diff --git "a/18_Feature_Tokenizer_Transformer.ipynb" "b/18_Feature_Tokenizer_Transformer.ipynb"
new file mode 100644--- /dev/null
+++ "b/18_Feature_Tokenizer_Transformer.ipynb"
@@ -0,0 +1,1303 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "8f37ab53",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: 19 Feature Tokenizer Transformer\n",
+    "description: An implementation of Feature Tokenizer Transformer on a classification task\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d41c7aa7",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/drive/1tdPifaZCTVpjzCh1FOyGPywMEfrbBZLh?usp=sharing\" target=\"_blank\"><img align=\"left\" alt=\"Colab\" title=\"Open in Colab\" src=\"https://colab.research.google.com/assets/colab-badge.svg\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "vYaXd7ISfCHG",
+   "metadata": {
+    "id": "vYaXd7ISfCHG"
+   },
+   "source": [
+    "<a href=\"https://www.kaggle.com/code/ritvik1909/feature-tokenizer-transformer/\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09826e04",
+   "metadata": {
+    "id": "09826e04",
+    "papermill": {
+     "duration": 0.020595,
+     "end_time": "2022-03-31T14:04:34.631874",
+     "exception": false,
+     "start_time": "2022-03-31T14:04:34.611279",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Feature Tokenizer Transformer\n",
+    "Featured in the paper [Revisiting Deep Learning Models for Tabular Data (2021, June)](https://arxiv.org/abs/2106.11959) Feature Tokenizer Transformer is a simple adaptation of the Transformer architecture for the tabular domain. In a nutshell, Feature Tokenizer Transformer transforms all features (categorical and numerical) to embeddings and applies a stack of Transformer layers to the embeddings. Thus, every Transformer layer operates on the feature level of one object.\n",
+    "\n",
+    "In this notebook we will be implementing Feature Tokenizer Transformer using TensorFlow 2 from scratch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "iW5s96JIyd6-",
+   "metadata": {
+    "id": "iW5s96JIyd6-"
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install tensorflow-addons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "457cf1b0",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2022-03-31T14:04:34.678481Z",
+     "iopub.status.busy": "2022-03-31T14:04:34.676340Z",
+     "iopub.status.idle": "2022-03-31T14:04:41.559310Z",
+     "shell.execute_reply": "2022-03-31T14:04:41.558566Z",
+     "shell.execute_reply.started": "2022-02-09T09:09:07.50688Z"
+    },
+    "id": "457cf1b0",
+    "papermill": {
+     "duration": 6.907577,
+     "end_time": "2022-03-31T14:04:41.559509",
+     "exception": false,
+     "start_time": "2022-03-31T14:04:34.651932",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import layers as L\n",
+    "from tensorflow_addons.activations import sparsemax\n",
+    "from tensorflow.data import Dataset\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.compose import make_column_selector, ColumnTransformer\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.metrics import confusion_matrix, classification_report\n",
+    "import joblib\n",
+    "from tensorflow.keras import utils"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a01faffd",
+   "metadata": {
+    "id": "a01faffd",
+    "papermill": {
+     "duration": 0.018805,
+     "end_time": "2022-03-31T14:04:41.597609",
+     "exception": false,
+     "start_time": "2022-03-31T14:04:41.578804",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Data\n",
+    "Loading the train and test csv files into `pandas.DataFrame` and splitting the columns as features and target.\n",
+    "\n",
+    "We will be using Stratified K folds as our local cross validation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f0e0c77",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 224
+    },
+    "execution": {
+     "iopub.execute_input": "2022-03-31T14:04:41.642070Z",
+     "iopub.status.busy": "2022-03-31T14:04:41.640798Z",
+     "iopub.status.idle": "2022-03-31T14:05:13.765418Z",
+     "shell.execute_reply": "2022-03-31T14:05:13.765978Z",
+     "shell.execute_reply.started": "2022-02-09T09:12:07.378505Z"
+    },
+    "id": "5f0e0c77",
+    "outputId": "4455f38a-cf9b-43b9-87a9-a2e2c8c2d275",
+    "papermill": {
+     "duration": 32.15078,
+     "end_time": "2022-03-31T14:05:13.766160",
+     "exception": false,
+     "start_time": "2022-03-31T14:04:41.615380",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(17000, 9)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-eef524a2-c896-48b1-8131-5a532152ff43\">\n",
+       "    <div class=\"colab-df-container\">\n",
+       "      <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "    .dataframe tbody tr th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-114.31</td>\n",
+       "      <td>34.19</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1015.0</td>\n",
+       "      <td>472.0</td>\n",
+       "      <td>1.4936</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-114.47</td>\n",
+       "      <td>34.40</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1129.0</td>\n",
+       "      <td>463.0</td>\n",
+       "      <td>1.8200</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-114.56</td>\n",
+       "      <td>33.69</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>333.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>1.6509</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-114.57</td>\n",
+       "      <td>33.64</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>515.0</td>\n",
+       "      <td>226.0</td>\n",
