Model backlog/Train/13-tweet-train-distilbert-base-uncased.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_kg_hide-input": true,
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "from tweet_utility_scripts import *\n",
    "from transformers import TFDistilBertModel, DistilBertConfig\n",
    "from tokenizers import BertWordPieceTokenizer\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras import optimizers, metrics, losses\n",
    "from tensorflow.keras.callbacks import EarlyStopping, TensorBoard\n",
    "from tensorflow.keras.layers import Dense, Input, Dropout, GlobalAveragePooling1D, GlobalMaxPooling1D, Concatenate\n",
    "\n",
    "\n",
    "SEED = 0\n",
    "seed_everything(SEED)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_kg_hide-input": true,
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
      "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>textID</th>\n",
       "      <th>text</th>\n",
       "      <th>selected_text</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text_len</th>\n",
       "      <th>selected_text_len</th>\n",
       "      <th>set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69a4e7a6f5</td>\n",
       "      <td>ouch, i'm sorry then</td>\n",
       "      <td>sorry</td>\n",
       "      <td>negative</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8022534fc2</td>\n",
       "      <td>thankyou, yes. off to a pre-wedding party now.</td>\n",
       "      <td>Thankyou,</td>\n",
       "      <td>positive</td>\n",
       "      <td>47</td>\n",
       "      <td>9</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f7beb13214</td>\n",
       "      <td>this is how we do itttt</td>\n",
       "      <td>this is how we do itttt</td>\n",
       "      <td>neutral</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89cdcbfb73</td>\n",
       "      <td>everyone's abandoning the office... i still ha...</td>\n",
       "      <td>everyone's abandoning the office... I still ha...</td>\n",
       "      <td>neutral</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29a4bb11d9</td>\n",
       "      <td>acquiring a centos live cd, now where did i pu...</td>\n",
       "      <td>I would like to boot from that. I wonder if I ...</td>\n",
       "      <td>neutral</td>\n",
       "      <td>126</td>\n",
       "      <td>61</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       textID                                               text  \\\n",
       "0  69a4e7a6f5                               ouch, i'm sorry then   \n",
       "1  8022534fc2     thankyou, yes. off to a pre-wedding party now.   \n",
       "2  f7beb13214                            this is how we do itttt   \n",
       "3  89cdcbfb73  everyone's abandoning the office... i still ha...   \n",
       "4  29a4bb11d9  acquiring a centos live cd, now where did i pu...   \n",
       "\n",
       "                                       selected_text sentiment  text_len  \\\n",
       "0                                              sorry  negative        21   \n",
       "1                                          Thankyou,  positive        47   \n",
       "2                            this is how we do itttt   neutral        23   \n",
       "3  everyone's abandoning the office... I still ha...   neutral        62   \n",
       "4  I would like to boot from that. I wonder if I ...   neutral       126   \n",
       "\n",
       "   selected_text_len    set  \n",
       "0                  5  train  \n",
       "1                  9  train  \n",
       "2                 23  train  \n",
       "3                 62  train  \n",
       "4                 61  train  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hold_out/\r\n",
      "hold_out/y_train.npy\r\n",
      "hold_out/x_train.npy\r\n",
      "hold_out/y_valid.npy\r\n",
      "hold_out/x_valid.npy\r\n"
     ]
    }
   ],
   "source": [
    "database_base_path = '/kaggle/input/tweet-dataset-split-distilbert-uncased-128/'\n",
    "hold_out = pd.read_csv(database_base_path + 'hold-out.csv')\n",
    "train = hold_out[hold_out['set'] == 'train']\n",
    "validation = hold_out[hold_out['set'] == 'validation']\n",
    "display(hold_out.head())\n",
    "\n",
    "# Unzip files\n",
    "!tar -xvf /kaggle/input/tweet-dataset-split-distilbert-uncased-128/hold_out.tar.gz\n",
    "\n",
    "base_data_path = 'hold_out/'\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",
    "# Delete data dir\n",
    "shutil.rmtree(base_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_LEN = 128\n",
