Diagram_Values.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "adb2997a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "1606aac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = ['Criteria i', 'Criteria ii', 'Criteria iii', 'Criteria iv', 'Criteria v', 'Criteria vi', \n",
    "              'Criteria vii', 'Criteria viii', 'Criteria ix', 'Criteria x', 'Others']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "fd0128f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Criteria i',\n",
       " 'Criteria ii',\n",
       " 'Criteria iii',\n",
       " 'Criteria iv',\n",
       " 'Criteria v',\n",
       " 'Criteria vi',\n",
       " 'Criteria vii',\n",
       " 'Criteria viii',\n",
       " 'Criteria ix',\n",
       " 'Criteria x',\n",
       " 'Others']"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "baa935f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentages = pd.DataFrame(classes,columns=['OUV']).set_index('OUV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "22fac344",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Criteria i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ii</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iii</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria v</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vii</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria viii</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ix</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria x</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [Criteria i, Criteria ii, Criteria iii, Criteria iv, Criteria v, Criteria vi, Criteria vii, Criteria viii, Criteria ix, Criteria x, Others]"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92696c9f",
   "metadata": {},
   "source": [
    "## Venezia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "ad16b06d",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert = pd.read_csv('Venezia/data_storage/metadata_bert.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "fab761f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert['max_1_eq'] = bert['max_1_eq'].fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "a0069af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert = bert[bert.max_1_eq!=-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "6f372dbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp = pd.read_csv('Venezia/data_storage/metadata_compare.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "0d1f15b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp = cmp.merge(bert[['max_1_eq','max_3_eq']], how='right',left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "5640773a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp['conf_3'] = (cmp['max_3_ulmfit'] + cmp['max_3_bert'])/2\n",
    "cmp['conf_1'] = (cmp['max_1_ulmfit'] + cmp['max_1_bert'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "279cf9d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1761.000000\n",
       "mean        0.787367\n",
       "std         0.069555\n",
       "min         0.517463\n",
       "25%         0.753484\n",
       "50%         0.795530\n",
       "75%         0.836271\n",
       "max         0.940598\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "0f29c78c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1761.000000\n",
       "mean        0.427770\n",
       "std         0.094998\n",
       "min         0.203504\n",
       "25%         0.356832\n",
       "50%         0.416490\n",
       "75%         0.503618\n",
       "max         0.802082\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "d6756e3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zzbn1\\AppData\\Local\\Temp\\ipykernel_6436\\3362900713.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  cmp_conf = cmp[cmp.conf_3>=0.75][cmp.same_3>=0.5]\n"
     ]
    }
   ],
   "source": [
    "cmp_conf = cmp[cmp.conf_3>=0.75][cmp.same_3>=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "f056aaec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
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       "      <th>max_3_eq</th>\n",
       "      <th>conf_3</th>\n",
       "      <th>conf_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>51870743082</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.830680</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.362388</td>\n",
       "      <td>0.787226</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.812573</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.808953</td>\n",
       "      <td>0.360209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>51871789478</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.759217</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.638658</td>\n",
       "      <td>0.898092</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.525624</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.828654</td>\n",
       "      <td>0.519206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>51870742942</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.814499</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.396352</td>\n",
       "      <td>0.812845</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.756251</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.813672</td>\n",
       "      <td>0.370950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>51870380541</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.470955</td>\n",
       "      <td>0.865256</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.823930</td>\n",
       "      <td>0.930945</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.462165</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.898100</td>\n",
       "      <td>0.647442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>51863124066</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.247691</td>\n",
       "      <td>0.672674</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.337761</td>\n",
       "      <td>0.871130</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.853685</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.771902</td>\n",
       "      <td>0.292726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2946</th>\n",
       "      <td>4981</td>\n",
       "      <td>51233430558</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.571989</td>\n",
       "      <td>0.903156</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.313572</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.884197</td>\n",
       "      <td>0.517292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>4982</td>\n",
       "      <td>51234286630</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.571989</td>\n",
       "      <td>0.903156</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.313572</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.884197</td>\n",
       "      <td>0.517292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2948</th>\n",
       "      <td>4983</td>\n",
       "      <td>51234294135</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.571989</td>\n",
       "      <td>0.903156</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.313572</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.884197</td>\n",
       "      <td>0.517292</td>\n",
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       "    <tr>\n",
       "      <th>2949</th>\n",
       "      <td>4984</td>\n",
       "      <td>51234291635</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.571989</td>\n",
       "      <td>0.903156</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.313572</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.884197</td>\n",
       "      <td>0.517292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2950</th>\n",
       "      <td>4985</td>\n",
       "      <td>51233434888</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.571989</td>\n",
       "      <td>0.903156</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.313572</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.884197</td>\n",
       "      <td>0.517292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>756 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin        index max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "0          3  51870743082     Criteria iii      Criteria ii      Criteria iv   \n",
       "1          4  51871789478     Criteria iii      Criteria ii      Criteria iv   \n",
       "2          5  51870742942     Criteria iii      Criteria ii      Criteria iv   \n",
       "3          6  51870380541     Criteria iii      Criteria vi      Criteria iv   \n",
       "4         13  51863124066      Criteria iv       Criteria i      Criteria ii   \n",
       "...      ...          ...              ...              ...              ...   \n",
       "2946    4981  51233430558     Criteria iii      Criteria iv      Criteria ii   \n",
       "2947    4982  51234286630     Criteria iii      Criteria iv      Criteria ii   \n",
       "2948    4983  51234294135     Criteria iii      Criteria iv      Criteria ii   \n",
       "2949    4984  51234291635     Criteria iii      Criteria iv      Criteria ii   \n",
       "2950    4985  51233434888     Criteria iii      Criteria iv      Criteria ii   \n",
       "\n",
       "      max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert max_3_col_bert  \\\n",
       "0         0.358030      0.830680    Criteria ii    Criteria iv     Criteria i   \n",
       "1         0.399754      0.759217    Criteria ii     Criteria i    Criteria iv   \n",
       "2         0.345548      0.814499    Criteria ii     Criteria i    Criteria iv   \n",
       "3         0.470955      0.865256    Criteria vi   Criteria iii    Criteria iv   \n",
       "4         0.247691      0.672674    Criteria vi    Criteria iv     Criteria i   \n",
       "...            ...           ...            ...            ...            ...   \n",
       "2946      0.462594      0.865238     Criteria i    Criteria ii    Criteria iv   \n",
       "2947      0.462594      0.865238     Criteria i    Criteria ii    Criteria iv   \n",
       "2948      0.462594      0.865238     Criteria i    Criteria ii    Criteria iv   \n",
       "2949      0.462594      0.865238     Criteria i    Criteria ii    Criteria iv   \n",
       "2950      0.462594      0.865238     Criteria i    Criteria ii    Criteria iv   \n",
       "\n",
       "      max_1_bert  max_3_bert  same_1  same_3  cosine_sim max_1_eq  max_3_eq  \\\n",
       "0       0.362388    0.787226   False     0.5    0.812573     True       1.0   \n",
       "1       0.638658    0.898092   False     0.5    0.525624     True       1.0   \n",
       "2       0.396352    0.812845   False     0.5    0.756251     True       1.0   \n",
       "3       0.823930    0.930945   False     1.0    0.462165     True       0.5   \n",
       "4       0.337761    0.871130   False     0.5    0.853685    False       1.0   \n",
       "...          ...         ...     ...     ...         ...      ...       ...   \n",
       "2946    0.571989    0.903156   False     0.5    0.313572     True       1.0   \n",
       "2947    0.571989    0.903156   False     0.5    0.313572     True       1.0   \n",
       "2948    0.571989    0.903156   False     0.5    0.313572     True       1.0   \n",
       "2949    0.571989    0.903156   False     0.5    0.313572     True       1.0   \n",
       "2950    0.571989    0.903156   False     0.5    0.313572     True       1.0   \n",
       "\n",
       "        conf_3    conf_1  \n",
       "0     0.808953  0.360209  \n",
       "1     0.828654  0.519206  \n",
       "2     0.813672  0.370950  \n",
       "3     0.898100  0.647442  \n",
       "4     0.771902  0.292726  \n",
       "...        ...       ...  \n",
       "2946  0.884197  0.517292  \n",
       "2947  0.884197  0.517292  \n",
       "2948  0.884197  0.517292  \n",
       "2949  0.884197  0.517292  \n",
       "2950  0.884197  0.517292  \n",
       "\n",
       "[756 rows x 19 columns]"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff3a7896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4293015332197615"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "756/1761"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "049eccd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    756.000000\n",
       "mean       0.813555\n",
       "std        0.040878\n",
       "min        0.750090\n",
       "25%        0.780984\n",
       "50%        0.808529\n",
       "75%        0.842688\n",
       "max        0.940598\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "9fdcfa5a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    756.000000\n",
       "mean       0.453253\n",
       "std        0.089525\n",
       "min        0.273961\n",
       "25%        0.380476\n",
       "50%        0.439975\n",
       "75%        0.517356\n",
       "max        0.755868\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "c03b34ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    756.000000\n",
       "mean       0.591270\n",
       "std        0.193272\n",
       "min        0.500000\n",
       "25%        0.500000\n",
       "50%        0.500000\n",
       "75%        0.500000\n",
       "max        1.000000\n",
       "Name: same_3, dtype: float64"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['same_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "d62008d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       756\n",
       "unique        2\n",
       "top       False\n",
       "freq        541\n",
       "Name: same_1, dtype: object"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['same_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "ac7850fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iv      0.240300\n",
       "Criteria iii     0.213845\n",
       "Criteria ii      0.202381\n",
       "Criteria vi      0.183862\n",
       "Criteria i       0.147707\n",
       "Criteria v       0.008598\n",
       "Criteria vii     0.001764\n",
       "Criteria x       0.000882\n",
       "Criteria ix      0.000441\n",
       "Criteria viii    0.000220\n",
       "dtype: float64"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf['max_1_col_bert'],\n",
    "           cmp_conf['max_2_col_bert'],\n",
    "           cmp_conf['max_3_col_bert'],\n",
    "           cmp_conf['max_1_col_ulmfit'],\n",
    "           cmp_conf['max_2_col_ulmfit'],\n",
    "           cmp_conf['max_3_col_ulmfit']]).value_counts()/(756*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "89725840",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentages['VEN']=pd.concat([cmp_conf['max_1_col_bert'],\n",
    "           cmp_conf['max_2_col_bert'],\n",
    "           cmp_conf['max_3_col_bert'],\n",
    "           cmp_conf['max_1_col_ulmfit'],\n",
    "           cmp_conf['max_2_col_ulmfit'],\n",
    "           cmp_conf['max_3_col_ulmfit']]).value_counts()/(756*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "a749d549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>VEN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUV</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Criteria i</th>\n",
       "      <td>0.147707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ii</th>\n",
       "      <td>0.202381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iii</th>\n",
       "      <td>0.213845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iv</th>\n",
       "      <td>0.240300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria v</th>\n",
       "      <td>0.008598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vi</th>\n",
       "      <td>0.183862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vii</th>\n",
       "      <td>0.001764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria viii</th>\n",
       "      <td>0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ix</th>\n",
       "      <td>0.000441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria x</th>\n",
       "      <td>0.000882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    VEN\n",
       "OUV                    \n",
       "Criteria i     0.147707\n",
       "Criteria ii    0.202381\n",
       "Criteria iii   0.213845\n",
       "Criteria iv    0.240300\n",
       "Criteria v     0.008598\n",
       "Criteria vi    0.183862\n",
       "Criteria vii   0.001764\n",
       "Criteria viii  0.000220\n",
       "Criteria ix    0.000441\n",
       "Criteria x     0.000882\n",
       "Others              NaN"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "c4429921",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iv      1090\n",
       "Criteria iii      970\n",
       "Criteria ii       918\n",
       "Criteria vi       834\n",
       "Criteria i        670\n",
       "Criteria v         39\n",
       "Criteria vii        8\n",
       "Criteria x          4\n",
       "Criteria ix         2\n",
       "Criteria viii       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf['max_1_col_bert'],\n",
    "           cmp_conf['max_2_col_bert'],\n",
    "           cmp_conf['max_3_col_bert'],\n",
    "           cmp_conf['max_1_col_ulmfit'],\n",
    "           cmp_conf['max_2_col_ulmfit'],\n",
    "           cmp_conf['max_3_col_ulmfit']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "393215cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert_s = pd.read_csv('Venezia/data_storage/sentences_bert.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "064947d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>...</th>\n",
       "      <th>BERT_766</th>\n",
       "      <th>BERT_767</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice, bri...</td>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>0.108202</td>\n",
       "      <td>0.362388</td>\n",
       "      <td>0.100627</td>\n",
       "      <td>0.316636</td>\n",
       "      <td>0.007993</td>\n",
       "      <td>0.080306</td>\n",
       "      <td>...</td>\n",
       "      <td>0.821890</td>\n",
       "      <td>-0.223941</td>\n",
       "      <td>0.362388</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.316636</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.108202</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.362388</td>\n",
       "      <td>0.787226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice</td>\n",
       "      <td>4</td>\n",
       "      <td>en</td>\n",
       "      <td>0.187734</td>\n",
       "      <td>0.638658</td>\n",
       "      <td>0.045533</td>\n",
       "      <td>0.071700</td>\n",
       "      <td>0.003141</td>\n",
       "      <td>0.030455</td>\n",
       "      <td>...</td>\n",
       "      <td>0.863881</td>\n",
       "      <td>0.210609</td>\n",
       "      <td>0.638658</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.187734</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.071700</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.638658</td>\n",
       "      <td>0.898092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice, water</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "      <td>0.268723</td>\n",
       "      <td>0.396352</td>\n",
       "      <td>0.098409</td>\n",
       "      <td>0.147769</td>\n",
       "      <td>0.006426</td>\n",
       "      <td>0.052501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.772562</td>\n",
       "      <td>0.284602</td>\n",
       "      <td>0.396352</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.268723</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.147769</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.396352</td>\n",
       "      <td>0.812845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>THE KNIGHT WITH A GRIM LOOK   The Carnival of ...</td>\n",
       "      <td>6</td>\n",
       "      <td>en</td>\n",
       "      <td>0.322504</td>\n",
       "      <td>0.042041</td>\n",
       "      <td>0.097346</td>\n",
       "      <td>0.059993</td>\n",
       "      <td>0.008586</td>\n",
       "      <td>0.424027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.468618</td>\n",
       "      <td>0.405893</td>\n",
       "      <td>0.424027</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.322504</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.097346</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.424027</td>\n",
       "      <td>0.843877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Who has never heard of it</td>\n",
       "      <td>6</td>\n",
       "      <td>en</td>\n",
       "      <td>0.339140</td>\n",
       "      <td>0.073987</td>\n",
       "      <td>0.281514</td>\n",
       "      <td>0.056507</td>\n",
       "      <td>0.011259</td>\n",
       "      <td>0.088447</td>\n",
       "      <td>...</td>\n",
       "      <td>0.206610</td>\n",
       "      <td>0.767832</td>\n",
       "      <td>0.339140</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.281514</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.088447</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.339140</td>\n",
       "      <td>0.709100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4552</th>\n",
       "      <td>1228</td>\n",
       "      <td>It has tags of venice, murano</td>\n",
       "      <td>4837</td>\n",
       "      <td>other</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.059518</td>\n",
       "      <td>0.024350</td>\n",
       "      <td>0.019918</td>\n",
       "      <td>0.001673</td>\n",
       "      <td>0.020364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.515820</td>\n",
       "      <td>0.863784</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.059518</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.024350</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.933448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4553</th>\n",
       "      <td>1229</td>\n",
       "      <td>It has tags of venice, murano</td>\n",
       "      <td>4838</td>\n",
       "      <td>other</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.059518</td>\n",
       "      <td>0.024350</td>\n",
       "      <td>0.019918</td>\n",
       "      <td>0.001673</td>\n",
       "      <td>0.020364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.515820</td>\n",
       "      <td>0.863784</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.059518</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.024350</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.933448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4554</th>\n",
       "      <td>1230</td>\n",
       "      <td>It has title of Gondolas on Canale Grande, Venice</td>\n",
       "      <td>4842</td>\n",
       "      <td>other</td>\n",
       "      <td>0.836220</td>\n",
       "      <td>0.064082</td>\n",
       "      <td>0.011721</td>\n",
       "      <td>0.047904</td>\n",
       "      <td>0.001575</td>\n",
       "      <td>0.015826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.765462</td>\n",
       "      <td>0.888029</td>\n",
       "      <td>0.836220</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.064082</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.047904</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.836220</td>\n",
       "      <td>0.948206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4555</th>\n",
       "      <td>1231</td>\n",
       "      <td>It has title of Frontage</td>\n",
       "      <td>4857</td>\n",
       "      <td>other</td>\n",
       "      <td>0.266114</td>\n",
       "      <td>0.056899</td>\n",
       "      <td>0.063980</td>\n",
       "      <td>0.543409</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.030839</td>\n",
       "      <td>...</td>\n",
       "      <td>0.738694</td>\n",
       "      <td>0.520473</td>\n",
       "      <td>0.543409</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.266114</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.063980</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.543409</td>\n",
       "      <td>0.873502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4556</th>\n",
       "      <td>1233</td>\n",
       "      <td>It has title of Venice, at sunset.</td>\n",
       "      <td>4866</td>\n",
       "      <td>other</td>\n",
       "      <td>0.308849</td>\n",
       "      <td>0.037880</td>\n",
       "      <td>0.163122</td>\n",
       "      <td>0.223433</td>\n",
       "      <td>0.013911</td>\n",
       "      <td>0.162307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438482</td>\n",
       "      <td>0.730638</td>\n",
       "      <td>0.308849</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.223433</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.163122</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.308849</td>\n",
       "      <td>0.695404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4557 rows × 791 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                                           sentence  origin   lang  \\\n",
       "0         0  It has tags of adriaticsea, italy, venice, bri...       3     en   \n",
       "1         1          It has tags of adriaticsea, italy, venice       4     en   \n",
       "2         2   It has tags of adriaticsea, italy, venice, water       5     en   \n",
       "3         3  THE KNIGHT WITH A GRIM LOOK   The Carnival of ...       6     en   \n",
       "4         4                          Who has never heard of it       6     en   \n",
       "...     ...                                                ...     ...    ...   \n",
       "4552   1228                      It has tags of venice, murano    4837  other   \n",
       "4553   1229                      It has tags of venice, murano    4838  other   \n",
       "4554   1230  It has title of Gondolas on Canale Grande, Venice    4842  other   \n",
       "4555   1231                           It has title of Frontage    4857  other   \n",
       "4556   1233                 It has title of Venice, at sunset.    4866  other   \n",
       "\n",
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.108202     0.362388      0.100627     0.316636    0.007993   \n",
       "1       0.187734     0.638658      0.045533     0.071700    0.003141   \n",
       "2       0.268723     0.396352      0.098409     0.147769    0.006426   \n",
       "3       0.322504     0.042041      0.097346     0.059993    0.008586   \n",
       "4       0.339140     0.073987      0.281514     0.056507    0.011259   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "4552    0.849580     0.059518      0.024350     0.019918    0.001673   \n",
       "4553    0.849580     0.059518      0.024350     0.019918    0.001673   \n",
       "4554    0.836220     0.064082      0.011721     0.047904    0.001575   \n",
       "4555    0.266114     0.056899      0.063980     0.543409    0.009950   \n",
       "4556    0.308849     0.037880      0.163122     0.223433    0.013911   \n",
       "\n",
       "      Criteria vi  ...  BERT_766  BERT_767  max_1_val    max_1_col  max_2_val  \\\n",
       "0        0.080306  ...  0.821890 -0.223941   0.362388  Criteria ii   0.316636   \n",
       "1        0.030455  ...  0.863881  0.210609   0.638658  Criteria ii   0.187734   \n",
       "2        0.052501  ...  0.772562  0.284602   0.396352  Criteria ii   0.268723   \n",
       "3        0.424027  ...  0.468618  0.405893   0.424027  Criteria vi   0.322504   \n",
       "4        0.088447  ...  0.206610  0.767832   0.339140   Criteria i   0.281514   \n",
       "...           ...  ...       ...       ...        ...          ...        ...   \n",
       "4552     0.020364  ...  0.515820  0.863784   0.849580   Criteria i   0.059518   \n",
       "4553     0.020364  ...  0.515820  0.863784   0.849580   Criteria i   0.059518   \n",
       "4554     0.015826  ...  0.765462  0.888029   0.836220   Criteria i   0.064082   \n",
       "4555     0.030839  ...  0.738694  0.520473   0.543409  Criteria iv   0.266114   \n",
       "4556     0.162307  ...  0.438482  0.730638   0.308849   Criteria i   0.223433   \n",
       "\n",
       "         max_2_col  max_3_val     max_3_col     max_1     max_3  \n",
       "0      Criteria iv   0.108202    Criteria i  0.362388  0.787226  \n",
       "1       Criteria i   0.071700   Criteria iv  0.638658  0.898092  \n",
       "2       Criteria i   0.147769   Criteria iv  0.396352  0.812845  \n",
       "3       Criteria i   0.097346  Criteria iii  0.424027  0.843877  \n",
       "4     Criteria iii   0.088447   Criteria vi  0.339140  0.709100  \n",
       "...            ...        ...           ...       ...       ...  \n",
       "4552   Criteria ii   0.024350  Criteria iii  0.849580  0.933448  \n",
       "4553   Criteria ii   0.024350  Criteria iii  0.849580  0.933448  \n",
       "4554   Criteria ii   0.047904   Criteria iv  0.836220  0.948206  \n",
       "4555    Criteria i   0.063980  Criteria iii  0.543409  0.873502  \n",
       "4556   Criteria iv   0.163122  Criteria iii  0.308849  0.695404  \n",
       "\n",
       "[4557 rows x 791 columns]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "53760b31",
   "metadata": {},
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>...</th>\n",
       "      <th>BERT_766</th>\n",
       "      <th>BERT_767</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice, bri...</td>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>0.108202</td>\n",
       "      <td>0.362388</td>\n",
       "      <td>0.100627</td>\n",
       "      <td>0.316636</td>\n",
       "      <td>0.007993</td>\n",
       "      <td>0.080306</td>\n",
       "      <td>...</td>\n",
       "      <td>0.821890</td>\n",
       "      <td>-0.223941</td>\n",
       "      <td>0.362388</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.316636</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.108202</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.362388</td>\n",
       "      <td>0.787226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice</td>\n",
       "      <td>4</td>\n",
       "      <td>en</td>\n",
       "      <td>0.187734</td>\n",
       "      <td>0.638658</td>\n",
       "      <td>0.045533</td>\n",
       "      <td>0.071700</td>\n",
       "      <td>0.003141</td>\n",
       "      <td>0.030455</td>\n",
       "      <td>...</td>\n",
       "      <td>0.863881</td>\n",
       "      <td>0.210609</td>\n",
       "      <td>0.638658</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.187734</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.071700</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.638658</td>\n",
       "      <td>0.898092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice, water</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "      <td>0.268723</td>\n",
       "      <td>0.396352</td>\n",
       "      <td>0.098409</td>\n",
       "      <td>0.147769</td>\n",
       "      <td>0.006426</td>\n",
       "      <td>0.052501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.772562</td>\n",
       "      <td>0.284602</td>\n",
       "      <td>0.396352</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.268723</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.147769</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.396352</td>\n",
       "      <td>0.812845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>THE KNIGHT WITH A GRIM LOOK   The Carnival of ...</td>\n",
       "      <td>6</td>\n",
       "      <td>en</td>\n",
       "      <td>0.322504</td>\n",
       "      <td>0.042041</td>\n",
       "      <td>0.097346</td>\n",
       "      <td>0.059993</td>\n",
       "      <td>0.008586</td>\n",
       "      <td>0.424027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.468618</td>\n",
       "      <td>0.405893</td>\n",
       "      <td>0.424027</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.322504</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.097346</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.424027</td>\n",
       "      <td>0.843877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Who has never heard of it</td>\n",
       "      <td>6</td>\n",
       "      <td>en</td>\n",
       "      <td>0.339140</td>\n",
       "      <td>0.073987</td>\n",
       "      <td>0.281514</td>\n",
       "      <td>0.056507</td>\n",
       "      <td>0.011259</td>\n",
       "      <td>0.088447</td>\n",
       "      <td>...</td>\n",
       "      <td>0.206610</td>\n",
       "      <td>0.767832</td>\n",
       "      <td>0.339140</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.281514</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.088447</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.339140</td>\n",
       "      <td>0.709100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4550</th>\n",
       "      <td>1226</td>\n",
       "      <td>It has title of Our apartment in Venice</td>\n",
       "      <td>4805</td>\n",
       "      <td>other</td>\n",
       "      <td>0.653249</td>\n",
       "      <td>0.147048</td>\n",
       "      <td>0.028190</td>\n",
       "      <td>0.109391</td>\n",
       "      <td>0.002627</td>\n",
       "      <td>0.032011</td>\n",
       "      <td>...</td>\n",
       "      <td>0.702520</td>\n",
       "      <td>0.687211</td>\n",
       "      <td>0.653249</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.147048</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.109391</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.653249</td>\n",
       "      <td>0.909687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4551</th>\n",
       "      <td>1227</td>\n",
       "      <td>It has tags of venice, murano</td>\n",
       "      <td>4836</td>\n",
       "      <td>other</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.059518</td>\n",
       "      <td>0.024350</td>\n",
       "      <td>0.019918</td>\n",
       "      <td>0.001673</td>\n",
       "      <td>0.020364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.515820</td>\n",
       "      <td>0.863784</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.059518</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.024350</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.933448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4554</th>\n",
       "      <td>1230</td>\n",
       "      <td>It has title of Gondolas on Canale Grande, Venice</td>\n",
       "      <td>4842</td>\n",
       "      <td>other</td>\n",
       "      <td>0.836220</td>\n",
       "      <td>0.064082</td>\n",
       "      <td>0.011721</td>\n",
       "      <td>0.047904</td>\n",
       "      <td>0.001575</td>\n",
       "      <td>0.015826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.765462</td>\n",
       "      <td>0.888029</td>\n",
       "      <td>0.836220</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.064082</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.047904</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.836220</td>\n",
       "      <td>0.948206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4555</th>\n",
       "      <td>1231</td>\n",
       "      <td>It has title of Frontage</td>\n",
       "      <td>4857</td>\n",
       "      <td>other</td>\n",
       "      <td>0.266114</td>\n",
       "      <td>0.056899</td>\n",
       "      <td>0.063980</td>\n",
       "      <td>0.543409</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.030839</td>\n",
       "      <td>...</td>\n",
       "      <td>0.738694</td>\n",
       "      <td>0.520473</td>\n",
       "      <td>0.543409</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.266114</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.063980</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.543409</td>\n",
       "      <td>0.873502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4556</th>\n",
       "      <td>1233</td>\n",
       "      <td>It has title of Venice, at sunset.</td>\n",
       "      <td>4866</td>\n",
       "      <td>other</td>\n",
       "      <td>0.308849</td>\n",
       "      <td>0.037880</td>\n",
       "      <td>0.163122</td>\n",
       "      <td>0.223433</td>\n",
       "      <td>0.013911</td>\n",
       "      <td>0.162307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438482</td>\n",
       "      <td>0.730638</td>\n",
       "      <td>0.308849</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.223433</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.163122</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.308849</td>\n",
       "      <td>0.695404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3249 rows × 791 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                                           sentence  origin   lang  \\\n",
       "0         0  It has tags of adriaticsea, italy, venice, bri...       3     en   \n",
       "1         1          It has tags of adriaticsea, italy, venice       4     en   \n",
       "2         2   It has tags of adriaticsea, italy, venice, water       5     en   \n",
       "3         3  THE KNIGHT WITH A GRIM LOOK   The Carnival of ...       6     en   \n",
       "4         4                          Who has never heard of it       6     en   \n",
       "...     ...                                                ...     ...    ...   \n",
       "4550   1226            It has title of Our apartment in Venice    4805  other   \n",
       "4551   1227                      It has tags of venice, murano    4836  other   \n",
       "4554   1230  It has title of Gondolas on Canale Grande, Venice    4842  other   \n",
       "4555   1231                           It has title of Frontage    4857  other   \n",
       "4556   1233                 It has title of Venice, at sunset.    4866  other   \n",
       "\n",
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.108202     0.362388      0.100627     0.316636    0.007993   \n",
       "1       0.187734     0.638658      0.045533     0.071700    0.003141   \n",
       "2       0.268723     0.396352      0.098409     0.147769    0.006426   \n",
       "3       0.322504     0.042041      0.097346     0.059993    0.008586   \n",
       "4       0.339140     0.073987      0.281514     0.056507    0.011259   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "4550    0.653249     0.147048      0.028190     0.109391    0.002627   \n",
       "4551    0.849580     0.059518      0.024350     0.019918    0.001673   \n",
       "4554    0.836220     0.064082      0.011721     0.047904    0.001575   \n",
       "4555    0.266114     0.056899      0.063980     0.543409    0.009950   \n",
       "4556    0.308849     0.037880      0.163122     0.223433    0.013911   \n",
       "\n",
       "      Criteria vi  ...  BERT_766  BERT_767  max_1_val    max_1_col  max_2_val  \\\n",
       "0        0.080306  ...  0.821890 -0.223941   0.362388  Criteria ii   0.316636   \n",
       "1        0.030455  ...  0.863881  0.210609   0.638658  Criteria ii   0.187734   \n",
       "2        0.052501  ...  0.772562  0.284602   0.396352  Criteria ii   0.268723   \n",
       "3        0.424027  ...  0.468618  0.405893   0.424027  Criteria vi   0.322504   \n",
       "4        0.088447  ...  0.206610  0.767832   0.339140   Criteria i   0.281514   \n",
       "...           ...  ...       ...       ...        ...          ...        ...   \n",
       "4550     0.032011  ...  0.702520  0.687211   0.653249   Criteria i   0.147048   \n",
       "4551     0.020364  ...  0.515820  0.863784   0.849580   Criteria i   0.059518   \n",
       "4554     0.015826  ...  0.765462  0.888029   0.836220   Criteria i   0.064082   \n",
       "4555     0.030839  ...  0.738694  0.520473   0.543409  Criteria iv   0.266114   \n",
       "4556     0.162307  ...  0.438482  0.730638   0.308849   Criteria i   0.223433   \n",
       "\n",
       "         max_2_col  max_3_val     max_3_col     max_1     max_3  \n",
       "0      Criteria iv   0.108202    Criteria i  0.362388  0.787226  \n",
       "1       Criteria i   0.071700   Criteria iv  0.638658  0.898092  \n",
       "2       Criteria i   0.147769   Criteria iv  0.396352  0.812845  \n",
       "3       Criteria i   0.097346  Criteria iii  0.424027  0.843877  \n",
       "4     Criteria iii   0.088447   Criteria vi  0.339140  0.709100  \n",
       "...            ...        ...           ...       ...       ...  \n",
       "4550   Criteria ii   0.109391   Criteria iv  0.653249  0.909687  \n",
       "4551   Criteria ii   0.024350  Criteria iii  0.849580  0.933448  \n",
       "4554   Criteria ii   0.047904   Criteria iv  0.836220  0.948206  \n",
       "4555    Criteria i   0.063980  Criteria iii  0.543409  0.873502  \n",
       "4556   Criteria iv   0.163122  Criteria iii  0.308849  0.695404  \n",
       "\n",
       "[3249 rows x 791 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert_s.drop_duplicates(subset='sentence',keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "ac4ecc74",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_s = pd.read_csv('Venezia/data_storage/sentences_compare.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "ca284c5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice, bri...</td>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.830680</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.362388</td>\n",
       "      <td>0.787226</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.812573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice</td>\n",
       "      <td>4</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.759217</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.638658</td>\n",
       "      <td>0.898092</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.525624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice, water</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.814499</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.396352</td>\n",
       "      <td>0.812845</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.756251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>THE KNIGHT WITH A GRIM LOOK   The Carnival of ...</td>\n",
       "      <td>6</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.348501</td>\n",