+       "      <td>3.1917</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-114.57</td>\n",
+       "      <td>33.57</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>624.0</td>\n",
+       "      <td>262.0</td>\n",
+       "      <td>1.9250</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eef524a2-c896-48b1-8131-5a532152ff43')\"\n",
+       "              title=\"Convert this dataframe to an interactive table.\"\n",
+       "              style=\"display:none;\">\n",
+       "        \n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+       "  </svg>\n",
+       "      </button>\n",
+       "      \n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      flex-wrap:wrap;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "      <script>\n",
+       "        const buttonEl =\n",
+       "          document.querySelector('#df-eef524a2-c896-48b1-8131-5a532152ff43 button.colab-df-convert');\n",
+       "        buttonEl.style.display =\n",
+       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "        async function convertToInteractive(key) {\n",
+       "          const element = document.querySelector('#df-eef524a2-c896-48b1-8131-5a532152ff43');\n",
+       "          const dataTable =\n",
+       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                     [key], {});\n",
+       "          if (!dataTable) return;\n",
+       "\n",
+       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "            + ' to learn more about interactive tables.';\n",
+       "          element.innerHTML = '';\n",
+       "          dataTable['output_type'] = 'display_data';\n",
+       "          await google.colab.output.renderOutput(dataTable, element);\n",
+       "          const docLink = document.createElement('div');\n",
+       "          docLink.innerHTML = docLinkHtml;\n",
+       "          element.appendChild(docLink);\n",
+       "        }\n",
+       "      </script>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "  "
+      ],
+      "text/plain": [
+       "   longitude  latitude  housing_median_age total_rooms total_bedrooms  \\\n",
+       "0    -114.31     34.19                15.0           2              3   \n",
+       "1    -114.47     34.40                19.0           0              1   \n",
+       "2    -114.56     33.69                17.0           0              4   \n",
+       "3    -114.57     33.64                14.0           1              7   \n",
+       "4    -114.57     33.57                20.0           4              6   \n",
+       "\n",
+       "   population  households  median_income  median_house_value  \n",
+       "0      1015.0       472.0         1.4936                 0.0  \n",
+       "1      1129.0       463.0         1.8200                 0.0  \n",
+       "2       333.0       117.0         1.6509                 0.0  \n",
+       "3       515.0       226.0         3.1917                 0.0  \n",
+       "4       624.0       262.0         1.9250                 0.0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('/content/sample_data/california_housing_train.csv')\n",
+    "data['total_rooms'] = (data['total_rooms'] % 10).astype(int).astype(str) # for demonstration purpose we will treat these columns as categorical\n",
+    "data['total_bedrooms'] = (data['total_bedrooms'] % 10).astype(int).astype(str) # for demonstration purpose we will treat these columns as categorical\n",
+    "data['median_house_value'] = data['median_house_value'] // 100_000 # converting the problem for a rgression to a classification problem\n",
+    "print(data.shape)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33890ed1",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "execution": {
+     "iopub.execute_input": "2022-03-31T14:05:13.814861Z",
+     "iopub.status.busy": "2022-03-31T14:05:13.814084Z",
+     "iopub.status.idle": "2022-03-31T14:05:28.504049Z",
+     "shell.execute_reply": "2022-03-31T14:05:28.503424Z",
+     "shell.execute_reply.started": "2022-02-07T16:57:49.893665Z"
+    },
+    "id": "33890ed1",
+    "outputId": "84355785-80fb-4fb3-86f1-4de0dc6c75fc",
+    "papermill": {
+     "duration": 14.715904,
+     "end_time": "2022-03-31T14:05:28.504193",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:13.788289",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3000, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "test = pd.read_csv('/content/sample_data/california_housing_test.csv')\n",
+    "test['total_rooms'] = (test['total_rooms'] % 10).astype(int).astype(str) # for demonstration purpose we will treat these columns as categorical\n",
+    "test['total_bedrooms'] = (test['total_bedrooms'] % 10).astype(int).astype(str) # for demonstration purpose we will treat these columns as categorical\n",
+    "test['median_house_value'] = test['median_house_value'] // 100_000 # converting the problem for a rgression to a classification problem\n",
+    "print(test.shape)\n",
+    "X_test = test.drop(['median_house_value'], axis=1)\n",
+    "y_test = pd.get_dummies(test['median_house_value'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba666ff7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-03-31T14:05:28.673783Z",
+     "iopub.status.busy": "2022-03-31T14:05:28.672869Z",
+     "iopub.status.idle": "2022-03-31T14:05:28.689473Z",
+     "shell.execute_reply": "2022-03-31T14:05:28.690014Z",
+     "shell.execute_reply.started": "2022-02-07T16:58:00.28421Z"
+    },
+    "id": "ba666ff7",
+    "papermill": {
+     "duration": 0.163677,