    "BATCH_SIZE = 64\n",
    "EPOCHS = 20\n",
    "LEARNING_RATE = 1e-5\n",
    "ES_PATIENCE = 3\n",
    "\n",
    "tokenizer_path = database_base_path + 'vocab.txt'\n",
    "base_path = '/kaggle/input/qa-transformers/distilbert/'\n",
    "base_model_path = base_path + 'distilbert-base-uncased-distilled-squad-tf_model.h5'\n",
    "config_path = base_path + 'distilbert-base-uncased-distilled-squad-config.json'\n",
    "model_path = 'model.h5'\n",
    "log_path = './'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "attention_mask (InputLayer)     [(None, 128)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_ids (InputLayer)          [(None, 128)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "token_type_ids (InputLayer)     [(None, 128)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "base_model (TFDistilBertModel)  ((None, 128, 768),)  66362880    attention_mask[0][0]             \n",
      "                                                                 input_ids[0][0]                  \n",
      "                                                                 token_type_ids[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d (Globa (None, 768)          0           base_model[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "y_start (Dense)                 (None, 128)          98432       global_average_pooling1d[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "y_end (Dense)                   (None, 128)          98432       global_average_pooling1d[0][0]   \n",
      "==================================================================================================\n",
      "Total params: 66,559,744\n",
      "Trainable params: 66,559,744\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "module_config = DistilBertConfig.from_pretrained(config_path, output_hidden_states=False)\n",
    "\n",
    "def model_fn():\n",
    "    input_ids = Input(shape=(MAX_LEN,), dtype=tf.int32, name='input_ids')\n",
    "    attention_mask = Input(shape=(MAX_LEN,), dtype=tf.int32, name='attention_mask')\n",
    "    token_type_ids = Input(shape=(MAX_LEN,), dtype=tf.int32, name='token_type_ids')\n",
    "    \n",
    "    base_model = TFDistilBertModel.from_pretrained(base_model_path, config=module_config, name=\"base_model\")\n",
    "    sequence_output = base_model({'input_ids': input_ids, 'attention_mask': attention_mask, 'token_type_ids': token_type_ids})\n",
    "    last_state = sequence_output[0]\n",
    "    \n",
    "    x = GlobalAveragePooling1D()(last_state)\n",
    "    \n",
    "    y_start = Dense(MAX_LEN, activation='softmax', name='y_start')(x)\n",
    "    y_end = Dense(MAX_LEN, activation='softmax', name='y_end')(x)\n",
    "    \n",
    "    model = Model(inputs=[input_ids, attention_mask, token_type_ids], outputs=[y_start, y_end])\n",
    "    model.compile(optimizers.Adam(lr=LEARNING_RATE), \n",
    "                  loss=losses.CategoricalCrossentropy(), \n",
    "                  metrics=[metrics.CategoricalAccuracy()])\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = model_fn()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_kg_hide-input": false,
    "_kg_hide-output": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 21988 samples, validate on 5497 samples\n",
      "Epoch 1/20\n",
      "21988/21988 - 191s - loss: 3.3044 - y_start_loss: 1.4702 - y_end_loss: 1.8357 - y_start_categorical_accuracy: 0.1881 - y_end_categorical_accuracy: 0.0300 - val_loss: 3.0925 - val_y_start_loss: 1.3674 - val_y_end_loss: 1.7251 - val_y_start_categorical_accuracy: 0.1903 - val_y_end_categorical_accuracy: 0.0367\n",
      "Epoch 2/20\n",
      "21988/21988 - 180s - loss: 2.9764 - y_start_loss: 1.3201 - y_end_loss: 1.6570 - y_start_categorical_accuracy: 0.1896 - y_end_categorical_accuracy: 0.0442 - val_loss: 2.9413 - val_y_start_loss: 1.3151 - val_y_end_loss: 1.6264 - val_y_start_categorical_accuracy: 0.1919 - val_y_end_categorical_accuracy: 0.0531\n",
      "Epoch 3/20\n",
      "21988/21988 - 180s - loss: 2.6453 - y_start_loss: 1.1691 - y_end_loss: 1.4764 - y_start_categorical_accuracy: 0.2002 - y_end_categorical_accuracy: 0.0880 - val_loss: 2.5128 - val_y_start_loss: 1.0872 - val_y_end_loss: 1.4258 - val_y_start_categorical_accuracy: 0.2112 - val_y_end_categorical_accuracy: 0.1152\n",
      "Epoch 4/20\n",