       "      <td>0.715528</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.424027</td>\n",
       "      <td>0.843877</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.645316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Who has never heard of it</td>\n",
       "      <td>6</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.774415</td>\n",
       "      <td>0.944572</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.339140</td>\n",
       "      <td>0.709100</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.317777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4552</th>\n",
       "      <td>1228</td>\n",
       "      <td>It has tags of venice, murano</td>\n",
       "      <td>4837</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>0.679870</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.933448</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.285384</td>\n",
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       "    <tr>\n",
       "      <th>4553</th>\n",
       "      <td>1229</td>\n",
       "      <td>It has tags of venice, murano</td>\n",
       "      <td>4838</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>0.679870</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.933448</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.285384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4554</th>\n",
       "      <td>1230</td>\n",
       "      <td>It has title of Gondolas on Canale Grande, Venice</td>\n",
       "      <td>4842</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.684141</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.836220</td>\n",
       "      <td>0.948206</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.452808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4555</th>\n",
       "      <td>1231</td>\n",
       "      <td>It has title of Frontage</td>\n",
       "      <td>4857</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>0.619725</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.543409</td>\n",
       "      <td>0.873502</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.550338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4556</th>\n",
       "      <td>1233</td>\n",
       "      <td>It has title of Venice, at sunset.</td>\n",
       "      <td>4866</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358972</td>\n",
       "      <td>0.730484</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.308849</td>\n",
       "      <td>0.695404</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.806718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4557 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                                           sentence  origin   lang  \\\n",
       "0         0  It has tags of adriaticsea, italy, venice, bri...       3     en   \n",
       "1         1          It has tags of adriaticsea, italy, venice       4     en   \n",
       "2         2   It has tags of adriaticsea, italy, venice, water       5     en   \n",
       "3         3  THE KNIGHT WITH A GRIM LOOK   The Carnival of ...       6     en   \n",
       "4         4                          Who has never heard of it       6     en   \n",
       "...     ...                                                ...     ...    ...   \n",
       "4552   1228                      It has tags of venice, murano    4837  other   \n",
       "4553   1229                      It has tags of venice, murano    4838  other   \n",
       "4554   1230  It has title of Gondolas on Canale Grande, Venice    4842  other   \n",
       "4555   1231                           It has title of Frontage    4857  other   \n",
       "4556   1233                 It has title of Venice, at sunset.    4866  other   \n",
       "\n",
       "     max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  max_1_ulmfit  \\\n",
       "0        Criteria iii      Criteria ii      Criteria iv      0.358030   \n",
       "1        Criteria iii      Criteria ii      Criteria iv      0.399754   \n",
       "2        Criteria iii      Criteria ii      Criteria iv      0.345548   \n",
       "3        Criteria iii      Criteria iv      Criteria vi      0.348501   \n",
       "4         Criteria vi     Criteria iii      Criteria ii      0.774415   \n",
       "...               ...              ...              ...           ...   \n",
       "4552     Criteria iii      Criteria vi      Criteria ii      0.368583   \n",
       "4553     Criteria iii      Criteria vi      Criteria ii      0.368583   \n",
       "4554      Criteria vi       Criteria i     Criteria iii      0.333300   \n",
       "4555     Criteria iii      Criteria vi      Criteria iv      0.293035   \n",
       "4556      Criteria vi     Criteria iii      Criteria iv      0.358972   \n",
       "\n",
       "      max_3_ulmfit max_1_col_bert max_2_col_bert max_3_col_bert  max_1_bert  \\\n",
       "0         0.830680    Criteria ii    Criteria iv     Criteria i    0.362388   \n",
       "1         0.759217    Criteria ii     Criteria i    Criteria iv    0.638658   \n",
       "2         0.814499    Criteria ii     Criteria i    Criteria iv    0.396352   \n",
       "3         0.715528    Criteria vi     Criteria i   Criteria iii    0.424027   \n",
       "4         0.944572     Criteria i   Criteria iii    Criteria vi    0.339140   \n",
       "...            ...            ...            ...            ...         ...   \n",
       "4552      0.679870     Criteria i    Criteria ii   Criteria iii    0.849580   \n",
       "4553      0.679870     Criteria i    Criteria ii   Criteria iii    0.849580   \n",
       "4554      0.684141     Criteria i    Criteria ii    Criteria iv    0.836220   \n",
       "4555      0.619725    Criteria iv     Criteria i   Criteria iii    0.543409   \n",
       "4556      0.730484     Criteria i    Criteria iv   Criteria iii    0.308849   \n",
       "\n",
       "      max_3_bert  same_1  same_3  cosine_sim  \n",
       "0       0.787226   False     0.5    0.812573  \n",
       "1       0.898092   False     0.5    0.525624  \n",
       "2       0.812845   False     0.5    0.756251  \n",
       "3       0.843877   False     0.5    0.645316  \n",
       "4       0.709100   False     0.5    0.317777  \n",
       "...          ...     ...     ...         ...  \n",
       "4552    0.933448   False     0.5    0.285384  \n",
       "4553    0.933448   False     0.5    0.285384  \n",
       "4554    0.948206   False     0.2    0.452808  \n",
       "4555    0.873502   False     0.5    0.550338  \n",
       "4556    0.695404   False     0.5    0.806718  \n",
       "\n",
       "[4557 rows x 17 columns]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "f3645585",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_s = cmp_s.merge(bert_s.drop_duplicates(subset='sentence',keep='first')[['max_1','max_3']], how='right',left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "4eb8a6b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_s['conf_3'] = (cmp_s['max_3_ulmfit'] + cmp_s['max_3_bert'])/2\n",
    "cmp_s['conf_1'] = (cmp_s['max_1_ulmfit'] + cmp_s['max_1_bert'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "1f6cb128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3249.000000\n",
       "mean        0.791598\n",
       "std         0.080637\n",
       "min         0.483390\n",
       "25%         0.746613\n",
       "50%         0.806321\n",
       "75%         0.849077\n",
       "max         0.965337\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_s['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "5439e3af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3249.000000\n",
       "mean        0.459456\n",
       "std         0.111867\n",
       "min         0.179329\n",
       "25%         0.377820\n",
       "50%         0.450008\n",
       "75%         0.533978\n",
       "max         0.890932\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_s['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "06fa90d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zzbn1\\AppData\\Local\\Temp\\ipykernel_6436\\3704144270.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  cmp_conf_s = cmp_s[cmp_s.conf_3>=0.75][cmp_s.same_3>=0.5]\n"
     ]
    }
   ],
   "source": [
    "cmp_conf_s = cmp_s[cmp_s.conf_3>=0.75][cmp_s.same_3>=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "4f022411",
   "metadata": {},
   "outputs": [
    {
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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>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>...</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>conf_3</th>\n",
       "      <th>conf_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice, bri...</td>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.830680</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.362388</td>\n",
       "      <td>0.787226</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.812573</td>\n",
       "      <td>0.362388</td>\n",
       "      <td>0.787226</td>\n",
       "      <td>0.808953</td>\n",
       "      <td>0.360209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice</td>\n",
       "      <td>4</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.759217</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.638658</td>\n",
       "      <td>0.898092</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.525624</td>\n",
       "      <td>0.638658</td>\n",
       "      <td>0.898092</td>\n",
       "      <td>0.828654</td>\n",
       "      <td>0.519206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice, water</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.814499</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.396352</td>\n",
       "      <td>0.812845</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.756251</td>\n",
       "      <td>0.396352</td>\n",
       "      <td>0.812845</td>\n",
       "      <td>0.813672</td>\n",
       "      <td>0.370950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>THE KNIGHT WITH A GRIM LOOK   The Carnival of ...</td>\n",
       "      <td>6</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.348501</td>\n",
       "      <td>0.715528</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.424027</td>\n",
       "      <td>0.843877</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.645316</td>\n",
       "      <td>0.424027</td>\n",
       "      <td>0.843877</td>\n",
       "      <td>0.779702</td>\n",
       "      <td>0.386264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Who has never heard of it</td>\n",
       "      <td>6</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.774415</td>\n",
       "      <td>0.944572</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.339140</td>\n",
       "      <td>0.709100</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.317777</td>\n",
       "      <td>0.339140</td>\n",
       "      <td>0.709100</td>\n",
       "      <td>0.826836</td>\n",
       "      <td>0.556777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4541</th>\n",
       "      <td>1217</td>\n",
       "      <td>More than twelve thousand elm wood poles were ...</td>\n",
       "      <td>4787</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.284607</td>\n",
       "      <td>0.700061</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.405187</td>\n",
       "      <td>0.858985</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.743985</td>\n",
       "      <td>0.405187</td>\n",
       "      <td>0.858985</td>\n",
       "      <td>0.779523</td>\n",
       "      <td>0.344897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4543</th>\n",
       "      <td>1219</td>\n",
       "      <td>In the central arches, overlooking the Grand C...</td>\n",
       "      <td>4787</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.912255</td>\n",
       "      <td>0.977944</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.839773</td>\n",
       "      <td>0.934699</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999086</td>\n",
       "      <td>0.839773</td>\n",
       "      <td>0.934699</td>\n",
       "      <td>0.956322</td>\n",
       "      <td>0.876014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4544</th>\n",
       "      <td>1220</td>\n",
       "      <td>The date of construction of the bridge is engr...</td>\n",
       "      <td>4787</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.387967</td>\n",
       "      <td>0.827922</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.509882</td>\n",
       "      <td>0.778598</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.910674</td>\n",
       "      <td>0.509882</td>\n",
       "      <td>0.778598</td>\n",
       "      <td>0.803260</td>\n",
       "      <td>0.448925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4545</th>\n",
       "      <td>1221</td>\n",
       "      <td>The total cost for the realization of this dar...</td>\n",
       "      <td>4787</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.519855</td>\n",
       "      <td>0.914659</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.868391</td>\n",
       "      <td>0.924623</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.875812</td>\n",
       "      <td>0.868391</td>\n",
       "      <td>0.924623</td>\n",
       "      <td>0.919641</td>\n",
       "      <td>0.694123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4551</th>\n",
       "      <td>1227</td>\n",
       "      <td>It has tags of venice, murano</td>\n",
       "      <td>4836</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>0.679870</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.933448</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.285384</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.933448</td>\n",
       "      <td>0.806659</td>\n",
       "      <td>0.609081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1659 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                                           sentence  origin   lang  \\\n",
       "0         0  It has tags of adriaticsea, italy, venice, bri...       3     en   \n",
       "1         1          It has tags of adriaticsea, italy, venice       4     en   \n",
       "2         2   It has tags of adriaticsea, italy, venice, water       5     en   \n",
       "3         3  THE KNIGHT WITH A GRIM LOOK   The Carnival of ...       6     en   \n",
       "4         4                          Who has never heard of it       6     en   \n",
       "...     ...                                                ...     ...    ...   \n",
       "4541   1217  More than twelve thousand elm wood poles were ...    4787  other   \n",
       "4543   1219  In the central arches, overlooking the Grand C...    4787  other   \n",
       "4544   1220  The date of construction of the bridge is engr...    4787  other   \n",
       "4545   1221  The total cost for the realization of this dar...    4787  other   \n",
       "4551   1227                      It has tags of venice, murano    4836  other   \n",
       "\n",
       "     max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  max_1_ulmfit  \\\n",
       "0        Criteria iii      Criteria ii      Criteria iv      0.358030   \n",
       "1        Criteria iii      Criteria ii      Criteria iv      0.399754   \n",
       "2        Criteria iii      Criteria ii      Criteria iv      0.345548   \n",
       "3        Criteria iii      Criteria iv      Criteria vi      0.348501   \n",
       "4         Criteria vi     Criteria iii      Criteria ii      0.774415   \n",
       "...               ...              ...              ...           ...   \n",
       "4541     Criteria iii      Criteria iv      Criteria ii      0.284607   \n",
       "4543       Criteria i      Criteria ii      Criteria iv      0.912255   \n",
       "4544      Criteria vi     Criteria iii      Criteria iv      0.387967   \n",
       "4545       Criteria i      Criteria ii      Criteria iv      0.519855   \n",
       "4551     Criteria iii      Criteria vi      Criteria ii      0.368583   \n",
       "\n",
       "      max_3_ulmfit max_1_col_bert  ... max_3_col_bert max_1_bert  max_3_bert  \\\n",
       "0         0.830680    Criteria ii  ...     Criteria i   0.362388    0.787226   \n",
       "1         0.759217    Criteria ii  ...    Criteria iv   0.638658    0.898092   \n",
       "2         0.814499    Criteria ii  ...    Criteria iv   0.396352    0.812845   \n",
       "3         0.715528    Criteria vi  ...   Criteria iii   0.424027    0.843877   \n",
       "4         0.944572     Criteria i  ...    Criteria vi   0.339140    0.709100   \n",
       "...            ...            ...  ...            ...        ...         ...   \n",
       "4541      0.700061     Criteria i  ...   Criteria iii   0.405187    0.858985   \n",
       "4543      0.977944     Criteria i  ...    Criteria iv   0.839773    0.934699   \n",
       "4544      0.827922    Criteria vi  ...   Criteria iii   0.509882    0.778598   \n",
       "4545      0.914659     Criteria i  ...   Criteria iii   0.868391    0.924623   \n",
       "4551      0.679870     Criteria i  ...   Criteria iii   0.849580    0.933448   \n",
       "\n",
       "      same_1  same_3  cosine_sim     max_1     max_3    conf_3    conf_1  \n",
       "0      False     0.5    0.812573  0.362388  0.787226  0.808953  0.360209  \n",
       "1      False     0.5    0.525624  0.638658  0.898092  0.828654  0.519206  \n",
       "2      False     0.5    0.756251  0.396352  0.812845  0.813672  0.370950  \n",
       "3      False     0.5    0.645316  0.424027  0.843877  0.779702  0.386264  \n",
       "4      False     0.5    0.317777  0.339140  0.709100  0.826836  0.556777  \n",
       "...      ...     ...         ...       ...       ...       ...       ...  \n",
       "4541   False     0.5    0.743985  0.405187  0.858985  0.779523  0.344897  \n",
       "4543    True     1.0    0.999086  0.839773  0.934699  0.956322  0.876014  \n",
       "4544    True     1.0    0.910674  0.509882  0.778598  0.803260  0.448925  \n",
       "4545    True     0.5    0.875812  0.868391  0.924623  0.919641  0.694123  \n",
       "4551   False     0.5    0.285384  0.849580  0.933448  0.806659  0.609081  \n",
       "\n",
       "[1659 rows x 21 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "548f16da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5106186518928901"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1659/3249"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "caf15165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1659.000000\n",
       "mean        0.832242\n",
       "std         0.045607\n",
       "min         0.750220\n",
       "25%         0.797144\n",
       "50%         0.830273\n",
       "75%         0.862706\n",
       "max         0.965337\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "d36c11cf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1659.000000\n",
       "mean        0.495822\n",
       "std         0.102316\n",
       "min         0.298501\n",
       "25%         0.418508\n",
       "50%         0.485789\n",
       "75%         0.561000\n",
       "max         0.890932\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "675c6a4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1659.000000\n",
       "mean        0.621157\n",
       "std         0.214306\n",
       "min         0.500000\n",
       "25%         0.500000\n",
       "50%         0.500000\n",
       "75%         0.500000\n",
       "max         1.000000\n",
       "Name: same_3, dtype: float64"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['same_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "31cf6f9e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1659\n",
       "unique        2\n",
       "top       False\n",
       "freq       1083\n",
       "Name: same_1, dtype: object"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['same_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "0847ddfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iv      0.186693\n",
       "Criteria iii     0.141448\n",
       "Criteria ii      0.139297\n",
       "Criteria i       0.135514\n",
       "Criteria vi      0.120234\n",
       "Criteria vii     0.004895\n",
       "Criteria v       0.003486\n",
       "Criteria ix      0.002596\n",
       "Criteria x       0.002077\n",
       "Criteria viii    0.002077\n",
       "dtype: float64"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf_s['max_1_col_bert'],\n",
    "           cmp_conf_s['max_2_col_bert'],\n",
    "           cmp_conf_s['max_3_col_bert'],\n",
    "           cmp_conf_s['max_1_col_ulmfit'],\n",
    "           cmp_conf_s['max_2_col_ulmfit'],\n",
    "           cmp_conf_s['max_3_col_ulmfit']]).value_counts()/(1659*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "4ecf47a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentages['VEN_s'] = pd.concat([cmp_conf_s['max_1_col_bert'],\n",
    "           cmp_conf_s['max_2_col_bert'],\n",
    "           cmp_conf_s['max_3_col_bert'],\n",
    "           cmp_conf_s['max_1_col_ulmfit'],\n",
    "           cmp_conf_s['max_2_col_ulmfit'],\n",
    "           cmp_conf_s['max_3_col_ulmfit']]).value_counts()/(1659*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "dfbfddd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VEN</th>\n",
       "      <th>VEN_s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUV</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Criteria i</th>\n",
       "      <td>0.147707</td>\n",
       "      <td>0.135514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ii</th>\n",
       "      <td>0.202381</td>\n",
       "      <td>0.139297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iii</th>\n",
       "      <td>0.213845</td>\n",
       "      <td>0.141448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iv</th>\n",
       "      <td>0.240300</td>\n",
       "      <td>0.186693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria v</th>\n",
       "      <td>0.008598</td>\n",
       "      <td>0.003486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vi</th>\n",
       "      <td>0.183862</td>\n",
       "      <td>0.120234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vii</th>\n",
       "      <td>0.001764</td>\n",
       "      <td>0.004895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria viii</th>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.002077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ix</th>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.002596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria x</th>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.002077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    VEN     VEN_s\n",
       "OUV                              \n",
       "Criteria i     0.147707  0.135514\n",
       "Criteria ii    0.202381  0.139297\n",
       "Criteria iii   0.213845  0.141448\n",
       "Criteria iv    0.240300  0.186693\n",
       "Criteria v     0.008598  0.003486\n",
       "Criteria vi    0.183862  0.120234\n",
       "Criteria vii   0.001764  0.004895\n",
       "Criteria viii  0.000220  0.002077\n",
       "Criteria ix    0.000441  0.002596\n",
       "Criteria x     0.000882  0.002077\n",
       "Others              NaN       NaN"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "7bccc8c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iv      2517\n",
       "Criteria iii     1907\n",
       "Criteria ii      1878\n",
       "Criteria i       1827\n",
       "Criteria vi      1621\n",
       "Criteria vii       66\n",
       "Criteria v         47\n",
       "Criteria ix        35\n",
       "Criteria x         28\n",
       "Criteria viii      28\n",
       "dtype: int64"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf_s['max_1_col_bert'],\n",
    "           cmp_conf_s['max_2_col_bert'],\n",
    "           cmp_conf_s['max_3_col_bert'],\n",
    "           cmp_conf_s['max_1_col_ulmfit'],\n",
    "           cmp_conf_s['max_2_col_ulmfit'],\n",
    "           cmp_conf_s['max_3_col_ulmfit']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "d59a3744",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_sorted = cmp_conf_s.sort_values(by='conf_1',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "3a5979f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>...</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>conf_3</th>\n",
       "      <th>conf_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>1370</td>\n",
       "      <td>The clouds make some fascinating shapes which ...</td>\n",
       "      <td>3451</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>Criteria ix</td>\n",
       "      <td>0.916736</td>\n",
       "      <td>0.983704</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.865129</td>\n",
       "      <td>0.938411</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.999324</td>\n",
       "      <td>0.865129</td>\n",
       "      <td>0.938411</td>\n",
       "      <td>0.961057</td>\n",
       "      <td>0.890932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>522</td>\n",
       "      <td>The blue glass piece of art is the Comet Glass...</td>\n",
       "      <td>1301</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.924289</td>\n",
       "      <td>0.984567</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.836732</td>\n",
       "      <td>0.910302</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.997880</td>\n",
       "      <td>0.836732</td>\n",
       "      <td>0.910302</td>\n",
       "      <td>0.947434</td>\n",
       "      <td>0.880511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4543</th>\n",
       "      <td>1219</td>\n",
       "      <td>In the central arches, overlooking the Grand C...</td>\n",
       "      <td>4787</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.912255</td>\n",
       "      <td>0.977944</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.839773</td>\n",
       "      <td>0.934699</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999086</td>\n",
       "      <td>0.839773</td>\n",
       "      <td>0.934699</td>\n",
       "      <td>0.956322</td>\n",
       "      <td>0.876014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>844</td>\n",
       "      <td>It was erected in 1492 under the design by Ren...</td>\n",
       "      <td>1949</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.857239</td>\n",
       "      <td>0.964832</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.858235</td>\n",
       "      <td>0.924835</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999066</td>\n",
       "      <td>0.858235</td>\n",
       "      <td>0.924835</td>\n",
       "      <td>0.944834</td>\n",
       "      <td>0.857737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>1069</td>\n",
       "      <td>The marble altarpiece of the Coronation of the...</td>\n",
       "      <td>2348</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.850773</td>\n",
       "      <td>0.959638</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.840216</td>\n",
       "      <td>0.930501</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.999203</td>\n",
       "      <td>0.840216</td>\n",
       "      <td>0.930501</td>\n",
       "      <td>0.945070</td>\n",
       "      <td>0.845495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4142</th>\n",
       "      <td>681</td>\n",
       "      <td>It has title of Gradual from the Saints, from ...</td>\n",
       "      <td>2621</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.301329</td>\n",
       "      <td>0.795631</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.310447</td>\n",
       "      <td>0.730925</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.810307</td>\n",
       "      <td>0.310447</td>\n",
       "      <td>0.730925</td>\n",
       "      <td>0.763278</td>\n",
       "      <td>0.305888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>1047</td>\n",
       "      <td>The bridge links the sestieri of Dorsoduro and...</td>\n",
       "      <td>2324</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.289439</td>\n",
       "      <td>0.731315</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.321646</td>\n",
       "      <td>0.814361</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.848575</td>\n",
       "      <td>0.321646</td>\n",
       "      <td>0.814361</td>\n",
       "      <td>0.772838</td>\n",
       "      <td>0.305543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3069</th>\n",
       "      <td>1340</td>\n",
       "      <td>It has title of Clock Tower, 15th century</td>\n",
       "      <td>3106</td>\n",
       "      <td>it</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.303250</td>\n",
       "      <td>0.739121</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.306004</td>\n",
       "      <td>0.778634</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.815392</td>\n",
       "      <td>0.306004</td>\n",
       "      <td>0.778634</td>\n",
       "      <td>0.758877</td>\n",
       "      <td>0.304627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>988</td>\n",
       "      <td>The San Marco Gondola Station at dawn, in Veni...</td>\n",
       "      <td>2193</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.280548</td>\n",
       "      <td>0.729708</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.322177</td>\n",
       "      <td>0.789243</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.791409</td>\n",
       "      <td>0.322177</td>\n",
       "      <td>0.789243</td>\n",
       "      <td>0.759476</td>\n",
       "      <td>0.301362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2282</th>\n",
       "      <td>550</td>\n",
       "      <td>It has title of Doge's Palace - Palazzo Ducale</td>\n",
       "      <td>1384</td>\n",
       "      <td>it</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.278102</td>\n",
       "      <td>0.748535</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.318900</td>\n",
       "      <td>0.780112</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.807303</td>\n",
       "      <td>0.318900</td>\n",
       "      <td>0.780112</td>\n",
       "      <td>0.764324</td>\n",
       "      <td>0.298501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1659 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                                           sentence  origin   lang  \\\n",
       "1280   1370  The clouds make some fascinating shapes which ...    3451     en   \n",
       "462     522  The blue glass piece of art is the Comet Glass...    1301     en   \n",
       "4543   1219  In the central arches, overlooking the Grand C...    4787  other   \n",
       "774     844  It was erected in 1492 under the design by Ren...    1949     en   \n",
       "989    1069  The marble altarpiece of the Coronation of the...    2348     en   \n",
       "...     ...                                                ...     ...    ...   \n",
       "4142    681  It has title of Gradual from the Saints, from ...    2621  other   \n",
       "969    1047  The bridge links the sestieri of Dorsoduro and...    2324     en   \n",
       "3069   1340          It has title of Clock Tower, 15th century    3106     it   \n",
       "912     988  The San Marco Gondola Station at dawn, in Veni...    2193     en   \n",
       "2282    550     It has title of Doge's Palace - Palazzo Ducale    1384     it   \n",
       "\n",
       "     max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  max_1_ulmfit  \\\n",
       "1280     Criteria vii    Criteria viii      Criteria ix      0.916736   \n",
       "462        Criteria i      Criteria ii      Criteria iv      0.924289   \n",
       "4543       Criteria i      Criteria ii      Criteria iv      0.912255   \n",
       "774        Criteria i      Criteria ii      Criteria iv      0.857239   \n",
       "989        Criteria i      Criteria ii      Criteria iv      0.850773   \n",
       "...               ...              ...              ...           ...   \n",
       "4142      Criteria vi     Criteria iii      Criteria iv      0.301329   \n",
       "969      Criteria iii      Criteria vi      Criteria ii      0.289439   \n",
       "3069      Criteria iv       Criteria i     Criteria iii      0.303250   \n",
       "912       Criteria vi     Criteria iii      Criteria iv      0.280548   \n",
       "2282     Criteria iii      Criteria vi      Criteria iv      0.278102   \n",
       "\n",
       "      max_3_ulmfit max_1_col_bert  ... max_3_col_bert max_1_bert  max_3_bert  \\\n",
       "1280      0.983704   Criteria vii  ...     Criteria x   0.865129    0.938411   \n",
       "462       0.984567     Criteria i  ...    Criteria ii   0.836732    0.910302   \n",
       "4543      0.977944     Criteria i  ...    Criteria iv   0.839773    0.934699   \n",
       "774       0.964832     Criteria i  ...    Criteria ii   0.858235    0.924835   \n",
       "989       0.959638     Criteria i  ...    Criteria vi   0.840216    0.930501   \n",
       "...            ...            ...  ...            ...        ...         ...   \n",
       "4142      0.795631    Criteria vi  ...   Criteria iii   0.310447    0.730925   \n",
       "969       0.731315   Criteria iii  ...    Criteria ii   0.321646    0.814361   \n",
       "3069      0.739121    Criteria ii  ...   Criteria iii   0.306004    0.778634   \n",
       "912       0.729708    Criteria iv  ...    Criteria vi   0.322177    0.789243   \n",
       "2282      0.748535    Criteria iv  ...    Criteria vi   0.318900    0.780112   \n",
       "\n",
       "      same_1  same_3  cosine_sim     max_1     max_3    conf_3    conf_1  \n",
       "1280    True     0.5    0.999324  0.865129  0.938411  0.961057  0.890932  \n",
       "462     True     0.5    0.997880  0.836732  0.910302  0.947434  0.880511  \n",
       "4543    True     1.0    0.999086  0.839773  0.934699  0.956322  0.876014  \n",
       "774     True     1.0    0.999066  0.858235  0.924835  0.944834  0.857737  \n",
       "989     True     0.5    0.999203  0.840216  0.930501  0.945070  0.845495  \n",
       "...      ...     ...         ...       ...       ...       ...       ...  \n",
       "4142    True     0.5    0.810307  0.310447  0.730925  0.763278  0.305888  \n",
       "969     True     0.5    0.848575  0.321646  0.814361  0.772838  0.305543  \n",
       "3069   False     0.5    0.815392  0.306004  0.778634  0.758877  0.304627  \n",
       "912    False     0.5    0.791409  0.322177  0.789243  0.759476  0.301362  \n",
       "2282   False     0.5    0.807303  0.318900  0.780112  0.764324  0.298501  \n",
       "\n",
       "[1659 rows x 21 columns]"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "ed2d5556",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Criteria i ['The blue glass piece of art is the Comet Glass Star sculpture made for Christmas 2007 by Master glass maker Simone Cenedese'\n",
      " 'In the central arches, overlooking the Grand Canal, there are sculptures representing the Annunciation on one side: a dove between the Archangel Gabriel and the Virgin Mary (by Agostino Rubini), on the other San Marco and San Teodoro (by Tiziano Aspetti ), late 16th century'\n",
      " 'It was erected in 1492 under the design by Renaissance architect Mauro Codussi'] [0.88051069 0.87601391 0.85773666]\n",
      "Criteria ii ['was introduced here, as clients went with such a bill of exchange with a credit inscribed from one banker to another'\n",
      " 'They first engaged Andrea Musato, but he died in 1721 and they turned to Giorgio Massari, whose model for the new church was accepted in 1724'\n",
      " 'He made great improvements to this and in 1565, was asked to prepare a model for a new church'] [0.83973262 0.74967072 0.72950038]\n",
      "Criteria iii ['In the sixth century, the Lagoon gave security to Romanised people fleeing invaders'\n",
      " 'A third, older theory is that they represent a dynastic group of the Constantinian dynasty'\n",
      " 'It has tags of coins, v'] [0.67597562 0.66838098 0.65518486]\n",
      "Criteria iv ['It has title of Venice, Sestiere Cannaregio, window shopping in the true sense of the word'\n",
      " 'Although elegant palazzos were built facing the Grand Canal, the area grew primarily with working class housing and manufacturing'\n",
      " 'The western part of the quarter is now known for its churches, while the eastern part, sometimes just called the Rialto, is known for its palaces and smaller houses'] [0.76850954 0.75414894 0.72652203]\n",
      "Criteria v ['It has tags of masstourism, mass tourism, tourismedemasse, venezia, venice, venecia, venedig, venice, veneto, v, italy, italy, italie, italien, nikon'\n",
      " 'It still provides a base for a seaport, the Venetian Arsenal, and for fishing, as well as a limited amount of hunting and the newer industry of fish farming'\n",
      " 'A Tale (tail.) of Daily life in Venice where the locals and merchants are not tourists and have to sell, or shop for and buy, their food locally'] [0.63194543 0.53397839 0.43832913]\n",
      "Criteria vi ['Onuphrius was the co-titular patron saint who was revered by the confraternity of the Tentori (dyers of fabrics, covers, and sheets)'\n",
      " 'It is the story of the martyrdom of the saint and his welcome by Jesus in paradise in a whirlwind of cheering angels, with crowns, palms and musical instruments'\n",
      " 'We have reached sixty-seven and the threshold of seventy is getting closer and closer but who cares, we know people much younger than us who look like our grandparents in mentality and attitudes .... it seems to me yesterday that we were at the Excelsior of the Lido of Venice to celebrate an important event, a lot of water has passed under the bridges and we are still here to tell us about it'] [0.80702653 0.77996573 0.77143142]\n",
      "Criteria vii ['The clouds make some fascinating shapes which only amplified the unique surroundings.'\n",
      " 'Sited at the end of a largely enclosed sea, the lagoon is subject to high variations in water level, the most extreme being the spring tides known as the acqua alta (high water), which regularly flood much of Venice'\n",
      " 'Early in the morning, heavy clouds, sometimes interspersed with clearings, produce a magnificent light above the San Marco basin.'] [0.89093235 0.7633681  0.75325755]\n",
      "Criteria viii [] []\n",
      "Criteria ix ['About 11% is permanently covered by open water, or canal, as the network of dredged channels are called, while around 80% consists of mud flats, tidal shallows and salt marshes'] [0.41847115]\n",
      "Criteria x ['It has tags of owl, athenenoctua, owl, november' 'Black 7 White'\n",
      " 'The lagoon is the largest wetland in the Mediterranean Basin'] [0.73264592 0.62032694 0.61168175]\n",
      "Others [] []\n"
     ]
    }
   ],
   "source": [
    "for c in classes:\n",
    "    temp = cmp_sorted[cmp_sorted.max_1_col_bert==c].iloc[:3]\n",
    "    print(c, temp['sentence'].values,temp['conf_1'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "68b2a090",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Criteria i ['The blue glass piece of art is the Comet Glass Star sculpture made for Christmas 2007 by Master glass maker Simone Cenedese'\n",
      " 'In the central arches, overlooking the Grand Canal, there are sculptures representing the Annunciation on one side: a dove between the Archangel Gabriel and the Virgin Mary (by Agostino Rubini), on the other San Marco and San Teodoro (by Tiziano Aspetti ), late 16th century'\n",
      " 'It was erected in 1492 under the design by Renaissance architect Mauro Codussi'] [0.88051069 0.87601391 0.85773666]\n",
      "Criteria ii ['was introduced here, as clients went with such a bill of exchange with a credit inscribed from one banker to another'\n",
      " \"The English art critic John Ruskin was particularly entranced with and wrote about the palace's Gothic marble-encrusted oculi\"\n",
      " 'They first engaged Andrea Musato, but he died in 1721 and they turned to Giorgio Massari, whose model for the new church was accepted in 1724'] [0.83973262 0.77317945 0.74967072]\n",
      "Criteria iii ['It has tags of xtiandugard, italie, murano, canal, landscape, paysage, waterscape, bateaux, boats, clo, perspective, architecture'\n",
      " 'In the sixth century, the Lagoon gave security to Romanised people fleeing invaders'\n",
      " 'Today supersnob shopping center :-)'] [0.72165605 0.67597562 0.66719067]\n",
      "Criteria iv ['Although elegant palazzos were built facing the Grand Canal, the area grew primarily with working class housing and manufacturing'\n",
      " 'The western part of the quarter is now known for its churches, while the eastern part, sometimes just called the Rialto, is known for its palaces and smaller houses'\n",
      " 'The two story brick building is unusual for standing detached in the large square'] [0.75414894 0.72652203 0.71424113]\n",
      "Criteria v ['Much of the industry went into decline after World War II, but it is now once more regarded as a quiet residential area of largely working-class housing with some chic apartments and exclusive houses'\n",
      " 'Today, the areas of the district along the Grand Canal from the train station to the Rialto Bridge are packed with tourists, but the rest of Cannaregio is residential and relatively peaceful, with morning markets, neighbourhood shops, and small cafés'] [0.6209664  0.50667883]\n",
      "Criteria vi ['Onuphrius was the co-titular patron saint who was revered by the confraternity of the Tentori (dyers of fabrics, covers, and sheets)'\n",
      " 'It is the story of the martyrdom of the saint and his welcome by Jesus in paradise in a whirlwind of cheering angels, with crowns, palms and musical instruments'\n",
      " 'We have reached sixty-seven and the threshold of seventy is getting closer and closer but who cares, we know people much younger than us who look like our grandparents in mentality and attitudes .... it seems to me yesterday that we were at the Excelsior of the Lido of Venice to celebrate an important event, a lot of water has passed under the bridges and we are still here to tell us about it'] [0.80702653 0.77996573 0.77143142]\n",