+     "end_time": "2022-03-31T14:05:28.690210",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:28.526533",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X = data.drop(['median_house_value'], axis=1)\n",
+    "y = pd.get_dummies(data['median_house_value'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36986880",
+   "metadata": {
+    "id": "36986880",
+    "papermill": {
+     "duration": 0.022543,
+     "end_time": "2022-03-31T14:05:28.793311",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:28.770768",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Model\n",
+    "**Creating some data utility classes**:\n",
+    "\n",
+    "`DataConfig` helps to segregate the features into numeric features and categorical features and maintain a vocabulary for the categorical ones.\n",
+    "\n",
+    "`DataLoader` class creates `tf.data.Dataset` objects from `pandas.DataFrame` to ensure efficiency in the input pipeline to the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11ad8c7b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-03-31T14:05:28.848441Z",
+     "iopub.status.busy": "2022-03-31T14:05:28.847411Z",
+     "iopub.status.idle": "2022-03-31T14:05:28.850963Z",
+     "shell.execute_reply": "2022-03-31T14:05:28.850342Z",
+     "shell.execute_reply.started": "2022-02-07T16:58:00.460541Z"
+    },
+    "id": "11ad8c7b",
+    "papermill": {
+     "duration": 0.035478,
+     "end_time": "2022-03-31T14:05:28.851093",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:28.815615",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class DataConfig:\n",
+    "    def __init__(self, numeric_feature_names, categorical_features_with_vocabulary):\n",
+    "        self.NUMERIC_FEATURE_NAMES = numeric_feature_names\n",
+    "        self.CATEGORICAL_FEATURES_WITH_VOCABULARY = categorical_features_with_vocabulary\n",
+    "        self.CATEGORICAL_FEATURE_NAMES = list(self.CATEGORICAL_FEATURES_WITH_VOCABULARY.keys())\n",
+    "        self.FEATURE_NAMES = self.NUMERIC_FEATURE_NAMES + self.CATEGORICAL_FEATURE_NAMES\n",
+    "        \n",
+    "class DataLoader:\n",
+    "    @classmethod\n",
+    "    def from_df(cls, X, y=None, batch_size=1024):\n",
+    "        return (\n",
+    "            Dataset.from_tensor_slices(({col: X[col].values.tolist() for col in X.columns}, y.values.tolist())).batch(\n",
+    "                batch_size\n",
+    "            )\n",
+    "            if y is not None\n",
+    "            else Dataset.from_tensor_slices({col: X[col].values.tolist() for col in X.columns}).batch(batch_size)\n",
+    "        )        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2e41252",
+   "metadata": {
+    "id": "f2e41252",
+    "papermill": {
+     "duration": 0.022767,
+     "end_time": "2022-03-31T14:05:28.898555",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:28.875788",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**Creating Input Layers and Feature Encoding Layers**\n",
+    "\n",
+    "`get_inputs` returns a dictionary of Input Layers based on the data types of the feature columns mentioned in the `DataConfig` object.\n",
+    "\n",
+    "`encode_inputs` applies StringLookup and Embedding Layer to the categorical features and Reshapes the Numeric Features in order to encode the inputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "536dd575",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-03-31T14:05:28.958742Z",
+     "iopub.status.busy": "2022-03-31T14:05:28.956589Z",
+     "iopub.status.idle": "2022-03-31T14:05:28.959664Z",
+     "shell.execute_reply": "2022-03-31T14:05:28.960277Z",
+     "shell.execute_reply.started": "2022-02-07T16:58:00.472966Z"
+    },
+    "id": "536dd575",
+    "papermill": {
+     "duration": 0.039,
+     "end_time": "2022-03-31T14:05:28.960429",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:28.921429",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_inputs(config):\n",
+    "    return {\n",
+    "        feature_name: L.Input(\n",
+    "            name=feature_name,\n",
+    "            shape=(),\n",
+    "            dtype=(tf.float32 if feature_name in config.NUMERIC_FEATURE_NAMES else tf.string),\n",
+    "        )\n",
+    "        for feature_name in config.FEATURE_NAMES\n",
+    "    }\n",
+    "\n",
+    "def encode_inputs(inputs, config, use_embeddings=False, embedding_dim=32, prefix=\"\", concat_features=False):\n",
+    "    cat_features = []\n",
+    "    num_features = []\n",
+    "    for feature_name in inputs:\n",
+    "        if feature_name in config.CATEGORICAL_FEATURE_NAMES:\n",
+    "            vocabulary = config.CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]\n",
+    "            lookup = L.StringLookup(\n",
+    "                vocabulary=vocabulary,\n",
+    "                mask_token=None,\n",
+    "                num_oov_indices=0,\n",
+    "                output_mode=\"int\" if use_embeddings else \"binary\",\n",
+    "                name=f\"{prefix}{feature_name}_lookup\",\n",
+    "            )\n",
+    "            if use_embeddings:\n",
+    "                encoded_feature = lookup(inputs[feature_name])\n",
+    "                embedding = L.Embedding(\n",
+    "                    input_dim=len(vocabulary),\n",
+    "                    output_dim=embedding_dim,\n",
+    "                    name=f\"{prefix}{feature_name}_embeddings\",\n",
+    "                )\n",
+    "                encoded_feature = embedding(encoded_feature)\n",
+    "            else:\n",
+    "                encoded_feature = lookup(\n",
+    "                    L.Reshape((1,), name=f\"{prefix}{feature_name}_reshape\")(inputs[feature_name])\n",
