view_database.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train': {'file_path': 'https://raw.githubusercontent.com/google-research-datasets/gap-coreference/master/gap-test.tsv',\n",
       "  'pickle_path': '../temp_result/train_data.pkl',\n",
       "  'pickle_path_augument': '../temp_result/train_data_a.pkl'},\n",
       " 'test': {'file_path': 'https://raw.githubusercontent.com/google-research-datasets/gap-coreference/master/gap-development.tsv',\n",
       "  'pickle_path': '../temp_result/test_data.pkl',\n",
       "  'pickle_path_augument': '../temp_result/test_data_a.pkl'},\n",
       " 'valid': {'file_path': 'https://raw.githubusercontent.com/google-research-datasets/gap-coreference/master/gap-validation.tsv',\n",
       "  'pickle_path': '../temp_result/valid_data.pkl',\n",
       "  'pickle_path_augument': '../temp_result/valid_data_a.pkl'}}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils.get_settings import parse\n",
    "import pandas as pd\n",
    "\n",
    "settings = parse(\"./utils\")\n",
    "\n",
    "settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_pickle(settings[\"train\"][\"pickle_path\"][1:])\n",
    "test_data = pd.read_pickle(settings[\"test\"][\"pickle_path\"][1:])\n",
    "valid_data = pd.read_pickle(settings[\"valid\"][\"pickle_path\"][1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>ID</th>\n",
       "      <th>Text</th>\n",
       "      <th>Pronoun</th>\n",
       "      <th>Pronoun-offset</th>\n",
       "      <th>A</th>\n",
       "      <th>A-offset</th>\n",
       "      <th>A-coref</th>\n",
       "      <th>B</th>\n",
       "      <th>B-offset</th>\n",
       "      <th>B-coref</th>\n",
       "      <th>...</th>\n",
       "      <th>B_vector_bert_1024_mean</th>\n",
       "      <th>pron_vector_bert_1024_mean</th>\n",
       "      <th>product_vector_A_bert_1024</th>\n",
       "      <th>product_vector_B_bert_1024</th>\n",
       "      <th>A_vector_bert_256_mean</th>\n",
       "      <th>B_vector_bert_256_mean</th>\n",
       "      <th>pron_vector_bert_256_mean</th>\n",
       "      <th>product_vector_A_bert_256</th>\n",
       "      <th>product_vector_B_bert_256</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>test-1996</td>\n",
       "      <td>The sole exception was Wimbledon, where she pl...</td>\n",
       "      <td>She</td>\n",
       "      <td>479</td>\n",
       "      <td>Goolagong Cawley</td>\n",
       "      <td>400</td>\n",
       "      <td>True</td>\n",
       "      <td>Peggy Michel</td>\n",
       "      <td>432</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>[-0.34410498, -0.23151395, 0.4869321, -0.07703...</td>\n",
       "      <td>[-0.7649605, -0.4366843, 0.38759252, 0.5346223...</td>\n",
       "      <td>[0.11406859, 0.03454206, 0.2283183, 0.04782291...</td>\n",
       "      <td>[0.26322672, 0.10109851, 0.18873124, -0.041186...</td>\n",
       "      <td>[-0.171403, -0.4057482, -0.47666842, -0.270443...</td>\n",
       "      <td>[-0.47419462, 0.5055274, -0.074567735, 0.30300...</td>\n",
       "      <td>[-0.13855383, -0.1971774, 0.37948307, 0.144610...</td>\n",
       "      <td>[0.023748543, 0.08000437, -0.1808876, -0.03910...</td>\n",
       "      <td>[0.06570148, -0.09967857, -0.028297193, 0.0438...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>test-1997</td>\n",
       "      <td>According to news reports, both Moore and Fily...</td>\n",
       "      <td>her</td>\n",
       "      <td>338</td>\n",
       "      <td>Esther Sheryl Wood</td>\n",
       "      <td>263</td>\n",
       "      <td>True</td>\n",
       "      <td>Barbara Morgan</td>\n",
       "      <td>404</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>[-0.34959722, 0.15987018, 0.21756673, 0.148476...</td>\n",
