Model backlog/Train/185-tweet-train-5fold-roberta-113-seq-len64.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "execution_count": null,
   "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_aux 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 scripts_step_lr_schedulers import *\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "SEED = 0\n",
    "seed_everything(SEED)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_kg_hide-input": true,
    "_kg_hide-output": true
   },
   "outputs": [],
   "source": [
    "# Unzip files\n",
    "!tar -xf /kaggle/input/tweet-dataset-5fold-roberta-64/fold_1.tar.gz\n",
    "!tar -xf /kaggle/input/tweet-dataset-5fold-roberta-64/fold_2.tar.gz\n",
    "!tar -xf /kaggle/input/tweet-dataset-5fold-roberta-64/fold_3.tar.gz\n",
    "!tar -xf /kaggle/input/tweet-dataset-5fold-roberta-64/fold_4.tar.gz\n",
    "!tar -xf /kaggle/input/tweet-dataset-5fold-roberta-64/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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set samples: 27480\n"
     ]
    },
    {
     "data": {
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       "  <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>text_wordCnt</th>\n",
       "      <th>text_tokenCnt</th>\n",
       "      <th>selected_text_len</th>\n",
       "      <th>selected_text_wordCnt</th>\n",
       "      <th>selected_text_tokenCnt</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",
       "      <th>imputed</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>7</td>\n",
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       "      <td>validation</td>\n",
       "      <td>train</td>\n",
       "      <td>False</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>10</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>validation</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>False</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>5</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>2</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",
       "      <td>False</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>5</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>3</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",
       "      <td>False</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>14</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>validation</td>\n",
       "      <td>train</td>\n",
       "      <td>train</td>\n",
       "      <td>False</td>\n",
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      "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",
       "   text_wordCnt  text_tokenCnt  selected_text_len  selected_text_wordCnt  \\\n",
       "0             7             10                 35                      7   \n",
       "1            10             13                  8                      2   \n",
       "2             5              6                 11                      2   \n",
       "3             5              6                 14                      3   \n",
       "4            14             17                 13                      3   \n",
       "\n",
       "   selected_text_tokenCnt      fold_1      fold_2      fold_3      fold_4  \\\n",
       "0                      10       train       train       train  validation   \n",
       "1                       3  validation       train       train       train   \n",
       "2                       2       train  validation       train       train   \n",
       "3                       3       train  validation       train       train   \n",
       "4                       4       train       train  validation       train   \n",
       "\n",
       "  fold_5  imputed  \n",
       "0  train    False  \n",
       "1  train    False  \n",
       "2  train    False  \n",
       "3  train    False  \n",
       "4  train    False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "database_base_path = '/kaggle/input/tweet-dataset-5fold-roberta-64/'\n",
    "k_fold = pd.read_csv(database_base_path + '5-fold.csv')\n",
    "print(f'Training set samples: {len(k_fold)}')\n",
    "display(k_fold.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "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': 64,\n",
    "  'BATCH_SIZE': 32,\n",
    "  'EPOCHS': 4,\n",
    "  'LEARNING_RATE': 3e-5,\n",
    "  'ES_PATIENCE': 4,\n",
    "  'N_FOLDS': 5,\n",
    "  'question_size': 4,\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",
   "execution_count": null,
   "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",
   "execution_count": null,
   "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 1.52e-05\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_start = 1e-4\n",
    "lr_min = 1e-6\n",
    "lr_max = config['LEARNING_RATE']\n",
    "warmup_steps=0\n",
    "hold_max_steps=0\n",
    "num_cycles=0.5\n",
    "train_size = len(k_fold[k_fold['fold_1'] == 'train'])\n",
    "step_size = train_size // config['BATCH_SIZE']\n",
    "total_steps = config['EPOCHS'] * step_size\n",
    "\n",
    "rng = [i for i in range(0, total_steps, config['BATCH_SIZE'])]\n",
    "y = [cosine_schedule_with_warmup(tf.cast(x, tf.float32), total_steps=total_steps, warmup_steps=warmup_steps, \n",
    "                                 hold_max_steps=hold_max_steps, lr_start=lr_start, lr_max=lr_max, \n",
    "                                 lr_min=lr_min, num_cycles=num_cycles) for x in rng]\n",
    "\n",
    "sns.set(style='whitegrid')\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",
   "execution_count": null,
   "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 = layers.Dropout(.1)(last_hidden_state)\n",
    "    \n",
    "    x_start = layers.Dense(1)(x)\n",
    "    x_start = layers.Flatten()(x_start)\n",
    "    y_start = layers.Activation('softmax', name='y_start')(x_start)\n",
    "\n",
    "    x_end = layers.Dense(1)(x)\n",
    "    x_end = layers.Flatten()(x_end)\n",
    "    y_end = layers.Activation('softmax', name='y_end')(x_end)\n",
    "    \n",
    "    model = Model(inputs=[input_ids, attention_mask], outputs=[y_start, y_end])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
      "Epoch 1/4\n",
      "687/687 - 192s - loss: 2.1895 - y_start_loss: 1.0731 - y_end_loss: 1.1165 - val_loss: 1.6667 - val_y_start_loss: 0.8415 - val_y_end_loss: 0.8253\n",
      "Epoch 2/4\n",
      "687/687 - 180s - loss: 1.7267 - y_start_loss: 0.8587 - y_end_loss: 0.8680 - val_loss: 1.6410 - val_y_start_loss: 0.8339 - val_y_end_loss: 0.8071\n",
      "Epoch 3/4\n",
      "687/687 - 180s - loss: 1.5388 - y_start_loss: 0.7804 - y_end_loss: 0.7584 - val_loss: 1.6211 - val_y_start_loss: 0.8249 - val_y_end_loss: 0.7963\n",
      "Epoch 4/4\n",
      "687/687 - 179s - loss: 1.3835 - y_start_loss: 0.7162 - y_end_loss: 0.6672 - val_loss: 1.6146 - val_y_start_loss: 0.8311 - val_y_end_loss: 0.7835\n",
      "\n",
      "FOLD: 2\n",
      "Train for 687 steps, validate for 171 steps\n",
      "Epoch 1/4\n",
      "687/687 - 191s - loss: 2.2160 - y_start_loss: 1.0692 - y_end_loss: 1.1469 - val_loss: 1.6991 - val_y_start_loss: 0.8760 - val_y_end_loss: 0.8231\n",
      "Epoch 2/4\n",
      "687/687 - 179s - loss: 1.6307 - y_start_loss: 0.8480 - y_end_loss: 0.7827 - val_loss: 1.6259 - val_y_start_loss: 0.8457 - val_y_end_loss: 0.7802\n",
      "Epoch 3/4\n",
      "687/687 - 179s - loss: 1.4502 - y_start_loss: 0.7556 - y_end_loss: 0.6946 - val_loss: 1.6073 - val_y_start_loss: 0.8331 - val_y_end_loss: 0.7742\n",
      "Epoch 4/4\n",
      "687/687 - 178s - loss: 1.3099 - y_start_loss: 0.6856 - y_end_loss: 0.6243 - val_loss: 1.6846 - val_y_start_loss: 0.8674 - val_y_end_loss: 0.8172\n",
      "\n",
      "FOLD: 3\n",
      "Train for 687 steps, validate for 171 steps\n",
      "Epoch 1/4\n",
      "687/687 - 191s - loss: 2.1804 - y_start_loss: 1.0746 - y_end_loss: 1.1058 - val_loss: 1.6806 - val_y_start_loss: 0.8438 - val_y_end_loss: 0.8368\n",
      "Epoch 2/4\n",
      "687/687 - 180s - loss: 1.6144 - y_start_loss: 0.8350 - y_end_loss: 0.7794 - val_loss: 1.6143 - val_y_start_loss: 0.8299 - val_y_end_loss: 0.7844\n",
      "Epoch 3/4\n",
      "687/687 - 178s - loss: 1.5071 - y_start_loss: 0.7780 - y_end_loss: 0.7290 - val_loss: 1.6143 - val_y_start_loss: 0.8285 - val_y_end_loss: 0.7859\n",
      "Epoch 4/4\n",
      "687/687 - 178s - loss: 1.3399 - y_start_loss: 0.6859 - y_end_loss: 0.6540 - val_loss: 1.7259 - val_y_start_loss: 0.8661 - val_y_end_loss: 0.8598\n",
      "\n",
      "FOLD: 4\n",
      "Train for 687 steps, validate for 171 steps\n",
      "Epoch 1/4\n",
      "687/687 - 192s - loss: 2.1595 - y_start_loss: 1.0538 - y_end_loss: 1.1058 - val_loss: 1.6761 - val_y_start_loss: 0.8650 - val_y_end_loss: 0.8111\n",
      "Epoch 2/4\n",
      "687/687 - 179s - loss: 1.6014 - y_start_loss: 0.8224 - y_end_loss: 0.7790 - val_loss: 1.6186 - val_y_start_loss: 0.8564 - val_y_end_loss: 0.7622\n",
      "Epoch 3/4\n",
      "687/687 - 178s - loss: 1.4218 - y_start_loss: 0.7403 - y_end_loss: 0.6815 - val_loss: 1.6392 - val_y_start_loss: 0.8775 - val_y_end_loss: 0.7617\n",
      "Epoch 4/4\n",
      "687/687 - 178s - loss: 1.2660 - y_start_loss: 0.6601 - y_end_loss: 0.6058 - val_loss: 1.6597 - val_y_start_loss: 0.8770 - val_y_end_loss: 0.7827\n",
      "\n",
      "FOLD: 5\n",
      "Train for 687 steps, validate for 171 steps\n",
      "Epoch 1/4\n",
      "687/687 - 191s - loss: 2.2483 - y_start_loss: 1.0835 - y_end_loss: 1.1648 - val_loss: 1.7665 - val_y_start_loss: 0.8899 - val_y_end_loss: 0.8766\n",
      "Epoch 2/4\n",
      "687/687 - 180s - loss: 1.7174 - y_start_loss: 0.8722 - y_end_loss: 0.8452 - val_loss: 1.6243 - val_y_start_loss: 0.8375 - val_y_end_loss: 0.7868\n",
      "Epoch 3/4\n",
      "687/687 - 178s - loss: 1.6016 - y_start_loss: 0.8091 - y_end_loss: 0.7924 - val_loss: 1.7113 - val_y_start_loss: 0.9019 - val_y_end_loss: 0.8094\n",
      "Epoch 4/4\n",
      "687/687 - 178s - loss: 1.4249 - y_start_loss: 0.7326 - y_end_loss: 0.6923 - val_loss: 1.6536 - val_y_start_loss: 0.8707 - val_y_end_loss: 0.7829\n"
     ]
    }
   ],
   "source": [
    "AUTO = tf.data.experimental.AUTOTUNE\n",
    "strategy = tf.distribute.get_strategy()\n",
    "k_fold_best = k_fold.copy()\n",
    "history_list = []\n",
    "\n",
    "for n_fold in range(config['N_FOLDS']):\n",
    "    n_fold +=1\n",
    "    print('\\nFOLD: %d' % (n_fold))\n",
    "    # Load data\n",
    "    base_data_path = 'fold_%d/' % (n_fold)\n",