+    "                )\n",
+    "            cat_features.append(encoded_feature)\n",
+    "        else:\n",
+    "            encoded_feature = L.Reshape((1,), name=f\"{prefix}{feature_name}_reshape\")(inputs[feature_name])\n",
+    "            num_features.append(encoded_feature)\n",
+    "\n",
+    "    features = (\n",
+    "        L.Concatenate(name=f\"{prefix}inputs_concatenate\")(cat_features + num_features)\n",
+    "        if concat_features\n",
+    "        else (cat_features, num_features)\n",
+    "    )\n",
+    "\n",
+    "    return features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9a367da",
+   "metadata": {
+    "id": "d9a367da",
+    "papermill": {
+     "duration": 0.022304,
+     "end_time": "2022-03-31T14:05:29.005787",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:28.983483",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**Defining Model Configurations**\n",
+    "* Number of Outputs\n",
+    "* Activation of the Output Layer\n",
+    "* Number of Transformer Blocks\n",
+    "* Number of heads in the Transformer Blocks\n",
+    "* Embedding Dimension for the features\n",
+    "* Dimesion of the Dense Projections in the transfomer blocks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5770219",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-03-31T14:05:29.058084Z",
+     "iopub.status.busy": "2022-03-31T14:05:29.057315Z",
+     "iopub.status.idle": "2022-03-31T14:05:29.061191Z",
+     "shell.execute_reply": "2022-03-31T14:05:29.060562Z",
+     "shell.execute_reply.started": "2022-02-07T16:58:00.487521Z"
+    },
+    "id": "c5770219",
+    "papermill": {
+     "duration": 0.032786,
+     "end_time": "2022-03-31T14:05:29.061345",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:29.028559",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class FeatureTokenizerTransformerConfig:\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        num_outputs,\n",
+    "        out_activation,\n",
+    "        num_transformer_blocks=2,\n",
+    "        num_heads=8,\n",
+    "        embedding_dim=32,\n",
+    "        dense_dim=16,\n",
+    "    ):\n",
+    "        self.NUM_OUT = num_outputs\n",
+    "        self.OUT_ACTIVATION = out_activation\n",
+    "        self.NUM_TRANSFORMER_BLOCKS = num_transformer_blocks\n",
+    "        self.NUM_HEADS = num_heads\n",
+    "        self.EMBEDDING_DIM = embedding_dim\n",
+    "        self.DENSE_DIM = dense_dim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "956b6ba0",
+   "metadata": {
+    "id": "956b6ba0",
+    "papermill": {
+     "duration": 0.022108,
+     "end_time": "2022-03-31T14:05:29.108372",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:29.086264",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**Defining a standard Transformer Block**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c5ca0fb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-03-31T14:05:29.162977Z",
+     "iopub.status.busy": "2022-03-31T14:05:29.162287Z",
+     "iopub.status.idle": "2022-03-31T14:05:29.166968Z",
+     "shell.execute_reply": "2022-03-31T14:05:29.166387Z",
+     "shell.execute_reply.started": "2022-02-07T16:58:00.496789Z"
+    },
+    "id": "9c5ca0fb",
+    "papermill": {
+     "duration": 0.036378,
+     "end_time": "2022-03-31T14:05:29.167099",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:29.130721",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class TransformerBlock(L.Layer):\n",
+    "    def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.embed_dim = embed_dim\n",
+    "        self.dense_dim = dense_dim\n",
+    "        self.num_heads = num_heads\n",
+    "        self.attention = L.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)\n",
+    "        self.dense_proj = tf.keras.Sequential([L.Dense(dense_dim, activation=\"relu\"), L.Dense(embed_dim)])\n",
+    "        self.layernorm1 = L.LayerNormalization()\n",
+    "        self.layernorm2 = L.LayerNormalization()\n",
+    "\n",
+    "    def call(self, inputs, mask=None):\n",
+    "        if mask is not None:\n",
+    "            mask = mask[: tf.newaxis, :]\n",
+    "        attention_output = self.attention(inputs, inputs, attention_mask=mask)\n",
+    "        proj_input = self.layernorm1(inputs + attention_output)\n",
+    "        proj_output = self.dense_proj(proj_input)\n",
+    "        return self.layernorm2(proj_input + proj_output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d148c442",
+   "metadata": {
+    "id": "d148c442",
+    "papermill": {
+     "duration": 0.023095,
+     "end_time": "2022-03-31T14:05:29.212849",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:29.189754",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**Defining the Model**\n",
+    "The model takes Inputs Layers and then encodes the features from the functions defined above, the numerical features are then passed through a Dense layer of the same dimensions as the embeddings of the categorical features.\n",
+    "\n",
+    "All the feature embeddings are then stacked and then passed through a series of Transformer Blocks followed by the Global Average Pooling and Final Output Layer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92ebcc05",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-03-31T14:05:29.270498Z",
+     "iopub.status.busy": "2022-03-31T14:05:29.266313Z",
+     "iopub.status.idle": "2022-03-31T14:05:29.273531Z",
+     "shell.execute_reply": "2022-03-31T14:05:29.272918Z",
+     "shell.execute_reply.started": "2022-02-07T16:58:00.496789Z"
+    },
+    "id": "92ebcc05",