      "Criteria vii ['The clouds make some fascinating shapes which only amplified the unique surroundings.'\n",
      " 'Sited at the end of a largely enclosed sea, the lagoon is subject to high variations in water level, the most extreme being the spring tides known as the acqua alta (high water), which regularly flood much of Venice'\n",
      " 'Early in the morning, heavy clouds, sometimes interspersed with clearings, produce a magnificent light above the San Marco basin.'] [0.89093235 0.7633681  0.75325755]\n",
      "Criteria viii ['Another morning long exposure view'\n",
      " 'The Rio de Santa Caterina has a length of 310 m'\n",
      " 'It is bordering the Cannaregio desert, a short distance along the rio del Fontego dei Tedeschi, then the Sestiere de Castello, at the intersection of this last channel with the Rio de la Fava'] [0.61383036 0.59470157 0.5397788 ]\n",
      "Criteria ix ['The area of Dorsoduro includes the highest land areas of the city and also Giudecca island and Isola Sacca Fisola'\n",
      " 'Those factors together, along with the more frequent extreme weather events associated with climate change, contribute to floods.'] [0.47557254 0.45234148]\n",
      "Criteria x ['It has tags of owl, athenenoctua, owl, november' 'Black 7 White'\n",
      " 'The lagoon is the largest wetland in the Mediterranean Basin'] [0.73264592 0.62032694 0.61168175]\n",
      "Others [] []\n"
     ]
    }
   ],
   "source": [
    "for c in classes:\n",
    "    temp = cmp_sorted[cmp_sorted.max_1_col_ulmfit==c].iloc[:3]\n",
    "    print(c, temp['sentence'].values,temp['conf_1'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9806a581",
   "metadata": {},
   "source": [
    "## Venezia_Large"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "31b2a52b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zzbn1\\AppData\\Local\\Temp\\ipykernel_6436\\208614092.py:1: DtypeWarning: Columns (5,824) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  bert = pd.read_csv('data_storage/images/metadata_bert.csv',sep='\\t',index_col='Unnamed: 0')\n"
     ]
    }
   ],
   "source": [
    "bert = pd.read_csv('data_storage/images/metadata_bert.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "95f994c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert['max_1_eq'] = bert['max_1_eq'].fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "0486355a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert = bert[bert.max_1_eq!=-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "b0e89106",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp = pd.read_csv('data_storage/images/metadata_compare.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "997ede2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp = cmp.merge(bert[['max_1_eq','max_3_eq']], how='right',left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "cbd30d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp['conf_3'] = (cmp['max_3_ulmfit'] + cmp['max_3_bert'])/2\n",
    "cmp['conf_1'] = (cmp['max_1_ulmfit'] + cmp['max_1_bert'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "72a136b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    49823.000000\n",
       "mean         0.780775\n",
       "std          0.065504\n",
       "min          0.463679\n",
       "25%          0.742605\n",
       "50%          0.793073\n",
       "75%          0.829303\n",
       "max          0.969183\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "1fb8209d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    49823.000000\n",
       "mean         0.425755\n",
       "std          0.087479\n",
       "min          0.166067\n",
       "25%          0.362628\n",
       "50%          0.424253\n",
       "75%          0.472822\n",
       "max          0.858133\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "8fde63fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zzbn1\\AppData\\Local\\Temp\\ipykernel_6436\\3362900713.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  cmp_conf = cmp[cmp.conf_3>=0.75][cmp.same_3>=0.5]\n"
     ]
    }
   ],
   "source": [
    "cmp_conf = cmp[cmp.conf_3>=0.75][cmp.same_3>=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "a890a61f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>max_1_eq</th>\n",
       "      <th>max_3_eq</th>\n",
       "      <th>conf_3</th>\n",
       "      <th>conf_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.391179</td>\n",
       "      <td>0.801656</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.624199</td>\n",
       "      <td>0.891726</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.846691</td>\n",
       "      <td>0.507689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.387872</td>\n",
       "      <td>0.799923</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.690831</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.852021</td>\n",
       "      <td>0.539351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.351008</td>\n",
       "      <td>0.785742</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria v</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.594140</td>\n",
       "      <td>0.834888</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.810315</td>\n",
       "      <td>0.472574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.322676</td>\n",
       "      <td>0.781370</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria v</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.467533</td>\n",
       "      <td>0.822071</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.801720</td>\n",
       "      <td>0.395105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358107</td>\n",
       "      <td>0.725466</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.664241</td>\n",
       "      <td>0.883973</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.804719</td>\n",
       "      <td>0.511174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80952</th>\n",
       "      <td>116664</td>\n",
       "      <td>123090</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.332850</td>\n",
       "      <td>0.736266</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.628652</td>\n",
       "      <td>0.871310</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.803788</td>\n",
       "      <td>0.480751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80953</th>\n",
       "      <td>116665</td>\n",
       "      <td>123091</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.343672</td>\n",
       "      <td>0.825905</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.528331</td>\n",
       "      <td>0.889617</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.857761</td>\n",
       "      <td>0.436002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80954</th>\n",
       "      <td>116666</td>\n",
       "      <td>123092</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.355907</td>\n",
       "      <td>0.841061</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.387768</td>\n",
       "      <td>0.819758</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.830410</td>\n",
       "      <td>0.371838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80956</th>\n",
       "      <td>116668</td>\n",
       "      <td>123094</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.310185</td>\n",
       "      <td>0.871311</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.406246</td>\n",
       "      <td>0.772845</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.822078</td>\n",
       "      <td>0.358216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80958</th>\n",
       "      <td>116670</td>\n",
       "      <td>123096</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.416799</td>\n",
       "      <td>0.815098</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.390654</td>\n",
       "      <td>0.895209</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.855154</td>\n",
       "      <td>0.403726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25771 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       origin   index max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "0           6       6      Criteria iv       Criteria i     Criteria iii   \n",
       "1           7       7      Criteria iv       Criteria i     Criteria iii   \n",
       "4          12      12      Criteria iv     Criteria iii      Criteria ii   \n",
       "5          13      13      Criteria iv     Criteria iii      Criteria ii   \n",
       "7          19      19      Criteria vi     Criteria iii      Criteria iv   \n",
       "...       ...     ...              ...              ...              ...   \n",
       "80952  116664  123090     Criteria iii      Criteria iv      Criteria vi   \n",
       "80953  116665  123091      Criteria ii     Criteria iii      Criteria iv   \n",
       "80954  116666  123092      Criteria ii     Criteria iii      Criteria iv   \n",
       "80956  116668  123094     Criteria iii      Criteria ii      Criteria iv   \n",
       "80958  116670  123096     Criteria iii      Criteria iv      Criteria ii   \n",
       "\n",
       "       max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert  \\\n",
       "0          0.391179      0.801656    Criteria iv     Criteria i   \n",
       "1          0.387872      0.799923    Criteria iv     Criteria i   \n",
       "4          0.351008      0.785742    Criteria iv     Criteria v   \n",
       "5          0.322676      0.781370    Criteria iv     Criteria v   \n",
       "7          0.358107      0.725466    Criteria vi   Criteria iii   \n",
       "...             ...           ...            ...            ...   \n",
       "80952      0.332850      0.736266    Criteria iv     Criteria i   \n",
       "80953      0.343672      0.825905    Criteria ii    Criteria iv   \n",
       "80954      0.355907      0.841061    Criteria iv    Criteria ii   \n",
       "80956      0.310185      0.871311    Criteria iv    Criteria vi   \n",
       "80958      0.416799      0.815098    Criteria ii    Criteria iv   \n",
       "\n",
       "      max_3_col_bert  max_1_bert  max_3_bert  same_1  same_3 max_1_eq  \\\n",
       "0       Criteria iii    0.624199    0.891726    True     1.0     True   \n",
       "1       Criteria iii    0.690831    0.904119    True     1.0     True   \n",
       "4       Criteria iii    0.594140    0.834888    True     0.5     True   \n",
       "5       Criteria iii    0.467533    0.822071    True     0.5     True   \n",
       "7        Criteria iv    0.664241    0.883973    True     1.0     True   \n",
       "...              ...         ...         ...     ...     ...      ...   \n",
       "80952   Criteria iii    0.628652    0.871310   False     0.5     True   \n",
       "80953     Criteria i    0.528331    0.889617    True     0.5    False   \n",
       "80954     Criteria i    0.387768    0.819758   False     0.5     True   \n",
       "80956    Criteria ii    0.406246    0.772845   False     0.5     True   \n",
       "80958     Criteria i    0.390654    0.895209   False     0.5    False   \n",
       "\n",
       "       max_3_eq    conf_3    conf_1  \n",
       "0           0.5  0.846691  0.507689  \n",
       "1           0.5  0.852021  0.539351  \n",
       "4           0.5  0.810315  0.472574  \n",
       "5           0.5  0.801720  0.395105  \n",
       "7           1.0  0.804719  0.511174  \n",
       "...         ...       ...       ...  \n",
       "80952       1.0  0.803788  0.480751  \n",
       "80953       0.5  0.857761  0.436002  \n",
       "80954       0.5  0.830410  0.371838  \n",
       "80956       0.2  0.822078  0.358216  \n",
       "80958       0.5  0.855154  0.403726  \n",
       "\n",
       "[25771 rows x 18 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6b2e7ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5172510687834936"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "25771/49823"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "6fd3a94a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25771.000000\n",
       "mean         0.813904\n",
       "std          0.035058\n",
       "min          0.750000\n",
       "25%          0.786381\n",
       "50%          0.815929\n",
       "75%          0.831907\n",
       "max          0.969183\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "c09cd003",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25771.000000\n",
       "mean         0.455643\n",
       "std          0.078946\n",
       "min          0.267133\n",
       "25%          0.397055\n",
       "50%          0.461202\n",
       "75%          0.501165\n",
       "max          0.858133\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "9c981332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25771.000000\n",
       "mean         0.615634\n",
       "std          0.210826\n",
       "min          0.500000\n",
       "25%          0.500000\n",
       "50%          0.500000\n",
       "75%          0.500000\n",
       "max          1.000000\n",
       "Name: same_3, dtype: float64"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['same_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "41860d73",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     25771\n",
       "unique        2\n",
       "top       False\n",
       "freq      17847\n",
       "Name: same_1, dtype: object"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['same_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "07c68d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     0.270847\n",
       "Criteria vi      0.249893\n",
       "Criteria iv      0.233602\n",
       "Criteria ii      0.162301\n",
       "Criteria i       0.075867\n",
       "Criteria v       0.002794\n",
       "Criteria vii     0.002231\n",
       "Criteria x       0.001520\n",
       "Criteria ix      0.000595\n",
       "Criteria viii    0.000349\n",
       "dtype: float64"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf['max_1_col_bert'],\n",
    "           cmp_conf['max_2_col_bert'],\n",
    "           cmp_conf['max_3_col_bert'],\n",
    "           cmp_conf['max_1_col_ulmfit'],\n",
    "           cmp_conf['max_2_col_ulmfit'],\n",
    "           cmp_conf['max_3_col_ulmfit']]).value_counts()/(25771*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "d832516b",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentages['VEN_XL']=pd.concat([cmp_conf['max_1_col_bert'],\n",
    "           cmp_conf['max_2_col_bert'],\n",
    "           cmp_conf['max_3_col_bert'],\n",
    "           cmp_conf['max_1_col_ulmfit'],\n",
    "           cmp_conf['max_2_col_ulmfit'],\n",
    "           cmp_conf['max_3_col_ulmfit']]).value_counts()/(25771*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "9d01e9e4",
   "metadata": {},
   "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>VEN</th>\n",
       "      <th>VEN_s</th>\n",
       "      <th>VEN_XL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUV</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Criteria i</th>\n",
       "      <td>0.147707</td>\n",
       "      <td>0.135514</td>\n",
       "      <td>0.075867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ii</th>\n",
       "      <td>0.202381</td>\n",
       "      <td>0.139297</td>\n",
       "      <td>0.162301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iii</th>\n",
       "      <td>0.213845</td>\n",
       "      <td>0.141448</td>\n",
       "      <td>0.270847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iv</th>\n",
       "      <td>0.240300</td>\n",
       "      <td>0.186693</td>\n",
       "      <td>0.233602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria v</th>\n",
       "      <td>0.008598</td>\n",
       "      <td>0.003486</td>\n",
       "      <td>0.002794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vi</th>\n",
       "      <td>0.183862</td>\n",
       "      <td>0.120234</td>\n",
       "      <td>0.249893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vii</th>\n",
       "      <td>0.001764</td>\n",
       "      <td>0.004895</td>\n",
       "      <td>0.002231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria viii</th>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.002077</td>\n",
       "      <td>0.000349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ix</th>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.000595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria x</th>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.002077</td>\n",
       "      <td>0.001520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    VEN     VEN_s    VEN_XL\n",
       "OUV                                        \n",
       "Criteria i     0.147707  0.135514  0.075867\n",
       "Criteria ii    0.202381  0.139297  0.162301\n",
       "Criteria iii   0.213845  0.141448  0.270847\n",
       "Criteria iv    0.240300  0.186693  0.233602\n",
       "Criteria v     0.008598  0.003486  0.002794\n",
       "Criteria vi    0.183862  0.120234  0.249893\n",
       "Criteria vii   0.001764  0.004895  0.002231\n",
       "Criteria viii  0.000220  0.002077  0.000349\n",
       "Criteria ix    0.000441  0.002596  0.000595\n",
       "Criteria x     0.000882  0.002077  0.001520\n",
       "Others              NaN       NaN       NaN"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "9ce6b04c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     41880\n",
       "Criteria vi      38640\n",
       "Criteria iv      36121\n",
       "Criteria ii      25096\n",
       "Criteria i       11731\n",
       "Criteria v         432\n",
       "Criteria vii       345\n",
       "Criteria x         235\n",
       "Criteria ix         92\n",
       "Criteria viii       54\n",
       "dtype: int64"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf['max_1_col_bert'],\n",
    "           cmp_conf['max_2_col_bert'],\n",
    "           cmp_conf['max_3_col_bert'],\n",
    "           cmp_conf['max_1_col_ulmfit'],\n",
    "           cmp_conf['max_2_col_ulmfit'],\n",
    "           cmp_conf['max_3_col_ulmfit']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "53ea51e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert_s = pd.read_csv('data_storage/images/sentences_bert.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "0059d7c9",
   "metadata": {},
   "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>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>...</th>\n",
       "      <th>BERT_766</th>\n",
       "      <th>BERT_767</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>It has title of Lonely window</td>\n",
       "      <td>20</td>\n",
       "      <td>en</td>\n",
       "      <td>0.168730</td>\n",
       "      <td>0.043053</td>\n",
       "      <td>0.149717</td>\n",
       "      <td>0.085379</td>\n",
       "      <td>0.033142</td>\n",
       "      <td>0.074294</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.116627</td>\n",
       "      <td>0.942009</td>\n",
       "      <td>0.180201</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.168730</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.149717</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.180201</td>\n",
       "      <td>0.498648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has title of Reflection and bridge - Venice</td>\n",
       "      <td>24</td>\n",
       "      <td>en</td>\n",
       "      <td>0.626143</td>\n",
       "      <td>0.111832</td>\n",
       "      <td>0.037493</td>\n",
       "      <td>0.066675</td>\n",
       "      <td>0.005745</td>\n",
       "      <td>0.082481</td>\n",
       "      <td>...</td>\n",
       "      <td>0.615546</td>\n",
       "      <td>0.812848</td>\n",
       "      <td>0.626143</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.111832</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.082481</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.626143</td>\n",
       "      <td>0.820457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>It has tags of nopeople, architecture, outdoor...</td>\n",
       "      <td>24</td>\n",
       "      <td>en</td>\n",
       "      <td>0.078370</td>\n",
       "      <td>0.066554</td>\n",
       "      <td>0.056322</td>\n",
       "      <td>0.306494</td>\n",
       "      <td>0.300336</td>\n",
       "      <td>0.082072</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.056501</td>\n",
       "      <td>0.092934</td>\n",
       "      <td>0.306494</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.300336</td>\n",
       "      <td>Criteria v</td>\n",
       "      <td>0.082072</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.306494</td>\n",
       "      <td>0.688902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>It has title of Venice fish market</td>\n",
       "      <td>28</td>\n",
       "      <td>en</td>\n",
       "      <td>0.084237</td>\n",
       "      <td>0.185749</td>\n",
       "      <td>0.160403</td>\n",
       "      <td>0.287541</td>\n",
       "      <td>0.060195</td>\n",
       "      <td>0.088886</td>\n",
       "      <td>...</td>\n",
       "      <td>0.362237</td>\n",
       "      <td>0.529293</td>\n",
       "      <td>0.287541</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.185749</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.160403</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.287541</td>\n",
       "      <td>0.633693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>It has tags of venice, venezia, italia, panora...</td>\n",
       "      <td>28</td>\n",
       "      <td>en</td>\n",
       "      <td>0.030712</td>\n",
       "      <td>0.210020</td>\n",
       "      <td>0.322502</td>\n",
       "      <td>0.343468</td>\n",
       "      <td>0.026379</td>\n",
       "      <td>0.045347</td>\n",
       "      <td>...</td>\n",
       "      <td>0.507087</td>\n",
       "      <td>-0.684952</td>\n",
       "      <td>0.343468</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.322502</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.210020</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.343468</td>\n",
       "      <td>0.875990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171019</th>\n",
       "      <td>91498</td>\n",
       "      <td>It has tags of Venezia, Italy, Veneto, Metropo...</td>\n",
       "      <td>116671</td>\n",
       "      <td>other</td>\n",
       "      <td>0.282043</td>\n",
       "      <td>0.466190</td>\n",
       "      <td>0.053860</td>\n",
       "      <td>0.144850</td>\n",
       "      <td>0.004639</td>\n",
       "      <td>0.010852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.723148</td>\n",
       "      <td>0.655239</td>\n",
       "      <td>0.466190</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.282043</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.144850</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.466190</td>\n",
       "      <td>0.893082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171020</th>\n",
       "      <td>91499</td>\n",
       "      <td>It has title of Ave Maria</td>\n",
       "      <td>116672</td>\n",
       "      <td>other</td>\n",
       "      <td>0.452509</td>\n",
       "      <td>0.110522</td>\n",
       "      <td>0.084270</td>\n",
       "      <td>0.091483</td>\n",
       "      <td>0.007040</td>\n",
       "      <td>0.207345</td>\n",
       "      <td>...</td>\n",
       "      <td>0.801728</td>\n",
       "      <td>0.383296</td>\n",
       "      <td>0.452509</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.207345</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.110522</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.452509</td>\n",
       "      <td>0.770377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171021</th>\n",
       "      <td>91500</td>\n",
       "      <td>It has tags of Venezia, Italy, Veneto, Metropo...</td>\n",
       "      <td>116672</td>\n",
       "      <td>other</td>\n",
       "      <td>0.321161</td>\n",
       "      <td>0.411557</td>\n",
       "      <td>0.064540</td>\n",
       "      <td>0.148818</td>\n",
       "      <td>0.003883</td>\n",
       "      <td>0.019492</td>\n",
       "      <td>...</td>\n",
       "      <td>0.790162</td>\n",
       "      <td>0.515132</td>\n",
       "      <td>0.411557</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.321161</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.148818</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.411557</td>\n",
       "      <td>0.881536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171022</th>\n",
       "      <td>91501</td>\n",
       "      <td>It has title of Door in the Jewish district of...</td>\n",
       "      <td>116674</td>\n",
       "      <td>other</td>\n",
       "      <td>0.366306</td>\n",
       "      <td>0.116810</td>\n",
       "      <td>0.180822</td>\n",
       "      <td>0.238843</td>\n",
       "      <td>0.004660</td>\n",
       "      <td>0.051460</td>\n",
       "      <td>...</td>\n",
       "      <td>0.688031</td>\n",
       "      <td>0.608966</td>\n",
       "      <td>0.366306</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.238843</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.180822</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.366306</td>\n",
       "      <td>0.785971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171023</th>\n",
       "      <td>91502</td>\n",
       "      <td>It has tags of venezia, italy, veneto, metropo...</td>\n",
       "      <td>116674</td>\n",
       "      <td>other</td>\n",
       "      <td>0.219246</td>\n",
       "      <td>0.566851</td>\n",
       "      <td>0.025993</td>\n",
       "      <td>0.136470</td>\n",
       "      <td>0.005333</td>\n",
       "      <td>0.010028</td>\n",
       "      <td>...</td>\n",
       "      <td>0.782135</td>\n",
       "      <td>0.657558</td>\n",
       "      <td>0.566851</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.219246</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.136470</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.566851</td>\n",
       "      <td>0.922566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171024 rows × 791 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index                                           sentence  origin  \\\n",
       "0           0                      It has title of Lonely window      20   \n",
       "1           1     It has title of Reflection and bridge - Venice      24   \n",
       "2           2  It has tags of nopeople, architecture, outdoor...      24   \n",
       "3           3                 It has title of Venice fish market      28   \n",
       "4           4  It has tags of venice, venezia, italia, panora...      28   \n",
       "...       ...                                                ...     ...   \n",
       "171019  91498  It has tags of Venezia, Italy, Veneto, Metropo...  116671   \n",
       "171020  91499                          It has title of Ave Maria  116672   \n",
       "171021  91500  It has tags of Venezia, Italy, Veneto, Metropo...  116672   \n",
       "171022  91501  It has title of Door in the Jewish district of...  116674   \n",
       "171023  91502  It has tags of venezia, italy, veneto, metropo...  116674   \n",
       "\n",
       "         lang  Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0          en    0.168730     0.043053      0.149717     0.085379    0.033142   \n",
       "1          en    0.626143     0.111832      0.037493     0.066675    0.005745   \n",
       "2          en    0.078370     0.066554      0.056322     0.306494    0.300336   \n",
       "3          en    0.084237     0.185749      0.160403     0.287541    0.060195   \n",
       "4          en    0.030712     0.210020      0.322502     0.343468    0.026379   \n",
       "...       ...         ...          ...           ...          ...         ...   \n",
       "171019  other    0.282043     0.466190      0.053860     0.144850    0.004639   \n",
       "171020  other    0.452509     0.110522      0.084270     0.091483    0.007040   \n",
       "171021  other    0.321161     0.411557      0.064540     0.148818    0.003883   \n",
       "171022  other    0.366306     0.116810      0.180822     0.238843    0.004660   \n",
       "171023  other    0.219246     0.566851      0.025993     0.136470    0.005333   \n",
       "\n",
       "        Criteria vi  ...  BERT_766  BERT_767  max_1_val    max_1_col  \\\n",
       "0          0.074294  ... -0.116627  0.942009   0.180201   Criteria x   \n",
       "1          0.082481  ...  0.615546  0.812848   0.626143   Criteria i   \n",
       "2          0.082072  ... -0.056501  0.092934   0.306494  Criteria iv   \n",
       "3          0.088886  ...  0.362237  0.529293   0.287541  Criteria iv   \n",
       "4          0.045347  ...  0.507087 -0.684952   0.343468  Criteria iv   \n",
       "...             ...  ...       ...       ...        ...          ...   \n",
       "171019     0.010852  ...  0.723148  0.655239   0.466190  Criteria ii   \n",
       "171020     0.207345  ...  0.801728  0.383296   0.452509   Criteria i   \n",
       "171021     0.019492  ...  0.790162  0.515132   0.411557  Criteria ii   \n",
       "171022     0.051460  ...  0.688031  0.608966   0.366306   Criteria i   \n",
       "171023     0.010028  ...  0.782135  0.657558   0.566851  Criteria ii   \n",
       "\n",
       "        max_2_val     max_2_col  max_3_val     max_3_col     max_1     max_3  \n",
       "0        0.168730    Criteria i   0.149717  Criteria iii  0.180201  0.498648  \n",
       "1        0.111832   Criteria ii   0.082481   Criteria vi  0.626143  0.820457  \n",
       "2        0.300336    Criteria v   0.082072   Criteria vi  0.306494  0.688902  \n",
       "3        0.185749   Criteria ii   0.160403  Criteria iii  0.287541  0.633693  \n",
       "4        0.322502  Criteria iii   0.210020   Criteria ii  0.343468  0.875990  \n",
       "...           ...           ...        ...           ...       ...       ...  \n",
       "171019   0.282043    Criteria i   0.144850   Criteria iv  0.466190  0.893082  \n",
       "171020   0.207345   Criteria vi   0.110522   Criteria ii  0.452509  0.770377  \n",
       "171021   0.321161    Criteria i   0.148818   Criteria iv  0.411557  0.881536  \n",
       "171022   0.238843   Criteria iv   0.180822  Criteria iii  0.366306  0.785971  \n",
       "171023   0.219246    Criteria i   0.136470   Criteria iv  0.566851  0.922566  \n",
       "\n",
       "[171024 rows x 791 columns]"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "2ddb6424",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_s = pd.read_csv('data_storage/images/sentences_compare.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "896aa897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
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       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>It has title of Lonely window</td>\n",
       "      <td>20</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.250929</td>\n",
       "      <td>0.628690</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.180201</td>\n",
       "      <td>0.498648</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has title of Reflection and bridge - Venice</td>\n",
       "      <td>24</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.280426</td>\n",
       "      <td>0.692337</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.626143</td>\n",
       "      <td>0.820457</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>It has tags of nopeople, architecture, outdoor...</td>\n",
       "      <td>24</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.650610</td>\n",
       "      <td>0.894722</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria v</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.306494</td>\n",
       "      <td>0.688902</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>It has title of Venice fish market</td>\n",
       "      <td>28</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.216419</td>\n",
       "      <td>0.552309</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.287541</td>\n",
       "      <td>0.633693</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>It has tags of venice, venezia, italia, panora...</td>\n",
       "      <td>28</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.606865</td>\n",
       "      <td>0.829000</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.343468</td>\n",
       "      <td>0.875990</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171019</th>\n",
       "      <td>91498</td>\n",
       "      <td>It has tags of Venezia, Italy, Veneto, Metropo...</td>\n",
       "      <td>116671</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.357152</td>\n",
       "      <td>0.866123</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.466190</td>\n",
       "      <td>0.893082</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171020</th>\n",
       "      <td>91499</td>\n",
       "      <td>It has title of Ave Maria</td>\n",
       "      <td>116672</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.283526</td>\n",
       "      <td>0.691920</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.452509</td>\n",
       "      <td>0.770377</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171021</th>\n",
       "      <td>91500</td>\n",
       "      <td>It has tags of Venezia, Italy, Veneto, Metropo...</td>\n",
       "      <td>116672</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.365819</td>\n",
       "      <td>0.867949</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.411557</td>\n",
       "      <td>0.881536</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171022</th>\n",
       "      <td>91501</td>\n",
       "      <td>It has title of Door in the Jewish district of...</td>\n",
       "      <td>116674</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.407372</td>\n",
       "      <td>0.856523</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.366306</td>\n",
       "      <td>0.785971</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171023</th>\n",
       "      <td>91502</td>\n",
       "      <td>It has tags of venezia, italy, veneto, metropo...</td>\n",
       "      <td>116674</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.406759</td>\n",
       "      <td>0.882695</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.566851</td>\n",
       "      <td>0.922566</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171024 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index                                           sentence  origin  \\\n",
       "0           0                      It has title of Lonely window      20   \n",
       "1           1     It has title of Reflection and bridge - Venice      24   \n",
       "2           2  It has tags of nopeople, architecture, outdoor...      24   \n",
       "3           3                 It has title of Venice fish market      28   \n",
       "4           4  It has tags of venice, venezia, italia, panora...      28   \n",
       "...       ...                                                ...     ...   \n",
       "171019  91498  It has tags of Venezia, Italy, Veneto, Metropo...  116671   \n",
       "171020  91499                          It has title of Ave Maria  116672   \n",
       "171021  91500  It has tags of Venezia, Italy, Veneto, Metropo...  116672   \n",
       "171022  91501  It has title of Door in the Jewish district of...  116674   \n",
       "171023  91502  It has tags of venezia, italy, veneto, metropo...  116674   \n",
       "\n",
       "         lang max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "0          en     Criteria iii      Criteria vi       Criteria i   \n",
       "1          en     Criteria iii      Criteria vi       Criteria i   \n",
       "2          en     Criteria iii      Criteria vi      Criteria iv   \n",
       "3          en     Criteria iii      Criteria vi     Criteria vii   \n",
       "4          en     Criteria iii      Criteria iv      Criteria ii   \n",
       "...       ...              ...              ...              ...   \n",
       "171019  other      Criteria ii     Criteria iii      Criteria iv   \n",
       "171020  other     Criteria iii      Criteria vi      Criteria iv   \n",
       "171021  other     Criteria iii      Criteria ii      Criteria iv   \n",
       "171022  other      Criteria vi     Criteria iii      Criteria iv   \n",
       "171023  other      Criteria ii     Criteria iii      Criteria iv   \n",
       "\n",
       "        max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert  \\\n",
       "0           0.250929      0.628690     Criteria x     Criteria i   \n",
       "1           0.280426      0.692337     Criteria i    Criteria ii   \n",
       "2           0.650610      0.894722    Criteria iv     Criteria v   \n",
       "3           0.216419      0.552309    Criteria iv    Criteria ii   \n",
       "4           0.606865      0.829000    Criteria iv   Criteria iii   \n",
       "...              ...           ...            ...            ...   \n",
       "171019      0.357152      0.866123    Criteria ii     Criteria i   \n",
       "171020      0.283526      0.691920     Criteria i    Criteria vi   \n",
       "171021      0.365819      0.867949    Criteria ii     Criteria i   \n",
       "171022      0.407372      0.856523     Criteria i    Criteria iv   \n",
       "171023      0.406759      0.882695    Criteria ii     Criteria i   \n",
       "\n",
       "       max_3_col_bert  max_1_bert  max_3_bert  same_1  same_3  \n",
       "0        Criteria iii    0.180201    0.498648   False     0.5  \n",
       "1         Criteria vi    0.626143    0.820457   False     0.5  \n",
       "2         Criteria vi    0.306494    0.688902   False     0.5  \n",
       "3        Criteria iii    0.287541    0.633693   False     0.2  \n",
       "4         Criteria ii    0.343468    0.875990   False     1.0  \n",
       "...               ...         ...         ...     ...     ...  \n",
       "171019    Criteria iv    0.466190    0.893082    True     0.5  \n",
       "171020    Criteria ii    0.452509    0.770377   False     0.2  \n",
       "171021    Criteria iv    0.411557    0.881536   False     0.5  \n",
       "171022   Criteria iii    0.366306    0.785971   False     0.5  \n",
       "171023    Criteria iv    0.566851    0.922566    True     0.5  \n",
       "\n",
       "[171024 rows x 16 columns]"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "e99c7c67",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_s = cmp_s.merge(bert_s.drop_duplicates(subset='sentence',keep='first')[['max_1','max_3']], how='right',left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "3f4dadf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_s['conf_3'] = (cmp_s['max_3_ulmfit'] + cmp_s['max_3_bert'])/2\n",
    "cmp_s['conf_1'] = (cmp_s['max_1_ulmfit'] + cmp_s['max_1_bert'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "c351bfdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    61253.000000\n",
       "mean         0.767896\n",
       "std          0.087492\n",
       "min          0.449389\n",
       "25%          0.716231\n",
       "50%          0.781218\n",
       "75%          0.833607\n",
       "max          0.978242\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_s['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "b6703747",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    61253.000000\n",
       "mean         0.435439\n",
       "std          0.112225\n",
       "min          0.166067\n",
       "25%          0.351004\n",
       "50%          0.428198\n",
       "75%          0.511996\n",
       "max          0.940294\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_s['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "ac2e7dd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zzbn1\\AppData\\Local\\Temp\\ipykernel_6436\\3704144270.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  cmp_conf_s = cmp_s[cmp_s.conf_3>=0.75][cmp_s.same_3>=0.5]\n"
     ]
    }
   ],
   "source": [
    "cmp_conf_s = cmp_s[cmp_s.conf_3>=0.75][cmp_s.same_3>=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "c5d8ff5e",
   "metadata": {},
   "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>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>conf_3</th>\n",
       "      <th>conf_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has title of Reflection and bridge - Venice</td>\n",
       "      <td>24</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.280426</td>\n",
       "      <td>0.692337</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.626143</td>\n",
       "      <td>0.820457</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.626143</td>\n",
       "      <td>0.820457</td>\n",
       "      <td>0.756397</td>\n",
       "      <td>0.453285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>It has tags of nopeople, architecture, outdoor...</td>\n",
       "      <td>24</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.650610</td>\n",
       "      <td>0.894722</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria v</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.306494</td>\n",
       "      <td>0.688902</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.306494</td>\n",
       "      <td>0.688902</td>\n",
       "      <td>0.791812</td>\n",
       "      <td>0.478552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>It has tags of venice, venezia, italia, panora...</td>\n",
       "      <td>28</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.606865</td>\n",
       "      <td>0.829000</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.343468</td>\n",
       "      <td>0.875990</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.343468</td>\n",
       "      <td>0.875990</td>\n",
