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diff --git a/experiments/poleval19/all-results-fixed.csv b/experiments/poleval19/all-results-fixed.csv
new file mode 100644
index 0000000..62df148
--- /dev/null
+++ b/experiments/poleval19/all-results-fixed.csv
@@ -0,0 +1,337 @@
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diff --git a/experiments/poleval19/all-results-halftest.csv b/experiments/poleval19/all-results-halftest.csv
new file mode 100644
index 0000000..2738983
--- /dev/null
+++ b/experiments/poleval19/all-results-halftest.csv
@@ -0,0 +1,39 @@
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diff --git a/experiments/poleval19/correlation between test and dev.ipynb b/experiments/poleval19/correlation between test and dev.ipynb
new file mode 100644
index 0000000..f0ed292
--- /dev/null
+++ b/experiments/poleval19/correlation between test and dev.ipynb	
@@ -0,0 +1,696 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastai.text import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "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>LMseed</th>\n",
+       "      <th>ft</th>\n",
+       "      <th>clsseed</th>\n",
+       "      <th>dataset</th>\n",
+       "      <th>F1_score</th>\n",
+       "      <th>loss</th>\n",
+       "      <th>accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ft-seed0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>test</td>\n",
+       "      <td>0.564516</td>\n",
+       "      <td>0.430445</td>\n",
+       "      <td>0.8920</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>120</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ft-seed0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>valid</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>0.422563</td>\n",
+       "      <td>0.8839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ft-seed0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>test</td>\n",
+       "      <td>0.592593</td>\n",
+       "      <td>0.416533</td>\n",
+       "      <td>0.8900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>126</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ft-seed0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>valid</td>\n",
+       "      <td>0.460674</td>\n",
+       "      <td>0.434416</td>\n",
+       "      <td>0.8863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ft-seed0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>test</td>\n",
+       "      <td>0.606557</td>\n",
+       "      <td>0.481438</td>\n",
+       "      <td>0.9040</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index  LMseed        ft  clsseed dataset  F1_score      loss  accuracy\n",
+       "0      0       0  ft-seed0        2    test  0.564516  0.430445    0.8920\n",
+       "1    120       0  ft-seed0        2   valid  0.416667  0.422563    0.8839\n",
+       "2      6       0  ft-seed0        3    test  0.592593  0.416533    0.8900\n",
+       "3    126       0  ft-seed0        3   valid  0.460674  0.434416    0.8863\n",
+       "4     12       0  ft-seed0        4    test  0.606557  0.481438    0.9040"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"valvstest-fixed.tsv\", sep=\"\\t\")\n",
+    "# fix issue in the data accuracy is actually F1_batch_avg\n",
+    "#df = df.rename(columns={\"accuracy\": \"F1_batch_avg\"})\n",
+    "df[\"dataset\"]=df[\"dataset\"].str.strip()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(count    240.000000\n",
+       " mean       0.454483\n",
+       " std        0.086787\n",
+       " min        0.271477\n",
+       " 25%        0.405409\n",
+       " 50%        0.440903\n",
+       " 75%        0.498287\n",
+       " max        0.787635\n",
+       " Name: loss, dtype: float64, count    240.000000\n",
+       " mean       0.499472\n",
+       " std        0.067876\n",
+       " min        0.379310\n",
+       " 25%        0.441921\n",
+       " 50%        0.483615\n",
+       " 75%        0.565879\n",
+       " max        0.636704\n",
+       " Name: F1_score, dtype: float64)"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"loss\"].describe(), df[\"F1_score\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['model_name'] = df[\"LMseed\"].map(str)+ \"-\"+df[\"ft\"]+\"-\"+df[\"clsseed\"].map(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">F1_score</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">loss</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">accuracy</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dataset</th>\n",
+       "      <th>test</th>\n",
+       "      <th>valid</th>\n",
+       "      <th>test</th>\n",
+       "      <th>valid</th>\n",
+       "      <th>test</th>\n",
+       "      <th>valid</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_name</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>1-ft-seed1-4</th>\n",
+       "      <td>0.583630</td>\n",
+       "      <td>0.379310</td>\n",
+       "      <td>0.381665</td>\n",