       "      <td>[-0.14545152, -0.01811517, 0.8394235, -0.93023...</td>\n",
       "      <td>[0.06410436, -0.0016351662, 0.12631176, 0.1000...</td>\n",
       "      <td>[0.050849445, -0.0028960756, 0.18263061, -0.13...</td>\n",
       "      <td>[0.56031704, 0.03621099, -0.5594447, 0.0859372...</td>\n",
       "      <td>[-0.5140588, 0.19695783, -0.0828488, 0.2230080...</td>\n",
       "      <td>[0.6599262, 0.74836516, -0.17937782, 0.5602119...</td>\n",
       "      <td>[0.36976787, 0.027099045, 0.100351974, 0.04814...</td>\n",
       "      <td>[-0.33924088, 0.14739637, 0.014861238, 0.12493...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>test-1998</td>\n",
       "      <td>In June 2009, due to the popularity of the Sab...</td>\n",
       "      <td>She</td>\n",
       "      <td>328</td>\n",
       "      <td>Kayla</td>\n",
       "      <td>364</td>\n",
       "      <td>True</td>\n",
       "      <td>Natasha Henstridge</td>\n",
       "      <td>412</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>[-0.16807513, -0.124387555, 0.22357456, 0.3628...</td>\n",
       "      <td>[-0.8260292, -0.59548515, 0.04561778, -0.17821...</td>\n",
       "      <td>[-0.09484517, -0.080319546, -0.0022544423, -0....</td>\n",
       "      <td>[0.13883497, 0.074070945, 0.010198976, -0.0646...</td>\n",
       "      <td>[0.6203897, 0.4300091, 0.30379778, 1.3983557, ...</td>\n",
       "      <td>[0.5873137, 0.12510478, 0.081058554, 0.6736881...</td>\n",
       "      <td>[0.24391061, -0.6219016, -0.14943314, 0.694560...</td>\n",
       "      <td>[0.15131964, -0.26742333, -0.045397457, 0.9712...</td>\n",
       "      <td>[0.14325204, -0.07780286, -0.012112834, 0.4679...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>test-1999</td>\n",
       "      <td>She was delivered to the Norwegian passenger s...</td>\n",
       "      <td>she</td>\n",
       "      <td>305</td>\n",
       "      <td>Irma</td>\n",
       "      <td>255</td>\n",
       "      <td>True</td>\n",
       "      <td>Bergen</td>\n",
       "      <td>274</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>[-0.30956715, 0.79081154, 0.31109822, 0.405848...</td>\n",
       "      <td>[-0.97279143, 0.2389887, -0.22996032, -0.00321...</td>\n",
       "      <td>[0.75074035, -0.050938085, 0.077419795, -0.000...</td>\n",
       "      <td>[0.30114427, 0.18899502, -0.071540244, -0.0013...</td>\n",
       "      <td>[-0.69979453, -0.44887957, -0.21628278, -0.509...</td>\n",
       "      <td>[-0.81550246, -0.13277012, 0.50108314, -0.4330...</td>\n",
       "      <td>[-1.2459968, 0.3374083, -0.12519275, -0.435292...</td>\n",
       "      <td>[0.87194175, -0.1514557, 0.027077036, 0.221742...</td>\n",
       "      <td>[1.0161135, -0.04479774, -0.062731974, 0.18848...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>test-2000</td>\n",
       "      <td>Meg and Vicky each have three siblings, and ha...</td>\n",
       "      <td>her</td>\n",
       "      <td>275</td>\n",
       "      <td>Vicky Austin</td>\n",
       "      <td>217</td>\n",
       "      <td>True</td>\n",
       "      <td>Polly O'Keefe</td>\n",
       "      <td>260</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>[-0.65313727, 0.0947511, -0.21495889, 0.017656...</td>\n",
       "      <td>[-0.56508684, 0.04315004, -0.17839415, -0.3307...</td>\n",
       "      <td>[0.32946235, 0.0031793315, 0.01654137, 0.07624...</td>\n",
       "      <td>[0.36907926, 0.0040885136, 0.03834741, -0.0058...</td>\n",
       "      <td>[-0.5419043, -0.036526673, -0.13798435, -0.255...</td>\n",
       "      <td>[-0.39947483, -0.098152794, 0.25745136, -0.009...</td>\n",