    "    x_train = np.load(base_data_path + 'x_train.npy')\n",
    "    y_train = np.load(base_data_path + 'y_train.npy')\n",
    "    x_valid = np.load(base_data_path + 'x_valid.npy')\n",
    "    y_valid = np.load(base_data_path + 'y_valid.npy')\n",
    "    \n",
    "    step_size = x_train.shape[1] // config['BATCH_SIZE']\n",
    "    valid_step_size = x_valid.shape[1] // config['BATCH_SIZE']\n",
    "\n",
    "    # Train model\n",
    "    model_path = 'model_fold_%d.h5' % (n_fold)\n",
    "    model = model_fn(config['MAX_LEN'])\n",
    "    optimizer = optimizers.Adam(learning_rate=lambda: cosine_schedule_with_warmup(tf.cast(optimizer.iterations, tf.float32), total_steps=total_steps, \n",
    "                                                                                  warmup_steps=warmup_steps, hold_max_steps=hold_max_steps, \n",
    "                                                                                  lr_start=lr_start, lr_max=lr_max, lr_min=lr_min, \n",
    "                                                                                  num_cycles=num_cycles))\n",
    "    model.compile(optimizer, loss={'y_start': losses.CategoricalCrossentropy(),\n",
    "                                   'y_end': losses.CategoricalCrossentropy()})\n",
    "    es = EarlyStopping(monitor='val_loss', mode='min', patience=config['ES_PATIENCE'], \n",
    "                       restore_best_weights=False, verbose=1)\n",
    "    checkpoint = ModelCheckpoint(model_path, monitor='val_loss', mode='min', \n",
    "                                 save_best_only=True, save_weights_only=True)\n",
    "    \n",
    "    history = model.fit(get_training_dataset(x_train, y_train, config['BATCH_SIZE'], AUTO, seed=SEED),\n",
    "                        validation_data=(get_validation_dataset(x_valid, y_valid, config['BATCH_SIZE'], AUTO, repeated=False, seed=SEED)),\n",
    "                        epochs=config['EPOCHS'], \n",
    "                        steps_per_epoch=step_size,\n",
    "                        callbacks=[checkpoint, es],\n",
    "                        verbose=2).history\n",
    "    \n",
    "    history_list.append(history)\n",
    "    model.save_weights('last_' + model_path)\n",
    "    \n",
    "    # Make predictions (last model)\n",
    "    predict_eval_df(k_fold, model, x_train, x_valid, get_test_dataset, decode, n_fold, tokenizer, config, config['question_size'])\n",
    "\n",
    "    # Make predictions (best model)\n",
    "    model.load_weights(model_path)\n",
    "    predict_eval_df(k_fold_best, model, x_train, x_valid, get_test_dataset, decode, n_fold, tokenizer, config, config['question_size'])\n",
    "    \n",
    "    ### Delete data dir\n",
    "    shutil.rmtree(base_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Model loss graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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x6aefkpaWRk5OTqOnjF0MRqORCRMm8MYbb/Duu++yd+9e4uLiePrpp7nrrrsaxlmtVubMmcOECROoq6vDYrHw9NNPc+eddwLQrVs3Pv/8c2bMmMGBAweIjIxkyJAhjBw58qJ+HndjcDqdbv3YrOrqagoLC0lNTcXLy8vV5TRbQUEBWVlZri7jtCoO1rBoRQl5dgfrt+7BaIDMlEhsWRZ6pcXgZTb98kWkxWoJa1RaN61RcXdao+LutEbF3bnzGl23bh2dOnVydRniYpWVlfj5+bm6jDPSnDWrHUbShL+vJ9f2bsu1vdtSsrOCPLuD/AIHr31UgK+3B30y4rBZLXRuG6qWNREREREREZFLkAIjOa24CH/uuLYTt13dkcLNu8hd6mDh8mLmfbeV6DBfbFkWBlotRIe1jHRVRERERERERH6ZAiM5I0ajgfT2EaS3j+CBG9NZsrqU3KUOpvxnAx/P20CXpDBsVgt9MmLx9Ta7ulwRERERERERaYZmBUZ79uzhiSeeoKioCE9PTxITExkzZkzDnciP+eabb3jjjTfYuHEjd9xxB08++WSzihbX8vHywGZNwGZNoGzPQeYXFJNnL+LNf63g71+s5rLUGGzdLWR0iMBkVMuaiIiIiIiISEvTrMDIYDBw77330rNnTwDGjh3La6+9xssvv9xonMVi4cUXX2Tu3LnU1NQ0Z0pxM5EhvvzmimRuyu7AxqI95NodLFpewoLlxYQGejMwKx6b1UJC9MV9vKKIiIiIiIiInLtmBUbBwcENYRFAZmYmU6ZMaTIuMTERgNzcXAVGlyiDwUBKYigpiaGMGJrK92t2kGsv4osFPzIt/wfaW4LJtlromxlHkH/Lf9qdiIiIiIiIyKXsvN3DqL6+nilTpmCz2c7XJaWFMnuYuDwjlsszYtl7oJoFy4vJW+rg71+s5h9fFmLtFIXNmoC1UxRmD6OryxURERERERGRExicTqfzfFzohRdeYMeOHYwfPx6j8eQhwJtvvsnBgwfP6h5G1dXVFBYWno8SxcW276lh5ZaDrPrpIJWH6vHxMpKW6ENGWz9iQ80YDLrfkYiIiIiIuC8PDw/at2/v6jJEztgPP/zA4cOHT3k+KyvrlOfOyw6jsWPHsnXrVnJyck4ZFjVXamoqXl4tv5WpoKDgtL8gl7rrgbq6epZv3Enu0iK+W7Od7zdWYokKINtqYUBWPGFBPq4us1Vr7WtU3J/WqLg7rVFxd1qj4u7ceY2uW7cOPz8/V5chLlZZWdli1oGnpycZGRnn9N5mB0bjxo2jsLCQCRMm4Onp2dzLSStgMhmxdorC2imKiqpavllRQp7dwXuz1vL+7LVkdIjA1j2BXqnReHuet65JERERERERETlDzdoOtGnTJnJycigrK+Pmm29m6NChPPzwwwCMGDGC1atXA2C32+nXrx+TJ0/mk08+oV+/fixatKj51UuL5+9j5prL2vCXR/vy96eyuemKZEp2VvD6RwXc+fxc/vbpcgp/3EV9/XnpnBQRERERERHgwQcfpEePHqd8MFVFRQWZmZk89dRTZ3xNm83WaPznn39OSkoKxcXFp31fcXExKSkpfP7552c81zHvvfce8+bNa3L8zTffJCUl5ayv11wnfg9asmZt3+jQoQMbNmw46bmJEyc2fG21Wlm4cGFzppJWIDbCn9uv6cStV3Vkzebd5NqL+GZlCf/5voioUF9sVgsDsyzEhLeMrX8iIiIiIiLu6oYbbiAvL4/8/HyuvvrqJufnzp1LVVUVw4YNO+c5BgwYwKeffkpkZGRzSj2t999/n27dunHVVVc1On7TTTfRt2/fCzZva6B+H3E7RqOBtPbhpLUP54Eb0llSuI28pQ4++c8GpszbQOe2odisCfTJiMXPx+zqckVERERERFqcAQMGEBwczPTp008aGE2fPp3Y2Fh69ux5znOEhoYSGhranDLPWXR0NNHR0S6Z+1KhZ5qLW/P28mBgloU/P9Cbf/zvVdx5XSf2V9YwfuoK7nz+K/7vAzsF63dQp5Y1ERERERGRM+bp6cn111/PokWLKC8vb3SutLSUpUuXMmTIEAwGA9988w0jRoygT58+ZGRkMGjQICZNmkRdXd1p5zhZS1pVVRXPP/88PXv2pGvXrjzwwANs3769yXtXrVrFyJEj6devH+np6Vx99dW88cYbHDp0qGGMzWajpKSEmTNnkpKSQkpKSkM72Mla0ioqKhgzZgx9+vQhNTWVq6++mvfee4/jHx7/3XffkZKSQm5uLmPGjKFnz5706tWL//mf/2H//v1n/g0+4bMMHz6crl27kpmZyV133cWqVauajLn77rvp2bMnGRkZZGdn8/zzzzec37lzJ08++WRD7X369OH+++9n9+7d51TTmdAOI2kxIkJ8uCk7mV/bOrDJsZfcpUUsXF7CwhUlhAZ6MaCbBVt3C4nRga4uVURERERExO0NGzaMjz76iDlz5nDbbbc1HP/yyy9xOp0N7WgOh4PLLruM22+/HS8vLwoLC3nzzTcpLy/nf/7nf85qzj/96U/MmTOHhx9+mLS0NL799tuTXmPbtm107NiRG264AT8/PzZt2sTbb7+Nw+Fg3LhxAIwfP5777ruPlJQUHn30UYBT7miqr6/nvvvuY+3atYwcOZLk5GTmz5/PK6+8Qnl5OY899lij8S+99BIDBw7k9ddfZ8uWLfzf//0fJpOJsWPHntXnXb9+Pbfffjvt27fnlVdewWAwMGHCBG6//Xb+9a9/0bFjRyorK7n33ntJS0vjlVdewc/Pj5KSEpYvX95wnSeeeILS0lKeeOIJYmJi2LVrF0uWLKGqquqs6jkbCoykxTEYDCQnhJCcEMK9Q1NZunYHeXYHMxb+yOfzf6BdfBA2q4X+XeMJ8vdydbkiIiIiInKJO7BqPgdW5rm0hoAMGwHpA87qPenp6bRv357p06c3CoxmzJhB165dadu2LQC33HJLwzmn04nVaqW2tpZJkybx2GOPYTSeWfPS5s2b+fe//82oUaO47777AOjTpw8HDx7kk08+aTT2+DY5p9NJt27d8Pf358knn+RPf/oTISEhdO7cGU9PT0JCQsjMzDzt3AsWLKCgoIBXXnmFG2+8sWHuqqoqJk2axPDhwxuFTd27d+fZZ59tGLdlyxamTp3Kq6++ekaf9Zi3334bT09P3nvvPQIDj2xuuPzyy7HZbIwfP57x48ezefNm9u3bx+OPP07Hjh0b3nusToAVK1YwatQohgwZ0nDs2muvPatazpYCI2nRzB4meqfH0js9lr0Hqlm4vJhcu4OJ0wuZ9OUarJ2isFktdO8cjdlDHZgiIiIiIiLHGzp0aMMumrZt27Jq1So2b97cqB2qrKyM8ePHs2jRIsrKyjh8+HDDud27dxMREXFGc61atYr6+vomQcf111/fJDCqqKjgnXfeYe7cuWzfvp3a2tqGc1u3biUkJOSsPufSpUsxGo0MGjSo0fEhQ4bw2WefsWLFCmw2W8Px/v37NxqXnJxMTU0Nu3btwtfX96zmHTBgQENYBODv74/NZiM/Px+ANm3aEBgYyHPPPcett95Kjx49iImJaXSd1NRU/vGPf+B0OunVqxfJyckYDIYzruNcKDCSS0ZwgBdD+rVjSL92/LRtP3l2B/MLHHy3ZjsBvmb6dY3HZrXQwRJ8wX9jiYiIiIhI6xGQPuCsd/e4i6FDhzJu3DimT5/OqFGjmD59Op6enlx33XXAkVauBx98kLKyMh599FGSkpLw8vLi66+/Jicnh+rq6jOeq6ysDICwsLBGx098DfD000+zePFiRo4cSadOnfDx8WHVqlWMGTPmrOY8Zt++fQQFBeHp6dnoeHh4eMP54wUHBzd6fex91dXVZxUY7du376SBWnh4eMOcAQEBvP/++7z99tu88MILVFZW0qFDBx599NGGnVb/7//9P8aPH8+7777Lyy+/TEREBDfffDMPPfTQGe/wOlsKjOSS1CYmkHsGd+Gu6zqxYtNO8pY6+M93W5n17RYsUf4MzLIwMMtCeLCPq0sVERERERFxmaioKHr37s3MmTN5+OGHmT17NjabjaCgIACKioooLCzkL3/5C0OHDm1437HdMWcjMjISOLIr6fjQ5cQbN1dXV5Obm8sjjzzCXXfd1XB848aNZz3nMUFBQezbt4+amppGodGuXbuApgHR+RIUFNQwx/F27drV8D0G6NSpE2+++SaHDx+msLCQv//97/zhD39gxowZJCcnExYWxnPPPcdzzz3H5s2bmT59Om+++SahoaHceuutF6R29ejIJc1kMpLVMYrH77Dy/vPX8MhNGfj7ePL+7HXc8+I8nv37YuYXODhUffiXLyYiIiIiInIJGjZsGCUlJbzxxhvs2bOn4WbXQMNTycxmc8Ox2tpaZs6cedbzpKenYzQamTNnTqPjs2bNavS6pqaGuro6PDwa73H54osvmlzTbDaf0Y6jHj16UF9fz1dffdXo+MyZMzGbzWRkZJzpxzgr3bt3Z8GCBVRUVDQcq6ioID8/nx49ejQZ7+HhQWZmJr///e+pr6/nxx9/bDImKSmJxx57jKCgIDZt2nRB6gbtMJJWxM/HzNW92nB1rzZs21VJnt1BXoGD1z9eho+XicvT47B1t9ClbRhGo1rWRERERESkdbjyyivx9/fnvffeIywsjL59+zacS0pKIi4ujnHjxmE0GvHw8OCf//znOc2TlJTEoEGD+Nvf/kZ9fX3DU9IWLlzYaFxAQACZmZlMnjyZyMhIQkJCmDZtGjt27Ghyzfbt22O328nPzyc8PJyQkBDi4+ObjOvXrx9ZWVk899xzlJeX06FDBxYsWMDUqVO5//77T/l0teZ66KGHmD9/PsOHD2fEiBEYDAYmTpxIVVUVDz/8MHBkt9ann37KFVdcQXx8PFVVVXzwwQf4+fnRtWtXDhw4wPDhwxk8eDBJSUmYzWZyc3PZt28fl19++QWpGxQYSSsVE+7Hbdd05JarUlizZTf5dgffrCzl66VFRIb6MjDryP2OYsP9XV2qiIiIiIjIBeXt7c0111zDZ599xqBBgxrt7PH09OStt95izJgxPPnkkwQFBfGrX/2K2NhYRo8efdZzjRkzBl9fXyZNmkRtbS09e/bktddea9JW9frrr/P888/zwgsv4O3tzbXXXsv//u//cv/99zca99hjj/Hss8/yhz/8gUOHDnHDDTec9ElmRqORCRMm8MYbb/Duu++yd+9e4uLiePrppxu1vZ1vHTt25IMPPmDcuHE89dRTOJ1OMjIy+PDDDxueiJaYmIi3tzdvv/02O3fuxM/Pj7S0NCZPnkx0dDQ1NTV06dKFqVOnUlpaisFgoG3btrz22mtcccUVF6x2g9PpdF6wq58H1dXVFBYWkpqaipdXy39EekFBAVlZWa4uQ07iUM1h/rt6G7l2Bys37cTphE5tQsnubuHyjDj8fcy/fJFLgNaouDutUXF3WqPi7rRGxd258xpdt24dnTp1cnUZ4mKVlZX4+fm5uowz0pw1qx1GIkd5e3owIMvCgCwLu/dVkV9QTJ69iPFTV/L3L1bTKzUGm9VC1+QITCbd/ktEREREREQuXQqMRE4iLMiHX9s68KuB7dnk2Eu+3cGC5cUsWlFCSIAX/bvFk909gTYxga4uVUREREREROS8U2AkchoGg4HkhBCSE0K4Z0gq9nXbyV3qYOaizUxf8CNJcUFkWy306xpPcEDLb5kUERERERERAQVGImfM7GHksrRYLkuLZV9FNQuXl5BnL2LijEImzVxDVscobN0t9OgchdnD5OpyRURERERERM6ZAiORcxDk78XgvkkM7pvE1u37ybc7yC8o5vu12/H3MdO3axzZVgvJCSEYDAZXlysiIiIiIiJyVhQYiTRTYnQgwwd14Y7rOrNy405y7UXkfl/EnMU/ERfhT3Z3CwO6WYgI8XF1qSIiIiIi0kxOp1N/KSwtgtPpbNb7FRiJnCcmo4FuHSPp1jGSyqpavl1VSp7dwfuz1/HBnHWktw/HZk2gd1oM3l76rSciIiIi0tKYzWaqqqrw9fV1dSkiv6iqqgqz2XzO72/WT6179uzhiSeeoKioCE9PTxITExkzZgyhoaGNxtXV1fHiiy+yaCcy1BcAACAASURBVNEiDAYD9913HzfddFNzphZxa34+Zq7qmchVPRPZvruSfLuDvAIH46Ys451pJnqnx5Ld3UJqUjhGo/52QkRERESkJYiMjKSkpIS4uDh8fHy000jcktPppKqqipKSEqKios75Os0KjAwGA/feey89e/YEYOzYsbz22mu8/PLLjcbNnDmToqIi5s2bx969exk2bBiXXXYZ8fHxzZlepEWIDvPjlqs7cvNVKazdUk7u0iK+WXlk91FkiA8DsyzYrBZiI/xdXaqIiIiIiJxGYGAgAKWlpdTW1rq4GnGVmpoaPD09XV3GaZnNZqKiohrW7LloVmAUHBzcEBYBZGZmMmXKlCbjZs+ezU033YTRaCQ0NJQrrriCr776invvvbc504u0KAaDgS5JYXRJCuO+G9L4rnA7eXYHU3M38unXG+mYGIKtewJ9M+Pw9zn3bYMiIiIiInLhBAYGNuuHcGn5CgoKyMjIcHUZF9x5u5FKfX09U6ZMwWazNTm3bds2YmNjG17HxMSwffv28zW1SIvj7elB/27x9O8Wz+59VcwvKCbX7uDtz1YycfpqenaJxma10C0lEpPJ6OpyRUREREREpJU5b4HRn//8Z3x9fbn99tvP1yUbKSwsvCDXdYWCggJXlyBupk0Q3GMLZNseH1ZsPkjB+u18s7IUP28j6W18yWjrS3TIxdvyqDUq7k5rVNyd1qi4O61RcXdao+LuLpU1mpWVdcpz5yUwGjt2LFu3biUnJwejseluiJiYGEpLS0lPTwea7jg6E6mpqXh5eZ2Pcl2qoKDgtL8gIoOB2sP1FKzfQZ7dwdK121myvoK2sYHYrAn07xZHSID3BZtfa1TcndaouDutUXF3WqPi7rRGxd21ljXa7MBo3LhxFBYWMmHChFPe9Omaa65h6tSpXHXVVezdu5evv/6ajz76qLlTi1yyzB5GeqXG0Cs1hn0V1SxaUUKu3cE/vixk8r/XkNUxEpvVQo/O0XiaTa4uV0RERERERC4xzQqMNm3aRE5ODm3atOHmm28GID4+nrfeeosRI0YwcuRI0tLSGDp0KCtXruSqq64C4OGHH8ZisTS/epFWIMjfi0F9khjUJ4mi7fvJszvILyhm6dod+PmY6ZcZh627hZSEED3WU0RERERERM6LZgVGHTp0YMOGDSc9N3HixIavTSYTL7zwQnOmEhEgITqQ4YO6cMd1nVm5aSf5dge5dgdzlvxEXIQfA60WBmZZiAzxdXWpIiIiIiIi0oKdt5tei8jFYzIa6JYSSbeUSB48VMu3K0vJtTv4cM56PvpqPWntwsnubuGytFh8vPTbXERERERERM6OfpIUaeF8vc1c2TORK3smsn13JfkFxeTbHYybspx3pq2id3osNquFtHbhGI1qWRMREREREZFfpsBI5BISHebHLVelcPOVyazdUk5+gYNFK0rIszsID/ZhYFY82d0TiIvwd3WpIiIiIiIi4sYUGIlcggwGA12SwuiSFMaIYWl8V7iNXLuDaXmbmJq7iZTEELKtFvpmxuHve/KnG4qIiIiIiEjrpcBI5BLnZTbRr2s8/brGU77/EPMLismzF/H2tFVMmF5Izy7R2Lpb6JYSiYfJ6OpyRURERERExA0oMBJpRUIDvblxYHtuGNCOH0v2kWd3sGBZMd+uKiXY34t+3eKI8atxdZkiIiIiIiLiYgqMRFohg8FA+/hg2scHc8/gLhSs20Gu3cHsb7dwuM7J3BX5ZHe30L9rPCGB3q4uV0RERERERC4yBUYirZyHyUjP1Bh6psawv7KGj75cwg9l8I8v1zD532vplhKJzWqhZ5doPM0mV5crIiIiIiIiF4ECIxFpEOjnSY9kfx68JQvHjgPk2R3kFzj4ywc78PP2oE9mHNnWBDq2CcFgMLi6XBEREREREblAFBiJyElZogK46/rO3H5tJ1b/sJNcu4P5y4qZ+9+txIb7YbNaGJhlITLU19WlioiIiIiIyHmmwEhETstkNJCZHElmciQHD9WyeNU28uwOPvxqPR9+tZ60duHYrBZ6p8fg6212dbkiIiIiIiJyHigwEpEz5utt5ooeCVzRI4Ed5QfJL3CQZ3fw10+Xk/PFKi5LiyHbaiGtfQQmo1rWREREREREWioFRiJyTqJCfbn5yhR+e0Uy63/aQ669iG9WlDC/oJjwIG8GWi3YrBbiIwNcXaqIiIiIiIicJQVGItIsBoOBTm1D6dQ2lBHD0vh+zXby7A6m5f/A1NxNJCcEY7Mm0K9rHAG+nq4uV0RERERERM6AAiMROW+8zCb6ZsbRNzOOPfsPMX9ZMXl2Bzmfr+LdGYV07xxFttVCVqcoPExGV5crIiIiIiIip6DASEQuiJBAb24Y0J5h/duxpXQ/ufYiFiwrZsnqbQT5e9K/azw2q4WkuCAMBt3vSERERERExJ0oMBKRC8pgMJAUF0RSXBp3D+rCsg1l5C11MHvxT3y5aDOJ0QHYrAkMyIonNNDb1eWKiIiIiIgICoxE5CLyMBnp0TmaHp2jOXCwhkUrSshb6mDyv9fwz1lr6JoSic1qoWdqDF5mk6vLFRERERERabWaFRiNHTuWuXPnUlJSwsyZM0lOTm4yZufOnfzpT3+iuLiYw4cP88ADDzB06NDmTCsil4AAX0+u692W63q3pbjsAHl2B/kFxfzfhwX4eXvQJzMOm9VCpzahalkTERERERG5yJoVGGVnZ3PnnXdy2223nXLMq6++SmpqKu+88w7l5eXceOON9OjRg5iYmOZMLSKXkPjIAO68rjO3X9OJ1T/sIq/Awfxlxcz971ZiwvwYaLVgs1qICvV1dakiIiIiIiKtQrMCI6vV+otj1q9fz1133QVAaGgoHTt2ZM6cOdxzzz3NmVpELkFGo4GM5AgykiN44MZ0Fq8qJc/u4OO56/l47npS24Vhy7JweUYsvt5mV5crIiIiIiJyybrg9zDq0qULs2fPJi0tjeLiYpYvX058fPyFnlZEWjgfLw+yuyeQ3T2BsvKD5C9zkLfUwd/+tYKcL1bTOy0Gm9VCeocITEa1rImIiIiIiJxPBqfT6WzuRWw2Gzk5OSe9h1F5eTkvv/wyGzduJDY2Fi8vL2JiYnjqqafO6NrV1dUUFhY2t0QRuQQ4nU6Kd9WwcstBCrce5FCtkwAfE+ltfcls60tEkHYdiYiIiIiInKmsrKxTnrvgO4xCQ0N57bXXGl6PGDGCdu3anfV1UlNT8fLyOp+luURBQcFpf0FEXM3d16gVGAbU1Nbx/drt5C51sGR9Gd+uPUAHSzDZVgt9u8YT6Ofp6lLlAnH3NSqiNSruTmtU3J3WqLi71rJGL3hgtGfPHgICAvDw8GDJkiVs3LiRv/3tbxd6WhG5xHmaTfTJiKNPRhx7DhxiwbIS8uxF5Hyxmne/LKR752hsVgtZHaMwexhdXa6IiIiIiEiL0qzA6MUXX2TevHns2rWLu+++m+DgYGbNmsWIESMYOXIkaWlprFq1ipdeegmj0UhISAg5OTn4+Picr/pFRAgJ8GZY/3YM69+OLaX7yF3qYMGyYpas3kagnyf9usaRbU2gXXwQBoPudyQiIiIiIvJLmhUYjR49mtGjRzc5PnHixIav+/fvT//+/ZszjYjIGWsbG8S9Q4O4e1Bnlm0oI9fu4KslW/n3N1tIiA4g22qhf7d4woIUXIuIiIiIiJzKBW9JExFxBZPJSPfO0XTvHE3FwRoWrSwlb2kRk/+9ln/OWktmciQ2q4VeaTF4mU2uLldERERERMStKDASkUuev68n117Whmsva0PJzgry7A7yCxy89lEBvt4e9MmIw2a10LltqFrWREREREREUGAkIq1MXIQ/d1zbiduu7kjh5l3kLnWwcHkx877bSnSYL7YsCwOtFqLD/FxdqoiIiIiIiMsoMBKRVsloNJDePoL09hE8cGM6S1ZvI89exJT/bODjeRvokhSGzWqhT0Ysvt5mV5crIiIiIiJyUSkwEpFWz8fLA5vVgs1qoWzPQeYXFJNnL+LNf63g71+s5rLUGGxWCxnJEZiMalkTEREREZFLnwIjEZHjRIb48psrkrkpuwMbi/aQa3ewaHkJC5YXExrozcCseGxWCwnRga4uVURERERE5IJRYCQichIGg4GUxFBSEkMZMTSV79fuIG+pgy8W/Mi0/B9oHx+EzZpAv65xBPl7ubpcERERERGR80qBkYjILzB7mLg8PZbL02PZe6CaBcuLyVvqYML01UyaWYi1UxQ2qwVrp2jMHkZXlysiIiIiItJsCoxERM5CcIAXQ/u1Y2i/dmwp3Uee3cH8ZcX8t3A7Ab6e9O8ah627hfbxwRgMut+RiIiIiIi0TAqMRETOUdvYIH43JIjh13dm+cad5NkdzP1uK//+dguWqABsVgsDs+IJC/JxdakiIiIiIiJnRYGRiEgzmUxGrJ2isHaKoqKqlm9WlJBnd/DPWWv5YPZaMjpEYLNa6JUWg7en/rMrIiIiIiLuTz+5iIicR/4+Zq65rA3XXNaG0l0V5Nkd5NsdvP7xMny8POiTEYvNaqFz2zCMRrWsiYiIiIiIe1JgJCJygcSG+3P7NZ249aqOrNm8mzy7g29WlvCf74uICvVlYJYFm9VCTLifq0sVERERERFpRIGRiMgFZjQaSGsfTlr7cO6/IY0lhdvIW+rg06838Ml/NtC5bSg2q4U+GXH4+ZhdXa6IiIiIiIgCIxGRi8nby4OBWRYGZlnYtbeK/AIHeXYH46euZMIXq+mVGoOtu4XMDhGYTEZXlysiIiIiIq2UAiMRERcJD/bhpuxkfm3rwCbHXvLsDhYuL2bhihJCA73o381CttVCYkygq0sVEREREZFWRoGRiIiLGQwGkhNCSE4I4XdDurB07Q7y7A6+XPgjX8z/gXbxQdisFvp3jSfI38vV5YqIiIiISCugwEhExI2YPUz0To+ld3os+yqqWbC8mDy7g4nTC5n05RqsnaKwWS107xyF2cPk6nJFREREROQS1azAaOzYscydO5eSkhJmzpxJcnJykzG7d+/m6aefZtu2bdTW1tKrVy9Gjx6Nh4eyKhGR0wny92JI33YM6duOrdv2k2t3ML/AwXdrthPga6ZvZhzZ3RPoYAnGYDC4ulwREREREbmENCu1yc7O5s477+S222475ZicnBzatWvHhAkTqK2t5dZbb2XevHlcd911zZlaRKRVSYwJ5J7BXbjruk6s2LSTvKUOvv6+iNmLfyI+0h+b9ciNtMODfVxdqoiIiIiIXAKaFRhZrdZfHGMwGKisrKS+vp6amhpqa2uJiopqzrQiIq2WyWQkq2MUWR2jqKyq5ZuVpeTZi3h/9jo+mLOOjPYR2LpbuCw1Bm8v7eQUEREREZFzc8F/mnjooYd49NFH6dOnD1VVVdx2221kZWVd6GlFRC55fj5mru6VyNW9Etm2q5I8u4O8AgdvfLwMHy8Tl6fHYbNa6JIUhtGoljURERERETlzBqfT6WzuRWw2Gzk5OSe9h9Enn3zCDz/8wDPPPENlZSUjRoxg+PDhXHPNNWd07erqagoLC5tboohIq1DvdFK0s4aVmytZU1RFzWEnwX4m0tv6ktHWj7AA7ToSEREREZEjTreh54L/5PDhhx/y8ssvYzQaCQgIwGaz8d13351xYHRMamoqXl4t/3HSBQUF2mElbk1rtOXrDvwKOFRzmP8WbidvaRGL1uxkYeEBOrUJxWa10CczDn8fs6tLPSdao+LutEbF3WmNirvTGhV311rW6AUPjOLj41m4cCHp6enU1NSwZMkSrrzyygs9rYhIq+ft6cGAbvEM6BbP7n1V5BcUk2cv4q3PVjJh+mp6pcZgs1romhyByWR0dbkiIiIiIuJGmhUYvfjii8ybN49du3Zx9913ExwczKxZsxgxYgQjR44kLS2NZ555hueee47BgwdTV1dHz549+c1vfnO+6hcRkTMQFuTDr20d+NXA9vxQvJe8pQ4WLC9h0YoSQgK86N8tHpvVQtvYIFeXKiIiIiIibqBZgdHo0aMZPXp0k+MTJ05s+DohIYHJkyc3ZxoRETlPDAYDHSwhdLCEcM+QVOzrtpNndzBz0WamL/iRpNggbN0t9O8aT3BAy28DFhERERGRc6O7n4qItFJmDyOXpcVyWVos+yqqWbi8hDx7Ee/OKGTyzDVkdYzCZrXQo0sUZg+Tq8sVEREREZGLSIGRiIgQ5O/F4L5JDO6bxNbt+8m3O8gvKOb7tdvx9zHTt2sc2VYLyQkhGAwGV5crIiIiIiIXmAIjERFpJDE6kOGDunDHdZ1ZuXEneXYHuUsdzFn8E3ER/tisFgZmWYgI8XF1qSIiIiIicoEoMBIRkZMyGQ106xhJt46RHDxUyzcrS8mzO/hgzjo+/God6e3DsVktXJYWi4+X/nciIiIiInIp0Z/wRUTkF/l6m7mqZyJX9Uxk++5K8u0O8gocjJuynHemraJ3eizZ3S2kJoVjNKplTURERESkpVNgJCIiZyU6zI9bru7IzVelsHZLOblLixp2H0WE+DAwy4LNaiEuwt/VpYqIiIiIyDlSYCQiIufEYDDQJSmMLklh3HdDGt8VbifP7uCz3I386+uNdEwMwWa10DczDn9fT1eXKyIiIiIiZ0GBkYiINJu3pwf9u8XTv1s8u/dVsWBZMbl2B29PW8XEGYX06BJNttVCt5RITCajq8sVEREREZFfoMBIRETOq7AgH24c2IEbBrTnx+J95NqLWLCshG9XlhLs70X/bvFkd7fQNjbI1aWKiIiIiMgpKDASEZELwmAw0N4STHtLMPcMTqVg/Q7y7A5mfbuZGQt/pG1sIDarhf7d4gkJ8HZ1uSIiIiIichwFRiIicsGZPYz0So2hV2oM+ytrWLT8SMvaP75cw+R/r6VbSiTZ3S306ByNp9nk6nJFRERERFo9BUYiInJRBfp5cn2fJK7vk0TR9v3k2R3MX1bM2Pd34Odjpm9mHNlWCymJIRgMBleXKyIiIiLSKikwEhERl0mIDmT4oC7ccV1nVm3aSZ7dQZ7dwVdLfiIuwo+BVgsDu1mIDPV1dakiIiIiIq2KAiMREXE5k9FA15RIuqZE8uChWhavKiXX7uDDOev5cM560tuHY7Na6J0e6+pSRURERERaBQVGIiLiVny9zVzRI5EreiSyfXcl+QXF5Nsd/L9PlvPO56tICDezvLiQuEh/4iL8iIvwJzTQW+1rIiIiIiLnkQIjERFxW9FhftxyVQo3X5nMup/KybM7WLG+lDlLfqKmtq5hnI+XidgIf+LC/YmL9Cc2wp/4CH9iI/zw9Ta77gOIiIiIiLRQCoxERMTtGQwGOrcNo3PbMAoK6ujatRu79lVRurOCkrIKSnZVUlJWwYaiPSxaWYLT+fN7QwO9joRJx/6JPPLvqFBfPExG130oERERERE3psBIRERaHKPRQGSIL5EhvmQmRzY6V1Nbx7bdRwKkkp1H/indWcniVds4cLCmYZzJaCA6zJe4iABij7a2HQuTQgK81OImIiIiIq2aAiMREbmkeJpNJEYHkhgd2OTc/sqaI7uSdjYOk1ZsLKPmcH3DOB8vj6P3Rwo48u+jbW5xEf74eOl/nSIiIiJy6WvWn3rHjh3L3LlzKSkpYebMmSQnJzcZ88QTT7Bhw4aG1xs2bOCtt94iOzu7OVOLiIictUA/TwL9QunYJrTR8fp6J7v2VlG8s+LnNredFaz7aTcLVxSf0OLmTfxxAdKxQCkqxBeTWtxERERE5BLRrMAoOzubO++8k9tuu+2UY/7yl780fL1+/Xruuusu+vbt25xpRUREziuj0UBkqC+Rob50S2nc4lZdW8e2XZVHdiQd1+b2zYoSKqpqG8Z5mAxEh/k13CspNsL/aLDkR7C/WtxEREREpGVpVmBktVrPavxnn33G4MGD8fT0bM60IiIiF42X2USbmEDaxDRtcdtXUU3pzkpKdh6gZGdlQ5hUsL6Mw3U/t7j5eXv8vCMp8rinuYX74a0WNxERERFxQwan8/iN9ufGZrORk5Nz0pa0Y2pqaujbty/vvfcenTp1OuNrV1dXU1hY2NwSRURELpr6eif7Dtaxe/9hdh2oZff+w+w+cJjd+w+z72Bdo7GBvibCAjwIC/QgLMCD8EAPwgLMBPuZMBq1K0lERERELpysrKxTnrtof6359ddfExsbe1Zh0fFSU1Px8vI6z1VdfAUFBaf9BRFxNa1RcXctfY0eqjn8c4vb0Ta30p2VrCuuoLKqsmGch8lITLhvQ4vb8W1ugX6eanFzYy19jcqlT2tU3J3WqLi71rJGL1pgNG3aNH71q19drOlERETckrenB21jg2gbG9TouNPpZH9lDcVlFU2e5GZft4PDdT9vCPbzMR99ittxbW4R/sSE++HtqRY3EREREWm+i/Knyu3bt1NQUMDrr79+MaYTERFpcQwGA0H+XgT5e9ElKazRubq6enburWoIk449zW31D7vILyhuNDY82If4iCM32z4WJMVF+BMR4otJLW4iIiIicoaaFRi9+OKLzJs3j127dnH33XcTHBzMrFmzGDFiBCNHjiQtLQ2AL774goEDBxIcHHxeihYREWlNTCYj0WF+RIf5QaeoRucOVR+m9PgWt6NtbvOXFXPw0OGGcWYPIzHhx+1KivAjLiKA2Ag/gvxbfsu3iIiIiJxfzQqMRo8ezejRo5scnzhxYqPXDz74YHOmERERkVPw9vIgKS6IpLimLW57jz7F7fg2N8eOAyxdu71Ri1uAr/nnp7gd1+YWE+6Hl9l0sT+SiIiIiLgB3ehARETkEmQwGAgJ8CYkwPukLW479hxsEiat3LSTPLvjuGtARLDPkZttH73p9rE2t4hgHz3FTUREROQSpsBIRESklTGZjMSG+xMb7o/1hBa3qurDx910u5KSsgpKdlWQa3dQVf1zi5vnsRa3yKY7kwJ8PS/2RxIRERGR80yB0UVUsfZbvDcsobxiIwajBwaTB5hMR742msDkgeHo64bjpmPnTBhM5iNfG01Hjh87d2yc6edzGIx65LKIiJw1Hy8P2sUH0y6+8X0HnU4new9UN9xw+8jOpEq2btvPd4Xbqas/vsXN88g9kk4Ik2LC/fBUi5uIiIhIi6DA6CKqKFyI90/L2Lul/iLMZjgudDoaIhl/fo3J47ig6mgAZfQ4LoxqGlwdOXfs+JH3Hguujlz76DzHzjUKxY7VcMK1G8173GuTCYPBeBG+TyIiciYMBgMhgd6EBHqT1i680bnDdfWUlR9sEiYt31BG7tITWtxCfBue4nZ8m1t4kFrcRERERNyJAqOLKPo3T1NQUEC3bt3AWY+z7jDUHcZZX4ezrg7qD+NseH3cufrDUFf387lGr49+few99Ydx1h0Zc+T1kWs3nDs67vhzx+atP1zVpA6Oq6fhdd1hcF6E0MtgPE1QdVwA1SSoOrZb6/gdWKbjdmt5/BycnXiuUcD281zHv+fEOhqHb8fPa9IuLxFpFTxMRmKPhj8nOnioltKdJzzFbWcF637aTVV1XcM4T7OJ2GNPcYs89hS3IzuT/NXiJiIiInLRKTByAYPBAIajwYa5ZT7K2OmsPyGMqmscdjUEV8dCpyOvG76urzsaetWeNDDjuNDrxOCKE0OvY3MdrsVZf+iE9xydt/6EgK3uMOD8xc/ZbMYTg6rjdnudIrg6k51gP7cfmn8+d+JOsJO0MB5fz4mvj83F4WrqD9f8HL6JiDSDr7eZ9pZg2luatriV7z905Mbbx+1M2lK6jyWF26g/rsUtyN+T2HB/4iOP7kg6GibFhPth9tB/p0REREQuBAVGck4MBiOYjBhMZleXcs6O7Naqa7xb6yQ7sBrv1jp9gHXKcyfsBPv5deO56murj+7iOskusfqmO9IuhBDgp6+PvTKcIqg6rnXwJC2LJwZXJ29ZbLwj6+QB1glznaSF8fg2x6Y70tTaKOKuDAYDYUE+hAX5kNa+aYvb9t2VPz/FbdeRMMm+bgd7vi9qGGc0QGSob6OnuB379/9n787Doyrv94/fsyaTfbJCIEQQCSgBIRBUsGAARQEBK+64VcEFqWvVnyiCWKutUgUrLX4FtbVYFa0ICIqCS1uIICIKgoBAAmQhJCH7MvP7I8lkJpOEhOzk/bouLybnnDnnOTMHhdvP53nCgn1pcQMAAGgCAiN0Wq7QooP+LnA6nZUVVjVbB2u0Kda3z62Fseq4QwcOqHt0l8oQrLxGkFZL62SNIK2itdGzysyzxbItWxu9WwerK7zqqwSrGU7VaFOsZ5/nuWtOZn+SfbQ2opMym4zqHhmo7pGBSjzHc19+YakOZ+ZVrN7m1ur2475jKiqpbnHzsbq1uLna3Cr+8bd13P/ZAQAA0Fo66F+VARgMBlfg0JytjT8btygkIaHZzlcfp9PhVmnl2TroEU5V7auquqp6Ty2hmFd7ZC1BldNRda1Sr3N7tjbWXgnmPo7WaW2sK4yqUf3VgEqw2lsYa6nqqmefd0tlzQnwa0x4T2sjmpG/zaKzYuw6K8busb2qxa1iwu28yja3fO1NydF/th+WW4ebQgJ81C0yQNHh/m5tbgHqEuYvi5mqRAAAAInACEAbMhiMktkogzru/+33DKNqVmTVNr9XzTm4agRXXpVgpbUGVdUT4Htf11FSXMcE+LW3TbY4g7HuubZqrLrotVJjLQGW7ViWMo99V1mBZayuxDKaZDAaXa9d13XfbjJXvKdqHrka7/f82eh23pMcQ+tjm3NvcRt4VoTHvtKyiha3VPdV3DLzlfxjmj5xb3EzGhQV6qduNVZx6x4ZoNAgX6r9AABAp0JgBABNUN3a2DFXcXJvbfRenbH2yeu99nlUgrmvxlhea9tjbSs31qwEc5QWe527ap+1pFh5aTvldDiq2zJbo72xXgavkKkqWKo7bKolfHIPslxtiRUBV13nrzUsq+969YZlZq8xnQ5hmcVshxlhMAAAIABJREFUVExUoGKiAr325RWW6nDV6m3p1au4bf85UyWl1YGqr9XkqkTqVjXxdmWbm59vxw29AQAA6kJgBACdWEu1NrakLVu2KKFG26TT6ZScjup2Q/cwyePX6u3ex7j/7JDTWe5WzeV2jNPtGI/ze/588mPczlVeJkdpeXU1mbOOMdU21vYSlplqhEyG+gOuBgdq9YRltQVcDQrLTNXbzEaTephMio02Sd1tMhgDJKNJThmVnV+qtONFOpxVqCPHCpV6rFD7D2bov98dUrnTIKcqKo7sgT6uSqTo8AAV5RYqKuaEuoT5y2xq/4EaAABAbQiMAAAdnsFgqK7O6WRcYVl5Wd2B1EnDsroDqnrDsqrKL2dt5z1JWOYok9PhkKOsVHIUVb/3pGFZ9bbWCMsCJPWp/MelcvokpwxyGoxyyKjybIPKjxtUvsugchl09DujDssgg8kkk9kss9kss8Uii8Uiq49FFou54WFZrWFc3S2UjQrU6jiXwWSuP/SjPQ8AgNMegREAAB1Y5w7LHG5hUgPCshoBV51VW7WFZZUBl1eIVSMsKykp1eHDabJYbSosKlZRYYmKi0tUfKJEBqdDRhXLaCiUxSj5WgzyMRvkY5asJoMsJqfMBsmg6vHWFca1eWWZwXiS9kX3tsXa5gWrrxqsjhCr3rDMXMu8Yw0Iy0y1jPdkFXKEZQCAToLACAAAdEgGg1EyVVbDtCNHtmzRwBptkw6HU5k5hUqtXMVtb+UqbikZecpIK5DTbRW30CAfdYsIVHRU9Spu3SMCFBnq52pxqzcsq5ofrJ6Aq66wrP6qsHrCsnK3QM158gozz8qystqv1+7DMnM9wVc9YZmp4S2bXmGZqZZjDDXDuJMHaqbswyo6HFzRVGkwqvKFZDBUBmIVr1UVjhmMlS/dt1ce6368DJUvK1sxa+