d3y+PHjunLlitvOAQDIGU1NAIBHO3v2rOnyO02aNHHL8kBNmzZ1euyuXbtM440bN3Y655+VK1fOdMaS2dKyt2RmZpruGWexWHTvvfe6rbb8UqZMGTVo0MAqfuHCBTVq1Ehvv/32HbGMqa3XrGHDhk7tpWmmefPmpnFX9hA0+9k7ymxPTUm6evWqy7kLs7Jly6ps2bIu5ciPzylfX19FRERYxXP6nJLMa/Pz81OdOnXcVlulSpWsYteuXdOZM2fcdg4AAHBna9KkSUGXIOm37TXMvrdHRkYqMDDQ5fw1a9ZUUFCQ0+PNbjaWfruecQdXrzvy43uv2XdLKffvvQAA96GpCQDwaEePHjWNu+vCqWTJkipXrpxTYw8ePGgaj4yMdKUkK2FhYVaxnJp4Z86cMd3LrkaNGipevLhba8svL7zwgmn86tWreuWVVxQREaFu3bpp5syZNn9nCtqJEydM440aNXLbOWzlOnnypNM5bS3/64hixYqZxpOTk13OXZjZmuHqiPz6nDL7Pcnpcyo7O9t0Bmn16tXl7e2dp3XlVhsAALi7uOM7lzucPn1a6enpVnF3XftaLBab20LY48KFC6ZxV3K6M4/Z995ixYqpfPnyLuX9I75bAkDBc32zGgAA8lBiYqJpvHLlym47R5UqVXTx4kWHx5nt1yH9tgxoXrP1c5Fs11WjRo28KifPDRo0SF999ZXNJYXS09O1cuVKrVy5UpJUqlQptWjRQtHR0Wrbtq2aNGnilj36XGFrH9SqVau67Ry2/iATExPjdE537D9ja1Y1S4DmLCQkxOUctj4PXF1+zB45fU7FxcWZ/tHu0KFDbpmFn5ucagMAAHcXd3zncof8uvbNbSsTW5KSkkzj4eHhLlT0f8LCwuTt7W26xK09zJquycnJfLcEgEKGmZoAAI9m68LJ1nKWznB2CR6zZXHzS0JCgsPH7tRZmpLk5eWlhQsXqnXr1nY9/tKlS1q2bJlefvlltWjRQmFhYerbt68WL15sOos1P9i60HXn77KtXLbeR/Zw56w5OMYdvxue+jlVkHVJOdcGAADuLu78Pu4KT772lTy7vqtXrxbYdZ7Ed0sAyE80NQEAHi0/LpycvTM3JSXFbTU4KjU11eaxmzdvmsbdMeOuIIWGhmrt2rX629/+pqJFizo09vr161q8eLH69u2rcuXK6e2339aNGzfyqFJzti6y3XlnuK1cOf2+wHP5+vq6nMNTP6cKsi6J9wQAAPg/7vjO5Q6efO0r2d46wh37fd5ia9uK3PDdEgDuHjQ1AQBwUmZmZkGXYMrW8jqGYeRzJe7n6+ur119/XSdPntT48eNVsWJFh3PEx8frlVdeUa1atbRt27Y8qNIx7nxdCsNrDPfy1M8pT60LAAAA5mw1HN3ZUHT2xlO+WwLA3YM9NQEAHs3W7MJr16657RzO5vL39zeNN23aVH5+fq6UlKt69erZPGarLleWIPU0ZcqU0aRJk/T6669r165dWrt2rTZs2KBt27bZ/XqeP39ebdq00bJly3T//ffnccWy+TuRH7/Ltn4nUPjZeu3r1q2b5/tHlS1b1uYxW3UFBwfn+PnmLqVLl87zcwAAADjCk699pZzrc2VZ2z/ncoat75Z+fn5q2rSpKyXZxZmbbQEAzqGpCQDwaJ58YWerITB79ux8+aO8LSVKlDCNF6am5i0Wi0VRUVGKiorSyy+/rOzsbB04cEA//vijfvzxR61Zs8bmXpbSb3f0DhgwQPv371eVKlXytFZbe5rmx+/ynbyfKlxj63Pqn//8p3r06JHP1fwfW3VVr15dmzdvzudqAAAACp4nX/tKtuu7fPmyypUr53TeWxITE5WVleXU2ODgYFksFquVawIDA/luCQCFDMvPAgA8mq