       "      <td>[-0.17940697, 0.15530047, 0.05025411, -0.31306...</td>\n",
       "      <td>[0.097221404, -0.0056726094, -0.006934281, 0.0...</td>\n",
       "      <td>[0.07166857, -0.0152431745, 0.012937989, 0.002...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID                                               Text Pronoun  \\\n",
       "1995  test-1996  The sole exception was Wimbledon, where she pl...     She   \n",
       "1996  test-1997  According to news reports, both Moore and Fily...     her   \n",
       "1997  test-1998  In June 2009, due to the popularity of the Sab...     She   \n",
       "1998  test-1999  She was delivered to the Norwegian passenger s...     she   \n",
       "1999  test-2000  Meg and Vicky each have three siblings, and ha...     her   \n",
       "\n",
       "      Pronoun-offset                   A  A-offset  A-coref  \\\n",
       "1995             479    Goolagong Cawley       400     True   \n",
       "1996             338  Esther Sheryl Wood       263     True   \n",
       "1997             328               Kayla       364     True   \n",
       "1998             305                Irma       255     True   \n",
       "1999             275        Vicky Austin       217     True   \n",
       "\n",
       "                       B  B-offset  B-coref  ...  \\\n",
       "1995        Peggy Michel       432    False  ...   \n",
       "1996      Barbara Morgan       404    False  ...   \n",
       "1997  Natasha Henstridge       412    False  ...   \n",
       "1998              Bergen       274    False  ...   \n",
       "1999       Polly O'Keefe       260    False  ...   \n",
       "\n",
       "                                B_vector_bert_1024_mean  \\\n",
       "1995  [-0.34410498, -0.23151395, 0.4869321, -0.07703...   \n",
       "1996  [-0.34959722, 0.15987018, 0.21756673, 0.148476...   \n",
       "1997  [-0.16807513, -0.124387555, 0.22357456, 0.3628...   \n",
       "1998  [-0.30956715, 0.79081154, 0.31109822, 0.405848...   \n",
       "1999  [-0.65313727, 0.0947511, -0.21495889, 0.017656...   \n",
       "\n",
       "                             pron_vector_bert_1024_mean  \\\n",
       "1995  [-0.7649605, -0.4366843, 0.38759252, 0.5346223...   \n",
       "1996  [-0.14545152, -0.01811517, 0.8394235, -0.93023...   \n",
       "1997  [-0.8260292, -0.59548515, 0.04561778, -0.17821...   \n",
       "1998  [-0.97279143, 0.2389887, -0.22996032, -0.00321...   \n",
       "1999  [-0.56508684, 0.04315004, -0.17839415, -0.3307...   \n",
       "\n",
       "                             product_vector_A_bert_1024  \\\n",
       "1995  [0.11406859, 0.03454206, 0.2283183, 0.04782291...   \n",
       "1996  [0.06410436, -0.0016351662, 0.12631176, 0.1000...   \n",
       "1997  [-0.09484517, -0.080319546, -0.0022544423, -0....   \n",
       "1998  [0.75074035, -0.050938085, 0.077419795, -0.000...   \n",
       "1999  [0.32946235, 0.0031793315, 0.01654137, 0.07624...   \n",
       "\n",
       "                             product_vector_B_bert_1024  \\\n",
       "1995  [0.26322672, 0.10109851, 0.18873124, -0.041186...   \n",
       "1996  [0.050849445, -0.0028960756, 0.18263061, -0.13...   \n",
       "1997  [0.13883497, 0.074070945, 0.010198976, -0.0646...   \n",
       "1998  [0.30114427, 0.18899502, -0.071540244, -0.0013...   \n",
       "1999  [0.36907926, 0.0040885136, 0.03834741, -0.0058...   \n",
       "\n",
       "                                 A_vector_bert_256_mean  \\\n",
       "1995  [-0.171403, -0.4057482, -0.47666842, -0.270443...   \n",
       "1996  [0.56031704, 0.03621099, -0.5594447, 0.0859372...   \n",
       "1997  [0.6203897, 0.4300091, 0.30379778, 1.3983557, ...   \n",
       "1998  [-0.69979453, -0.44887957, -0.21628278, -0.509...   \n",