w3uB0jt2O8r2kgmAOAdqB9/QkLAADgNGQ0GhRp91Ok3U+D4iI99pWUluvIsXyPSbcPZ+TrP9uP6ERBies4k9GgLmH+FZNuR1ZMvF0VJoUE+neav2B7hmV1tS2eJCxza+FsSMDV4ECtnnNVbXOUFjd4fjP36jg5yprtMwySdPh/zXa6FmSoETo1LowyeIVZlcdKpxCU1R9w1TW25gvVajne4/zV92TwGlsD79vtuoYGBnvV2xtybEPuu+Ify9F9yttVIoM8x1txH97fU133Xef32oAxeD8DLfG9en9OneXf5egYCIwAAADakNViUmyXIMV2CfLal5tfUr2Km9tqbt/uTldpWXWljZ+vuWIVt/DqMKlbREV1ks3n9PrjXnutLGsNFWHZyeb4qm2OMs9j9uzerd69z5SrtM3pkJyS5KyYF03Oin2u15XXrnxdVeXl9Dim+rXT7Ziqfd7nrTjG49jKfU6v6zvdxlbLeGs5t9PtGM+xVd+T+3WdNcbgfe467tvt/E73sTkqjnU05DNyOiVVfha13Huj77vGfXh+/jXvu5bvux0IkJS+ra1H0ZaaOwhs/bDUKyhzC/+8ArfK6xoMJwsIawvtmuszqrzvBgZ7lmMnJHlWE5+OOt9/aQEAADqIIH+rgvxD1feMUI/tDodTGdmFrgDpcEaeUjLytPOXY/piW4pHi1tYsG9FVVJlgFTR5uavKLufTKzi1qFUh2VNO09ZtkP+Z53+f9HBqfEIkSRXUNWYMMor0Ko6tq7QsEZo9cMPP+jsfv08rl9rUFZPoFlnIFbHvbRJWFpboNlCQWCrhaVVi3E04vtuzPdU12fa2mGpn9VPGn99o97TETUpMHr22We1du1apaamauXKlerTp0+tx61evVqvvPKKnE6nDAaDli5dqvDw8KZcGgAAoNMyGg2KCvVTVKifBtdocSsuLdeRzHxXmFRVmfTltlTlFZa6jjOb3FrcaoRJIQE+tEUAnZRnVYgkVSSUrflvBEdghnyizmjFKwKqOyytpfLv2+072nKoraZJgdHo0aN144036vrr607Wvv/+ey1atEivv/66IiIidOLECVmt1qZcFgAAAHXwsZh0RtcgndHVu8UtJ69YhzPylZpxQqkZ+a4wacuudJWVV7e4+Ve1uEUGuAKlbhEBig73l+9p1uIGAIDUyLC0k6xO26T/4g8ZMuSkxyxbtky33nqrIiIiJEmBgYFNuSQAAABOUXCAj4IDfNSvp2eLW7nDqYzjBR7zJB3OyNeOvce0YUuKx7Hhwb7q5rZ6W1VlUoTdTyYjVUkAAJwuDE7XLGqnLikpSYsXL661JW3y5MkaOXKkvvnmGxUUFGjs2LG68847G1zmXFxcrB07Oke5FwAAQHtTUuZQ1okyHTtRpmO5ZcrMrXpdqqLS6j9GmoxSaKBZYYFmhQVZFBZoVnhQxc9+PkZa3AAAaIcSEuqe067Fa4rLy8v1008/aenSpSopKdFtt92m6OhoTZ48uVHn6d+/v3x8fFpolK1ny5Yt9X4hQFvjGUV7xzOK9q6zPKNOp1O5+SVKqZx0230lt59/ylNZeXWY5G+zVFYj+Xu0uXUN95evlRa31tZZnlF0XDyjaO86yzPa4v+Fjo6O1rhx42S1WmW1WjV69Ght37690YERAAAA2g+DweBqcTunV5jHvvJyh9KPF3qESKnpefr+50x9XqPFLcJuU7fwgMo2N391jwhUdIQ/LW4AALSxFg+MJkyYoI0bN2rSpEkqKyvT//73P11yySUtfVkAAAC0EZPJqK7h/uoa7q8h/aI89hUVl+lwZr5XmPT5lkMqKCpzHWcxV5zDfdLtbpUTcQf5s4AKAAAtrUmB0fz587Vu3TplZmbqlltuUUhIiFatWqXbb79ds2bNUnx8vMaPH68dO3bosssuk9Fo1IgRI3TllVc21/gBAADQgfj6mNWrW7B6dQv22O50OpVduYqbe5vbobQTSv7xqEeLW6CfpWLVNrcQqarFzcfSOVauAQCgpTUpMJo9e7Zmz57ttX3JkiWu10ajUY8++qgeffTRplwKAAAApzGDwSB7oK/sgb61trilHS9QanqeUjMqqpMOZ+Rp2+4MffbNIbdzSBEhNo9qpKrV3MJDbDLS4gYAQIMxyyAAAADaNZPJqOjwAEWHB2hojX2FxWVuk27nV4RKmXla/80hFRZXt7hZzUZFV028XaMyKdCPFjcAAGoiMAIAAECHZfMx68zuITqze4jHdqfTqewTxUqprEaqaHPL14Ejudq046jKHdUtbkH+VleIFB3hr+6VlUnR4f6ymGlxAwB0TgRGAAAAOO0YDAbZg3xlD/JV/JnhHvvKyh1KyypwTbhdNfn21p/S9Glyses4o0GKsPtVVyOF+7va3MKDaXEDAJzeCIwAAADQqZhNRldFkc723FdQVKrDGTVWccvI087Nx1RYXO46zmoxKboyQKpexc1f3SIDFWCztPIdAQDQ/AiMAAAAgEp+vhb1jglR7xjvFres3KKKVdzc2tz2p+bov98fkcOtxS04wOoWIlVOvB0ZoC5hfrS4AQA6DAIjAAAA4CQMBoPCgm0KC7Ypvrdni1tpmUNpWfkeq7ilZuQpeWeaPtl80HWc0SBFhvp5TrodXvFrWLCvDAZa3AAA7QeBEQAAANAEFrNR3SMD1T0y0GtffmGpUisrktzDpB/2HVNRSXWLm4/VpG7hlau4ebS5BcifFjcAQBsgMAIAAABaiL/Noj497OrTw+6xvarFrWL1trzKNrd87U3J0X+2H5Zbh5tCAn0850mqbHPrEuYvi9nYyncEAOgsCIwAAACAVube4jbwrAiPfaVlDh09lu+1itvmH44qO89tFTejQVHuLW5u1UmhQbS4AQCahsAIAAAAaEcsZqNiogIVE+Xd4pZXWFrZ3uYZJm3/OVMlpdUtbr5WU8Vk25XVSBVBUkV1EgAADUFgBAAAAHQQAXW0uDkcTh3LKXJrb6v4dfeh4/rqu1SPFjd/X6O6f5WvCLtNUaF+igz1U6TdT1Ghfoqw2+Rr5a8IAAACIwAAAKDDMxoNirDbFGG3aWCfmi1u5TqSWTXhdr6+33VADpNZ+1Jz9L8dR1VW7vA4PjjAqkh7RZAUZfdTpN1WESpVBks2H/4KAQCdAf+2BwAAAE5jFrNJPboEqUeXIElSz+BcJSQkSKqoTDp+okjpWYVKP16g9OMFSssqUHpWgX45nKPNPxxVaZlnoBTkb60OkSork6peR9pt8vNlVTcAOB0QGAEAAACdlNFYPfl2v56hXvsdDqdy8oqVdrwiRErLKlDG8UKlHS/QwaMn9M2PaSqpESgF+lncAiQ/RYbaKiqVQivCJQIlAOgYCIwAAAAA1MpoNMge5Ct7kK/6xnoHSk6nU9l5xUrPKlD68cKKUKkyXEpJz9PWn9JVXFLu8Z4Am8UVJLna3qoCplA/BdgIlACgPSAwAgAAAHBKDAaD7IG+sgf6Ki7We7/T6VRufklFm9vxAlfrW1pWgQ5n5mvb7gwV1QiU/H3NHgFSRdubzdX+5m+zyGAwtNIdAkDnRWAEAAAAoEUYDAYFB/goOMDHa2U3qSJQOlFQ6lGZVPU6LatA23/OUGGxZ6Bk8zFXzJtU1e4W6qcIe3WlUqAfgRIANAcCIwAAAABtwmAwKMjfqiB/q3rHhHjtdzqdyissdU3EnX68cnLuyvmUduzLVEFRmcd7bD4mRdorQySPibkrqpSC/K0ESgDQAE0KjJ599lmtXbtWqampWrlypfr06eN1zMKFC/XWW28pMjJSkjR48GDNmTOnKZcFAAAA0AkYDAYF+lkV6GdV7+7egZIk5RWWugKkqjCpqv1t5y9Zyi8s9Tjex2qqDpHsNlfrW1W4FBxAoAQAUhMDo9GjR+vGG2/U9ddfX+9xkydP1sMPP9yUSwEAAACAlwCbRQHdgtWrW3Ct+/MKS5VxvMCt7a16HqWfDmTpRIFnoGS1mCqCJLcJuaPsfoqoXO0tJNCHQAlAp9CkwGjIkCHNNQ4AAAAAaHYBNosCbMHqGV17oFRQVFq9wltVdVJlwLTnYLZOFJR4HG81G13tbhF2W422Nz+FBPjIaCRQAtDxGZxOp7OpJ0lKStLixYvrbEl75513FBwcrIiICN1zzz0aNGhQg89dXFysHTt2NHWIAAAAANBoxaUOZeeXKye/TNn55crOL1N2XuWv+eUqKHZ4HG8ySsH+ZoX4mxTib1ZIgEkhfpW/+psVYDPKSIUSgHYiISGhzn0tPun1NddcozvuuEMWi0Vff/217rrrLq1evVp2u/cqCfXp37+/fHx8WmiUrWfLli31fiFAW+MZRXvHM4r2jmcU7R3PaPMqKi6rrEoqVFpWgTKOV1cq7UsrVPbefI/jzSZjRWWS3a1Cya1KyR7kK1Mnr1DiGUV711me0RYPjCIiIlyvhw8frq5du2rPnj1KTExs6UsDAAAAQIvy9TGrR5cg9egSVOv+opIyZdRY3a1qtbdvdqbp+Ilij+NNRoMiKifjrg6TqifnDgu2dfpACUDraPHAKC0tTVFRUZKknTt3KjU1VT179mzpywIAAABAm/O1mhUTFaiYqMBa9xeXlldOyl3omj8pLasiXNqyK01Zud6BUnhIVYBkc03MXVWlFB7sK5PJ2Bq3BuA016TAaP78+Vq3bp0yMzN1yy23KCQkRKtWrdLtt9+uWbNmKT4+Xi+88IJ++OEHGY1GWSwWPffccx5VRwAAAADQWflYTOoeGajukbUHSiWl5crMLnS1uVWESRXh0rbdGcrKLZL7rLRGo0Hhwb6uAKmiUslWHSiF2GQmUALQAE0KjGbPnq3Zs2d7bV+yZInr9bPPPtuUSwAAAABAp2W1mBQdEaDoiIBa95eWlSsju2KVN9dqb5Xtb9v3ZOhYzUDJIIUGV63uVhEkRVUGS5GhFYGSxUygBKAVWtIAAAAAAC3DYjYpOjxA0eF1BUoOHcspdLW5pR0vUEblBN079h3Tsa0pcrgFSgaDFBbkVqEU6lmlFBFik8VsaqW7A9CWCIwAAAAA4DRlMRvVJcxfXcL8a91fVu5QZnahK0RKd1vl7cdfsvTFtlQ53BIlg0GyB/pWVihVzKNUFSxFVQZKVguBEnA6IDACAAAAgE7KbKoOlOJr2V9e7tCxnCJXm1tV21v68QLtOpClr74rVLl7iZKk0CAf1/xJVRNyR1WGSxF2P/kQKAEdAoERAAAAAKBWJpPRFfroTO/95Q6nsnKKPCqTqgKlPYey9Z/vD6us3DNQCgn0qV7dzW5za3vzU4Td1kp3BuBkCIwAAAAAAKfEZDQowm5ThN2mc3qFee0vdzh1PLeoOkw6XrnKW1aBfk7J1n9rCZT8fY2K/jLPrTKpOkyKtPvJ5sNfY4HWwO80AAAAAECLMBkNCg+xKTzEpnPkHSg5HE4dP1Gk9KxCV9vbj3sOymG06JfDOdr8w1GVljk83hPkb3WFSRH2yhXf3H7287W01u0BpzUCIwAAAABAmzAaDQoLtiks2KZ+PUMlSVvsJ5SQkCCpIlDKzit2tbmluc2j9MuRXCX/eFQlNQKlQD+LR5tbxXxKNlelEoES0DAERgAAAACAdsloNCg0yFehQb7qe0ao136n0y1QqqpSqqxUSkk/oS270lVSWu7xngCbxWP+JM+2Nz8F2AiUAInACAAAAADQQRkMBtkDfWUP9FVcrPd+p9OpnLwSt/mTqquUDmfm69vdGSou8QyU/H3NNSbi9lNUqM31s7/NIoPB0Ep3CLQdAiMAAAAAwGnJYDAoJNBHIYE+6tPD7rXf6XQqN7+kejLuqlDpeIGOHsvX9p8zVFjsGSj5+Zor29z8FBlqc2t7q6hUCvQjUMLpgcAIAAAAANApGQwGBQf4KDjAR2fF1B4o5RWWVlQluc2jlHG8UGlZBfp+b6YKi8s83mPzMbnCo4qJuCvnUqqsUgrytxIooUMgMAIAAAAAoBYGg0GBflYF+lnVu3uI136n06n8qkDpeGGNtrcC/bjvmPKLPAMlX6upOkRyW+WtqkopOIBACe0DgREAAAAAAKfAYDAowM+qAD+rzqwlUJKkvMJSZVSt8FbZ7lZVobTrlyzlFZZ6HG+1mFxzJrlXKkWGVkzSHRLgQ6CEVkFgBAAAAABACwmwWRRgC1bP6OBa9+cXlirdLUSqantLP16g3QezdaKgxON4q9no1uZWudqb288hAT4yGgmU0HQERgAAAAAAtBF/m0U96wmUCopKK8Kk41XzKBW6KpV+TslWbr5noGQxGxVpt7m1vXlWKdkDfQmU0CAERgAAAAAAtFN+vhbFdrUotmtQrfsLi8uUcbwiSHJve0vPKtCmHUeVnVfscbzZZFSE3VYZIFVWKFUGS1GhfrIH+cpEoAQRGAEAAAAA0GHZfMzq0SVIPbrUHigVFZcpI7u63c29Smnzj0eVfaJmoGRQeIhnm1vV6wi7TWHBNgKlToLACAAAAACA05Svj1kxUYGKiQqsdX9xaXlFhVKWW9tbZbi0ZVeasnI9AyWTsSJQcrW7VVUoVba9hQX7ymQytsatoYU1KTB69tlntXbtWqWmpmrlypXq06dPncfu27dPU6ZM0XXXXaeHH364KZcFAAAAAADNwMdiUvfIQHWPrD1QKiktd1UoVa/2Vqj04wXa+lO6jp8oktNZfbzRaFB4sK9HZVJk1Spvdj+Fh9hkJlDqEJoUGI0ePVo33nijrr/++nqPKy8v15w5czRmzJimXA4AAAAAALQiq8WkbhEB6hYRUOv+0rKKQCk9q0BplUFSVevb9j0ZOpZbI1AySGGVLW9V1UlV8ylFhfopLNgmi5lAqT1oUmA0ZMiQBh33t7/9TaNGjVJBQYEKCgqackkAAAAAANBOWMwmRYcHKDq8rkDJoczsQleI5L7a2/d7jylra4ocboGSwSCFBfl6tLlVrPhWES5FhNhkMZta6e46N4PT6Z71nZqkpCQtXry41pa0Xbt26amnntIbb7yhv/zlLyooKGhUS1pxcbF27Nj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      "text/plain": [
       "<Figure size 1440x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 3 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 1440x1080 with 3 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 1440x1080 with 3 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 1440x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Model evaluation (best model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
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       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col7 {\n",
       "            color:  green;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col8 {\n",
       "            color:  green;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col9 {\n",
       "            color:  green;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col10 {\n",
       "            color:  green;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col11 {\n",
       "            color:  green;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col12 {\n",
       "            color:  green;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col0 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col1 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col2 {\n",
       "            color:  red;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col3 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col4 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col5 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col6 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col7 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col8 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col9 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col10 {\n",
       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col2 {\n",
       "            color:  red;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_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",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  red;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  red;\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:  red;\n",
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       "            color:  black;\n",
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       "            color:  orange;\n",
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       "            color:  black;\n",
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       "            color:  red;\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_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col2 {\n",
       "            color:  red;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_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",
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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_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col0 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col1 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col2 {\n",
       "            color:  red;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col3 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col4 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col5 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col6 {\n",
       "            color:  black;\n",