+    "papermill": {
+     "duration": 0.037916,
+     "end_time": "2022-03-31T14:05:29.273653",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:29.235737",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class FeatureTokenizerTransformer:\n",
+    "    @classmethod\n",
+    "    def from_config(cls, data_config, model_config, name):\n",
+    "        inputs = get_inputs(data_config)\n",
+    "        cat_features, num_features = encode_inputs(\n",
+    "            inputs,\n",
+    "            data_config,\n",
+    "            use_embeddings=True,\n",
+    "            embedding_dim=model_config.EMBEDDING_DIM,\n",
+    "            prefix=\"\",\n",
+    "            concat_features=False,\n",
+    "        )\n",
+    "        num_feat_emb = [\n",
+    "            L.Dense(model_config.EMBEDDING_DIM, name=f\"{feature_name}_embeddings\")\n",
+    "            for _, feature_name in zip(range(len(num_features)), data_config.NUMERIC_FEATURE_NAMES)\n",
+    "        ]\n",
+    "        num_features = [emb(feat) for emb, feat in zip(num_feat_emb, num_features)]\n",
+    "\n",
+    "        features = L.Concatenate(axis=1, name=\"feature_embeddings_stack\")(\n",
+    "            [\n",
+    "                L.Reshape((1, 32), name=f\"{feat_name}_reshape_2\")(feat)\n",
+    "                for feat, feat_name in zip((num_features + cat_features), data_config.FEATURE_NAMES)\n",
+    "            ]\n",
+    "        )\n",
+    "\n",
+    "        for _ in range(model_config.NUM_TRANSFORMER_BLOCKS):\n",
+    "            features = TransformerBlock(\n",
+    "                embed_dim=model_config.EMBEDDING_DIM,\n",
+    "                dense_dim=model_config.DENSE_DIM,\n",
+    "                num_heads=model_config.NUM_HEADS,\n",
+    "            )(features)\n",
+    "        features = L.GlobalMaxPooling1D()(features)\n",
+    "        outputs = L.Dense(\n",
+    "            units=model_config.NUM_OUT,\n",
+    "            activation=model_config.OUT_ACTIVATION,\n",
+    "            name=\"outputs\",\n",
+    "        )(features)\n",
+    "        model = keras.Model(inputs=inputs, outputs=outputs, name=name)\n",
+    "        return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7b3a0ee",
+   "metadata": {
+    "id": "e7b3a0ee",
+    "papermill": {
+     "duration": 0.022378,
+     "end_time": "2022-03-31T14:05:29.318065",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:29.295687",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**Creating instances of the various classes defined so far**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d324e5a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-03-31T14:05:29.369088Z",
+     "iopub.status.busy": "2022-03-31T14:05:29.367848Z",
+     "iopub.status.idle": "2022-03-31T14:05:29.370809Z",
+     "shell.execute_reply": "2022-03-31T14:05:29.371496Z",
+     "shell.execute_reply.started": "2022-02-07T16:58:00.515632Z"
+    },
+    "id": "6d324e5a",
+    "papermill": {
+     "duration": 0.030625,
+     "end_time": "2022-03-31T14:05:29.371645",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:29.341020",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data_config = DataConfig(\n",
+    "    numeric_feature_names=['longitude', 'latitude', 'housing_median_age', 'population', 'households', 'median_income'], \n",
+    "    categorical_features_with_vocabulary={\n",
+    "        'total_rooms': [str(i) for i in range(10)],\n",
+    "        'total_bedrooms': [str(i) for i in range(10)],\n",
+    "    }\n",
+    ")\n",
+    "model_config = FeatureTokenizerTransformerConfig(num_outputs=y.shape[1], out_activation='softmax')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf11fc2c",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "execution": {
+     "iopub.execute_input": "2022-03-31T14:05:29.420812Z",
+     "iopub.status.busy": "2022-03-31T14:05:29.419831Z",
+     "iopub.status.idle": "2022-03-31T14:05:39.684695Z",
+     "shell.execute_reply": "2022-03-31T14:05:39.685657Z",
+     "shell.execute_reply.started": "2022-02-07T16:58:00.52588Z"
+    },
+    "id": "bf11fc2c",
+    "outputId": "0869a413-ef6d-45f7-ccf0-21ec5bf4635d",
+    "papermill": {
+     "duration": 10.291903,
+     "end_time": "2022-03-31T14:05:39.686006",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:29.394103",
+     "status": "completed"
+    },
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/dist-packages/numpy/core/numeric.py:2446: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  return bool(asarray(a1 == a2).all())\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"ftt\"\n",
+      "__________________________________________________________________________________________________\n",
+      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      " longitude (InputLayer)         [(None,)]            0           []                               \n",
+      "                                                                                                  \n",
+      " latitude (InputLayer)          [(None,)]            0           []                               \n",
+      "                                                                                                  \n",
+      " housing_median_age (InputLayer  [(None,)]           0           []                               \n",
+      " )                                                                                                \n",
+      "                                                                                                  \n",
