Model backlog/Train/132-tweet-train-5fold-roberta-base-aux-sentiment.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": {
    "_kg_hide-input": true,
    "_kg_hide-output": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold_1/\r\n",
      "fold_1/y_valid_mask.npy\r\n",
      "fold_1/y_valid_aux.npy\r\n",
      "fold_1/x_valid.npy\r\n",
      "fold_1/x_train.npy\r\n",
      "fold_1/x_valid_aux.npy\r\n",
      "fold_1/y_train.npy\r\n",
      "fold_1/x_valid_aux2.npy\r\n",
      "fold_1/x_train_aux2.npy\r\n",
      "fold_1/y_train_mask.npy\r\n",
      "fold_1/y_train_aux.npy\r\n",
      "fold_1/x_train_aux.npy\r\n",
      "fold_1/y_valid.npy\r\n",
      "fold_2/\r\n",
      "fold_2/y_valid_mask.npy\r\n",
      "fold_2/y_valid_aux.npy\r\n",
      "fold_2/x_valid.npy\r\n",
      "fold_2/x_train.npy\r\n",
      "fold_2/x_valid_aux.npy\r\n",
      "fold_2/y_train.npy\r\n",
      "fold_2/x_valid_aux2.npy\r\n",
      "fold_2/x_train_aux2.npy\r\n",
      "fold_2/y_train_mask.npy\r\n",
      "fold_2/y_train_aux.npy\r\n",
      "fold_2/x_train_aux.npy\r\n",
      "fold_2/y_valid.npy\r\n",
      "fold_3/\r\n",
      "fold_3/y_valid_mask.npy\r\n",
      "fold_3/y_valid_aux.npy\r\n",
      "fold_3/x_valid.npy\r\n",
      "fold_3/x_train.npy\r\n",
      "fold_3/x_valid_aux.npy\r\n",
      "fold_3/y_train.npy\r\n",
      "fold_3/x_valid_aux2.npy\r\n",
      "fold_3/x_train_aux2.npy\r\n",
      "fold_3/y_train_mask.npy\r\n",
      "fold_3/y_train_aux.npy\r\n",
      "fold_3/x_train_aux.npy\r\n",
      "fold_3/y_valid.npy\r\n",
      "fold_4/\r\n",
      "fold_4/y_valid_mask.npy\r\n",
      "fold_4/y_valid_aux.npy\r\n",
      "fold_4/x_valid.npy\r\n",
      "fold_4/x_train.npy\r\n",
      "fold_4/x_valid_aux.npy\r\n",
      "fold_4/y_train.npy\r\n",
      "fold_4/x_valid_aux2.npy\r\n",
      "fold_4/x_train_aux2.npy\r\n",
      "fold_4/y_train_mask.npy\r\n",
      "fold_4/y_train_aux.npy\r\n",
      "fold_4/x_train_aux.npy\r\n",
      "fold_4/y_valid.npy\r\n",
      "fold_5/\r\n",
      "fold_5/y_valid_mask.npy\r\n",
      "fold_5/y_valid_aux.npy\r\n",
      "fold_5/x_valid.npy\r\n",
      "fold_5/x_train.npy\r\n",
      "fold_5/x_valid_aux.npy\r\n",
      "fold_5/y_train.npy\r\n",
      "fold_5/x_valid_aux2.npy\r\n",
      "fold_5/x_train_aux2.npy\r\n",
      "fold_5/y_train_mask.npy\r\n",
      "fold_5/y_train_aux.npy\r\n",
      "fold_5/x_train_aux.npy\r\n",
      "fold_5/y_valid.npy\r\n"
     ]
    }
   ],
   "source": [
    "# Unzip files\n",
    "!tar -xvf /kaggle/input/tweet-dataset-split-roberta-base-96-aux/fold_1.tar.gz\n",
    "!tar -xvf /kaggle/input/tweet-dataset-split-roberta-base-96-aux/fold_2.tar.gz\n",
    "!tar -xvf /kaggle/input/tweet-dataset-split-roberta-base-96-aux/fold_3.tar.gz\n",
    "!tar -xvf /kaggle/input/tweet-dataset-split-roberta-base-96-aux/fold_4.tar.gz\n",
    "!tar -xvf /kaggle/input/tweet-dataset-split-roberta-base-96-aux/fold_5.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_kg_hide-input": true,
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>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>cb774db0d1</td>\n",
       "      <td>i`d have responded, if i were going</td>\n",
       "      <td>i`d have responded, if i were going</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>7</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>1</th>\n",
       "      <td>549e992a42</td>\n",
       "      <td>sooo sad i will miss you here in san diego!!!</td>\n",
       "      <td>sooo sad</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>45</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2</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>2</th>\n",
       "      <td>088c60f138</td>\n",
       "      <td>my boss is bullying me...</td>\n",
       "      <td>bullying me</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2</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>3</th>\n",
       "      <td>9642c003ef</td>\n",
       "      <td>what interview! leave me alone</td>\n",
       "      <td>leave me alone</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>3</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>358bd9e861</td>\n",
       "      <td>sons of ****, why couldn`t they put them on th...</td>\n",
       "      <td>sons of ****,</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>74</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>validation</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       textID                                               text  \\\n",
       "0  cb774db0d1                i`d have responded, if i were going   \n",
       "1  549e992a42      sooo sad i will miss you here in san diego!!!   \n",
       "2  088c60f138                          my boss is bullying me...   \n",
       "3  9642c003ef                     what interview! leave me alone   \n",
       "4  358bd9e861  sons of ****, why couldn`t they put them on th...   \n",
       "\n",
       "                         selected_text sentiment   jaccard  text_len  \\\n",
       "0  i`d have responded, if i were going   neutral  1.000000        35   \n",
       "1                             sooo sad  negative  0.200000        45   \n",
       "2                          bullying me  negative  0.166667        25   \n",
       "3                       leave me alone  negative  0.600000        30   \n",
       "4                        sons of ****,  negative  0.214286        74   \n",
       "\n",
       "   selected_text_len  text_wordCnt  selected_text_wordCnt      fold_1  \\\n",
       "0                 35             7                      7       train   \n",
       "1                  8            10                      2  validation   \n",
       "2                 11             5                      2       train   \n",
       "3                 14             5                      3       train   \n",
       "4                 13            14                      3       train   \n",
       "\n",
       "       fold_2      fold_3      fold_4 fold_5  \n",
       "0       train       train  validation  train  \n",
       "1       train       train       train  train  \n",
       "2  validation       train       train  train  \n",
       "3  validation       train       train  train  \n",
       "4       train  validation       train  train  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "database_base_path = '/kaggle/input/tweet-dataset-split-roberta-base-96-aux/'\n",
    "k_fold = pd.read_csv(database_base_path + '5-fold.csv')\n",
    "display(k_fold.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {
    "_kg_hide-output": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./vocab.json', './merges.txt']"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "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": 7,
   "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",
    "    x_sentiment = layers.GlobalAveragePooling1D()(last_hidden_state)\n",
    "    x_sentiment = layers.Dropout(.1)(x_sentiment) \n",
    "    y_sentiment = layers.Dense(3, activation='softmax', name='y_sentiment')(x_sentiment)\n",
    "    \n",
    "    model = Model(inputs=[input_ids, attention_mask], outputs=[y_start, y_end, y_sentiment])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Datasets\n",
    "def get_training_dataset(x_train, y_train, batch_size, buffer_size, seed=0):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices(({'input_ids': x_train[0],'attention_mask': x_train[1]}, \n",
    "                                                  {'y_start': y_train[0],'y_end': y_train[1],'y_sentiment': y_train[2]}))\n",
    "    dataset = dataset.repeat()\n",
    "    dataset = dataset.shuffle(2048, seed=seed)\n",
    "    dataset = dataset.batch(batch_size, drop_remainder=True)\n",
    "    dataset = dataset.prefetch(buffer_size)\n",
    "    return dataset\n",
    "\n",
    "def get_validation_dataset(x_valid, y_valid, batch_size, buffer_size, repeated=False, seed=0):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices(({'input_ids': x_valid[0],'attention_mask': x_valid[1]}, \n",
    "                                                  {'y_start': y_valid[0],'y_end': y_valid[1],'y_sentiment': y_valid[2]}))\n",
    "    if repeated:\n",
    "        dataset = dataset.repeat()\n",
    "        dataset = dataset.shuffle(2048, seed=seed)\n",
    "    dataset = dataset.batch(batch_size, drop_remainder=True)\n",
    "    dataset = dataset.cache()\n",
    "    dataset = dataset.prefetch(buffer_size)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_kg_hide-input": true,
    "_kg_hide-output": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "FOLD: 1\n",
      "Train for 687 steps, validate for 171 steps\n",
      "\n",
      "EPOCH 1/5\n",
      "time: 284.5s loss: 4.5489 loss_start: 2.1992 loss_end: 2.2591 loss_sentiment: 0.0907 val_loss: 2.0462 val_loss_start: 1.0253 val_loss_end: 1.0191 val_loss_sentiment: 0.0018\n",
      "Saved model weights at \"model_fold_1.h5\"\n",
      "\n",
      "EPOCH 2/5\n",
      "time: 268.3s loss: 4.0678 loss_start: 2.0302 loss_end: 2.0358 loss_sentiment: 0.0019 val_loss: 1.8757 val_loss_start: 0.9857 val_loss_end: 0.8894 val_loss_sentiment: 0.0006\n",
      "Saved model weights at \"model_fold_1.h5\"\n",
      "\n",
      "EPOCH 3/5\n",
      "time: 268.9s loss: 3.9445 loss_start: 1.9822 loss_end: 1.9609 loss_sentiment: 0.0014 val_loss: 1.8654 val_loss_start: 0.9424 val_loss_end: 0.9224 val_loss_sentiment: 0.0005\n",
      "Saved model weights at \"model_fold_1.h5\"\n",
      "\n",
      "EPOCH 4/5\n",
      "time: 268.6s loss: 3.8805 loss_start: 1.9551 loss_end: 1.9241 loss_sentiment: 0.0012 val_loss: 1.8392 val_loss_start: 0.9490 val_loss_end: 0.8897 val_loss_sentiment: 0.0006\n",
      "Saved model weights at \"model_fold_1.h5\"\n",
      "\n",
      "EPOCH 5/5\n",
      "time: 268.7s loss: 3.8395 loss_start: 1.9385 loss_end: 1.8998 loss_sentiment: 0.0012 val_loss: 1.8366 val_loss_start: 0.9501 val_loss_end: 0.8860 val_loss_sentiment: 0.0004\n",
      "Training finished saved model weights at \"model_fold_1.h5\"\n",
      "\n",
      "FOLD: 2\n",
      "Train for 687 steps, validate for 171 steps\n",
      "\n",
      "EPOCH 1/5\n",
      "time: 282.6s loss: 4.5302 loss_start: 2.1936 loss_end: 2.2794 loss_sentiment: 0.0572 val_loss: 1.9952 val_loss_start: 1.0179 val_loss_end: 0.9765 val_loss_sentiment: 0.0009\n",
      "Saved model weights at \"model_fold_2.h5\"\n",
      "\n",
      "EPOCH 2/5\n",
      "time: 268.0s loss: 3.9718 loss_start: 2.0045 loss_end: 1.9656 loss_sentiment: 0.0018 val_loss: 1.8821 val_loss_start: 0.9756 val_loss_end: 0.9059 val_loss_sentiment: 0.0007\n",
      "Saved model weights at \"model_fold_2.h5\"\n",
      "\n",
      "EPOCH 3/5\n",
      "time: 268.3s loss: 3.8633 loss_start: 1.9529 loss_end: 1.9091 loss_sentiment: 0.0013 val_loss: 1.9611 val_loss_start: 1.0181 val_loss_end: 0.9422 val_loss_sentiment: 0.0007\n",