       "1999  [-0.5419043, -0.036526673, -0.13798435, -0.255...   \n",
       "\n",
       "                                 B_vector_bert_256_mean  \\\n",
       "1995  [-0.47419462, 0.5055274, -0.074567735, 0.30300...   \n",
       "1996  [-0.5140588, 0.19695783, -0.0828488, 0.2230080...   \n",
       "1997  [0.5873137, 0.12510478, 0.081058554, 0.6736881...   \n",
       "1998  [-0.81550246, -0.13277012, 0.50108314, -0.4330...   \n",
       "1999  [-0.39947483, -0.098152794, 0.25745136, -0.009...   \n",
       "\n",
       "                              pron_vector_bert_256_mean  \\\n",
       "1995  [-0.13855383, -0.1971774, 0.37948307, 0.144610...   \n",
       "1996  [0.6599262, 0.74836516, -0.17937782, 0.5602119...   \n",
       "1997  [0.24391061, -0.6219016, -0.14943314, 0.694560...   \n",
       "1998  [-1.2459968, 0.3374083, -0.12519275, -0.435292...   \n",
       "1999  [-0.17940697, 0.15530047, 0.05025411, -0.31306...   \n",
       "\n",
       "                              product_vector_A_bert_256  \\\n",
       "1995  [0.023748543, 0.08000437, -0.1808876, -0.03910...   \n",
       "1996  [0.36976787, 0.027099045, 0.100351974, 0.04814...   \n",
       "1997  [0.15131964, -0.26742333, -0.045397457, 0.9712...   \n",
       "1998  [0.87194175, -0.1514557, 0.027077036, 0.221742...   \n",
       "1999  [0.097221404, -0.0056726094, -0.006934281, 0.0...   \n",
       "\n",
       "                              product_vector_B_bert_256  label  \n",
       "1995  [0.06570148, -0.09967857, -0.028297193, 0.0438...      0  \n",
       "1996  [-0.33924088, 0.14739637, 0.014861238, 0.12493...      0  \n",
       "1997  [0.14325204, -0.07780286, -0.012112834, 0.4679...      0  \n",
       "1998  [1.0161135, -0.04479774, -0.062731974, 0.18848...      0  \n",
       "1999  [0.07166857, -0.0152431745, 0.012937989, 0.002...      0  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Text', 'Pronoun', 'Pronoun-offset', 'A', 'A-offset', 'A-coref',\n",
       "       'B', 'B-offset', 'B-coref', 'URL', 'vector_bert_1024', 'bert_tokens',\n",
       "       'name_list', 'token_map_bert', 'sentence_map_bert', 'A_dist_bert',\n",
       "       'B_dist_bert', 'A_pos_bert', 'B_pos_bert', 'pron_pos_bert',\n",
       "       'A_idx_bert', 'B_idx_bert', 'pron_idx_bert', 'A_vector_bert_1024',\n",
       "       'B_vector_bert_1024', 'pron_vector_bert_1024', 'topic', 'topic_A',\n",
       "       'topic_B', 'vector_bert_256', 'A_vector_bert_256', 'B_vector_bert_256',\n",
       "       'pron_vector_bert_256', 'name_idx_bert', 'neither_list',\n",
       "       'neither_idx_bert', 'name_vector_bert_1024', 'neither_vector_bert_1024',\n",
       "       'name_vector_bert_256', 'neither_vector_bert_256',\n",
       "       'A_vector_bert_1024_mean', 'B_vector_bert_1024_mean',\n",
       "       'pron_vector_bert_1024_mean', 'product_vector_A_bert_1024',\n",
       "       'product_vector_B_bert_1024', 'A_vector_bert_256_mean',\n",
       "       'B_vector_bert_256_mean', 'pron_vector_bert_256_mean',\n",
       "       'product_vector_A_bert_256', 'product_vector_B_bert_256', 'label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_a = pd.read_pickle(settings[\"train\"][\"pickle_path_augument\"][1:])\n",
    "test_data_a = pd.read_pickle(settings[\"test\"][\"pickle_path_augument\"][1:])\n",
    "valid_data_a = pd.read_pickle(settings[\"valid\"][\"pickle_path_augument\"][1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>ID</th>\n",
       "      <th>Text</th>\n",
       "      <th>Pronoun</th>\n",
       "      <th>Pronoun-offset</th>\n",
       "      <th>A</th>\n",
       "      <th>A-offset</th>\n",
       "      <th>A-coref</th>\n",
       "      <th>B</th>\n",