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       "            color:  black;\n",
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       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col9 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col10 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col11 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col12 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col0 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col1 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col2 {\n",
       "            color:  red;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col3 {\n",
       "            color:  green;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col4 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col5 {\n",
       "            color:  green;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col6 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col7 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col8 {\n",
       "            color:  green;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col9 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col10 {\n",
       "            color:  green;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col11 {\n",
       "            color:  black;\n",
       "        }    #T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col12 {\n",
       "            color:  green;\n",
       "        }</style><table id=\"T_2a102ee6_9e08_11ea_92eb_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_2a102ee6_9e08_11ea_92eb_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >Overall</th>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row0_col0\" class=\"data row0 col0\" >0.740471</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row0_col1\" class=\"data row0 col1\" >0.700863</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row0_col2\" class=\"data row0 col2\" >0.039608</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row0_col3\" class=\"data row0 col3\" >0.761175</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row0_col4\" class=\"data row0 col4\" >0.695240</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row0_col5\" class=\"data row0 col5\" >0.748891</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row0_col6\" class=\"data row0 col6\" >0.698870</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row0_col7\" class=\"data row0 col7\" >0.733217</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row0_col8\" class=\"data row0 col8\" >0.707914</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row0_col9\" class=\"data row0 col9\" >0.739689</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row0_col10\" class=\"data row0 col10\" >0.698798</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row0_col11\" class=\"data row0 col11\" >0.719383</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row0_col12\" class=\"data row0 col12\" >0.703491</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >Sentiment negative</th>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row1_col0\" class=\"data row1 col0\" >0.586959</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row1_col1\" class=\"data row1 col1\" >0.518273</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row1_col2\" class=\"data row1 col2\" >0.068686</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row1_col3\" class=\"data row1 col3\" >0.625408</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row1_col4\" class=\"data row1 col4\" >0.507543</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row1_col5\" class=\"data row1 col5\" >0.603021</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row1_col6\" class=\"data row1 col6\" >0.513297</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row1_col7\" class=\"data row1 col7\" >0.578276</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row1_col8\" class=\"data row1 col8\" >0.528886</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row1_col9\" class=\"data row1 col9\" >0.584853</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row1_col10\" class=\"data row1 col10\" >0.521268</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row1_col11\" class=\"data row1 col11\" >0.543235</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row1_col12\" class=\"data row1 col12\" >0.520372</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >Sentiment neutral</th>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col0\" class=\"data row2 col0\" >0.972857</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col1\" class=\"data row2 col1\" >0.969985</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col2\" class=\"data row2 col2\" >0.002872</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col3\" class=\"data row2 col3\" >0.970167</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col4\" class=\"data row2 col4\" >0.965153</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col5\" class=\"data row2 col5\" >0.973951</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col6\" class=\"data row2 col6\" >0.972573</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col7\" class=\"data row2 col7\" >0.972080</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col8\" class=\"data row2 col8\" >0.970092</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col9\" class=\"data row2 col9\" >0.975399</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col10\" class=\"data row2 col10\" >0.971060</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col11\" class=\"data row2 col11\" >0.972688</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row2_col12\" class=\"data row2 col12\" >0.971047</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >Sentiment positive</th>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col0\" class=\"data row3 col0\" >0.578624</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col1\" class=\"data row3 col1\" >0.517793</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col2\" class=\"data row3 col2\" >0.060831</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col3\" class=\"data row3 col3\" >0.613576</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col4\" class=\"data row3 col4\" >0.515619</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col5\" class=\"data row3 col5\" >0.589638</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col6\" class=\"data row3 col6\" >0.512413</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col7\" class=\"data row3 col7\" >0.564253</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col8\" class=\"data row3 col8\" >0.530713</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col9\" class=\"data row3 col9\" >0.574713</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col10\" class=\"data row3 col10\" >0.507185</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col11\" class=\"data row3 col11\" >0.550936</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row3_col12\" class=\"data row3 col12\" >0.523035</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >0 <= text wordCnt < 3</th>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col0\" class=\"data row4 col0\" >0.861969</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col1\" class=\"data row4 col1\" >0.860587</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col2\" class=\"data row4 col2\" >0.001382</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col3\" class=\"data row4 col3\" >0.871795</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col4\" class=\"data row4 col4\" >0.835821</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col5\" class=\"data row4 col5\" >0.866667</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col6\" class=\"data row4 col6\" >0.859823</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col7\" class=\"data row4 col7\" >0.852907</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col8\" class=\"data row4 col8\" >0.868766</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col9\" class=\"data row4 col9\" >0.866364</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col10\" class=\"data row4 col10\" >0.847222</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col11\" class=\"data row4 col11\" >0.852113</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row4_col12\" class=\"data row4 col12\" >0.891304</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >3 <= text wordCnt < 10</th>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row5_col0\" class=\"data row5 col0\" >0.794183</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row5_col1\" class=\"data row5 col1\" >0.763995</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row5_col2\" class=\"data row5 col2\" >0.030188</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row5_col3\" class=\"data row5 col3\" >0.815974</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row5_col4\" class=\"data row5 col4\" >0.750263</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row5_col5\" class=\"data row5 col5\" >0.798959</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row5_col6\" class=\"data row5 col6\" >0.768763</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row5_col7\" class=\"data row5 col7\" >0.787044</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row5_col8\" class=\"data row5 col8\" >0.776667</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row5_col9\" class=\"data row5 col9\" >0.795916</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row5_col10\" class=\"data row5 col10\" >0.756152</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row5_col11\" class=\"data row5 col11\" >0.773020</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row5_col12\" class=\"data row5 col12\" >0.768129</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >10 <= text wordCnt < 30</th>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row6_col0\" class=\"data row6 col0\" >0.707544</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row6_col1\" class=\"data row6 col1\" >0.661774</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row6_col2\" class=\"data row6 col2\" >0.045770</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row6_col3\" class=\"data row6 col3\" >0.728498</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row6_col4\" class=\"data row6 col4\" >0.659948</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row6_col5\" class=\"data row6 