       "      <td>0.852495</td>\n",
       "      <td>0.475166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>It has tags of venice, venezia, italia, panora...</td>\n",
       "      <td>29</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.522550</td>\n",
       "      <td>0.821304</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.342281</td>\n",
       "      <td>0.817564</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.342281</td>\n",
       "      <td>0.817564</td>\n",
       "      <td>0.819434</td>\n",
       "      <td>0.432415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>It has tags of venice, venezia, italia, panora...</td>\n",
       "      <td>30</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.537576</td>\n",
       "      <td>0.825881</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.425743</td>\n",
       "      <td>0.823486</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.425743</td>\n",
       "      <td>0.823486</td>\n",
       "      <td>0.824683</td>\n",
       "      <td>0.481660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171017</th>\n",
       "      <td>91496</td>\n",
       "      <td>It has tags of venezia, italy, veneto, metropo...</td>\n",
       "      <td>116670</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.401659</td>\n",
       "      <td>0.834411</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.440942</td>\n",
       "      <td>0.916597</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.440942</td>\n",
       "      <td>0.916597</td>\n",
       "      <td>0.875504</td>\n",
       "      <td>0.421301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171019</th>\n",
       "      <td>91498</td>\n",
       "      <td>It has tags of Venezia, Italy, Veneto, Metropo...</td>\n",
       "      <td>116671</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.357152</td>\n",
       "      <td>0.866123</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.466190</td>\n",
       "      <td>0.893082</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.466190</td>\n",
       "      <td>0.893082</td>\n",
       "      <td>0.879602</td>\n",
       "      <td>0.411671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171021</th>\n",
       "      <td>91500</td>\n",
       "      <td>It has tags of Venezia, Italy, Veneto, Metropo...</td>\n",
       "      <td>116672</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.365819</td>\n",
       "      <td>0.867949</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.411557</td>\n",
       "      <td>0.881536</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.411557</td>\n",
       "      <td>0.881536</td>\n",
       "      <td>0.874742</td>\n",
       "      <td>0.388688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171022</th>\n",
       "      <td>91501</td>\n",
       "      <td>It has title of Door in the Jewish district of...</td>\n",
       "      <td>116674</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.407372</td>\n",
       "      <td>0.856523</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.366306</td>\n",
       "      <td>0.785971</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.366306</td>\n",
       "      <td>0.785971</td>\n",
       "      <td>0.821247</td>\n",
       "      <td>0.386839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171023</th>\n",
       "      <td>91502</td>\n",
       "      <td>It has tags of venezia, italy, veneto, metropo...</td>\n",
       "      <td>116674</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.406759</td>\n",
       "      <td>0.882695</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.566851</td>\n",
       "      <td>0.922566</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.566851</td>\n",
       "      <td>0.922566</td>\n",
       "      <td>0.902631</td>\n",
       "      <td>0.486805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25754 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index                                           sentence  origin  \\\n",
       "1           1     It has title of Reflection and bridge - Venice      24   \n",
       "2           2  It has tags of nopeople, architecture, outdoor...      24   \n",
       "4           4  It has tags of venice, venezia, italia, panora...      28   \n",
       "7           7  It has tags of venice, venezia, italia, panora...      29   \n",
       "9           9  It has tags of venice, venezia, italia, panora...      30   \n",
       "...       ...                                                ...     ...   \n",
       "171017  91496  It has tags of venezia, italy, veneto, metropo...  116670   \n",
       "171019  91498  It has tags of Venezia, Italy, Veneto, Metropo...  116671   \n",
       "171021  91500  It has tags of Venezia, Italy, Veneto, Metropo...  116672   \n",
       "171022  91501  It has title of Door in the Jewish district of...  116674   \n",
       "171023  91502  It has tags of venezia, italy, veneto, metropo...  116674   \n",
       "\n",
       "         lang max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "1          en     Criteria iii      Criteria vi       Criteria i   \n",
       "2          en     Criteria iii      Criteria vi      Criteria iv   \n",
       "4          en     Criteria iii      Criteria iv      Criteria ii   \n",
       "7          en     Criteria iii      Criteria iv      Criteria ii   \n",
       "9          en     Criteria iii      Criteria iv      Criteria ii   \n",
       "...       ...              ...              ...              ...   \n",
       "171017  other     Criteria iii      Criteria iv      Criteria ii   \n",
       "171019  other      Criteria ii     Criteria iii      Criteria iv   \n",
       "171021  other     Criteria iii      Criteria ii      Criteria iv   \n",
       "171022  other      Criteria vi     Criteria iii      Criteria iv   \n",
       "171023  other      Criteria ii     Criteria iii      Criteria iv   \n",
       "\n",
       "        max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert  \\\n",
       "1           0.280426      0.692337     Criteria i    Criteria ii   \n",
       "2           0.650610      0.894722    Criteria iv     Criteria v   \n",
       "4           0.606865      0.829000    Criteria iv   Criteria iii   \n",
       "7           0.522550      0.821304    Criteria iv   Criteria iii   \n",
       "9           0.537576      0.825881    Criteria iv    Criteria ii   \n",
       "...              ...           ...            ...            ...   \n",
       "171017      0.401659      0.834411    Criteria iv    Criteria ii   \n",
       "171019      0.357152      0.866123    Criteria ii     Criteria i   \n",
       "171021      0.365819      0.867949    Criteria ii     Criteria i   \n",
       "171022      0.407372      0.856523     Criteria i    Criteria iv   \n",
       "171023      0.406759      0.882695    Criteria ii     Criteria i   \n",
       "\n",
       "       max_3_col_bert  max_1_bert  max_3_bert  same_1  same_3     max_1  \\\n",
       "1         Criteria vi    0.626143    0.820457   False     0.5  0.626143   \n",
       "2         Criteria vi    0.306494    0.688902   False     0.5  0.306494   \n",
       "4         Criteria ii    0.343468    0.875990   False     1.0  0.343468   \n",
       "7         Criteria ii    0.342281    0.817564   False     1.0  0.342281   \n",
       "9          Criteria i    0.425743    0.823486   False     0.5  0.425743   \n",
       "...               ...         ...         ...     ...     ...       ...   \n",
       "171017     Criteria i    0.440942    0.916597   False     0.5  0.440942   \n",
       "171019    Criteria iv    0.466190    0.893082    True     0.5  0.466190   \n",
       "171021    Criteria iv    0.411557    0.881536   False     0.5  0.411557   \n",
       "171022   Criteria iii    0.366306    0.785971   False     0.5  0.366306   \n",
       "171023    Criteria iv    0.566851    0.922566    True     0.5  0.566851   \n",
       "\n",
       "           max_3    conf_3    conf_1  \n",
       "1       0.820457  0.756397  0.453285  \n",
       "2       0.688902  0.791812  0.478552  \n",
       "4       0.875990  0.852495  0.475166  \n",
       "7       0.817564  0.819434  0.432415  \n",
       "9       0.823486  0.824683  0.481660  \n",
       "...          ...       ...       ...  \n",
       "171017  0.916597  0.875504  0.421301  \n",
       "171019  0.893082  0.879602  0.411671  \n",
       "171021  0.881536  0.874742  0.388688  \n",
       "171022  0.785971  0.821247  0.386839  \n",
       "171023  0.922566  0.902631  0.486805  \n",
       "\n",
       "[25754 rows x 20 columns]"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7862c63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15058705210964543"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "25754/171024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "633acb47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['the sky was grey, the water green and the camera sensor dirty'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s[cmp_conf_s.max_3_col_bert=='Others']['sentence'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "e0c47623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25754.000000\n",
       "mean         0.825144\n",
       "std          0.044890\n",
       "min          0.750012\n",
       "25%          0.789387\n",
       "50%          0.822347\n",
       "75%          0.856033\n",
       "max          0.978242\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "f15c6255",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25754.000000\n",
       "mean         0.490181\n",
       "std          0.097513\n",
       "min          0.260655\n",
       "25%          0.418234\n",
       "50%          0.479712\n",
       "75%          0.550364\n",
       "max          0.940294\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "5b2f8f7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25754.000000\n",
       "mean         0.603071\n",
       "std          0.202271\n",
       "min          0.500000\n",
       "25%          0.500000\n",
       "50%          0.500000\n",
       "75%          0.500000\n",
       "max          1.000000\n",
       "Name: same_3, dtype: float64"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['same_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "719bee98",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     25754\n",
       "unique        2\n",
       "top       False\n",
       "freq      16606\n",
       "Name: same_1, dtype: object"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['same_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "f33d76e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iv      0.241005\n",
       "Criteria iii     0.203632\n",
       "Criteria ii      0.190178\n",
       "Criteria vi      0.172038\n",
       "Criteria i       0.171714\n",
       "Criteria vii     0.007203\n",
       "Criteria v       0.005177\n",
       "Criteria x       0.004401\n",
       "Criteria viii    0.002543\n",
       "Criteria ix      0.002103\n",
       "Others           0.000006\n",
       "dtype: float64"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf_s['max_1_col_bert'],\n",
    "           cmp_conf_s['max_2_col_bert'],\n",
    "           cmp_conf_s['max_3_col_bert'],\n",
    "           cmp_conf_s['max_1_col_ulmfit'],\n",
    "           cmp_conf_s['max_2_col_ulmfit'],\n",
    "           cmp_conf_s['max_3_col_ulmfit']]).value_counts()/(25754*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "ac5c5937",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentages['VEN_XL_s'] = pd.concat([cmp_conf_s['max_1_col_bert'],\n",
    "           cmp_conf_s['max_2_col_bert'],\n",
    "           cmp_conf_s['max_3_col_bert'],\n",
    "           cmp_conf_s['max_1_col_ulmfit'],\n",
    "           cmp_conf_s['max_2_col_ulmfit'],\n",
    "           cmp_conf_s['max_3_col_ulmfit']]).value_counts()/(25754*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "7632bb29",
   "metadata": {},
   "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>VEN</th>\n",
       "      <th>VEN_s</th>\n",
       "      <th>VEN_XL</th>\n",
       "      <th>VEN_XL_s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUV</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Criteria i</th>\n",
       "      <td>0.147707</td>\n",
       "      <td>0.183544</td>\n",
       "      <td>0.075867</td>\n",
       "      <td>0.171714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ii</th>\n",
       "      <td>0.202381</td>\n",
       "      <td>0.188668</td>\n",
       "      <td>0.162301</td>\n",
       "      <td>0.190178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iii</th>\n",
       "      <td>0.213845</td>\n",
       "      <td>0.191581</td>\n",
       "      <td>0.270847</td>\n",
       "      <td>0.203632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iv</th>\n",
       "      <td>0.240300</td>\n",
       "      <td>0.252863</td>\n",
       "      <td>0.233602</td>\n",
       "      <td>0.241005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria v</th>\n",
       "      <td>0.008598</td>\n",
       "      <td>0.004722</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.005177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vi</th>\n",
       "      <td>0.183862</td>\n",
       "      <td>0.162849</td>\n",
       "      <td>0.249893</td>\n",
       "      <td>0.172038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vii</th>\n",
       "      <td>0.001764</td>\n",
       "      <td>0.006631</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>0.007203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria viii</th>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>0.002543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ix</th>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.003516</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.002103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria x</th>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.001520</td>\n",
       "      <td>0.004401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    VEN     VEN_s    VEN_XL  VEN_XL_s\n",
       "OUV                                                  \n",
       "Criteria i     0.147707  0.183544  0.075867  0.171714\n",
       "Criteria ii    0.202381  0.188668  0.162301  0.190178\n",
       "Criteria iii   0.213845  0.191581  0.270847  0.203632\n",
       "Criteria iv    0.240300  0.252863  0.233602  0.241005\n",
       "Criteria v     0.008598  0.004722  0.002794  0.005177\n",
       "Criteria vi    0.183862  0.162849  0.249893  0.172038\n",
       "Criteria vii   0.001764  0.006631  0.002231  0.007203\n",
       "Criteria viii  0.000220  0.002813  0.000349  0.002543\n",
       "Criteria ix    0.000441  0.003516  0.000595  0.002103\n",
       "Criteria x     0.000882  0.002813  0.001520  0.004401\n",
       "Others              NaN       NaN       NaN  0.000006"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "2d0c021b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iv      37241\n",
       "Criteria iii     31466\n",
       "Criteria ii      29387\n",
       "Criteria vi      26584\n",
       "Criteria i       26534\n",
       "Criteria vii      1113\n",
       "Criteria v         800\n",
       "Criteria x         680\n",
       "Criteria viii      393\n",
       "Criteria ix        325\n",
       "Others               1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf_s['max_1_col_bert'],\n",
    "           cmp_conf_s['max_2_col_bert'],\n",
    "           cmp_conf_s['max_3_col_bert'],\n",
    "           cmp_conf_s['max_1_col_ulmfit'],\n",
    "           cmp_conf_s['max_2_col_ulmfit'],\n",
    "           cmp_conf_s['max_3_col_ulmfit']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "41291a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_sorted = cmp_conf_s.sort_values(by='conf_1',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "35a96299",
   "metadata": {},
   "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>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>conf_3</th>\n",
       "      <th>conf_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168027</th>\n",
       "      <td>86702</td>\n",
       "      <td>The facade, a masterpiece of decorated niches,...</td>\n",
       "      <td>104616</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.965096</td>\n",
       "      <td>0.995026</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.915491</td>\n",
       "      <td>0.961458</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.915491</td>\n",
       "      <td>0.961458</td>\n",
       "      <td>0.978242</td>\n",
       "      <td>0.940294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164938</th>\n",
       "      <td>81007</td>\n",
       "      <td>The ceiling, with rich carvings and eight near...</td>\n",
       "      <td>89729</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.928368</td>\n",
       "      <td>0.977318</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.902832</td>\n",
       "      <td>0.947419</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.902832</td>\n",
       "      <td>0.947419</td>\n",
       "      <td>0.962368</td>\n",
       "      <td>0.915600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157249</th>\n",
       "      <td>67777</td>\n",
       "      <td>Masterful work whose intensity is matched only...</td>\n",
       "      <td>59437</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.982605</td>\n",
       "      <td>0.997875</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.842241</td>\n",
       "      <td>0.937806</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.842241</td>\n",
       "      <td>0.937806</td>\n",
       "      <td>0.967841</td>\n",
       "      <td>0.912423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8071</th>\n",
       "      <td>8364</td>\n",
       "      <td>It took 50 years to erect and is a masterpiece...</td>\n",
       "      <td>20717</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.901978</td>\n",
       "      <td>0.985675</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.911216</td>\n",
       "      <td>0.951821</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.911216</td>\n",
       "      <td>0.951821</td>\n",
       "      <td>0.968748</td>\n",
       "      <td>0.906597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67113</th>\n",
       "      <td>9394</td>\n",
       "      <td>Tiffany glass is an Art Nouveau icon known for...</td>\n",
       "      <td>25396</td>\n",
       "      <td>it</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.924449</td>\n",
       "      <td>0.994325</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.879572</td>\n",
       "      <td>0.940908</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.879572</td>\n",
       "      <td>0.940908</td>\n",
       "      <td>0.967617</td>\n",
       "      <td>0.902010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72159</th>\n",
       "      <td>14515</td>\n",
       "      <td>It has title of marine dry street</td>\n",
       "      <td>42041</td>\n",
       "      <td>it</td>\n",
       "      <td>Criteria ix</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.268377</td>\n",
       "      <td>0.780456</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria ix</td>\n",
       "      <td>0.291963</td>\n",
       "      <td>0.762115</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.291963</td>\n",
       "      <td>0.762115</td>\n",
       "      <td>0.771285</td>\n",
       "      <td>0.280170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63326</th>\n",
       "      <td>5525</td>\n",
       "      <td>It has title of Minelli building</td>\n",
       "      <td>14090</td>\n",
       "      <td>it</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.260268</td>\n",
       "      <td>0.724218</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.290063</td>\n",
       "      <td>0.846133</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.290063</td>\n",
       "      <td>0.846133</td>\n",
       "      <td>0.785176</td>\n",
       "      <td>0.275165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47183</th>\n",
       "      <td>49008</td>\n",
       "      <td>In his will dated 1587, Giovanni Grimani expre...</td>\n",
       "      <td>79567</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.290412</td>\n",
       "      <td>0.827162</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.253536</td>\n",
       "      <td>0.728589</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.253536</td>\n",
       "      <td>0.728589</td>\n",
       "      <td>0.777876</td>\n",
       "      <td>0.271974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54477</th>\n",
       "      <td>56678</td>\n",
       "      <td>In the distance: Ponte San Alvise</td>\n",
       "      <td>107477</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.252147</td>\n",
       "      <td>0.698171</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.282588</td>\n",
       "      <td>0.811586</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.282588</td>\n",
       "      <td>0.811586</td>\n",
       "      <td>0.754878</td>\n",
       "      <td>0.267367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63502</th>\n",
       "      <td>5702</td>\n",
       "      <td>From 7 February the Casa dei Tre Oci inaugurat...</td>\n",
       "      <td>14350</td>\n",
       "      <td>it</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.252573</td>\n",
       "      <td>0.714298</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.268736</td>\n",
       "      <td>0.796323</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.268736</td>\n",
       "      <td>0.796323</td>\n",
       "      <td>0.755310</td>\n",
       "      <td>0.260655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25754 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index                                           sentence  origin  \\\n",
       "168027  86702  The facade, a masterpiece of decorated niches,...  104616   \n",
       "164938  81007  The ceiling, with rich carvings and eight near...   89729   \n",
       "157249  67777  Masterful work whose intensity is matched only...   59437   \n",
       "8071     8364  It took 50 years to erect and is a masterpiece...   20717   \n",
       "67113    9394  Tiffany glass is an Art Nouveau icon known for...   25396   \n",
       "...       ...                                                ...     ...   \n",
       "72159   14515                  It has title of marine dry street   42041   \n",
       "63326    5525                   It has title of Minelli building   14090   \n",
       "47183   49008  In his will dated 1587, Giovanni Grimani expre...   79567   \n",
       "54477   56678                  In the distance: Ponte San Alvise  107477   \n",
       "63502    5702  From 7 February the Casa dei Tre Oci inaugurat...   14350   \n",
       "\n",
       "         lang max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "168027  other       Criteria i      Criteria iv      Criteria ii   \n",
       "164938  other       Criteria i      Criteria iv      Criteria ii   \n",
       "157249  other       Criteria i      Criteria iv      Criteria ii   \n",
       "8071       en       Criteria i      Criteria ii      Criteria iv   \n",
       "67113      it       Criteria i      Criteria ii      Criteria iv   \n",
       "...       ...              ...              ...              ...   \n",
       "72159      it      Criteria ix     Criteria vii    Criteria viii   \n",
       "63326      it     Criteria iii       Criteria i      Criteria iv   \n",
       "47183      en      Criteria ii     Criteria iii      Criteria vi   \n",
       "54477      en      Criteria ii       Criteria i      Criteria vi   \n",
       "63502      it      Criteria iv      Criteria ii     Criteria iii   \n",
       "\n",
       "        max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert  \\\n",
       "168027      0.965096      0.995026     Criteria i    Criteria ii   \n",
       "164938      0.928368      0.977318     Criteria i    Criteria ii   \n",
       "157249      0.982605      0.997875     Criteria i    Criteria ii   \n",
       "8071        0.901978      0.985675     Criteria i    Criteria ii   \n",
       "67113       0.924449      0.994325     Criteria i    Criteria ii   \n",
       "...              ...           ...            ...            ...   \n",
       "72159       0.268377      0.780456   Criteria vii     Criteria x   \n",
       "63326       0.260268      0.724218     Criteria i    Criteria ii   \n",
       "47183       0.290412      0.827162   Criteria iii    Criteria ii   \n",
       "54477       0.252147      0.698171    Criteria iv     Criteria i   \n",
       "63502       0.252573      0.714298    Criteria ii     Criteria i   \n",
       "\n",
       "       max_3_col_bert  max_1_bert  max_3_bert  same_1  same_3     max_1  \\\n",
       "168027    Criteria iv    0.915491    0.961458    True     1.0  0.915491   \n",
       "164938    Criteria vi    0.902832    0.947419    True     0.5  0.902832   \n",
       "157249    Criteria iv    0.842241    0.937806    True     1.0  0.842241   \n",
       "8071      Criteria iv    0.911216    0.951821    True     1.0  0.911216   \n",
       "67113     Criteria vi    0.879572    0.940908    True     0.5  0.879572   \n",
       "...               ...         ...         ...     ...     ...       ...   \n",
       "72159     Criteria ix    0.291963    0.762115   False     0.5  0.291963   \n",
       "63326     Criteria iv    0.290063    0.846133   False     0.5  0.290063   \n",
       "47183     Criteria vi    0.253536    0.728589   False     1.0  0.253536   \n",
       "54477     Criteria vi    0.282588    0.811586   False     0.5  0.282588   \n",
       "63502     Criteria iv    0.268736    0.796323   False     0.5  0.268736   \n",
       "\n",
       "           max_3    conf_3    conf_1  \n",
       "168027  0.961458  0.978242  0.940294  \n",
       "164938  0.947419  0.962368  0.915600  \n",
       "157249  0.937806  0.967841  0.912423  \n",
       "8071    0.951821  0.968748  0.906597  \n",
       "67113   0.940908  0.967617  0.902010  \n",
       "...          ...       ...       ...  \n",
       "72159   0.762115  0.771285  0.280170  \n",
       "63326   0.846133  0.785176  0.275165  \n",
       "47183   0.728589  0.777876  0.271974  \n",
       "54477   0.811586  0.754878  0.267367  \n",
       "63502   0.796323  0.755310  0.260655  \n",
       "\n",
       "[25754 rows x 20 columns]"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "9a2b363c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Criteria i ['The facade, a masterpiece of decorated niches, pilasters and white or polychrome marble statues, was later finished by Mauro Codussi'\n",
      " 'The ceiling, with rich carvings and eight nearly life-size angels, is by Francesco Bernardoni and contains in the center La Gloria di S Domenico, a masterpiece by Giovanni Battista Piazzetta, completed in 1727: one of the greatest works of the 18th century'\n",
      " 'Masterful work whose intensity is matched only by its extreme beauty: a masterpiece of European painting.'] [0.94029367 0.91560015 0.91242285]\n",
      "Criteria ii [\"He renewed this artistic gesture in 1972 at the Galerie L'Attico in Rome, and then in 1978, 1981 and 2004 in various cultural venues.\"\n",
      " \"The Ballets Russes' influence on the world of art cannot be overestimated, as opposed to the influence it had on the development of classical ballet.\"\n",
      " 'Cohen this amalgamation, perhaps less perceptible by foreigners than by French people, but which is very relevant in the context of this biennale questioning the meaning of modernity in architecture'] [0.8786885  0.84931443 0.84134892]\n",
      "Criteria iii ['Cityscapes like this are anthropological snapshots that provide clues about the inhabitants'\n",
      " 'Funerary stele of Lysandra from Smyrna, Asia Minor, 2nd half of 2'\n",
      " 'Detail with the inscription from the funerary stele of Lysandra from Smyrna, Asia Minor, 2nd half of 2'] [0.89160293 0.85037328 0.83806774]\n",
      "Criteria iv ['Drawing Together: Assembling the Maison du Peuple, 2018 [Close Encounter]'\n",
      " 'Typical example of a 15th century palazzo with a beautiful restored piano nobile loggia'\n",
      " \"It's a very good conserved example of a late-gothic two-family palace\"] [0.88834855 0.84027299 0.80330986]\n",
      "Criteria v ['Farming The Life'\n",
      " 'But the Inuit wanted to continue living on their land'\n",
      " 'Deliveries of parcels or subsistence are made here by water.'] [0.6825126  0.65595031 0.65494198]\n",
      "Criteria vi ['stresses that it is a place of memory for traditional Tibetan spirituality (endorsed by His Holiness the Dalai Lama) but also a space of reflection for visitors who should ask them about the history of Tibet'\n",
      " 'My images may not be used for any profane or immoral purpose or to incite violence or hatred'\n",
      " 'Founder of the Society of Jesus - the Jesuits'] [0.8972536  0.88257377 0.86751729]\n",
      "Criteria vii ['If you look carefully, you can even see the snow-capped peaks of the Dolomites on the distant horizon'\n",
      " 'With the window slid open briefly there is a wonderful lick of sea air'\n",
      " 'That is why you can enjoy the beauty of this vivid colourful paradise'] [0.88293409 0.87734389 0.86099892]\n",
      "Criteria viii ['Future Fossil Spaces, 2017' 'It has title of Future Fossil Spaces'\n",
      " 'Explore Mar 16, 2018 71'] [0.89910773 0.7544241  0.72675537]\n",
      "Criteria ix ['It has title of Mucous layer aqueous layer lipid layer.'\n",
      " 'The sinking of the Costa is however a warning, zero risk does not exist and even less in an environment as fragile as the lagoon'\n",
      " 'It has tags of trees, forest, baum, b, wald, bw, supershot, pine, pines, blinkagain'] [0.62945747 0.53692087 0.45368697]\n",
      "Criteria x ['The bacteria killed 50,000 people, almost one third of the total population'\n",
      " 'It must not be confused with the in general yellow-legged Larus michahellis, which are more common in the Mediterranean area but single birds may reach more Northern seas.'\n",
      " 'It has title of Rhino in the garden'] [0.82094068 0.80676856 0.80198786]\n",
      "Others [] []\n"
     ]
    }
   ],
   "source": [
    "for c in classes:\n",
    "    temp = cmp_sorted[cmp_sorted.max_1_col_bert==c].iloc[:3]\n",
    "    print(c, temp['sentence'].values,temp['conf_1'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "0d36688d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Criteria i ['The facade, a masterpiece of decorated niches, pilasters and white or polychrome marble statues, was later finished by Mauro Codussi'\n",
      " 'The ceiling, with rich carvings and eight nearly life-size angels, is by Francesco Bernardoni and contains in the center La Gloria di S Domenico, a masterpiece by Giovanni Battista Piazzetta, completed in 1727: one of the greatest works of the 18th century'\n",
      " 'Masterful work whose intensity is matched only by its extreme beauty: a masterpiece of European painting.'] [0.94029367 0.91560015 0.91242285]\n",
      "Criteria ii ['Drawing Together: Assembling the Maison du Peuple, 2018 [Close Encounter]'\n",
      " \"He renewed this artistic gesture in 1972 at the Galerie L'Attico in Rome, and then in 1978, 1981 and 2004 in various cultural venues.\"\n",
      " \"The Ballets Russes' influence on the world of art cannot be overestimated, as opposed to the influence it had on the development of classical ballet.\"] [0.88834855 0.8786885  0.84931443]\n",
      "Criteria iii ['Cityscapes like this are anthropological snapshots that provide clues about the inhabitants'\n",
      " 'Funerary stele of Lysandra from Smyrna, Asia Minor, 2nd half of 2'\n",
      " 'Detail with the inscription from the funerary stele of Lysandra from Smyrna, Asia Minor, 2nd half of 2'] [0.89160293 0.85037328 0.83806774]\n",
      "Criteria iv ['Typical example of a 15th century palazzo with a beautiful restored piano nobile loggia'\n",
      " \"It's a very good conserved example of a late-gothic two-family palace\"\n",
      " 'Hall churches are typical of the brick Gothic of the coastal region'] [0.84027299 0.80330986 0.80229973]\n",
      "Criteria v ['old conserved) residential buildings.' 'Farming The Life'\n",
      " 'But the Inuit wanted to continue living on their land'] [0.70350584 0.6825126  0.65595031]\n",
      "Criteria vi ['stresses that it is a place of memory for traditional Tibetan spirituality (endorsed by His Holiness the Dalai Lama) but also a space of reflection for visitors who should ask them about the history of Tibet'\n",
      " 'My images may not be used for any profane or immoral purpose or to incite violence or hatred'\n",
      " 'Founder of the Society of Jesus - the Jesuits'] [0.8972536  0.88257377 0.86751729]\n",
      "Criteria vii ['If you look carefully, you can even see the snow-capped peaks of the Dolomites on the distant horizon'\n",
      " 'With the window slid open briefly there is a wonderful lick of sea air'\n",
      " 'That is why you can enjoy the beauty of this vivid colourful paradise'] [0.88293409 0.87734389 0.86099892]\n",
      "Criteria viii ['Future Fossil Spaces, 2017' 'It has title of Future Fossil Spaces'\n",
      " 'Explore Mar 16, 2018 71'] [0.89910773 0.7544241  0.72675537]\n",
      "Criteria ix ['It has title of Climate Change, Castello, Venice, Italy'\n",
      " 'It has tags of coast, delightful, cruises, golf, putting, green'\n",
      " 'It has tags of coast, delightful, cruises, cabin, suite, bathroom, tub, whirlpool'] [0.66413835 0.6208429  0.60997017]\n",
      "Criteria x ['The bacteria killed 50,000 people, almost one third of the total population'\n",
      " 'It must not be confused with the in general yellow-legged Larus michahellis, which are more common in the Mediterranean area but single birds may reach more Northern seas.'\n",
      " 'It has title of Rhino in the garden'] [0.82094068 0.80676856 0.80198786]\n",
      "Others [] []\n"
     ]
    }
   ],
   "source": [
    "for c in classes:\n",
    "    temp = cmp_sorted[cmp_sorted.max_1_col_ulmfit==c].iloc[:3]\n",
    "    print(c, temp['sentence'].values,temp['conf_1'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d50781be",
   "metadata": {},
   "source": [
    "## Amsterdam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "1aad8db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert = pd.read_csv('Amsterdam/data_storage/metadata_bert.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "84d278ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert['max_1_eq'] = bert['max_1_eq'].fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "982b39c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert = bert[bert.max_1_eq!=-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "c94f1361",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp = pd.read_csv('Amsterdam/data_storage/metadata_compare.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "9115a4ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp = cmp.merge(bert[['max_1_eq','max_3_eq']], how='right',left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "73057a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp['conf_3'] = (cmp['max_3_ulmfit'] + cmp['max_3_bert'])/2\n",
    "cmp['conf_1'] = (cmp['max_1_ulmfit'] + cmp['max_1_bert'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "7238171d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2904.000000\n",
       "mean        0.759687\n",
       "std         0.081750\n",
       "min         0.450763\n",
       "25%         0.701647\n",
       "50%         0.768293\n",
       "75%         0.820700\n",
       "max         0.954249\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "0a88e5f4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2904.000000\n",
       "mean        0.426400\n",
       "std         0.108188\n",
       "min         0.183077\n",
       "25%         0.344021\n",
       "50%         0.407107\n",
       "75%         0.489209\n",
       "max         0.771919\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "7b6a9997",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zzbn1\\AppData\\Local\\Temp\\ipykernel_6436\\3362900713.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  cmp_conf = cmp[cmp.conf_3>=0.75][cmp.same_3>=0.5]\n"
     ]
    }
   ],
   "source": [
    "cmp_conf = cmp[cmp.conf_3>=0.75][cmp.same_3>=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "c2e55609",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "      <th>max_1_eq</th>\n",
       "      <th>max_3_eq</th>\n",
       "      <th>conf_3</th>\n",
       "      <th>conf_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>51885929339</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.280558</td>\n",
       "      <td>0.813833</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.531683</td>\n",
       "      <td>0.877878</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.805216</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.845855</td>\n",
       "      <td>0.406120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>51884357517</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.779117</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.509236</td>\n",
       "      <td>0.742402</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.670258</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.760760</td>\n",
       "      <td>0.475271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>51884423594</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.401489</td>\n",
       "      <td>0.787524</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.470036</td>\n",
       "      <td>0.846035</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.705522</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.816779</td>\n",
       "      <td>0.435763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>51882154188</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.304299</td>\n",
       "      <td>0.730016</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.440501</td>\n",
       "      <td>0.829711</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.776164</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.779864</td>\n",
       "      <td>0.372400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>51882056851</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.413302</td>\n",
       "      <td>0.834710</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.428247</td>\n",
       "      <td>0.810964</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.937864</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.822837</td>\n",
       "      <td>0.420775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3718</th>\n",
       "      <td>4988</td>\n",
       "      <td>51364036007</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.401202</td>\n",
       "      <td>0.736451</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.707430</td>\n",
       "      <td>0.854239</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.853071</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.795345</td>\n",
       "      <td>0.554316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3719</th>\n",
       "      <td>4989</td>\n",
       "      <td>51365804155</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.401202</td>\n",
       "      <td>0.736451</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.707430</td>\n",
       "      <td>0.854239</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.853071</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.795345</td>\n",
       "      <td>0.554316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3720</th>\n",
       "      <td>4990</td>\n",
       "      <td>51364790451</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.401202</td>\n",
       "      <td>0.736451</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.707430</td>\n",
       "      <td>0.854239</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.853071</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.795345</td>\n",
       "      <td>0.554316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3722</th>\n",
       "      <td>4993</td>\n",
       "      <td>51361633644</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.365005</td>\n",
       "      <td>0.752462</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.461521</td>\n",
       "      <td>0.763086</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.739271</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.757774</td>\n",
       "      <td>0.413263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3726</th>\n",
       "      <td>4999</td>\n",
       "      <td>51368197299</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.328312</td>\n",
       "      <td>0.726001</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.437944</td>\n",
       "      <td>0.795807</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.883181</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.760904</td>\n",
       "      <td>0.383128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1247 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin        index max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "0          2  51885929339      Criteria iv     Criteria iii      Criteria ii   \n",
       "1          3  51884357517     Criteria iii      Criteria iv      Criteria ii   \n",
       "3          9  51884423594      Criteria ii      Criteria iv     Criteria iii   \n",
       "4         13  51882154188     Criteria iii      Criteria iv      Criteria vi   \n",
       "8         17  51882056851      Criteria iv      Criteria ii       Criteria i   \n",
       "...      ...          ...              ...              ...              ...   \n",
