Model backlog/Train/128-tweet-train-5fold-roberta-base-balanced.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_kg_hide-input": true,
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import json, warnings, shutil\n",
    "from tweet_utility_scripts import *\n",
    "from tweet_utility_preprocess_roberta_scripts import *\n",
    "from transformers import TFRobertaModel, RobertaConfig\n",
    "from tokenizers import ByteLevelBPETokenizer\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras import optimizers, metrics, losses, layers\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, LearningRateScheduler\n",
    "\n",
    "SEED = 0\n",
    "seed_everything(SEED)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_kg_hide-input": true,
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>textID</th>\n",
       "      <th>text</th>\n",
       "      <th>selected_text</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>jaccard</th>\n",
       "      <th>text_len</th>\n",
       "      <th>selected_text_len</th>\n",
       "      <th>text_wordCnt</th>\n",
       "      <th>selected_text_wordCnt</th>\n",
       "      <th>fold_1</th>\n",
       "      <th>fold_2</th>\n",
       "      <th>fold_3</th>\n",
       "      <th>fold_4</th>\n",
       "      <th>fold_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6c3221ae9b</td>\n",
       "      <td>so instead of laying out in this 90 degrees i`...</td>\n",
       "      <td>fml</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>115</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "      <td>validation</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6d059e310b</td>\n",
       "      <td>hitting the (fairly empty) shops in orlando! o...</td>\n",
       "      <td>hitting</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>100</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>validation</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ce7f8a5210</td>\n",
       "      <td>spent some time with the guild wars 4th birthd...</td>\n",
       "      <td>welcome</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>114</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>validation</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>456c4304cd</td>\n",
       "      <td>first weekend at home</td>\n",
       "      <td>first weekend at home</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>train</td>\n",
       "      <td>validation</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6b78e2821a</td>\n",
       "      <td>would like june the nineteenth to hurry it sel...</td>\n",
       "      <td>she is waiting very impatiently</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>102</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>validation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       textID                                               text  \\\n",
       "0  6c3221ae9b  so instead of laying out in this 90 degrees i`...   \n",
       "1  6d059e310b  hitting the (fairly empty) shops in orlando! o...   \n",
       "2  ce7f8a5210  spent some time with the guild wars 4th birthd...   \n",
       "3  456c4304cd                              first weekend at home   \n",
       "4  6b78e2821a  would like june the nineteenth to hurry it sel...   \n",
       "\n",
       "                     selected_text sentiment   jaccard  text_len  \\\n",
       "0                              fml  negative  0.052632       115   \n",
       "1                          hitting  negative  0.076923       100   \n",
       "2                          welcome  positive  0.052632       114   \n",
       "3            first weekend at home   neutral  1.000000        21   \n",
       "4  she is waiting very impatiently  negative  0.263158       102   \n",
       "\n",
       "   selected_text_len  text_wordCnt  selected_text_wordCnt      fold_1  \\\n",
       "0                  3            21                      1       train   \n",
       "1                  7            14                      1       train   \n",
       "2                  7            19                      1  validation   \n",
       "3                 21             4                      4       train   \n",
       "4                 31            20                      5       train   \n",
       "\n",
       "       fold_2 fold_3      fold_4      fold_5  \n",
       "0  validation  train       train       train  \n",
       "1       train  train  validation       train  \n",
       "2       train  train       train       train  \n",
       "3  validation  train       train       train  \n",
       "4       train  train       train  validation  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold_1/\r\n",
      "fold_1/y_valid.npy\r\n",
      "fold_1/x_valid_aux.npy\r\n",
      "fold_1/y_train.npy\r\n",
      "fold_1/x_train_aux2.npy\r\n",
      "fold_1/x_train.npy\r\n",
      "fold_1/y_train_aux.npy\r\n",
      "fold_1/y_train_mask.npy\r\n",
      "fold_1/x_train_aux.npy\r\n",
      "fold_1/x_valid.npy\r\n",
      "fold_1/y_valid_mask.npy\r\n",
      "fold_1/x_valid_aux2.npy\r\n",
      "fold_1/y_valid_aux.npy\r\n",
      "fold_2/\r\n",
      "fold_2/y_valid.npy\r\n",
      "fold_2/x_valid_aux.npy\r\n",
      "fold_2/y_train.npy\r\n",
      "fold_2/x_train_aux2.npy\r\n",
      "fold_2/x_train.npy\r\n",
      "fold_2/y_train_aux.npy\r\n",
      "fold_2/y_train_mask.npy\r\n",
      "fold_2/x_train_aux.npy\r\n",
      "fold_2/x_valid.npy\r\n",
      "fold_2/y_valid_mask.npy\r\n",
      "fold_2/x_valid_aux2.npy\r\n",
      "fold_2/y_valid_aux.npy\r\n",
      "fold_3/\r\n",
      "fold_3/y_valid.npy\r\n",
      "fold_3/x_valid_aux.npy\r\n",
      "fold_3/y_train.npy\r\n",
      "fold_3/x_train_aux2.npy\r\n",
      "fold_3/x_train.npy\r\n",
      "fold_3/y_train_aux.npy\r\n",
      "fold_3/y_train_mask.npy\r\n",
      "fold_3/x_train_aux.npy\r\n",
      "fold_3/x_valid.npy\r\n",
      "fold_3/y_valid_mask.npy\r\n",
      "fold_3/x_valid_aux2.npy\r\n",
      "fold_3/y_valid_aux.npy\r\n",
      "fold_4/\r\n",
      "fold_4/y_valid.npy\r\n",
      "fold_4/x_valid_aux.npy\r\n",
      "fold_4/y_train.npy\r\n",
      "fold_4/x_train_aux2.npy\r\n",
      "fold_4/x_train.npy\r\n",
      "fold_4/y_train_aux.npy\r\n",
      "fold_4/y_train_mask.npy\r\n",
      "fold_4/x_train_aux.npy\r\n",
      "fold_4/x_valid.npy\r\n",
      "fold_4/y_valid_mask.npy\r\n",
      "fold_4/x_valid_aux2.npy\r\n",
      "fold_4/y_valid_aux.npy\r\n",
      "fold_5/\r\n",
      "fold_5/y_valid.npy\r\n",
      "fold_5/x_valid_aux.npy\r\n",
      "fold_5/y_train.npy\r\n",
      "fold_5/x_train_aux2.npy\r\n",
      "fold_5/x_train.npy\r\n",
      "fold_5/y_train_aux.npy\r\n",
      "fold_5/y_train_mask.npy\r\n",
      "fold_5/x_train_aux.npy\r\n",
      "fold_5/x_valid.npy\r\n",
      "fold_5/y_valid_mask.npy\r\n",
      "fold_5/x_valid_aux2.npy\r\n",
      "fold_5/y_valid_aux.npy\r\n"
     ]
    }
   ],
   "source": [
    "database_base_path = '/kaggle/input/tweet-dataset-split-roberta-base-96-aux-balanced/'\n",
    "k_fold = pd.read_csv(database_base_path + '5-fold.csv')\n",
    "display(k_fold.head())\n",
    "\n",
    "# Unzip files\n",
    "!tar -xvf /kaggle/input/tweet-dataset-split-roberta-base-96-aux-balanced/fold_1.tar.gz\n",
    "!tar -xvf /kaggle/input/tweet-dataset-split-roberta-base-96-aux-balanced/fold_2.tar.gz\n",
    "!tar -xvf /kaggle/input/tweet-dataset-split-roberta-base-96-aux-balanced/fold_3.tar.gz\n",
    "!tar -xvf /kaggle/input/tweet-dataset-split-roberta-base-96-aux-balanced/fold_4.tar.gz\n",
    "!tar -xvf /kaggle/input/tweet-dataset-split-roberta-base-96-aux-balanced/fold_5.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_path = database_base_path + 'vocab.json'\n",
    "merges_path = database_base_path + 'merges.txt'\n",
    "base_path = '/kaggle/input/qa-transformers/roberta/'\n",
    "\n",
    "config = {\n",
    "  'MAX_LEN': 96,\n",
    "  'BATCH_SIZE': 32,\n",
    "  'EPOCHS': 5,\n",
    "  'LEARNING_RATE': 3e-5,\n",
    "  'ES_PATIENCE': 1,\n",
    "  'question_size': 4,\n",
    "  'N_FOLDS': 5,\n",
    "  'base_model_path': base_path + 'roberta-base-tf_model.h5',\n",
    "  'config_path': base_path + 'roberta-base-config.json'\n",
    "}\n",
    "\n",
    "with open('config.json', 'w') as json_file:\n",
    "    json.dump(json.loads(json.dumps(config)), json_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_kg_hide-output": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./vocab.json', './merges.txt']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = ByteLevelBPETokenizer(vocab_file=vocab_path, merges_file=merges_path, \n",
    "                                  lowercase=True, add_prefix_space=True)\n",
    "tokenizer.save('./')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning rate schedule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate schedule: 3e-05 to 3e-05 to 1.87e-06\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LR_MIN = 1e-6\n",
    "LR_MAX = config['LEARNING_RATE']\n",
    "LR_EXP_DECAY = .5\n",
    "\n",
    "@tf.function\n",
    "def lrfn(epoch):\n",
    "    lr = LR_MAX * LR_EXP_DECAY**epoch\n",
    "    if lr < LR_MIN:\n",
    "        lr = LR_MIN\n",
    "    return lr\n",
    "    \n",
    "rng = [i for i in range(config['EPOCHS'])]\n",
    "y = [lrfn(x) for x in rng]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 6))\n",
    "plt.plot(rng, y)\n",
    "print(\"Learning rate schedule: {:.3g} to {:.3g} to {:.3g}\".format(y[0], max(y), y[-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "module_config = RobertaConfig.from_pretrained(config['config_path'], output_hidden_states=False)\n",
    "\n",
    "def model_fn(MAX_LEN):\n",
    "    input_ids = layers.Input(shape=(MAX_LEN,), dtype=tf.int32, name='input_ids')\n",
    "    attention_mask = layers.Input(shape=(MAX_LEN,), dtype=tf.int32, name='attention_mask')\n",
    "    \n",