+      " population (InputLayer)        [(None,)]            0           []                               \n",
+      "                                                                                                  \n",
+      " households (InputLayer)        [(None,)]            0           []                               \n",
+      "                                                                                                  \n",
+      " median_income (InputLayer)     [(None,)]            0           []                               \n",
+      "                                                                                                  \n",
+      " total_rooms (InputLayer)       [(None,)]            0           []                               \n",
+      "                                                                                                  \n",
+      " total_bedrooms (InputLayer)    [(None,)]            0           []                               \n",
+      "                                                                                                  \n",
+      " longitude_reshape (Reshape)    (None, 1)            0           ['longitude[0][0]']              \n",
+      "                                                                                                  \n",
+      " latitude_reshape (Reshape)     (None, 1)            0           ['latitude[0][0]']               \n",
+      "                                                                                                  \n",
+      " housing_median_age_reshape (Re  (None, 1)           0           ['housing_median_age[0][0]']     \n",
+      " shape)                                                                                           \n",
+      "                                                                                                  \n",
+      " population_reshape (Reshape)   (None, 1)            0           ['population[0][0]']             \n",
+      "                                                                                                  \n",
+      " households_reshape (Reshape)   (None, 1)            0           ['households[0][0]']             \n",
+      "                                                                                                  \n",
+      " median_income_reshape (Reshape  (None, 1)           0           ['median_income[0][0]']          \n",
+      " )                                                                                                \n",
+      "                                                                                                  \n",
+      " total_rooms_lookup (StringLook  (None,)             0           ['total_rooms[0][0]']            \n",
+      " up)                                                                                              \n",
+      "                                                                                                  \n",
+      " total_bedrooms_lookup (StringL  (None,)             0           ['total_bedrooms[0][0]']         \n",
+      " ookup)                                                                                           \n",
+      "                                                                                                  \n",
+      " longitude_embeddings (Dense)   (None, 32)           64          ['longitude_reshape[0][0]']      \n",
+      "                                                                                                  \n",
+      " latitude_embeddings (Dense)    (None, 32)           64          ['latitude_reshape[0][0]']       \n",
+      "                                                                                                  \n",
+      " housing_median_age_embeddings   (None, 32)          64          ['housing_median_age_reshape[0][0\n",
+      " (Dense)                                                         ]']                              \n",
+      "                                                                                                  \n",
+      " population_embeddings (Dense)  (None, 32)           64          ['population_reshape[0][0]']     \n",
+      "                                                                                                  \n",
+      " households_embeddings (Dense)  (None, 32)           64          ['households_reshape[0][0]']     \n",
+      "                                                                                                  \n",
+      " median_income_embeddings (Dens  (None, 32)          64          ['median_income_reshape[0][0]']  \n",
+      " e)                                                                                               \n",
+      "                                                                                                  \n",
+      " total_rooms_embeddings (Embedd  (None, 32)          320         ['total_rooms_lookup[0][0]']     \n",
+      " ing)                                                                                             \n",
+      "                                                                                                  \n",
+      " total_bedrooms_embeddings (Emb  (None, 32)          320         ['total_bedrooms_lookup[0][0]']  \n",
+      " edding)                                                                                          \n",
+      "                                                                                                  \n",
+      " longitude_reshape_2 (Reshape)  (None, 1, 32)        0           ['longitude_embeddings[0][0]']   \n",