       "3718    4988  51364036007      Criteria vi     Criteria iii      Criteria iv   \n",
       "3719    4989  51365804155      Criteria vi     Criteria iii      Criteria iv   \n",
       "3720    4990  51364790451      Criteria vi     Criteria iii      Criteria iv   \n",
       "3722    4993  51361633644     Criteria iii      Criteria iv      Criteria vi   \n",
       "3726    4999  51368197299      Criteria vi     Criteria iii      Criteria iv   \n",
       "\n",
       "      max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert max_3_col_bert  \\\n",
       "0         0.280558      0.813833    Criteria iv    Criteria ii    Criteria vi   \n",
       "1         0.441306      0.779117    Criteria iv   Criteria iii    Criteria vi   \n",
       "3         0.401489      0.787524    Criteria iv    Criteria vi    Criteria ii   \n",
       "4         0.304299      0.730016    Criteria iv    Criteria vi    Criteria ii   \n",
       "8         0.413302      0.834710    Criteria iv     Criteria i    Criteria vi   \n",
       "...            ...           ...            ...            ...            ...   \n",
       "3718      0.401202      0.736451    Criteria vi     Criteria i    Criteria iv   \n",
       "3719      0.401202      0.736451    Criteria vi     Criteria i    Criteria iv   \n",
       "3720      0.401202      0.736451    Criteria vi     Criteria i    Criteria iv   \n",
       "3722      0.365005      0.752462    Criteria vi   Criteria iii    Criteria iv   \n",
       "3726      0.328312      0.726001    Criteria vi    Criteria ii    Criteria iv   \n",
       "\n",
       "      max_1_bert  max_3_bert  same_1  same_3  cosine_sim max_1_eq  max_3_eq  \\\n",
       "0       0.531683    0.877878    True     0.5    0.805216    False       0.5   \n",
       "1       0.509236    0.742402   False     0.5    0.670258     True       1.0   \n",
       "3       0.470036    0.846035   False     0.5    0.705522    False       1.0   \n",
       "4       0.440501    0.829711   False     0.5    0.776164     True       1.0   \n",
       "8       0.428247    0.810964    True     0.5    0.937864     True       0.5   \n",
       "...          ...         ...     ...     ...         ...      ...       ...   \n",
       "3718    0.707430    0.854239    True     0.5    0.853071     True       1.0   \n",
       "3719    0.707430    0.854239    True     0.5    0.853071     True       1.0   \n",
       "3720    0.707430    0.854239    True     0.5    0.853071     True       1.0   \n",
       "3722    0.461521    0.763086   False     1.0    0.739271    False       0.5   \n",
       "3726    0.437944    0.795807    True     0.5    0.883181     True       0.5   \n",
       "\n",
       "        conf_3    conf_1  \n",
       "0     0.845855  0.406120  \n",
       "1     0.760760  0.475271  \n",
       "3     0.816779  0.435763  \n",
       "4     0.779864  0.372400  \n",
       "8     0.822837  0.420775  \n",
       "...        ...       ...  \n",
       "3718  0.795345  0.554316  \n",
       "3719  0.795345  0.554316  \n",
       "3720  0.795345  0.554316  \n",
       "3722  0.757774  0.413263  \n",
       "3726  0.760904  0.383128  \n",
       "\n",
       "[1247 rows x 19 columns]"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "44d1ed01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4294077134986226"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1247/2904"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "b899f463",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1247.000000\n",
       "mean        0.812231\n",
       "std         0.039370\n",
       "min         0.750001\n",
       "25%         0.780613\n",
       "50%         0.807355\n",
       "75%         0.837889\n",
       "max         0.954249\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "38f9bb54",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1247.000000\n",
       "mean        0.460604\n",
       "std         0.099890\n",
       "min         0.292825\n",
       "25%         0.377878\n",
       "50%         0.442460\n",
       "75%         0.550534\n",
       "max         0.703789\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "870e6f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1247.000000\n",
       "mean        0.544507\n",
       "std         0.142439\n",
       "min         0.500000\n",
       "25%         0.500000\n",
       "50%         0.500000\n",
       "75%         0.500000\n",
       "max         1.000000\n",
       "Name: same_3, dtype: float64"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['same_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "2e1e40b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1247\n",
       "unique        2\n",
       "top       False\n",
       "freq        805\n",
       "Name: same_1, dtype: object"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['same_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "3b5fe42d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iv      0.313018\n",
       "Criteria ii      0.219995\n",
       "Criteria vi      0.200080\n",
       "Criteria iii     0.149425\n",
       "Criteria i       0.103849\n",
       "Criteria v       0.010960\n",
       "Criteria vii     0.000802\n",
       "Criteria x       0.000802\n",
       "Criteria viii    0.000535\n",
       "Criteria ix      0.000535\n",
       "dtype: float64"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf['max_1_col_bert'],\n",
    "           cmp_conf['max_2_col_bert'],\n",
    "           cmp_conf['max_3_col_bert'],\n",
    "           cmp_conf['max_1_col_ulmfit'],\n",
    "           cmp_conf['max_2_col_ulmfit'],\n",
    "           cmp_conf['max_3_col_ulmfit']]).value_counts()/(1247*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "a788fe55",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentages['AMS']=pd.concat([cmp_conf['max_1_col_bert'],\n",
    "           cmp_conf['max_2_col_bert'],\n",
    "           cmp_conf['max_3_col_bert'],\n",
    "           cmp_conf['max_1_col_ulmfit'],\n",
    "           cmp_conf['max_2_col_ulmfit'],\n",
    "           cmp_conf['max_3_col_ulmfit']]).value_counts()/(1247*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "3a3df3c0",
   "metadata": {},
   "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>VEN</th>\n",
       "      <th>VEN_s</th>\n",
       "      <th>VEN_XL</th>\n",
       "      <th>VEN_XL_s</th>\n",
       "      <th>AMS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUV</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Criteria i</th>\n",
       "      <td>0.147707</td>\n",
       "      <td>0.183544</td>\n",
       "      <td>0.075867</td>\n",
       "      <td>0.171714</td>\n",
       "      <td>0.103849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ii</th>\n",
       "      <td>0.202381</td>\n",
       "      <td>0.188668</td>\n",
       "      <td>0.162301</td>\n",
       "      <td>0.190178</td>\n",
       "      <td>0.219995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iii</th>\n",
       "      <td>0.213845</td>\n",
       "      <td>0.191581</td>\n",
       "      <td>0.270847</td>\n",
       "      <td>0.203632</td>\n",
       "      <td>0.149425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iv</th>\n",
       "      <td>0.240300</td>\n",
       "      <td>0.252863</td>\n",
       "      <td>0.233602</td>\n",
       "      <td>0.241005</td>\n",
       "      <td>0.313018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria v</th>\n",
       "      <td>0.008598</td>\n",
       "      <td>0.004722</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.005177</td>\n",
       "      <td>0.010960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vi</th>\n",
       "      <td>0.183862</td>\n",
       "      <td>0.162849</td>\n",
       "      <td>0.249893</td>\n",
       "      <td>0.172038</td>\n",
       "      <td>0.200080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vii</th>\n",
       "      <td>0.001764</td>\n",
       "      <td>0.006631</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>0.007203</td>\n",
       "      <td>0.000802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria viii</th>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>0.002543</td>\n",
       "      <td>0.000535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ix</th>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.003516</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.002103</td>\n",
       "      <td>0.000535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria x</th>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.001520</td>\n",
       "      <td>0.004401</td>\n",
       "      <td>0.000802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    VEN     VEN_s    VEN_XL  VEN_XL_s       AMS\n",
       "OUV                                                            \n",
       "Criteria i     0.147707  0.183544  0.075867  0.171714  0.103849\n",
       "Criteria ii    0.202381  0.188668  0.162301  0.190178  0.219995\n",
       "Criteria iii   0.213845  0.191581  0.270847  0.203632  0.149425\n",
       "Criteria iv    0.240300  0.252863  0.233602  0.241005  0.313018\n",
       "Criteria v     0.008598  0.004722  0.002794  0.005177  0.010960\n",
       "Criteria vi    0.183862  0.162849  0.249893  0.172038  0.200080\n",
       "Criteria vii   0.001764  0.006631  0.002231  0.007203  0.000802\n",
       "Criteria viii  0.000220  0.002813  0.000349  0.002543  0.000535\n",
       "Criteria ix    0.000441  0.003516  0.000595  0.002103  0.000535\n",
       "Criteria x     0.000882  0.002813  0.001520  0.004401  0.000802\n",
       "Others              NaN       NaN       NaN  0.000006       NaN"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "cbdf0a1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iv      2342\n",
       "Criteria ii      1646\n",
       "Criteria vi      1497\n",
       "Criteria iii     1118\n",
       "Criteria i        777\n",
       "Criteria v         82\n",
       "Criteria vii        6\n",
       "Criteria x          6\n",
       "Criteria viii       4\n",
       "Criteria ix         4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf['max_1_col_bert'],\n",
    "           cmp_conf['max_2_col_bert'],\n",
    "           cmp_conf['max_3_col_bert'],\n",
    "           cmp_conf['max_1_col_ulmfit'],\n",
    "           cmp_conf['max_2_col_ulmfit'],\n",
    "           cmp_conf['max_3_col_ulmfit']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "42a169ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert_s = pd.read_csv('Amsterdam/data_storage/sentences_bert.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "fe164e15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>...</th>\n",
       "      <th>BERT_766</th>\n",
       "      <th>BERT_767</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Amsterdam, the Netherlands.</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "      <td>0.142302</td>\n",
       "      <td>0.613584</td>\n",
       "      <td>0.033604</td>\n",
       "      <td>0.114368</td>\n",
       "      <td>0.007062</td>\n",
       "      <td>0.038919</td>\n",
       "      <td>...</td>\n",
       "      <td>0.823750</td>\n",
       "      <td>0.598916</td>\n",
       "      <td>0.613584</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.142302</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.114368</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.613584</td>\n",
       "      <td>0.870255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has tags of citro, citro, eend, geit, deuch...</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "      <td>0.067704</td>\n",
       "      <td>0.170978</td>\n",
       "      <td>0.030062</td>\n",
       "      <td>0.557452</td>\n",
       "      <td>0.017852</td>\n",
       "      <td>0.127049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.728472</td>\n",
       "      <td>-0.094057</td>\n",
       "      <td>0.557452</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.170978</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.127049</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.557452</td>\n",
       "      <td>0.855478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>It has tags of xt, rfo, station, amsterdam, wh...</td>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>0.055612</td>\n",
       "      <td>0.104151</td>\n",
       "      <td>0.120792</td>\n",
       "      <td>0.509236</td>\n",
       "      <td>0.033778</td>\n",
       "      <td>0.112375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.549450</td>\n",
       "      <td>0.060106</td>\n",
       "      <td>0.509236</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.120792</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.112375</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.509236</td>\n",
       "      <td>0.742402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>There was a single swan in the water which see...</td>\n",
       "      <td>8</td>\n",
       "      <td>en</td>\n",
       "      <td>0.096401</td>\n",
       "      <td>0.010414</td>\n",
       "      <td>0.051497</td>\n",
       "      <td>0.054470</td>\n",
       "      <td>0.043407</td>\n",
       "      <td>0.096124</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.438962</td>\n",
       "      <td>0.746187</td>\n",
       "      <td>0.485289</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.096401</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.096124</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.485289</td>\n",
       "      <td>0.677814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>It has title of Ford Transit minibus 1978</td>\n",
       "      <td>9</td>\n",
       "      <td>en</td>\n",
       "      <td>0.034096</td>\n",
       "      <td>0.302022</td>\n",
       "      <td>0.065662</td>\n",
       "      <td>0.349770</td>\n",
       "      <td>0.038628</td>\n",
       "      <td>0.155254</td>\n",
       "      <td>...</td>\n",
       "      <td>0.801603</td>\n",
       "      <td>-0.415707</td>\n",
       "      <td>0.349770</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.302022</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.155254</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.349770</td>\n",
       "      <td>0.807046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9594</th>\n",
       "      <td>2498</td>\n",
       "      <td>It has tags of Holland, Netherlands, Amsterdam</td>\n",
       "      <td>4936</td>\n",
       "      <td>other</td>\n",
       "      <td>0.074664</td>\n",
       "      <td>0.819251</td>\n",
       "      <td>0.017535</td>\n",
       "      <td>0.050233</td>\n",
       "      <td>0.003286</td>\n",
       "      <td>0.013226</td>\n",
       "      <td>...</td>\n",
       "      <td>0.881062</td>\n",
       "      <td>0.335289</td>\n",
       "      <td>0.819251</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.074664</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.050233</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.819251</td>\n",
       "      <td>0.944148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9595</th>\n",
       "      <td>2499</td>\n",
       "      <td>It has tags of Holland, Netherlands, Amsterdam</td>\n",
       "      <td>4937</td>\n",
       "      <td>other</td>\n",
       "      <td>0.074664</td>\n",
       "      <td>0.819251</td>\n",
       "      <td>0.017535</td>\n",
       "      <td>0.050233</td>\n",
       "      <td>0.003286</td>\n",
       "      <td>0.013226</td>\n",
       "      <td>...</td>\n",
       "      <td>0.881062</td>\n",
       "      <td>0.335289</td>\n",
       "      <td>0.819251</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.074664</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.050233</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.819251</td>\n",
       "      <td>0.944148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9596</th>\n",
       "      <td>2538</td>\n",
       "      <td>It has title of Ben Paul H087 Municipal museum...</td>\n",
       "      <td>4985</td>\n",
       "      <td>other</td>\n",
       "      <td>0.275405</td>\n",
       "      <td>0.569107</td>\n",
       "      <td>0.031185</td>\n",
       "      <td>0.055734</td>\n",
       "      <td>0.003697</td>\n",
       "      <td>0.012832</td>\n",
       "      <td>...</td>\n",
       "      <td>0.811676</td>\n",
       "      <td>0.903536</td>\n",
       "      <td>0.569107</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.275405</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.055734</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.569107</td>\n",
       "      <td>0.900247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9597</th>\n",
       "      <td>2542</td>\n",
       "      <td>It has title of Rijksmuseum</td>\n",
       "      <td>4993</td>\n",
       "      <td>other</td>\n",
       "      <td>0.145588</td>\n",
       "      <td>0.498142</td>\n",
       "      <td>0.067416</td>\n",
       "      <td>0.197442</td>\n",
       "      <td>0.007795</td>\n",
       "      <td>0.029427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.695847</td>\n",
       "      <td>0.646813</td>\n",
       "      <td>0.498142</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.197442</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.145588</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.498142</td>\n",
       "      <td>0.841173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9598</th>\n",
       "      <td>2544</td>\n",
       "      <td>It has tags of amsterdam, pont, d, digitalphotos</td>\n",
       "      <td>4995</td>\n",
       "      <td>other</td>\n",
       "      <td>0.125670</td>\n",
       "      <td>0.729283</td>\n",
       "      <td>0.046187</td>\n",
       "      <td>0.050647</td>\n",
       "      <td>0.002971</td>\n",
       "      <td>0.018610</td>\n",
       "      <td>...</td>\n",
       "      <td>0.878206</td>\n",
       "      <td>0.400915</td>\n",
       "      <td>0.729283</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.125670</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.050647</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.729283</td>\n",
       "      <td>0.905600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9599 rows × 791 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                                           sentence  origin   lang  \\\n",
       "0         0                        Amsterdam, the Netherlands.       2     en   \n",
       "1         1  It has tags of citro, citro, eend, geit, deuch...       2     en   \n",
       "2         2  It has tags of xt, rfo, station, amsterdam, wh...       3     en   \n",
       "3         4  There was a single swan in the water which see...       8     en   \n",
       "4         6          It has title of Ford Transit minibus 1978       9     en   \n",
       "...     ...                                                ...     ...    ...   \n",
       "9594   2498     It has tags of Holland, Netherlands, Amsterdam    4936  other   \n",
       "9595   2499     It has tags of Holland, Netherlands, Amsterdam    4937  other   \n",
       "9596   2538  It has title of Ben Paul H087 Municipal museum...    4985  other   \n",
       "9597   2542                        It has title of Rijksmuseum    4993  other   \n",
       "9598   2544   It has tags of amsterdam, pont, d, digitalphotos    4995  other   \n",
       "\n",
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.142302     0.613584      0.033604     0.114368    0.007062   \n",
       "1       0.067704     0.170978      0.030062     0.557452    0.017852   \n",
       "2       0.055612     0.104151      0.120792     0.509236    0.033778   \n",
       "3       0.096401     0.010414      0.051497     0.054470    0.043407   \n",
       "4       0.034096     0.302022      0.065662     0.349770    0.038628   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "9594    0.074664     0.819251      0.017535     0.050233    0.003286   \n",
       "9595    0.074664     0.819251      0.017535     0.050233    0.003286   \n",
       "9596    0.275405     0.569107      0.031185     0.055734    0.003697   \n",
       "9597    0.145588     0.498142      0.067416     0.197442    0.007795   \n",
       "9598    0.125670     0.729283      0.046187     0.050647    0.002971   \n",
       "\n",
       "      Criteria vi  ...  BERT_766  BERT_767  max_1_val     max_1_col  \\\n",
       "0        0.038919  ...  0.823750  0.598916   0.613584   Criteria ii   \n",
       "1        0.127049  ...  0.728472 -0.094057   0.557452   Criteria iv   \n",
       "2        0.112375  ...  0.549450  0.060106   0.509236   Criteria iv   \n",
       "3        0.096124  ... -0.438962  0.746187   0.485289  Criteria vii   \n",
       "4        0.155254  ...  0.801603 -0.415707   0.349770   Criteria iv   \n",
       "...           ...  ...       ...       ...        ...           ...   \n",
       "9594     0.013226  ...  0.881062  0.335289   0.819251   Criteria ii   \n",
       "9595     0.013226  ...  0.881062  0.335289   0.819251   Criteria ii   \n",
       "9596     0.012832  ...  0.811676  0.903536   0.569107   Criteria ii   \n",
       "9597     0.029427  ...  0.695847  0.646813   0.498142   Criteria ii   \n",
       "9598     0.018610  ...  0.878206  0.400915   0.729283   Criteria ii   \n",
       "\n",
       "      max_2_val     max_2_col  max_3_val    max_3_col     max_1     max_3  \n",
       "0      0.142302    Criteria i   0.114368  Criteria iv  0.613584  0.870255  \n",
       "1      0.170978   Criteria ii   0.127049  Criteria vi  0.557452  0.855478  \n",
       "2      0.120792  Criteria iii   0.112375  Criteria vi  0.509236  0.742402  \n",
       "3      0.096401    Criteria i   0.096124  Criteria vi  0.485289  0.677814  \n",
       "4      0.302022   Criteria ii   0.155254  Criteria vi  0.349770  0.807046  \n",
       "...         ...           ...        ...          ...       ...       ...  \n",
       "9594   0.074664    Criteria i   0.050233  Criteria iv  0.819251  0.944148  \n",
       "9595   0.074664    Criteria i   0.050233  Criteria iv  0.819251  0.944148  \n",
       "9596   0.275405    Criteria i   0.055734  Criteria iv  0.569107  0.900247  \n",
       "9597   0.197442   Criteria iv   0.145588   Criteria i  0.498142  0.841173  \n",
       "9598   0.125670    Criteria i   0.050647  Criteria iv  0.729283  0.905600  \n",
       "\n",
       "[9599 rows x 791 columns]"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "30ec7db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_s = pd.read_csv('Amsterdam/data_storage/sentences_compare.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "85465774",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
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       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
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       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Amsterdam, the Netherlands.</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.410730</td>\n",
       "      <td>0.768343</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.613584</td>\n",
       "      <td>0.870255</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.495698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has tags of citro, citro, eend, geit, deuch...</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.472180</td>\n",
       "      <td>0.767179</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.557452</td>\n",
       "      <td>0.855478</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.457918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>It has tags of xt, rfo, station, amsterdam, wh...</td>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.779117</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.509236</td>\n",
       "      <td>0.742402</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.670258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>There was a single swan in the water which see...</td>\n",
       "      <td>8</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.216921</td>\n",
       "      <td>0.607379</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.485289</td>\n",
       "      <td>0.677814</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.329462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>It has title of Ford Transit minibus 1978</td>\n",
       "      <td>9</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.273391</td>\n",
       "      <td>0.562010</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.349770</td>\n",
       "      <td>0.807046</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.474408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9594</th>\n",
       "      <td>2498</td>\n",
       "      <td>It has tags of Holland, Netherlands, Amsterdam</td>\n",
       "      <td>4936</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>0.759667</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.819251</td>\n",
       "      <td>0.944148</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.341877</td>\n",
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       "    <tr>\n",
       "      <th>9595</th>\n",
       "      <td>2499</td>\n",
       "      <td>It has tags of Holland, Netherlands, Amsterdam</td>\n",
       "      <td>4937</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>0.759667</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.819251</td>\n",
       "      <td>0.944148</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.341877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9596</th>\n",
       "      <td>2538</td>\n",
       "      <td>It has title of Ben Paul H087 Municipal museum...</td>\n",
       "      <td>4985</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.359905</td>\n",
       "      <td>0.773720</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.569107</td>\n",
       "      <td>0.900247</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.307787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9597</th>\n",
       "      <td>2542</td>\n",
       "      <td>It has title of Rijksmuseum</td>\n",
       "      <td>4993</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>0.619725</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.498142</td>\n",
       "      <td>0.841173</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.444536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9598</th>\n",
       "      <td>2544</td>\n",
       "      <td>It has tags of amsterdam, pont, d, digitalphotos</td>\n",
       "      <td>4995</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.663798</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.729283</td>\n",
       "      <td>0.905600</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.492806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9599 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                                           sentence  origin   lang  \\\n",
       "0         0                        Amsterdam, the Netherlands.       2     en   \n",
       "1         1  It has tags of citro, citro, eend, geit, deuch...       2     en   \n",
       "2         2  It has tags of xt, rfo, station, amsterdam, wh...       3     en   \n",
       "3         4  There was a single swan in the water which see...       8     en   \n",
       "4         6          It has title of Ford Transit minibus 1978       9     en   \n",
       "...     ...                                                ...     ...    ...   \n",
       "9594   2498     It has tags of Holland, Netherlands, Amsterdam    4936  other   \n",
       "9595   2499     It has tags of Holland, Netherlands, Amsterdam    4937  other   \n",
       "9596   2538  It has title of Ben Paul H087 Municipal museum...    4985  other   \n",
       "9597   2542                        It has title of Rijksmuseum    4993  other   \n",
       "9598   2544   It has tags of amsterdam, pont, d, digitalphotos    4995  other   \n",
       "\n",
       "     max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  max_1_ulmfit  \\\n",
       "0         Criteria vi      Criteria ii      Criteria iv      0.410730   \n",
       "1        Criteria iii      Criteria iv      Criteria ii      0.472180   \n",
       "2        Criteria iii      Criteria iv      Criteria ii      0.441306   \n",
       "3          Criteria i      Criteria iv      Criteria ii      0.216921   \n",
       "4         Criteria vi     Criteria vii    Criteria viii      0.273391   \n",
       "...               ...              ...              ...           ...   \n",
       "9594     Criteria iii      Criteria iv      Criteria ii      0.438163   \n",
       "9595     Criteria iii      Criteria iv      Criteria ii      0.438163   \n",
       "9596      Criteria vi     Criteria iii      Criteria iv      0.359905   \n",
       "9597     Criteria iii      Criteria vi      Criteria iv      0.293035   \n",
       "9598     Criteria iii      Criteria iv      Criteria vi      0.292644   \n",
       "\n",
       "      max_3_ulmfit max_1_col_bert max_2_col_bert max_3_col_bert  max_1_bert  \\\n",
       "0         0.768343    Criteria ii     Criteria i    Criteria iv    0.613584   \n",
       "1         0.767179    Criteria iv    Criteria ii    Criteria vi    0.557452   \n",
       "2         0.779117    Criteria iv   Criteria iii    Criteria vi    0.509236   \n",
       "3         0.607379   Criteria vii     Criteria i    Criteria vi    0.485289   \n",
       "4         0.562010    Criteria iv    Criteria ii    Criteria vi    0.349770   \n",
       "...            ...            ...            ...            ...         ...   \n",
       "9594      0.759667    Criteria ii     Criteria i    Criteria iv    0.819251   \n",
       "9595      0.759667    Criteria ii     Criteria i    Criteria iv    0.819251   \n",
       "9596      0.773720    Criteria ii     Criteria i    Criteria iv    0.569107   \n",
       "9597      0.619725    Criteria ii    Criteria iv     Criteria i    0.498142   \n",
       "9598      0.663798    Criteria ii     Criteria i    Criteria iv    0.729283   \n",
       "\n",
       "      max_3_bert  same_1  same_3  cosine_sim  \n",
       "0       0.870255   False     0.5    0.495698  \n",
       "1       0.855478   False     0.5    0.457918  \n",
       "2       0.742402   False     0.5    0.670258  \n",
       "3       0.677814   False     0.2    0.329462  \n",
       "4       0.807046   False     0.2    0.474408  \n",
       "...          ...     ...     ...         ...  \n",
       "9594    0.944148   False     0.5    0.341877  \n",
       "9595    0.944148   False     0.5    0.341877  \n",
       "9596    0.900247   False     0.2    0.307787  \n",
       "9597    0.841173   False     0.2    0.444536  \n",
       "9598    0.905600   False     0.2    0.492806  \n",
       "\n",
       "[9599 rows x 17 columns]"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "b081baa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_s = cmp_s.merge(bert_s.drop_duplicates(subset='sentence',keep='first')[['max_1','max_3']], how='right',left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "b4685dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_s['conf_3'] = (cmp_s['max_3_ulmfit'] + cmp_s['max_3_bert'])/2\n",
    "cmp_s['conf_1'] = (cmp_s['max_1_ulmfit'] + cmp_s['max_1_bert'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "f2472fac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2247.000000\n",
       "mean        0.755707\n",
       "std         0.093868\n",
       "min         0.450763\n",
       "25%         0.692280\n",
       "50%         0.774614\n",
       "75%         0.825901\n",
       "max         0.963648\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_s['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "f2d64bc3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2247.000000\n",
       "mean        0.416188\n",
       "std         0.108683\n",
       "min         0.183077\n",
       "25%         0.339095\n",
       "50%         0.406609\n",
       "75%         0.483847\n",
       "max         0.896366\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_s['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "2fcfca57",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zzbn1\\AppData\\Local\\Temp\\ipykernel_6436\\3704144270.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  cmp_conf_s = cmp_s[cmp_s.conf_3>=0.75][cmp_s.same_3>=0.5]\n"
     ]
    }
   ],
   "source": [
    "cmp_conf_s = cmp_s[cmp_s.conf_3>=0.75][cmp_s.same_3>=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "bf01660f",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>...</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>conf_3</th>\n",
       "      <th>conf_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Amsterdam, the Netherlands.</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.410730</td>\n",
       "      <td>0.768343</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.613584</td>\n",
       "      <td>0.870255</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.495698</td>\n",
       "      <td>0.613584</td>\n",
       "      <td>0.870255</td>\n",
       "      <td>0.819299</td>\n",
       "      <td>0.512157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has tags of citro, citro, eend, geit, deuch...</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.472180</td>\n",
       "      <td>0.767179</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.557452</td>\n",
       "      <td>0.855478</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.457918</td>\n",
       "      <td>0.557452</td>\n",
       "      <td>0.855478</td>\n",
       "      <td>0.811329</td>\n",
       "      <td>0.514816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>It has tags of xt, rfo, station, amsterdam, wh...</td>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.779117</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.509236</td>\n",
       "      <td>0.742402</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.670258</td>\n",
       "      <td>0.509236</td>\n",
       "      <td>0.742402</td>\n",
       "      <td>0.760760</td>\n",
       "      <td>0.475271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>It has tags of bt, renault, dauphine, renaultd...</td>\n",
       "      <td>13</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.466868</td>\n",
       "      <td>0.755010</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.584791</td>\n",
       "      <td>0.852904</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.480572</td>\n",
       "      <td>0.584791</td>\n",
       "      <td>0.852904</td>\n",
       "      <td>0.803957</td>\n",
       "      <td>0.525830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>Nieuwe Kerk ( New Church ) is a 15th-century c...</td>\n",
       "      <td>17</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.431527</td>\n",
       "      <td>0.867928</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.671613</td>\n",
       "      <td>0.934666</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.574561</td>\n",
       "      <td>0.671613</td>\n",
       "      <td>0.934666</td>\n",
       "      <td>0.901297</td>\n",
       "      <td>0.551570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9481</th>\n",
       "      <td>2224</td>\n",
       "      <td>It is literally in the shadow of the very rece...</td>\n",
       "      <td>4643</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.513813</td>\n",
       "      <td>0.809617</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.390583</td>\n",
       "      <td>0.856561</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.825572</td>\n",
       "      <td>0.390583</td>\n",
       "      <td>0.856561</td>\n",
       "      <td>0.833089</td>\n",
       "      <td>0.452198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9488</th>\n",
       "      <td>2231</td>\n",
       "      <td>Mixed goods train with NS 1113 is waiting on S...</td>\n",
       "      <td>4649</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.618295</td>\n",
       "      <td>0.865281</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.753205</td>\n",
       "      <td>0.906073</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.983455</td>\n",
       "      <td>0.753205</td>\n",
       "      <td>0.906073</td>\n",
       "      <td>0.885677</td>\n",
       "      <td>0.685750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9492</th>\n",
       "      <td>2236</td>\n",
       "      <td>It has title of In a mid-20th century Dutch dw...</td>\n",
       "      <td>4653</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.299676</td>\n",
       "      <td>0.775949</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.455223</td>\n",
       "      <td>0.839420</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.843010</td>\n",
       "      <td>0.455223</td>\n",
       "      <td>0.839420</td>\n",
       "      <td>0.807684</td>\n",
       "      <td>0.377450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9541</th>\n",
       "      <td>2369</td>\n",
       "      <td>It has title of old telephone exchange</td>\n",
       "      <td>4759</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.361776</td>\n",
       "      <td>0.737252</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.765006</td>\n",
       "      <td>0.890530</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.421305</td>\n",
       "      <td>0.765006</td>\n",
       "      <td>0.890530</td>\n",
       "      <td>0.813891</td>\n",
       "      <td>0.563391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9554</th>\n",
       "      <td>2393</td>\n",
       "      <td>It has tags of mural</td>\n",
       "      <td>4779</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.387440</td>\n",
       "      <td>0.859070</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.560513</td>\n",
       "      <td>0.817086</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.890272</td>\n",
       "      <td>0.560513</td>\n",
       "      <td>0.817086</td>\n",
       "      <td>0.838078</td>\n",
       "      <td>0.473977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>942 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                                           sentence  origin   lang  \\\n",
       "0         0                        Amsterdam, the Netherlands.       2     en   \n",
       "1         1  It has tags of citro, citro, eend, geit, deuch...       2     en   \n",
       "2         2  It has tags of xt, rfo, station, amsterdam, wh...       3     en   \n",
       "5         7  It has tags of bt, renault, dauphine, renaultd...      13     en   \n",
       "10       12  Nieuwe Kerk ( New Church ) is a 15th-century c...      17     en   \n",
       "...     ...                                                ...     ...    ...   \n",
       "9481   2224  It is literally in the shadow of the very rece...    4643  other   \n",
       "9488   2231  Mixed goods train with NS 1113 is waiting on S...    4649  other   \n",
       "9492   2236  It has title of In a mid-20th century Dutch dw...    4653  other   \n",
       "9541   2369             It has title of old telephone exchange    4759  other   \n",
       "9554   2393                               It has tags of mural    4779  other   \n",
       "\n",
       "     max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  max_1_ulmfit  \\\n",
       "0         Criteria vi      Criteria ii      Criteria iv      0.410730   \n",
       "1        Criteria iii      Criteria iv      Criteria ii      0.472180   \n",
       "2        Criteria iii      Criteria iv      Criteria ii      0.441306   \n",
       "5        Criteria iii      Criteria iv      Criteria vi      0.466868   \n",
       "10        Criteria iv       Criteria i      Criteria ii      0.431527   \n",
       "...               ...              ...              ...           ...   \n",
       "9481       Criteria i      Criteria vi      Criteria iv      0.513813   \n",
       "9488      Criteria ii      Criteria iv     Criteria iii      0.618295   \n",
       "9492      Criteria iv     Criteria iii      Criteria vi      0.299676   \n",
       "9541     Criteria iii      Criteria vi      Criteria iv      0.361776   \n",
       "9554       Criteria i     Criteria iii      Criteria iv      0.387440   \n",
       "\n",
       "      max_3_ulmfit max_1_col_bert  ... max_3_col_bert max_1_bert  max_3_bert  \\\n",
       "0         0.768343    Criteria ii  ...    Criteria iv   0.613584    0.870255   \n",
       "1         0.767179    Criteria iv  ...    Criteria vi   0.557452    0.855478   \n",
       "2         0.779117    Criteria iv  ...    Criteria vi   0.509236    0.742402   \n",
       "5         0.755010    Criteria iv  ...    Criteria ii   0.584791    0.852904   \n",
       "10        0.867928    Criteria ii  ...     Criteria i   0.671613    0.934666   \n",
       "...            ...            ...  ...            ...        ...         ...   \n",
       "9481      0.809617    Criteria iv  ...    Criteria vi   0.390583    0.856561   \n",
       "9488      0.865281    Criteria ii  ...     Criteria i   0.753205    0.906073   \n",
       "9492      0.775949    Criteria iv  ...   Criteria iii   0.455223    0.839420   \n",
       "9541      0.737252    Criteria iv  ...   Criteria iii   0.765006    0.890530   \n",
       "9554      0.859070     Criteria i  ...    Criteria ii   0.560513    0.817086   \n",
       "\n",
       "      same_1  same_3  cosine_sim     max_1     max_3    conf_3    conf_1  \n",
       "0      False     0.5    0.495698  0.613584  0.870255  0.819299  0.512157  \n",
       "1      False     0.5    0.457918  0.557452  0.855478  0.811329  0.514816  \n",
       "2      False     0.5    0.670258  0.509236  0.742402  0.760760  0.475271  \n",
       "5      False     0.5    0.480572  0.584791  0.852904  0.803957  0.525830  \n",