    "    base_model = TFRobertaModel.from_pretrained(config['base_model_path'], config=module_config, name=\"base_model\")\n",
    "    last_hidden_state, _ = base_model({'input_ids': input_ids, 'attention_mask': attention_mask})\n",
    "    \n",
    "    x_start = layers.Dropout(.1)(last_hidden_state) \n",
    "    x_start = layers.Dense(1)(x_start)\n",
    "    x_start = layers.Flatten()(x_start)\n",
    "    y_start = layers.Activation('softmax', name='y_start')(x_start)\n",
    "\n",
    "    x_end = layers.Dropout(.1)(last_hidden_state) \n",
    "    x_end = layers.Dense(1)(x_end)\n",
    "    x_end = layers.Flatten()(x_end)\n",
    "    y_end = layers.Activation('softmax', name='y_end')(x_end)\n",
    "    \n",
    "    model = Model(inputs=[input_ids, attention_mask], outputs=[y_start, y_end])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_kg_hide-input": true,
    "_kg_hide-output": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "FOLD: 1\n",
      "Train for 583 steps, validate for 145 steps\n",
      "\n",
      "EPOCH 1/5\n",
      "time: 243.8s loss: 4.6261 loss_start: 2.3010 loss_end: 2.3251 acc_start: 0.3660 acc_end: 0.3669 val_loss: 2.1480 val_loss_start: 1.1126 val_loss_end: 1.0354 val_acc_start: 0.3738 val_acc_end: 0.3819\n",
      "Saved model weights at \"model_fold_1.h5\"\n",
      "\n",
      "EPOCH 2/5\n",
      "time: 227.9s loss: 4.1253 loss_start: 2.0860 loss_end: 2.0394 acc_start: 0.4151 acc_end: 0.4303 val_loss: 2.0182 val_loss_start: 1.0478 val_loss_end: 0.9704 val_acc_start: 0.3877 val_acc_end: 0.3983\n",
      "Saved model weights at \"model_fold_1.h5\"\n",
      "\n",
      "EPOCH 3/5\n",
      "time: 228.4s loss: 3.9811 loss_start: 2.0134 loss_end: 1.9677 acc_start: 0.4326 acc_end: 0.4426 val_loss: 1.9906 val_loss_start: 1.0315 val_loss_end: 0.9591 val_acc_start: 0.4280 val_acc_end: 0.4236\n",
      "Saved model weights at \"model_fold_1.h5\"\n",
      "\n",
      "EPOCH 4/5\n",
      "time: 228.3s loss: 3.9177 loss_start: 1.9839 loss_end: 1.9338 acc_start: 0.4372 acc_end: 0.4498 val_loss: 2.0705 val_loss_start: 1.0801 val_loss_end: 0.9905 val_acc_start: 0.4143 val_acc_end: 0.4199\n",
      "Epoch 00004: early stopping\n",
      "\n",
      "FOLD: 2\n",
      "Train for 583 steps, validate for 145 steps\n",
      "\n",
      "EPOCH 1/5\n",
      "time: 243.3s loss: 4.5593 loss_start: 2.2798 loss_end: 2.2795 acc_start: 0.3709 acc_end: 0.3739 val_loss: 2.1131 val_loss_start: 1.0907 val_loss_end: 1.0224 val_acc_start: 0.4113 val_acc_end: 0.4248\n",
      "Saved model weights at \"model_fold_2.h5\"\n",
      "\n",
      "EPOCH 2/5\n",
      "time: 228.0s loss: 4.0559 loss_start: 2.0576 loss_end: 1.9982 acc_start: 0.4197 acc_end: 0.4361 val_loss: 2.1165 val_loss_start: 1.0974 val_loss_end: 1.0191 val_acc_start: 0.3796 val_acc_end: 0.3876\n",
      "Epoch 00002: early stopping\n",
      "\n",
      "FOLD: 3\n",
      "Train for 583 steps, validate for 145 steps\n",
      "\n",
      "EPOCH 1/5\n",
      "time: 243.1s loss: 4.6118 loss_start: 2.2984 loss_end: 2.3135 acc_start: 0.3736 acc_end: 0.3719 val_loss: 2.0963 val_loss_start: 1.0929 val_loss_end: 1.0034 val_acc_start: 0.3827 val_acc_end: 0.4221\n",
      "Saved model weights at \"model_fold_3.h5\"\n",
      "\n",
      "EPOCH 2/5\n",
      "time: 227.7s loss: 4.1315 loss_start: 2.0980 loss_end: 2.0335 acc_start: 0.4135 acc_end: 0.4284 val_loss: 1.9462 val_loss_start: 1.0131 val_loss_end: 0.9331 val_acc_start: 0.4343 val_acc_end: 0.4512\n",
      "Saved model weights at \"model_fold_3.h5\"\n",
      "\n",
      "EPOCH 3/5\n",
      "time: 228.1s loss: 4.0127 loss_start: 2.0368 loss_end: 1.9759 acc_start: 0.4258 acc_end: 0.4401 val_loss: 2.0023 val_loss_start: 1.0374 val_loss_end: 0.9649 val_acc_start: 0.4214 val_acc_end: 0.4416\n",
      "Epoch 00003: early stopping\n",
      "\n",
      "FOLD: 4\n",
      "Train for 583 steps, validate for 145 steps\n",
      "\n",
      "EPOCH 1/5\n",
      "time: 244.2s loss: 4.6905 loss_start: 2.3156 loss_end: 2.3749 acc_start: 0.3691 acc_end: 0.3654 val_loss: 2.1345 val_loss_start: 1.0897 val_loss_end: 1.0448 val_acc_start: 0.4150 val_acc_end: 0.4366\n",
      "Saved model weights at \"model_fold_4.h5\"\n",
      "\n",
      "EPOCH 2/5\n",
      "time: 228.2s loss: 4.1461 loss_start: 2.0845 loss_end: 2.0617 acc_start: 0.4186 acc_end: 0.4286 val_loss: 2.0655 val_loss_start: 1.0453 val_loss_end: 1.0202 val_acc_start: 0.4032 val_acc_end: 0.4330\n",
      "Saved model weights at \"model_fold_4.h5\"\n",
      "\n",
      "EPOCH 3/5\n",
      "time: 228.5s loss: 4.0286 loss_start: 2.0301 loss_end: 1.9986 acc_start: 0.4318 acc_end: 0.4362 val_loss: 2.0483 val_loss_start: 1.0578 val_loss_end: 0.9905 val_acc_start: 0.4206 val_acc_end: 0.4412\n",
      "Saved model weights at \"model_fold_4.h5\"\n",
      "\n",
      "EPOCH 4/5\n",
      "time: 228.5s loss: 3.9586 loss_start: 1.9965 loss_end: 1.9621 acc_start: 0.4392 acc_end: 0.4495 val_loss: 2.0254 val_loss_start: 1.0471 val_loss_end: 0.9783 val_acc_start: 0.4216 val_acc_end: 0.4316\n",
      "Saved model weights at \"model_fold_4.h5\"\n",
      "\n",
      "EPOCH 5/5\n",
      "time: 228.3s loss: 3.9251 loss_start: 1.9817 loss_end: 1.9434 acc_start: 0.4411 acc_end: 0.4522 val_loss: 2.0192 val_loss_start: 1.0453 val_loss_end: 0.9739 val_acc_start: 0.4241 val_acc_end: 0.4324\n",
      "Training finished saved model weights at \"model_fold_4.h5\"\n",
      "\n",
      "FOLD: 5\n",
      "Train for 583 steps, validate for 145 steps\n",
      "\n",
      "EPOCH 1/5\n",
      "time: 242.0s loss: 4.5645 loss_start: 2.2798 loss_end: 2.2846 acc_start: 0.3730 acc_end: 0.3800 val_loss: 2.0500 val_loss_start: 1.0428 val_loss_end: 1.0071 val_acc_start: 0.4145 val_acc_end: 0.4525\n",
      "Saved model weights at \"model_fold_5.h5\"\n",
      "\n",
      "EPOCH 2/5\n",
      "time: 227.3s loss: 4.1471 loss_start: 2.0784 loss_end: 2.0687 acc_start: 0.4174 acc_end: 0.4235 val_loss: 2.0562 val_loss_start: 1.0475 val_loss_end: 1.0087 val_acc_start: 0.4339 val_acc_end: 0.4369\n",
      "Epoch 00002: early stopping\n"
     ]
    }
   ],
   "source": [
    "AUTO = tf.data.experimental.AUTOTUNE\n",
    "strategy = tf.distribute.get_strategy()\n",
    "history_list = []\n",
    "\n",
    "for n_fold in range(config['N_FOLDS']):\n",
    "    n_fold +=1\n",
    "    print('\\nFOLD: %d' % (n_fold))\n",
    "    # Load data\n",
    "    base_data_path = 'fold_%d/' % (n_fold)\n",
    "    x_train = np.load(base_data_path + 'x_train.npy')\n",
    "    y_train = np.load(base_data_path + 'y_train.npy')\n",
    "    x_valid = np.load(base_data_path + 'x_valid.npy')\n",
    "    y_valid = np.load(base_data_path + 'y_valid.npy')\n",
    "    \n",
    "    step_size = x_train.shape[1] // config['BATCH_SIZE']\n",
    "    valid_step_size = x_valid.shape[1] // config['BATCH_SIZE']\n",
    "    \n",
    "    ### Delete data dir\n",
    "    shutil.rmtree(base_data_path)\n",
    "\n",
    "    # Build TF datasets\n",
    "    train_dist_ds = strategy.experimental_distribute_dataset(get_training_dataset(x_train, y_train, config['BATCH_SIZE'], AUTO, seed=SEED))\n",
    "    valid_dist_ds = strategy.experimental_distribute_dataset(get_validation_dataset(x_valid, y_valid, config['BATCH_SIZE'], AUTO, repeated=True, seed=SEED))\n",
    "    train_data_iter = iter(train_dist_ds)\n",
    "    valid_data_iter = iter(valid_dist_ds)\n",
    "    \n",
    "    # Step functions\n",
    "    @tf.function\n",
    "    def train_step(data_iter):\n",
    "        def train_step_fn(x, y):\n",
    "            with tf.GradientTape() as tape:\n",
    "                probabilities = model(x, training=True)\n",
    "                loss_start = loss_fn_start(y['y_start'], probabilities[0], label_smoothing=0.2)\n",
    "                loss_end = loss_fn_end(y['y_end'], probabilities[1], label_smoothing=0.2)\n",
    "                loss = tf.math.add(loss_start, loss_end)\n",
    "            grads = tape.gradient(loss, model.trainable_variables)\n",
    "            optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
    "            # update metrics\n",
    "            train_acc_start.update_state(y['y_start'], probabilities)\n",
    "            train_acc_end.update_state(y['y_end'], probabilities)\n",
    "            train_loss.update_state(loss)\n",
    "            train_loss_start.update_state(loss_start)\n",
    "            train_loss_end.update_state(loss_end)\n",
    "        for _ in tf.range(step_size):\n",
    "            strategy.experimental_run_v2(train_step_fn, next(data_iter))\n",
    "\n",
    "    @tf.function\n",
    "    def valid_step(data_iter):\n",
    "        def valid_step_fn(x, y):\n",
    "            probabilities = model(x, training=False)\n",
    "            loss_start = loss_fn_start(y['y_start'], probabilities[0])\n",
    "            loss_end = loss_fn_end(y['y_end'], probabilities[1])\n",
    "            loss = tf.math.add(loss_start, loss_end)\n",
    "            # update metrics\n",
    "            valid_acc_start.update_state(y['y_start'], probabilities)\n",
    "            valid_acc_end.update_state(y['y_end'], probabilities)\n",
    "            valid_loss.update_state(loss)\n",
    "            valid_loss_start.update_state(loss_start)\n",
    "            valid_loss_end.update_state(loss_end)\n",
    "        for _ in tf.range(valid_step_size):\n",
    "            strategy.experimental_run_v2(valid_step_fn, next(data_iter))\n",
    "\n",
    "    # Train model\n",
    "    model_path = 'model_fold_%d.h5' % (n_fold)\n",
    "    \n",
    "    model = model_fn(config['MAX_LEN'])\n",
    "    optimizer = optimizers.Adam(learning_rate=lambda: lrfn(tf.cast(optimizer.iterations, tf.float32)//step_size))\n",
    "    loss_fn_start = losses.categorical_crossentropy\n",
    "    loss_fn_end = losses.categorical_crossentropy\n",
    "\n",
    "    train_acc_start = metrics.CategoricalAccuracy()\n",
    "    valid_acc_start = metrics.CategoricalAccuracy()\n",
    "    train_acc_end = metrics.CategoricalAccuracy()\n",