1mzJkzZ9x2jtOnTzs1rkKFCqbx+Ph4V8pxma26jh8/ns+V5D8vLy81bNhQo0eP1qJFi3Tp0iWtX79eQ4YMsXn37vXr1zVhwoQ8ry0iIsI0furUKbed48SJE6bxnGbMoXC70z6nCrouAACAguLJ176SbDYuDxw44HROd+Xx8/NTeHi4VTwpKUnZ2dmulAUA8DA0NQEAHi0yMtI0vm/fPrfkv3Llii5evOjU2Jo1a5rGL1y44EpJLqtUqZJpA+/48eNKSEgogIoKjo+Pj9q2basvvvhChw8fVnR0tOnjFi1a5Na9YMxUr17dNL537163ncPW+6JGjRpuOwfuLJ76ORUQEGDa2IyLi2NPJAAAcFeqUqWK6ZYV7rr2NQzDpcZhVFSUadxd9bmax+x7b3Z2tmJiYlzKCwDwLDQ1AQAerVKlSqZ7n+3evdst+Xft2uX02GbNmpnGN23a5HROd/Dx8VHDhg2t4oZhaMuWLflfkIeoUqWKVq5caXqHcVpaWp4vS1S/fn3T+L59+5SRkeGWc+zYscM03qBBA7fkx53HUz+nJPPaUlNTtXPnzgKoBgAAoGAVKVLE9Hv70aNHdePGDZfzHzt2TNevX3d6fNOmTeXlZf2n5BUrVrhS1u+WL1/u0nhP/t4LAHAfmpoAAI9ndkdoTEyMtm7d6nLub775xumxbdq0UZEiRazia9eudaUkt+jUqZNpfM6cOflciWcJDg7WyJEjTY+5shSTPcqUKWM6W/P69etauXKly/l3795tuvxssWLF1KhRI5fz383M7piXpPT09HyuxHF16tQxXX54y5YtunnzZgFU9H86duxoGl+zZk0+VwIAAOAZzBpzGRkZWrp0qcu5Xbn2lX67rqhXr55V/NixY/rpp59cyh0bG+vydTTfLQHg7kBTEwDg8dq3b28anzdvnkt5U1NTtXjxYqfHBwcHm144nTp1yi0Xna7o1auXaXzZsmU6f/58/hbjYcxmsUrKl6V5u3btahqfO3euy7m//PJL03iHDh3k4+Pjcv67WXBwsGncHXfM5zWLxaLevXtbxVNSUjRr1qwCqOj/9OrVS97e3lbxjz76SKmpqQVQEQAAQMHKq2tfSZo/f77LOQYPHmwanzx5skt53333XZf3vmzfvr3pvqRff/21fv31V5dyAwA8B01NAIDHGzx4sOmMyNmzZ+vs2bNO533vvfeUlJTkQmWyOetv4sSJBbovXOPGjU1n52VlZWn06NEFUJHnsHWxHBoamufnfuSRR0zjixcvdmlJ5dOnT9tsUA0aNMjpvPiNrabmnXKDgK3PqcmTJ7u0BJmrypQpo549e1rFY2NjNWPGjAKoCAAAoGA98MADKlWqlFV85cqV2r59u9N5582bp6NHj7pSmiRp2LBhptfm3377rVavXu1UzkOHDmn69OmulqYiRYpo+PDhVvGbN2/qzTffdDk/AMAz0NQEAHi88PBw0z98p6am6q9//atTOc+dO6e33nrL1dLUvXt3070S9+3bV+DNw5deesk0vnTpUn322Wf5XI3nsNU8NNtr091atmxpOlM0OztbTz/9tNON8DFjxpjObCtfvrzpeweOMVs2WHJtT978VK9ePT3wwANW8djYWD388MMu3xXvildeeUUWi8Uq/uqrr7L/EQAAuOv4+vpq6NChVnHDMPTss886db1w7do1jRs3zh3lqWTJkhowYIDpscGDBzvcOI2NjVWvXr3cdkPwX//6VwUEBFjFP/zwQ3399dduOQcAoGDR1AQA3BGee+450z98f/fdd5o4caJDuRITE9WtWze3LB3p5eWladOmmR776KOPNHbsWLddoKWkpGjmzJn67rvv7Hr8gAED1LRpU9NjTz75pMv7axqGoSNHjriUwx7jxo3