      "21988/21988 - 180s - loss: 2.3730 - y_start_loss: 1.0425 - y_end_loss: 1.3312 - y_start_categorical_accuracy: 0.2264 - y_end_categorical_accuracy: 0.1310 - val_loss: 2.4376 - val_y_start_loss: 1.0863 - val_y_end_loss: 1.3514 - val_y_start_categorical_accuracy: 0.2416 - val_y_end_categorical_accuracy: 0.1454\n",
      "Epoch 5/20\n",
      "21988/21988 - 180s - loss: 2.2082 - y_start_loss: 0.9688 - y_end_loss: 1.2384 - y_start_categorical_accuracy: 0.2420 - y_end_categorical_accuracy: 0.1623 - val_loss: 2.2862 - val_y_start_loss: 1.0056 - val_y_end_loss: 1.2807 - val_y_start_categorical_accuracy: 0.2494 - val_y_end_categorical_accuracy: 0.1612\n",
      "Epoch 6/20\n",
      "21988/21988 - 180s - loss: 2.0621 - y_start_loss: 0.9042 - y_end_loss: 1.1595 - y_start_categorical_accuracy: 0.2557 - y_end_categorical_accuracy: 0.1832 - val_loss: 2.2464 - val_y_start_loss: 0.9904 - val_y_end_loss: 1.2561 - val_y_start_categorical_accuracy: 0.2449 - val_y_end_categorical_accuracy: 0.1683\n",
      "Epoch 7/20\n",
      "21988/21988 - 180s - loss: 1.9290 - y_start_loss: 0.8481 - y_end_loss: 1.0808 - y_start_categorical_accuracy: 0.2664 - y_end_categorical_accuracy: 0.2057 - val_loss: 2.3095 - val_y_start_loss: 1.0130 - val_y_end_loss: 1.2964 - val_y_start_categorical_accuracy: 0.2378 - val_y_end_categorical_accuracy: 0.1655\n",
      "Epoch 8/20\n",
      "21988/21988 - 180s - loss: 1.8185 - y_start_loss: 0.7984 - y_end_loss: 1.0200 - y_start_categorical_accuracy: 0.2770 - y_end_categorical_accuracy: 0.2196 - val_loss: 2.3552 - val_y_start_loss: 1.0451 - val_y_end_loss: 1.3100 - val_y_start_categorical_accuracy: 0.2341 - val_y_end_categorical_accuracy: 0.1721\n",
      "Epoch 9/20\n",
      "Restoring model weights from the end of the best epoch.\n",
      "21988/21988 - 180s - loss: 1.7111 - y_start_loss: 0.7576 - y_end_loss: 0.9582 - y_start_categorical_accuracy: 0.2849 - y_end_categorical_accuracy: 0.2344 - val_loss: 2.2955 - val_y_start_loss: 1.0123 - val_y_end_loss: 1.2831 - val_y_start_categorical_accuracy: 0.2387 - val_y_end_categorical_accuracy: 0.1761\n",
      "Epoch 00009: early stopping\n"
     ]
    }
   ],
   "source": [
    "tb_callback = TensorBoard(log_dir=log_path)\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, \n",
    "                   restore_best_weights=True, verbose=1)\n",
    "\n",
    "history = model.fit(list(x_train), list(y_train),\n",
    "                    validation_data=(list(x_valid), list(y_valid)),\n",
    "                    callbacks=[es, tb_callback],\n",
    "                    epochs=EPOCHS, \n",
    "                    verbose=2).history\n",
    "\n",
    "model.save_weights(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_kg_hide-input": true,
    "_kg_hide-output": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train/\r\n",
      "train/events.out.tfevents.1586305719.7199e9c41607.12.5418.v2\r\n",
      "train/events.out.tfevents.1586305728.7199e9c41607.profile-empty\r\n",
      "train/plugins/\r\n",
      "train/plugins/profile/\r\n",
      "train/plugins/profile/2020-04-08_00-28-48/\r\n",
      "train/plugins/profile/2020-04-08_00-28-48/local.trace\r\n",
      "validation/\r\n",
      "validation/events.out.tfevents.1586305909.7199e9c41607.12.28047.v2\r\n"
     ]
    }
   ],
   "source": [
    "# Compress logs dir\n",
    "!tar -cvzf train.tar.gz train\n",
    "!tar -cvzf validation.tar.gz validation\n",
    "\n",
    "# Delete logs dir\n",
    "if os.path.exists('/kaggle/working/train/'):\n",
    "    shutil.rmtree('/kaggle/working/train/')\n",
    "if os.path.exists('/kaggle/working/validation/'):\n",
    "    shutil.rmtree('/kaggle/working/validation/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model loss graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "plot_metrics(history, metric_list=['loss', 'y_start_loss', 'y_end_loss', 'y_start_categorical_accuracy', 'y_end_categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = BertWordPieceTokenizer(tokenizer_path , lowercase=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Train</th>\n",
       "      <th>Validation</th>\n",
       "      <th>Train samples</th>\n",
       "      <th>Validation samples</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metric/Jaccard</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>0.505423</td>\n",
       "      <td>0.454979</td>\n",
       "      <td>21988</td>\n",
       "      <td>5497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sentiment negative</th>\n",
       "      <td>0.430474</td>\n",
       "      <td>0.344570</td>\n",
       "      <td>6142</td>\n",
       "      <td>1644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sentiment positive</th>\n",