       "      <th>B-offset</th>\n",
       "      <th>B-coref</th>\n",
       "      <th>...</th>\n",
       "      <th>B_vector_bert_1024_mean</th>\n",
       "      <th>pron_vector_bert_1024_mean</th>\n",
       "      <th>product_vector_A_bert_1024</th>\n",
       "      <th>product_vector_B_bert_1024</th>\n",
       "      <th>label</th>\n",
       "      <th>A_vector_bert_256_mean</th>\n",
       "      <th>B_vector_bert_256_mean</th>\n",
       "      <th>pron_vector_bert_256_mean</th>\n",
       "      <th>product_vector_A_bert_256</th>\n",
       "      <th>product_vector_B_bert_256</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>test-1982</td>\n",
       "      <td>Still sure there was some sort of error in her...</td>\n",
       "      <td>her</td>\n",
       "      <td>291</td>\n",
       "      <td>Donna</td>\n",
       "      <td>249</td>\n",
       "      <td>False</td>\n",
       "      <td>Weston</td>\n",
       "      <td>80</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>[-0.282752, 0.44045272, -0.16815029, -0.126975...</td>\n",
       "      <td>[-0.34913534, 0.2077736, 0.18587857, -0.080315...</td>\n",
       "      <td>[0.18407728, 0.11221954, 0.031704463, 0.042853...</td>\n",
       "      <td>[0.09871872, 0.091514446, -0.031255536, 0.0101...</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.049995765, -0.60634166, 0.20255497, 0.6519...</td>\n",
       "      <td>[0.1495116, -0.34842277, 0.5431875, 0.5604353,...</td>\n",
       "      <td>[0.19477811, -0.13037822, 0.4184101, -0.613263...</td>\n",
       "      <td>[-0.009738081, 0.079053745, 0.08475105, -0.399...</td>\n",
       "      <td>[0.029121589, 0.045426738, 0.22727513, -0.3436...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>test-1983</td>\n",
       "      <td>Prince Edward is an important character in Nor...</td>\n",
       "      <td>her</td>\n",
       "      <td>280</td>\n",
       "      <td>Norah Lofts</td>\n",
       "      <td>43</td>\n",
       "      <td>False</td>\n",
       "      <td>Edward</td>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>[-0.37290993, 1.098882, 0.049777627, -0.186013...</td>\n",
       "      <td>[-0.43210465, 0.5562654, -0.51887923, -0.29208...</td>\n",
       "      <td>[0.21747549, -0.10036211, -0.021217879, -0.173...</td>\n",
       "      <td>[0.16113612, 0.61127, -0.025828578, 0.05433245...</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.3109052, -0.26638955, 0.18921939, -0.30856...</td>\n",
       "      <td>[-0.24005964, -0.36902916, -0.26978564, -0.839...</td>\n",
       "      <td>[-0.2885028, -0.0913441, 0.82021976, 0.0228908...</td>\n",
       "      <td>[0.08969702, 0.024333114, 0.15520148, -0.00706...</td>\n",
       "      <td>[0.069257885, 0.03370864, -0.22128351, -0.0192...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>test-1984</td>\n",
       "      <td>Cook lives in Oxfordshire with her husband, th...</td>\n",
       "      <td>Her</td>\n",
       "      <td>330</td>\n",
       "      <td>Megan Macqueen</td>\n",
       "      <td>206</td>\n",
       "      <td>False</td>\n",
       "      <td>Ian</td>\n",
       "      <td>62</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>[-0.22983211, -0.149786, -0.19198531, 0.549313...</td>\n",
       "      <td>[-0.37197685, -0.08051762, -0.25786197, -0.476...</td>\n",
       "      <td>[0.07772554, -0.013079676, 0.0030740618, -0.06...</td>\n",
       "      <td>[0.08549222, 0.012060412, 0.04950571, -0.26161...</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.087708816, -0.62591285, 0.05713142, 0.08633...</td>\n",
       "      <td>[0.2812255, -0.48392832, -0.048053026, 0.06798...</td>\n",
       "      <td>[0.15799408, -0.29900932, 0.36529776, 0.506040...</td>\n",