+      "                                                                                                  \n",
+      " latitude_reshape_2 (Reshape)   (None, 1, 32)        0           ['latitude_embeddings[0][0]']    \n",
+      "                                                                                                  \n",
+      " housing_median_age_reshape_2 (  (None, 1, 32)       0           ['housing_median_age_embeddings[0\n",
+      " Reshape)                                                        ][0]']                           \n",
+      "                                                                                                  \n",
+      " population_reshape_2 (Reshape)  (None, 1, 32)       0           ['population_embeddings[0][0]']  \n",
+      "                                                                                                  \n",
+      " households_reshape_2 (Reshape)  (None, 1, 32)       0           ['households_embeddings[0][0]']  \n",
+      "                                                                                                  \n",
+      " median_income_reshape_2 (Resha  (None, 1, 32)       0           ['median_income_embeddings[0][0]'\n",
+      " pe)                                                             ]                                \n",
+      "                                                                                                  \n",
+      " total_rooms_reshape_2 (Reshape  (None, 1, 32)       0           ['total_rooms_embeddings[0][0]'] \n",
+      " )                                                                                                \n",
+      "                                                                                                  \n",
+      " total_bedrooms_reshape_2 (Resh  (None, 1, 32)       0           ['total_bedrooms_embeddings[0][0]\n",
+      " ape)                                                            ']                               \n",
+      "                                                                                                  \n",
+      " feature_embeddings_stack (Conc  (None, 8, 32)       0           ['longitude_reshape_2[0][0]',    \n",
+      " atenate)                                                         'latitude_reshape_2[0][0]',     \n",
+      "                                                                  'housing_median_age_reshape_2[0]\n",
+      "                                                                 [0]',                            \n",
+      "                                                                  'population_reshape_2[0][0]',   \n",
+      "                                                                  'households_reshape_2[0][0]',   \n",
+      "                                                                  'median_income_reshape_2[0][0]',\n",
+      "                                                                  'total_rooms_reshape_2[0][0]',  \n",
+      "                                                                  'total_bedrooms_reshape_2[0][0]'\n",
+      "                                                                 ]                                \n",
+      "                                                                                                  \n",
+      " transformer_block (Transformer  (None, 8, 32)       34768       ['feature_embeddings_stack[0][0]'\n",
+      " Block)                                                          ]                                \n",
+      "                                                                                                  \n",
+      " transformer_block_1 (Transform  (None, 8, 32)       34768       ['transformer_block[0][0]']      \n",
+      " erBlock)                                                                                         \n",
+      "                                                                                                  \n",
+      " global_max_pooling1d (GlobalMa  (None, 32)          0           ['transformer_block_1[0][0]']    \n",
+      " xPooling1D)                                                                                      \n",
+      "                                                                                                  \n",
+      " outputs (Dense)                (None, 6)            198         ['global_max_pooling1d[0][0]']   \n",
+      "                                                                                                  \n",
+      "==================================================================================================\n",
+      "Total params: 70,758\n",
+      "Trainable params: 70,758\n",
+      "Non-trainable params: 0\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = FeatureTokenizerTransformer.from_config(data_config, model_config, name='ftt')\n",
+    "model.summary()\n",
+    "utils.plot_model(model, show_shapes=True, expand_nested=True, rankdir='LR')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "668dd940",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-03-31T14:05:39.761148Z",
+     "iopub.status.busy": "2022-03-31T14:05:39.760110Z",
+     "iopub.status.idle": "2022-03-31T14:05:39.762782Z",
+     "shell.execute_reply": "2022-03-31T14:05:39.763361Z",
+     "shell.execute_reply.started": "2022-02-07T16:59:04.408088Z"
+    },
+    "id": "668dd940",
+    "papermill": {
+     "duration": 0.042795,
+     "end_time": "2022-03-31T14:05:39.763510",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:39.720715",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "MAX_EPOCHS  = 10_000\n",