      "Epoch 00003: early stopping\n",
      "\n",
      "FOLD: 3\n",
      "Train for 687 steps, validate for 171 steps\n",
      "\n",
      "EPOCH 1/5\n",
      "time: 282.6s loss: 4.6122 loss_start: 2.2168 loss_end: 2.2493 loss_sentiment: 0.1461 val_loss: 2.0066 val_loss_start: 1.0014 val_loss_end: 1.0044 val_loss_sentiment: 0.0008\n",
      "Saved model weights at \"model_fold_3.h5\"\n",
      "\n",
      "EPOCH 2/5\n",
      "time: 267.9s loss: 4.0421 loss_start: 2.0324 loss_end: 2.0078 loss_sentiment: 0.0018 val_loss: 1.9083 val_loss_start: 0.9741 val_loss_end: 0.9337 val_loss_sentiment: 0.0005\n",
      "Saved model weights at \"model_fold_3.h5\"\n",
      "\n",
      "EPOCH 3/5\n",
      "time: 268.1s loss: 3.9108 loss_start: 1.9737 loss_end: 1.9356 loss_sentiment: 0.0015 val_loss: 1.8497 val_loss_start: 0.9519 val_loss_end: 0.8973 val_loss_sentiment: 0.0005\n",
      "Saved model weights at \"model_fold_3.h5\"\n",
      "\n",
      "EPOCH 4/5\n",
      "time: 267.9s loss: 3.8479 loss_start: 1.9446 loss_end: 1.9019 loss_sentiment: 0.0013 val_loss: 1.8827 val_loss_start: 0.9758 val_loss_end: 0.9066 val_loss_sentiment: 0.0004\n",
      "Epoch 00004: early stopping\n",
      "\n",
      "FOLD: 4\n",
      "Train for 687 steps, validate for 171 steps\n",
      "\n",
      "EPOCH 1/5\n",
      "time: 282.7s loss: 4.4508 loss_start: 2.1883 loss_end: 2.1779 loss_sentiment: 0.0846 val_loss: 1.9624 val_loss_start: 1.0137 val_loss_end: 0.9473 val_loss_sentiment: 0.0014\n",
      "Saved model weights at \"model_fold_4.h5\"\n",
      "\n",
      "EPOCH 2/5\n",
      "time: 268.0s loss: 3.9633 loss_start: 2.0001 loss_end: 1.9618 loss_sentiment: 0.0014 val_loss: 1.8879 val_loss_start: 0.9795 val_loss_end: 0.9071 val_loss_sentiment: 0.0013\n",
      "Saved model weights at \"model_fold_4.h5\"\n",
      "\n",
      "EPOCH 3/5\n",
      "time: 268.4s loss: 3.8567 loss_start: 1.9504 loss_end: 1.9052 loss_sentiment: 0.0011 val_loss: 1.8993 val_loss_start: 1.0030 val_loss_end: 0.8951 val_loss_sentiment: 0.0012\n",
      "Epoch 00003: early stopping\n",
      "\n",
      "FOLD: 5\n",
      "Train for 687 steps, validate for 171 steps\n",
      "\n",
      "EPOCH 1/5\n",
      "time: 282.4s loss: 4.5011 loss_start: 2.2121 loss_end: 2.2386 loss_sentiment: 0.0504 val_loss: 1.9012 val_loss_start: 0.9604 val_loss_end: 0.9395 val_loss_sentiment: 0.0013\n",
      "Saved model weights at \"model_fold_5.h5\"\n",
      "\n",
      "EPOCH 2/5\n",
      "time: 268.5s loss: 3.9901 loss_start: 2.0146 loss_end: 1.9739 loss_sentiment: 0.0017 val_loss: 1.8488 val_loss_start: 0.9523 val_loss_end: 0.8955 val_loss_sentiment: 0.0010\n",
      "Saved model weights at \"model_fold_5.h5\"\n",
      "\n",
      "EPOCH 3/5\n",
      "time: 268.6s loss: 3.8820 loss_start: 1.9605 loss_end: 1.9202 loss_sentiment: 0.0013 val_loss: 1.8945 val_loss_start: 0.9801 val_loss_end: 0.9135 val_loss_sentiment: 0.0009\n",
      "Epoch 00003: 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",
    "    y_train_sentiment = np.load(base_data_path + 'x_train_aux2.npy')\n",
    "    y_valid_sentiment = np.load(base_data_path + 'x_valid_aux2.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, y_train_sentiment), config['BATCH_SIZE'], AUTO, seed=SEED))\n",
    "    valid_dist_ds = strategy.experimental_distribute_dataset(get_validation_dataset(x_valid, (*y_valid, y_valid_sentiment), 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_sentiment = loss_fn_sentiment(y['y_sentiment'], probabilities[2])\n",
    "                loss = tf.math.add(loss_start, loss_end)\n",
    "                loss = tf.math.add(loss, loss_sentiment)\n",
    "            grads = tape.gradient(loss, model.trainable_variables)\n",
    "            optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
    "            # update metrics\n",
    "            train_loss.update_state(loss)\n",
    "            train_loss_start.update_state(loss_start)\n",
    "            train_loss_end.update_state(loss_end)\n",
    "            train_loss_sentiment.update_state(loss_sentiment)\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_sentiment = loss_fn_sentiment(y['y_sentiment'], probabilities[2])\n",
    "            loss = tf.math.add(loss_start, loss_end)\n",
    "            loss = tf.math.add(loss, loss_sentiment)\n",
    "            # update metrics\n",
    "            valid_loss.update_state(loss)\n",
    "            valid_loss_start.update_state(loss_start)\n",
    "            valid_loss_end.update_state(loss_end)\n",
    "            valid_loss_sentiment.update_state(loss_sentiment)\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",
    "    loss_fn_sentiment = losses.categorical_crossentropy\n",
    "\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",
    "    train_loss_sentiment = metrics.Sum()\n",
    "    valid_loss_sentiment = metrics.Sum()\n",
    "        \n",
    "    metrics_dict = {'loss': train_loss, 'loss_start': train_loss_start, \n",
    "                    'loss_end': train_loss_end, 'loss_sentiment': train_loss_sentiment, \n",
    "                    'val_loss': valid_loss, 'val_loss_start': valid_loss_start, \n",
    "                    'val_loss_end': valid_loss_end, 'val_loss_sentiment': valid_loss_sentiment}\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": 10,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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jRuntt9/Wtm3b5HA4lJ+fr3/84x/Kzc2t8tpWrVopOztbOTk5tTIWAADQNLHCCAAANAr333+/8vLyNHToUElSSkqK7r//fknSwYMHNX36dGVlZalFixa69dZb1adPH+3atUsvvvii9u3bp+DgYPXu3VvPPvtspf1s3LhRf/rTn1RQUKCYmBjNmzdPISEhio6O1quvvqrZs2frkUcekdlsVkJCgqZNm1bl2Js1a6Z7771Xt956q0pKSvTaa6/59bXo1auXpk+frmeffVYHDx5UaGioLrnkEiUlJVV5bbdu3TR48GBdffXVstlsWrduHRtfAwAANyaHw+Fo6EEAAAAAAADg3OFTSdr+/fs1evRoXXfddRo9erQOHDjg1sZms+mZZ57R1VdfrWuuuUbvvfee4fz69euVmpqqIUOGKDU1VSdOnKiVDwAAAAAAAIDa5VNJ2h//+EfddtttGjZsmD7++GM9/fTTevPNNw1t1qxZo4yMDG3YsEHZ2dkaPny4Lr/8cnXs2FE7duzQK6+8ouXLl6tNmzbKycmR1Wqtkw8EAABQlXHjxik9Pd3t+Pjx43Xvvfc2wIgAAADOLVWWpJ08eVLXXXedtmzZIovFIpvNpj59+mjDhg2Kiopytrvnnns0YsQIpaSkSJKeffZZxcTEaNy4cXrkkUd0+eWXa+TIkXX7aQAAAAAAAOC3KkvSDh8+rHbt2jmfKGKxWNS2bVsdPnzYrV1MTIzzfXR0tPOpIfv27dPPP/+s22+/XTfeeKNeffVV+bp1ksPhUGFhoc/tAQAAAAAA4J96eUqazWbT7t1nD6gVAAAgAElEQVS79cYbb6ioqEjjxo1TTEyMhg8fXuW1RUVF2rlzZz2MEgAAAAAAIHAkJiZ6PVdlYBQdHa2jR4/KZrM5S9KOHTum6Ohot3aZmZlKSEiQZFxxFBMTo5SUFFmtVlmtVg0aNEjbt2/3KTAqFx8fr5CQEJ/bn6vS09MrnRA0Xcx9YGLeAxdzH7iY+8DF3Acu5j5wMfeBKVDmvcqStFatWumCCy7Q2rVrJUlr167VBRdcYNi/SJJSUlL03nvvyW63KysrS3/729903XXXSZKGDBmiTZs2yeFwqLi4WP/5z3/0m9/8pg4+DgAAAAAAAPzlU0natGnT9Pjjj+vVV19VixYtNGvWLElSWlqaJk6cqF69emnYsGHatm2brr32WknShAkTdP7550uSBv8/e3ceF1Xd9nH8MzPsIPu+yQiKCIIoLiyaKSqCqVlqWXnbYt53ZmWlad25VNZtmmZlWbaoZaVmaQoqWeat4K65bxlIijtuuC88f/g4TzxW4jrKfN+vl68Xc85vzrnOXMPoXP5+18nKYt26dWRmZmI0GklLS1MDbBERERERERGRW1SFCkaRkZFMmTLlku1jx461/GwymRg8ePCfPt9oNNK/f3/69+9/lWGKiIiIiIiIiMjNctklaSIiIiIiIiIiYltUMBIRERERERERkXJUMBIRERERERERkXIq1MNIREREREREbm1Hjhxh7969nDlzxtqh2Aw7Ozs2btxo7TDkJrsd8m5vb4+/vz/u7u5XfQwVjERERERERG5zR44cYc+ePYSEhODs7IzBYLB2SDbh2LFjuLq6WjsMuclu9byXlZVx4sQJdu7cCXDVRSMtSbuJ+o1eyOjs3WTnFXD8pKr+IiIiIiJyfezdu5eQkBBcXFxULBKxcQaDARcXF0JCQti7d+9VH0cFo5uow51R2JkMjPl2Dd1emcMHU1ezffcRa4clIiIiIiK3uTNnzuDs7GztMETkFuLs7HxNS1S1JO0malArENOJAKr4VSM7r4AflhaRk19IXKQPmSlmkmsHYWdSDU9ERERERK6cZhaJyB9d62eCCkZWUCPcixrhXjxyVyw/LrtQNHrz8+V4VXGkVaMIWjWqiq+n/ndARERERERERKxDBSMr8nBzpMOd1Wl/RxQrN+8lO6+ASXM3M/nHLTSMDSQr1Ux8lK/+p0BEREREREREbioVjG4BRqOBpJgAkmIC2H3gGLMXFZK7pIhFa3cR6u9G65QImieF4+psb+1QRUREREREbqjo6OjLjgkJCeGnn3665nOdOnWK+Ph4nnvuOR5//PFrPt62bdvIzMxkxIgRZGVlXfPxbmdnz57lgw8+oFGjRtSvX7/cvt69e7Nx40Zmz55tpej+Xn5+PitXruTJJ5+0dihWpYLRLSbQx5VubWLp0qomC1cXk5NfwNhp65iQs5GmdUPJSjVjDvawdpgiIiIiIiI3xKRJk8o9fvLJJ4mOjqZXr16WbQ4ODtflXA4ODkyaNImgoKDrcjz5P+fOneO9997DZDJdUjB65plnOHHihJUiu7z8/Hw+/fRTFYysHYD8OQd7E82SwmiWFMavOw6Rk1fAvBU7mLN4OzER3mSmmkmND8LezmTtUEVERERERK6bOnXqlHvs4OCAl5fXJdv/yunTpytcUDIYDBU+rlw/VatWtXYIUgG6JddtICrUk6c6JzJ+QEsebRvHodJTvDVxBY+8+gMTcjaw9+Bxa4coIiIiIiJy0/Xu3ZsWLVqwbNkyOnXqRHx8PKNGjQJg2rRpPPjggzRq1IjExEQ6dOjAjBkzyj3/1KlTREdH89FHH1m2DR8+nFq1alFYWMijjz5KnTp1aNasGR9++CFlZWVXFefUqVO56667iIuLo1GjRvTv358DBw6UG/Ptt9/Stm1b6tSpQ7169Wjbti3ffPONZf/KlSvp2rUrDRo0ICEhgfT0dIYMGVLhGI4ePcqgQYO44447iIuLIyUlhUceeYTt27dbxpw5c4bRo0fTqlUr4uLiaNKkCcOGDeP06dOWMdu2bSM6OpqpU6fy1ltvkZqaSv369XniiSfYu3cv8H9L/QBGjRpFdHR0ude5d+/eZGRkXHLMb775hqFDh5KcnEzdunXp378/p06dYtu2bXTr1o3ExERatmxJdnb2Jde3bt06evToQVJSEvHx8TzwwAOsWrWq3JiL75c1a9Zw3333kZCQQKtWrcq9zsOHD2fs2LGcO3fOEnft2rUr/DpXJpphdBtxc3Gg/R2RtG1cjdVb95GdV8DUn7Yy9aet1K8VSGaKmTo1/DAa1SRbRERERERsw8GDB+nbty+PPfYYzz33HM7OF+44vWPHDjIzMy2zWZYsWULfvn05c+YMHTp0+NtjlpWV0atXL+655x4effRRcnNzGTFiBCEhIbRp0+aK4pswYQJDhgyhbdu2PP/88+zatYuRI0eyZs0apk6dipOTE4sWLeLFF1/k4Ycfpn///pw9e5Zt27Zx9OhRAA4fPkz37t2pX78+Q4cOxdnZmZ07d7J27doKx/HKK6+wePFinnnmGcLDwzl48CDLli2jtLTUMuaZZ54hPz+fHj16EB8fz5YtW3jnnXfYvXs3b731VrnjjR49mvr16/PGG2+wZ88e3nzzTfr3788nn3yCg4MDX3zxBQ8++CCdO3e2vN6XW/o3evRoUlJSGDZsGJs2bWLEiBEArFmzhi5duvDYY4/x+eef8/zzzxMbG0tERAQAv/zyC127diUhIYEhQ4bg6OjIF198wT/+8Q+mTJlSri/WoUOHeOGFF3jkkUcICgpi8uTJvPTSS0RGRpKYmMgDDzzA3r17mTlzJl9++SUARqNtzrVRweg2ZDQaSIz2JzHan70HjzNn8XZyF29nyfrdBPm6kpkSQfP64VRxuT7rekVERERE5Pbz0/IiflhaZNUYWjQIp1lS+A09x9GjRxk5ciSNGzcut/2P/WfOnz9Pw4YN2bVrF1999dVlC0bnz5+nR48eluJQcnIyixYtYubMmVdUMDp9+jSjR48mLS2NYcOGWbaHh4fz8MMPM336dDp37syqVavw9fXlhRdesIz54/Vs3bqV0tJSXnjhBcxms2X7PffcU+FYfvnlF+6+++5yz2nZsqXl57y8PObOncvIkSPJzMwEICUlBVdXV/7973/zxBNPEBkZaRlvNpsZOnSo5fG+ffsYNWoUhw4dwtPT0zLDKDAwsMLL/qKioiyzptLS0liyZAnffvsto0aNssxIiomJISUlhdzcXEuj8v/85z9ERETw2WefYWd3ocyRmppK69atGTNmDCNHjrSc48iRI3z00UckJiYCUK9ePfLy8sjOziYxMZGgoCD8/f2BS5dH2hrbLJNVIv5eLjzUOoZPX27B8w/Uw6uKI598v55ug+fwzqRV/Pr7IWuHKCIiIiIicsM4OztfUiyCC8ucnn76adLS0qhVqxaxsbF8//33FBQUVOi4TZs2tfxsMBiIiopi165dVxTb1q1bOXToEG3bti23PSUlBV9fX5YuXQpAfHw8+/bto1+/fsyfP7/crB+AatWq4erqyksvvcSMGTPYs2fPFcUBULt2bSZNmsRHH33E+vXrOX/+fLn9CxYswMXFhfT0dM6ePWv5k5aWBsDy5cvLjb/jjjvKPa5RowbAFb9Gf9SkSZNyj6tVqwZgiQHAx8cHDw8Pdu/eDUBpaSm//PKLpch1MW6DwUCjRo1YtmxZuWN6eHhYikVw4f0TFhZGcXHxVcddWWmGUSVhb2fijrqh3FE3lILiw+TkF/Lzit/5YWkRNcI9yUo1k5YQgoO9mmSLiIiIiNiCZkk3fnbPrcDX1/eSbUeOHOHhhx/Gw8ODvn37Ehoair29PePHj2fOnDmXPabJZMLNza3cNgcHB06dOnVFsR0+fBjAMmPl/8d9cX9aWhojRoxg4sSJ/Otf/7IUO/r160f16tXx9vZm/PjxfPDBBwwYMIDjx48THR3N008/TfPmzSsUyyuvvML777/PpEmTeOutt/Dy8uLuu+/mmWeewdHRkQMHDnD8+PG/7Ndz6FD5yQgeHuXv3n2x0fiVvkZ/5O7uXu6xvb39n+bC3t7ecp6SkhLKysoYOXJkuZlEfxz7d3FfjP1a4q6sVDCqhMzBHvS8N4FuWbX4afnv5OQXMPKrVXw8fT0tGoTTOiWCQB9Xa4cpIiIiIiJyzQyGS3u4Ll++nD179jB69OhyBZCzZ8/ezNAsxYl9+/Zdsm///v1Ur17d8jgrK4usrCxKS0tZvHgxw4YNo0ePHvz000/AhRlC77//PmfOnGHt2rV88MEHPPXUU2RnZ1t6+fwdNzc3+vbtS9++ffn999+ZNWsWb7/9Ns7Ozjz11FN4enri6urK+PHj//T5AQEBV/EK3HgXX+NHHnnEMsvoj/7s/SEVo4JRJebqbM9djavRJs3M2m37yckrZNp/t/Hd/F+pVzOAzJQI6tYMwKQm2SIiIiIiUomcPHkSKD+75MCBA8yfP/+mxlG9enU8PT3Jyckptyxt0aJF7N+/nwYNGlzyHDc3N9LT0ykoKGD48OGUlpaWm2Fjb29P3bp16dWrF//973/57bffKlQw+qOwsDAef/xxpk2bxtatW4ELPZMmTJjA6dOnqVev3tVd8B/Y29tjMBhu+MwdDw8PEhIS2Lx5M3379r0uBSIHBwfOnTvHmTNnLpmhZEtUMLIBBoOB+Cg/4qP8OHD4BHMWb2fO4kJe+WQJAd4utE6OIL1BOB5ujtYOVURERERE5JrVq1cPFxcXBgwYQM+ePSktLWX06NH4+vpaet/cDA4ODvTs2ZMhQ4bQv39/WrdubblLWlRUlKWIdLEw1KBBA/z8/CguLuarr76iTp06uLm5MWfOHL7//nuaN29OcHAwx44dY9y4cbi7u1uaS19Ohw4daN26NdWrV8fZ2Zn8/Hx+++03unbtClzoH9SyZUueeOIJHn74YeLi4oALd5v7+eefefnllwkJCanwtRuNRsxmMz/++CONGjXCzc2NwMBA/Pz8rvBVvLwXX3yRf/zjH3Tv3p0OHTrg6+tLSUkJ69atw87OjmeeeeaKjhcVFQXAJ598QkpKCiaTidjY2Ose961OBSMb4+PhTJdWNemUXoPF63aRk1fIuOwNTJyzibSEYLJSzdQI99K0PRERERERuW0FBATwzjvvMGzYMHr16kVgYCDdunWjuLj4L5dc3Shdu3bF1dWVcePGMWPGDNzc3GjatCl9+vTByckJuHA3rokTJ/LDDz9w+PBhfH19ady4MU8//TRwofmzyWTivffeY9++fbi5uREfH8+4ceP+tIfTn6lfvz4zZ85kx44dnD9/nrCwMF5++WXuu+8+y5i3336b8ePH89133zF69GgcHR0JDQ0lLS0NT0/PK772QYMG8frrr9O9e3fOnDnDc889Z7mz2fVUp04dJk+ezOjRo3nllVcoLS3Fx8eHuLg47r///is+XsuWLenUqROfffYZb7/9Nvb29qxdu/a6x32rM5SVlZVZO4i/c+rUKdatW0dcXByOjrf/DJgVK1Zcl+l919P23UeYlV/IT8t/58Sps0SGepCVYqZxYghODqopXi+3Yu7lxlPebZdyb7uUe9ul3NuuWyH3GzduJCYmxqox2KJjx47h6qr+sLbmdsr7tXw2qBogVA10558d4umaGcP8lTvIzivgncm/8MmM9aTXDyczJYJgP7fLH0hEREREREREKgUVjMTCxcme1ilmMpIj2FBQQk5eATMX/sb0/24jsYYfmalm6tcKVJNsERERERGRW8jl7v5mMpnUdkSumApGcgmDwUBsNR9iq/nw2JGT5C7ZzuxFhQz5bCl+Xs5kNIqgRcNwvKo4WTtUERERERERm3bq1KnLNr4eMWIEWVlZNykiqSxUMJK/5eXuROcW0dzbrDpLN+wmJ6+Qz2dt5KvcTaTEX2iSHRPhrWq1iIiIiIiIFTg4OPDNN9/87Zjw8PCbFI1UJioYSYWYTEaSaweTXDuYHXuPMiu/kB+XFfHfVTuJCHInM9VM07qhODvqLSUiIiIiInKzGAwGateube0wpBLSt3u5YqH+VejevjYPtY5h/qqd5OQV8P43qxk3cz3NksLITDETFlDF2mGKiIiIiIiIyFWqUMGooKCAfv36cejQITw9PRk6dCgRERHlxpw7d47XXnuNBQsWYDAYePzxx+nYsSMA7777Ll9++SX+/v4A1K1bl4EDB17fK5GbzsnRjlaNqtKyYTibtx8kO7+A2Yu2M3NhAfFRvmSmmmkYG4idyWjtUEVERERERETkClSoYDRw4EC6dOlCu3btmD59OgMGDGDChAnlxsyYMYOioiJyc3M5dOgQ7du3Jzk5mdDQUADat2/PCy+8cP2vQKzOYDBQM8KbmhHePNY2ztIk+z/jl+Ht7kRGcgStGlXF211NskVERERERERuB5ed+nHgwAE2bNhAmzZtAGjTpg0bNmygpKSk3LicnBw6duyI0WjE29ub9PR0Zs+efWOilluWh5sjHZvX4KMXW/DyIw2JCHbnyzmbeOTVXP4zYRlrt+2nrKzM2mGKiIiIiIiIyN+47AyjXbt2ERAQgMlkAsBkMuHv78+uXbvw9vYuNy44ONjyOCgoiN27d1seZ2dns3DhQvz8/OjVqxeJiYnX8zrkFmMyGmgQG0iD2ECK95cye9F25i7dTt7qYsICqpCVEsGdSWG4ONlbO1QRERERERER+X9uStPr++67j3/+85/Y29uTl5fHE088QU5ODl5eXhU+xrp1625ghDfXihUrrB3CTZcQDLXa+LOu6DjLtpQy5ru1fDpjHfFmF+pXdyPA0zYKR7aYe1HebZlyb7uUe9ul3Nsua+fezs6OY8eOWTUGW6XX3TbdLnk/ffr0334+1atX7y/3XbZgFBQUxJ49ezh37hwmk4lz586xd+9egoKCLhlXXFxMfHw8UH7GkZ+fn2VcamoqQUFBbN26lQYNGlzu9BZxcXE4OjpWePytasWKFX+bkMquUUN4DNhSdJCc/AIWrNrJ8q3HiK3mQ1aKmUa1g7C3q5xNsm0997ZKebddyr3tUu5tl3Jvu26F3G/cuBFXV1erxnC9/Otf/2LFihUsXLgQBweHS/aXlpaSlpZGRkYG//nPfyp83Oeff57Vq1fzww8/ALB9+3ZatmzJm2++Sbt27f72uU2aNKFx48YMGTKk3PZjx4797euem5tLcXEx3bp1K7c9Pz+fhx9+mIkTJ5KUlFTha7hW//81sGWLFi1i+fLl9OrVq9z2irwvLpf3G+n8+fOMHj2aBg0a0LBhw8uOd3BwICEh4arOddlv5j4+PsTExDBz5kwAZs6cSUxMTLnlaAAZGRlMmTKF8+fPU1JSwty5c2nVqhUAe/bssYzbuHEjO3fuxGw2X1XAUjnUCPfimfvq8tmAVjzcJpYDh0/w5hfLefS1XL6YvZH9h05YO0QREREREbGCu+++m8OHDzNv3rw/3T9nzhxOnDhB+/btr+k8QUFBTJo0icaNG1/Tcf5Obm7uJTeMAoiPj2fSpEnUrFnzhp1b/t7ixYt57733Ltl+M94X1+L8+fO89957LF269Iafq0JL0gYNGkS/fv14//33cXd3Z+jQoQB0796dp556itq1a9OuXTtWr15Ny5YtAejZsydhYWEAjBgxgvXr12M0GrG3t+fNN98sN+tIbJe7qwMd7oyi/R2RrNqyl+y8AibP3cKUH7fSMDaQzJQIEqr7YTAYrB2qiIiIiIjcBE2bNsXT05Np06ZZJiH80bRp0wgODq7Q7Iq/4+DgQJ06da7pGFfLzc3NaueWv2fN98WtpkIFo8jISKZMmXLJ9rFjx1p+NplMDB48+E+ff7HAJPJXjEYD9WoGUK9mAHtKjjN7USG5S7azaO0uQvzcyEyJoFn9cNycbaPXkYiIiIiIrXJwcCArK4vJkydTUlJSbnVLcXExy5Yto0ePHpb/VC4oKGD06NGsXLmS/fv34+/vT+PGjenduzfu7u5/eZ6/Wno0btw4xo8fz/79+4mOjubFF1+85Ln79+9n1KhRLFq0iH379uHl5UVSUhJ9+vQhICAAuLD8a8aMGQBER0cDEB4ezg8//PCnS9LKysr47LPPmDRpEjt37sTT05NWrVrx7LPPWpY/nT17ltjYWJ588kmqVKnChAkTOHjwIHFxcQwaNIjIyMgrfr337NnDsGHDWLBgAcePH8dsNtO9e3fuuuuucmOGDx/OokWLOHToEJ6ensTFxfHGG2/g5eXF2bNneffdd5k5cyZ79+7FxcWFyMhInn/+eerWrVuhOObPn8/777/Pr7/+yvnz5wkICKBdu3b861//sozZsGED77zzDsuXL+f06dPExsby/PPPl1sS+vzzz7Ny5UreeecdhgwZwoYNGwgMDOTRRx+lU6dOAIwcOZIxY8aUy43JZGLDhg1/+r64eMyRI0fy+uuvs3HjRoKDg3nxxRdp0qQJH3/8MRMnTuTIkSM0atSIV199tdz79uzZs4wdO5Zp06axc+dOvLy8aNOmDb1797Ysu7x43tdee40dO3YwdepUTp06RVJSEoMGDSIgIMCSf4D33nvPMkPq6aef5oknnriyxFfATWl6LXIlArxd+EdWLe5vGU3emmJy8goYO30dE2ZtpGndUDJTzFQL8bB2mCIiIiIicoO0b9+eiRMnMmvWLB544AHL9u+//56ysrJyy9H27NlDSEgIGRkZeHh4sH37dj788EM2bdrEV199dUXn/frrr3njjTe49957ycjIoKCggN69e1NaWlpu3KFDh3BycuKpp56y9P399NNPeeCBB8jJycHBwYGnnnqKgwcPsnnzZssX+7/ryzts2DA++eQTHnroIZo2bcrWrVsZNWoUW7ZsYfz48RiN/9dR5rvvvqNatWr8+9//5uTJkwwbNsxyc6mLdziviNLSUh588EFKS0t57rnnCAgIYPr06Tz//POcOnWKe++9F7hQMNm3bx8vvPACgYGB7N+/n/z8fE6dOgXAmDFjmDBhAs8++yzR0dEcPXqUtWvXcuTIkQrFUVhYSM+ePcnMzKRnz57Y29tTWFhIcXGxZcyaNWt46KGHiIuL47XXXsPJyYkvv/ySbt26MXnyZGJiYixjjxw5Qp8+fXj44Yd58sknmTJlCi+//DLVqlUjKSmJ+++/n7179/Ltt98yadIkgMuuajl8+DD9+/fn0UcfpUqVKnz66af06tWL++67j99//52BAweyb98+Xn/9dYYMGcJbb71lee6zzz7LggUL6N69O3Xq1GHr1q2888477Nq1i7fffrvceT744APq1avH66+/zr59+xg6dCh9+/Zl/Pjx2NnZ8eWXX9KlSxfuvfdeOnbsCHBJj+nrRQUjuWU52Ju4s14Yd9YL49cdh5iVX8i8FTuYs3g7Nat6kZVqJjUhGHu7in8gioiIiIjYiqNrfubo6p+sGkOVhGZUiW96xc+Lj48nKiqKadOmlSsYTZ8+ncTExHI9cRs1akSjRo0sjxMTEwkLC6Nr165s3rzZMoPkcs6dO8fo0aNp2rSppbl148aN8fT0pE+fPuXGRkVF8dJLL1maH589e5aEhATS09NZuHAhzZo1Izw8HC8vrwotcSopKWHChAnce++9/Pvf/wYgLS0NDw8P+vfvz4IFC7jjjjss4x0cHBgzZgx2dhe+0peVlfHss8+yfv16y42oKuKbb76hqKio3EynO+64g3379vH222/ToUMHjEYjq1ev5oUXXig366h169aWn1etWkWTJk146KGHLNuaN29e4TjWr1/PmTNnGDRoEC4uLgAkJyeXG/Pmm28SFhbGuHHjsLe/sPIkLS2NrKwsPvjgA9555x3L2KNHjzJmzBjLNSUlJZGXl8fMmTNJSkoiMDAQf39/gAovPystLWXs2LHUrVuXY8eOERISQocOHViwYAEzZ860FPQ2b97M119/zbBhwzAajSxevJg5c+YwfPhwy+uXkpKCu7s7/fr141//+le592hYWBjDhg2zPD5w4ABvvfUWBw4cwMfHx9LAOjAw8IYvnauct6OSSicq1JNeneowfmArureL4+jx07z15UoefjWX8dkb2Fty3NohioiIiIjIddSuXTvWrFlDQUEBcGGGyW+//XbJnatOnz7N+++/T0ZGBvHx8cTGxtK1a1cAy3Mrori4mL1795YrhMCFGzz9cXYPXCjQTJw4kU6dOpGYmEhsbCzp6elXfM6LVq1axZkzZ2jbtm257W3atMFoNF7S4DgtLc1SLAKoUaOG5RquxPLlywkJCbnkTm1t27Zl3759lmuJi4vj448/5vPPP2fLli2XHKd27drMmzePkSNHsmLFCk6fPn1FccTExGBnZ0fv3r2ZM2cOJSUl5fYfP36cFStWkJGRgcFg4OzZs5w9exa4UDBctmxZufGurq7lrsnR0ZHw8HB27dp1RXH9kZubW7nlddWqVQMuFH/++P4wm82cOXOGAwcOALBgwQIcHR1p0aKFJe6zZ8+SmpoKXMjBHzVt2rTc46vN7fWgGUZyW3Fztqdtk0japFVjza/7yM4r4Nt5W5k6byv1YwLJTI0gsYY/RqOaZIuIiIiIbasS3/SqZvfcKtq1a8fIkSOZNm0avXv3Ztq0aTg4OJCZmVlu3JtvvsnXX3/Nk08+SUJCAq6urhQXF/P0009blkxVxN69e4ELdwr/IwcHh0t6IY0fP5433niDhx56iDvvvJMqVapw9uxZ7r///is650WHDx8GuOTmUBfPfXH/RR4eHpeMA6743IcPH/7TG1L5+voCF5beAbzzzju89957fPjhh7z22mv4+/vTpUsXevTogdFo5IknnsDZ2ZkZM2YwZswYXF1dycjIoG/fvnh6el42jmrVqvHxxx/z8ccf06dPH06fPk1CQgJ9+vQhKSmJgwcPcv78ed59913efffdS57//wt6f3ZOBweHq8rNRZ1LJaQAACAASURBVP//Nb84y+lyuSgpKeHUqVN/eWv7i6/xX53nanN7PahgJLclo9FAnRr+1Knhz76DJ5izuJA5S7azdOxugnxcaZ0SQXqDcKq4OFg7VBERERERuQoBAQGkpKQwY8YMevbsSU5ODs2aNbvkC3VOTg4dOnTgn//8p2Xb0aNHr/h8F5coXZwZctHp06cv6cWTnZ1NWloavXv3tjSkLiwsvOJzXnTxmvbv32+ZufLHc1ek6HK1592wYcMl2/fv3w/8X+HF19eXQYMGMWjQILZt28a3337L22+/jY+PD506dcLBwYEePXrQo0cP9u3bx7x583jjjTc4depUuV4+fyc5OZnk5GROnTrFypUrGTVqFN27d2fevHl4eHhgMBjo2rVruWVxtwNPT0+cnZ35/PPP/3T/xffdrUhL0uS25+flzIOtY/j03y3p82A9vD2c+HTGeroNnsOor1ex9feD1g5RRERERESuQvv27dm5cycjRozg4MGD5ZpdX3Ty5EnLbI+Lvv322ys+V3BwMP7+/syaNavc9tmzZ3P+/PmrOqeDgwMnT5687LkTExOxt7cnOzu73Pbs7GzOnz9P/fr1K3oZV6R+/frs3LmTX375pdz2GTNm4OfnV65X1EWRkZH06dMHV1fXP12e5ufnR6dOnWjYsCFbt2694pgcHR1JTk7mkUce4fjx4xQXF+Pm5kZiYiKbN28mLi6O2rVrX/LnSl2cuVOR/FyLxo0bc+LECU6cOPGncV+8q15F2dnZYTQab3jcoBlGUonY2xlpkhhKk8RQCncdISevgHkrfmfusiKqh3mSlWomrU4IjvZqki0iIiIicjto0aIFbm5ujBs3Dh8fHxo3bnzJmLS0NKZOnUpkZCTh4eHMnj2bNWvWXPG5TCYTPXv2ZODAgbz00ktkZGRQWFjIxx9/bJlF9Mdzjhs3js8++4y6deuSn5/PDz/8cMkxo6KimDp1KpMmTSImJgYnJydLT5o/8vb2pmvXrnz66ac4OTnRuHFjfv31V0aNGkWDBg3+9Lqvh3vuuYcvvviCJ598kqeffhp/f3++//57lixZwpAhQzAajRw8eJDu3btz1113Ua1aNezs7MjNzeXYsWOWPjw9evQgNjaWWrVq4e7uzoYNG8jPzy/XsPzvTJw4kVWrVtG4cWOCgoIoKSnhww8/JDAwkKioKAD69+9P165deeyxx7jnnnvw9fXl4MGDrFu3DoPBwLPPPntF137xuJ988glpaWmYTCbi4uKu6BgVkZKSQkZGBk8++STdunWzNCXfuXMn8+fPp1+/foSHh19x7PPmzSM1NZUqVaoQEBBwQ2YqqWAklVJEkDtP3JtAtza1mLf8d7LzC3j761V88v060htUpXVyBEG+rpc/kIiIiIiIWI2TkxMZGRl88803tGnTplyj54sGDRrE4MGDGTFiBAaDgaZNmzJ8+HA6dep0xee77777OHHiBOPHj+f777+nRo0ajBw5kmeeeabcuF69enHs2DE+//xzPvnkExo2bMhHH31Ey5Yty43r1KkTa9asYfjw4Rw5coTw8PA/LSwB9OnTBx8fHyZPnszEiRPx9PSkQ4cOPPvss5f06Lle3Nzc+OKLLxg2bBjDhw/n2LFjVKtWrdwdvZydnYmJiWHy5MkUFxdjNBoxm82MGDGCO++8E7hwF7Lc3Fy++OILTp48SVBQEI8//ni5ZYJ/p2bNmixYsIC33nqLkpISPD09SUpKYsSIEZaZQPHx8UyZMoX33nuPV155hdLSUnx8fIiNjeW+++674mtv3rw5nTt35vPPP+fdd9/FaDT+6fK862HEiBF8/vnnTJ06lQ8++ABHR0dCQkJIS0vD29v7io83YMAAhgwZwuOPP86ZM2d4+umneeKJJ6573IaysrKy637U6+jUqVOsW7eOuLg4HB0drR3ONVuxYgX16tWzdhg2p6ysjHXbDpCdV8CidbsoKyujbrQ/malm6tUMwHQTmmQr97ZJebddyr3tUu5tl3Jvu26F3G/cuJGYmBirxmCLjh07dsnsI6n8bqe8X8tng2YYiU0wGAzUjvKldpQvBw6fIHfxdmYv3s6rnyzB39uF1skRtGgQjofb7V+UFBEREREREblWKhiJzfHxcOb+VjXpmF6DJet2k5NfwPjsDUycvYnGdYLJTDUTHe6FwXDjZx2JiIiIiIhUZufPn7+kafgfGQwGTCb1mb0VqWAkNsvOZCQ1IZjUhGCKdh9h1qJCflr+O/NW7KBaiAeZKWbuqBuCk4N+TURERERERK7GqFGjGDNmzF/u/7u+TmJd+iYsAoQHutPj7ni6Ztbi55U7yMkr4L0pv/DZzPU0rx9GZoqZED83a4cpIiIiIiJyW+nSpQvp6el/ub8y9CqurFQwEvkDZ0c7WidHkNGoKhsKSsjJLyAnr4Dv//sbdWr4kZlipkGtAEymG3OXAhERERERkcokICCAgIAAa4chV0EFI5E/YTAYiK3mQ2w1Hw4eOUnu0u3MXrSd18ctxdfTmYzkqrRsWBWvKk7WDlVERERERETkulPBSOQyvNyd6Jwezb13VmfZxj1k5xXwxaxNfJ27mZTaF5pk1zJ7q0m2iIiIiFhVWVmZ/k0qIhZlZWXX9HwVjEQqyGQy0iguiEZxQezcV8qs/ELmLiviv7/sJCLIncyUCJrWC8PZUb9WIiIiInJz2dvbc+LECVxcXKwdiojcIk6cOIG9vf1VP1+NWESuQoifG4+1i2PcgJb06lQHo9HA+1PX8I/Bc/jw2zUU7T5i7RBFRERExIb4+/uzc+dOjh8/fs2zCkTk9lZWVsbx48fZuXMn/v7+V30cTYUQuQZODna0bFiVFg3C2Vx0kJy8AmYv3s7MvAJqR/qSlWqmYVwgdmqSLSIiIiI3kLu7OwDFxcWcOXPGytHYjtOnT+Pg4GDtMOQmux3ybm9vT0BAgOWz4WqoYCRyHRgMBmpW9aZmVW8ebRvHD0uLmLWokP9MWIa3uxMZjaoS6HLO2mGKiIiISCXm7u5+TV8O5cqtWLGChIQEa4chN5mt5F0FI5HrzMPNkXubVefuplGs3HShSfZXP2wGYMlvy8hMjaB2pK8aEoqIiIiIiMgtSwUjkRvEZDRQv1Yg9WsFsmv/McZNW8yaX/eRt6aYsAA3MlPMNEsKw8Xp6puQiYiIiIiIiNwIKhiJ3ARBvq60TPTk2X/UYeEvO8nJL+DD79YyPnsDd9YLIzPVTESQpg+LiIiIiIjIrUEFI5GbyNHeRPP64TSvH87W3w+Sk1fIj8su9DuqZfYmK9VMcu1g7O3UJFtERERERESsRwUjESupHubF0/d58UjbWOYuLWJWfiHDvliBp9s6WjaqSkajCPy8nK0dpoiIiIiIiNggFYxErKyKiwN3N42iXZNIftmyj+y8Aqb8uIVvftxCg9hAMlPMJFT3w2hUk2wRERERERG5OVQwErlFGI0G6tb0p25Nf/aUHGfO4kJyl2xn8brdhPi50jrFTPOkMNxcHKwdqoiIiIiIiFRyKhiJ3IICvF3omlmL+1tGk7e6mJz8Qj6evo4JORtpWjeUzJQIIkM9rR2miIiIiIiIVFIqGIncwuztTDStF0bTemH8tvMwOfkF/LxyB7lLthNd1YvMFDNpCcE42JusHaqIiIiIiIhUIhW6FVNBQQGdO3emVatWdO7cmcLCwkvGnDt3jsGDB5Oenk6LFi2YMmXKJWN+++03EhISGDp06DUHLmJrqoV48GTHOowb0Iru7eMoPX6GkV+t5OFXcxk3cz17So5bO0QRERERERGpJCo0w2jgwIF06dKFdu3aMX36dAYMGMCECRPKjZkxYwZFRUXk5uZy6NAh2rdvT3JyMqGhocCFgtLAgQNJT0+//lchYkPcnO1p2ziSu9KqsWbrfrLzC/hu/ja+/flXkmICyEwxUzfaX02yRURERERE5KpddobRgQMH2LBhA23atAGgTZs2bNiwgZKSknLjcnJy6NixI0ajEW9vb9LT05k9e7Zl/0cffUTTpk2JiIi4vlcgYqMMBgMJNfx4sVsDPnmpBZ3Sa7D190MM/ngxPf4zl2/n/cqRY6etHaaIiIiIiIjchi5bMNq1axcBAQGYTBd6pJhMJvz9/dm1a9cl44KDgy2Pg4KC2L17NwCbNm1i4cKFdOvW7TqGLiIX+Xo682BGDJ/+uyV9H0zCx8OZz2aup9srcxj51Uq2FB20dogiIiIiIiJyG7nhTa/PnDnDyy+/zBtvvGEpOl2NdevWXceorGvFihXWDkGs5Gbk3gXo2MiZJjUDWLa1lIW/7OCn5b8T7G1P/epuxFV1wd5Oy9VuJv3O2y7l3nYp97ZLubddyr3tUu5tU2XJe7169f5y32ULRkFBQezZs4dz585hMpk4d+4ce/fuJSgo6JJxxcXFxMfHA/8342jfvn0UFRXx+OOPA3DkyBHKysooLS3l1VdfrfBFxMXF4ejoWOHxt6oVK1b8bUKk8rJG7jObw/GTZ5i3YgfZeQVMX3KQH9eUkt4gnNYpEQT7ut3UeGyRfudtl3Jvu5R726Xc2y7l3nYp97bJVvJ+2YKRj48PMTExzJw5k3bt2jFz5kxiYmLw9vYuNy4jI4MpU6bQsmVLDh06xNy5c5k4cSLBwcEsWbLEMu7dd9/l+PHjvPDCC9f/akTkEi5O9mSlmslMiWDdbwfIyStgxoLfmDZ/G3Vr+pOVYqZeTAAmNckWERERERGR/1WhJWmDBg2iX79+vP/++7i7uzN06FAAunfvzlNPPUXt2rVp164dq1evpmXLlgD07NmTsLCwGxe5iFwRg8FA7Uhfakf6cuDwCXKXFDF7USGvfroEfy9nMpIjaNmwKh5ut/9MPhEREREREbk2FSoYRUZGMmXKlEu2jx071vKzyWRi8ODBlz1Wr169riA8EbkRfDycub9lNB2bV2fJ+t3k5BUwIWcjX87ZTFpCMFmpZqKremEwaNaRiIiIiIiILbrhTa9F5NZlZzKSGh9Manwwv+85Sk5+AT8t/52fV+6gWrAHmakR3JEYipOjPipERERERERsib4FiggAYQFV6HF3PF0zazF/5YUm2e9NWc1nM9bTvP6FJtmh/lWsHaaIiIiIiIjcBCoYiUg5zo52ZCRH0KpRVTYWlpCTV0hOfgHfL/iNOtX9yEyNoEGtQEwmo7VDFRERERERkRtEBSMR+VMGg4FaZh9qmX149GgsPywpYtaiQl4ftwwfD6cLRaWGVfFyd7J2qCIiIiIiInKdqWAkIpflVcWJTuk1uOfOKJZv3EN2XgETZ2/i69zNpMQHk5kSQWw1HzXJFhERERERqSRUMBKRCjOZjDSMC6JhXBDF+0qZtaiQH5YWseCXnVQNrEJmqpmmdUNxcbK3dqgiIiIiIiJyDVQwEpGrEuznxqNt43ggoyYLVu0kO7+AD6auYdzMDTRLCqN1SgRVA92tHaaIiIiIiIhcBRWMROSaODnY0aJhVdIbhLOl6CA5+YXkLtlOdl4BtSN9yUyNoFFcEHZqki0iIiIiInLbUMFIRK4Lg8FAdFVvoqt688hdscxdWkTOokKGTliOt7sjLRtGkJFcFR8PZ2uHKiIiIiIiIpehgpGIXHcebo7c06w67ZtGsWrzXrLzCpg0dzOTf9xCo7hAMlPMxEf5qkm2iIiIiIjILUoFIxG5YUxGA0kxASTFBLD7wDFmLyokd0kR+Wt2EervRmaKmWZJYbg6q0m2iIiIiIjIrUQFIxG5KQJ9XOnWJpYurWqycPVOcvIK+WjaWibkbKBpvTAyUyIwB3tYO0wRERERERFBBSMRuckc7E00SwqnWVI4v/5+iJz8An5aVsTsRYXERHiTlWomJT4IezuTtUMVERERERGxWSoYiYjVRIV58lTnRB6+K5YflxWRk1/I8Ikr8JzuSIuG4WQkR+Dv5WLtMEVERERERGyOCkYiYnVVXBxof0cUbRtH8svWfeTkFTD1p61M/Wkr9WsFkplqpk51P4xGNckWERERERG5GVQwEpFbhtFooG60P3Wj/dlbcpzZiwvJXbKdJet3E+zrSusUM+n1w3BzcbB2qCIiIiIiIpWaCkYickvy93aha2Yt7m8ZTd6aXeTkFfDJ9+v4fNZG7kgMITPVTFSop7XDFBERERERqZRUMBKRW5q9nYmmdUNpWjeUguLDZOcV8PPKHfywtIjocC8yUyNISwjBwV5NskVERERERK4Xo7UDEBGpKHOwB092rMP4Aa14vH1tjp08w8ivVtHtlVzGzVzP7gPHrB2iiIiIiIhIpaAZRiJy23F1tueuxtVok2Zmza/7yckv4Lv52/j251+pVzOArFQzidH+mNQkW0RERERE5KqoYCQity2DwUBCdT8Sqvux/9AJ5izezpzFhQz+eDEB3i5kpkTQvH44Hm6O1g5VRERERETktqKCkYhUCr6ezjyQUZPOLWqweN0usvMK+GzmBr6YvYnGdULISjVTPcwTg0GzjkRERERERC5HBSMRqVTsTEbSEkJISwhh++4jzMov5KflRfy0/HeiQj3ITDHTODEEJwd9/ImIiIiIiPwVfWMSkUqraqA7/+wQT9fMGH5euYPsvALemfwLn85YT3qDcFonRxDs52btMEVERERERG45KhiJSKXn4mRPZoqZ1skRrP/tADn5hcxY8BvT5m+jbrQ/mSkRJNUKVJNsERERERGR/6WCkYjYDIPBQFykL3GRvhw8cpI5S7Yze1Ehr322FD8vZzIaRdCyYVU8q6hJtoiIiIiI2DYVjETEJnm5O3Ffi2g6NqvOkvW7yckv4PNZG/kqdxOp8ReaZNeM8FKTbBERERERsUkqGImITTOZjKTEB5MSH8zve44ye1EhPy4rYv6qHZiD3clMMXNH3VCcHfVxKSIiIiIitkPfgERE/ldYQBW6t6/NQ61jmL/qQpPs0d+s5rOZ62le/0KT7LCAKtYOU0RERERE5IarUMGooKCAfv36cejQITw9PRk6dCgRERHlxpw7d47XXnuNBQsWYDAYePzxx+nYsSMAU6dOZdy4cRiNRs6fP0/Hjh3p2rXrdb8YEZHrwcnRjlb/289oU+FBcvILmPW/jbLjo3zJSjXTMDYQk8lo7VBFRERERERuiAoVjAYOHEiXLl1o164d06dPZ8CAAUyYMKHcmBkzZlBUVERubi6HDh2iffv2JCcnExoaSqtWrejQoQMGg4HS0lLuuusuGjRoQM2aNW/IRYmIXA8Gg4EYszcxZm8ebRvHD0u3M2tRIW+MX4aPhxOtGkXQqlFVvN2drB2qiIiIiIjIdXXZ/x4/cOAAGzZsoE2bNgC0adOGDRs2UFJSUm5cTk4OHTt2xGg04u3tTXp6OrNnzwbAzc3N0jj25MmTnDlzRo1kReS24lnFkY7NazD2xRa8/EhDqga68+WcTTzyai5DJyxj7bb9lJWVWTtMERERERGR6+KyM4x27dpFQEAAJpMJAJPJhL+/P7t27cLb27vcuODgYMvjoKAgdu/ebXn8448/MmLECIqKinjuueeIjo6+ntchInJTmIwGGsQG0iA2kOL9pczKL2Tu0iIWri4mPLAKmSlm7qwXiouTvbVDFRERERERuWo3rel18+bNad68OcXFxfTs2ZMmTZpQrVq1Cj9/3bp1NzC6m2vFihXWDkGsRLmvfOqEQK27/Fi//QRLt5Yy5ts1fPr9WhLMLiRVdyPA0155t2HKve1S7m2Xcm+7lHvbpdzbpsqS93r16v3lvssWjIKCgtizZw/nzp3DZDJx7tw59u7dS1BQ0CXjiouLiY+PBy6dcXRRcHAwtWvX5ueff76iglFcXByOjo4VHn+rWrFixd8mRCov5b5yS24IjwFbig6SnVfAgl92smzrMar6O9C5VTyN4oKwt1OTbFui33nbpdzbLuXedin3tku5t022kvfLfnvx8fEhJiaGmTNnAjBz5kxiYmLKLUcDyMjIYMqUKZw/f56SkhLmzp1Lq1atANi2bZtlXElJCUuWLKFGjRrX8zpERG4JNcK96H1/XcYNaMXDbWI5cvwcb36+nEdfy+WL2RvZf+iEtUMUERERERG5rAotSRs0aBD9+vXj/fffx93dnaFDhwLQvXt3nnrqKWrXrk27du1YvXo1LVu2BKBnz56EhYUBMGnSJPLy8rCzs6OsrIwHH3yQtLS0G3RJIiLW5+7qQIc7owircgiDaxjZeQVMnruFKT9upWFsIFkpZuKr++oGACIiIiIickuqUMEoMjKSKVOmXLJ97Nixlp9NJhODBw/+0+e/+OKLVxmeiMjtzWgwUC8mgKSYAHYfOMbsRYXkLili0dpdhPi5kZkaQbOkcNyc1SRbRERERERuHTet6bWIiK0L9HGlW5tYurSqycLVxeTkFzB22jom5Gykad1QslLNmIM9rB2miIiIiIiICkYiIjebg72JZklhNEsK49cdh8jJK2Deih3MWbydmAhvMlMiSE0Ixt7OZO1QRURERETERumWPSIiVhQV6slTnRMZP6Alj7WL43DpKd76ciUPv5rLhJwN7C05bu0QRURERETEBmmGkYjILcDNxYF2TSK5K60aq7fuIye/gKk/bWXqT1upXyuQzBQzdWr4YTSqSbaIiIiIiNx4KhiJiNxCjEYDidH+JEb7s/fgceYs3k7u4u0sWb+bIF9XMlMiaF4/nCouDtYOVUREREREKjEVjEREblH+Xi481DqG+1pEk7/mQpPsT75fz+c5G2mSeKFJdlSYp7XDFBERERGRSkgFIxGRW5y9nZE76oZyR91QCooPMyu/kHkrfmfusiJqhHuSmWImrU4IjvZqki0iIiIiIteHml6LiNxGzMEePHFvAuMGtKLH3bU5ceosb3+9iodfmcNnM9az+8Axa4coIiIiIiKVgGYYiYjchlyd7WmTVo2sVDNrt+0nJ6+Qaf/dxnfzf6VezQAyUyKoWzMAk5pki4iIiIjIVVDBSETkNmYwGIiP8iM+yo8Dh08wZ/F25iwu5JVPlhDg7ULr5AjSG4Tj4eZo7VBFREREROQ2ooKRiEgl4ePhTJdWNemUXoMl63aTnVfAuOwNTJyzibSEYDJTzUSHe2EwaNaRiIiIiIj8PRWMREQqGTuTkdSEYFITginafYRZ+YX8uPx35q3YQWSoB5kpZpokhuDkoL8CRERERETkz+nbgohIJRYe6E6PDvE8lBnD/JU7yM4r4N3Jv/DpjPWk1w+ndUoEIX5u1g5TRERERERuMSoYiYjYABcne1qnmMlIjmBDQQk5eQXMXPgb0/+7jTo1/MhKNVM/JgCTSTfPFBERERERFYxERGyKwWAgtpoPsdV8eOzISXKXbGf2okKGfLYUX09nMpKr0rJhVbyqOFk7VBERERERsSIVjEREbJSXuxOdW0Rzb7PqLN2wh5y8Ar6YtYmvczeTEh9MZoqZWmZvNckWEREREbFBKhiJiNg4k8lIcu0gkmsHsWPvUWYtKuTHpUX8d9VOIoLcyUw107RuKM6O+itDRERERMRW6F//IiJiEepfhe7tavNQRgzzV+0kJ6+A979ZzWcz1tM8KYzMVDNhAVWsHaaIiIiIiNxgKhiJiMglnBztaNWoKi0bhrN5+0Gy8wuYvXg7M/MKiI/yJTPVTMPYQOzUJFtEREREpFJSwUhERP6SwWCgZoQ3NSO8eaxtnKVJ9n/GL8Pb3YmMRlVp2agqPh7O1g5VRERERESuIxWMRESkQjzcHOnYvAYd7qzOik17yM4r4MvczUyau4VGtYPISjETF+mjJtkiIiIiIpWACkYiInJFTEYDDWoF0qBWILv2H2PWokLmLt1O3upiwgKqkJUSwZ1JYbg42Vs7VBERERERuUoqGImIyFUL8nXlkbtieSCjJgtW7SQnv4Ax361lXPYG7qx3oUl2RJC7tcMUEREREZErpIKRiIhcM0d7E+kNwklvEM6WooPk5Bfw47IiZi0qJLaaD5kpESTXDsbeTk2yRURERERuByoYiYjIdVUj3Isa4V48clfchaJRfiHDvliBZ5V1tGpYlVaNIvDzUpNsEREREZFbmQpGIiJyQ7i7OnB30yjaNYlk1Za95OQVMvnHLUz5cQsN44LITIkgobqfmmSLiIiIiNyCVDASEZEbymg0UK9mAPVqBrCn5DizFxWSu2Q7i9buIsTPlcwUM83qh+PmrCbZIiIiIiK3ChWMRETkpgnwduEfWbXo0iqahauLyckrYOz0dYzP2UjTuqFkpZqpFuJh7TBFRERERGyeCkYiInLT2duZuLNeGHfWC2PbjkPk5Bfy88od5C7ZTs2qXmSmmklLCMbezmTtUEVEREREbFKFbldTUFBA586dadWqFZ07d6awsPCSMefOnWPw4MGkp6fTokULpkyZYtk3evRosrKyaNu2LR06dGDBggXX7QJEROT2FhnqSa9OdRg/sBXd28Vx9PhpRny5km6v5DI+ewN7So5bO0QREREREZtToRlGAwcOpEuXLrRr147p06czYMAAJkyYUG7MjBkzKCoqIjc3l0OHDtG+fXuSk5MJDQ0lPj6eRx55BGdnZzZt2sSDDz7IwoULcXJyuiEXJSIitx83Z3vaNonkrsbVWL11Hzn5hXz7P+3de1hU57n38d8wIOIBOchhUGAGFUQHPJ9Aq4nEA2K0SXhN3Ul3mtYkTXbcl+kVY/a73xgT2x3S9Iox1e5e5lTTNmlpjEZANOakgkYlHoIYNQmokYMRPESjsYV5/1CRYQYYFBhgvp9/ZNbzrGfda91rscabNc98dETvfHREI+PDNCPZomGxofLyYpJsAAAAoLU1+YRRZWWlioqKlJaWJklKS0tTUVGRqqqq7Prl5OQoPT1dXl5eCgoKUkpKinJzcyVJEyZMkJ/fla9QjouLk81m05kzZ1p6XwAAnYDBYNDQ2FD9132j9cr/naL/MzlWR46f0dOrduih5z7Qmo++1LkLl90dJgAAANCp6izsKAAAIABJREFUNfmEUVlZmcLCwmQ0XplHwmg0KjQ0VGVlZQoKCrLrFxERUfvaZDKpvLzcYby1a9cqKipK4eHhLRE/AKATCwn00z3T4zXntjht/7xUOfklej3rgP6Se1AThvVRapJFsVGB7g4TAAAA6HTadNLrnTt36qWXXtJrr73W7HULCwtbISL3KCgocHcIcBNy75nIe8voLil9rJ9+NDBMuw6f15Y93+iDXccVEeSjUbE9ZI3qJh/v9vVxNXLvuci95yL3novcey5y75k6S95HjBjRYFuTBSOTyaSKigpVV1fLaDSqurpaJ0+elMlkcuhXWlqqxMRESY5PHO3Zs0ePP/64Vq5cqZiYmGbvhNVqla+vb7PXa28KCgoaTQg6L3Lvmch760idLH1/6Z/6aPdxZeeXaN2O0/pw/3mljI7W9HFmmXp3d3eI5N6DkXvPRe49F7n3XOTeM3lK3pucwyg4OFjx8fHKysqSJGVlZSk+Pt7u42iSNG3aNGVmZqqmpkZVVVXavHmzpk6dKknav3+/FixYoOXLl2vw4MGtsBsAAE/TrauPZoyP0YrHb9FvfpmsxAEhem/LV3rgfzZr8art2llUruoam7vDBAAAADoklz6S9vTTT2vRokVauXKl/P39lZGRIUmaN2+e5s+fr4SEBM2aNUv79u3TlClTJEmPPPKIIiMjJUlLlizRpUuX9NRTT9WO+fzzzysuLq6l9wcA4GEMBoMS+vdWQv/eqjx7UZt2HFXujqN69tVPFRrop2njzJoyJlq9enT8p1QBAACAtuJSwahfv37KzMx0WL5q1aran41Go5YsWeJ0/XfeeecGwwMAwHXBvfz0k6kDlZ4Sq08PlCsnr1ircw7qrxsPafzQCM1IsiguOlAGQ/ua6wgAAABob9p00msAANqCt9FLyYkRSk6M0PGK75STX6wPdx/XxwXfKKZPL6UmWTRxWB919eU2CAAAADjDO2UAQKcWGdZTD/44UT9NHaSPP/tGOXnF+n3mXr2+vlCTR0cpNcmiPiE93B0mAAAA0K5QMAIAeAQ/X29NH2fWtLHRKiquUk5+sXLyivXelq81dECIUpMtGj0oTEZjk98HAQAAAHR6FIwAAB7FYDBocEywBscE6/R3l7Tp06PK3X5Uv3ljp3r36lo7SXagf1d3hwoAAAC4DQUjAIDHCuzZVXNS4nTXLQO062CFcvKK9efcL/T2+4eUlBCh1GSLBlmCmCQbAAAAHoeCEQDA4xmNXhprNWms1aQT357XhvwSbd51TFv2npDZ5K/UJLMmDu+rbl193B0qAAAA0CYoGAEAUEefkB76xSyr7pk+UFv2nFB2XrFWvrNfr2cVafLISE1PMisq3N/dYQIAAACtioIRAABOdO3irSljonXb6CgdPnZa2XnFyt1xVFl5xUro11upyWaNtZrkzSTZAAAA6IQoGAEA0AiDwaC46CDFRQfp57db9f7OY9qwvUQZq3cryN9XU8eaNXVstIJ7+bk7VAAAAKDFUDACAMBFvXr46q5bB+jHk/rrsy8qlJNforffP6S/bT6scVaTUpPNSujXm0myAQAA0OFRMAIAoJmMXgaNGhSuUYPCVXbqgnK3l+j9nUeVt79UkWE9lJpkUaCxxt1hAgAAADeMghEAADfB1Lu7fjZzsOZOG6hte08oJ79Yf3z3c/l4G7T3xD6lJplliejl7jABAACAZqFgBABAC/D1MWryqChNHhWlI8dPa/V7u/XhrmPK3V6iQZYgpSZZlJQYIR9vJskGAABA+0fBCACAFjYgMlCzxwZp4c8S9MGuY8rJK9ELfylQwLpCTRkbraljoxUa2M3dYQIAAAANomAEAEAr6dmti2ZP7K/bJ/TT3sPfKie/WP/44LD+8cFhjRoUrhnJFg0ZECIvLybJBgAAQPtCwQgAgFbm5WXQ8IGhGj4wVCervlfujhJt+vSoPj1Qroje3ZWabNHkkZHq0a2Lu0MFAAAAJFEwAgCgTYUGddNPUwfpJ1PilLe/TDl5xXplXaFW5xzUxGF9lJpsUf++Ae4OEwAAAB6OghEAAG7g423UpOF9NWl4X3194qxy8ov18Wff6P2dxxQXHajUJIvGD4lQFx+ju0MFAACAB+KrWgAAcLOYPr30H+lD9cZTUzVvtlXnv/+nXnzrM/3s2U16I+uAyisvuDtEAAAAeBieMAIAoJ3o4eej2yf008zxMdp/5JSy84v17idfac3HX2rEwDDNSLZoeFwok2QDAACg1VEwAgCgnTEYDBoSG6IhsSE6deaicneUaOOOo1ryyg6FB3fT9HFmpYyOln93JskGAABA66BgBABAO9Y7wE/3TIvXnJQ47SgsU3ZesV7PKtKfc7/QhKF9NCPZotioQHeHCQAAgE6GghEAAB2Aj7eXJgztowlD++ho2Tnl5Bfro4Lj+nD3cfWPDNCMJLMmDOsrXybJBgAAQAtg0msAADqYaJO/fnnnEL3x1FQ9dEeifrhcrZf+tlf3LdmoV98rVOmp8+4OEQAAAB0cTxgBANBBdevqoxnJFqUmmVX4daVy8oq1fuvXWvvJVxoeF6rUJLNGDgqXkUmyAQAA0EwUjAAA6OAMBoMS+vVWQr/eqjp3SRt3HFXu9hItfX2nQgL9NH2cWbeNjlZAT193hwoAAIAOgoIRAACdSJB/V/1kSpzSJw/QzgPlyskv1uqcg/rrxkMaPyRCqUkWDTQHymDgqSMAAAA0jIIRAACdkLfRS0mJEUpKjNDxiu+0YXuJPth1TB9/9o1iInopNdmsicP6qqsvbwUAAADgiHeJAAB0cpFhPfXA7ATdOz1en3z2jbLzivX7zH16ff0B3ToqSqlJZvUN7enuMAEAANCOuPQtacXFxZozZ46mTp2qOXPmqKSkxKFPdXW1lixZopSUFN12223KzMysbdu2bZvuuOMOWa1WZWRktFjwAADAdX6+3po2zqzlv5qkjP8Yr5Hx4dqQX6xfZnyo//7fPOXvL1V1dY27wwQAAEA74NITRosXL9bcuXM1a9YsrVu3Tk899ZRWr15t12f9+vU6duyYNm3apDNnzmj27NkaN26c+vbtq8jISC1dulQbN27U5cuXW2VHAACAawwGgwZZgjXIEqxffGfV+zuPasP2Ev3Pn3YpuFdXTRtn1pQx0Qry7+ruUAEAAOAmTT5hVFlZqaKiIqWlpUmS0tLSVFRUpKqqKrt+OTk5Sk9Pl5eXl4KCgpSSkqLc3FxJUnR0tAYNGiRvbz4BBwBAexLQ01fpk2O16r9u03//bLSiw/31l9wvdP+zm/T8m7tV+NUp2Ww2d4cJAACANtZkBaesrExhYWEyGo2SJKPRqNDQUJWVlSkoKMiuX0RERO1rk8mk8vLyVggZAAC0NKOXQWOsJo2xmlT67Xlt2F6i93ce09a9JxQd3lOpyRZNGt5X3br6uDtUAAAAtIEO88hPYWGhu0NoMQUFBe4OAW5C7j0TefdcHTn3Q/tIg2aGqPDoRe06fF5/eGe/Xl33uYZYumnUgB4KDaBw1JiOnHvcHHLvuci95yL3nqmz5H3EiBENtjVZMDKZTKqoqFB1dbWMRqOqq6t18uRJmUwmh36lpaVKTEyU5PjE0c2yWq3y9fVtsfHcpaCgoNGEoPMi956JvHuuzpL7cWOkX9hsOnL8jLLzirV17wntOnJB1n7BSk2yaFyCSd5Gl75Dw2N0ltyj+ci95yL3novceyZPyXuT7/CCg4MVHx+vrKwsSVJWVpbi4+PtPo4mSdOmTVNmZqZqampUVVWlzZs3a+rUqa0TNQAAaDMGg0GxUYFa8JPhev3/TdHP0gbp29MX9fybu3X/s5v0l9wvVHn2orvDBAAAQAty6SNpTz/9tBYtWqSVK1fK399fGRkZkqR58+Zp/vz5SkhI0KxZs7Rv3z5NmTJFkvTII48oMjJSkrR792499thjOn/+vGw2m7Kzs/XrX/9aEyZMaKXdAgAAraFXD1/dccsAzZrYX3sOnVR2XrH+tvmQ/v7BYY21his1yaLE/r1lMBjcHSoAAABugksFo379+ikzM9Nh+apVq2p/NhqNWrJkidP1R44cqS1bttxgiAAAoL0xehk0Mj5MI+PDVF55QbnbS7Tp02PK31+mvqE9ND3JrMkjo9Tdj7mOAAAAOqIOM+k1AABon8KDu+u+tMGaO3Wgtu07oZy8Eq1aW6jVOQc1aXhfzUi2yBLRy91hAgAAoBkoGAEAgBbRxceoW0dG6daRUfry+Bnl5Bfro4JvtHHHUcWbg5SabFFyokk+3kZ3hwoAAIAmUDACAAAtrn9kgObPGab7Zw7W5l3HlZNfrN/9pUCvrvPVbWOiNG2cWaGB3dwdJgAAABpAwQgAALSaHt26aPbEfrp9Qoz2HvlWOXnFeufDI3rnwyMaNShcqckWDR0QIi8vJskGAABoTygYAQCAVuflZdDwuFANjwvVyarvlbujRJs+PapPD5TL1Lu7UpPMShkVpR7durg7VAAAAIiCEQAAaGOhQd3009RB+smUOOXvL1N2XrFefe+A3tzwhSYO66PUJIv6Rwa4O0wAAACPRsEIAAC