+       "      <td>0.368908</td>\n",
+       "      <td>0.883</td>\n",
+       "      <td>0.8720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0-noft-4</th>\n",
+       "      <td>0.540541</td>\n",
+       "      <td>0.381395</td>\n",
+       "      <td>0.517359</td>\n",
+       "      <td>0.590351</td>\n",
+       "      <td>0.847</td>\n",
+       "      <td>0.8424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1-noft-3</th>\n",
+       "      <td>0.564315</td>\n",
+       "      <td>0.386667</td>\n",
+       "      <td>0.480363</td>\n",
+       "      <td>0.479577</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.8910</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1-ft-seed1-21</th>\n",
+       "      <td>0.582781</td>\n",
+       "      <td>0.388626</td>\n",
+       "      <td>0.361739</td>\n",
+       "      <td>0.397997</td>\n",
+       "      <td>0.874</td>\n",
+       "      <td>0.8472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1-noft-4</th>\n",
+       "      <td>0.582192</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.398434</td>\n",
+       "      <td>0.410791</td>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.8720</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               F1_score                loss           accuracy        \n",
+       "dataset            test     valid      test     valid     test   valid\n",
+       "model_name                                                            \n",
+       "1-ft-seed1-4   0.583630  0.379310  0.381665  0.368908    0.883  0.8720\n",
+       "0-noft-4       0.540541  0.381395  0.517359  0.590351    0.847  0.8424\n",
+       "1-noft-3       0.564315  0.386667  0.480363  0.479577    0.895  0.8910\n",
+       "1-ft-seed1-21  0.582781  0.388626  0.361739  0.397997    0.874  0.8472\n",
+       "1-noft-4       0.582192  0.393258  0.398434  0.410791    0.878  0.8720"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfp = df.pivot(index=\"model_name\", columns=\"dataset\", values=[\"F1_score\", \"loss\", \"accuracy\"]).sort_values((\"F1_score\",\"valid\"))\n",
+    "dfp.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10adc23550>"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dfp.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10adb80d30>"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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JdM4QOGwEY6nSFwZO0ho5yp/kO7hiEt1Hz1hD4HQ6Wbp06elehkZzVpNLH9XB4ukklkzjdRnvscdpZygYL33h0ccB2OnaVLa1TSnOWGlIo9GcfmLJDDYBvuwmpYvKpgdDGlIxAnt5Ce7I74nYGhnwrpjU/bUh0Gg0U0ZJFi6HsZUktUcwLcSSadxmHYGtdNbQ6GHYcS/bPRfR5J1cTFEbAo1GM2WMbJacITgTPQIpJaPhyQ17n2liyTQehwoWl2gxISU8dDvYXXzH9a5JDaUBbQg0Gs0pEEtm8DhsuOxZQzBLPIKvPLyPW7/7dE3XPryrn4v//jdMTHLO70wSS1mkoVJ1BLvvg4P/D175KQ4nmmiZxFAa0IZAo9GcAkXS0CzxCF7oGWdvf7Cma4+PRoinMoxFZq9XYM0a8jjt+UH5WMCYyTJ3I1zwXmM62SQ9gjM2a0ij0Zx+YskMbqcdZ9YjmC1ZQ0PBOOF4mRTLAgJR47pKbRtOJ1LKrCHItphw2EhnJMl0BmcmDj/7cwgNwM0/IS3sBGMpbQg0Gs3MoTYot2N2SUPDoTjRKlO8FMGYIQlFE7PTECTTkowk13Qu+//4eD/On/0Z9GyDa/8BFpxPIBvrUANsakUbAo1GM2VUEDMnDZ3+Sv9UOsNIOIGUhmFSaytHIFucVW0g/OlCeSo5achGF2M0/OAaCA/AjT+Ac64DYDw7HEgbAo1GM2PEUmn8HocpDSVmgbwyGjGMABin/GqGQHkEs1UaUvOK3ZYWE9fZn8A+cQz+1yOwKDcWXk2J01lDGo1mxoglMzS4Zlf6qLXqNpKsHicwYwSzVBoyp5M5cjGCVaKHVENnnhEAGM8GvJsbdNaQRqOZIUxpyD57pKHhUC77JxyvvrkHZrtHUCQN2Vll6yHWurLo2okpSkPaEGg0mimTyxoy+trMhqwhq0dQSwBYNXCLJk7/2kuhagZMQ+AQrBQ9RJpXFV2rpSGNRjPjxLNZQ0IIXHbbrMgaGg7lDEE4UYM0pDyCGrOMZprc4Hpju26K9+MTcYJNxf2ExiPaEGg0mhlGtZgAcDlss6KgbDIeQSYjCcVndx2BMlAqfbQ5eBCA8cblRdeOR5I0unPB+1rRhkCj0UyJVDpDMi3NHjhOuzjjPIJQImVmGM3WYHG0QBpqDBiGYMRbbAgmopOvKgZtCDQazRRRjc+UZOFyzA5paCgYZ06TG4BIlc09EM31Fypq5DZLyMUIjPfZO36AftlK2FY8gWwimtCGQKPRzByFQczZJA0tbjM2yWrSkHXS12ytLI5nYwRqeL17bB/7MwuKCuBiyTQ7eibobpn8WFttCDQazZQo1K6ddhvxWWAIhkNxFrV7gerSUJ5HMFuDxdb00UwGx+gB9ssFRev90Z+OMRiM855Ll036ObQh0Gg0U0KdSN1KGpoFWUPJdIaxSJIFrQ0IMTmP4IyQhsaPIlLRrCHIrTcUT/EfvzvEZSs7uHh5+6SfQxsCjUYzJQqlIfcskIZGssVknX43PpejakFZMG54BF6XfdZKQ7n0UTsM7gXgQGYBcUuW03/94Qij4QR//ZrVU3oObQg0Gs2UiBdUvDpngUegUkc7G900uOxEq7SYUO0luvzuvI11NhFLpnHYhJESOrgbgKO2haaBGI8k+PZjh3nNOXM4d2HLlJ5DGwKNRjMlCnvgzIasIZU62uF343PZq2YNqYZznX73rI0RRC1DaRjaC80LSTkbzfX+8MljhBKpKXsDoA2BRqOZIkpKmU1ZQ/keQXVpKBBL4XHa8HucNc8vmGliydyYSgb3Quea7JQyY73bj4+xeo6f1XP9U36OuhsCIcQ1Qoh9QoiDQog7ylxzoxBitxBilxDiJ/Veg0ajmX4Km6E57baaeg1lMhIpp6c53VDWI+j0uw3dv4o0FIwlafI48Thts3YeQTyZNlJH0ykY3g9da/A4bWZa6b7+IGtOwQhAnQ2BEMIOfB24FjgHeJsQ4pyCa1YCnwAukVKuAz5azzVoNJqZobAHjsthq6kN9ZX/9Du+/8TRaVnTUDCO3+3A47TjddmrewTRFH6Pcf2sDRansmMqjz4G6Th0rsXtsBNLpZmIJDk5EWP13KZTeo56ewQvAw5KKQ9LKRPAXcAbC655H/B1KeUYgJRysM5r0Gg0M0BhHYHbXl0aymQkx0Yi7D4ZmJY1DYfidPiNquJaMoECsSRNDc48qWW2EUtm2MxeuPtd0L4CVl9rejD7BoIAs8sjAOYDJyxf92S/Z2UVsEoI8UchxJ+EENfUeQ0ajWYGUIbAPYmsISUdjVhmBtSToWCczkbDEPhcjqqDaQKxFH6PE4/DPmulocWhF/h86LPgnwu3PAjeNjwOw3Dt6zcM6qnEB+D0BIsdwErgCuBtwLeFEEU5T0KI24QQ24QQ24aGhmZ4iRqNphrxKfQaUgFZa2O4ejIcitOZ9QgaXHYi1eoIYkmaPA4aXLbZGSzu3c7/HvkU4/Z2wwj45wJGEV8smWFvfxC/x8G85sm3lbBSb0PQCyy0fL0g+z0