       "      <td>0.420342</td>\n",
       "      <td>0.352170</td>\n",
       "      <td>6948</td>\n",
       "      <td>1634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sentiment neutral</th>\n",
       "      <td>0.623594</td>\n",
       "      <td>0.612484</td>\n",
       "      <td>8898</td>\n",
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       "      <td>0.459430</td>\n",
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       "      <th>100 &lt;= selected text &lt; 200</th>\n",
       "      <td>0.244274</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Train  Validation  Train samples  \\\n",
       "Metric/Jaccard                                                     \n",
       "Overall                      0.505423    0.454979          21988   \n",
       "Sentiment negative           0.430474    0.344570           6142   \n",
       "Sentiment positive           0.420342    0.352170           6948   \n",
       "Sentiment neutral            0.623594    0.612484           8898   \n",
       "0 <= text < 10               0.927083    0.950980            192   \n",
       "10 <= text < 50              0.709915    0.657669           7776   \n",
       "50 <= text < 100             0.459430    0.402682           8816   \n",
       "100 <= text < 200            0.262224    0.222222           5204   \n",
       "0 <= selected text < 10      0.486951    0.386629           6190   \n",
       "10 <= selected text < 50     0.575990    0.543982           9712   \n",
       "50 <= selected text < 100    0.493627    0.475958           4084   \n",
       "100 <= selected text < 200   0.244274    0.222139           2002   \n",
       "\n",
       "                             Validation samples  \n",
       "Metric/Jaccard                                   \n",
       "Overall                                    5497  \n",
       "Sentiment negative                         1644  \n",
       "Sentiment positive                         1634  \n",
       "Sentiment neutral                          2219  \n",
       "0 <= text < 10                               51  \n",
       "10 <= text < 50                            1947  \n",
       "50 <= text < 100                           2186  \n",
       "100 <= text < 200                          1313  \n",
       "0 <= selected text < 10                    1602  \n",
       "10 <= selected text < 50                   2345  \n",
       "50 <= selected text < 100                  1031  \n",
       "100 <= selected text < 200                  519  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_preds = model.predict(list(x_train))\n",
    "valid_preds = model.predict(list(x_valid))\n",
    "\n",
    "train['start'] = train_preds[0].argmax(axis=-1)\n",
    "train['end'] = train_preds[1].argmax(axis=-1)\n",
    "train['prediction'] = train.apply(lambda x: decode(x['start'], x['end'], x['text'], tokenizer), axis=1)\n",
    "train[\"prediction\"] = train[\"prediction\"].apply(lambda x: '.' if x.strip() == '' else x)\n",
    "\n",
    "validation['start'] = valid_preds[0].argmax(axis=-1)\n",
    "validation['end'] = valid_preds[1].argmax(axis=-1)\n",
    "validation['prediction'] = validation.apply(lambda x: decode(x['start'], x['end'], x['text'], tokenizer), axis=1)\n",
    "validation[\"prediction\"] = validation[\"prediction\"].apply(lambda x: '.' if x.strip() == '' else x)\n",
    "  \n",
    "display(evaluate_model(train, validation))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set\n"
     ]
    },
    {
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       "       textID                                               text  \\\n",
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       "1  8022534fc2     thankyou, yes. off to a pre-wedding party now.   \n",
       "2  f7beb13214                            this is how we do itttt   \n",
       "3  89cdcbfb73  everyone's abandoning the office... i still ha...   \n",
       "4  29a4bb11d9  acquiring a centos live cd, now where did i pu...   \n",
       "5  b456369c87   alough i will hold you to your word now for w...   \n",
       "6  4e815dd602                       im not at litle oven tonight   \n",
       "7  3ef24ff6fa                           tgif. i don't feel good.   \n",
       "8  f3b0e2db98  twenty20 cricket, beer and sunshine very good....   \n",
       "9  bb97edb997                       off to bed, night every onee   \n",
       "\n",