4hY+3UROH99XE4X1VXHpWOfkl+rjguN7feUyxUQGakWzR+CF91MWHSbIBAADampe7AwAAALBE9NIjdw3RG09N1QOzE/T9pX/pxbf26L5nNumNrAMqr7zg7hABAAA8Ck8YAQCAdqO7n49mTohR2niL9n95Sjn5xXr3k6+05uMvNWJgmGYkWzQsLlRGJskGAABoVRSMAABAu2MwGDRkQIiGDAhR5dmLyt1+VBt3lGjJKzsUFtRN08eZlTI6Sr16+Lo7VAAAgE6JghEAAGjXgnv56d+mDdSc22K1o/DKJNlvZBfpLxu/0IShfZSaZFZsVKAMBp46AgAAaCkUjAAAQIfgbfTS+CF9NH5IHx0tP6cN+SX6cPdxfbj7uPr17aUZSRZNGNZHXbvw9gYAAOBmMek1AADocKLD/fXQHYl646kp+uWdifrnv2q0/O97dd8zm/TKukKVfnve3SECAAB0aPwJDgAAdFjduvooNcmi6ePMOvB1pXLyS5S17Wut2/KVhsWGKDXZolGDwpkkGwAAoJkoGAEAgA7PYDDI2q+3rP166/S5S9r06VHlbi/Rr1/fqZBAP00ba9ZtY6IU2LOru0MFAADoECgYAQCATiXQv6vm3Banu24doJ1F5crOK9abGw7qrU1fKDmxj1KTzYo3BzFJNgAAQCMoGAEAgE7JaPTSuIQIjUuI0PGK75S7vUQf7DqmT/Z8I7PJX6nJFk0a3ld+vrwdAgAAqI93SAAAoNOLDOupebMTdO/0eH2y5xtl5xVr5T/26Y2sA7p1ZKRSkyyKDOvp7jABAADaDQpGAADAY3T19dbUsWZNGROtQ0dPKzuvWLnbjyprW7ES+/dWarJFYwaHy9vIF8kCAADPRsEIAAB4HIPBoIHmIA00B+nnt1v1/s4rk2Q/96ddCvLvqmnjzJo6NlpB/kySDQAAPBMFIwAA4NECevoqfXKs7rhlgAoOVig7v1h/3fiF/vb+IY1LMCk12SJrTDCTZAMAAI9CwQgAAECS0cug0YPDNXpwuEpPndeG/BJt3nlM2/aVKjKsp2YkmXXLyEh16+rj7lABAABaHQUjAACAeiJ699DPb7fqnunx2np1kuz/ffdz/SmnSJNGRGpGkkXRJn93hwkAANBqKBgBAAA0wNfHqJTR0UoZHa3Dx65Mkr155zFtyC/R4JhgzUiyaGyCST7eTJINAAA6FwpGAAAALoiNClRsVKB+frv1StFoe7Ge//NuBfb01ZSx0Zo21qzeAX7uDhMAAKBFUDACAABoBv/uXXTHLf01e2I/fXbopHLyi/X3zYeV+cERjRkcrpBul3S6+pgMBunKPNlXJss2GK7+ZDDIoGttkkFXGq60X+ukOn0M19fVlW94u9at7kTcddc3OFt27eerg9ddv24sBoNje8PjG66ve20sJ+PXrn9131Unlrrr1zlctbE0PP71bdeNxVBnULvjbKjd+6vHo24emsgRE54DADwQBSMAAIAb4OVl0Mj4MI2MD1N55QXlbi/R+zuP6dyFy9Knp90dHlpBwwWpK8WqmpoaGf9eeqXgZLAvWNkV/uoUr+yKgk6Letfbr21TjRTHGi06OomlwaJgncJg3QKa4/iOhb+6Y9kX9ZwXHRsrUF4f1/n49WNxVnR0ZfxrYzdYYHQ2fp3XJyvOaO+JQvux6sTtbPzaoerEYnfMDfbt9Y+D0/PLaQG03rF0GN/5+dzQOdpkgbXRIvX1sa5vs/FzQPVicaUIXrdv3XPA7tg3dA41mCMn4xsMqjz3T5WeOt9gjq7HV/86tD+H2qIIXjduwBUUjAAAAG5SeHB33Zc2WP82baA+ydstq9UqSbLZJJtsVzrZJJskm80mm+36ujbb1R512q+31Wmv7XN9/Wt9a9ev1+44vq123LpjNTS+w/p1YrFdXVi7fu1u2hzGvx6j7eo+Xl/BZnMylrPx669fLxbb1QN4ffw669cZ69qWa4+d0xxdH8TmZHzVX//qz+Xl5QoLC3cY/9pxsctxvVjsj6Hz8evG23COGjjnnOXoWpvdWE2fA7VDOe1b97yyyVZT7zg0EIutTqfrOWg4FlfOIYdz3GF8189nZ+dQ3fOxurpaxpKS2vWbHr9eLOjYsircHcENaagwXL+odzNF8KuvHIqGdts3GBqMpSWL4Hb7U7f9akPd9ZsqghtrvteIEU0e4g6PghEAAEAL8fE2KrCHt8KDu7s7FLhBQcEljRgx2N1hwA0KCgo04ib/92hfxFS9AqtUt+DkrOhot2699Z0VHRsugDovOl5rvxqJfUGyoQKj0/GbKjo6L4A2VXSsP5Zr49cv3DVSRK8Xy7UcfV1cLLPZUrtCw0Vqx2X1i+B1+zrmyHH8urE0nqPrg9Qf//o2HXPkbHzJMT5bnZ2qPSfscuQkvnpF+8bOAcf9ceEPKbWvXTiH7M7Zxovg186ba8Xwzs6lglFxcbEWLVqkM2fOKCAgQBkZGTKbzXZ9qqurtXTpUm3dulUGg0EPPPCA0tPTm2zzJBVrX1SvI3t0dKuPY6PdY4EGu38cXtStmja2votjXF/U1BiNr99QHIaGYmhqvAbiMdxkPA0dE6ePZjbnuDrLS53lPc6dU9mhbCdjuL6NBh8fbYHcXR/7xs4f+/2/sdw6356z3Dcvnoa2YXCSuybHaM51Kcnv5ElVnv68wRhu+Lp0Gqfr2zC0QJ6bzE1bX5dN9Gnp86epOLucOKrvfM41MkZD50/L/f51er43M56GtmFoIndtel3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ux/zdd9/pvvvu08yZMxUTEyMfHx998MEHOnv2rJKTk2/yiAAAADhHwQgAAHiM//zP/3S6fPv27bU///a3v9Vrr72mt99+Wz4+PkpPT6/91jRJ6tatm95880399re/1QsvvKALFy7IYrHo+eef16xZs2r79e3bV5mZmVq2bJl+97vf6cKFCwoLC9PkyZObFbOvr68GDx6szMxMlZaWymAwyGKx6IUXXlBKSkozjwAAAIBrDDabzebuIAAAANxtzZo1evLJJ7Vp06YmJ8YGAADo7JjDCAAAAAAAAHYoGAEAAAAAAMAOH0kDAAAAAACAHZ4wAgAAAAAAgB0KRgAAAAAAALBDwQgAAAAAAAB2KBgBAAAAAADADgUjAAAAAAAA2KFgBAAAAAAAADv/H+2bWoZDSoIiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 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 1440x1440 with 4 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 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold: 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 4 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": 11,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
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       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col7 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col8 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col9 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col10 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col11 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col12 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col0 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col1 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col2 {\n",
       "            color:  red;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col3 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col4 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col5 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col6 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col7 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col8 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col9 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col10 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col11 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col12 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col0 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col1 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col2 {\n",
       "            color:  red;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col3 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col4 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col5 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col6 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col7 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col8 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col9 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col10 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col11 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col12 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col0 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col1 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col2 {\n",
       "            color:  red;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col3 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col4 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col5 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col6 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col7 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col8 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col9 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col10 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col11 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col12 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col0 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col1 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col2 {\n",
       "            color:  red;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col3 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col4 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col5 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col6 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col7 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col8 {\n",
       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col12 {\n",
       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col2 {\n",
       "            color:  red;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col3 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col4 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col5 {\n",
       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col8 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col9 {\n",
       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col0 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col1 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col2 {\n",
       "            color:  red;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col3 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col4 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col5 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col6 {\n",
       "            color:  orange;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col7 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col8 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col9 {\n",
       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col2 {\n",
       "            color:  red;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col3 {\n",
       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col7 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col8 {\n",
       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col12 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col0 {\n",
       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  red;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col3 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col4 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col5 {\n",
       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col0 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col1 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col2 {\n",
       "            color:  red;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col3 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col4 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col5 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col6 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col7 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col8 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col9 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col10 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col11 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col12 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col0 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col1 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col2 {\n",
       "            color:  red;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col3 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col4 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col5 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col6 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col7 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col8 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col9 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col10 {\n",
       "            color:  green;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col11 {\n",
       "            color:  black;\n",
       "        }    #T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col12 {\n",
       "            color:  green;\n",