       "      <td>[0.013857474, 0.18715377, 0.02086998, 0.043686...</td>\n",
       "      <td>[0.044431962, 0.14469908, -0.017553663, 0.0344...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>test-1989</td>\n",
       "      <td>Sobered by the diagnosis, Sadie sacrifices her...</td>\n",
       "      <td>she</td>\n",
       "      <td>457</td>\n",
       "      <td>Dolly</td>\n",
       "      <td>367</td>\n",
       "      <td>False</td>\n",
       "      <td>Tommy</td>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>[-0.76646227, 0.37736088, 0.43795377, 0.415202...</td>\n",
       "      <td>[-1.3185914, 0.8088266, -0.21499316, 0.1407138...</td>\n",
       "      <td>[0.1726877, -0.34794542, -0.018675223, 0.04540...</td>\n",
       "      <td>[1.0106505, 0.30521953, -0.09415707, 0.0584246...</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.38088372, -0.47499472, -0.23724352, 0.17390...</td>\n",
       "      <td>[0.17742214, -0.84216654, 0.5229067, -0.234712...</td>\n",
       "      <td>[0.010341406, -0.3127415, 0.66500294, 0.174266...</td>\n",
       "      <td>[0.0039388733, 0.14855056, -0.15776764, 0.0303...</td>\n",
       "      <td>[0.0018347943, 0.2633804, 0.3477345, -0.040902...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>test-1992</td>\n",
       "      <td>She has the distinction of being the first fem...</td>\n",
       "      <td>She</td>\n",
       "      <td>363</td>\n",
       "      <td>Vivian Colobella</td>\n",
       "      <td>264</td>\n",
       "      <td>False</td>\n",
       "      <td>Kandee</td>\n",
       "      <td>247</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>[-0.751409, 0.43532157, 0.20915435, -0.4139840...</td>\n",
       "      <td>[-0.74860764, -0.44908255, 1.1458538, -0.47485...</td>\n",
       "      <td>[0.24488813, -0.09391636, 0.43098232, -0.12861...</td>\n",
       "      <td>[0.5625105, -0.19549532, 0.23966031, 0.1965840...</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.3937323, -0.091270685, -0.08154824, 0.0990...</td>\n",
       "      <td>[0.05794612, -0.50453717, 0.7336615, 0.2957242...</td>\n",
       "      <td>[0.3959269, -0.58405113, -0.43202242, -0.17479...</td>\n",
       "      <td>[-0.15588921, 0.053306747, 0.035230666, -0.017...</td>\n",
       "      <td>[0.022942428, 0.2946755, -0.31695822, -0.05169...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID                                               Text Pronoun  \\\n",
       "1440  test-1982  Still sure there was some sort of error in her...     her   \n",
       "1441  test-1983  Prince Edward is an important character in Nor...     her   \n",
       "1442  test-1984  Cook lives in Oxfordshire with her husband, th...     Her   \n",
       "1443  test-1989  Sobered by the diagnosis, Sadie sacrifices her...     she   \n",
       "1444  test-1992  She has the distinction of being the first fem...     She   \n",
       "\n",
       "      Pronoun-offset                 A  A-offset  A-coref       B  B-offset  \\\n",
       "1440             291             Donna       249    False  Weston        80   \n",
       "1441             280       Norah Lofts        43    False  Edward         7   \n",
       "1442             330    Megan Macqueen       206    False     Ian        62   \n",
       "1443             457             Dolly       367    False   Tommy        70   \n",
       "1444             363  Vivian Colobella       264    False  Kandee       247   \n",
       "\n",
       "      B-coref  ...                            B_vector_bert_1024_mean  \\\n",
       "1440    False  ...  [-0.282752, 0.44045272, -0.16815029, -0.126975...   \n",
       "1441    False  ...  [-0.37290993, 1.098882, 0.049777627, -0.186013...   \n",