    "    valid_acc_end = metrics.CategoricalAccuracy()\n",
    "    train_loss = metrics.Sum()\n",
    "    valid_loss = metrics.Sum()\n",
    "    train_loss_start = metrics.Sum()\n",
    "    valid_loss_start = metrics.Sum()\n",
    "    train_loss_end = metrics.Sum()\n",
    "    valid_loss_end = metrics.Sum()\n",
    "        \n",
    "    metrics_dict = {'loss': train_loss, 'loss_start': train_loss_start, 'loss_end': train_loss_end, \n",
    "                    'acc_start': train_acc_start, 'acc_end': train_acc_end, \n",
    "                    'val_loss': valid_loss, 'val_loss_start': valid_loss_start, 'val_loss_end': valid_loss_end, \n",
    "                    'val_acc_start': valid_acc_start, 'val_acc_end': valid_acc_end}\n",
    "        \n",
    "    history = custom_fit(model, metrics_dict, train_step, valid_step, train_data_iter, valid_data_iter, \n",
    "                         step_size, valid_step_size, config['BATCH_SIZE'], config['EPOCHS'], config['ES_PATIENCE'], model_path)\n",
    "    \n",
    "    history_list.append(history)\n",
    "    model.load_weights(model_path)\n",
    "    \n",
    "    # Make predictions\n",
    "    train_preds = model.predict(list(x_train))\n",
    "    valid_preds = model.predict(list(x_valid))\n",
    "    \n",
    "    k_fold.loc[k_fold['fold_%d' % (n_fold)] == 'train', 'start_fold_%d' % (n_fold)] = train_preds[0].argmax(axis=-1)\n",
    "    k_fold.loc[k_fold['fold_%d' % (n_fold)] == 'train', 'end_fold_%d' % (n_fold)] = train_preds[1].argmax(axis=-1)\n",
    "    k_fold.loc[k_fold['fold_%d' % (n_fold)] == 'validation', 'start_fold_%d' % (n_fold)] = valid_preds[0].argmax(axis=-1)\n",
    "    k_fold.loc[k_fold['fold_%d' % (n_fold)] == 'validation', 'end_fold_%d' % (n_fold)] = valid_preds[1].argmax(axis=-1)\n",
    "    \n",
    "    k_fold['end_fold_%d' % (n_fold)] = k_fold['end_fold_%d' % (n_fold)].astype(int)\n",
    "    k_fold['start_fold_%d' % (n_fold)] = k_fold['start_fold_%d' % (n_fold)].astype(int)\n",
    "    k_fold['end_fold_%d' % (n_fold)].clip(0, k_fold['text_len'], inplace=True)\n",
    "    k_fold['start_fold_%d' % (n_fold)].clip(0, k_fold['end_fold_%d' % (n_fold)], inplace=True)\n",
    "    k_fold['prediction_fold_%d' % (n_fold)] = k_fold.apply(lambda x: decode(x['start_fold_%d' % (n_fold)], x['end_fold_%d' % (n_fold)], x['text'], config['question_size'], tokenizer), axis=1)\n",
    "    k_fold['prediction_fold_%d' % (n_fold)].fillna(k_fold[\"text\"], inplace=True)\n",
    "    k_fold['jaccard_fold_%d' % (n_fold)] = k_fold.apply(lambda x: jaccard(x['selected_text'], x['prediction_fold_%d' % (n_fold)]), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model loss graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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t2X9UX36XJ6erthi3xZCSE6KChklpydFKiKUYNwAAAICWUV5eLkmy2+2+fQUFBfq///u/Vh3H6aefroSEBK1Zs8a0LG3Dhg3Kz8/X+eefH3BObGysRowYod27d2vOnDkqLi42zXay2+369a9/rbvvvlufffaZfvzxx0YFRoMHD9aKFSt02mmnBcweOh7nnHOO3n//fX3//fe+ZWkul6tRM7hOZgRGOCXFRNnVo1M79egUvBh34ZHy6qe7VQdKhSXKKyjVN9vzVHjEPHUzwmH1hke+MKm2jlJaUrQiHfxnBgAAAOD49O/fX9HR0Xrsscc0adIkFRcXa968eUpJSVFubm6rjcPhcGjSpEl66qmn9PDDD+uyyy7zPSWtV69evhCpJhg6//zz1b59e+Xk5Gj58uU655xzFBsbqw8//FCrVq3S8OHDlZ6erpKSEi1evFjx8fHq169fo8Yybtw4ffjhh7rpppv07//+7+rWrZtKSkq0a9cubd68WS+//HKTPtt1112nv/zlL7rzzjs1ZcoU31PSmrps72TDb7JAHRaLoZSEKKUkBC/GXV7p1IHqYtz+YVJeYak2/3BQ5ZXmYtyJcRG+p7qZi3LHKKldJMW4AQAAANQrLS1NL730kmbPnq27775bHTp00G233aacnBwtWbKkVccyZswYxcTEaPHixVq9erViY2M1dOhQTZs2TZGRkZK8s3WWLVumjz76SIcPH1ZKSooGDx6se++9V5J3uZjVatUrr7yigwcPKjY2Vv369dPixYuD1nAKJiEhQW+99ZbmzZun+fPn6+DBg4qPj1ePHj102WWXNflzRUZG6vXXX9fMmTP12GOPKSYmRqNHj9bFF1+smTNnNvl6JwvDU1Ne/TgcOnRIDzzwgPbu3SuHw6GuXbvqiSeeUFJSkqndvHnztGbNGlmtVtlsNk2ZMiVo5fdgKioqlJ2drT59+igiIuJ4hxo2srKy1L9//7YeBlqIx+PR4eLK2qVufmFSbkGJ8ovK5FeLWzarRamJUUHCJG/AFBNlr7+zFsI9inDHPYpwxz2KcMc9inC2detWdenSRTExMW09FKBeJSUlJ8w9unXrVp155pnHdW5IM4wMw9C4ceM0cOBASdKsWbM0Z84cPf3006Z2/fr10+23366oqCht27ZNt9xyi9avX+9LIIGThWEYSoiLUEJchDK6JgUcr3J6i3HX1EzK83vdsfeQisuqTO3jou1Kq/tkt+rX9olRslkpxg0AAAAAaH4hBUYJCQm+sEjyTj1bvnx5QDv/2UQZGRnyeDwqKipShw4dQukeOOHYbRZ1TIlRx5TgaXRxWZUpRPK+lmr3vsPamL1fTlft9CSLIaUkRJlCJO+rdzs+xkExbgAAAAAnlGM9/c1qtfJ7TisJaUmaP7fbrdtvv13Dhg3TmDFj6m23YsUKLV26VCtWrGjUdWuWpAGnOrfbo6NlLh0qdulQsVOHSpy128VOlZS7Te0dNkMJsTYlxlqVGGNTYs12rE0JMTbZbfyQBQAAQHix2Wzq1atXWw8DbaSiokIXXHBBg22eeeYZXXrppa00ohPfDz/80GAI19AS5WYrev3kk08qOjpat9xyS71tvvzyS82dO1eLFi1q8vWpYQQ0rLzCqbxD3hlJuQUlpqLcP+0uVUWdYtxJ8RG+J7v5z1I6sG+XLr7oPFkoxo0wxc9RhDvuUYQ77lGEs61bt0rSCVMfBs0rOjpab7/9doNtwqHG1YlUw8jhcOjss88+rnObJTCaNWuW9uzZo/nz58tiCV5T5dtvv9W0adP06quvqkePHs3RLQA/kRE2de0Qr64d4gOOeTweFRVXBA2Ttuwq0D+++UX+cw1ffv99pSZG1wmTarejI1u/GDcAAACAk5thGOrbt29bDwPVQg6MXnjhBWVnZ2vBggVyOBxB22zevFlTpkzRSy+9pLPOOivULgE0kWEYSoyLVGJcpM7oFqwYt0sHD5Upt6BUX/5rqyJiUpRb/aS3bT8VqqTcPIUxLtrhC5A6JEd7A6XqJ7y1T4iSlWLcAAAAAHBCCykw2rlzp+bPn69u3brp97//vSSpc+fOmjdvnsaPH6977rlHffv21eOPP67y8nI99thjvnOfffZZZWRkhDZ6AM3CbrMqvX2s0tvHylPys/r3Nwe7xaWVvhlJeQWlyi30zlT64ecifbE5Ry63XzFui6H2CVHm2UnVYVJaUjTFuAEAAADgBBBSYHT66adr+/btQY8tXLjQt/3OO++E0g2ANhYb7VCvaId6nZYQcMzlcqvgcHlAmJRXUKqN2bkqKq4wtY+KsNUJk6KVVr2dlhQth93aWh8LAAAAAFCPZit6DeDUZLValJoUrdSkaCnIAy3KKpzKKyxVXkGJL0zKLSjVLweKlbU1T5VO89PdkttFBoRJNcveEuMiKcYNAAAAAK2AwAhAi4qKsKlbx3h16xi8GPehoxXmQtzV25t3HtSnR8pNxbgdNkv10raYgDCJYtwAAAAA0HwIjAC0GcMwlBQfqaT4SP2qe3LA8coqlw4cKg0Ik/IKSvXdjwUqqzAX424X6/DWS0qKVpqpKHeMUtpFUowbAAAAABqJwAhA2HLYreqcGqfOqXEBxzwej46WVimv+mlu/mHSjp8Paf3mHLn9inFbLYZSE6ODhEne7dgoO8W4AQAA0KzuvPNOZWVlaf369UGfKl5cXKxBgwYpMzNT//Vf/9Xo606dOlWbNm3SRx99JEnas2ePLrnkEj377LO66qqrGjx3yJAhGjx4sJ566qkmfZZ169YpJydHt912m2n/F198obFjx2rZsmUaMGBAk64Zirpfg1PR8X4vG4vACMAJyTAMxcc4FB/j0OmnJQYcd7ncOlhU5pudlOcryl2iDVv260hJpal9TKTNV3y7bpiUmhglu41i3AAAAGiaf/u3f9Mnn3yiTz/9VJdeemnA8Q8//FBlZWUaPXp0SP107NhRb775prp06RLSdRqybt06ffPNNwGBUb9+/fTmm2+qV68gBU1xQiMwAnBSslot1cFPjM4+PfB4aXmVKUzyvpbq57yj+nprnqr8inEbhpTcLqo6QKoOlJKqayklRyshLoLZSQAAAAgwdOhQJSQkaOXKlUEDo5UrVyo9PV0DBw4MqR+Hw6FzzjknpGscr9jY2DbrGy2LwAjAKSk60q7u6e3UPb1dwDG326NDR8sDwqTcghJ9u/2gCo/8bGrvsFtNM5I6JNVupyVFKzKCH7UAAACnIofDoZEjR+qtt95SYWGhkpKSfMdycnL01VdfaeLEib7/+bh7927NmzdP33zzjfLz85WamqrBgwdrypQpio8PfIhMjfqWpC1evFhLlixRfn6+MjIy9MgjjwScm5+fr7lz52rjxo3Ky8tTYmKiBgwYoGnTpiktLU2Sd/nX6tWrJUkZGRmSpC5duuijjz4KuiTN4/Ho9ddf15tvvql9+/YpISFBl156qe677z7FxMRIkpxOp8466yxNnjxZcXFxWrp0qQ4dOqQ+ffpoxowZ6tmzZ5O/3nl5eZo9e7Y+//xzlZaWqnv37ho/frxGjRplajNnzhxt2LBBRUVFSkhIUJ8+ffTMM88oMTFRTqdTL7/8st5//30dOHBA0dHR6tmzp6ZOnapf//rXjR7L2rVr9Ze//EU7duyQ3W7XRRddpIcfflgdOnTwtRkyZIguvPBCXXjhhXr11VeVm5urnj176tFHHw3oqzHfy+bGbzEAUIfFYii5XZSS20XprB6Bxbgrqlw6UFgbIvkHS9m78lVW4TK1T4iL8AZI1TOSagpxpyVHK7ldlKwWZicBAACcrEaPHq1ly5bp73//u26++Wbf/lWrVsnj8ZiWo+Xl5alTp07KzMxUu3bttGfPHv3pT3/Stm3btHz58ib1+9e//lXPPPOMrr32WmVmZmr37t2aMmWKiouLTe2KiooUGRmpqVOnKjExUXl5eVq0aJFuvvlmrVmzRg6HQ/fcc48OHTqk7du365VXXpEkRURE1Nv37Nmz9Ze//EW33nqrhg4dqp07d2ru3LnasWOHlixZIoul9mE0K1asUI8ePTR9+nSVl5dr9uzZuuuuu7RmzRpZrY0vC1FcXKxbbrlFxcXFuv/++5WWlqb33ntPU6dOVUVFha699lpJ3vDr4MGDevDBB9WhQwfl5+friy++UEVFhSRp/vz5Wrp0qe677z5lZGTo6NGj2rJli44cOdLosbzxxhuaOXOmrrnmGk2ePFnFxcV66aWXdOutt2rlypW+0EySNm7cqF27dmnKlCmy2+168cUXdccdd+iTTz5RbGyspMZ/L5sbgREANFGE3arT0uJ0WlrwYtxHSirrhEne7a17CvX5pn2mYtw2q6H2idHqkGR+qltNYe7YKHtrfjQAAICwdHTzP3R00ydtOoa4s4cprt/QJp/Xr18/9erVSytXrjQFRu+9957OPfdcde/e3bfvN7/5jX7zm9/43p977rk67bTTNGbMGG3fvt03u+dYXC6X5s2bp6FDh/oKIg8ePFgJCQmaNm2aqW2vXr306KOP+t47nU6dffbZGjFihNavX69hw4apS5cuSkxMbNTSt8LCQi1dulTXXnutpk+fLkkaNGiQ2rVrp4cffliff/65Lr74Yl97h8Oh+fPny2bzxhMej0f33XefvvvuO/Xr169Rn1eS3n77be3du9c00+niiy/WwYMH9eKLL+rqq6+WxWLRpk2b9OCDD5pmHV122WW+7W+//VZDhgzRrbfe6ts3fPjwRo+juLhYzz33nK677jo9+eSTvv19+/bVZZddphUrVuiWW27x7S8tLdWiRYsUF+f93SIpKUk33HCDPvvsM11++eVN+l42NwIjAGhGhmGoXWyE2sVGqHeXwGLcTpdbh1aa+wAAIABJREFU+UVlAWFSbmGpfti0T0dLq0ztY6Ps3vAoqbYQd1p1sNQ+IVp2myWgDwAAAISXq666Ss8995x2796t7t27a/Pmzfrxxx81Y8YMU7vKykr9+c9/1qpVq5STk+Ob9SJ5l6s1NjDKycnRgQMHdP/995v2Z2Zm6sEHHzTt83g8+p//+R/99a9/1S+//KLS0lJTn0317bffqqqqSldeeaVp/xVXXKFHH31UX375pSkwGjRokC8skqTevXv7PkNTAqOvv/5anTp1CnhS25VXXqnp06dr9+7d6tmzp/r06aM///nPcrvdGjhwoK+/Gn379tWiRYv0wgsvaMiQIerbt2/QJ9zVJysrS6WlpRo1apScTqdvf3p6urp27aqvvvrKFBide+65vrDI//Pv37/f93Vo7PeyuREYAUArsvkV4w6mpKzKFyL5h0k/7T+sjd/lyumqLcZtMaTkhKigYVKHpBi1i3VQjBsAAJwU4voNPa7ZPeHiqquu0gsvvKCVK1dqypQpWrlypRwOhy6//HJTu2effVZ//etfNXnyZJ199tmKiYlRTk6O7r33XlN4dCwHDhyQJCUnm8srOByOgFpIS5Ys0TPPPKPbb79dgwYNUlxcnJxOp2688cYm9Vnj8OHDkqT27dsH7bvmeI127doFtJPU5L4PHz4c0KckpaSkSPIuvZOkl156Sa+88or+9Kc/aebMmUpNTdVNN92kiRMnymKx6K677lJUVJRWr16t+fPnKyYmRpmZmXrggQeUkJBwzHEUFhZKkmmGkr+6Y6x7zbqfvynfy+ZGYAQAYSQmyq4endqpR6fgxbgLj5SbZycVliivoFRZ2/JUeMT8l2qEw+pb6pZWpyh3alK0Ih38FQAAANAa0tLSdOGFF2r16tWaNGmS1qxZo2HDhgWEJWvWrNHVV1+tO+64w7fv6NGjTe4vNTVVklRQUGDaX1lZGVCL54MPPtCgQYNMs1V++umnJvdZo+Yz5efnq0ePHgF9NyZ0Od5+v//++4D9+fn5kmqDmZSUFM2YMUMzZszQrl279O677+rFF19UcnKyrr/+ejkcDk2cOFETJ07UwYMH9emnn+qZZ55RRUWFnnvuuWOOo6afZ5991vT5a/jXL2qMpnwvmxu/LQDACcJiMZSSEKWUhCj1CfLQiPJKpw4Uliq3sFR5BbVhUm5BiTbtPKjySnMx7sS4CHOY5CvKHaOk+EhZKMYNAADQbEaPHq2pU6fq+eef16FDh0zFrmuUl5fLbjfXsHz33Xeb3Fd6erpSU1P197//3dTP2rVr5Xa7TW3Ly8sDZq8E69PhcKi8vPyYfZ977rmy2+364IMPdP755/v2f/DBB3K73TrvvPOa+nEa5bzzztNHH32kf/3rX6Y6S6tXr1b79u1NtaJq9OzZU9OmTdPy5cu1Y8eOgOPt27fX9ddfr08++UQ7d+5s1Dj69++v6Oho/fzzz6Yn1h2vpnwvmxuBEQCcJCIdNnXpEK8uHQKnpno8Hh0urqwNkXxhUqm+/7FAn33zi/xqcctmtSgtKUppyTEBYVJaUrRiKMYNAADQJL/73e8UGxurxYsXKzk5WYMHDw5oM2jQIL3zzjvq2bOnunTporVr12rz5s1N7stqtWrSpEn64x//qEcffVSZmZn66aef9Oc//zlghsugQYO0ePFiLViwQH369NEXX3yhjz76KOCavXr10jvvvKM333xTZ555piIjIwPq/0jeos1jxozRokWLFBkZqcGDB+uHH37Q3Llzdf755wf93M3hmmuu0RtvvKHJkyfr3nvvVWpqqlatWqWNGzfqqaeeksVi0aFDhzR+/HiNGjVKPXr0kM1m07p161RSUqKLLrpIkjRx4kSdddZZ+tWvfqX4+Hh9//33+uKLL0wFyxsSHx+vqVOn6umnn9bBgwc1ZMgQxcbGKi8vTxs3btRFF10UsBSxIU35XjY3AiMAOAUYhqGEuAglxEXojK5JAcernG4dLCr1LXXLq172lltYoh17Dqm4zFyMOy7a7q2XlOS31K06UEpJiJLNSjFuAAAAf5GRkcrMzNTbb7+tK664wlToucaMGTP0+OOP6/nnn5dhGBo6dKjmzJmj66+/vsn9/f73v1dZWZmWLFmiVatWqXfv3nrhhRf0H//xH6Z2d999t0pKSrRo0SJVVFRo4MCBWrBggS655BJTu+uvv16bN2/WnDlzdOTIEXXp0iVosCRJ06ZNU3Jyst566y0tW7ZMCQkJuvrqq3XffffJYmmZfyfGxsbqjTfe0OzZszVnzhyVlJSoR48emjNnju+JaFFRUTrzzDP11ltvKScnRxaLRd27d9fzzz+v3/72t5KkAQMGaN26dXrjjTdUXl6ujh07asKECaZlgsdy8803Kz09XYsWLdKqVavkdruVlpamAQMGNLpwub/Gfi+bm+HxeDzHbtZ2KioqlJ2drT59+igiIqKthxOyrKws9e/fv62HAdSLexTBFJdWepe6+YdJ1YW5DxwqldNV+1eJxZBSEqODhklpSdGKjwmtGDf3KMId9yjCHfcowtnWrVvVpUuXFp85AYSipKTkhLlHt27dqjPPPPO4zmWGEQDgmGKjHeoV7VCvzoFFCl1ujwoOlymvoFR5hTVhknf7q615KjpqLsYdFWFVmu/JbuYwKTUpWhF2a2t9LAAAAAD1IDACAITEajGUmhit1MRo9VVKwPHyCqd3ZlKhd1ZSTVHunPwSfbP9oCqrzMW4k+Ijq8Ok2tlJNcFSYlxka30sAAAAwMTtdsvtdsvpdMrpdAYcNwxDVuvJ8z8/CYwAAC0qMsKmrh3j1bVj8GLcRUcrTGFSzVK3LbsK9I9vfpH/wmm7zaIoh6HYdR/LYbfKbrPIYbfKUfNqt8pht8hh89v2O263Be5z+O8zXcsim9US0vI5AAAAnDzmzp2r+fPn13u8obpOJyICIwBAmzEMQ4nxkUqMj9QZ3YIV43bp4KEyXwHu3IJS/bQ3R3HtElTldKuiyqWqKrdKy50qKq5QZZVLlU63qqqqjzldpvpKTR+fvCGTX4gUGDZZZbdbFFFfgBVwrnfbHF6ZQyu71SKLhaAKAAAgnNx0000aMWKEysvLFRkZOPP9ZKi77I/ACAAQtuw2q9Lbxyq9faxvX1ZWeZOKtbpc7tpwyen2hUqVVa7qP25VOl2mkKmiyl396t1vOsdZfU6V9/iR0srac2qu6fT2Gdpnt9TOjLJbFVETMtU3m8rXtia88j/e8Gwqu636HLtVVoIqAACAoNLS0pSWlnZCFb0OBYERAOCkZrVaZLVaFBnRun/lud0eVblqgyn/GVE1wVRNWOULruqEWf5Blzm8cqm0vMqvrdsXWtWtCdVUVovR+NlU9S3nCxJ0RVSf67DVCbqqz7daDJb/AQAAhJGQ/vV86NAhPfDAA9q7d68cDoe6du2qJ554QklJ5mUFLpdLM2fO1Oeffy7DMDRhwgRdd911IQ0cAIBwZrEYirBYW/2pbx6PR06XO2gAZQqonN4ZUTWzqcxtA2dT1RwvLq1UYZ19Ndvu41/9J4shX52pCLtF9nqX9vnPiKqdTRVR3zK/IEFXzWwqh80iu406VQAQbjyeEP5CAeAT6n9LIQVGhmFo3LhxGjhwoCRp1qxZmjNnjp5++mlTu9WrV2vv3r1at26dioqKNHr0aF1wwQXq3LlzKN0DAIA6DMOQ3eYNUWKi7K3at9PlNgVTVfWFVQ0s86tyuoO2L62oqVMVuIQwlDpVkgLqTPkHUL6ZUfUEWP4BVM05P/9SJkvsgXqLsNfMzqJOFQAEcjgcqqioUGxs7LEbA2hQWVmZ7Pbj//dgSIFRQkKCLyySpHPOOUfLly8PaLdmzRpdd911slgsSkpK0ogRI7R27VqNGzculO4BAEAYsVm9T5aLDqwB2aJcbo+3flSQAMo/mDItB6xvNlWdUMu/TlXdoKvBOlWfbTjmuG1WS+1sqpoQqm4BdFuQmVEBtan8g64gs6nqtLVaLc341QeA5pWSkqK9e/fKMAzFxcXJZrMxExRoIo/Ho7KyMu3bt09paWnHfZ1mK+jgdru1fPlyDRs2LODY/v37lZ6e7nvfsWNH5ebmNun62dnZIY8xXGRlZbX1EIAGcY8i3HGPoqns1X+irZKskhp8iElNo4b/j5zb45HLJTndHlU5PXK6PXLWvLo8qnJ5X2v+mN8r4LjT5VKVy6nKCo9KSxs+JxQWQ7JZDd8fu9+2zao674O1CXxvs0h2myGbxZCt+rXue6tF/NIXRvg5inC3Z88eWSwWeTwefnYATVTz343T6dSRI0cabNvQw2SaLTB68sknFR0drVtuuaW5LmnSp0+fk+IRdVlZWU16ug/Q2rhHEe64RxHuWvoeNdWpqjObKrBQun+dqjozqKq3a2de1c7AKq9yq7K8ZjaV01dU3R1CoSqLoeraUeZZUt7lfObZVAFPAgzyFEDfbCrbsZcN8sumGT9HEe6ysrJ07rnntvUwgHqdKj9HmyUwmjVrlvbs2aP58+fLYgmc5tyxY0fl5OSoX79+kgJnHAEAAKBxTHWqjjELqrm5XHXDpWB1qmqW7NWGVRV1gqmA0MrpVlmFU0eKK4M+RbA56lT515ly1Fna16jlfHWeBBgQdNnrnEOdKgDACS7kwOiFF15Qdna2FixYIIfDEbRNZmam/va3v+mSSy5RUVGRPv74Yy1btizUrgEAANCKrFaLotugBpJ/napgAVTNbKq6TwKsCaZ85/jauk3BVHFZVUBB9pr+QmGzWo4ZTJme6nfMpwAG1q4KVuuKOlUAgOYQUmC0c+dOzZ8/X926ddPvf/97SVLnzp01b948jR8/Xvfcc4/69u2rq666Sps2bdIll1wiSZo0aZJOO+200EcPAACAk57VYsgaYVNkK1cncLs9tU//q++pf063KYCqCaTMTwIMPLfK6VZpSWW9QVcoT0K2WAxzyOT3JEC7zary0mK9/+0/ZbUYslgM79fXYpHVapj3WS3Vx2r21bbx7qs+3uA+i9/1qq/RpD7MbVheCACtJ6TA6PTTT9f27duDHlu4cKFv22q16vHHHw+lKwAAAKBVWSyGHBZv6NKavHWqPAFP/as6xpMA6y7zM4dXtcfLqzwqKq6Q2+W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      "text/plain": [
       "<Figure size 1440x1800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold: 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold: 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "for n_fold in range(config['N_FOLDS']):\n",
    "    print('Fold: %d' % (n_fold+1))\n",
    "    plot_metrics(history_list[n_fold])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col0 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col1 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col2 {\n",
       "            color:  red;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col3 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col4 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col5 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col6 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col7 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col8 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col9 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col10 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col11 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col12 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col0 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col1 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col2 {\n",
       "            color:  red;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col3 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col4 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col5 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col6 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col7 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col8 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col9 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col10 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col11 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col12 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col0 {\n",
       "            color:  green;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col1 {\n",
       "            color:  green;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col2 {\n",
       "            color:  red;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col3 {\n",
       "            color:  green;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col4 {\n",
       "            color:  green;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col5 {\n",
       "            color:  green;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col6 {\n",
       "            color:  green;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col7 {\n",
       "            color:  green;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col8 {\n",
       "            color:  green;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col9 {\n",
       "            color:  green;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col10 {\n",
       "            color:  green;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col11 {\n",
       "            color:  green;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col12 {\n",
       "            color:  green;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col0 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col1 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col2 {\n",
       "            color:  red;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col3 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col4 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col5 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col6 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col7 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col8 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col9 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col10 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col11 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col12 {\n",
       "            color:  orange;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col0 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col1 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col2 {\n",
       "            color:  red;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col3 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col4 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col5 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col6 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col7 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col8 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col9 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col10 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col11 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col12 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col0 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col1 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col2 {\n",
       "            color:  red;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col3 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col4 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col5 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col6 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col7 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col8 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col9 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col10 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col11 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col12 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col0 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col1 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col2 {\n",
       "            color:  red;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col3 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col4 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col5 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col6 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col7 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col8 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col9 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col10 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col11 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col12 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col0 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col1 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col2 {\n",
       "            color:  red;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col3 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col4 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col5 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col6 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col7 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col8 {\n",