       "        }</style><table id=\"T_21b588f6_93b1_11ea_a8f5_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_21b588f6_93b1_11ea_a8f5_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >Overall</th>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row0_col0\" class=\"data row0 col0\" >0.742824</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row0_col1\" class=\"data row0 col1\" >0.705239</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row0_col2\" class=\"data row0 col2\" >0.037585</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row0_col3\" class=\"data row0 col3\" >0.754093</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row0_col4\" class=\"data row0 col4\" >0.710125</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row0_col5\" class=\"data row0 col5\" >0.738800</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row0_col6\" class=\"data row0 col6\" >0.696016</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row0_col7\" class=\"data row0 col7\" >0.743224</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row0_col8\" class=\"data row0 col8\" >0.708976</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row0_col9\" class=\"data row0 col9\" >0.742614</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row0_col10\" class=\"data row0 col10\" >0.699107</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row0_col11\" class=\"data row0 col11\" >0.735390</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row0_col12\" class=\"data row0 col12\" >0.711969</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >Sentiment negative</th>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col0\" class=\"data row1 col0\" >0.590642</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col1\" class=\"data row1 col1\" >0.520816</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col2\" class=\"data row1 col2\" >0.069827</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col3\" class=\"data row1 col3\" >0.612174</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col4\" class=\"data row1 col4\" >0.522616</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col5\" class=\"data row1 col5\" >0.585104</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col6\" class=\"data row1 col6\" >0.504237</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col7\" class=\"data row1 col7\" >0.596478</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col8\" class=\"data row1 col8\" >0.525760</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col9\" class=\"data row1 col9\" >0.587272</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col10\" class=\"data row1 col10\" >0.520721</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col11\" class=\"data row1 col11\" >0.572183</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row1_col12\" class=\"data row1 col12\" >0.530743</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >Sentiment neutral</th>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col0\" class=\"data row2 col0\" >0.974486</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col1\" class=\"data row2 col1\" >0.973264</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col2\" class=\"data row2 col2\" >0.001222</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col3\" class=\"data row2 col3\" >0.974671</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col4\" class=\"data row2 col4\" >0.973741</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col5\" class=\"data row2 col5\" >0.973791</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col6\" class=\"data row2 col6\" >0.972954</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col7\" class=\"data row2 col7\" >0.974677</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col8\" class=\"data row2 col8\" >0.975001</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col9\" class=\"data row2 col9\" >0.974524</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col10\" class=\"data row2 col10\" >0.970763</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col11\" class=\"data row2 col11\" >0.974766</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row2_col12\" class=\"data row2 col12\" >0.973859</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >Sentiment positive</th>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col0\" class=\"data row3 col0\" >0.580711</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col1\" class=\"data row3 col1\" >0.525253</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col2\" class=\"data row3 col2\" >0.055458</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col3\" class=\"data row3 col3\" >0.597064</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col4\" class=\"data row3 col4\" >0.538494</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col5\" class=\"data row3 col5\" >0.573782</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col6\" class=\"data row3 col6\" >0.510990</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col7\" class=\"data row3 col7\" >0.576432</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col8\" class=\"data row3 col8\" >0.530593</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col9\" class=\"data row3 col9\" >0.583020</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col10\" class=\"data row3 col10\" >0.509054</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col11\" class=\"data row3 col11\" >0.573258</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row3_col12\" class=\"data row3 col12\" >0.537134</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >0 <= text wordCnt < 3</th>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col0\" class=\"data row4 col0\" >0.864558</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col1\" class=\"data row4 col1\" >0.857769</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col2\" class=\"data row4 col2\" >0.006789</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col3\" class=\"data row4 col3\" >0.870629</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col4\" class=\"data row4 col4\" >0.828358</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col5\" class=\"data row4 col5\" >0.860060</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col6\" class=\"data row4 col6\" >0.863135</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col7\" class=\"data row4 col7\" >0.862406</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col8\" class=\"data row4 col8\" >0.863517</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col9\" class=\"data row4 col9\" >0.868485</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col10\" class=\"data row4 col10\" >0.846154</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col11\" class=\"data row4 col11\" >0.861209</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row4_col12\" class=\"data row4 col12\" >0.887681</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >3 <= text wordCnt < 10</th>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col0\" class=\"data row5 col0\" >0.794831</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col1\" class=\"data row5 col1\" >0.766664</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col2\" class=\"data row5 col2\" >0.028167</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col3\" class=\"data row5 col3\" >0.805754</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col4\" class=\"data row5 col4\" >0.759886</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col5\" class=\"data row5 col5\" >0.791222</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col6\" class=\"data row5 col6\" >0.766494</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col7\" class=\"data row5 col7\" >0.792793</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col8\" class=\"data row5 col8\" >0.774468</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col9\" class=\"data row5 col9\" >0.796591</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col10\" class=\"data row5 col10\" >0.756833</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col11\" class=\"data row5 col11\" >0.787795</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row5_col12\" class=\"data row5 col12\" >0.775638</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >10 <= text wordCnt < 30</th>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col0\" class=\"data row6 col0\" >0.710769</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col1\" class=\"data row6 col1\" >0.667127</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col2\" class=\"data row6 col2\" >0.043642</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col3\" class=\"data row6 col3\" >0.722683</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col4\" class=\"data row6 col4\" >0.678553</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col5\" class=\"data row6 col5\" >0.705879</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col6\" class=\"data row6 col6\" >0.655384</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col7\" class=\"data row6 col7\" >0.712239</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col8\" class=\"data row6 col8\" >0.670575</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col9\" class=\"data row6 col9\" >0.709921</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col10\" class=\"data row6 col10\" >0.661462</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col11\" class=\"data row6 col11\" >0.703124</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row6_col12\" class=\"data row6 col12\" >0.669662</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >30 <= text wordCnt < 100</th>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col0\" class=\"data row7 col0\" >0.684877</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col1\" class=\"data row7 col1\" >0.636591</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col2\" class=\"data row7 col2\" >0.048286</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col3\" class=\"data row7 col3\" >0.688083</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col4\" class=\"data row7 col4\" >0.667141</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col5\" class=\"data row7 col5\" >0.729698</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col6\" class=\"data row7 col6\" >0.467437</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col7\" class=\"data row7 col7\" >0.687613</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col8\" class=\"data row7 col8\" >0.578710</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col9\" class=\"data row7 col9\" >0.660443</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col10\" class=\"data row7 col10\" >0.648840</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col11\" class=\"data row7 col11\" >0.658547</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row7_col12\" class=\"data row7 col12\" >0.820825</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >0 <= selected text wordCnt < 3</th>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col0\" class=\"data row8 col0\" >0.670550</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col1\" class=\"data row8 col1\" >0.604406</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col2\" class=\"data row8 