       "10     False     1.0    0.574561  0.671613  0.934666  0.901297  0.551570  \n",
       "...      ...     ...         ...       ...       ...       ...       ...  \n",
       "9481   False     1.0    0.825572  0.390583  0.856561  0.833089  0.452198  \n",
       "9488    True     0.5    0.983455  0.753205  0.906073  0.885677  0.685750  \n",
       "9492    True     0.5    0.843010  0.455223  0.839420  0.807684  0.377450  \n",
       "9541   False     0.5    0.421305  0.765006  0.890530  0.813891  0.563391  \n",
       "9554    True     0.5    0.890272  0.560513  0.817086  0.838078  0.473977  \n",
       "\n",
       "[942 rows x 21 columns]"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "77d3bea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    942.000000\n",
       "mean       0.825585\n",
       "std        0.043301\n",
       "min        0.750331\n",
       "25%        0.791902\n",
       "50%        0.821172\n",
       "75%        0.854514\n",
       "max        0.963512\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "e8794667",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    942.000000\n",
       "mean       0.476646\n",
       "std        0.093903\n",
       "min        0.289831\n",
       "25%        0.410012\n",
       "50%        0.464307\n",
       "75%        0.528907\n",
       "max        0.896366\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "8ebf2d09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    942.000000\n",
       "mean       0.596603\n",
       "std        0.197511\n",
       "min        0.500000\n",
       "25%        0.500000\n",
       "50%        0.500000\n",
       "75%        0.500000\n",
       "max        1.000000\n",
       "Name: same_3, dtype: float64"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['same_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "c8488c00",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       942\n",
       "unique        2\n",
       "top       False\n",
       "freq        671\n",
       "Name: same_1, dtype: object"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['same_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "95861eee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iv      0.289986\n",
       "Criteria ii      0.237792\n",
       "Criteria iii     0.170028\n",
       "Criteria i       0.147912\n",
       "Criteria vi      0.127212\n",
       "Criteria v       0.009023\n",
       "Criteria vii     0.007077\n",
       "Criteria x       0.005662\n",
       "Criteria ix      0.002831\n",
       "Criteria viii    0.002477\n",
       "dtype: float64"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf_s['max_1_col_bert'],\n",
    "           cmp_conf_s['max_2_col_bert'],\n",
    "           cmp_conf_s['max_3_col_bert'],\n",
    "           cmp_conf_s['max_1_col_ulmfit'],\n",
    "           cmp_conf_s['max_2_col_ulmfit'],\n",
    "           cmp_conf_s['max_3_col_ulmfit']]).value_counts()/(942*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "e770c54d",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentages['AMS_s'] = pd.concat([cmp_conf_s['max_1_col_bert'],\n",
    "           cmp_conf_s['max_2_col_bert'],\n",
    "           cmp_conf_s['max_3_col_bert'],\n",
    "           cmp_conf_s['max_1_col_ulmfit'],\n",
    "           cmp_conf_s['max_2_col_ulmfit'],\n",
    "           cmp_conf_s['max_3_col_ulmfit']]).value_counts()/(942*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "ec87a3b4",
   "metadata": {},
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VEN</th>\n",
       "      <th>VEN_s</th>\n",
       "      <th>VEN_XL</th>\n",
       "      <th>VEN_XL_s</th>\n",
       "      <th>AMS</th>\n",
       "      <th>AMS_s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUV</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Criteria i</th>\n",
       "      <td>0.147707</td>\n",
       "      <td>0.183544</td>\n",
       "      <td>0.075867</td>\n",
       "      <td>0.171714</td>\n",
       "      <td>0.103849</td>\n",
       "      <td>0.147912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ii</th>\n",
       "      <td>0.202381</td>\n",
       "      <td>0.188668</td>\n",
       "      <td>0.162301</td>\n",
       "      <td>0.190178</td>\n",
       "      <td>0.219995</td>\n",
       "      <td>0.237792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iii</th>\n",
       "      <td>0.213845</td>\n",
       "      <td>0.191581</td>\n",
       "      <td>0.270847</td>\n",
       "      <td>0.203632</td>\n",
       "      <td>0.149425</td>\n",
       "      <td>0.170028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iv</th>\n",
       "      <td>0.240300</td>\n",
       "      <td>0.252863</td>\n",
       "      <td>0.233602</td>\n",
       "      <td>0.241005</td>\n",
       "      <td>0.313018</td>\n",
       "      <td>0.289986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria v</th>\n",
       "      <td>0.008598</td>\n",
       "      <td>0.004722</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.005177</td>\n",
       "      <td>0.010960</td>\n",
       "      <td>0.009023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vi</th>\n",
       "      <td>0.183862</td>\n",
       "      <td>0.162849</td>\n",
       "      <td>0.249893</td>\n",
       "      <td>0.172038</td>\n",
       "      <td>0.200080</td>\n",
       "      <td>0.127212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vii</th>\n",
       "      <td>0.001764</td>\n",
       "      <td>0.006631</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>0.007203</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.007077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria viii</th>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>0.002543</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.002477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ix</th>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.003516</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.002103</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.002831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria x</th>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.001520</td>\n",
       "      <td>0.004401</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.005662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    VEN     VEN_s    VEN_XL  VEN_XL_s       AMS     AMS_s\n",
       "OUV                                                                      \n",
       "Criteria i     0.147707  0.183544  0.075867  0.171714  0.103849  0.147912\n",
       "Criteria ii    0.202381  0.188668  0.162301  0.190178  0.219995  0.237792\n",
       "Criteria iii   0.213845  0.191581  0.270847  0.203632  0.149425  0.170028\n",
       "Criteria iv    0.240300  0.252863  0.233602  0.241005  0.313018  0.289986\n",
       "Criteria v     0.008598  0.004722  0.002794  0.005177  0.010960  0.009023\n",
       "Criteria vi    0.183862  0.162849  0.249893  0.172038  0.200080  0.127212\n",
       "Criteria vii   0.001764  0.006631  0.002231  0.007203  0.000802  0.007077\n",
       "Criteria viii  0.000220  0.002813  0.000349  0.002543  0.000535  0.002477\n",
       "Criteria ix    0.000441  0.003516  0.000595  0.002103  0.000535  0.002831\n",
       "Criteria x     0.000882  0.002813  0.001520  0.004401  0.000802  0.005662\n",
       "Others              NaN       NaN       NaN  0.000006       NaN       NaN"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "7fe8f472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iv      1639\n",
       "Criteria ii      1344\n",
       "Criteria iii      961\n",
       "Criteria i        836\n",
       "Criteria vi       719\n",
       "Criteria v         51\n",
       "Criteria vii       40\n",
       "Criteria x         32\n",
       "Criteria ix        16\n",
       "Criteria viii      14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf_s['max_1_col_bert'],\n",
    "           cmp_conf_s['max_2_col_bert'],\n",
    "           cmp_conf_s['max_3_col_bert'],\n",
    "           cmp_conf_s['max_1_col_ulmfit'],\n",
    "           cmp_conf_s['max_2_col_ulmfit'],\n",
    "           cmp_conf_s['max_3_col_ulmfit']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "b6313e2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_sorted = cmp_conf_s.sort_values(by='conf_1',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "a8da5d16",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
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       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>...</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>conf_3</th>\n",
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       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>579</td>\n",
       "      <td>Inaugurated in 1997, it is the work of archite...</td>\n",
       "      <td>1353</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.898873</td>\n",
       "      <td>0.977705</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.893859</td>\n",
       "      <td>0.944379</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.999473</td>\n",
       "      <td>0.893859</td>\n",
       "      <td>0.944379</td>\n",
       "      <td>0.961042</td>\n",
       "      <td>0.896366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9003</th>\n",
       "      <td>584</td>\n",
       "      <td>Inaugurated in 1997, it is the work of the arc...</td>\n",
       "      <td>1353</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria i</td>\n",
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       "      <td>Criteria iv</td>\n",
       "      <td>0.874940</td>\n",
       "      <td>0.973507</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.895739</td>\n",
       "      <td>0.946531</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.998918</td>\n",
       "      <td>0.895739</td>\n",
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       "      <td>0.960019</td>\n",
       "      <td>0.885339</td>\n",
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       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>607</td>\n",
       "      <td>One of these is his giant 'Beanfield' painting...</td>\n",
       "      <td>1383</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.849365</td>\n",
       "      <td>0.921980</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.807620</td>\n",
       "      <td>0.901502</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.999184</td>\n",
       "      <td>0.807620</td>\n",
       "      <td>0.901502</td>\n",
       "      <td>0.911741</td>\n",
       "      <td>0.828492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5549</th>\n",
       "      <td>5973</td>\n",
       "      <td>It is a living testimony of the time</td>\n",
       "      <td>3240</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.888029</td>\n",
       "      <td>0.965490</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.747824</td>\n",
       "      <td>0.908523</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.992759</td>\n",
       "      <td>0.747824</td>\n",
       "      <td>0.908523</td>\n",
       "      <td>0.937006</td>\n",
       "      <td>0.817926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>52</td>\n",
       "      <td>The tower was then rebuilt in Amsterdam Renais...</td>\n",
       "      <td>19</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.739586</td>\n",
       "      <td>0.983559</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.868267</td>\n",
       "      <td>0.943464</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.984731</td>\n",
       "      <td>0.868267</td>\n",
       "      <td>0.943464</td>\n",
       "      <td>0.963512</td>\n",
       "      <td>0.803927</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1535</td>\n",
       "      <td>It has tags of amsterdam, best, bestof, citytr...</td>\n",
       "      <td>1626</td>\n",
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       "      <td>0.823984</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.260288</td>\n",
       "      <td>0.693995</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.730301</td>\n",
       "      <td>0.260288</td>\n",
       "      <td>0.693995</td>\n",
       "      <td>0.758989</td>\n",
       "      <td>0.303457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8725</th>\n",
       "      <td>2441</td>\n",
       "      <td>The Willem Frederik Hermans Institute organize...</td>\n",
       "      <td>4895</td>\n",
       "      <td>it</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.298067</td>\n",
       "      <td>0.798239</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.296875</td>\n",
       "      <td>0.706907</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.870103</td>\n",
       "      <td>0.296875</td>\n",
       "      <td>0.706907</td>\n",
       "      <td>0.752573</td>\n",
       "      <td>0.297471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6682</th>\n",
       "      <td>329</td>\n",
       "      <td>It has tags of amsterdam, gvba, gvba, north ho...</td>\n",
       "      <td>338</td>\n",
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       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.304795</td>\n",
       "      <td>0.770579</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.283121</td>\n",
       "      <td>0.739457</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.803420</td>\n",
       "      <td>0.283121</td>\n",
       "      <td>0.739457</td>\n",
       "      <td>0.755018</td>\n",
       "      <td>0.293958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8000</th>\n",
       "      <td>1697</td>\n",
       "      <td>It has title of Square Frederiksplein in Amste...</td>\n",
       "      <td>3478</td>\n",
       "      <td>it</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.267437</td>\n",
       "      <td>0.671734</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.317985</td>\n",
       "      <td>0.886422</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.750401</td>\n",
       "      <td>0.317985</td>\n",
       "      <td>0.886422</td>\n",
       "      <td>0.779078</td>\n",
       "      <td>0.292711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6974</th>\n",
       "      <td>637</td>\n",
       "      <td>It has title of statues of saints</td>\n",
       "      <td>1004</td>\n",
       "      <td>it</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
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       "      <td>0.290052</td>\n",
       "      <td>0.819296</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.289609</td>\n",
       "      <td>0.729429</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.941349</td>\n",
       "      <td>0.289609</td>\n",
       "      <td>0.729429</td>\n",
       "      <td>0.774362</td>\n",
       "      <td>0.289831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>942 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                                           sentence  origin   lang  \\\n",
       "557     579  Inaugurated in 1997, it is the work of archite...    1353     en   \n",
       "9003    584  Inaugurated in 1997, it is the work of the arc...    1353  other   \n",
       "584     607  One of these is his giant 'Beanfield' painting...    1383     en   \n",
       "5549   5973               It is a living testimony of the time    3240     en   \n",
       "49       52  The tower was then rebuilt in Amsterdam Renais...      19     en   \n",
       "...     ...                                                ...     ...    ...   \n",
       "1458   1535  It has tags of amsterdam, best, bestof, citytr...    1626     en   \n",
       "8725   2441  The Willem Frederik Hermans Institute organize...    4895     it   \n",
       "6682    329  It has tags of amsterdam, gvba, gvba, north ho...     338     it   \n",
       "8000   1697  It has title of Square Frederiksplein in Amste...    3478     it   \n",
       "6974    637                  It has title of statues of saints    1004     it   \n",
       "\n",
       "     max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  max_1_ulmfit  \\\n",
       "557        Criteria i      Criteria ii      Criteria iv      0.898873   \n",
       "9003       Criteria i      Criteria ii      Criteria iv      0.874940   \n",
       "584        Criteria i      Criteria iv      Criteria ii      0.849365   \n",
       "5549     Criteria iii      Criteria iv      Criteria vi      0.888029   \n",
       "49         Criteria i      Criteria iv      Criteria ii      0.739586   \n",
       "...               ...              ...              ...           ...   \n",
       "1458     Criteria iii      Criteria vi      Criteria iv      0.346625   \n",
       "8725      Criteria vi     Criteria iii      Criteria ii      0.298067   \n",
       "6682     Criteria iii      Criteria iv      Criteria ii      0.304795   \n",
       "8000      Criteria vi       Criteria i      Criteria iv      0.267437   \n",
       "6974     Criteria iii      Criteria vi       Criteria i      0.290052   \n",
       "\n",
       "      max_3_ulmfit max_1_col_bert  ... max_3_col_bert max_1_bert  max_3_bert  \\\n",
       "557       0.977705     Criteria i  ...    Criteria vi   0.893859    0.944379   \n",
       "9003      0.973507     Criteria i  ...    Criteria vi   0.895739    0.946531   \n",
       "584       0.921980     Criteria i  ...    Criteria vi   0.807620    0.901502   \n",
       "5549      0.965490   Criteria iii  ...    Criteria iv   0.747824    0.908523   \n",
       "49        0.983559     Criteria i  ...    Criteria ii   0.868267    0.943464   \n",
       "...            ...            ...  ...            ...        ...         ...   \n",
       "1458      0.823984    Criteria ii  ...    Criteria iv   0.260288    0.693995   \n",
       "8725      0.798239    Criteria vi  ...     Criteria i   0.296875    0.706907   \n",
       "6682      0.770579    Criteria iv  ...    Criteria ii   0.283121    0.739457   \n",
       "8000      0.671734    Criteria iv  ...     Criteria i   0.317985    0.886422   \n",
       "6974      0.819296   Criteria iii  ...    Criteria iv   0.289609    0.729429   \n",
       "\n",
       "      same_1  same_3  cosine_sim     max_1     max_3    conf_3    conf_1  \n",
       "557     True     0.5    0.999473  0.893859  0.944379  0.961042  0.896366  \n",
       "9003    True     0.5    0.998918  0.895739  0.946531  0.960019  0.885339  \n",
       "584     True     0.5    0.999184  0.807620  0.901502  0.911741  0.828492  \n",
       "5549    True     0.5    0.992759  0.747824  0.908523  0.937006  0.817926  \n",
       "49      True     1.0    0.984731  0.868267  0.943464  0.963512  0.803927  \n",
       "...      ...     ...         ...       ...       ...       ...       ...  \n",
       "1458   False     0.5    0.730301  0.260288  0.693995  0.758989  0.303457  \n",
       "8725    True     0.5    0.870103  0.296875  0.706907  0.752573  0.297471  \n",
       "6682   False     0.5    0.803420  0.283121  0.739457  0.755018  0.293958  \n",
       "8000   False     0.5    0.750401  0.317985  0.886422  0.779078  0.292711  \n",
       "6974    True     0.5    0.941349  0.289609  0.729429  0.774362  0.289831  \n",
       "\n",
       "[942 rows x 21 columns]"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "38f06d88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Criteria i ['Inaugurated in 1997, it is the work of architect Renzo Piano'\n",
      " 'Inaugurated in 1997, it is the work of the architect Renzo Piano'\n",
      " \"One of these is his giant 'Beanfield' painting, a beautiful piece that is displayed on the ground floor\"] [0.89636591 0.88533941 0.82849249]\n",
      "Criteria ii ['Accented corner solutions were an important part of the urban planning repertoire in the expansion plan-Berlage Amsterdam School architecture by Jop van Epen from the 1920s in Amsterdam'\n",
      " 'In 2017, I met Betteke and Michiel Hennus, when they attended a book presentation at Athenaeum Bookshop in Amsterdam'\n",
      " 'During the summer months, the Tram Museum in Amsterdam made several tours through the city on Sundays (line 20), from Haarlemmermeer station along, among other things,'] [0.76927333 0.76689643 0.73418105]\n",
      "Criteria iii ['It is a living testimony of the time' 'Candies Kingdom of Sweets'\n",
      " 'This is where the cooking was done and where the household took its meals'] [0.8179262  0.74420058 0.63455843]\n",
      "Criteria iv ['Along the south side of Sarphatistraat, the former Schans, east of Roetersstraat there are almost only buildings of military origin, such as barracks and warehouses.'\n",
      " 'The present guard house is not the original medieval structure but a 19th-century fantasy'\n",
      " 'The Sluyswacht is one of the few surviving monuments in the neighborhood and after years of various uses and sometimes abandonment the building is fully restored for use as a café'] [0.78978235 0.74273312 0.71052479]\n",
      "Criteria v ['I believe these mainly old bikes are used by people arriving here by train or ferry boat, for transport around town to various workplaces'] [0.4236356]\n",
      "Criteria vi ['Read: BOOKWEEK ESSAY THE GENOCIDE FAX This year, Roxane van Iperen is writing the essay on the theme of Two Battles'\n",
      " '\\ufeff \\ufeffIt tells us that we, as human beings, are affected by the moon, just as much as we are affected by the sun.'\n",
      " 'An entire shop in the Dutch capital, Amsterdam, dedicated to the worship (and sales) of the rubber duck.'] [0.75077191 0.70301859 0.70103232]\n",
      "Criteria vii ['No effort is spared to ensure that this breathtaking mansion makes an impact on visitors, adding an extra layer to the Moco experience'\n",
      " 'The combination gray + gray in gray weather is not the most ideal of course'\n",
      " 'Storm clouds over Amsterdam, Netherlands'] [0.78423479 0.60129788 0.57629063]\n",
      "Criteria viii ['The artwork represents this phenomenon’s data and shows that we are all connected to earth’s natural systems.'] [0.61065478]\n",
      "Criteria ix [] []\n",
      "Criteria x ['It has title of Alpenmarmot / Alpine marmot'\n",
      " 'The Sensi Seed Bank is the location of the first true cannabis gene bank in the Netherlands'\n",
      " 'The Sensi Seed Bank is the location of the first true cannabis gene-bank in the Netherlands'] [0.62180261 0.61909446 0.61909446]\n",
      "Others [] []\n"
     ]
    }
   ],
   "source": [
    "for c in classes:\n",
    "    temp = cmp_sorted[cmp_sorted.max_1_col_bert==c].iloc[:3]\n",
    "    print(c, temp['sentence'].values,temp['conf_1'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "107cbe0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Criteria i ['Inaugurated in 1997, it is the work of architect Renzo Piano'\n",
      " 'Inaugurated in 1997, it is the work of the architect Renzo Piano'\n",
      " \"One of these is his giant 'Beanfield' painting, a beautiful piece that is displayed on the ground floor\"] [0.89636591 0.88533941 0.82849249]\n",
      "Criteria ii ['Accented corner solutions were an important part of the urban planning repertoire in the expansion plan-Berlage Amsterdam School architecture by Jop van Epen from the 1920s in Amsterdam'\n",
      " 'In 2017, I met Betteke and Michiel Hennus, when they attended a book presentation at Athenaeum Bookshop in Amsterdam'\n",
      " 'During the summer months, the Tram Museum in Amsterdam made several tours through the city on Sundays (line 20), from Haarlemmermeer station along, among other things,'] [0.76927333 0.76689643 0.73418105]\n",
      "Criteria iii ['It is a living testimony of the time' 'Candies Kingdom of Sweets'\n",
      " 'Today it houses various businesses.'] [0.8179262  0.74420058 0.6609439 ]\n",
      "Criteria iv ['Along the south side of Sarphatistraat, the former Schans, east of Roetersstraat there are almost only buildings of military origin, such as barracks and warehouses.'\n",
      " 'The present guard house is not the original medieval structure but a 19th-century fantasy'\n",
      " 'The Sluyswacht is one of the few surviving monuments in the neighborhood and after years of various uses and sometimes abandonment the building is fully restored for use as a café'] [0.78978235 0.74273312 0.71052479]\n",
      "Criteria v [\"The Sluyswacht is a former lock keeper's house: in the past, the locks served both to prevent enemy ships from entering the city and to regulate the flow of water from the Amstel through the canals to the estuary of the Zuiderzee, the IJ\"\n",
      " 'Old brick bridge over the canal water of the Reguliergracht'\n",
      " 'Built 1610-1613 as city storage houses, for the storage of grain, butter and peat that was distributed to the poor'] [0.68340171 0.61159781 0.55562601]\n",
      "Criteria vi ['Read: BOOKWEEK ESSAY THE GENOCIDE FAX This year, Roxane van Iperen is writing the essay on the theme of Two Battles'\n",
      " '\\ufeff \\ufeffIt tells us that we, as human beings, are affected by the moon, just as much as we are affected by the sun.'\n",
      " 'An entire shop in the Dutch capital, Amsterdam, dedicated to the worship (and sales) of the rubber duck.'] [0.75077191 0.70301859 0.70103232]\n",
      "Criteria vii ['No effort is spared to ensure that this breathtaking mansion makes an impact on visitors, adding an extra layer to the Moco experience'\n",
      " 'It has title of Alpenmarmot / Alpine marmot'\n",
      " 'It has tags of heron, heron, amsterdam, artis, zoo, fp'] [0.78423479 0.62180261 0.61718118]\n",
      "Criteria viii ['The artwork represents this phenomenon’s data and shows that we are all connected to earth’s natural systems.'] [0.61065478]\n",
      "Criteria ix ['The Sensi Seed Bank is the location of the first true cannabis gene bank in the Netherlands'\n",
      " 'The Sensi Seed Bank is the location of the first true cannabis gene-bank in the Netherlands'] [0.61909446 0.61909446]\n",
      "Criteria x ['It has title of No weed zone' 'Birds by Johann Altorf'\n",
      " 'It has title of Big bird'] [0.59569729 0.59531221 0.49864099]\n",
      "Others [] []\n"
     ]
    }
   ],
   "source": [
    "for c in classes:\n",
    "    temp = cmp_sorted[cmp_sorted.max_1_col_ulmfit==c].iloc[:3]\n",
    "    print(c, temp['sentence'].values,temp['conf_1'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8cd3379",
   "metadata": {},
   "source": [
    "## Suzhou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "21cd7e7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zzbn1\\AppData\\Local\\Temp\\ipykernel_6436\\2667814804.py:1: DtypeWarning: Columns (824,825,826,827) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  bert = pd.read_csv('Suzhou/data_storage/metadata_bert.csv',sep='\\t',index_col='Unnamed: 0')\n"
     ]
    }
   ],
   "source": [
    "bert = pd.read_csv('Suzhou/data_storage/metadata_bert.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "ad146eac",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert['max_1_eq'] = bert['max_1_eq'].fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "7a70c9d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert = bert[bert.max_1_eq!=-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "e04d3e16",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp = pd.read_csv('Suzhou/data_storage/metadata_compare.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "2e7476f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp = cmp.merge(bert[['max_1_eq','max_3_eq']], how='right',left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "aa45387c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp['conf_3'] = (cmp['max_3_ulmfit'] + cmp['max_3_bert'])/2\n",
    "cmp['conf_1'] = (cmp['max_1_ulmfit'] + cmp['max_1_bert'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "77eb66be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    754.000000\n",
       "mean       0.766681\n",
       "std        0.055568\n",
       "min        0.541545\n",
       "25%        0.743970\n",
       "50%        0.765029\n",
       "75%        0.801086\n",
       "max        0.917763\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "ae1ea4d2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    754.000000\n",
       "mean       0.408007\n",
       "std        0.071565\n",
       "min        0.195560\n",
       "25%        0.367483\n",
       "50%        0.392667\n",
       "75%        0.444362\n",
       "max        0.681063\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "6a5dd1ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zzbn1\\AppData\\Local\\Temp\\ipykernel_6436\\3362900713.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  cmp_conf = cmp[cmp.conf_3>=0.75][cmp.same_3>=0.5]\n"
     ]
    }
   ],
   "source": [
    "cmp_conf = cmp[cmp.conf_3>=0.75][cmp.same_3>=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "b056531c",
   "metadata": {},
   "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>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "      <th>max_1_eq</th>\n",
       "      <th>max_3_eq</th>\n",
       "      <th>conf_3</th>\n",
       "      <th>conf_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>51859675789</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>Criteria ix</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.592425</td>\n",
       "      <td>0.898150</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.455594</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.848600</td>\n",
       "      <td>0.453421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>23</td>\n",
       "      <td>51849953700</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.402479</td>\n",
       "      <td>0.745595</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.450524</td>\n",
       "      <td>0.812665</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.838563</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.779130</td>\n",
       "      <td>0.426501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>108</td>\n",
       "      <td>51273169730</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.377143</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.276083</td>\n",
       "      <td>0.708932</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.938834</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.821132</td>\n",
       "      <td>0.326613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>135</td>\n",
       "      <td>51399368631</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.337336</td>\n",
       "      <td>0.778288</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.380843</td>\n",
       "      <td>0.784962</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.931472</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.781625</td>\n",
       "      <td>0.359090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>138</td>\n",
       "      <td>51354675299</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.362317</td>\n",
       "      <td>0.734053</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.477917</td>\n",
       "      <td>0.773208</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.612494</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.753631</td>\n",
       "      <td>0.420117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3008</th>\n",
       "      <td>4080</td>\n",
       "      <td>41140126144</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.503243</td>\n",
       "      <td>0.866574</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.278920</td>\n",
       "      <td>0.734373</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.814454</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.800473</td>\n",
       "      <td>0.391082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>4111</td>\n",
       "      <td>27910400829</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.440712</td>\n",
       "      <td>0.869378</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.316912</td>\n",
       "      <td>0.691579</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.679194</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.780479</td>\n",
       "      <td>0.378812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041</th>\n",
       "      <td>4129</td>\n",
       "      <td>38689625892</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.404491</td>\n",
       "      <td>0.724738</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.694318</td>\n",
       "      <td>0.815191</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.891406</td>\n",
       "      <td>True</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.769965</td>\n",
       "      <td>0.549405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3048</th>\n",
       "      <td>4136</td>\n",
       "      <td>26874316829</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>0.948481</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.759633</td>\n",
       "      <td>0.887045</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.931110</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.917763</td>\n",
       "      <td>0.681063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3049</th>\n",
       "      <td>4137</td>\n",
       "      <td>38618080352</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>0.948481</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.759633</td>\n",
       "      <td>0.887045</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.931110</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.917763</td>\n",
       "      <td>0.681063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>322 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin        index max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "0          8  51859675789       Criteria x    Criteria viii     Criteria vii   \n",
       "15        23  51849953700      Criteria vi     Criteria iii      Criteria iv   \n",
       "95       108  51273169730     Criteria iii      Criteria vi      Criteria ii   \n",
       "120      135  51399368631      Criteria ii       Criteria i      Criteria iv   \n",
       "123      138  51354675299       Criteria i      Criteria iv     Criteria iii   \n",
       "...      ...          ...              ...              ...              ...   \n",
       "3008    4080  41140126144     Criteria iii      Criteria vi      Criteria iv   \n",
       "3024    4111  27910400829      Criteria vi     Criteria iii      Criteria ii   \n",
       "3041    4129  38689625892       Criteria i      Criteria iv     Criteria iii   \n",
       "3048    4136  26874316829     Criteria iii      Criteria vi      Criteria ii   \n",
       "3049    4137  38618080352     Criteria iii      Criteria vi      Criteria ii   \n",
       "\n",
       "      max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert max_3_col_bert  \\\n",
       "0         0.314417      0.799051    Criteria ix     Criteria x  Criteria viii   \n",
       "15        0.402479      0.745595   Criteria iii    Criteria vi    Criteria iv   \n",
       "95        0.377143      0.933333   Criteria iii    Criteria ii    Criteria vi   \n",
       "120       0.337336      0.778288     Criteria i    Criteria ii    Criteria iv   \n",
       "123       0.362317      0.734053    Criteria vi    Criteria iv   Criteria iii   \n",
       "...            ...           ...            ...            ...            ...   \n",
       "3008      0.503243      0.866574    Criteria ii   Criteria iii    Criteria iv   \n",
       "3024      0.440712      0.869378     Criteria i   Criteria iii    Criteria vi   \n",
       "3041      0.404491      0.724738     Criteria i    Criteria vi   Criteria iii   \n",
       "3048      0.602493      0.948481   Criteria iii    Criteria iv    Criteria ii   \n",
       "3049      0.602493      0.948481   Criteria iii    Criteria iv    Criteria ii   \n",
       "\n",
       "      max_1_bert  max_3_bert  same_1  same_3  cosine_sim max_1_eq  max_3_eq  \\\n",
       "0       0.592425    0.898150   False     0.5    0.455594     True       1.0   \n",
       "15      0.450524    0.812665   False     1.0    0.838563     True       1.0   \n",
       "95      0.276083    0.708932    True     1.0    0.938834     True       1.0   \n",
       "120     0.380843    0.784962   False     1.0    0.931472     True       0.5   \n",
       "123     0.477917    0.773208   False     0.5    0.612494     True       1.0   \n",
       "...          ...         ...     ...     ...         ...      ...       ...   \n",
       "3008    0.278920    0.734373   False     0.5    0.814454     True       1.0   \n",
       "3024    0.316912    0.691579   False     0.5    0.679194     True       0.5   \n",
       "3041    0.694318    0.815191    True     0.5    0.891406     True       0.2   \n",
       "3048    0.759633    0.887045    True     0.5    0.931110     True       1.0   \n",
       "3049    0.759633    0.887045    True     0.5    0.931110     True       1.0   \n",
       "\n",
       "        conf_3    conf_1  \n",
       "0     0.848600  0.453421  \n",
       "15    0.779130  0.426501  \n",
       "95    0.821132  0.326613  \n",
       "120   0.781625  0.359090  \n",
       "123   0.753631  0.420117  \n",
       "...        ...       ...  \n",
       "3008  0.800473  0.391082  \n",
       "3024  0.780479  0.378812  \n",
       "3041  0.769965  0.549405  \n",
       "3048  0.917763  0.681063  \n",
       "3049  0.917763  0.681063  \n",
       "\n",
       "[322 rows x 19 columns]"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "d8b57930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    322.000000\n",
       "mean       0.802907\n",
       "std        0.036613\n",
       "min        0.752205\n",
       "25%        0.768768\n",
       "50%        0.800473\n",
       "75%        0.831376\n",
       "max        0.917763\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "cc29e2a8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    322.000000\n",
       "mean       0.435893\n",
       "std        0.068704\n",
       "min        0.311336\n",
       "25%        0.378172\n",
       "50%        0.411087\n",
       "75%        0.491913\n",
       "max        0.681063\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "b3f7bf34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    322.000000\n",
       "mean       0.588509\n",
       "std        0.191139\n",
       "min        0.500000\n",
       "25%        0.500000\n",
       "50%        0.500000\n",
       "75%        0.500000\n",
       "max        1.000000\n",
       "Name: same_3, dtype: float64"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['same_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "2c74d22a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       322\n",
       "unique        2\n",
       "top       False\n",
       "freq        247\n",
       "Name: same_1, dtype: object"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf['same_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f3e0eb19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4270557029177719"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "322/754"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c45588a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat([cmp_conf['max_1_col_bert'],\n",
    "           cmp_conf['max_2_col_bert'],\n",
    "           cmp_conf['max_3_col_bert'],\n",
    "           cmp_conf['max_1_col_ulmfit'],\n",
    "           cmp_conf['max_2_col_ulmfit'],\n",
    "           cmp_conf['max_3_col_ulmfit']]).value_counts()/(322*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "f02ce419",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentages['SUZ']=pd.concat([cmp_conf['max_1_col_bert'],\n",
    "           cmp_conf['max_2_col_bert'],\n",
    "           cmp_conf['max_3_col_bert'],\n",
    "           cmp_conf['max_1_col_ulmfit'],\n",
    "           cmp_conf['max_2_col_ulmfit'],\n",
    "           cmp_conf['max_3_col_ulmfit']]).value_counts()/(322*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "9fc82b06",
   "metadata": {},
   "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>VEN</th>\n",
       "      <th>VEN_s</th>\n",
       "      <th>VEN_XL</th>\n",
       "      <th>VEN_XL_s</th>\n",
       "      <th>AMS</th>\n",
       "      <th>AMS_s</th>\n",
       "      <th>SUZ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUV</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Criteria i</th>\n",
       "      <td>0.147707</td>\n",
       "      <td>0.183544</td>\n",
       "      <td>0.075867</td>\n",
       "      <td>0.171714</td>\n",
       "      <td>0.103849</td>\n",
       "      <td>0.147912</td>\n",
       "      <td>0.068841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ii</th>\n",
       "      <td>0.202381</td>\n",
       "      <td>0.188668</td>\n",
       "      <td>0.162301</td>\n",
       "      <td>0.190178</td>\n",
       "      <td>0.219995</td>\n",
       "      <td>0.237792</td>\n",
       "      <td>0.262422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iii</th>\n",
       "      <td>0.213845</td>\n",
       "      <td>0.191581</td>\n",
       "      <td>0.270847</td>\n",
       "      <td>0.203632</td>\n",
       "      <td>0.149425</td>\n",
       "      <td>0.170028</td>\n",
       "      <td>0.243789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iv</th>\n",
       "      <td>0.240300</td>\n",
       "      <td>0.252863</td>\n",
       "      <td>0.233602</td>\n",
       "      <td>0.241005</td>\n",
       "      <td>0.313018</td>\n",
       "      <td>0.289986</td>\n",
       "      <td>0.114907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria v</th>\n",
       "      <td>0.008598</td>\n",
       "      <td>0.004722</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.005177</td>\n",
       "      <td>0.010960</td>\n",
       "      <td>0.009023</td>\n",
       "      <td>0.005694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vi</th>\n",
       "      <td>0.183862</td>\n",
       "      <td>0.162849</td>\n",