       "            color:  orange;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col9 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col10 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col11 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col12 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col0 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col1 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col2 {\n",
       "            color:  red;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col3 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col4 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col5 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col6 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col7 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col8 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col9 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col10 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col11 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col12 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col0 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col1 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col2 {\n",
       "            color:  red;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col3 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col4 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col5 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col6 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col7 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col8 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col9 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col10 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col11 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col12 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col0 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col1 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col2 {\n",
       "            color:  red;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col3 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col4 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col5 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col6 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col7 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col8 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col9 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col10 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col11 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col12 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col0 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col1 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col2 {\n",
       "            color:  red;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col3 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col4 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col5 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col6 {\n",
       "            color:  green;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col7 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col8 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col9 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col10 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col11 {\n",
       "            color:  black;\n",
       "        }    #T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col12 {\n",
       "            color:  black;\n",
       "        }</style><table id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Train</th>        <th class=\"col_heading level0 col1\" >Valid</th>        <th class=\"col_heading level0 col2\" >Var</th>        <th class=\"col_heading level0 col3\" >Train_fold_1</th>        <th class=\"col_heading level0 col4\" >Valid_fold_1</th>        <th class=\"col_heading level0 col5\" >Train_fold_2</th>        <th class=\"col_heading level0 col6\" >Valid_fold_2</th>        <th class=\"col_heading level0 col7\" >Train_fold_3</th>        <th class=\"col_heading level0 col8\" >Valid_fold_3</th>        <th class=\"col_heading level0 col9\" >Train_fold_4</th>        <th class=\"col_heading level0 col10\" >Valid_fold_4</th>        <th class=\"col_heading level0 col11\" >Train_fold_5</th>        <th class=\"col_heading level0 col12\" >Valid_fold_5</th>    </tr>    <tr>        <th class=\"index_name level0\" >Metric/Jaccard</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >Overall</th>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col0\" class=\"data row0 col0\" >0.704530</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col1\" class=\"data row0 col1\" >0.670277</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col2\" class=\"data row0 col2\" >0.034253</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col3\" class=\"data row0 col3\" >0.723325</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col4\" class=\"data row0 col4\" >0.681007</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col5\" class=\"data row0 col5\" >0.689199</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col6\" class=\"data row0 col6\" >0.660891</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col7\" class=\"data row0 col7\" >0.702828</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col8\" class=\"data row0 col8\" >0.672051</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col9\" class=\"data row0 col9\" >0.733207</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col10\" class=\"data row0 col10\" >0.678319</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col11\" class=\"data row0 col11\" >0.674090</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row0_col12\" class=\"data row0 col12\" >0.659117</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >Sentiment negative</th>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col0\" class=\"data row1 col0\" >0.573444</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col1\" class=\"data row1 col1\" >0.518421</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col2\" class=\"data row1 col2\" >0.055023</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col3\" class=\"data row1 col3\" >0.600852</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col4\" class=\"data row1 col4\" >0.553596</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col5\" class=\"data row1 col5\" >0.545495</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col6\" class=\"data row1 col6\" >0.500304</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col7\" class=\"data row1 col7\" >0.575332</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col8\" class=\"data row1 col8\" >0.501768</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col9\" class=\"data row1 col9\" >0.617244</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col10\" class=\"data row1 col10\" >0.529707</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col11\" class=\"data row1 col11\" >0.528297</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row1_col12\" class=\"data row1 col12\" >0.506732</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >Sentiment neutral</th>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col0\" class=\"data row2 col0\" >0.974431</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col1\" class=\"data row2 col1\" >0.973552</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col2\" class=\"data row2 col2\" >0.000879</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col3\" class=\"data row2 col3\" >0.975138</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col4\" class=\"data row2 col4\" >0.973303</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col5\" class=\"data row2 col5\" >0.972503</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col6\" class=\"data row2 col6\" >0.975042</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col7\" class=\"data row2 col7\" >0.974063</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col8\" class=\"data row2 col8\" >0.975116</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col9\" class=\"data row2 col9\" >0.975742</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col10\" class=\"data row2 col10\" >0.972420</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col11\" class=\"data row2 col11\" >0.974707</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row2_col12\" class=\"data row2 col12\" >0.971879</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >Sentiment positive</th>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col0\" class=\"data row3 col0\" >0.565750</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col1\" class=\"data row3 col1\" >0.518894</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col2\" class=\"data row3 col2\" >0.046856</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col3\" class=\"data row3 col3\" >0.594005</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col4\" class=\"data row3 col4\" >0.516205</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col5\" class=\"data row3 col5\" >0.549623</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col6\" class=\"data row3 col6\" >0.507425</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col7\" class=\"data row3 col7\" >0.559133</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col8\" class=\"data row3 col8\" >0.539270</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col9\" class=\"data row3 col9\" >0.606674</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col10\" class=\"data row3 col10\" >0.532829</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col11\" class=\"data row3 col11\" >0.519316</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row3_col12\" class=\"data row3 col12\" >0.498741</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >0 <= text wordCnt < 3</th>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col0\" class=\"data row4 col0\" >0.849180</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col1\" class=\"data row4 