col2\" >0.066144</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col3\" class=\"data row8 col3\" >0.699027</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col4\" class=\"data row8 col4\" >0.621417</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col5\" class=\"data row8 col5\" >0.667427</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col6\" class=\"data row8 col6\" >0.597804</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col7\" class=\"data row8 col7\" >0.655874</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col8\" class=\"data row8 col8\" >0.588236</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col9\" class=\"data row8 col9\" >0.663897</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col10\" class=\"data row8 col10\" >0.588584</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col11\" class=\"data row8 col11\" >0.666526</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row8_col12\" class=\"data row8 col12\" >0.625988</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >3 <= selected text wordCnt < 10</th>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col0\" class=\"data row9 col0\" >0.714619</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col1\" class=\"data row9 col1\" >0.680478</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col2\" class=\"data row9 col2\" >0.034141</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col3\" class=\"data row9 col3\" >0.717498</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col4\" class=\"data row9 col4\" >0.673668</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col5\" class=\"data row9 col5\" >0.706158</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col6\" class=\"data row9 col6\" >0.664185</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col7\" class=\"data row9 col7\" >0.728455</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col8\" class=\"data row9 col8\" >0.699838</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col9\" class=\"data row9 col9\" >0.721305</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col10\" class=\"data row9 col10\" >0.684111</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col11\" class=\"data row9 col11\" >0.699678</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row9_col12\" class=\"data row9 col12\" >0.680590</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >10 <= selected text wordCnt < 30</th>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col0\" class=\"data row10 col0\" >0.865792</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col1\" class=\"data row10 col1\" >0.858186</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col2\" class=\"data row10 col2\" >0.007607</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col3\" class=\"data row10 col3\" >0.867572</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col4\" class=\"data row10 col4\" >0.861178</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col5\" class=\"data row10 col5\" >0.866288</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col6\" class=\"data row10 col6\" >0.854619</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col7\" class=\"data row10 col7\" >0.867176</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col8\" class=\"data row10 col8\" >0.866565</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col9\" class=\"data row10 col9\" >0.863713</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col10\" class=\"data row10 col10\" >0.854837</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col11\" class=\"data row10 col11\" >0.864213</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row10_col12\" class=\"data row10 col12\" >0.853728</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >30 <= selected text wordCnt < 100</th>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col0\" class=\"data row11 col0\" >0.894393</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col1\" class=\"data row11 col1\" >0.888104</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col2\" class=\"data row11 col2\" >0.006289</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col3\" class=\"data row11 col3\" >0.912676</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col4\" class=\"data row11 col4\" >0.835627</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col5\" class=\"data row11 col5\" >0.918638</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col6\" class=\"data row11 col6\" >0.794436</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col7\" class=\"data row11 col7\" >0.891529</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col8\" class=\"data row11 col8\" >0.908788</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col9\" class=\"data row11 col9\" >0.890488</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col10\" class=\"data row11 col10\" >0.908811</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col11\" class=\"data row11 col11\" >0.858632</td>\n",
       "                        <td id=\"T_21b588f6_93b1_11ea_a8f5_0242ac130202row11_col12\" class=\"data row11 col12\" >0.992857</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9cda1d4bd0>"
      ]
     },
     "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": 12,
   "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>i`d have responded, if i were going</td>\n",
       "      <td>i`d have responded, if i were going</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>i`d have responded, if i were going</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>i`d have responded, if i were going</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>i`d have responded, if i were going</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>i`d have responded, if i were going</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>i`d have responded, if i were going</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sooo sad i will miss you here in san diego!!!</td>\n",
       "      <td>sooo sad</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>sooo sad</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>sooo sad</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>sooo sad</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>sooo sad</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>sooo sad</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>my boss is bullying me...</td>\n",
       "      <td>bullying me</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>bullying</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>bullying me...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>bullying me...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>bullying me...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>bullying me...</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>what interview! leave me alone</td>\n",
       "      <td>leave me alone</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>leave me alone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>leave me alone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>leave me alone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>leave me alone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>leave me alone</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sons of ****, why couldn`t they put them on th...</td>\n",
       "      <td>sons of ****,</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>sons of ****,</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>sons of ****,</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>****,</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>sons of ****,</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>sons of ****,</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n",
       "      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2am feedings for the baby are fun when he is a...</td>\n",
       "      <td>fun</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>fun</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>fun</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>fun</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>fun</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>fun</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>soooo high</td>\n",
       "      <td>soooo high</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>soooo high</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>soooo high</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>soooo high</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>soooo high</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>soooo high</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>both of you</td>\n",
       "      <td>both of you</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>both of you</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>both of you</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>both of you</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>both of you</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>both of you</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>journey!? wow... u just became cooler.  hehe.....</td>\n",
       "      <td>wow... u just became cooler.</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>wow...</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>wow... u just became cooler.</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>wow...</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>wow... u just became cooler.</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>wow...</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>as much as i love to be hopeful, i reckon the ...</td>\n",
       "      <td>as much as i love to be hopeful, i reckon the ...</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>as much as i love to be hopeful, i reckon the ...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>as much as i love to be hopeful, i reckon the ...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>as much as i love to be hopeful, i reckon the ...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>as much as i love to be hopeful, i reckon the ...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>as much as i love to be hopeful, i reckon the ...</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>i really really like the song love story by ta...</td>\n",
       "      <td>like</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>i really really like</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>like</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>i really really like</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>i really really like</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>i really really like</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>my sharpie is running dangerously low on ink</td>\n",