col5\" >0.717537</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row6_col6\" class=\"data row6 col6\" >0.658730</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row6_col7\" class=\"data row6 col7\" >0.699763</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row6_col8\" class=\"data row6 col8\" >0.667574</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row6_col9\" class=\"data row6 col9\" >0.705728</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row6_col10\" class=\"data row6 col10\" >0.661612</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row6_col11\" class=\"data row6 col11\" >0.686196</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row6_col12\" class=\"data row6 col12\" >0.661008</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >30 <= text wordCnt < 100</th>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row7_col0\" class=\"data row7 col0\" >0.679767</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row7_col1\" class=\"data row7 col1\" >0.610729</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row7_col2\" class=\"data row7 col2\" >0.069038</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row7_col3\" class=\"data row7 col3\" >0.681239</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row7_col4\" class=\"data row7 col4\" >0.647417</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row7_col5\" class=\"data row7 col5\" >0.723223</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row7_col6\" class=\"data row7 col6\" >0.476135</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row7_col7\" class=\"data row7 col7\" >0.692264</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row7_col8\" class=\"data row7 col8\" >0.579785</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row7_col9\" class=\"data row7 col9\" >0.661414</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row7_col10\" class=\"data row7 col10\" >0.609410</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row7_col11\" class=\"data row7 col11\" >0.640694</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row7_col12\" class=\"data row7 col12\" >0.740898</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >0 <= selected text wordCnt < 3</th>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row8_col0\" class=\"data row8 col0\" >0.660270</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row8_col1\" class=\"data row8 col1\" >0.589061</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row8_col2\" class=\"data row8 col2\" >0.071209</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row8_col3\" class=\"data row8 col3\" >0.671900</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row8_col4\" class=\"data row8 col4\" >0.558856</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row8_col5\" class=\"data row8 col5\" >0.698159</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row8_col6\" class=\"data row8 col6\" >0.611610</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row8_col7\" class=\"data row8 col7\" >0.650008</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row8_col8\" class=\"data row8 col8\" >0.596811</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row8_col9\" class=\"data row8 col9\" >0.654374</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row8_col10\" class=\"data row8 col10\" >0.578798</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row8_col11\" class=\"data row8 col11\" >0.626909</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row8_col12\" class=\"data row8 col12\" >0.599227</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >3 <= selected text wordCnt < 10</th>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col0\" class=\"data row9 col0\" >0.717341</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col1\" class=\"data row9 col1\" >0.685491</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col2\" class=\"data row9 col2\" >0.031850</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col3\" class=\"data row9 col3\" >0.754062</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col4\" class=\"data row9 col4\" >0.699257</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col5\" class=\"data row9 col5\" >0.704605</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col6\" class=\"data row9 col6\" >0.660590</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col7\" class=\"data row9 col7\" >0.709901</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col8\" class=\"data row9 col8\" >0.688232</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col9\" class=\"data row9 col9\" >0.723455</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col10\" class=\"data row9 col10\" >0.692601</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col11\" class=\"data row9 col11\" >0.694684</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row9_col12\" class=\"data row9 col12\" >0.686775</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >10 <= selected text wordCnt < 30</th>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col0\" class=\"data row10 col0\" >0.866473</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col1\" class=\"data row10 col1\" >0.855388</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col2\" class=\"data row10 col2\" >0.011085</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col3\" class=\"data row10 col3\" >0.877872</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col4\" class=\"data row10 col4\" >0.854314</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col5\" class=\"data row10 col5\" >0.866267</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col6\" class=\"data row10 col6\" >0.852734</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col7\" class=\"data row10 col7\" >0.863195</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col8\" class=\"data row10 col8\" >0.866687</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col9\" class=\"data row10 col9\" >0.862353</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col10\" class=\"data row10 col10\" >0.854256</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col11\" class=\"data row10 col11\" >0.862675</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row10_col12\" class=\"data row10 col12\" >0.848949</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >30 <= selected text wordCnt < 100</th>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col0\" class=\"data row11 col0\" >0.891441</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col1\" class=\"data row11 col1\" >0.880483</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col2\" class=\"data row11 col2\" >0.010958</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col3\" class=\"data row11 col3\" >0.916708</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col4\" class=\"data row11 col4\" >0.806774</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col5\" class=\"data row11 col5\" >0.916394</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col6\" class=\"data row11 col6\" >0.800150</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col7\" class=\"data row11 col7\" >0.886653</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col8\" class=\"data row11 col8\" >0.945152</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col9\" class=\"data row11 col9\" >0.884737</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col10\" class=\"data row11 col10\" >0.908811</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col11\" class=\"data row11 col11\" >0.852711</td>\n",
       "                        <td id=\"T_2a102ee6_9e08_11ea_92eb_0242ac130202row11_col12\" class=\"data row11 col12\" >0.941528</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc89c25f310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(evaluate_model_kfold(k_fold_best, config['N_FOLDS']).style.applymap(color_map))"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Model evaluation (last model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
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       "            color:  black;\n",
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       "            color:  red;\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_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col1 {\n",
       "            color:  black;\n",
       "        }    #T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col2 {\n",
       "            color:  red;\n",
       "        }    #T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col3 {\n",
       "            color:  black;\n",
       "        }    #T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col4 {\n",
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       "            color:  black;\n",
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       "            color:  green;\n",
       "        }</style><table id=\"T_2e6dd628_9e08_11ea_92eb_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_2e6dd628_9e08_11ea_92eb_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >Overall</th>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row0_col0\" class=\"data row0 col0\" >0.767191</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row0_col1\" class=\"data row0 col1\" >0.699351</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row0_col2\" class=\"data row0 col2\" >0.067840</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row0_col3\" class=\"data row0 col3\" >0.761175</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row0_col4\" class=\"data row0 col4\" >0.695240</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row0_col5\" class=\"data row0 col5\" >0.772978</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row0_col6\" class=\"data row0 col6\" >0.701610</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row0_col7\" class=\"data row0 col7\" >0.756242</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row0_col8\" class=\"data row0 col8\" >0.698703</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row0_col9\" class=\"data row0 col9\" >0.792815</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row0_col10\" class=\"data row0 col10\" >0.693923</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row0_col11\" class=\"data row0 col11\" >0.752747</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row0_col12\" class=\"data row0 col12\" >0.707279</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >Sentiment negative</th>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row1_col0\" class=\"data