col1\" >0.834586</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col2\" class=\"data row4 col2\" >0.014595</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col3\" class=\"data row4 col3\" >0.858531</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col4\" class=\"data row4 col4\" >0.847222</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col5\" class=\"data row4 col5\" >0.852662</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col6\" class=\"data row4 col6\" >0.828431</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col7\" class=\"data row4 col7\" >0.854552</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col8\" class=\"data row4 col8\" >0.813648</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col9\" class=\"data row4 col9\" >0.846442</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col10\" class=\"data row4 col10\" >0.866959</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col11\" class=\"data row4 col11\" >0.833713</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row4_col12\" class=\"data row4 col12\" >0.816667</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >3 <= text wordCnt < 10</th>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col0\" class=\"data row5 col0\" >0.760370</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col1\" class=\"data row5 col1\" >0.735542</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col2\" class=\"data row5 col2\" >0.024829</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col3\" class=\"data row5 col3\" >0.774497</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col4\" class=\"data row5 col4\" >0.744277</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col5\" class=\"data row5 col5\" >0.745568</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col6\" class=\"data row5 col6\" >0.729300</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col7\" class=\"data row5 col7\" >0.759339</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col8\" class=\"data row5 col8\" >0.728877</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col9\" class=\"data row5 col9\" >0.782871</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col10\" class=\"data row5 col10\" >0.741930</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col11\" class=\"data row5 col11\" >0.739577</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row5_col12\" class=\"data row5 col12\" >0.733324</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >10 <= text wordCnt < 30</th>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col0\" class=\"data row6 col0\" >0.670689</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col1\" class=\"data row6 col1\" >0.631287</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col2\" class=\"data row6 col2\" >0.039402</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col3\" class=\"data row6 col3\" >0.692417</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col4\" class=\"data row6 col4\" >0.643109</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col5\" class=\"data row6 col5\" >0.653995</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col6\" class=\"data row6 col6\" >0.620940</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col7\" class=\"data row6 col7\" >0.668573</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col8\" class=\"data row6 col8\" >0.637765</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col9\" class=\"data row6 col9\" >0.703652</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col10\" class=\"data row6 col10\" >0.639404</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col11\" class=\"data row6 col11\" >0.634806</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row6_col12\" class=\"data row6 col12\" >0.615217</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >30 <= text wordCnt < 100</th>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col0\" class=\"data row7 col0\" >0.613823</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col1\" class=\"data row7 col1\" >0.532343</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col2\" class=\"data row7 col2\" >0.081480</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col3\" class=\"data row7 col3\" >0.618914</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col4\" class=\"data row7 col4\" >0.523898</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col5\" class=\"data row7 col5\" >0.605989</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col6\" class=\"data row7 col6\" >0.572619</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col7\" class=\"data row7 col7\" >0.627083</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col8\" class=\"data row7 col8\" >0.444024</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col9\" class=\"data row7 col9\" >0.653831</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col10\" class=\"data row7 col10\" >0.539106</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col11\" class=\"data row7 col11\" >0.563301</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row7_col12\" class=\"data row7 col12\" >0.582070</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >0 <= selected text wordCnt < 3</th>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col0\" class=\"data row8 col0\" >0.665945</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col1\" class=\"data row8 col1\" >0.612747</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col2\" class=\"data row8 col2\" >0.053198</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col3\" class=\"data row8 col3\" >0.676542</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col4\" class=\"data row8 col4\" >0.601708</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col5\" class=\"data row8 col5\" >0.641110</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col6\" class=\"data row8 col6\" >0.597262</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col7\" class=\"data row8 col7\" >0.698077</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col8\" class=\"data row8 col8\" >0.645272</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col9\" class=\"data row8 col9\" >0.711174</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col10\" class=\"data row8 col10\" >0.628985</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col11\" class=\"data row8 col11\" >0.602821</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row8_col12\" class=\"data row8 col12\" >0.590507</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >3 <= selected text wordCnt < 10</th>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col0\" class=\"data row9 col0\" >0.657811</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col1\" class=\"data row9 col1\" >0.626968</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col2\" class=\"data row9 col2\" >0.030843</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col3\" class=\"data row9 col3\" >0.694659</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col4\" class=\"data row9 col4\" >0.659128</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col5\" class=\"data row9 col5\" >0.653656</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col6\" class=\"data row9 col6\" >0.618000</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col7\" class=\"data row9 col7\" >0.625075</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col8\" class=\"data row9 col8\" >0.602443</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col9\" class=\"data row9 col9\" >0.683005</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col10\" class=\"data row9 col10\" >0.641942</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col11\" class=\"data row9 col11\" >0.632659</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row9_col12\" class=\"data row9 col12\" >0.613328</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >10 <= selected text wordCnt < 30</th>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col0\" class=\"data row10 col0\" >0.831484</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col1\" class=\"data row10 col1\" >0.820962</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col2\" class=\"data row10 col2\" >0.010522</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col3\" class=\"data row10 col3\" >0.836055</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col4\" class=\"data row10 col4\" >0.835825</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col5\" class=\"data row10 col5\" >0.814582</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col6\" class=\"data row10 col6\" >0.817378</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col7\" class=\"data row10 col7\" >0.824398</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col8\" class=\"data row10 col8\" >0.812655</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col9\" class=\"data row10 col9\" >0.839972</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col10\" class=\"data row10 col10\" >0.807657</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col11\" class=\"data row10 col11\" >0.842414</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row10_col12\" class=\"data row10 col12\" >0.831297</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >30 <= selected text wordCnt < 100</th>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col0\" class=\"data row11 col0\" >0.836579</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col1\" class=\"data row11 col1\" >0.814632</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col2\" class=\"data row11 col2\" >0.021947</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col3\" class=\"data row11 col3\" >0.824478</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col4\" class=\"data row11 col4\" >0.778846</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col5\" class=\"data row11 col5\" >0.799704</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col6\" class=\"data row11 col6\" >0.987143</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col7\" class=\"data row11 col7\" >0.855415</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col8\" class=\"data row11 col8\" >0.683448</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col9\" class=\"data row11 col9\" >0.827631</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col10\" class=\"data row11 col10\" >0.811602</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col11\" class=\"data row11 col11\" >0.875665</td>\n",