       "                                       selected_text sentiment  text_len  \\\n",
       "0                                              sorry  negative        21   \n",
       "1                                          Thankyou,  positive        47   \n",
       "2                            this is how we do itttt   neutral        23   \n",
       "3  everyone's abandoning the office... I still ha...   neutral        62   \n",
       "4  I would like to boot from that. I wonder if I ...   neutral       126   \n",
       "5  Alough I will hold you to your word now for wh...   neutral        61   \n",
       "6                       im not at litle oven tonight   neutral        29   \n",
       "7                                   don't feel good.  negative        24   \n",
       "8  Twenty20 cricket, beer and sunshine very good....  positive        93   \n",
       "9                       off to bed, night every onee   neutral        28   \n",
       "\n",
       "   selected_text_len    set  start  end  \\\n",
       "0                  5  train      6    8   \n",
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       "2                 23  train      1    8   \n",
       "3                 62  train      1    6   \n",
       "4                 61  train      1    3   \n",
       "5                 60  train      1   11   \n",
       "6                 28  train      1    8   \n",
       "7                 16  train      1   11   \n",
       "8                 93  train      8    9   \n",
       "9                 28  train      1    8   \n",
       "\n",
       "                                     prediction   jaccard  \n",
       "0                                  m sorry then  0.333333  \n",
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       "8                                        good.   0.066667  \n",
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     "metadata": {},
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    {
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     "text": [
      "Validation set\n"
     ]
    },
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       "      <td>At the pub with the dog but seems to have misp...</td>\n",
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       "      <td>4c1897aa51</td>\n",
       "      <td>ugh, still sick. this calgary cold has lasted ...</td>\n",
       "      <td>Ugh, still sick. This Calgary cold has lasted ...</td>\n",
       "      <td>negative</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>validation</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>ugh, still sick</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21992</th>\n",
       "      <td>daf57be394</td>\n",
       "      <td>rt  #barcampbkk3 for entertainment at night, b...</td>\n",
       "      <td>enjoy</td>\n",
       "      <td>positive</td>\n",
       "      <td>123</td>\n",
       "      <td>5</td>\n",
       "      <td>validation</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>way</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21993</th>\n",
       "      <td>1381ed35a5</td>\n",
       "      <td>you two are hilaaaaarious, i love it. vernae...</td>\n",
       "      <td>you two are hilaaaaarious, I love it.</td>\n",
       "      <td>positive</td>\n",
       "      <td>69</td>\n",
       "      <td>37</td>\n",
       "      <td>validation</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>, i love</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21994</th>\n",
       "      <td>424754c08d</td>\n",
       "      <td>_iain well my team couldn't</td>\n",
       "      <td>_iain well my team couldn't</td>\n",
       "      <td>neutral</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>validation</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>_iain well my team couldn't</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>21995</th>\n",
       "      <td>981713769d</td>\n",
       "      <td>gotta drop off some car parts for a buddy to p...</td>\n",
       "      <td>Gotta drop off some car parts for a buddy to p...</td>\n",
       "      <td>neutral</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>validation</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>gotta drop off some car parts</td>\n",
       "      <td>0.375000</td>\n",
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       "    <tr>\n",
       "      <th>21996</th>\n",
       "      <td>8aef9288d8</td>\n",