row1 col0\" >0.638070</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row1_col1\" class=\"data row1 col1\" >0.516488</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row1_col2\" class=\"data row1 col2\" >0.121582</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row1_col3\" class=\"data row1 col3\" >0.625408</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row1_col4\" class=\"data row1 col4\" >0.507543</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row1_col5\" class=\"data row1 col5\" >0.651234</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row1_col6\" class=\"data row1 col6\" >0.519156</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row1_col7\" class=\"data row1 col7\" >0.625023</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row1_col8\" class=\"data row1 col8\" >0.519805</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row1_col9\" class=\"data row1 col9\" >0.681666</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row1_col10\" class=\"data row1 col10\" >0.511913</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row1_col11\" class=\"data row1 col11\" >0.607019</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row1_col12\" class=\"data row1 col12\" >0.524024</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >Sentiment neutral</th>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row2_col0\" class=\"data row2 col0\" >0.972150</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row2_col1\" class=\"data row2 col1\" >0.967987</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row2_col2\" class=\"data row2 col2\" >0.004163</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row2_col3\" class=\"data row2 col3\" >0.970167</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row2_col4\" class=\"data row2 col4\" >0.965153</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row2_col5\" class=\"data row2 col5\" >0.974836</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row2_col6\" class=\"data row2 col6\" >0.971986</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row2_col7\" class=\"data row2 col7\" >0.964013</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row2_col8\" class=\"data row2 col8\" >0.959475</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row2_col9\" class=\"data row2 col9\" >0.977531</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row2_col10\" class=\"data row2 col10\" >0.969329</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row2_col11\" class=\"data row2 col11\" >0.974201</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row2_col12\" class=\"data row2 col12\" >0.973992</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >Sentiment positive</th>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row3_col0\" class=\"data row3 col0\" >0.618761</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row3_col1\" class=\"data row3 col1\" >0.517159</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row3_col2\" class=\"data row3 col2\" >0.101602</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row3_col3\" class=\"data row3 col3\" >0.613576</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row3_col4\" class=\"data row3 col4\" >0.515619</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row3_col5\" class=\"data row3 col5\" >0.621905</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row3_col6\" class=\"data row3 col6\" >0.516634</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row3_col7\" class=\"data row3 col7\" >0.606052</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row3_col8\" class=\"data row3 col8\" >0.523206</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row3_col9\" class=\"data row3 col9\" >0.654290</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row3_col10\" class=\"data row3 col10\" >0.502298</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row3_col11\" class=\"data row3 col11\" >0.597983</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row3_col12\" class=\"data row3 col12\" >0.528038</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >0 <= text wordCnt < 3</th>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row4_col0\" class=\"data row4 col0\" >0.867710</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row4_col1\" class=\"data row4 col1\" >0.860051</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row4_col2\" class=\"data row4 col2\" >0.007659</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row4_col3\" class=\"data row4 col3\" >0.871795</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row4_col4\" class=\"data row4 col4\" >0.835821</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row4_col5\" class=\"data row4 col5\" >0.867568</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row4_col6\" class=\"data row4 col6\" >0.859823</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row4_col7\" class=\"data row4 col7\" >0.856649</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row4_col8\" class=\"data row4 col8\" >0.880577</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row4_col9\" class=\"data row4 col9\" >0.884848</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row4_col10\" class=\"data row4 col10\" >0.847222</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row4_col11\" class=\"data row4 col11\" >0.857688</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row4_col12\" class=\"data row4 col12\" >0.876812</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >3 <= text wordCnt < 10</th>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row5_col0\" class=\"data row5 col0\" >0.814342</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row5_col1\" class=\"data row5 col1\" >0.763600</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row5_col2\" class=\"data row5 col2\" >0.050743</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row5_col3\" class=\"data row5 col3\" >0.815974</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row5_col4\" class=\"data row5 col4\" >0.750263</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row5_col5\" class=\"data row5 col5\" >0.815776</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row5_col6\" class=\"data row5 col6\" >0.777450</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row5_col7\" class=\"data row5 col7\" >0.807633</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row5_col8\" class=\"data row5 col8\" >0.770056</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row5_col9\" class=\"data row5 col9\" >0.834028</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row5_col10\" class=\"data row5 col10\" >0.753600</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row5_col11\" class=\"data row5 col11\" >0.798300</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row5_col12\" class=\"data row5 col12\" >0.766630</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >10 <= text wordCnt < 30</th>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row6_col0\" class=\"data row6 col0\" >0.738456</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row6_col1\" class=\"data row6 col1\" >0.659546</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row6_col2\" class=\"data row6 col2\" >0.078910</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row6_col3\" class=\"data row6 col3\" >0.728498</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row6_col4\" class=\"data row6 col4\" >0.659948</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row6_col5\" class=\"data row6 col5\" >0.746485</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row6_col6\" class=\"data row6 col6\" >0.658093</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row6_col7\" class=\"data row6 col7\" >0.724841</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row6_col8\" class=\"data row6 col8\" >0.656740</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row6_col9\" class=\"data row6 col9\" >0.767764</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row6_col10\" class=\"data row6 col10\" >0.654811</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row6_col11\" class=\"data row6 col11\" >0.724692</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row6_col12\" class=\"data row6 col12\" >0.668136</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >30 <= text wordCnt < 100</th>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row7_col0\" class=\"data row7 col0\" >0.720625</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row7_col1\" class=\"data row7 col1\" >0.618852</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row7_col2\" class=\"data row7 col2\" >0.101773</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row7_col3\" class=\"data row7 col3\" >0.681239</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row7_col4\" class=\"data row7 col4\" >0.647417</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row7_col5\" class=\"data row7 col5\" >0.744495</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row7_col6\" class=\"data row7 col6\" >0.509783</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row7_col7\" class=\"data row7 col7\" >0.721007</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row7_col8\" class=\"data row7 col8\" >0.515651</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row7_col9\" class=\"data row7 col9\" >0.752313</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row7_col10\" class=\"data row7 col10\" >0.658584</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row7_col11\" class=\"data row7 col11\" >0.704071</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row7_col12\" class=\"data row7 col12\" >0.762825</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >0 <= selected text wordCnt < 3</th>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row8_col0\" class=\"data row8 col0\" >0.702273</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row8_col1\" class=\"data row8 col1\" >0.589046</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row8_col2\" class=\"data row8 col2\" >0.113227</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row8_col3\" class=\"data row8 col3\" >0.671900</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row8_col4\" class=\"data row8 col4\" >0.558856</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row8_col5\" class=\"data row8 col5\" >0.677106</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row8_col6\" class=\"data row8 