+    "\n",
+    "get_callbacks = lambda : [\n",
+    "    keras.callbacks.EarlyStopping(min_delta=1e-4, patience=10, verbose=1, restore_best_weights=True),\n",
+    "    keras.callbacks.ReduceLROnPlateau(patience=3, verbose=1),\n",
+    "    keras.callbacks.CSVLogger(\"training_log.csv\"),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "D7i50Z7R_LAr",
+   "metadata": {
+    "id": "D7i50Z7R_LAr"
+   },
+   "outputs": [],
+   "source": [
+    "get_preprocessor = lambda : ColumnTransformer(\n",
+    "    transformers=[(\"num\", StandardScaler(), data_config.NUMERIC_FEATURE_NAMES)],\n",
+    "    remainder='passthrough'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a54ce176",
+   "metadata": {
+    "id": "a54ce176",
+    "papermill": {
+     "duration": 0.029339,
+     "end_time": "2022-03-31T14:05:39.822871",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:39.793532",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Training Loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26c8d63d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-03-31T14:05:39.896069Z",
+     "iopub.status.busy": "2022-03-31T14:05:39.894947Z",
+     "iopub.status.idle": "2022-03-31T22:43:20.878373Z",
+     "shell.execute_reply": "2022-03-31T22:43:20.879492Z",
+     "shell.execute_reply.started": "2022-02-07T16:59:04.417412Z"
+    },
+    "id": "26c8d63d",
+    "papermill": {
+     "duration": 31061.028116,
+     "end_time": "2022-03-31T22:43:20.879702",
+     "exception": false,
+     "start_time": "2022-03-31T14:05:39.851586",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "preprocessor = get_preprocessor().fit(X)\n",
+    "X = pd.DataFrame(preprocessor.transform(X), columns=data_config.NUMERIC_FEATURE_NAMES + data_config.CATEGORICAL_FEATURE_NAMES)\n",
+    "X_test = pd.DataFrame(preprocessor.transform(X_test), columns=data_config.NUMERIC_FEATURE_NAMES + data_config.CATEGORICAL_FEATURE_NAMES)\n",
+    "\n",
+    "for col in data_config.NUMERIC_FEATURE_NAMES:\n",
+    "  X[col] = X[col].astype(float)\n",
+    "  X_test[col] = X_test[col].astype(float)\n",
+    "\n",
+    "for col in data_config.CATEGORICAL_FEATURE_NAMES:\n",
+    "  X[col] = X[col].astype(str)\n",
+    "  X_test[col] = X_test[col].astype(str)\n",
+    "    \n",
+    "data_train = DataLoader.from_df(X, y, batch_size=512)\n",
+    "data_test = DataLoader.from_df(X_test, y_test, batch_size=512)\n",
+    "    \n",
+    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics='accuracy')\n",
+    "model.fit(\n",
+    "    data_train, validation_data=data_test, callbacks=get_callbacks(), \n",
+    "    epochs=MAX_EPOCHS\n",
+    ")  \n",
+    "preds = model.predict(data_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ec702ad",
+   "metadata": {
+    "id": "4ec702ad",
+    "papermill": {
+     "duration": 15.733372,
+     "end_time": "2022-03-31T22:43:52.092011",
+     "exception": false,
+     "start_time": "2022-03-31T22:43:36.358639",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5eacd805",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "execution": {
+     "iopub.execute_input": "2022-03-31T22:44:23.944481Z",
+     "iopub.status.busy": "2022-03-31T22:44:23.943887Z",
+     "iopub.status.idle": "2022-03-31T22:44:24.411346Z",
+     "shell.execute_reply": "2022-03-31T22:44:24.410395Z",
+     "shell.execute_reply.started": "2022-02-07T17:05:49.950752Z"
+    },
+    "id": "5eacd805",
+    "outputId": "4a7f1209-d4b1-40ab-e394-c57116574aac",
+    "papermill": {
+     "duration": 16.863791,
+     "end_time": "2022-03-31T22:44:24.411495",
+     "exception": false,
+     "start_time": "2022-03-31T22:44:07.547704",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[357, 144,  11,   0,   0,   0],\n",
+       "       [101, 960, 160,   3,   0,   1],\n",
+       "       [  3, 213, 442,  50,   2,   4],\n",
+       "       [  2,  28, 132, 120,   6,  16],\n",
+       "       [  1,   7,  30,  42,  11,  25],\n",
+       "       [  2,   4,  12,  22,   3,  86]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(test['median_house_value'], preds.argmax(axis=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6Z3YRbCey9nG",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6Z3YRbCey9nG",
+    "outputId": "303edea8-9a17-4b9d-e1bd-39033117f5e7"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       0.77      0.70      0.73       512\n",
+      "         1.0       0.71      0.78      0.74      1225\n",
+      "         2.0       0.56      0.62      0.59       714\n",
+      "         3.0       0.51      0.39      0.44       304\n",
+      "         4.0       0.50      0.09      0.16       116\n",
+      "         5.0       0.65      0.67      0.66       129\n",
+      "\n",
+      "    accuracy                           0.66      3000\n",
+      "   macro avg       0.62      0.54      0.55      3000\n",
+      "weighted avg       0.65      0.66      0.65      3000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(test['median_house_value'], preds.argmax(axis=1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "Guet_Des7W13",
+   "metadata": {
+    "id": "Guet_Des7W13"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Feature-Tokenizer-Transformer.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.7"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 31218.584849,
+   "end_time": "2022-03-31T22:44:43.439536",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2022-03-31T14:04:24.854687",
+   "version": "2.3.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}