       "1442    False  ...  [-0.22983211, -0.149786, -0.19198531, 0.549313...   \n",
       "1443    False  ...  [-0.76646227, 0.37736088, 0.43795377, 0.415202...   \n",
       "1444    False  ...  [-0.751409, 0.43532157, 0.20915435, -0.4139840...   \n",
       "\n",
       "                             pron_vector_bert_1024_mean  \\\n",
       "1440  [-0.34913534, 0.2077736, 0.18587857, -0.080315...   \n",
       "1441  [-0.43210465, 0.5562654, -0.51887923, -0.29208...   \n",
       "1442  [-0.37197685, -0.08051762, -0.25786197, -0.476...   \n",
       "1443  [-1.3185914, 0.8088266, -0.21499316, 0.1407138...   \n",
       "1444  [-0.74860764, -0.44908255, 1.1458538, -0.47485...   \n",
       "\n",
       "                             product_vector_A_bert_1024  \\\n",
       "1440  [0.18407728, 0.11221954, 0.031704463, 0.042853...   \n",
       "1441  [0.21747549, -0.10036211, -0.021217879, -0.173...   \n",
       "1442  [0.07772554, -0.013079676, 0.0030740618, -0.06...   \n",
       "1443  [0.1726877, -0.34794542, -0.018675223, 0.04540...   \n",
       "1444  [0.24488813, -0.09391636, 0.43098232, -0.12861...   \n",
       "\n",
       "                             product_vector_B_bert_1024 label  \\\n",
       "1440  [0.09871872, 0.091514446, -0.031255536, 0.0101...     2   \n",
       "1441  [0.16113612, 0.61127, -0.025828578, 0.05433245...     2   \n",
       "1442  [0.08549222, 0.012060412, 0.04950571, -0.26161...     2   \n",
       "1443  [1.0106505, 0.30521953, -0.09415707, 0.0584246...     2   \n",
       "1444  [0.5625105, -0.19549532, 0.23966031, 0.1965840...     2   \n",
       "\n",
       "                                 A_vector_bert_256_mean  \\\n",
       "1440  [-0.049995765, -0.60634166, 0.20255497, 0.6519...   \n",
       "1441  [-0.3109052, -0.26638955, 0.18921939, -0.30856...   \n",
       "1442  [0.087708816, -0.62591285, 0.05713142, 0.08633...   \n",
       "1443  [0.38088372, -0.47499472, -0.23724352, 0.17390...   \n",
       "1444  [-0.3937323, -0.091270685, -0.08154824, 0.0990...   \n",
       "\n",
       "                                 B_vector_bert_256_mean  \\\n",
       "1440  [0.1495116, -0.34842277, 0.5431875, 0.5604353,...   \n",
       "1441  [-0.24005964, -0.36902916, -0.26978564, -0.839...   \n",
       "1442  [0.2812255, -0.48392832, -0.048053026, 0.06798...   \n",
       "1443  [0.17742214, -0.84216654, 0.5229067, -0.234712...   \n",
       "1444  [0.05794612, -0.50453717, 0.7336615, 0.2957242...   \n",
       "\n",
       "                              pron_vector_bert_256_mean  \\\n",
       "1440  [0.19477811, -0.13037822, 0.4184101, -0.613263...   \n",
       "1441  [-0.2885028, -0.0913441, 0.82021976, 0.0228908...   \n",
       "1442  [0.15799408, -0.29900932, 0.36529776, 0.506040...   \n",
       "1443  [0.010341406, -0.3127415, 0.66500294, 0.174266...   \n",
       "1444  [0.3959269, -0.58405113, -0.43202242, -0.17479...   \n",
       "\n",
       "                              product_vector_A_bert_256  \\\n",
       "1440  [-0.009738081, 0.079053745, 0.08475105, -0.399...   \n",
       "1441  [0.08969702, 0.024333114, 0.15520148, -0.00706...   \n",
       "1442  [0.013857474, 0.18715377, 0.02086998, 0.043686...   \n",
       "1443  [0.0039388733, 0.14855056, -0.15776764, 0.0303...   \n",
       "1444  [-0.15588921, 0.053306747, 0.035230666, -0.017...   \n",
       "\n",
       "                              product_vector_B_bert_256  \n",
       "1440  [0.029121589, 0.045426738, 0.22727513, -0.3436...  \n",
       "1441  [0.069257885, 0.03370864, -0.22128351, -0.0192...  \n",
       "1442  [0.044431962, 0.14469908, -0.017553663, 0.0344...  \n",