       "                        <td id=\"T_d54d8bf8_933d_11ea_aa60_0242ac130202row11_col12\" class=\"data row11 col12\" >0.812120</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc5ad0e4690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(evaluate_model_kfold(k_fold, config['N_FOLDS']).style.applymap(color_map))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_kg_hide-input": true
   },
   "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>text</th>\n",
       "      <th>selected_text</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>jaccard</th>\n",
       "      <th>prediction_fold_1</th>\n",
       "      <th>jaccard_fold_1</th>\n",
       "      <th>prediction_fold_2</th>\n",
       "      <th>jaccard_fold_2</th>\n",
       "      <th>prediction_fold_3</th>\n",
       "      <th>jaccard_fold_3</th>\n",
       "      <th>prediction_fold_4</th>\n",
       "      <th>jaccard_fold_4</th>\n",
       "      <th>prediction_fold_5</th>\n",
       "      <th>jaccard_fold_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>so instead of laying out in this 90 degrees i`...</td>\n",
       "      <td>fml</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>fml</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>so instead of laying out in this 90 degrees i`...</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>fml</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>fml</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>so instead of laying out in this 90 degrees i`...</td>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hitting the (fairly empty) shops in orlando! o...</td>\n",
       "      <td>hitting</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>hitting</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>empty)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>hitting</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>empty)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>hitting</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spent some time with the guild wars 4th birthd...</td>\n",
       "      <td>welcome</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>welcome</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>welcome</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>welcome</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>welcome</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>welcome new feature... for rangers anyway.</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>first weekend at home</td>\n",
       "      <td>first weekend at home</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>first weekend at home</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>first weekend at home</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>first weekend at home</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>first weekend at home</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>first weekend at home</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>would like june the nineteenth to hurry it sel...</td>\n",
       "      <td>she is waiting very impatiently</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>impatiently</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>impatiently</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>impatiently</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>impatiently</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>impatiently</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>you realize you just did  a solid. better than...</td>\n",
       "      <td>you realize you just did  a solid. better than...</td>\n",
       "      <td>positive</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>better</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>better</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>better</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>better</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>better than a full-page in the ajc.</td>\n",
       "      <td>0.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>still a bit of a warning signal in my head say...</td>\n",
       "      <td>still a bit of a warning signal in my head</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>warning</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>warning</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>warning</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>warning</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>warning</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>im finally going home but didnt get to finish ...</td>\n",
       "      <td>im finally going home but didnt get to finish ...</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>im finally going home but didnt get to finish ...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>im finally going home but didnt get to finish ...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>im finally going home but didnt get to finish ...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>im finally going home but didnt get to finish ...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>im finally going home but didnt get to finish ...</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>is actually feeling awake today</td>\n",
       "      <td>is actually feeling awake today</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>is actually feeling awake today</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>is actually feeling awake today</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>is actually feeling awake today</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>is actually feeling awake today</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>is actually feeling awake today</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sorry  friends - i`m swamped with deadlines ri...</td>\n",
       "      <td>sorry  friends</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>sorry</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>sorry</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>sorry</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>sorry</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>sorry  friends - i`m swamped with deadlines ri...</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>i wish i could of been at commencement this yr...</td>\n",
       "      <td>wish</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>wish</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>wish</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>wish</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>wish</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>wish i could of been at commencement this yr  ...</td>\n",
       "      <td>0.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>i do too, but not heavy rain, which we`ve had ...</td>\n",
       "      <td>heavy</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>but not heavy rain,</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>heavy rain,</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>heavy rain,</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>heavy rain,</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>heavy rain, which we`ve had 4 more than a wk.</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>i have to pee (**** coffee)  sooo long until b...</td>\n",
       "      <td>i have to pee (**** coffee)  sooo long until b...</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>i have to pee (**** coffee)  sooo long until b...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>i have to pee (**** coffee)  sooo long until b...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>i have to pee (**** coffee)  sooo long until b...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>i have to pee (**** coffee)  sooo long until b...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>i have to pee (**** coffee)  sooo long until b...</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>lakers tomorrow  kb and df ftw!</td>\n",
       "      <td>lakers tomorrow  kb and df ftw!</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>lakers tomorrow  kb and df ftw!</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>lakers tomorrow  kb and df ftw!</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>lakers tomorrow  kb and df ftw!</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>lakers tomorrow  kb and df ftw!</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>lakers tomorrow  kb and df ftw!</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>an hour of walking in hot weather = a satisfie...</td>\n",
       "      <td>a satisfied but hurting nellie.  ow, blisters.</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>an hour of walking in hot weather = a satisfie...</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>an hour of walking in hot weather = a satisfie...</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>an hour of walking in hot weather = a satisfie...</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>an hour of walking in hot weather = a satisfie...</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>an hour of walking in hot weather = a satisfie...</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 text  \\\n",
       "0   so instead of laying out in this 90 degrees i`...   \n",
       "1   hitting the (fairly empty) shops in orlando! o...   \n",