       "      <td>dangerously</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>dangerously</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>dangerously low on ink</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>dangerously</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>dangerously low on ink</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>dangerously low on ink</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>i want to go to music tonight but i lost my vo...</td>\n",
       "      <td>lost</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>lost</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>lost my voice.</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>lost my voice.</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>lost my voice.</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>lost my voice.</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>test test from the lg env2</td>\n",
       "      <td>test test from the lg env2</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>test test from the lg env2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>test test from the lg env2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>test test from the lg env2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>test test from the lg env2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>test test from the lg env2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 text  \\\n",
       "0                 i`d have responded, if i were going   \n",
       "1       sooo sad i will miss you here in san diego!!!   \n",
       "2                           my boss is bullying me...   \n",
       "3                      what interview! leave me alone   \n",
       "4   sons of ****, why couldn`t they put them on th...   \n",
       "5   http://www.dothebouncy.com/smf - some shameles...   \n",
       "6   2am feedings for the baby are fun when he is a...   \n",
       "7                                          soooo high   \n",
       "8                                         both of you   \n",
       "9   journey!? wow... u just became cooler.  hehe.....   \n",
       "10  as much as i love to be hopeful, i reckon the ...   \n",
       "11  i really really like the song love story by ta...   \n",
       "12       my sharpie is running dangerously low on ink   \n",
       "13  i want to go to music tonight but i lost my vo...   \n",
       "14                         test test from the lg env2   \n",
       "\n",
       "                                        selected_text sentiment   jaccard  \\\n",
       "0                 i`d have responded, if i were going   neutral  1.000000   \n",
       "1                                            sooo sad  negative  0.200000   \n",
       "2                                         bullying me  negative  0.166667   \n",
       "3                                      leave me alone  negative  0.600000   \n",
       "4                                       sons of ****,  negative  0.214286   \n",
       "5   http://www.dothebouncy.com/smf - some shameles...   neutral  1.000000   \n",
       "6                                                 fun  positive  0.071429   \n",
       "7                                          soooo high   neutral  1.000000   \n",
       "8                                         both of you   neutral  1.000000   \n",
       "9                        wow... u just became cooler.  positive  0.500000   \n",
       "10  as much as i love to be hopeful, i reckon the ...   neutral  1.000000   \n",
       "11                                               like  positive  0.100000   \n",
       "12                                        dangerously  negative  0.125000   \n",
       "13                                               lost  negative  0.100000   \n",
       "14                         test test from the lg env2   neutral  1.000000   \n",
       "\n",
       "                                    prediction_fold_1  jaccard_fold_1  \\\n",
       "0                 i`d have responded, if i were going        1.000000   \n",
       "1                                            sooo sad        1.000000   \n",
       "2                                            bullying        0.500000   \n",
       "3                                      leave me alone        1.000000   \n",
       "4                                       sons of ****,        1.000000   \n",
       "5   http://www.dothebouncy.com/smf - some shameles...        1.000000   \n",
       "6                                                 fun        1.000000   \n",
       "7                                          soooo high        1.000000   \n",
       "8                                         both of you        1.000000   \n",
       "9                                              wow...        0.200000   \n",
       "10  as much as i love to be hopeful, i reckon the ...        1.000000   \n",
       "11                               i really really like        0.333333   \n",
       "12                                        dangerously        1.000000   \n",
       "13                                               lost        1.000000   \n",
       "14                         test test from the lg env2        1.000000   \n",
       "\n",
       "                                    prediction_fold_2  jaccard_fold_2  \\\n",
       "0                 i`d have responded, if i were going        1.000000   \n",
       "1                                            sooo sad        1.000000   \n",
       "2                                      bullying me...        0.333333   \n",
       "3                                      leave me alone        1.000000   \n",
       "4                                       sons of ****,        1.000000   \n",
       "5   http://www.dothebouncy.com/smf - some shameles...        1.000000   \n",
       "6                                                 fun        1.000000   \n",
       "7                                          soooo high        1.000000   \n",
       "8                                         both of you        1.000000   \n",
       "9                        wow... u just became cooler.        1.000000   \n",
       "10  as much as i love to be hopeful, i reckon the ...        1.000000   \n",
       "11                                               like        1.000000   \n",
       "12                             dangerously low on ink        0.250000   \n",
       "13                                     lost my voice.        0.333333   \n",
       "14                         test test from the lg env2        1.000000   \n",
       "\n",
       "                                    prediction_fold_3  jaccard_fold_3  \\\n",
       "0                 i`d have responded, if i were going        1.000000   \n",
       "1                                            sooo sad        1.000000   \n",
       "2                                      bullying me...        0.333333   \n",
       "3                                      leave me alone        1.000000   \n",
       "4                                               ****,        0.333333   \n",
       "5   http://www.dothebouncy.com/smf - some shameles...        1.000000   \n",
       "6                                                 fun        1.000000   \n",
       "7                                          soooo high        1.000000   \n",
       "8                                         both of you        1.000000   \n",
       "9                                              wow...        0.200000   \n",
       "10  as much as i love to be hopeful, i reckon the ...        1.000000   \n",
       "11                               i really really like        0.333333   \n",
       "12                                        dangerously        1.000000   \n",
       "13                                     lost my voice.        0.333333   \n",
       "14                         test test from the lg env2        1.000000   \n",
       "\n",
       "                                    prediction_fold_4  jaccard_fold_4  \\\n",
       "0                 i`d have responded, if i were going        1.000000   \n",
       "1                                            sooo sad        1.000000   \n",
       "2                                      bullying me...        0.333333   \n",
       "3                                      leave me alone        1.000000   \n",
       "4                                       sons of ****,        1.000000   \n",
       "5   http://www.dothebouncy.com/smf - some shameles...        1.000000   \n",
       "6                                                 fun        1.000000   \n",
       "7                                          soooo high        1.000000   \n",
       "8                                         both of you        1.000000   \n",
       "9                        wow... u just became cooler.        1.000000   \n",
       "10  as much as i love to be hopeful, i reckon the ...        1.000000   \n",
       "11                               i really really like        0.333333   \n",
       "12                             dangerously low on ink        0.250000   \n",
       "13                                     lost my voice.        0.333333   \n",
       "14                         test test from the lg env2        1.000000   \n",
       "\n",
       "                                    prediction_fold_5  jaccard_fold_5  \n",
       "0                 i`d have responded, if i were going        1.000000  \n",
       "1                                            sooo sad        1.000000  \n",
       "2                                      bullying me...        0.333333  \n",
       "3                                      leave me alone        1.000000  \n",
       "4                                       sons of ****,        1.000000  \n",
       "5   http://www.dothebouncy.com/smf - some shameles...        1.000000  \n",
       "6                                                 fun        1.000000  \n",
       "7                                          soooo high        1.000000  \n",
       "8                                         both of you        1.000000  \n",
       "9                                              wow...        0.200000  \n",
       "10  as much as i love to be hopeful, i reckon the ...        1.000000  \n",
       "11                               i really really like        0.333333  \n",
       "12                             dangerously low on ink        0.250000  \n",
       "13                                     lost my voice.        0.333333  \n",
       "14                         test test from the lg env2        1.000000  "
      ]
     },
     "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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