       "      <td>just read up on your mom   hope she's okay!!</td>\n",
       "      <td>Hope she's okay!</td>\n",
       "      <td>positive</td>\n",
       "      <td>45</td>\n",
       "      <td>16</td>\n",
       "      <td>validation</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>hope she</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21997</th>\n",
       "      <td>db5005c4d5</td>\n",
       "      <td>that's sad. trauma... future serial killer...</td>\n",
       "      <td>s sad</td>\n",
       "      <td>negative</td>\n",
       "      <td>47</td>\n",
       "      <td>5</td>\n",
       "      <td>validation</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>that's sad.</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           textID                                               text  \\\n",
       "21988  e45d640ba0     is sooo stressed about everything i have to do   \n",
       "21989  dfa0d5ca14                            i just dyededed my hair   \n",
       "21990  051f106b59  at the pub with the dog but seems to have misp...   \n",
       "21991  4c1897aa51  ugh, still sick. this calgary cold has lasted ...   \n",
       "21992  daf57be394  rt  #barcampbkk3 for entertainment at night, b...   \n",
       "21993  1381ed35a5    you two are hilaaaaarious, i love it. vernae...   \n",
       "21994  424754c08d                        _iain well my team couldn't   \n",
       "21995  981713769d  gotta drop off some car parts for a buddy to p...   \n",
       "21996  8aef9288d8       just read up on your mom   hope she's okay!!   \n",
       "21997  db5005c4d5      that's sad. trauma... future serial killer...   \n",
       "\n",
       "                                           selected_text sentiment  text_len  \\\n",
       "21988     is sooo stressed about everything I have to do  negative        46   \n",
       "21989                            I just dyededed my hair   neutral        23   \n",
       "21990  At the pub with the dog but seems to have misp...   neutral        70   \n",
       "21991  Ugh, still sick. This Calgary cold has lasted ...  negative        54   \n",
       "21992                                              enjoy  positive       123   \n",
       "21993              you two are hilaaaaarious, I love it.  positive        69   \n",
       "21994                        _iain well my team couldn't   neutral        27   \n",
       "21995  Gotta drop off some car parts for a buddy to p...   neutral       103   \n",
       "21996                                   Hope she's okay!  positive        45   \n",
       "21997                                              s sad  negative        47   \n",
       "\n",
       "       selected_text_len         set  start  end  \\\n",
       "21988                 46  validation      4    4   \n",
       "21989                 23  validation      1    6   \n",
       "21990                 70  validation      1   13   \n",
       "21991                 54  validation      1    5   \n",
       "21992                  5  validation     24   24   \n",
       "21993                 37  validation      9   11   \n",
       "21994                 27  validation      1    8   \n",
       "21995                103  validation      1    6   \n",
       "21996                 16  validation      7    8   \n",
       "21997                  5  validation      1    5   \n",
       "\n",
       "                                              prediction   jaccard  \n",
       "21988                                          stressed   0.111111  \n",
       "21989                            i just dyededed my hair  1.000000  \n",
       "21990  at the pub with the dog but seems to have misp...  0.916667  \n",
       "21991                                    ugh, still sick  0.200000  \n",
       "21992                                               way   0.000000  \n",
       "21993                                          , i love   0.250000  \n",
       "21994                        _iain well my team couldn't  1.000000  \n",
       "21995                     gotta drop off some car parts   0.375000  \n",
       "21996                                           hope she  0.250000  \n",
       "21997                                       that's sad.   0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Train set')\n",
    "display(train.head(10))\n",
    "\n",
    "print('Validation set')\n",
    "display(validation.head(10))"
   ]
  }
 ],
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