col6\" >0.565355</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row8_col7\" class=\"data row8 col7\" >0.730790</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row8_col8\" class=\"data row8 col8\" >0.627477</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row8_col9\" class=\"data row8 col9\" >0.748771</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row8_col10\" class=\"data row8 col10\" >0.584816</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row8_col11\" class=\"data row8 col11\" >0.682799</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row8_col12\" class=\"data row8 col12\" >0.608725</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >3 <= selected text wordCnt < 10</th>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row9_col0\" class=\"data row9 col0\" >0.742411</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row9_col1\" class=\"data row9 col1\" >0.682460</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row9_col2\" class=\"data row9 col2\" >0.059951</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row9_col3\" class=\"data row9 col3\" >0.754062</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row9_col4\" class=\"data row9 col4\" >0.699257</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row9_col5\" class=\"data row9 col5\" >0.769508</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row9_col6\" class=\"data row9 col6\" >0.699412</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row9_col7\" class=\"data row9 col7\" >0.700853</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row9_col8\" class=\"data row9 col8\" >0.652925</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row9_col9\" class=\"data row9 col9\" >0.758151</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row9_col10\" class=\"data row9 col10\" >0.671694</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row9_col11\" class=\"data row9 col11\" >0.729484</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row9_col12\" class=\"data row9 col12\" >0.689012</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >10 <= selected text wordCnt < 30</th>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col0\" class=\"data row10 col0\" >0.877117</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col1\" class=\"data row10 col1\" >0.853507</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col2\" class=\"data row10 col2\" >0.023611</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col3\" class=\"data row10 col3\" >0.877872</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col4\" class=\"data row10 col4\" >0.854314</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col5\" class=\"data row10 col5\" >0.893207</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col6\" class=\"data row10 col6\" >0.866562</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col7\" class=\"data row10 col7\" >0.858065</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col8\" class=\"data row10 col8\" >0.841722</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col9\" class=\"data row10 col9\" >0.889427</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col10\" class=\"data row10 col10\" >0.857726</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col11\" class=\"data row10 col11\" >0.867017</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row10_col12\" class=\"data row10 col12\" >0.847211</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >30 <= selected text wordCnt < 100</th>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row11_col0\" class=\"data row11 col0\" >0.889409</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row11_col1\" class=\"data row11 col1\" >0.894870</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row11_col2\" class=\"data row11 col2\" >-0.005461</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row11_col3\" class=\"data row11 col3\" >0.916708</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row11_col4\" class=\"data row11 col4\" >0.806774</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row11_col5\" class=\"data row11 col5\" >0.915003</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row11_col6\" class=\"data row11 col6\" >0.864776</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row11_col7\" class=\"data row11 col7\" >0.872560</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row11_col8\" class=\"data row11 col8\" >0.901795</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row11_col9\" class=\"data row11 col9\" >0.886695</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row11_col10\" class=\"data row11 col10\" >0.908811</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row11_col11\" class=\"data row11 col11\" >0.856079</td>\n",
       "                        <td id=\"T_2e6dd628_9e08_11ea_92eb_0242ac130202row11_col12\" class=\"data row11 col12\" >0.992194</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcdaf623910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(evaluate_model_kfold(k_fold, config['N_FOLDS']).style.applymap(color_map))"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "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>text_tokenCnt</th>\n",
       "      <th>selected_text_tokenCnt</th>\n",
       "      <th>imputed</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>10</td>\n",
       "      <td>10</td>\n",
       "      <td>False</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>13</td>\n",
       "      <td>3</td>\n",
       "      <td>False</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>sad</td>\n",
       "      <td>0.500000</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>6</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>bullying</td>\n",
       "      <td>0.500000</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",
       "    </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>6</td>\n",
       "      <td>3</td>\n",
       "      <td>False</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>17</td>\n",
       "      <td>4</td>\n",
       "      <td>False</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>27</td>\n",
       "      <td>27</td>\n",
       "      <td>False</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>some shameless plugging for the best rangers f...</td>\n",
       "      <td>0.833333</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>17</td>\n",
       "      <td>1</td>\n",
       "      <td>False</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>3</td>\n",
       "      <td>3</td>\n",
       "      <td>False</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>3</td>\n",
       "      <td>3</td>\n",
       "      <td>False</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>19</td>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "      <td>wow... u just became cooler.</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>wow... u just became cooler.</td>\n",
       "      <td>1.000000</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...</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>27</td>\n",
       "      <td>27</td>\n",
       "      <td>False</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>12</td>\n",
       "      <td>1</td>\n",
       "      <td>False</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>like</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>i really really like</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>like</td>\n",
       "      <td>1.000000</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>9</td>\n",
       "      <td>1</td>\n",
       "      <td>False</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</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>dangerously low</td>\n",
       "      <td>0.500000</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>13</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>lost my voice.</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>lost</td>\n",
       "      <td>1.000000</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",
       "    </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>8</td>\n",
       "      <td>8</td>\n",
       "      <td>False</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",
       "    text_tokenCnt  selected_text_tokenCnt  imputed  \\\n",
       "0              10                      10    False   \n",
       "1              13                       3    False   \n",
       "2               6                       2    False   \n",
       "3               6                       3    False   \n",
       "4              17                       4    False   \n",
       "5              27                      27    False   \n",
       "6              17                       1    False   \n",
       "7               3                       3    False   \n",
       "8               3                       3    False   \n",
       "9              19                       7    False   \n",
       "10             27                      27    False   \n",
       "11             12                       1    False   \n",
       "12              9                       1    False   \n",
       "13             13                       1    False   \n",
       "14              8                       8    False   \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... 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_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        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... 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        1.000000   \n",
       "13                                               lost        1.000000   \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... 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        1.000000   \n",
       "13                                               lost        1.000000   \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                                                 sad        0.500000   \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        0.500000   \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   some shameless plugging for the best rangers f...        0.833333  \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                                               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  "
      ]
     },
     "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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