TG2+84ZblYS9duqSPP/7YKu7j46Po6GiX89tjwoQJpvEdO3ZoxIgRMgzDoXxjx47V999/b3rs5ZdfNr2TGo4pW7asSpcubRVftmyZLl26VAAVOW7KlCmme4MuX75cQ4YMUUpKilvOk5GRoXnz5umTTz6x6/FNmzbVkCFDrOLp6enq3bu31q1b55a6JOnEiRMaPXp0gc5OBQAAyM1TTz0lf39/q/hPP/2kxx9/3KFcaWlp6tWrly5evOiu8jR58mTTVW4uX76sDh06aP369Xbl2bt3r9q3b68TJ064rbby5cvbvLl3+PDhWrhwodvOFRMTo9dee03Hjx93W04AQO5oagIA7gjR0dF69NFHTY/94x//0Lhx45SWlpZrniNHjqhDhw46dOiQ22pr166dnn32WdNj7777rtq2bau9e/c6nX/Pnj16+eWXVaFCBY0aNcruJXctFovmzJlj2sjIysrSiBEjNHr0aMXHxztUT1pamr7++ms1aNBAr7zyikNjnREXF6e//e1vqlChgkaOHKmNGzc6NbPszJkz6tq1q+Li4qyOde7cWSVLlnRHubnq1auXunTpYnps7ty5evjhh+16TW7cuKFRo0bp3XffNT3eqFEjm8uOwnFt27a1il29elWdOnXSqlWrlJGRkf9FOSAyMtLm7PR58+YpKipKGzdudDr/0aNH9cYbb6hKlSoaPHiwQ5+x06ZNU8WKFa3iCQkJ6tSpk1599VUlJiY6VVdKSoqWLFmiXr16qWbNmvrwww+VlZXlVC4AAID8UKVKFY0fP9702Oeff66hQ4fq2rVruea5ePGiunfvbneT0V5ly5a1eQ0SExOj9u3b65FHHtHq1autbvDNzs7Wjz/+qJEjRyoqKuq2mZ1t27Y1/U7oqNdee8305t7U1FQNHDhQjz/+uGJiYpzKnZGRodWrV2vIkCGqXLmy/vnPf7rlZmkAgAMMAADuEImJiUbp0qUNSab/ateubXz00UdGfHy81dg9e/YYzz//vOHv73/bmPDwcKN79+6m+RyRlpZmdOzY0WZtkoz27dsbn3zyiXHkyBGbedLT0419+/YZX3zxhfH0008bFStWtMozdepUh2r7/PPPc6wrJCTEeP755421a9caaWlppjlOnjxpLFy40Bg6dKgREhLy+9iePXvaVcOECRNMz3369Olcxw4dOtRqXJkyZYwnn3zSmDNnjnHgwAEjMzPTdOzNmzeN9evXG6NGjbJ67W/98/HxMXbv3m3X8zCrxZmvUxcuXDBKlSpl8zUpXbq08eabbxqnTp2yGhsbG2tMnTrVqFKlis3xxYoVMw4fPuxQTevXrzfNNWfOHIefX37mzi//+9//cnwfFSlSxKhZs6YRFRVl3HvvvVb/+vXrl2N+s5xDhw51+/MYMmRIjs+jSZMmxgcffGDs3bvXyMrKMs2RmZlpHD582FiwYIHxwgsvGDVr1rTKM2bMGIfq2rt3rxEcHGyzrsDAQOOpp54yli5daly5csVmnvj4eGP9+vXG1KlTjZ49exoBAQFWuRITEx2qDQAAFA5z5swx/Z6xfv16jztHenq6UbduXZvfjSpUqGC89957xsWLF63GHjt2zJgwYYIRGhp62xh/f3/j4Ycfdvq67M/69++f4/fKW+esWrWqERUVZVSvXt0IDAw0fVxYWJhx8uRJo1KlSlbH2rZt63Bt586dM8qVK5fjd/e//OUvxqJFi4yYmBibea5du2Zs2bLFmDFjhvHwww/fdi1869/evXsdrg8A4DwfAQBwhwgNDdWCBQvUpUsX01lRhw8f1siRIzVq1CiVLl1aERERSklJ0cWLF03vZLVYLPrkk0/sXs41J0WKFNHixYvVs2dPm3fCrlu37velFIsVK6aSJUsqLCxM3t7eunr1qq5evar4+Hi3LVd7y9ChQ3X58mWNHTvW9PjVq1f1r3/9S//617/k6+urUqVKKTw8XD4+PkpMTNTly5ftuhM4P/3666/6+OOPf19KtkiRIipRooSKFy+u4OBgpaWlKTExUefOnct1Vuff//53NW7cOD/K/l25cuW0ZMkSderUyfTO3ri4OL322mt67bXXVKJECZUtW1Y+Pj6KiYnR5cuXc1yi1tfXV19//bVq1aqVl0/hrtOhQwe1a9fO5vs7PT1dx44dszm+UqVKeVWaQz777DNlZGRowYIFpsd37979+76zAQEBCg8PV1hYmIoUKaJr164pKSlJCQkJSk9Pd2tdDRs21PLly9WjRw8lJSVZHb9x44ZmzpypmTNnymKx/P75GRoaqtTUVF29elVJSUmmYwEAAO40t77T33vvvbp69arV8fPnz+uFF17Qiy++qPDwcEVERCgzM1MxMTE2t+145513HF6lJydz585VSkqKli9fbvMxqampOnXqlE6dOmXzMUWLFtXixYtVtWpV0+O+vr4O11ahQgWtWbNGnTt31vnz562Op6ena+7cuZo7d64kqUSJEgoLC1Px4sWVkZHx+/V5QkKCw9uDAADyFk1NAMAdpV27dpo/f74GDRpk84/q2dnZio2NVWxsbI653n33XfXs2dMtTU1JCgoK0sqVKzV69Ohc95NLTk5WcnKyzpw545Zz5+bFF19UiRIlNGrUKKWmptp8XEZGhi5evOjWPVfyQ3p6ul2v+Z+NHz9ef/vb3/Koqpy1bNlSP/zwg3r37q0rV67YfFx8fLzdf3woVqyYFixYoO7du7urTPx/FotF8+fPV+vWrd26709+8/Hx0bx581S9enW9+eabOTb9b968qXPnzuncuXP5UlurVq20efNm9e/fP8f9eg3D0OXLl3X58uV8qQsAAKAg1KlTR99//70eeOABmzeZGoahS5cu5brP+7PPPqvRo0dr4sSJbqvPz89PS5Ys0dixY/X+++871fwrV66cFi9erGbNmkmS6fM021/UHpGRkdq2bZseeughbd26NcfHOnLNBQAoWOypCQC44/Tr10+rV69W2bJlnRofEBCg2bNn6/nnn3dzZb9d2M2aNUtLly5VtWrV3Jrb29tbnTt3VuvWrZ0aP3z4cO3YsUMtWrRwW03BwcFuy2VLUFCQ23NGRERowYIFmjRpkttzO6JVq1batWuX2rdv73KuqKgo7dixQw888IAbKoOZMmXKaO/evXrmmWcUGBhY0OU4zWKx6PXXX9emTZvUqFEjt+ePjo52urF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",
       "text/plain": [
        "<Figure size 1920x1440 with 1 Axes>"
       ]
@@ -608,7 +212,7 @@
     }
    ],
    "source": [
-    "sns.barplot(data=ravg, x='model_name', y='accuracy_test') #, hue='dataset_name')\n",
+    "sns.barplot(data=ravg_cv, x='model_name', y='accuracy_test') #, hue='dataset_name')\n",
     "plt.show()"
    ]
   },
@@ -662,7 +266,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10 (default, Nov 14 2022, 12:59:47) \n[GCC 9.4.0]"
+   "version": "3.8.10"
   },
   "orig_nbformat": 4,
   "vscode": {