       "1443  [0.0018347943, 0.2633804, 0.3477345, -0.040902...  \n",
       "1444  [0.022942428, 0.2946755, -0.31695822, -0.05169...  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_a.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Text', 'Pronoun', 'Pronoun-offset', 'A', 'A-offset', 'A-coref',\n",
       "       'B', 'B-offset', 'B-coref', 'URL', 'vector_bert_1024', 'bert_tokens',\n",
       "       'name_list', 'token_map_bert', 'sentence_map_bert', 'A_dist_bert',\n",
       "       'B_dist_bert', 'A_pos_bert', 'B_pos_bert', 'pron_pos_bert',\n",
       "       'A_idx_bert', 'B_idx_bert', 'pron_idx_bert', 'A_vector_bert_1024',\n",
       "       'B_vector_bert_1024', 'pron_vector_bert_1024', 'topic', 'topic_A',\n",
       "       'topic_B', 'vector_bert_256', 'A_vector_bert_256', 'B_vector_bert_256',\n",
       "       'pron_vector_bert_256', 'neither_name', 'len', 'name_idx_bert',\n",
       "       'neither_list', 'neither_idx_bert', 'name_vector_bert_1024',\n",
       "       'neither_vector_bert_1024', 'name_vector_bert_256',\n",
       "       'neither_vector_bert_256', 'A_vector_bert_1024_mean',\n",
       "       'B_vector_bert_1024_mean', 'pron_vector_bert_1024_mean',\n",
       "       'product_vector_A_bert_1024', 'product_vector_B_bert_1024', 'label',\n",
       "       'A_vector_bert_256_mean', 'B_vector_bert_256_mean',\n",
       "       'pron_vector_bert_256_mean', 'product_vector_A_bert_256',\n",
       "       'product_vector_B_bert_256'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_a.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                            1445\n",
       "Text                          1445\n",
       "Pronoun                       1445\n",
       "Pronoun-offset                1445\n",
       "A                             1445\n",
       "A-offset                      1445\n",
       "A-coref                       1445\n",
       "B                             1445\n",
       "B-offset                      1445\n",
       "B-coref                       1445\n",
       "URL                           1445\n",
       "vector_bert_1024              1445\n",
       "bert_tokens                   1445\n",
       "token_map_bert                1445\n",
       "sentence_map_bert             1445\n",
       "A_dist_bert                   1445\n",
       "B_dist_bert                   1445\n",
       "A_pos_bert                    1445\n",
       "B_pos_bert                    1445\n",
       "pron_pos_bert                 1445\n",
       "A_idx_bert                    1445\n",
       "B_idx_bert                    1445\n",
       "pron_idx_bert                 1445\n",
       "A_vector_bert_1024            1445\n",
       "B_vector_bert_1024            1445\n",
       "pron_vector_bert_1024         1445\n",
       "topic                         1445\n",
       "topic_A                       1445\n",
       "topic_B                       1445\n",
       "name_list                     1445\n",
       "name_idx_bert                 1445\n",
       "neither_list                  1445\n",
       "neither_idx_bert              1445\n",
       "name_vector_bert_1024         1445\n",
       "neither_vector_bert_1024      1445\n",
       "A_vector_bert_1024_mean       1445\n",
       "B_vector_bert_1024_mean       1445\n",
       "pron_vector_bert_1024_mean    1445\n",
       "product_vector_A_bert_1024    1445\n",
       "product_vector_B_bert_1024    1445\n",
       "label                         1445\n",
       "neither_name                  1445\n",
       "len                           1445\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_a.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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