       "      <td>0.249893</td>\n",
       "      <td>0.172038</td>\n",
       "      <td>0.200080</td>\n",
       "      <td>0.127212</td>\n",
       "      <td>0.290890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vii</th>\n",
       "      <td>0.001764</td>\n",
       "      <td>0.006631</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>0.007203</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.007077</td>\n",
       "      <td>0.002070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria viii</th>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>0.002543</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.002477</td>\n",
       "      <td>0.001553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ix</th>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.003516</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.002103</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.002831</td>\n",
       "      <td>0.001035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria x</th>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.001520</td>\n",
       "      <td>0.004401</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.005662</td>\n",
       "      <td>0.008799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    VEN     VEN_s    VEN_XL  VEN_XL_s       AMS     AMS_s  \\\n",
       "OUV                                                                         \n",
       "Criteria i     0.147707  0.183544  0.075867  0.171714  0.103849  0.147912   \n",
       "Criteria ii    0.202381  0.188668  0.162301  0.190178  0.219995  0.237792   \n",
       "Criteria iii   0.213845  0.191581  0.270847  0.203632  0.149425  0.170028   \n",
       "Criteria iv    0.240300  0.252863  0.233602  0.241005  0.313018  0.289986   \n",
       "Criteria v     0.008598  0.004722  0.002794  0.005177  0.010960  0.009023   \n",
       "Criteria vi    0.183862  0.162849  0.249893  0.172038  0.200080  0.127212   \n",
       "Criteria vii   0.001764  0.006631  0.002231  0.007203  0.000802  0.007077   \n",
       "Criteria viii  0.000220  0.002813  0.000349  0.002543  0.000535  0.002477   \n",
       "Criteria ix    0.000441  0.003516  0.000595  0.002103  0.000535  0.002831   \n",
       "Criteria x     0.000882  0.002813  0.001520  0.004401  0.000802  0.005662   \n",
       "Others              NaN       NaN       NaN  0.000006       NaN       NaN   \n",
       "\n",
       "                    SUZ  \n",
       "OUV                      \n",
       "Criteria i     0.068841  \n",
       "Criteria ii    0.262422  \n",
       "Criteria iii   0.243789  \n",
       "Criteria iv    0.114907  \n",
       "Criteria v     0.005694  \n",
       "Criteria vi    0.290890  \n",
       "Criteria vii   0.002070  \n",
       "Criteria viii  0.001553  \n",
       "Criteria ix    0.001035  \n",
       "Criteria x     0.008799  \n",
       "Others              NaN  "
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "5cf192fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria vi      562\n",
       "Criteria ii      507\n",
       "Criteria iii     471\n",
       "Criteria iv      222\n",
       "Criteria i       133\n",
       "Criteria x        17\n",
       "Criteria v        11\n",
       "Criteria vii       4\n",
       "Criteria viii      3\n",
       "Criteria ix        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf['max_1_col_bert'],\n",
    "           cmp_conf['max_2_col_bert'],\n",
    "           cmp_conf['max_3_col_bert'],\n",
    "           cmp_conf['max_1_col_ulmfit'],\n",
    "           cmp_conf['max_2_col_ulmfit'],\n",
    "           cmp_conf['max_3_col_ulmfit']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "f519df23",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert_s = pd.read_csv('Suzhou/data_storage/sentences_bert.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "eb284589",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>...</th>\n",
       "      <th>BERT_766</th>\n",
       "      <th>BERT_767</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>It has title of \"Raining Soochow\"</td>\n",
       "      <td>104</td>\n",
       "      <td>en</td>\n",
       "      <td>0.132901</td>\n",
       "      <td>0.025626</td>\n",
       "      <td>0.111723</td>\n",
       "      <td>0.045728</td>\n",
       "      <td>0.036301</td>\n",
       "      <td>0.158634</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.050052</td>\n",
       "      <td>0.825894</td>\n",
       "      <td>0.191816</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.158634</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.158438</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.191816</td>\n",
       "      <td>0.508888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has title of \"Glow In the Dark\"</td>\n",
       "      <td>106</td>\n",
       "      <td>en</td>\n",
       "      <td>0.185117</td>\n",
       "      <td>0.042183</td>\n",
       "      <td>0.066849</td>\n",
       "      <td>0.053831</td>\n",
       "      <td>0.021639</td>\n",
       "      <td>0.058710</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.135458</td>\n",
       "      <td>0.975341</td>\n",
       "      <td>0.224774</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.185117</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.181310</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.224774</td>\n",
       "      <td>0.591201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>and a family (in the background)</td>\n",
       "      <td>131</td>\n",
       "      <td>en</td>\n",
       "      <td>0.033132</td>\n",
       "      <td>0.110547</td>\n",
       "      <td>0.314518</td>\n",
       "      <td>0.330711</td>\n",
       "      <td>0.114962</td>\n",
       "      <td>0.039835</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101699</td>\n",
       "      <td>-0.195817</td>\n",
       "      <td>0.330711</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.314518</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.114962</td>\n",
       "      <td>Criteria v</td>\n",
       "      <td>0.330711</td>\n",
       "      <td>0.760191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>It has title of Goldfishes</td>\n",
       "      <td>131</td>\n",
       "      <td>en</td>\n",
       "      <td>0.008342</td>\n",
       "      <td>0.004857</td>\n",
       "      <td>0.015115</td>\n",
       "      <td>0.002584</td>\n",
       "      <td>0.007281</td>\n",
       "      <td>0.002083</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.617588</td>\n",
       "      <td>0.887453</td>\n",
       "      <td>0.722116</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.110830</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.087264</td>\n",
       "      <td>Criteria ix</td>\n",
       "      <td>0.722116</td>\n",
       "      <td>0.920210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>It has title of Tetrahedral shadow and rockery</td>\n",
       "      <td>132</td>\n",
       "      <td>en</td>\n",
       "      <td>0.059658</td>\n",
       "      <td>0.027018</td>\n",
       "      <td>0.067757</td>\n",
       "      <td>0.438848</td>\n",
       "      <td>0.072322</td>\n",
       "      <td>0.105282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.609779</td>\n",
       "      <td>0.456196</td>\n",
       "      <td>0.438848</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.108198</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.105282</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.438848</td>\n",
       "      <td>0.652328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>1761</td>\n",
       "      <td>It has tags of suzhoushi, jiangsusheng, china, cn</td>\n",
       "      <td>4116</td>\n",
       "      <td>other</td>\n",
       "      <td>0.164054</td>\n",
       "      <td>0.185454</td>\n",
       "      <td>0.247030</td>\n",
       "      <td>0.153396</td>\n",
       "      <td>0.013783</td>\n",
       "      <td>0.177106</td>\n",
       "      <td>...</td>\n",
       "      <td>0.592267</td>\n",
       "      <td>0.389208</td>\n",
       "      <td>0.247030</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.185454</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.177106</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.247030</td>\n",
       "      <td>0.609589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>1763</td>\n",
       "      <td>It has tags of suzhoushi, jiangsusheng, china, cn</td>\n",
       "      <td>4117</td>\n",
       "      <td>other</td>\n",
       "      <td>0.164054</td>\n",
       "      <td>0.185454</td>\n",
       "      <td>0.247030</td>\n",
       "      <td>0.153396</td>\n",
       "      <td>0.013783</td>\n",
       "      <td>0.177106</td>\n",
       "      <td>...</td>\n",
       "      <td>0.592267</td>\n",
       "      <td>0.389208</td>\n",
       "      <td>0.247030</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.185454</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.177106</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.247030</td>\n",
       "      <td>0.609589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>1764</td>\n",
       "      <td>Canglang Pavilion 20171120 adam0730</td>\n",
       "      <td>4129</td>\n",
       "      <td>other</td>\n",
       "      <td>0.619855</td>\n",
       "      <td>0.059418</td>\n",
       "      <td>0.058423</td>\n",
       "      <td>0.116926</td>\n",
       "      <td>0.008782</td>\n",
       "      <td>0.027466</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137768</td>\n",
       "      <td>0.965384</td>\n",
       "      <td>0.619855</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.116926</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.059418</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.619855</td>\n",
       "      <td>0.796199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>1773</td>\n",
       "      <td>It has tags of chinasuzhou</td>\n",
       "      <td>4139</td>\n",
       "      <td>other</td>\n",
       "      <td>0.069314</td>\n",
       "      <td>0.085946</td>\n",
       "      <td>0.398199</td>\n",
       "      <td>0.126585</td>\n",
       "      <td>0.033287</td>\n",
       "      <td>0.147490</td>\n",
       "      <td>...</td>\n",
       "      <td>0.419786</td>\n",
       "      <td>0.380366</td>\n",
       "      <td>0.398199</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.147490</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.126585</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.398199</td>\n",
       "      <td>0.672273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>1774</td>\n",
       "      <td>It has tags of cinasuzhouautumn</td>\n",
       "      <td>4140</td>\n",
       "      <td>other</td>\n",
       "      <td>0.046740</td>\n",
       "      <td>0.013995</td>\n",
       "      <td>0.092506</td>\n",
       "      <td>0.023016</td>\n",
       "      <td>0.025447</td>\n",
       "      <td>0.025495</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.612271</td>\n",
       "      <td>0.906253</td>\n",
       "      <td>0.595125</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.092506</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.088954</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.595125</td>\n",
       "      <td>0.776585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>984 rows × 791 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                                           sentence  origin   lang  \\\n",
       "0        0                  It has title of \"Raining Soochow\"     104     en   \n",
       "1        1                 It has title of \"Glow In the Dark\"     106     en   \n",
       "2        5                   and a family (in the background)     131     en   \n",
       "3        6                         It has title of Goldfishes     131     en   \n",
       "4        7     It has title of Tetrahedral shadow and rockery     132     en   \n",
       "..     ...                                                ...     ...    ...   \n",
       "979   1761  It has tags of suzhoushi, jiangsusheng, china, cn    4116  other   \n",
       "980   1763  It has tags of suzhoushi, jiangsusheng, china, cn    4117  other   \n",
       "981   1764                Canglang Pavilion 20171120 adam0730    4129  other   \n",
       "982   1773                         It has tags of chinasuzhou    4139  other   \n",
       "983   1774                    It has tags of cinasuzhouautumn    4140  other   \n",
       "\n",
       "     Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0      0.132901     0.025626      0.111723     0.045728    0.036301   \n",
       "1      0.185117     0.042183      0.066849     0.053831    0.021639   \n",
       "2      0.033132     0.110547      0.314518     0.330711    0.114962   \n",
       "3      0.008342     0.004857      0.015115     0.002584    0.007281   \n",
       "4      0.059658     0.027018      0.067757     0.438848    0.072322   \n",
       "..          ...          ...           ...          ...         ...   \n",
       "979    0.164054     0.185454      0.247030     0.153396    0.013783   \n",
       "980    0.164054     0.185454      0.247030     0.153396    0.013783   \n",
       "981    0.619855     0.059418      0.058423     0.116926    0.008782   \n",
       "982    0.069314     0.085946      0.398199     0.126585    0.033287   \n",
       "983    0.046740     0.013995      0.092506     0.023016    0.025447   \n",
       "\n",
       "     Criteria vi  ...  BERT_766  BERT_767  max_1_val     max_1_col  max_2_val  \\\n",
       "0       0.158634  ... -0.050052  0.825894   0.191816  Criteria vii   0.158634   \n",
       "1       0.058710  ... -0.135458  0.975341   0.224774    Criteria x   0.185117   \n",
       "2       0.039835  ...  0.101699 -0.195817   0.330711   Criteria iv   0.314518   \n",
       "3       0.002083  ... -0.617588  0.887453   0.722116    Criteria x   0.110830   \n",
       "4       0.105282  ...  0.609779  0.456196   0.438848   Criteria iv   0.108198   \n",
       "..           ...  ...       ...       ...        ...           ...        ...   \n",
       "979     0.177106  ...  0.592267  0.389208   0.247030  Criteria iii   0.185454   \n",
       "980     0.177106  ...  0.592267  0.389208   0.247030  Criteria iii   0.185454   \n",
       "981     0.027466  ...  0.137768  0.965384   0.619855    Criteria i   0.116926   \n",
       "982     0.147490  ...  0.419786  0.380366   0.398199  Criteria iii   0.147490   \n",
       "983     0.025495  ... -0.612271  0.906253   0.595125    Criteria x   0.092506   \n",
       "\n",
       "        max_2_col  max_3_val     max_3_col     max_1     max_3  \n",
       "0     Criteria vi   0.158438    Criteria x  0.191816  0.508888  \n",
       "1      Criteria i   0.181310  Criteria vii  0.224774  0.591201  \n",
       "2    Criteria iii   0.114962    Criteria v  0.330711  0.760191  \n",
       "3    Criteria vii   0.087264   Criteria ix  0.722116  0.920210  \n",
       "4    Criteria vii   0.105282   Criteria vi  0.438848  0.652328  \n",
       "..            ...        ...           ...       ...       ...  \n",
       "979   Criteria ii   0.177106   Criteria vi  0.247030  0.609589  \n",
       "980   Criteria ii   0.177106   Criteria vi  0.247030  0.609589  \n",
       "981   Criteria iv   0.059418   Criteria ii  0.619855  0.796199  \n",
       "982   Criteria vi   0.126585   Criteria iv  0.398199  0.672273  \n",
       "983  Criteria iii   0.088954  Criteria vii  0.595125  0.776585  \n",
       "\n",
       "[984 rows x 791 columns]"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "f81127a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_s = pd.read_csv('Suzhou/data_storage/sentences_compare.csv',sep='\\t',index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "c11383a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>It has title of \"Raining Soochow\"</td>\n",
       "      <td>104</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.279820</td>\n",
       "      <td>0.686922</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.191816</td>\n",
       "      <td>0.508888</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.722470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has title of \"Glow In the Dark\"</td>\n",
       "      <td>106</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.331788</td>\n",
       "      <td>0.598135</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.224774</td>\n",
       "      <td>0.591201</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>and a family (in the background)</td>\n",
       "      <td>131</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.381616</td>\n",
       "      <td>0.749891</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria v</td>\n",
       "      <td>0.330711</td>\n",
       "      <td>0.760191</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.903507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>It has title of Goldfishes</td>\n",
       "      <td>131</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>0.619725</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>Criteria ix</td>\n",
       "      <td>0.722116</td>\n",
       "      <td>0.920210</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>It has title of Tetrahedral shadow and rockery</td>\n",
       "      <td>132</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.207680</td>\n",
       "      <td>0.534065</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.438848</td>\n",
       "      <td>0.652328</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.522514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>1761</td>\n",
       "      <td>It has tags of suzhoushi, jiangsusheng, china, cn</td>\n",
       "      <td>4116</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.453741</td>\n",
       "      <td>0.741025</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.247030</td>\n",
       "      <td>0.609589</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.882950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>1763</td>\n",
       "      <td>It has tags of suzhoushi, jiangsusheng, china, cn</td>\n",
       "      <td>4117</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.453741</td>\n",
       "      <td>0.741025</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.247030</td>\n",
       "      <td>0.609589</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.882950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>1764</td>\n",
       "      <td>Canglang Pavilion 20171120 adam0730</td>\n",
       "      <td>4129</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.579131</td>\n",
       "      <td>0.868801</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.619855</td>\n",
       "      <td>0.796199</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.984780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>1773</td>\n",
       "      <td>It has tags of chinasuzhou</td>\n",
       "      <td>4139</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.333507</td>\n",
       "      <td>0.584716</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.398199</td>\n",
       "      <td>0.672273</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.928948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>1774</td>\n",
       "      <td>It has tags of cinasuzhouautumn</td>\n",
       "      <td>4140</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.333507</td>\n",
       "      <td>0.584716</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.595125</td>\n",
       "      <td>0.776585</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.358723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>984 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                                           sentence  origin   lang  \\\n",
       "0        0                  It has title of \"Raining Soochow\"     104     en   \n",
       "1        1                 It has title of \"Glow In the Dark\"     106     en   \n",
       "2        5                   and a family (in the background)     131     en   \n",
       "3        6                         It has title of Goldfishes     131     en   \n",
       "4        7     It has title of Tetrahedral shadow and rockery     132     en   \n",
       "..     ...                                                ...     ...    ...   \n",
       "979   1761  It has tags of suzhoushi, jiangsusheng, china, cn    4116  other   \n",
       "980   1763  It has tags of suzhoushi, jiangsusheng, china, cn    4117  other   \n",
       "981   1764                Canglang Pavilion 20171120 adam0730    4129  other   \n",
       "982   1773                         It has tags of chinasuzhou    4139  other   \n",
       "983   1774                    It has tags of cinasuzhouautumn    4140  other   \n",
       "\n",
       "    max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  max_1_ulmfit  \\\n",
       "0       Criteria iii      Criteria vi      Criteria iv      0.279820   \n",
       "1        Criteria vi     Criteria iii      Criteria iv      0.331788   \n",
       "2       Criteria iii      Criteria iv      Criteria vi      0.381616   \n",
       "3       Criteria iii      Criteria vi      Criteria iv      0.293035   \n",
       "4      Criteria viii     Criteria iii     Criteria vii      0.207680   \n",
       "..               ...              ...              ...           ...   \n",
       "979     Criteria iii      Criteria vi      Criteria iv      0.453741   \n",
       "980     Criteria iii      Criteria vi      Criteria iv      0.453741   \n",
       "981       Criteria i      Criteria iv      Criteria ii      0.579131   \n",
       "982     Criteria iii     Criteria vii      Criteria iv      0.333507   \n",
       "983     Criteria iii     Criteria vii      Criteria iv      0.333507   \n",
       "\n",
       "     max_3_ulmfit max_1_col_bert max_2_col_bert max_3_col_bert  max_1_bert  \\\n",
       "0        0.686922   Criteria vii    Criteria vi     Criteria x    0.191816   \n",
       "1        0.598135     Criteria x     Criteria i   Criteria vii    0.224774   \n",
       "2        0.749891    Criteria iv   Criteria iii     Criteria v    0.330711   \n",
       "3        0.619725     Criteria x   Criteria vii    Criteria ix    0.722116   \n",
       "4        0.534065    Criteria iv   Criteria vii    Criteria vi    0.438848   \n",
       "..            ...            ...            ...            ...         ...   \n",
       "979      0.741025   Criteria iii    Criteria ii    Criteria vi    0.247030   \n",
       "980      0.741025   Criteria iii    Criteria ii    Criteria vi    0.247030   \n",
       "981      0.868801     Criteria i    Criteria iv    Criteria ii    0.619855   \n",
       "982      0.584716   Criteria iii    Criteria vi    Criteria iv    0.398199   \n",
       "983      0.584716     Criteria x   Criteria iii   Criteria vii    0.595125   \n",
       "\n",
       "     max_3_bert  same_1  same_3  cosine_sim  \n",
       "0      0.508888   False     0.2    0.722470  \n",
       "1      0.591201   False     0.0    0.534548  \n",
       "2      0.760191   False     0.5    0.903507  \n",
       "3      0.920210   False     0.0    0.140760  \n",
       "4      0.652328   False     0.2    0.522514  \n",
       "..          ...     ...     ...         ...  \n",
       "979    0.609589    True     0.5    0.882950  \n",
       "980    0.609589    True     0.5    0.882950  \n",
       "981    0.796199    True     1.0    0.984780  \n",
       "982    0.672273    True     0.5    0.928948  \n",
       "983    0.776585   False     0.5    0.358723  \n",
       "\n",
       "[984 rows x 17 columns]"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "92067f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_s = cmp_s.merge(bert_s.drop_duplicates(subset='sentence',keep='first')[['max_1','max_3']], how='right',left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "5f4210fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_s['conf_3'] = (cmp_s['max_3_ulmfit'] + cmp_s['max_3_bert'])/2\n",
    "cmp_s['conf_1'] = (cmp_s['max_1_ulmfit'] + cmp_s['max_1_bert'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "f5bc6e34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    361.000000\n",
       "mean       0.754936\n",
       "std        0.082307\n",
       "min        0.484819\n",
       "25%        0.703652\n",
       "50%        0.761163\n",
       "75%        0.809322\n",
       "max        0.926604\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_s['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "5c8269fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    361.000000\n",
       "mean       0.417806\n",
       "std        0.108084\n",
       "min        0.190062\n",
       "25%        0.344645\n",
       "50%        0.402027\n",
       "75%        0.474313\n",
       "max        0.813775\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_s['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "cc9a51cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zzbn1\\AppData\\Local\\Temp\\ipykernel_6436\\3704144270.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  cmp_conf_s = cmp_s[cmp_s.conf_3>=0.75][cmp_s.same_3>=0.5]\n"
     ]
    }
   ],
   "source": [
    "cmp_conf_s = cmp_s[cmp_s.conf_3>=0.75][cmp_s.same_3>=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "ae3d4492",
   "metadata": {},
   "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>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>...</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>conf_3</th>\n",
       "      <th>conf_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>and a family (in the background)</td>\n",
       "      <td>131</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.381616</td>\n",
       "      <td>0.749891</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria v</td>\n",
       "      <td>0.330711</td>\n",
       "      <td>0.760191</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.903507</td>\n",
       "      <td>0.330711</td>\n",
       "      <td>0.760191</td>\n",
       "      <td>0.755041</td>\n",
       "      <td>0.356163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>It has title of The classic Soochow-style brunch</td>\n",
       "      <td>138</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.362317</td>\n",
       "      <td>0.734053</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.477917</td>\n",
       "      <td>0.773208</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.612494</td>\n",
       "      <td>0.477917</td>\n",
       "      <td>0.773208</td>\n",
       "      <td>0.753631</td>\n",
       "      <td>0.420117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>It has tags of chenyang, suzhou, jiangsu, peop...</td>\n",
       "      <td>349</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.509157</td>\n",
       "      <td>0.825236</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.333751</td>\n",
       "      <td>0.768451</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.936054</td>\n",
       "      <td>0.333751</td>\n",
       "      <td>0.768451</td>\n",
       "      <td>0.796843</td>\n",
       "      <td>0.421454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>It has tags of suzhou, jiangsu, peoplesrepubli...</td>\n",
       "      <td>350</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.480951</td>\n",
       "      <td>0.821939</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.443253</td>\n",
       "      <td>0.770803</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.771557</td>\n",
       "      <td>0.443253</td>\n",
       "      <td>0.770803</td>\n",
       "      <td>0.796371</td>\n",
       "      <td>0.462102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13</td>\n",
       "      <td>The Canglang Pavilion (沧浪亭), variously transla...</td>\n",
       "      <td>361</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.379311</td>\n",
       "      <td>0.758977</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.336931</td>\n",
       "      <td>0.793802</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.897677</td>\n",
       "      <td>0.336931</td>\n",
       "      <td>0.793802</td>\n",
       "      <td>0.776390</td>\n",
       "      <td>0.358121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>1444</td>\n",
       "      <td>Ieoh Ming Pei, 2006</td>\n",
       "      <td>3485</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.578377</td>\n",
       "      <td>0.899474</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.468294</td>\n",
       "      <td>0.780051</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.663891</td>\n",
       "      <td>0.468294</td>\n",
       "      <td>0.780051</td>\n",
       "      <td>0.839763</td>\n",
       "      <td>0.523336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>1445</td>\n",
       "      <td>It has title of Suzhou museum</td>\n",
       "      <td>3485</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.445002</td>\n",
       "      <td>0.807460</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.594494</td>\n",
       "      <td>0.804005</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.682554</td>\n",
       "      <td>0.594494</td>\n",
       "      <td>0.804005</td>\n",
       "      <td>0.805733</td>\n",
       "      <td>0.519748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>1448</td>\n",
       "      <td>It has title of Zhuozheng yuan, Suzhou</td>\n",
       "      <td>3487</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.375482</td>\n",
       "      <td>0.825209</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.475545</td>\n",
       "      <td>0.777265</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.933956</td>\n",
       "      <td>0.475545</td>\n",
       "      <td>0.777265</td>\n",
       "      <td>0.801237</td>\n",
       "      <td>0.425514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>1449</td>\n",
       "      <td>It has tags of china, jiangsu, suzhou, zh, cn</td>\n",
       "      <td>3487</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.460907</td>\n",
       "      <td>0.874101</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.600894</td>\n",
       "      <td>0.873927</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.491655</td>\n",
       "      <td>0.600894</td>\n",
       "      <td>0.873927</td>\n",
       "      <td>0.874014</td>\n",
       "      <td>0.530901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>1764</td>\n",
       "      <td>Canglang Pavilion 20171120 adam0730</td>\n",
       "      <td>4129</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.579131</td>\n",
       "      <td>0.868801</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.619855</td>\n",
       "      <td>0.796199</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.984780</td>\n",
       "      <td>0.619855</td>\n",
       "      <td>0.796199</td>\n",
       "      <td>0.832500</td>\n",
       "      <td>0.599493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                                           sentence  origin   lang  \\\n",
       "2        5                   and a family (in the background)     131     en   \n",
       "7       10   It has title of The classic Soochow-style brunch     138     en   \n",
       "8       11  It has tags of chenyang, suzhou, jiangsu, peop...     349     en   \n",
       "9       12  It has tags of suzhou, jiangsu, peoplesrepubli...     350     en   \n",
       "10      13  The Canglang Pavilion (沧浪亭), variously transla...     361     en   \n",
       "..     ...                                                ...     ...    ...   \n",
       "874   1444                                Ieoh Ming Pei, 2006    3485  other   \n",
       "875   1445                      It has title of Suzhou museum    3485  other   \n",
       "878   1448             It has title of Zhuozheng yuan, Suzhou    3487  other   \n",
       "879   1449      It has tags of china, jiangsu, suzhou, zh, cn    3487  other   \n",
       "981   1764                Canglang Pavilion 20171120 adam0730    4129  other   \n",
       "\n",
       "    max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  max_1_ulmfit  \\\n",
       "2       Criteria iii      Criteria iv      Criteria vi      0.381616   \n",
       "7         Criteria i      Criteria iv     Criteria iii      0.362317   \n",
       "8       Criteria iii      Criteria vi      Criteria iv      0.509157   \n",
       "9       Criteria iii      Criteria vi      Criteria ii      0.480951   \n",
       "10        Criteria i      Criteria vi      Criteria iv      0.379311   \n",
       "..               ...              ...              ...           ...   \n",
       "874     Criteria iii      Criteria vi      Criteria ii      0.578377   \n",
       "875      Criteria vi     Criteria iii       Criteria i      0.445002   \n",
       "878     Criteria iii      Criteria vi      Criteria iv      0.375482   \n",
       "879     Criteria iii      Criteria vi      Criteria ii      0.460907   \n",
       "981       Criteria i      Criteria iv      Criteria ii      0.579131   \n",
       "\n",
       "     max_3_ulmfit max_1_col_bert  ... max_3_col_bert max_1_bert  max_3_bert  \\\n",
       "2        0.749891    Criteria iv  ...     Criteria v   0.330711    0.760191   \n",
       "7        0.734053    Criteria vi  ...   Criteria iii   0.477917    0.773208   \n",
       "8        0.825236   Criteria iii  ...    Criteria iv   0.333751    0.768451   \n",
       "9        0.821939    Criteria vi  ...    Criteria iv   0.443253    0.770803   \n",
       "10       0.758977    Criteria iv  ...    Criteria vi   0.336931    0.793802   \n",
       "..            ...            ...  ...            ...        ...         ...   \n",
       "874      0.899474    Criteria vi  ...     Criteria i   0.468294    0.780051   \n",
       "875      0.807460   Criteria iii  ...     Criteria i   0.594494    0.804005   \n",
       "878      0.825209   Criteria iii  ...    Criteria iv   0.475545    0.777265   \n",
       "879      0.874101    Criteria ii  ...    Criteria vi   0.600894    0.873927   \n",
       "981      0.868801     Criteria i  ...    Criteria ii   0.619855    0.796199   \n",
       "\n",
       "     same_1  same_3  cosine_sim     max_1     max_3    conf_3    conf_1  \n",
       "2     False     0.5    0.903507  0.330711  0.760191  0.755041  0.356163  \n",
       "7     False     0.5    0.612494  0.477917  0.773208  0.753631  0.420117  \n",
       "8      True     1.0    0.936054  0.333751  0.768451  0.796843  0.421454  \n",
       "9     False     0.5    0.771557  0.443253  0.770803  0.796371  0.462102  \n",
       "10    False     1.0    0.897677  0.336931  0.793802  0.776390  0.358121  \n",
       "..      ...     ...         ...       ...       ...       ...       ...  \n",
       "874   False     0.5    0.663891  0.468294  0.780051  0.839763  0.523336  \n",
       "875   False     1.0    0.682554  0.594494  0.804005  0.805733  0.519748  \n",
       "878    True     1.0    0.933956  0.475545  0.777265  0.801237  0.425514  \n",
       "879   False     0.5    0.491655  0.600894  0.873927  0.874014  0.530901  \n",
       "981    True     1.0    0.984780  0.619855  0.796199  0.832500  0.599493  \n",
       "\n",
       "[154 rows x 21 columns]"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "123edaf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    154.000000\n",
       "mean       0.812553\n",
       "std        0.041879\n",
       "min        0.750136\n",
       "25%        0.779271\n",
       "50%        0.803779\n",
       "75%        0.838589\n",
       "max        0.926604\n",
       "Name: conf_3, dtype: float64"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['conf_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "8b5531f4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    154.000000\n",
       "mean       0.467371\n",
       "std        0.096271\n",
       "min        0.312379\n",
       "25%        0.397846\n",
       "50%        0.445358\n",
       "75%        0.521623\n",
       "max        0.813775\n",
       "Name: conf_1, dtype: float64"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['conf_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "6450b5b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    154.000000\n",
       "mean       0.620130\n",
       "std        0.214318\n",
       "min        0.500000\n",
       "25%        0.500000\n",
       "50%        0.500000\n",
       "75%        0.500000\n",
       "max        1.000000\n",
       "Name: same_3, dtype: float64"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['same_3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "e3c59482",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       154\n",
       "unique        2\n",
       "top       False\n",
       "freq         96\n",
       "Name: same_1, dtype: object"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_conf_s['same_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "d46a2075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     0.252165\n",
       "Criteria vi      0.226190\n",
       "Criteria iv      0.196970\n",
       "Criteria ii      0.180736\n",
       "Criteria i       0.093074\n",
       "Criteria v       0.017316\n",
       "Criteria vii     0.010823\n",
       "Criteria x       0.010823\n",
       "Criteria viii    0.007576\n",
       "Criteria ix      0.004329\n",
       "dtype: float64"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf_s['max_1_col_bert'],\n",
    "           cmp_conf_s['max_2_col_bert'],\n",
    "           cmp_conf_s['max_3_col_bert'],\n",
    "           cmp_conf_s['max_1_col_ulmfit'],\n",
    "           cmp_conf_s['max_2_col_ulmfit'],\n",
    "           cmp_conf_s['max_3_col_ulmfit']]).value_counts()/(154*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "c12e5d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentages['SUZ_s'] = pd.concat([cmp_conf_s['max_1_col_bert'],\n",
    "           cmp_conf_s['max_2_col_bert'],\n",
    "           cmp_conf_s['max_3_col_bert'],\n",
    "           cmp_conf_s['max_1_col_ulmfit'],\n",
    "           cmp_conf_s['max_2_col_ulmfit'],\n",
    "           cmp_conf_s['max_3_col_ulmfit']]).value_counts()/(154*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "cfa24ac8",
   "metadata": {},
   "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>VEN</th>\n",
       "      <th>VEN_s</th>\n",
       "      <th>VEN_XL</th>\n",
       "      <th>VEN_XL_s</th>\n",
       "      <th>AMS</th>\n",
       "      <th>AMS_s</th>\n",
       "      <th>SUZ</th>\n",
       "      <th>SUZ_s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUV</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Criteria i</th>\n",
       "      <td>0.147707</td>\n",
       "      <td>0.183544</td>\n",
       "      <td>0.075867</td>\n",
       "      <td>0.171714</td>\n",
       "      <td>0.103849</td>\n",
       "      <td>0.147912</td>\n",
       "      <td>0.068841</td>\n",
       "      <td>0.093074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ii</th>\n",
       "      <td>0.202381</td>\n",
       "      <td>0.188668</td>\n",
       "      <td>0.162301</td>\n",
       "      <td>0.190178</td>\n",
       "      <td>0.219995</td>\n",
       "      <td>0.237792</td>\n",
       "      <td>0.262422</td>\n",
       "      <td>0.180736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iii</th>\n",
       "      <td>0.213845</td>\n",
       "      <td>0.191581</td>\n",
       "      <td>0.270847</td>\n",
       "      <td>0.203632</td>\n",
       "      <td>0.149425</td>\n",
       "      <td>0.170028</td>\n",
       "      <td>0.243789</td>\n",
       "      <td>0.252165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iv</th>\n",
       "      <td>0.240300</td>\n",
       "      <td>0.252863</td>\n",
       "      <td>0.233602</td>\n",
       "      <td>0.241005</td>\n",
       "      <td>0.313018</td>\n",
       "      <td>0.289986</td>\n",
       "      <td>0.114907</td>\n",
       "      <td>0.196970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria v</th>\n",
       "      <td>0.008598</td>\n",
       "      <td>0.004722</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.005177</td>\n",
       "      <td>0.010960</td>\n",
       "      <td>0.009023</td>\n",
       "      <td>0.005694</td>\n",
       "      <td>0.017316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vi</th>\n",
       "      <td>0.183862</td>\n",
       "      <td>0.162849</td>\n",
       "      <td>0.249893</td>\n",
       "      <td>0.172038</td>\n",
       "      <td>0.200080</td>\n",
       "      <td>0.127212</td>\n",
       "      <td>0.290890</td>\n",
       "      <td>0.226190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vii</th>\n",
       "      <td>0.001764</td>\n",
       "      <td>0.006631</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>0.007203</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.007077</td>\n",
       "      <td>0.002070</td>\n",
       "      <td>0.010823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria viii</th>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>0.002543</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.002477</td>\n",
       "      <td>0.001553</td>\n",
       "      <td>0.007576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ix</th>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.003516</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.002103</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.002831</td>\n",
       "      <td>0.001035</td>\n",
       "      <td>0.004329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria x</th>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.001520</td>\n",
       "      <td>0.004401</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.005662</td>\n",
       "      <td>0.008799</td>\n",