       "2   spent some time with the guild wars 4th birthd...   \n",
       "3                               first weekend at home   \n",
       "4   would like june the nineteenth to hurry it sel...   \n",
       "5   you realize you just did  a solid. better than...   \n",
       "6   still a bit of a warning signal in my head say...   \n",
       "7   im finally going home but didnt get to finish ...   \n",
       "8                     is actually feeling awake today   \n",
       "9   sorry  friends - i`m swamped with deadlines ri...   \n",
       "10  i wish i could of been at commencement this yr...   \n",
       "11  i do too, but not heavy rain, which we`ve had ...   \n",
       "12  i have to pee (**** coffee)  sooo long until b...   \n",
       "13                    lakers tomorrow  kb and df ftw!   \n",
       "14  an hour of walking in hot weather = a satisfie...   \n",
       "\n",
       "                                        selected_text sentiment   jaccard  \\\n",
       "0                                                 fml  negative  0.052632   \n",
       "1                                             hitting  negative  0.076923   \n",
       "2                                             welcome  positive  0.052632   \n",
       "3                               first weekend at home   neutral  1.000000   \n",
       "4                     she is waiting very impatiently  negative  0.263158   \n",
       "5   you realize you just did  a solid. better than...  positive  1.000000   \n",
       "6          still a bit of a warning signal in my head  negative  0.428571   \n",
       "7   im finally going home but didnt get to finish ...   neutral  1.000000   \n",
       "8                     is actually feeling awake today   neutral  1.000000   \n",
       "9                                      sorry  friends  negative  0.100000   \n",
       "10                                               wish  positive  0.062500   \n",
       "11                                              heavy  negative  0.066667   \n",
       "12  i have to pee (**** coffee)  sooo long until b...   neutral  1.000000   \n",
       "13                    lakers tomorrow  kb and df ftw!   neutral  1.000000   \n",
       "14     a satisfied but hurting nellie.  ow, blisters.   neutral  0.466667   \n",
       "\n",
       "                                    prediction_fold_1  jaccard_fold_1  \\\n",
       "0                                                 fml        1.000000   \n",
       "1                                             hitting        1.000000   \n",
       "2                                             welcome        1.000000   \n",
       "3                               first weekend at home        1.000000   \n",
       "4                                         impatiently        0.200000   \n",
       "5                                              better        0.083333   \n",
       "6                                             warning        0.111111   \n",
       "7   im finally going home but didnt get to finish ...        1.000000   \n",
       "8                     is actually feeling awake today        1.000000   \n",
       "9                                               sorry        0.500000   \n",
       "10                                               wish        1.000000   \n",
       "11                                but not heavy rain,        0.250000   \n",
       "12  i have to pee (**** coffee)  sooo long until b...        1.000000   \n",
       "13                    lakers tomorrow  kb and df ftw!        1.000000   \n",
       "14  an hour of walking in hot weather = a satisfie...        0.466667   \n",
       "\n",
       "                                    prediction_fold_2  jaccard_fold_2  \\\n",
       "0   so instead of laying out in this 90 degrees i`...        0.052632   \n",
       "1                                              empty)        0.000000   \n",
       "2                                             welcome        1.000000   \n",
       "3                               first weekend at home        1.000000   \n",
       "4                                         impatiently        0.200000   \n",
       "5                                              better        0.083333   \n",
       "6                                             warning        0.111111   \n",
       "7   im finally going home but didnt get to finish ...        1.000000   \n",
       "8                     is actually feeling awake today        1.000000   \n",
       "9                                               sorry        0.500000   \n",
       "10                                               wish        1.000000   \n",
       "11                                        heavy rain,        0.500000   \n",
       "12  i have to pee (**** coffee)  sooo long until b...        1.000000   \n",
       "13                    lakers tomorrow  kb and df ftw!        1.000000   \n",
       "14  an hour of walking in hot weather = a satisfie...        0.466667   \n",
       "\n",
       "                                    prediction_fold_3  jaccard_fold_3  \\\n",
       "0                                                 fml        1.000000   \n",
       "1                                             hitting        1.000000   \n",
       "2                                             welcome        1.000000   \n",
       "3                               first weekend at home        1.000000   \n",
       "4                                         impatiently        0.200000   \n",
       "5                                              better        0.083333   \n",
       "6                                             warning        0.111111   \n",
       "7   im finally going home but didnt get to finish ...        1.000000   \n",
       "8                     is actually feeling awake today        1.000000   \n",
       "9                                               sorry        0.500000   \n",
       "10                                               wish        1.000000   \n",
       "11                                        heavy rain,        0.500000   \n",
       "12  i have to pee (**** coffee)  sooo long until b...        1.000000   \n",
       "13                    lakers tomorrow  kb and df ftw!        1.000000   \n",
       "14  an hour of walking in hot weather = a satisfie...        0.466667   \n",
       "\n",
       "                                    prediction_fold_4  jaccard_fold_4  \\\n",
       "0                                                 fml        1.000000   \n",
       "1                                              empty)        0.000000   \n",
       "2                                             welcome        1.000000   \n",
       "3                               first weekend at home        1.000000   \n",
       "4                                         impatiently        0.200000   \n",
       "5                                              better        0.083333   \n",
       "6                                             warning        0.111111   \n",
       "7   im finally going home but didnt get to finish ...        1.000000   \n",
       "8                     is actually feeling awake today        1.000000   \n",
       "9                                               sorry        0.500000   \n",
       "10                                               wish        1.000000   \n",
       "11                                        heavy rain,        0.500000   \n",
       "12  i have to pee (**** coffee)  sooo long until b...        1.000000   \n",
       "13                    lakers tomorrow  kb and df ftw!        1.000000   \n",
       "14  an hour of walking in hot weather = a satisfie...        0.466667   \n",
       "\n",
       "                                    prediction_fold_5  jaccard_fold_5  \n",
       "0   so instead of laying out in this 90 degrees i`...        0.052632  \n",
       "1                                             hitting        1.000000  \n",
       "2          welcome new feature... for rangers anyway.        0.166667  \n",
       "3                               first weekend at home        1.000000  \n",
       "4                                         impatiently        0.200000  \n",
       "5                 better than a full-page in the ajc.        0.583333  \n",
       "6                                             warning        0.111111  \n",
       "7   im finally going home but didnt get to finish ...        1.000000  \n",
       "8                     is actually feeling awake today        1.000000  \n",
       "9   sorry  friends - i`m swamped with deadlines ri...        0.100000  \n",
       "10  wish i could of been at commencement this yr  ...        0.062500  \n",
       "11      heavy rain, which we`ve had 4 more than a wk.        0.100000  \n",
       "12  i have to pee (**** coffee)  sooo long until b...        1.000000  \n",
       "13                    lakers tomorrow  kb and df ftw!        1.000000  \n",
       "14  an hour of walking in hot weather = a satisfie...        0.466667  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(k_fold[[c for c in k_fold.columns if not (c.startswith('textID') or \n",
    "                                                  c.startswith('text_len') or \n",
    "                                                  c.startswith('selected_text_len') or \n",
    "                                                  c.startswith('text_wordCnt') or \n",
    "                                                  c.startswith('selected_text_wordCnt') or \n",
    "                                                  c.startswith('fold_') or \n",
    "                                                  c.startswith('start_fold_') or \n",
    "                                                  c.startswith('end_fold_'))]].head(15))"
   ]
  }
 ],
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