       "      <td>0.010823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    VEN     VEN_s    VEN_XL  VEN_XL_s       AMS     AMS_s  \\\n",
       "OUV                                                                         \n",
       "Criteria i     0.147707  0.183544  0.075867  0.171714  0.103849  0.147912   \n",
       "Criteria ii    0.202381  0.188668  0.162301  0.190178  0.219995  0.237792   \n",
       "Criteria iii   0.213845  0.191581  0.270847  0.203632  0.149425  0.170028   \n",
       "Criteria iv    0.240300  0.252863  0.233602  0.241005  0.313018  0.289986   \n",
       "Criteria v     0.008598  0.004722  0.002794  0.005177  0.010960  0.009023   \n",
       "Criteria vi    0.183862  0.162849  0.249893  0.172038  0.200080  0.127212   \n",
       "Criteria vii   0.001764  0.006631  0.002231  0.007203  0.000802  0.007077   \n",
       "Criteria viii  0.000220  0.002813  0.000349  0.002543  0.000535  0.002477   \n",
       "Criteria ix    0.000441  0.003516  0.000595  0.002103  0.000535  0.002831   \n",
       "Criteria x     0.000882  0.002813  0.001520  0.004401  0.000802  0.005662   \n",
       "Others              NaN       NaN       NaN  0.000006       NaN       NaN   \n",
       "\n",
       "                    SUZ     SUZ_s  \n",
       "OUV                                \n",
       "Criteria i     0.068841  0.093074  \n",
       "Criteria ii    0.262422  0.180736  \n",
       "Criteria iii   0.243789  0.252165  \n",
       "Criteria iv    0.114907  0.196970  \n",
       "Criteria v     0.005694  0.017316  \n",
       "Criteria vi    0.290890  0.226190  \n",
       "Criteria vii   0.002070  0.010823  \n",
       "Criteria viii  0.001553  0.007576  \n",
       "Criteria ix    0.001035  0.004329  \n",
       "Criteria x     0.008799  0.010823  \n",
       "Others              NaN       NaN  "
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "33d48a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VEN         1.0\n",
       "VEN_s       1.0\n",
       "VEN_XL      1.0\n",
       "VEN_XL_s    1.0\n",
       "AMS         1.0\n",
       "AMS_s       1.0\n",
       "SUZ         1.0\n",
       "SUZ_s       1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentages.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "a51b362a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     233\n",
       "Criteria vi      209\n",
       "Criteria iv      182\n",
       "Criteria ii      167\n",
       "Criteria i        86\n",
       "Criteria v        16\n",
       "Criteria vii      10\n",
       "Criteria x        10\n",
       "Criteria viii      7\n",
       "Criteria ix        4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([cmp_conf_s['max_1_col_bert'],\n",
    "           cmp_conf_s['max_2_col_bert'],\n",
    "           cmp_conf_s['max_3_col_bert'],\n",
    "           cmp_conf_s['max_1_col_ulmfit'],\n",
    "           cmp_conf_s['max_2_col_ulmfit'],\n",
    "           cmp_conf_s['max_3_col_ulmfit']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "efa7165a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_sorted = cmp_conf_s.sort_values(by='conf_1',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "2b7fded0",
   "metadata": {},
   "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>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>...</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>conf_3</th>\n",
       "      <th>conf_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>37</td>\n",
       "      <td>It is also a popular tourist destination with ...</td>\n",
       "      <td>397</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.900342</td>\n",
       "      <td>0.973134</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.727207</td>\n",
       "      <td>0.846616</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.995447</td>\n",
       "      <td>0.727207</td>\n",
       "      <td>0.846616</td>\n",
       "      <td>0.909875</td>\n",
       "      <td>0.813775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>342</td>\n",
       "      <td>Master of the Nets Garden, one of the finest o...</td>\n",
       "      <td>1517</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.822567</td>\n",
       "      <td>0.963477</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.668362</td>\n",
       "      <td>0.857590</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.989583</td>\n",
       "      <td>0.668362</td>\n",
       "      <td>0.857590</td>\n",
       "      <td>0.910533</td>\n",
       "      <td>0.745465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>345</td>\n",
       "      <td>Master of the Nets Garden, one of the finest o...</td>\n",
       "      <td>1518</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.784132</td>\n",
       "      <td>0.956668</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.679439</td>\n",
       "      <td>0.879666</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.991576</td>\n",
       "      <td>0.679439</td>\n",
       "      <td>0.879666</td>\n",
       "      <td>0.918167</td>\n",
       "      <td>0.731785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>336</td>\n",
       "      <td>Master of the Nets Garden impressive front ent...</td>\n",
       "      <td>1487</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.838520</td>\n",
       "      <td>0.968865</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.614135</td>\n",
       "      <td>0.801764</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.982897</td>\n",
       "      <td>0.614135</td>\n",
       "      <td>0.801764</td>\n",
       "      <td>0.885314</td>\n",
       "      <td>0.726328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>708</td>\n",
       "      <td>Suzhou Ancient City</td>\n",
       "      <td>4136</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>0.948481</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.759633</td>\n",
       "      <td>0.887045</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.931110</td>\n",
       "      <td>0.759633</td>\n",
       "      <td>0.887045</td>\n",
       "      <td>0.917763</td>\n",
       "      <td>0.681063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>467</td>\n",
       "      <td>It has tags of china, lantern, suzhou, street</td>\n",
       "      <td>2446</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.361592</td>\n",
       "      <td>0.817160</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.294767</td>\n",
       "      <td>0.692504</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.829729</td>\n",
       "      <td>0.294767</td>\n",
       "      <td>0.692504</td>\n",
       "      <td>0.754832</td>\n",
       "      <td>0.328179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>1</td>\n",
       "      <td>It has tags of china, china, chinese, garden, ...</td>\n",
       "      <td>108</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.377143</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.276083</td>\n",
       "      <td>0.708932</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.938834</td>\n",
       "      <td>0.276083</td>\n",
       "      <td>0.708932</td>\n",
       "      <td>0.821132</td>\n",
       "      <td>0.326613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>475</td>\n",
       "      <td>It is often nicknamed the .Leaning Tower of Ch...</td>\n",
       "      <td>2818</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.260614</td>\n",
       "      <td>0.722566</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.380433</td>\n",
       "      <td>0.877508</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.794707</td>\n",
       "      <td>0.380433</td>\n",
       "      <td>0.877508</td>\n",
       "      <td>0.800037</td>\n",
       "      <td>0.320523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>274</td>\n",
       "      <td>It has title of Paifang Gate Suzhou</td>\n",
       "      <td>1243</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.367202</td>\n",
       "      <td>0.784018</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.261395</td>\n",
       "      <td>0.759471</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.945232</td>\n",
       "      <td>0.261395</td>\n",
       "      <td>0.759471</td>\n",
       "      <td>0.771745</td>\n",
       "      <td>0.314299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>35</td>\n",
       "      <td>(although many other cities often also claim t...</td>\n",
       "      <td>397</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.341612</td>\n",
       "      <td>0.789045</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.283147</td>\n",
       "      <td>0.737675</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.898347</td>\n",
       "      <td>0.283147</td>\n",
       "      <td>0.737675</td>\n",
       "      <td>0.763360</td>\n",
       "      <td>0.312379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                                           sentence  origin   lang  \\\n",
       "30      37  It is also a popular tourist destination with ...     397     en   \n",
       "237    342  Master of the Nets Garden, one of the finest o...    1517     en   \n",
       "240    345  Master of the Nets Garden, one of the finest o...    1518     en   \n",
       "231    336  Master of the Nets Garden impressive front ent...    1487     en   \n",
       "507    708                                Suzhou Ancient City    4136     en   \n",
       "..     ...                                                ...     ...    ...   \n",
       "325    467      It has tags of china, lantern, suzhou, street    2446     en   \n",
       "539      1  It has tags of china, china, chinese, garden, ...     108  other   \n",
       "333    475  It is often nicknamed the .Leaning Tower of Ch...    2818     en   \n",
       "204    274                It has title of Paifang Gate Suzhou    1243     en   \n",
       "28      35  (although many other cities often also claim t...     397     en   \n",
       "\n",
       "    max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  max_1_ulmfit  \\\n",
       "30      Criteria vii       Criteria x    Criteria viii      0.900342   \n",
       "237       Criteria i      Criteria iv      Criteria ii      0.822567   \n",
       "240       Criteria i      Criteria iv      Criteria ii      0.784132   \n",
       "231       Criteria i      Criteria iv      Criteria ii      0.838520   \n",
       "507     Criteria iii      Criteria vi      Criteria ii      0.602493   \n",
       "..               ...              ...              ...           ...   \n",
       "325     Criteria iii      Criteria vi      Criteria ii      0.361592   \n",
       "539     Criteria iii      Criteria vi      Criteria ii      0.377143   \n",
       "333     Criteria iii      Criteria iv       Criteria i      0.260614   \n",
       "204     Criteria iii      Criteria vi      Criteria iv      0.367202   \n",
       "28       Criteria iv     Criteria iii      Criteria ii      0.341612   \n",
       "\n",
       "     max_3_ulmfit max_1_col_bert  ... max_3_col_bert max_1_bert  max_3_bert  \\\n",
       "30       0.973134   Criteria vii  ...  Criteria viii   0.727207    0.846616   \n",
       "237      0.963477     Criteria i  ...    Criteria vi   0.668362    0.857590   \n",
       "240      0.956668     Criteria i  ...    Criteria vi   0.679439    0.879666   \n",
       "231      0.968865     Criteria i  ...    Criteria vi   0.614135    0.801764   \n",
       "507      0.948481   Criteria iii  ...    Criteria ii   0.759633    0.887045   \n",
       "..            ...            ...  ...            ...        ...         ...   \n",
       "325      0.817160    Criteria vi  ...     Criteria i   0.294767    0.692504   \n",
       "539      0.933333   Criteria iii  ...    Criteria vi   0.276083    0.708932   \n",
       "333      0.722566    Criteria iv  ...     Criteria i   0.380433    0.877508   \n",
       "204      0.784018    Criteria vi  ...    Criteria iv   0.261395    0.759471   \n",
       "28       0.789045    Criteria iv  ...    Criteria vi   0.283147    0.737675   \n",
       "\n",
       "     same_1  same_3  cosine_sim     max_1     max_3    conf_3    conf_1  \n",
       "30     True     1.0    0.995447  0.727207  0.846616  0.909875  0.813775  \n",
       "237    True     0.5    0.989583  0.668362  0.857590  0.910533  0.745465  \n",
       "240    True     0.5    0.991576  0.679439  0.879666  0.918167  0.731785  \n",
       "231    True     0.5    0.982897  0.614135  0.801764  0.885314  0.726328  \n",
       "507    True     0.5    0.931110  0.759633  0.887045  0.917763  0.681063  \n",
       "..      ...     ...         ...       ...       ...       ...       ...  \n",
       "325   False     0.5    0.829729  0.294767  0.692504  0.754832  0.328179  \n",
       "539    True     1.0    0.938834  0.276083  0.708932  0.821132  0.326613  \n",
       "333   False     0.5    0.794707  0.380433  0.877508  0.800037  0.320523  \n",
       "204   False     1.0    0.945232  0.261395  0.759471  0.771745  0.314299  \n",
       "28     True     0.5    0.898347  0.283147  0.737675  0.763360  0.312379  \n",
       "\n",
       "[154 rows x 21 columns]"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "0c1f0112",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Criteria i ['Master of the Nets Garden, one of the finest oriental gardens Impressive front entrance'\n",
      " 'Master of the Nets Garden, one of the finest oriental gardens, Impressive front entrance'\n",
      " 'Master of the Nets Garden impressive front entrance.'] [0.74546462 0.73178509 0.72632787]\n",
      "Criteria ii ['- Another artist working on drawing the scenes in the the Suzhou Lingering Garden'\n",
      " 'It has tags of china, jiangsu, suzhou, zh, temple, cn'\n",
      " 'It has tags of china, jiangsu, suzhou, zh, gate, cn'] [0.61054767 0.57623951 0.56505162]\n",
      "Criteria iii ['Suzhou Ancient City'\n",
      " 'It has title of Vase details, Humble Administrator Garden - Suzhou, Garden'\n",
      " 'It has tags of suzhou, china, shantang, street, canal, waterway, water, boats, trees, house, decoration, people, busy, shops, longexposure'] [0.6810633  0.58219239 0.58067985]\n",
      "Criteria iv ['It has tags of architectural, architecture, building, edifice, edifices, furniture, home, householdobjects, residence, residentialbuilding, rooms, structures, thing, things, suzhou, jiangsu, china'\n",
      " 'It has title of Suzhou Electric Two Wheeled Truck'\n",
      " 'Begun in 486 BC, the canal is still used to move industrial and agricultural products north-south.'] [0.59509464 0.56087004 0.5535316 ]\n",
      "Criteria v [\"It's a blend of the traditional with the new.\"\n",
      " 'It has tags of architectural, architecture, building, buildingmaterial, buildingmaterials, clayproducts, constructionmaterial, ecology, ecosystem, edifice, edifices, environment, environmentalism, nature, pavilion, plants, pond, recreationbuilding, recreationbuildings, rooftile, scenery, structures, tree, water, suzhou, jiangsu, china'] [0.43708589 0.36512244]\n",
      "Criteria vi ['It has title of Suzhou day1' 'It has title of The modern Chinese woman'\n",
      " 'These circular openings represent symbolically the heaven and perfection in Chinese culture'] [0.67077763 0.59228851 0.58602761]\n",
      "Criteria vii ['It is also a popular tourist destination with its many UNESCO sites and attractions, such as this one near Tiger Hill.'\n",
      " 'The best time to take a stroll would be in autumn time, when the weather is cool and not humid, and the leaves are starting to turn red or orange and shredding before winter sets in.'\n",
      " 'The moon descends, crows caw, frostiness fills the sky'] [0.81377455 0.67216309 0.62177487]\n",
      "Criteria viii [] []\n",
      "Criteria ix ['It has title of Extinction Bridge'] [0.4534211]\n",
      "Criteria x ['It has tags of china, jiangsu, pingjiang, suzhou, peoplesrepublicofchina'] [0.39010887]\n",
      "Others [] []\n"
     ]
    }
   ],
   "source": [
    "for c in classes:\n",
    "    temp = cmp_sorted[cmp_sorted.max_1_col_bert==c].iloc[:3]\n",
    "    print(c, temp['sentence'].values,temp['conf_1'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "940081a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Criteria i ['Master of the Nets Garden, one of the finest oriental gardens Impressive front entrance'\n",
      " 'Master of the Nets Garden, one of the finest oriental gardens, Impressive front entrance'\n",
      " 'Master of the Nets Garden impressive front entrance.'] [0.74546462 0.73178509 0.72632787]\n",
      "Criteria ii [\"The garden demonstrates Chinese garden designers' adept skills for synthesizing art, nature, and architecture to create unique metaphysical masterpieces\"\n",
      " '- Another artist working on drawing the scenes in the the Suzhou Lingering Garden'\n",
      " 'Had to buy an umbrella when started raining'] [0.62898004 0.61054767 0.48590109]\n",
      "Criteria iii ['Suzhou Ancient City'\n",
      " 'It has tags of pavilionofsurgingwaves, canglangpavilion, unescowhs, garden'\n",
      " 'It has tags of architectural, architecture, building, edifice, edifices, furniture, home, householdobjects, residence, residentialbuilding, rooms, structures, thing, things, suzhou, jiangsu, china'] [0.6810633  0.61868339 0.59509464]\n",
      "Criteria iv ['Begun in 486 BC, the canal is still used to move industrial and agricultural products north-south.'\n",
      " 'All along the street are these lanterns, the text has the name of the street: Shantang.'\n",
      " 'This one was happening in the middle of the old town with the couple dressed in traditional red Chinese wedding dress.'] [0.5535316  0.53858231 0.45937796]\n",
      "Criteria v ['With the traditional typical three entrances'\n",
      " 'It has tags of architectural, architecture, building, buildingmaterial, buildingmaterials, clayproducts, constructionmaterial, ecology, ecosystem, edifice, edifices, environment, environmentalism, nature, pavilion, plants, pond, recreationbuilding, recreationbuildings, rooftile, scenery, structures, tree, water, suzhou, jiangsu, china'] [0.5186333  0.36512244]\n",
      "Criteria vi ['It has title of Suzhou day1' 'It has title of The modern Chinese woman'\n",
      " 'These circular openings represent symbolically the heaven and perfection in Chinese culture'] [0.67077763 0.59228851 0.58602761]\n",
      "Criteria vii ['It is also a popular tourist destination with its many UNESCO sites and attractions, such as this one near Tiger Hill.'\n",
      " 'The best time to take a stroll would be in autumn time, when the weather is cool and not humid, and the leaves are starting to turn red or orange and shredding before winter sets in.'\n",
      " 'The moon descends, crows caw, frostiness fills the sky'] [0.81377455 0.67216309 0.62177487]\n",
      "Criteria viii [] []\n",
      "Criteria ix [] []\n",
      "Criteria x ['It has title of Extinction Bridge'] [0.4534211]\n",
      "Others [] []\n"
     ]
    }
   ],
   "source": [
    "for c in classes:\n",
    "    temp = cmp_sorted[cmp_sorted.max_1_col_ulmfit==c].iloc[:3]\n",
    "    print(c, temp['sentence'].values,temp['conf_1'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "6647871f",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentages.to_csv('data_storage/images/value_percentage.csv',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "cbd59316",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentages=pd.read_csv('data_storage/images/value_percentage.csv',sep='\\t',index_col='OUV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "a785b114",
   "metadata": {},
   "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>VEN</th>\n",
       "      <th>VEN_s</th>\n",
       "      <th>VEN_XL</th>\n",
       "      <th>VEN_XL_s</th>\n",
       "      <th>AMS</th>\n",
       "      <th>AMS_s</th>\n",
       "      <th>SUZ</th>\n",
       "      <th>SUZ_s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUV</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Criteria i</th>\n",
       "      <td>0.147707</td>\n",
       "      <td>0.183544</td>\n",
       "      <td>0.075867</td>\n",
       "      <td>0.171714</td>\n",
       "      <td>0.103849</td>\n",
       "      <td>0.147912</td>\n",
       "      <td>0.068841</td>\n",
       "      <td>0.093074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ii</th>\n",
       "      <td>0.202381</td>\n",
       "      <td>0.188668</td>\n",
       "      <td>0.162301</td>\n",
       "      <td>0.190178</td>\n",
       "      <td>0.219995</td>\n",
       "      <td>0.237792</td>\n",
       "      <td>0.262422</td>\n",
       "      <td>0.180736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iii</th>\n",
       "      <td>0.213845</td>\n",
       "      <td>0.191581</td>\n",
       "      <td>0.270847</td>\n",
       "      <td>0.203632</td>\n",
       "      <td>0.149425</td>\n",
       "      <td>0.170028</td>\n",
       "      <td>0.243789</td>\n",
       "      <td>0.252165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iv</th>\n",
       "      <td>0.240300</td>\n",
       "      <td>0.252863</td>\n",
       "      <td>0.233602</td>\n",
       "      <td>0.241005</td>\n",
       "      <td>0.313018</td>\n",
       "      <td>0.289986</td>\n",
       "      <td>0.114907</td>\n",
       "      <td>0.196970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria v</th>\n",
       "      <td>0.008598</td>\n",
       "      <td>0.004722</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.005177</td>\n",
       "      <td>0.010960</td>\n",
       "      <td>0.009023</td>\n",
       "      <td>0.005694</td>\n",
       "      <td>0.017316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vi</th>\n",
       "      <td>0.183862</td>\n",
       "      <td>0.162849</td>\n",
       "      <td>0.249893</td>\n",
       "      <td>0.172038</td>\n",
       "      <td>0.200080</td>\n",
       "      <td>0.127212</td>\n",
       "      <td>0.290890</td>\n",
       "      <td>0.226190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vii</th>\n",
       "      <td>0.001764</td>\n",
       "      <td>0.006631</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>0.007203</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.007077</td>\n",
       "      <td>0.002070</td>\n",
       "      <td>0.010823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria viii</th>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>0.002543</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.002477</td>\n",
       "      <td>0.001553</td>\n",
       "      <td>0.007576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ix</th>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.003516</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.002103</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.002831</td>\n",
       "      <td>0.001035</td>\n",
       "      <td>0.004329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria x</th>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.001520</td>\n",
       "      <td>0.004401</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.005662</td>\n",
       "      <td>0.008799</td>\n",
       "      <td>0.010823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    VEN     VEN_s    VEN_XL  VEN_XL_s       AMS     AMS_s  \\\n",
       "OUV                                                                         \n",
       "Criteria i     0.147707  0.183544  0.075867  0.171714  0.103849  0.147912   \n",
       "Criteria ii    0.202381  0.188668  0.162301  0.190178  0.219995  0.237792   \n",
       "Criteria iii   0.213845  0.191581  0.270847  0.203632  0.149425  0.170028   \n",
       "Criteria iv    0.240300  0.252863  0.233602  0.241005  0.313018  0.289986   \n",
       "Criteria v     0.008598  0.004722  0.002794  0.005177  0.010960  0.009023   \n",
       "Criteria vi    0.183862  0.162849  0.249893  0.172038  0.200080  0.127212   \n",
       "Criteria vii   0.001764  0.006631  0.002231  0.007203  0.000802  0.007077   \n",
       "Criteria viii  0.000220  0.002813  0.000349  0.002543  0.000535  0.002477   \n",
       "Criteria ix    0.000441  0.003516  0.000595  0.002103  0.000535  0.002831   \n",
       "Criteria x     0.000882  0.002813  0.001520  0.004401  0.000802  0.005662   \n",
       "Others              NaN       NaN       NaN  0.000006       NaN       NaN   \n",
       "\n",
       "                    SUZ     SUZ_s  \n",
       "OUV                                \n",
       "Criteria i     0.068841  0.093074  \n",
       "Criteria ii    0.262422  0.180736  \n",
       "Criteria iii   0.243789  0.252165  \n",
       "Criteria iv    0.114907  0.196970  \n",
       "Criteria v     0.005694  0.017316  \n",
       "Criteria vi    0.290890  0.226190  \n",
       "Criteria vii   0.002070  0.010823  \n",
       "Criteria viii  0.001553  0.007576  \n",
       "Criteria ix    0.001035  0.004329  \n",
       "Criteria x     0.008799  0.010823  \n",
       "Others              NaN       NaN  "
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "40df5b13",
   "metadata": {},
   "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>VEN</th>\n",
       "      <th>VEN_s</th>\n",
       "      <th>VEN_XL</th>\n",
       "      <th>VEN_XL_s</th>\n",
       "      <th>AMS</th>\n",
       "      <th>AMS_s</th>\n",
       "      <th>SUZ</th>\n",
       "      <th>SUZ_s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUV</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Criteria i</th>\n",
       "      <td>0.147707</td>\n",
       "      <td>0.183544</td>\n",
       "      <td>0.075867</td>\n",
       "      <td>0.171714</td>\n",
       "      <td>0.103849</td>\n",
       "      <td>0.147912</td>\n",
       "      <td>0.068841</td>\n",
       "      <td>0.093074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ii</th>\n",
       "      <td>0.202381</td>\n",
       "      <td>0.188668</td>\n",
       "      <td>0.162301</td>\n",
       "      <td>0.190178</td>\n",
       "      <td>0.219995</td>\n",
       "      <td>0.237792</td>\n",
       "      <td>0.262422</td>\n",
       "      <td>0.180736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iii</th>\n",
       "      <td>0.213845</td>\n",
       "      <td>0.191581</td>\n",
       "      <td>0.270847</td>\n",
       "      <td>0.203632</td>\n",
       "      <td>0.149425</td>\n",
       "      <td>0.170028</td>\n",
       "      <td>0.243789</td>\n",
       "      <td>0.252165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria iv</th>\n",
       "      <td>0.240300</td>\n",
       "      <td>0.252863</td>\n",
       "      <td>0.233602</td>\n",
       "      <td>0.241005</td>\n",
       "      <td>0.313018</td>\n",
       "      <td>0.289986</td>\n",
       "      <td>0.114907</td>\n",
       "      <td>0.196970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria v</th>\n",
       "      <td>0.008598</td>\n",
       "      <td>0.004722</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.005177</td>\n",
       "      <td>0.010960</td>\n",
       "      <td>0.009023</td>\n",
       "      <td>0.005694</td>\n",
       "      <td>0.017316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vi</th>\n",
       "      <td>0.183862</td>\n",
       "      <td>0.162849</td>\n",
       "      <td>0.249893</td>\n",
       "      <td>0.172038</td>\n",
       "      <td>0.200080</td>\n",
       "      <td>0.127212</td>\n",
       "      <td>0.290890</td>\n",
       "      <td>0.226190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria vii</th>\n",
       "      <td>0.001764</td>\n",
       "      <td>0.006631</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>0.007203</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.007077</td>\n",
       "      <td>0.002070</td>\n",
       "      <td>0.010823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria viii</th>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>0.002543</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.002477</td>\n",
       "      <td>0.001553</td>\n",
       "      <td>0.007576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria ix</th>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.003516</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.002103</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.002831</td>\n",
       "      <td>0.001035</td>\n",
       "      <td>0.004329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criteria x</th>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.001520</td>\n",
       "      <td>0.004401</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.005662</td>\n",
       "      <td>0.008799</td>\n",
       "      <td>0.010823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    VEN     VEN_s    VEN_XL  VEN_XL_s       AMS     AMS_s  \\\n",
       "OUV                                                                         \n",
       "Criteria i     0.147707  0.183544  0.075867  0.171714  0.103849  0.147912   \n",
       "Criteria ii    0.202381  0.188668  0.162301  0.190178  0.219995  0.237792   \n",
       "Criteria iii   0.213845  0.191581  0.270847  0.203632  0.149425  0.170028   \n",
       "Criteria iv    0.240300  0.252863  0.233602  0.241005  0.313018  0.289986   \n",
       "Criteria v     0.008598  0.004722  0.002794  0.005177  0.010960  0.009023   \n",
       "Criteria vi    0.183862  0.162849  0.249893  0.172038  0.200080  0.127212   \n",
       "Criteria vii   0.001764  0.006631  0.002231  0.007203  0.000802  0.007077   \n",
       "Criteria viii  0.000220  0.002813  0.000349  0.002543  0.000535  0.002477   \n",
       "Criteria ix    0.000441  0.003516  0.000595  0.002103  0.000535  0.002831   \n",
       "Criteria x     0.000882  0.002813  0.001520  0.004401  0.000802  0.005662   \n",
       "Others              NaN       NaN       NaN  0.000006       NaN       NaN   \n",
       "\n",
       "                    SUZ     SUZ_s  \n",
       "OUV                                \n",
       "Criteria i     0.068841  0.093074  \n",
       "Criteria ii    0.262422  0.180736  \n",
       "Criteria iii   0.243789  0.252165  \n",
       "Criteria iv    0.114907  0.196970  \n",
       "Criteria v     0.005694  0.017316  \n",
       "Criteria vi    0.290890  0.226190  \n",
       "Criteria vii   0.002070  0.010823  \n",
       "Criteria viii  0.001553  0.007576  \n",
       "Criteria ix    0.001035  0.004329  \n",
       "Criteria x     0.008799  0.010823  \n",
       "Others              NaN       NaN  "
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plotper = np.log2(500000*percentages)\n",
    "plotper= percentages\n",
    "plotper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "2362eb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "classes_new = ['Criterion i\\nMasterpiece', 'Criterion ii\\nInfluence', 'Criterion iii\\nTestimony', 'Criterion iv\\nTypology', \n",
    "           'Criterion v\\nLand-use', 'Criterion vi\\nAssociation', \n",
    "              'Criterion vii\\nNatural Beauty', 'Criterion viii\\nGeological Process', 'Criterion ix\\nEcological Process', \n",
    "           'Criterion x\\nBiodiversity', 'Others']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "2992a57d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Criterion i\\nMasterpiece',\n",
       " 'Criterion ii\\nInfluence',\n",
       " 'Criterion iii\\nTestimony',\n",
       " 'Criterion iv\\nTypology',\n",
       " 'Criterion v\\nLand-use',\n",
       " 'Criterion vi\\nAssociation',\n",
       " 'Criterion vii\\nNatural Beauty',\n",
       " 'Criterion viii\\nGeological Process',\n",
       " 'Criterion ix\\nEcological Process',\n",
       " 'Criterion x\\nBiodiversity',\n",
       " 'Others']"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "a2ddecfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 6000x4500 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "y = -np.arange(len(classes))\n",
    "width = 0.25\n",
    "\n",
    "#plt.figure(figsize=(40,40),dpi=300)\n",
    "fig, axes = plt.subplots(ncols=2, sharey=True,figsize=(20,15),dpi=300)\n",
    "\n",
    "p1=axes[0].barh(y+ width*1.3, plotper['AMS'], align='center', color='salmon', zorder=10, height=width*0.8, label='Amsterdam')\n",
    "p2=axes[0].barh(y+ width*0.44, plotper['SUZ'], align='center', color='yellowgreen', zorder=10, height=width*0.8, label='Suzhou')\n",
    "p3=axes[0].barh(y- width*0.44, plotper['VEN'], align='center', color='deepskyblue', zorder=10, height=width*0.8, label='Venice')\n",
    "p4=axes[0].barh(y- width*1.3, plotper['VEN_XL'], align='center', color='dodgerblue', zorder=10, height=width*0.8, label='Venice-Large')\n",
    "axes[0].set_title('Post-level Top-3 Prediction',size=28)\n",
    "\n",
    "#axes[0].set(title='Post-level Prediction')\n",
    "\n",
    "hbars_1 = axes[1].barh(y+ width*1.3, plotper['AMS_s'], align='center', color='salmon',height=width*0.8,  zorder=10)\n",
    "hbars_2 = axes[1].barh(y+ width*0.44, plotper['SUZ_s'], align='center', color='yellowgreen', height=width*0.8, zorder=10)\n",
    "hbars_3 = axes[1].barh(y- width*0.44, plotper['VEN_s'], align='center', color='deepskyblue',height=width*0.8,  zorder=10)\n",
    "hbars_4 = axes[1].barh(y- width*1.3, plotper['VEN_XL_s'], align='center', color='dodgerblue', height=width*0.8, zorder=10)\n",
    "axes[1].set_title('Sentence-level Top-3 Prediction', size=28)\n",
    "#axes[1].set(title='Sentence-level Prediction',size=20)\n",
    "\n",
    "axes[0].bar_label(p1, fmt='%.3f',padding=-55,size=15)\n",
    "axes[0].bar_label(p2, fmt='%.3f',padding=-55,size=15)\n",
    "axes[0].bar_label(p3, fmt='%.3f',padding=-55,size=15)\n",
    "axes[0].bar_label(p4, fmt='%.3f',padding=-55,size=15)\n",
    "axes[0].invert_xaxis()\n",
    "axes[0].set_yticks(y, labels=classes_new,size=20)\n",
    "#axes[0].set(yticks=y, yticklabels=classes)\n",
    "axes[0].yaxis.tick_right()\n",
    "\n",
    "axes[1].bar_label(hbars_1, fmt='%.3f',padding=10,size=15)\n",
    "axes[1].bar_label(hbars_2, fmt='%.3f',padding=10,size=15)\n",
    "axes[1].bar_label(hbars_3, fmt='%.3f',padding=10,size=15)\n",
    "axes[1].bar_label(hbars_4, fmt='%.3f',padding=10,size=15)\n",
    "\n",
    "for ax in axes.flat:\n",
    "    ax.margins(0.03)\n",
    "    ax.grid(False)\n",
    "    ax.set_xticks([0,0.1,0.2,0.3])\n",
    "    #ax.axis('off')\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_edgecolor('white')\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(wspace=0.45)\n",
    "axes[0].legend(frameon=False,fontsize=25,loc=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb66ac66",
   "metadata": {},
   "source": [
    "## Chi-square Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8239666e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=3.5, pvalue=0.6233876277495822)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chisquare\n",
    "chisquare([16, 18, 16, 14, 12, 12], f_exp=[16, 16, 16, 16, 16, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46377320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=0.35, pvalue=0.9965956325533629)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chisquare([1.6, 1.8, 1.6, 1.4, 1.2, 1.2], f_exp=[1.6, 1.6, 1.6, 1.6, 1.6, .8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6c34fe4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=35.0, pvalue=1.5046506621757205e-06)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chisquare([160, 180, 160, 140, 120, 120], f_exp=[160, 160, 160, 160, 160, 80])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7116e0ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.7777777777777777,\n",
       " 0.24935220877729622,\n",
       " 2,\n",
       " array([[12., 12., 16.],\n",
       "        [18., 18., 24.]]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "import numpy as np\n",
    "obs = np.array([[10, 10, 20], [20, 20, 20]])\n",
    "chi2_contingency(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c493ec05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25753.833333333332"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = np.array([26534, 29387, 31466, 37241, 800, 26584, 1113, 393, 325, 680])\n",
    "x1.sum()/6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d8dc209d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1659.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2 = np.array([1827,1878,1907,2517,47,1621,66,28,35,28])\n",
    "x2.sum()/6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8b577ce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25771.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x3 = np.array([11731,25096,41880,36121,432,38640,345,54,92,235])\n",
    "x3.sum()/6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ef0e83bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "756.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x4 = np.array([670,918,970,1090,39,834,8,1,2,4])\n",
    "x4.sum()/6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5045383c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1356"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x5 = np.array([274,196,392,28,177,65,188,28,8])\n",
    "x5.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "73ca72fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37289"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x6 = np.array([4578,4242,9146,436,7442,1797,7680,684,1284])\n",
    "x6.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "07b2103b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x3 = x1/x1.sum()*x2.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4facf9c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1709.2564602 , 1893.03985814, 2026.96403772, 2398.97564764,\n",
       "         51.53407583, 1712.47733994,   71.696783  ,   25.31611475,\n",
       "         20.93571831,   43.80396446])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c0d5a292",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=42.30954583591253, pvalue=2.8848385131644893e-06)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chisquare(x2, f_exp=x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1a355318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0020859149204444727"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import entropy\n",
    "entropy(x2,x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a33d8d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0514544153389116"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy(x4/x4.sum(),x3/x3.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0f3bd729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0020764607425106997"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy(x1,x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4deef932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04626216736989267"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy(x3,x4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "321cb8ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07604175339371703"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy(x5,x6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "19c22a1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08970406742804549"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy(x6,x5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "03061988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39.51525638157538,\n",
       " 9.299340740729766e-06,\n",
       " 9,\n",
       " array([[2.66446178e+04, 2.93728703e+04, 3.13532961e+04, 3.73518816e+04,\n",
       "         7.95740322e+02, 2.64980588e+04, 1.10764798e+03, 3.95521459e+02,\n",
       "         3.38213124e+02, 6.65152477e+02],\n",
       "        [1.71638219e+03, 1.89212966e+03, 2.01970392e+03, 2.40611838e+03,\n",
       "         5.12596776e+01, 1.70694121e+03, 7.13520188e+01, 2.54785411e+01,\n",
       "         2.17868760e+01, 4.28475228e+01]]))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_contingency([x1,x2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d62a6467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39.51525638157537,\n",
       " 9.29934074072979e-06,\n",
       " 9,\n",
       " array([[1.71638219e+03, 1.89212966e+03, 2.01970392e+03, 2.40611838e+03,\n",
       "         5.12596776e+01, 1.70694121e+03, 7.13520188e+01, 2.54785411e+01,\n",
       "         2.17868760e+01, 4.28475228e+01],\n",
       "        [2.66446178e+04, 2.93728703e+04, 3.13532961e+04, 3.73518816e+04,\n",
       "         7.95740322e+02, 2.64980588e+04, 1.10764798e+03, 3.95521459e+02,\n",
       "         3.38213124e+02, 6.65152477e+02]]))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_contingency([x2,x1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "94ddcbf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(518.894591066096,\n",
       " 5.162672219701555e-106,\n",
       " 9,\n",
       " array([[3.53419384e+02, 7.41379877e+02, 1.22119350e+03, 1.06048615e+03,\n",
       "         1.34231538e+01, 1.12497998e+03, 1.00602405e+01, 1.56745957e+00,\n",
       "         2.67893090e+00, 6.81132431e+00],\n",
       "        [1.20475806e+04, 2.52726201e+04, 4.16288065e+04, 3.61505139e+04,\n",
       "         4.57576846e+02, 3.83490200e+04, 3.42939759e+02, 5.34325404e+01,\n",
       "         9.13210691e+01, 2.32188676e+02]]))"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_contingency([x4,x3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1050083f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(188.2409381024855,\n",
       " 1.9090413680556476e-36,\n",
       " 8,\n",
       " array([[ 170.25001941,  155.72332773,  334.67532669,   16.28112304,\n",
       "          267.340251  ,   65.33502394,  276.07731919,   24.9831026 ,\n",
       "           45.3345064 ],\n",
       "        [4681.74998059, 4282.27667227, 9203.32467331,  447.71887696,\n",
       "         7351.659749  , 1796.66497606, 7591.92268081,  687.0168974 ,\n",
       "         1246.6654936 ]]))"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_contingency([x5,x6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58b10e28",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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