rPcD9UsqklPIIsB/DMOQhpfyWlHKLlHJLZ2dnnZep0WhOlVgyjRCY08mMrKHKQeCYaQimzyPoaDTGNHprSB81YgSGR5DOyLpnPe3oGadvPDq1Bw/ugR+9hXHRxFfmfcU0AkDWg0mzrz/I2rlNCCFOaZ31NgTPACuFEEuFEC7gZuD+gmvuw/AGEEJ0YEhFh+u8Do1GM82oMZVqE6pFGopZPIJ6Zw7FU2kCsZTpEXhdDqLJNJlM+ecJxpI0NThocBnyVr29gtt+sJ2bvvUkY+FJGD4pYWg//PB6sLv5G8/niTXMybvE47QTzRqCU5WFoM6GQEqZAj4IPAzsAe6RUu4SQnxeCHFd9rKHgREhxG7gUeB2KeVIPdeh0Wimn2h2OplCZQ1V2uDVRhtPZcyBMPVCeRkdjblgsfU5C4mn0sRTGZo8TjPOUc+isngqTX8gxonRKB++6zlS1byNx74C/7QGvtABX78AklF41884ku40Zz4o3E4bPWNRgvFUXQyB45TvUICU8iHgoYLvfdrybwn8VfY/jUZzhmIUOuU2KLcjN8De5SgtVVgDssOhBH7P5Hvnl2M4mKshAPC6je0tnEjhcxdvdarhXJPHYVZHx8sEjH/2XA8b5rewoqux5vX0T8QAuGRFO48fGObLD+/jE69dW/ribd+F334Bll0Jm94Gvk5YdTW0LyeW6s0zuGC0pFbe16lmDME0GAKNRnN2ELO2PgBzgH0incHlKC02xC0n7pFQnKUdxcNVpoqqKjY9guzayqWQqhbUfo8TR3btpbyHdEZy+707eNfFi/nMG9bVvJ6+ccMQ/OUVK1ja4eObjx3mspWdXLqyI//Cg7+BB/8aVrwK3nY32PO35cL3GcgzDKtmmzSk0WjOHgo9AhU0rhQnsG609c4cGg7lewQ+t7G2cgFj0yNocJjSSylpaCQcJ5WRhGKTk7JUkLi7pYH/k/UEXugZz79oaD/c827oWgs3fLfICEgps4Ygf6tW7/v8lgaa6uBVaY9Ao9FMiXiqMEZgbE6VMm+s0tBQHTOHArEk3/3jUVq8TtMjaHAZ21ukzHCaQCznEShJqFQtwWDAMDDVhtwUcnLCMATzmj14nHY8ThvjkYLX/Pt/ACHg7XeDp7g6eCAQJyMxayMUynDVIz4A2iPQaDRTRGUNKUxpqIJHECuQhupBMp3hL3/0LIeGQnz97ZtNWUoFi6t5BP5sHQGUloYGAobEE6pSk1BI30SMNp/LPL23NLgYj+QmohEahN0/h3PfAc0LSt5jZ+8EAOvnN+d9Xw0D0oZAo9GcVvK6YoK5AVdqPBdL1VcaklLyyZ+9yB8ODvP3b97AJSty+rsyBOX6DakYQZPHac4DLiUNDSiPYJJZTn3j0bz5wS1eJ2NWQ7D9+5BJwgXvLXuPXX0BhIC18/K9BRXcrkegGLQh0Gg0UyRaEMTMZQ1ViBFkT+ddfndd2kxsOzbGPdt6+OCVK3jrloV5P/NmpaFyHUjzPYJKhsDwCCZrCE6Ox5jX3GB+3eJ1MhHNvuZ0CrZ/18gS6lhR9h47+yZY1uErynpSGVFrTrHZnELHCDQazZQozGZRHkElaUh5CwtaG+riEaiA7Js3F7Y0A18VaSgQS2ITRk8ij9PY5EsZgsGgkoYm6RFMRLloWZv5davXxcHBkPHF/l9CoBde++WK99jVO8GWJW1F379m/VycdsGqObWns1ZCewQajWZKFEpDTpU1VDFYbLSlmNfSUJc2E8qraPO5in6mTvnl+g0FYyka3Q5sNmFKLaWCxVORhoKxJMFYinkt+R6BKQ09/W1oXgiryvfcHAnF6ZuIsX5+8am/yePk+vMWnHJrCYU2BBqNZkqYA1OyqPTRZKX00USaBqedzkZ3XTyCsUgCm6BkCqXXzBoqHyNoajAeV6nFRE4aqj1YfDJbTNadZwhcTEQTyKH9cOT3sOVWsNnL3YJdfUZn0fXdzWWvqRfaEGjOOn769HE+8T87Tvcy6oqUkruePj6jw1WMgSnF0lC8kkeQfUy7z0UwljKlmGMjYf7yx9sn3eJhNJyg1evCZis+GdttArfDViF9NGVWNleqI1AeQSKdqXkms1lDYOkK2tLgJJmWJLf9AGwOOO9dFe+xs8/IGFqnDYFGU38e3TvIz5/vm7ZxiaeD3ScD3PE/L/Lwrv4ZeT6jU6c0h9KARRqqmD6aweOwmcNjRrLN2B56sZ+HXuzPaeg1MhpOlJSFFJU6kAayLagBbDaBy1HcijqZzjASjpvX1SoPKY+gUBpykML+4l2GJNTYVfEeu3oDLGxroNlbvzYc5dCGQHPWMRZJEEkYnSpfKiitvD8rY0w36pSt8u+hxqyhZBqPy24WfalaAnX6VUVetTIaTtBa0RA4KtYRWHsdeRy2ol5DRpdUWNZpBGVrDRj3jUexCZjjzxWCtXhdXGF7AXtkCM57Z9V77OybmBFZCLQh0JyFqFOoqvx8KTCWrVhVVbDTzUQ2B7+5IbeR1pQ1lC1Ca8/ODFBxgt1ZPTw4SeM8FknQ5q3mEZSRhqJGC2qFx2kvkoaULLSs0+iJVGt1cd94jDlNHhz23Bbb0uDkRvvvSHg6YcWrKz4+EEtybCTCuu76pIdWQxsCzVnHqDIE4zNzep4JVL/7geDMvKZAVHXuzBmCmqUhp81smTAcTBCMJTkyHM7ed/IeQVvj1KQhYzpZbv0NLnuRNKQCxcuzHkHt0lC0aGpYhxjnlbbnOL7ouqKeQoUow7huvvYINJq6k0pnzNOs0nHPBO7ddqJigHs0m5Y4NAs8gmrSUIPL4hGE4+amB5PzCDIZyVgkWcUjcJT0CDIZSTCeMrV/yE39sjKYNQTLsl1Sa20zYVQVN+R9r+vIz3GIDLu6rivzqBxmawktDWk09Wc8mkTFiM8kaeh3+4b42XO9ZQPcqpnZ4Ex5BFktv6mEIajYYiIrDXldDrwuO8PBhJkmab1vLQRjKdIZWSVGUNojCCdSSEl+jCA7B9jKQCCO3SZY1O4FIFKDRyCl5ORELN8QSIlv90/ZnlnJCVtx8Vshu/oCzGlym51UpxtdWaw5q7CODDyTPIKxSIJYMkMglso7hSuU3DUQMEZA1qvQqBwlPYIaC8pUymlHtpZgPJqgy+82AvjR2j2C0azxa69kCNylg8UBSwtqhRr/aGUgEKOz0W1KSLUEi0fDCeKpTL401L8D2/B+7ud9OCLVjd0LJ8ZnzBsA7RFozjJUoFiIM8sjUBWpA2WyglSwOJpM130EZCnMhm0lDEEyVT4tN5bMmJ0zOxpdjITj7OoNsH5+M36Pg+AkPILRsCGDVfQInKWDxUFLC2qFx2nPG5wDMBCMM6fJbfb6qSVGUKqYjD0PgLCxreGS/A6kJegdj3J4OMzFy9urPle90IZAc1ahTs5LO3xnVLBYST9lDUE4t7kMBqc/ThCIJhEC/JZmaDabwGETJNLldfRYMm3WHrQ3uukdi3JwKMS67iaaPM5JSUOj2ddcKUbQUEYamojkOo+a15bwCAYDMTr9HnPITbiGgr1es5jMYgh23w+LL0F6O3KN58rwhwNDAFy2srPqc9ULbQg0ZxXKEKzrbubkRGxWFpWVqm5u219KAAAgAElEQVRVJ/7+MnLWWCTB/OwJtJyxsPLEwWFOjEamvMaJaBJ/tk+PFafdVnUegVUaOjoSIZ2RrOtupqnBMSlpSMl8rb7yBVc+t2EICn/PysNq8VaLEcSY0+TG7bDjtIuavK2TWUMwT7WgHtoHw/tg7XXFrahL8NiBYbr87ro1lKsFbQg0ZxXKEJwzr4loMm1q3bOF/QNB1n/mYfaczAVQY8m0uUGVO+2PhhNmb/qhGjyCD/70Oe78/aEprzMQS5WseHU5bCTTpY2rlJJYKteortOS9rl+fhN+j5NgvPbfh5L52n3lA6pel4N0RhbFLdSp3CorFdYRxFNpxiJJ5jQZG7rP7ahZGnI5bLnYxZ4HjP+vfT2tXlfxlDIL6YzkjweHuWxl57THeaxoQ6A5qxgNJ/B7HCxqM7JAZlvA+Pnj46QykgOWVgtjlo2jlEcQTaSJpzLmtKpqRWVSSsYjiVOaEDYRTZZs9OZy2MpmDSXTknRG5klDYJzKjdm7k/QIIgk8TpvZMK4U3jIdSE2PoCE/RmCVhtT7OKcpOwPZ5ajJI+gdj9Ld7Mlt5HvuhwUXQFM3zV5nxRjBzt4JxiNJLl/VUfaa6UAbAs1ZxWg4QbvPZbrtsy1gfGjYMADWTdqq/5eSfVT2zKI2Lx6nrao0FEmkycj8+06WQDRZMnvJVUEaUtPJrNIQwLruJoQQNDU4JxksrlxVDBZDUCC3jUeSuOw28+dqXdYWEyoVtyvrETTW6BEcHQmzMHvQYOwYnHwB1r4BgFavM5vCbHhNsWSag4NB87F/ODgMkDdpbSbQhkBzxvKbPQNs3d4zqceo3jQqta9vlgWMDw0aFbbWFs1KSii3yee0chdzmjxVg8WqaGskPD0eQbmCMiW7uE1DYGziKk3S73EQiKVqjttUqyqG3AD7aEHm0EQ0QbPXmSe/eJw2EukM6Yzx/Kq9xBy/kobsVVtRZzKSg4MhVs3JjpA0ZSHDELQ0uEhnpOlZ/Pip47zqq4/xq51Gs8DH9g9xzrwm00jOFLqOQDPt9I1HOTIcpnc8il0I3nJ+6UHdk+V7Txzl+GiEGyZxv5FwgvktHrr8Huw2UTb4ero4PKQ8gpwcpE78q+b4zc3JipKO2nwuuvzuqkVloawOXy1oWYmJqXgECeP7Shpa3O7DYRNcmJ3i1eRxks5IIol00WjGUqgW1JXwlZlbPBZO0loQ41DriiWN51dVxaY05HZUrXzuHY8SS2ZY2dUIyRjsuAvmbIC2ZUAuOD0eSeL3ONmVrSD+2N3P0+Z7Gc8eH+N/Xbq06muvN9oQaOpG33gUj9Oe1xb46HCYK//pd1gPeS9b2pZzncsgpeS3ewd5+fKOshpwMJYyM39qDayNhRNsmN+E3Sbo8rvpm0XSUDKd4Xg2k8fqEagNe/UcP7v6AqQzErslW0cFwFu9Lrr8nrxAcylUMdV4JFF0r1oJxJIlg8VOhyhbUJaThgwhYm6zh22fehUt2c1c5fQHY6maDMFYJMHi9sqfo4Yy4yrHowlaGvKNiKfAEAwE4zjtwjQ2jW5H1YPDgazMs6YlAz96M/S/CNd/y/y5eq3jkSQL22DfQJCNC5oZCsZ5138+RTItuXwG00YVWhrS1IVUOsMNdz7BF36xO+/7/YEYUsJn3nAOf3f9BiCX7VGJJw+N8J7vb+OhF0+WvSYYS5JIZWo+2Uop89oWz2v25P1hf/+Joxwamlw//HpybCRCKitLWMc4jmffr9Vz/aQzsijIa0pDXiddTe6q0lAoawgycvJN3sDIpoklM3l9ehQue3VpyGOZatZiOdGrKt9aawlGQ5VnEYAR4AWKisrGI8m81FHIeQQqYDwQiNHl95gpsrVkDR0YCDGfITY8cjOceBre8p+w6Sbz5+o5x7JG+OBgiAuXtvHtP9uCTQg8ThvnL26t9tLrjjYEZwnPHB0lVaH0/1R5dN8QfROxovGDamLWeYtaWTPP0E3HajAEP3n6OFBZx1Zueq3yTiieIpHOmGl981oazKyhff1BPnP/Lu7dNrmYQz1RstCyDl+RR+B12c1Mp8KZA6MRo7irucFJl99DKJ6quGFZ5Y3RCqmM5VCZPSWloQpZQ+qzUM7DUzGHWoxTIpUhGE/VHiwu9AhKGAJV8axSdQcCsbxeP43uCllDsQnY/n2ufPp9POb5GPbACXjHvbDhhrzLlBw1Hk1yfDRCPJVh1Rw/6+c385+3bOHvrt+QN/VtptCG4Czg6HCYt37jSR7Y0Tdtz/HT7MZd+Aen+rf7XHbzj3a0iiEYCcXNSVuVTvumIQjUJu+oLJm2bN75vCYPfeNRpJTc93wvQMUc7+nm0JARKL5gSVtejGA8YmjhKp+9ME4wHknQ3ODEYbfRld24KnkFIUuufi1GuZCJEu0lFJUKymLZ71sH3lvxZz0Mq6F64IU+vvTLvUWfGfV7qtReAoxeQ1DsEYxFEnneiLGu/HGVPWNR5rfmqoN9bjvhwuK0saPwq0/AV9fBAx/GFzvJ/f6b4c8fh+VXFq2nuUFJQwn29Rsykkr7ffnyDt68uT7xs8miDcEs5eFd/Tx7fKwu9+oZMzbKvf3BKlfmk0xn+PMfbufFnomK1/WNR/ndvkGguBeLyt/2uh3mH+1Ylc32f57tJZmWOO2ibM51Mp0xXfhaawGUd9GWrUSd19JAPJVhNJzg58/11rS26eTwUIguv5slHT6iybT5Xo5FErT6nMxtVoagwCOwBE2VsRiskEKa5xHU2RC4a8kacpTxCLL3s0pD3/3jEb7x+0O84suP8q3HDhHPxhmUvFhNGvI6iz2CWNKouygnDcWSaVLpDL1jURZbYlk+t1GcZno8u38OX9sMT38LVl+LfO9veE3yq7yw8oNmcLgQa7B4/4Dx97iia+YqiMtRd0MghLhGCLFPCHFQCHFHiZ/fIoQYEkI8n/3vvfVew0uBv3toD988hcpPKyqL5HD2xFkr/RMxfrWrn8eyvU/Kcc+2E0jg/MWtZT0Cr9NOk8eB3SYqbrZSSn76zHHOX9zK4nZf2b4sIctmVqs0NGpuHlmPILuxPvBCH30TMWzi1DJpyhGKp/J67isGA7G8vPlDQyGWdfrMtErlFYxFkrR6XbT7XNhEsSEYiyRMyaErm+EyUMEjsI7onIrhUxt1OWmorEeQzK8jKER5BFZpaCAQ56JlbWxZ3MrfPbSXf/zVPmPdNRqCUsHicbOYrJxHkKFvPEYqI/OC0Y1Z7yIUT8HwQbjvAzB/M3xkB7zl25xsXEc4kam4sTvtNhrdDsYjSfYNBFnU5sXrOv05O3U1BEIIO/B14FrgHOBtQohzSlx6t5Ty3Ox/36nnGl4qBGOpql0Ka0XJBJMNhKqNs9KpMZ2R3P3MCS5b2cnquf4iF1z9AXrddoQQtHqdZrOwUjx9ZJTDQ2FuvmAhLQ3OskVPVq22Vo9g1GxJkAsWA3z78SN4XXYuWdFhNiOrJz948ihv+vofi/Tlm771J26/1xg2I6Xk0FCY5Z2NZg75UDZOMJ6VMRx2G51+d7EhCCfNDdGUhip4BKFYCqfdCIBW+l2Uw+w8WqKOwGm3lc8aStYYI1DB7IxkMBjj3IWtfPfWl3HVmi4e2W1IhqOR2gyB22HDbhN5n0tl/ArTR5VkFU2mOTZqHJoWt/vMn5uB53AQ7vkzsDvhrd+DZmO+gKoGX1nlhN/idTIeSbC/P5irNzjN1NsjeBlwUEp5WEqZAO4C3ljn5zgrCMVSdeuDo0rlj49EKk6PKkT9sVUyBL/fP8jJiRhvu2AhPldxwU04nsJhE2aL4lavq6Iu/dOnj+P3OHj9xm5avC7Gy7wHVvmgliZr1teRyxoy9N/e8SivOWcO3c0N0yINDQbiJNIZ9lrSOsfCCY4Mh/n1ngEGgzFGwwkmokmWWQyByg4yPAJj05rT5KG/IEZg1bubG5y4HLaK/YaCsSTtPjcep21qHkGJWQQKl91GsqxHkI0ROEpvOx6nHZfDZv5uxyIJkmlp5vG/YnUnJ0ajHBsJ56XMVkIIkW1FXewRFKa/WqWhYyNGGq/VI/C5HbhJ4P/Nx2FwN7z529Cc0/QPZKWelVU29xavk6FQnCPD4RltLFeJehuC+cAJy9c92e8V8hYhxA4hxFYhxMJSNxJC3CaE2CaE2DY0VFmaOJNJZ2RRulw8lSZhGal4qqg5tqmMND/gtaA27Erpng+8cJI2n4tXnTMHr8tBNJk2KzPB8Ai8LruZ59/qc1XMVPnNnkGuXT+XBpedFq+TiTLXKp27yeOYlEfgctjMIqNOv9vMoX/TefNp8Rl9YOrdkVRtttZJXOrf6YzkZ8/2moHi5Z0+c4zjSDhhfj5aLDGAwtP+aDiXRimEUR9RyTiG4in8HgdtXtcpxghKpI86qnsElbJimjwOSxKA8RrmZuMel2bbLjx2YNhiCMp3HlV43fa8XkNmoLlCsPjYSBiXw2ZUFceDsPN/OP/pj/Gs+/207t8Kl/8NrHxV3uMPDoZo97mqeiktDS6eP2H0lFKB4tPN6QgWPwAskVJuBH4NfL/URVLKb0kpt0gpt3R2znyBxUxx77YTXPYPj+bpqkr/rpchGArETX1zMvJQThoqf7rc1TfBeQtbcNptZs92a+OuSCK/OKitgkeQzs6RVSf1lobyLXvVZrFqjn9SMYJ2n8s0SnabYI7fTUeji0tXdNDqdZFIZ8oOO58q6gS6qy8XdN+Z/ffqOX7u3nbC/L0s72zMzfMNxpnIjtbMeQTuvPRR1XDOuqkZ1cWV0279HiN4P5WsoUAshcdpKxn0ddorpI/WZAicpsehjNmcrIS3tMPH/JYG/nBgiLFwLlOqGl6Xw4xVAaaXWRgs9jjttBCkre/3dB/9H273/RLbT2+Cf1wOW2+lZfBp7ktfyvNXfBeu/GTR8xwYDLGyhhN+i9eZ9/mdDdTbEPQC1hP+guz3TKSUI1JK9Sn9DnB+nddwRnFgMMRENJm36SstOZJIT0rKKcdgMMaWJUaRymQMgTrJjoZKbxaJVIbDQ2HzVKOCXta5ruGsR6Bo9bnKyhHqj1UFDVt9LqLJdMn+/CrIunKOn1A8VVOzslItCd518RL++jWrcdht5mZbb3lIbTyFHkF3s4f3XLaUw0Nh7t12ArfDxvyWBtwOO36Pg5FwwqJnG+ue2+RhPJI035PREnp3tX5DwViSRo+TtireWTkmIqXbS0C1rKEMLrutYiWz6jcEll4/WY9ACMGlKzp44tAIQ6F41ZO3otPvzuvIWvieIiX0bKPl4Q/zlPuDXPXsB7h16Mu8L/59GNwDF7wXbv0lR9+9nU+m3sOJ1ouMEXcWpJQcGAiysqv6xq4MkN0mWNbpq3L1zFBvQ/AMsFIIsVQI4QJuBu63XiCEmGf58jpgT53XcEahCoesG5k1ve9UvQIpJYPBOMs7G5nT5DabmtWCCiSOhBMl5ZJDQ6E897bUFKdIPJWXFdGaHcxR6n7KE1IehNpsShUYqfdIBeZqiROMRhLmaVvxF1cs520vWwTkl//XEyVvHRgImZ7frr4Jzulu5nUb5uF12Xn2+DhLO3xmFWtno5uhUNyUMVrMrCCVHpqNHxTEPYCq0lAwKw1Vi9eUfT1lGs5B9awhd5kaAoW1A2n/RAwhcgFwgEtXdhCMpfjjwZGaDcGC1gZ6xnKSaCwwxhbHETw7fwr/83746jnwnatw7P8F96Rfwb0bvsVrMl/j7879f/DRHXDN38Hil+NrMNZRqlhvKBgnEEvV5BEoA7Sk3Vs2lXamqashkFKmgA8CD2Ns8PdIKXcJIT4vhLgue9mHhRC7hBAvAB8GbqnnGs40coYg9+GyZpec6qYUiqeIJNJ0+d0s72ycnEeQ3STiqdJyiSqIWTO3Cch5BOEKHkGbz5XVvYv/mNTjlIyVK8cvZQiM7ynXupY4gVVLL4X6A50Oj6DNZ8hOBwdDhOMpjgyHWdfdhM/t4PUbjbPR8s7cJtLe6GIkFDezpgrrBFTcZ6xE9kxXk4dgLFXSk4KsNOR2GB7BlKSh8h6B024jIylZxW4dU1kOqzQ0GIzR7nPjtMg/l6zoQAjDGFULFAOQCPOKzNP8Wfi7ZH7wZvjKav7q2Vex1fFJ+PkH4OCvYdGFcN2/If5qD5/LvIdn5Br2JzqY39WZd/L3WdNHC1AZQ7XUBKj3brbEB2Aams5JKR8CHir43qct//4E8Il6P++ZisoVtwaMQ3X0CJRE0NVkGIL7nu+tuUmbVTYYDSeKGoHt7Q/itOfc21xfl/wYQVe2jS9YNtusxmslVGAIWs0TevFmFYylcDlsZlZHTYYgVLlbZWsFwzNVMhnJRDTJazfM48EdJ9nVN0E0mUJKWD/faL9845aF3LOtJ08m6Gh0c2AwVFIaglztRKnsGXWCHgjE8tIfFaFsjKDR7SQQS5FMZ/I222pMRHNTuwpxZTOCkmlJ4WHXOqayHFZpqH8iZmYMKdp8LtZ3N/Ni74RZGFhEOmm0f35xKxz6LW9MRUnY7aQCa3AtfyX/fcLHi/EuPnvLm6B9Bdhyr93jtJsHnEUFDe3KdTJNZySP7jUKKmuRhtTvqpZrZwpdWXyaqeYRTLYp2H9v7+GXlkZtSkLo8ntY3ukjGEuZ+enVGAsnzNzqUplD+/oDLO9sNDcRrykN5dYfiRd7BFC6x41pCDz50lCpFNJALEWTx2EWUJUKGGcykicODRujCrO9adoreAQtFQzPVAnEjGDvuQtaaHDa2dUXMGMF67oNT+r8xa187rp13JyVqCDnEZjFT75csBhyUthYieyZcq0oIFeR7fc4zY10sl5nJY9ApQmXkodiyUzZ9hKKPGkoEDcNn5VLVxrZQ3ntJdIpOLkDHv17+Of1sPVWOPk8bP4zdr3qR6yLf5dnrrkfrr+Tu91vYU/TZdC5Ks8IgGEI9g8Yp/vFBR1yHXYbboct7/P9270DvPZfH+c7fzjCZSs7zGLASihP9yXtEbzUyWQk33zsMO+4aFFZnbRW0hlpnujyYgTxqXkEsWSaz9y/i0VtXq7dYMgNqqp4TpObTFaXPzQYzjull2M0nGB5ZyO7+gIlM4f29Qe5YGmb+bXpEcStHkHa/D7k/nhLadNKGlLX58rxSxsNv8eJ22Gn3ecq6RH86fAIb//OU9xx7RquP8/IYq40yMSUok5hclchapNt87lYO8/P7r4A0USaVq/TLGgTQvDuly/Je1y7z81YJMlwKI7DJvBb4iZuR25AzZil4ZxCtaIobE4H+fKbteWHtblaNSYiyZKdRyFXLBZKFM80jtYgDfndDmLJDIlUhsFAjHMXtuR+KCWEBnld436C9t9yTT9wXwLGj0Pfc5AIAQJWvApe9m/G/202mkYjJH/xKCeyLb7HIwmWdZSWcDxOG8OhNDYBC1qLW1xbG8/98eAw/+t721jS7uXf334er10/ryZP+2VL23jXRYu5bOXMTiGrhDYEk2TfQJB/+NVeuvzuUx6wMhZJoFLurbNardLQZE6nv907SCie4tBQyOwzrwqLOv0ec1rToaEQFy9vr3ivTEYyFklw6coOdvUF8pqggWGg+iZieacadfK3npjCiZTpKQAVG8+FsgakWBoqHSNQ2UVzmz0lg6NKt/3nX+83O3dW6lapyv+tMYJ0RrKjZ5xzF7aYf+SZjORbjx+mo9FddSiO8mZafU7WdTfzs+d6CSdSrOturrhpdGQ35kNDIVosk7SEEMxp8tA3kYsRFKZRmh5BCeOoPE9VRwCT6zeUyab4lvMI5rcYqb8nRiPmvxVGsLhKjCB735FwnJFwgteE7odvfhAioxAZgWSY9cAXnSBPOGC0C5rmwaa3wcILYfHFeUVeYFSQ223C7LlVqvOoQklX85obTJnLirUV9VOHR7DbBA995LJJtYnwe5x84U3ra75+JtCGYJLUY8yfwtpq2OoRhOJJbMLoFz8xiWHeP8920IynMpwYjbCkw8dAIIbbYaPJ48DvdtDgtJs9h/7zD0d4eFc/d992UdGmFIglyUhYkQ1gFm4WqmHWGoshUDEElT4qpSzyCFoqyBGh7HugpCGvy240niuTNaQMxrxmD70lRk4eGQ7T4LTjsAs++bMXgeotCVT5v+L+F3r52N0vcNWaLv7xho343A7+6p7neejFfpZ3+qobguy9mhtcrOtu4od/OsauvgDvv7x0UzJFR3adBwdDRV0ytyxu5cEXT3JiNFIyJbbJY/yeS3kEKhal6ghgch1Ig3EjvlGq4RzkKnGPj0S4aFn+YSOWypQ1IObas0VqhwbDXGzbxRWHvwzzNsKSS6ChDVoXQ+dq6FiFaJxbJO2UwmG3Ma/ZQ89YBCll1hCU/hzkpqeVHnhjNQS7TwZY3umbFb2CTpUz/xXMMKqFb6Vq27uePk4qI3nnRYsr3ms4mLuHNYvGCOYZY/tqlYYmIkke3TvE5kUtPHt8nP0DQZZ0+BgMxulqciOEQAhY1unj0FCIxw8M8cUHdyMl9E3Eik5vauNf0GacjAoNwV6zhW6T+b2cR2Cc7OMpY/6rtbeM3+3AYRMlYwTqcSoNVQhBc4OrTLA4SUeHEQid0+Rh+7HiTq2Hh8Os6GrkXRct5n//t9HTpzB9tJBWrysvWHxoMIwQ8PiBYa7918eZ2+zhxd4JVs/xc3g4VDXQOmEpXjqnO/deWf9dCuURHB+NFA0q+ZurV/PLnf188cHdhOPpoupaIQRzmz0lDUHI9AicRfGaE6MR/vqeF2j2OlnU5mXzolZet3Fe3uPNPkMVPAKHTZi9eqzEEmnmVJGg/G7jvsd6e/iq804i/qX4bv0luE4t335BawMnxqJEk0bVfnmPwPhdlgqyAzS67aY0tLsvkCeNnsnoYPEkMT2CMkVW4XiKLz64h+8/cbTqvaxeRaAgRtDodtDc4KzZEPxq10kS6Qx/85rVQE4WGQzEzeHbYKQo7uyd4CN3PU9L9o+5VGfMXFqim3afq8jw7esP4Pc46G7O3buwwZfKHvJZDIEQomxFazCWwmXPr1ht9TrLSEMpc7ThvGYPY5YiK8WR4RBLO3y8dcsCsz1Bu6/yRtTideZ5IL3jUbqbG/jZB16O3+NgX3+QO9+xmfddvoxkWpq6cznU62xpcLJqjt8splqXHdheDhXUzkiKTq/dLQ188JUreHjXAM8eHyvp5XT53SUbz6nPb6PbYW6GqmDw4V39PH10lCPDYX781DE+8JNni+4xUaHPEBin7/mtDSVbmcRS6bIN5xSGgZFsfO4ztDPB4Kv//ZSNABh6f89YxPwslWtN4anJI0gzHknQNxHjnHmVDfqZgjYEk0SdBsrpqvc930sonqqpwEnp993NnvysoWx6n2EIanPb73uuj6UdPi5e3k53s8dsgDUYjJmZNWAYgpFwgkQqww/fcyFClDYEqpiszesqmW++rz/I6jn+PElJCIHX0nhOudDegrTTcj1uwvGU6Q0oWrzOknn9qk0CwNxsSwrrex5Ppekdi7K0w4cQgn+5+Vz+/e3nVR1k0urN90B6xiLMb21gXXczD33kMv7w8Vdyzfp5LM+meh6q0tp73LJxepx2VnY14nXZWdpReXPrsJycS21a771sKUvavUQS6ZIyR1mPIJ6LEbgddhrdDtMj2H5sjAWtDfy/v3oFd992MQBPHRnNe3ylzqOKRW1ec/aylVgynTemshR+j4O32n/Phonf8U+pG2lefkHF62tlQWsDA4G4+Rlpbij9OTANQZmZ2koa2p1tIFjNsztT0IZgkoRMj6A4RiCl5Ed/MiZ1BWIpczRfOYZDCZx2wfzWhoIYweQ8gv6JGH86MsJ1m7oRQrByjt9MgRsMxPMyhDYsMD64X75hI+vnN7O03cfuk8WDZ3IVq4aEYH29Ukr29gdLpr/5XA7TI1C9ZXwFGmqrr/TmbhiC/GsNaSj/PUhnpJk1BLl20tbMoROjETISc8PtaHTz+o3dRc9ZSKvXmeet9I5FWZCdUuV22M3smmXZ2Em1Ar3xiBHUVsHcN503nxvOX1B1YLzf7cjr2FqI22Hn028wOryX8gjmNnkYCMSLKriDZozAeO9afcbrlVKy7dgYW7Iy1LruJhrdDp46MpL3+EqzCBSL270cHS42kNFEumr6aFv0GJ9zfJ+nWcf3xRtqaipXCwuzGUAqdbdasLiwhkDR6HLkzZdYqz2CsxN1oioVI3j2+Dh7TgZMTbeaVzASitPuc2erKfPrCBo9tRuCX+zoQ0p447nGRrdqjlFBHIqnCMZTeamBV67u4plPvspML107r4k9J4snl1n7vRdKQycnYgRjqbxAscKbHecHVo8g/xRYrqJVSWJWWr3F74H6HagUxjkFRVaQG8JT7eRdSIvXRSCWIpU2Uhj7AzEWFMRPwNgIO/1uc85wOSai+Rkqf/6K5Xz+jdUzRoQQZk56ucDmK9fM4R/esoG3W+oPFHOaPCRSmaLiuIAlawgM6W80kuTEaJShYJzzlxiat8NuDFH/0+F8j8CUhips0IvbfARiqaLYTiyVwVNJGkrF6Xj4L4jh5EOxv6CzqaGmdMxaUMZ8Z69x6ClXWNhQJUZg9Qi6/G6zZfiZjjYEk6RSjODHfzpGo9vBbdmMkGqGYDgUp8Pvwu9xELTMkQ3FJucRPHVklGWdPvOUunKOn3gqYwZQrVWgQog8w3BOdxPHRyNFrbDHwgncDhsNTruxWVg27n0lAsUKn8thZg3lYgT5m3uLt/iUD4bhKDQEpaShoCXzBXJ581aP4Ej2RLpkkoZAnUAnokn6J2JkZOl8cjCGzFeVhiKJoklYtdKe3WQqnYpvumBRyddo1hIUpJCG4sZQGnc2NbIt6wFtO2Zs+FssgekLl7VxcDCUl91mtqAuU0cAOX3dGifIZIv6KkpDv/k89oEXuSN1GwO05cW2TpUFWalHdX0t5xEsbPWyvNNX9DlUNGYPOrv7Ai8ZWQjOckOw52Rg0r3n1Wk0mkznST9j4fC7E+8AACAASURBVAS/ePEk1583n2XZP8xSGq2V4VCCjkZ3tpoy5xGopmAtZQKlhezuC7DeEnxUjdj+eHAYyG/aVYgKdu0t8ApUXx4hBO2NLiKJXBfQPf2GW7y6RAtdr6uER1BwCmzzGh1IM5n89155QlZavC5iyUxeIDincxt/zI1uB36Pg5MTuSH2R4bDdDS6qqYrFpIrskrSM25sZNYB5laWdzVycDBU8TM0Hi2fs16N9ioeQSVy1cX5n8FgLEmj25E/HyKcYNuxMfxuR15b5AuXGumfT1viBIFoCpug7EYJudP0MUucIG4Ori9hCKSEZ74DT/47XPBe/uS8yHgNzfUzBHObPDhswjzElPtc/OWVK3jww5eVvY+SLvcNBF8ygWI4iw3BY/uHuPZfH+eZo5MbEG8t9rJm/fzsuV4SqQzvvGhxUYfIcihpyJ8dxqE2FOURNDU4iacyZZuHgZE22jsezTudqAlJfziQNQRNFQxB9nG7+/LjBNapV0qDVvLQrr4AC1obSsoDPrejKGuo0BC0+lxkJEVeSKhEjKDFckJXBAvkDYC1c5vYZvldHhkOs6SMe18Ja5sJVYC0oJwh6GxkIpqsWJA1XqFlczWU7FBrl00r5QbdhyzZVpAzytuPjnHuopa82MXGBc00OO08dTgXJ5iIJmlqcFaUbFTx3vGRnLek4kUNhTGCZBTu+0t48K9hxavhNV80U1NLtZeYKnaboLulgWRa0uC0l+15ZLeJiv2Q1OdTypdOoBjO4jqC+54ziq/6xqNVrszH2gdoJJQwZYM9JwPMaXKzeq4fKWXZgh6FlNLwCPwus2YgkkjjdtiIJtM0up15bZjLfTjN7AXL6aTR7WB+S4P5s0rtJLr8Rnro7pP5mUOGR2A8v5lvHkoY9y3wQKx4XblpUKrCuHBzV/cdDSfyTrvheIrGQhmpIdcGQZ1yCwOeAK9c28WXfrmX/okYc5s9HBkO84pVkx9oZG081zsWRYjcSMtCrJlD7WW04vFI5UZ3lVAewVQCpp3Z9RR+Bq2FeGAY5Ugizf7BIK/dkF8z4MzGCayZQ5X6DCkaXHa6/G6OD4dg7CgMH8Tes4ePOp7lgsPtEPEZ7SBiE9D3PIwchFfcAa/4ONhs2d9rtKjh3KmyoLWB46ORUwpAW9+7l5JHcFYaglgyzSO7B4DKhWGlUOX1hSfB3vGoWZRltAGo3BM+EEuRSGfobHSblYnBWIpUdsNXwWIwTmFdZU5HagMvzF5YOaeR3vEoTruo+MEXQnBOd1ORIRiLJOnOvp520yOIE4wlOTIc5s3nlZpAasQDlAFQBqHIIyjT7jkUKyUNFVcil/IIXrnGMAS/3TvIded2MxiMs3QKQz+shqdnLMocv6dkqwHItY0+PBTiZUvbjF5PP9/F+1+xjGWdjWbn0alKQ/OaPAhBWSNTCZfDRkejq1gaiqfy3jdl5KXEHF5k5cKlbfzTr/czFk7Q6nMxEa1iCFJx6HuOTzt/yEV7/wC7Da+0GfioAzKH7XDUDq5G8DSDrwPefjesutq8RWESQL1Qnl3zFA0z5A41DU572YDymchZaQh+t2/QPNlPdjBHMJZkcbuXHT0TeUG0vvGo2VYY1GzZ8tKQSsdsb3ThyJbJB2NJUhlDS/VbCn7KDXAHIz7Q6XcXNQ1b2dXI7/YN0eX3VM28WDuvie89cTSvStbau9/0CMIJM8No3fzSpyHrfNicNFToEaj75V5XJiMJJ9JlpSGrISjMfFGvd0FrA7/dO8DGBcbvYelUpCGzBUaC3vFIWVkIjMIut8NmppD+YsdJ7t52gsUdXv7yihUE4ykysnKqZSVu2LKQFV3+KUlDkB10P1HsEViryJVRtttEfoO3LBdm20Q8fXSUq9fNzR9KEx2Dnm3Q8wz0PgvD+2HiBMgM1wgnT3Aul7/+k9Cxmr2pOVzznb18451buGb93IrrVp5evQ2BSiE9FY9A1bmsmeevmgJ8JnFWGoIHXjhJR6MxIGWyo/pCMaNh2I6eCdMjyGQkfRMxrl6X+4DPafLw/InxsvcZzmYddTS6zWHvgViSdDZOkOcRVAgY7z4ZKOmiqjhBLV0lz5nXlDd2MpXO5A3+UCfS0XDC3JDLSUPKIzD6DBmzbQv/YEp5BOaYyiJDUNwaWklD1qImIQRXreninm09XL3O8G6m4hGoFhhjkSQ9Y9G8LJpC7DbBUkvm0F1PGzUkagqc+r1NJdgLhgxx6Sl0qJxraU6nMJr15QLCysisnecvMsIAmxY243bY+Nmzvfzx4DAjJ49y87y98L0vwLE/gsyAsEHnWliwBTbdDF1r+U7PEr70aB97N12Dx2knfGwMEFXrCCDXb6ieMQIw2qVA+YyhWlDS0EtJFoKz0BCE4il+s3eAG7cs5MlDI2Xn8VZ6/By/G5fDZspKqlLXml2ipKFyQ2CUN9HR6DYDaYGY0dALMNNHoXwr6kQqw8HBIFesLtbCVfZHpYwhhRkwPjnB6rl+0wNRm0STx4HTLhgJJxgMxOlodJeVqrxuOxlpZImEE6mi1FEo3YpaVSMXbkatJbyiYCyFw5ZLgVRcuaaL7z95jJ9mN+SpBIuFELR4nYyE4vRPxMpmDCmWdxktO/YPBNmWTddVHsJ4NNde4nQwp9nDcwWHkVCRNGSsbcvibM+c8Iih36cTkIrhTsZ419xjyL0P8DrHM3zWfgDboISOVXDpX8GyK6D7PHDnt3Wel+wF+jg+GmHVHD/xGgbXK5qmySNQ8bypGmbIHWKs3v9LgbPOEPxmzwCxZIY3bOpmb39wUh6BCuj6PU46fC6zlkAFnLubrYbAQzyVyWrExR88qzSkyvatKaRWj6CcNHRgMEgyLUueTtTIvEoZQ4plHT5cDhu7+wJcf17xHFwhBK1eF6OhBLv6JlhfRhaCXM1AOJ4iEi/dW8bnsuOy2/Le+8KhNIoGp3FtfozAqNYtNLAXLWunwWnM/53f0lDTplOKFq+Lff1BUhlZtoZAsbzDxy9fPMkPnjyK0y549TlzePzAMFJKs5jrVE6gp8LcJg+j4QTxVBq3w46UsihYvKDFw5u6x3hf5mn4j1/D4K6i+3wKwAnprvXY1v0fWHsddK2p+NxmCumIYQhyWUPVfyevXNNFvIa+RJNFSUOnYpgXtnn57q0X8PIqbdzPNM46Q/DAC33Ma/Zw/qJW2n0uszlbLVg3q7ZGlzmsxTQELfmGAIwpUaUMwVAogRBG+p40ZxIkUVub3+3A73Ga81lLofT6UmlsjW4Hn7h2TdW5A2BUka6Z6+fFbNWlkrysvfvbfC76JqIcGAxx1dqusvdSgeFIIl3WIzAaz+W3csiNqbQXXdtc0Bo6WJACqfA47Vy6soNf7x5gSUflDbwSrV4nL/QY70VhV9ZClnc1kpFw19MnuHrdXLYsaeWhF/sLBs9P/QRaFSmN0YzpePb/CeO/TIrl9gEWi35Gj+xgnjNCIjjMDeJxLh94Ah6WMLATT++z/Es8AKMCFl0Er/qcEcC1u8HhBmcDODzQshB765Kal6V69RzLppDGkhXqCAq4fFUnl08h46saXX43166fe0pyGxjV+S81zipDEIgl+f3+Id598RJstvJdMMthFjK5HbT53KY01Js1BNZNwzolqlRPnuFQnDavC4fdZrrCwVjK1NMbPQ7s2clU5cZV7u4L0OC0l5VA3v+K5TW/touXt/Ofjx9hNJzIzcm1zIRtb3Sx/dgY6YwsGx+AnLQTTqSIJNJF7SUUrV5XXrC4cDpZ/rXOoqwhf5nK1qvWdPHr3QOTbi1hpcXrMkctVgoWQy5zKJWR3PyyhaZRPzQYzmtBPSmkNIawjB6BieO5oSyBXhg+YARlo+MgK/eyeh3wOjfwY+NrN/APTuAIcMIDHSthww0wfwusuAr8lYO4k6HF68TvcZjN52KmNHT6SpdsNsGd7zz/tD3/bOasMgSP7h0kmZZmn512X67C1VZDBoAqJmv0OOjwuTiU9Sb6xmP4XHYzyAWY5fHlUkiHg3EzT9zjtOGwCYKxJA5lCNRowhK9dhRK069H9sIbN83nm78/zEMvnsSWlVys2SptPreZBVSphbLXMuC7cCiNlbbse68IxkpLQ2CkdI5burCGKhiCV67pwuWwcc68qWu41qyS7ioegTI4C9sauGR5h5m3f2goZBqvmrKGklE4+BvY/XM48AjESiQa+DqhfSWseb1xahd2I1Brd4Ldlf0v+2+bg75AnH/81T7edelyzl+9nOOxBm7+4T7+zw2X8PotK2p8N6aGEMJoPpdtMxGdRIxAM/OcVYbg4V39dPndnJdNk2v1urJTwJJV2xNDbihNo9thdOTMSkO94xG6W/IbZCltvlRPeDACzKpyVAiB3+MgEEuaXSrVBtrc4Cw5mEVKye6+AK/fVL2jZi2snednRVcj97/QZxZiWQuhVC2B3+NgYVv5zdGcUpZIEa4wLL7V6zJbVUD+LN1Cmr3OvL7/gViShWXaBHc1efj97VeYBVVTQb3uTr+76sblczt407ndXLayE5tNMLfJg9dl59BQCIGg0e3IDa7J/P/2zjs6rupa+L89M9KMyqjblmW5V4zBBoxNLwEMBAIJkO8FkjxSSEI+0uCDFfLSILASSF7KS0LCSyG8EAgpBMKLaQ5gQgcD7rjj3iRLsno/3x/n3tHVaJqlURlr/9aapas75957ztx7zz57n3327oYdL8H+tdDeBO0NULsDqjbaRVXdHZBTDHMugXHzoGQaFE2yAiCnGPxH9rrmNrfz2BPLmBc+hpOmT6NmVx17OURu/tAkTZ9cmhcJ8taqgmBEM2oEQWtHF8s3VvGhEyZERv/eDE2pCALvqLU0P0hrRzfN7Z3srWvtM3IMZfkpys2Ku7q4urGN+ZU9fttuvKFsv40R79YxXuC5PXUt1Ld2ps2NTUS4fH4FP1y2ibL8bHKzey/Dd3+rYysKEq5L6KMRxIlJU5yX1Stwn+s+GksQFOdmsWZ3aqYhiL8SOFVcm34ys5DLTz5yQmTb11bH2cWHCOzaRUV2C1dl18DbB6F6I6x5BBr29hzoD0JBBYyZYxdUTTsHppxhR/VpIDrRvet2mx8cmsnr+ZWFLF29j921zZ5YQ6M2qs2IZtQIgpe3VNPc3sUSj6+/d6HU9BTmprxzBJHVto3tfRaTuYwL25jwsfCahoBIvKFgwNerMyzKyWb/4b6JY9x46OmMd3LZAisInl53oI8Pd48gSGxycTWZ5vZOmts743p+lBeEONxis4qFsvwRIRtLcBTl9jYNNbR2JEyMMlBc01AyjyE622Dnq7D1Odi3Cg6+C40H+GV0uccBXwBmnA8X3glTz4FQQdo6/Hj0pKy0z2BjjIV4g8kFc8v57hMbWLb+AK0dXYgQybGgjCxGjSB4et1+wqEAp3oSansFgcueuhb21bWwcErfXKSNvTSC7Ej5Q03tMUeP4wpDMecIWtq7aGrv6hXLPBzMcmIK+XrZyQtysmImsH93XwMisSOA9pfJpXnMn1jEql11fVazuoIvkeso9OQeaGrvoqmtq1eaSi9uVrH9h1uZUpZHU1vstQFgR7ZuBNJgwNfHFz7duBpBH4+h7m7rXrntBXjvX7D9JehoAl8WlM+zQdPGzuHJHcIDa5ooKR1HTm4+P7h6kTXtBIfGJOPFDkZcjSC+1jUYTC3LY+bYfJ5Zd4BjKwrIyfKnLb+Akl5GhSDo7Ormn+8ejEwkusQSBD9etonH3tnD0i+d2cfbp9Fjx3aPdW2gFUV9F7+MCwfZuL/vaN5dTOa1Y4dDAXYcaiYn29/rRXXTVUYvTNuwv57JJblxTS/95fL5FazaVdfHVHbi5GLOnFnGGTMSq06uRtDY2klLR1ef8BIublaxvYdbmFKWFwlBHaujKI6sLu4gPxSg2wzuqLY4BJVSxcLuRljxqvXU2bfSjvrbHXfj0pmw4Grb+U85o9eCqu78fbyy6m181VjHhKK+iWOGinGFIVbvthPPDZGEPkO3rmHJseO494VtlOZn6/zACGZUCIK3dtRS09TOkrm93eNiCYLt1U10dhu+9rfV/PX603p5E0XMF9mBSBJ01/e+IoZdurwwRFVDG13dppdnT7VnMZmLnSPoIDfo79XJFeZk0dFl+nSqG/c3MCdGYpiBcunx47lz6fo+8VjGFYR44NOLkx5vR309C+aicxC7jI9KnNLYFnvNAfTcpx2HmiIpBGOtI4hLdxfsfcd25B3N0NEKne6nDdrqrYtmSw3U72NRwz5eChp40zk+EILy42DBNTDhJJhyJhTGDroHMH2s9STqNsO3qthlckkuT6zZx6YDDZE5gnj3ZDC4YG459zy/lec3HOx3zCVl8MlIQWCM4RfLt/LhhZUJQyy7PL3uANkBH2dHhWIIZfnJzfb3Wkuws6aZseEgb++s48HXd/DxU6dEvnNzCft8EunEI4Ighpvh2IIQ3cZ2/N7l8gedpPXeOEDWa6iTvGBnL/u8Nx6/Kwha2rt471ATly1Ij8dQdJ1vv+xY5vRzEtrnE3Kz/FQ5giCeRhCdVawpgbnn9Bml5GX7+fOK3XzujImMpZaxXQfgkLOAqrPNftobbafeWg9NVdBUDXU7radOa1ReZvFBIMcumgqGIbfEmm/GzEEKK6FgApRMheKpdkLXl3rnOaU0DxG7HGC4VhW7fOL0KTz4+g5u+etqTphYRG62P+KZNhQcP6HQCbfSltZEM0p6yUhBsP1QMz94eiMFOVl8/JTJScu/uLmKU6eVxvFI6cmf29LexcGGNm66YBZvvFfD3U9t5IK55ZFOq9GzPD83208w4GNbVRMiPR2bl3FOR3+gvrWXIHBdISd5XCDDoSwa2zo53NLRxzQEVhC43jCbDjRgDDFzBqcDr/DrD7nBAFUNiTWC3GwbQqOXRhDLzFXzHuEN/+DRwscJrdtO5buHeCPUBU9jP4nIDttFUsdcBtPPhYmLIVhgR/j+LBgke3Uoy8/E4lx21jT3O01luijLD/Kdy+fxxT++w7aDjUM2Uezi89mwG394bWfiNJXKsJL2p0JELgL+C/ADvzHG3BWn3JXAX4GTjTErjuQa7kKkeCtuo6lubGPxtL6Tv2DNM27Mm921toOeXJrL5QsqWPLjf/GjZRv5/lXzgd6pFG1y8SB76mzM+qwYoyxv3tjjK3v27zjUTEEo0Cv0gBuDvbqxrddkcSTekGdl7QZn3mEwTEPpIC/bHxEEfTQCY6x5pqWOk/OroaoBDvgobdpCQdAHe0LWr373m7DztUjsm0kls3mmezpvF5zHipoQ1517DFPGFNkOPRC0rpjZedYbJ1hgF1xlDcyNdCBMH5NnBcEwawRgzX1LV+/jqXX7U4o9lW6WzC23gkBdR0csaRUEIuIH7gEuAHYDb4rI48aY9VHlwsCXgdf7cx13gZU3SFs8jDHUt3bGtU96NYJdjiCoLM5lcmkep04vZd3ensnehqjk6iV52eypa4k5UQyeeEMNvV1Id9Y0R2zdLu4Enp0I7alrrAik7+5rICfL30ujGEnkZgfY6+QPjiSl6eqA5d+DV++xtnngNwANwC/hp+7Bv3b+Zodhwomw5E6Ycymhkqk8cO8rkdSi1x53FqTRYyrdTB+Tz/MbqwY3zlCKiAh3fHAer713aFjqc8q0UsLBgE4Wj2DSrREsArYYY7YBiMjDwOXA+qhydwB3A7f05yK1TW60zuQaQVN7F13dJq4gKMnLjoQN3nmot8mmoiiHVZ4wvo1O1EsXd55gQhx/87L8ID7pu7p4Z01zn4Vg3vOG45iGXDbub2B2eTilsBjDQV7QH9FgcrMDNl3hI9fZUf6xH4LxCyBUyMOrali5u4G7rprPrY+u55iKQq49bRoUT4Exs/vY5T92yuSIIDiiyeJhYLoT/XUkaARg56MevG5xJBbSUJId8PEflxwz5GYpJXXSfWcmALs8/+8GermaiMiJwERjzFIR6Z8gOAKNwO1A47nMleT1aAQ7a1rIyfJTlt/jR17b3EFzeye52QGbi8Bj63e9WeJpBH4n5IA3PEJXt2F3bXOvJDZAJGE39I634yaId1fhGmPYsL8+aZanYcEYaG+k0ldDs2znRN9mZi3/Pex+0XbqV90H866MFD94eDMPb97EbbMuYmlHLrljJsKcuXFPf9G8ckrzsjnU1D7iO5X3zRnLB+ZXjKgEJskWAw4mVy8aPhdaJTlD+jaJiA/4EfCJFMp+FvgswKRJvR8id7SZikbgziMk0gia27to7ehiV20zE0t6Yga5C4r21rUwY2y412Qx9CyyShSqeFZ5mA37GyL/769vpaPL9DHreDs27zXCwQAzx+bz1Lr9fP6c6RxsaKO2uSOtC8mOmENbYccrNj7OoS3WM6ep2nrqdHfwY7ChLoHO6kqYdwWcdQsU957Yd+dQDtS30tje2ScEdTTBgJ9rT5vC71/d0ScP8khjXEGIn119QvKCijICSLcg2ANM9Pxf6exzCQPzgOVOZ1sOPC4il0VPGBtjfgX8CmDhwoW9FFp3crf+SDSCBIIArJaxq6a5VwftZqfaXWsFQUNUcnU3hWOsNQQus8vDvLylOpIP2I3PPrk0WhDE1ghEhGsWT+L2/13P2j09eZL7696ZMsbYnLQN++DwHpuLtnoTbF4GNVttGV+WDYxWPBnKj7cTtLml/HldA89tb2edmcxjN380bvJ1dy3BtqomjIkdXiKaL75vBp85c5quUFWUNJJuQfAmMFNEpmIFwEeAa9wvjTGHgUhWCBFZDtx8pF5DPZPFyTWCw0k0AnfV6qHGdnbWNPdK5FIR0Qha6e42NLZ39rLfuyuDE6UznFMepqPL8F51E7PGhWO6jkKP1xD0zdt7xQmV3P3UBh56Y2ck4ceguI621sPqP8E7D9i4OV1RUU/9QbuKdvHnYNq5VgjEiIi5rnotT23bASTu3F1BsMUJ5x0rBHU0IpL2zFWKMtpJqyAwxnSKyBewHt5+4D5jzDoR+Q6wwhjzeDqu0zNZnFwjSGYacid8txxspLm9q1cHPS4cxO8T9tQ109zRhTG9O6uLjyun25iEnfLscXbkvmF/A7PGhdlxqJmATyKdoEs8jQDsPMEHjq/g7+/s4bQZZYwvDKXX+6N6M7z2C1j1Jxs7Z/x8WHw9hMdDeBwUToTCSsgfl9LCqtyg62JLzNhBLm68oc0HrelsqGLgKIrSm7S/ecaYJ4AnovZ9K07Zc/pzjXROFrsawUrHO2iixwMo4PdRXhBib11rT8A5Twjf3OwAH17otYT1ZfrYPPw+sTGH5lews6aZCcU5fVZ3Zgd8BAM+2jq7Y3aI1yyexF/e2s2y9QdiJqvvF/vXwnN3wqYn7Wj/uA/DyZ+2bpsDwA00l5cdO3aQS34wQDgUiKQLVUGgKMNDRr557mRxY1tnnzg+0dS3diISP0iZO+G7ygnMFe3fX1EUYk9dS09SmiP0VgkG/Ewry2OjM2G8M2oewktBThZVDW2EY8SLXzCxiLnjC1i/r37gC8m6u+Dl/4Lnv2vDK5z9VTj5OshPTy5WdxFZKhO64wtDbDlgBUG6A+gpipIaGfnm1Ta3k+330d7VTWNrZ8TFMhb1TsiGeD73BTlZ+ITIwrGJUWsCJhTl8Ob22oj20R+3xdnl4YjGsbOmmUucVJnRhEM2NEOssAwiwkdPmcTXH117ZPMD3V2we4UNm9zeYP/f+RrsWQFzPwiX/tjG2Ukjbv1T6djLC3PYdEA1AkUZTjLuzWtp76Kts5tpZXlsq26ivrUjoSA43NKRMOqh3ycUOauLy/KDfSYiK4py2F+/L2Jiip7ITYU55WH+sXof+w63UNfcEVcjCIeyyMmKHxTsyhMraWrrZMmx4xJf0BibMGXlQ7DxSWiutvsDIZsgJVQEV/7W+vQPgvfNEWkEnnUZKggUZXjIuDfPnR+YWJLLtuqmpPME9UkEAfQsKpsUIxfvhOIcuroNW6us2+eRmoYAZjumnH+uPwD0dR11KQgFEp4/lOXns2dN7/tF1UY76m88YD+ui2d2Psy6CGZfDDPOs9E1h4CIRhAn8qgXb7A+NQ0pyvCQcW+eKwjczjSZC+nhluRpDUucCeNYI3XXhXTT/v57trimnGccQRA38Xo4RHVj30T1MWnYb9081z4aCcwG2M5//AI462aYe7kNxDbEuBpBKm6e3lXZI321sKIcrWTcm+dOFLuddlKNoLWDaWX5Ccu4i8piddCVjiDYcMAKglgTucmYUJRDXrafV7ceAmILHICvvX8OzW1d8U9kDNRsg1d+BisftH7+E0+Bi79v8+GGx0P28AeiczWBVBKguC6k/jhpKhVFGXwyThB4TUNgO/pEHG7poCAncTOLEwgCVyPY7AiC/mR38vmEWeVh3tlpcwHHC5hWlh+EfGykzhfuhj1vQUcLtDfZDFpNVdDVBv5smy3rtC9BaQxT0TDj5i2Ol5TGi7ueIj+Y2NVUUZTBIwMFwZFpBMkmi6HHhTTWSD0vGKAoN4u65o6EE7nJmOMIgnhmoQiNVfDnf4edr0DFCda9s6ACxs2zIRzC420Ez4LYnkcjgYhGkIJpqNwjCBRFGR4y7u2ra+qtESSaI2jr7KK1ozupIHCTdcSbxK0ozIkkTu8vbpC4yfEEQWc7bH0Olt5kR/9X/haOu6rf1xtOIhpBCp17OBggL9s/pHl0FUXpTcYJgtrmDvKy/eQHAwQDvoQaQX2L/S5ewDmXK06sZGJJbiQVZDQVRTms31ffL9dRF9dzaFJxCBoPQv1eqNsBtTtg30rr6dNWD4WT4NNP2zAPGUpedoDi3KyEUVldRITywpBqBIoyjGTc21fX3B6JsxMOZSWMQOrOHyTTCPKDAc6dHX9VbaUTVK7fGkHNNk7c+gdezHmICa9XwWtRdc4bA3Mvs7l1p54NWZmd5NvvE5bffG7Ko/xLjhs/pAnVFUXpTcYJgtrmdorzbMdeEAok0SsJIwAAEONJREFUnCxOFmcoVVwXx5RHrYe2wuZn4OB62L8G9r5DUHxMnHYuVCyA/HKbVL1okg3hPET+/UNJokV+0dy0ZPYg1kRRlGRkoCDoiASKC4cCSUxDiXMRpMqEImvXT0kQrH0E/v4Fm6A9txTGzoX3fRPmXw2FEwZUD0VRlMEg4wRBXXN7ZKK4ICcr4WRxslwEqRLRCBKZhjrb4dnb4dWfW9/+K/7b5t5VFEUZ4WScILAage3Yw6EA+w63xi3boxEMrJlu4pk+k8W1O+CVn8Ket+HAOuvjf/Jn4MLvQiCN+QIURVEGkYwSBF3dhvrWjp7J4mBijcCdSB7oHEFZXpCx4WDvNQBbn4e/fhI622DCSbDoMzZr18zzB3QtRVGUoSajBMHhlg6MoZdG4LqIxisfDPgIZQ3MR93nE56/+Rx7nq5Oa/559nYomw0feXBEru5VFEVJlYwSBG54iWKP+2hLR1ckMXw0qUQeTZU8aYM3/2CFQN1OG9Dt8l9AMHEcI0VRlJFOZgkCZ1VxkaMRuLb/xtbOSLwgL6mEl0iJ/WvhwQ9Dw16YuBguugtmv39QYvkriqIMNZklCJw4Q16NAGy8oXiCYKCuo+x4BR76iA3n/IknYMrpAzufoijKCCPDBIHVCNyw0W78+niLyupbOxgbPoJVuh0tULcLGvZB8yEbAmL5XVA4ET7+KBQlTlSvKIqSiWSUIKhr7m0aSiYIDrd0MGNMDBt+dxcc3g3Vm2yo511v2BXATQf7lq08Ga7+E+SVpqcRiqIoI4yMEASui2htcwcBn0RW+BZ4TEOxqG/p7JkjaDwIq/8Eq/8MVRtsUhcAxK7+nbkESqbYoG8FFXZVcG4J5I0Fn8bBURTl6CUjBMGu2hbqmtsjAefcBCaJBEG3s+agMCcLnv46vH4vdHfaEf4pn4eSaVAy3Ub5DBUMaXsURVFGEhkhCLq6DXc/tZHapp5VxdBjGoq1qKyhrRNjYGrHZnjz5zDvSjj7qzBGA5wpiqJ4yQhBUJYf5I9v7GRMOMjU0p5k7PkRQdBXI3DDS5y863cQKoRLf6Ijf0VRlBhkhPF7XEGQ8YUhqhraIhPFAFl+HzlZ/kin7+VwSwczZTeV+/8Jiz6nQkBRFCUOGSEIfCJ8+wNzgZ41BC7xQlHXt3ZwQ+AxugK5dk5AURRFiUlGmIYALjy2nFsunM2iqSW99hfkZNHQ1lcj6Kzawgd8r1I797OU5Zb0+V5RFEWxpF0jEJGLRGSjiGwRkVtjfH+9iKwRkZUi8pKIzE3xvNxw7gxOntK7U4+nEVSs/y2dBGhfpNqAoihKItIqCETED9wDXAzMBa6O0dE/ZIw5zhizAPg+8KOBXDNm3uKuDir3Ps2T3ScTLqscyOkVRVGOetKtESwCthhjthlj2oGHgcu9BYwx9Z5/8wAzkAuGQwEaoieLt79EqPMwT3YvJi87Y6xfiqIow0K6e8kJwC7P/7uBxdGFROQG4CYgG3jfQC5oE9hHaQTr/06bL4eV2Sfh82mEUEVRlEQMi9eQMeYeY8x04KvAN2KVEZHPisgKEVlRVVUV91zhUE+Wsr+9vZuz715G7dt/Y3n3AkK5mitAURQlGekWBHsAb4jOSmdfPB4GPhjrC2PMr4wxC40xC8eMGRP3BAWhAG2d3azYXsOtj6zhFP8mis1hto05n4+fMrk/bVAURRlVpNs09CYwU0SmYgXAR4BrvAVEZKYxZrPz7yXAZgaAm5Pgcw+8xZhwkNtnboE1OXz+uus1e5iiKEoKpFUQGGM6ReQLwNOAH7jPGLNORL4DrDDGPA58QUTOBzqAWuDagVwz7Akzcd+1JxH68/U2gbwKAUVRlJRIu0uNMeYJ4Imofd/ybH85ndcrL7CJZ7516THMb34NGg/A3JjWJkVRFCUGme1b2dXJKbKOlYuepej1r9mMYjklNreAoiiKkhKZKQg6WuCZb8K6v+FrPkRRIARTz4YzvmKTymuAOUVRlJTJPEFgDPz9Blj7CMy7CuZeBjPOt8nlFUVRlCMm8wTBC3dbIXD+bXDGjcNdG0VRlIwncwRBWwOs+Qss/x4s+Cic/pXhrpGiKMpRQWYIgoPvwvcmAgYmn26zjYmGjlAURUkHmSEIAiE491YYf7ydFA5kJz9GURRFSYnMEAQlU+HsW4a7FoqiKEclGZGqUlEURRk8VBAoiqKMclQQKIqijHJUECiKooxyVBAoiqKMclQQKIqijHJUECiKooxyVBAoiqKMcsQYM9x1SIqINAAbh7seg0gZUD3clRhkjvY2avsym6O1fZONMfGTvjtkxspi2GiMWTjclRgsRGTF0dw+OPrbqO3LbI729iVDTUOKoiijHBUEiqIoo5xMEQS/Gu4KDDJHe/vg6G+jti+zOdrbl5CMmCxWFEVRBo9M0QgURVGUQSKpIBCR+0TkoIisTVDmSyLyrog8KCLniMhp6a1m0jpuF5EyZ/siEdkoIltE5NYExzwlInUi8o+o/ZtFpEtEWkRkpYgsiHFsRrXXKWNEpE1E9orI4zHK/EBE1jl/Pygicwe7HVHXb/Rsx7w3UeUXiMirTp1Xi8i/eb671rl/7SKySkT6ZDIaYe291nnuNovItXHKXyAib4nIGufv+1K8TkrvwxHW/Uznt1spIseIyDUJyt4mInucsitF5P39recIu2dJn9FE5UTkt86zuVpE/ioi+YNV75QwxiT8AGcBJwJrE5TZAFQ627cBNyc7bzo/wHasH7Af2ApMA7KBVcDcOMecB3wA+EfU/ieBW46W9nrKNCX6TYDDgN/Zvh+4aojb1Jjs3kSVnwXMdLYrgH1AkdPeRuBLTnurgdtHanuBEmCb87fY2S6OUf4EoMLZngfsSeEaKb8PR1j3e4GPOdvnJLlPSd+PVOs5Uu5Zqs9oonJAgWf7R8CtQ9mW6E9SjcAY8y+gJt73InKvcwOfFJEbgeuBGx3pf2ZUWb+I3C8ia52RzY3O/umO5HxLRF4UkTnO/jEi8oiIvOl8Tnf2l4rIM87o4DeAm8B4EbDFGLPNGNMOPAxcHqddzwINMb46gO1Ijpb2LgK22CbHLuNoCPnAWyLybeAy4AdOm6ZHlc0TkaXOaGatOxIXkZNE5AWnTU+LyPgkbZ3qjOjXiMidKd4bb5lNxpjNzvZe4CAwxmlvNvALp71/Aa4ewe29EFhmjKkxxtQCy4CLYrT3HaedAOuAHBEJJvqNSOH5EKvRLndGpRvEarnifHeeiLzj1Pk+EQmKyHXA/wHuEJEHgbuAM53f7sYk9RlIPUfSPUvpGU1UzhhT71xDgBxgeCdrU5SEU0g8Qt4OlCUbAQAnYR969/8i5++z9IzuFgPPOdsPAWc425OAd53tnwLfcrYvwf6IZcBVwG885/848PME9T6HvpL6fuzopBX4MRDM5Pa6ZYBOYAVWKCyNUdfGqN8g5mgLuBL4tef/QiALeAUY4+z7N+C+JG19HPh3Z/sG7/Xj3ZsE93ER8C7W1PlJ4LDnuy8Dh0Zqe4GbgW94zv/NeM9T1D39Zwq/SyrPxznYkXal8/u9CpwBhIBdwCyn3O+Br0T/XsnuE/b92A6sBu4jtraT0ns7Uu7ZkT6j8coBv8MOPJ8HclN51gfrM9Qri7cB00TkZ8BS4BmxtrHTgL84AxEAd6RzPjDXs7/AKX8WcAWAMWapiNSmsY5fc66/FKuufxX4Tj/PNZLaO9kYs0dEbgJuE5Hpxpit/TjPGuCHInI39uF+UUTmYc0Vy5y6+4F9Sdp6OvaFBXgAuLsfdcEZ1T0AXGuM6fZcJ12MtPYe6xy7pD/Hx+ENY8xu5/wrsQO/BuA9Y8wmp8z/YDvDnxzhuX8J3IEdvNwB/BD4VBrqnIgRdc/iYYz5pIj4gZ9hBdPv0nn+I+GIvYZEZKL0TPxcn6Ss31P2O8aqvvOB5ViTym+cOtQZYxZ4Psd46neKZ/8EY0xcsw2wB5jo+b8S2CMiiz31uCxRnY0x+9xN7I05K5PbC0wFJhpj9jhlgliN55+p/B7R99vpGE7Evmx3isi3sKaqdZ56H2eMWZKkre5vnBKx7qGIFGAF7NeNMa85RTdgzSbuIGcOUJvq/R+G9sa7hx/y1GOhU7dK4FHsKDUVIR7r3FUxfos2T5kuBhB6RkR+55z7CQBjzAFjTJcxphv4NVZ76289411zxD6jyTDGdGFNYVcmKzuopKI2cGSmof9HjMk557synEkSrHRe6Wy/AnzY2RZgvrP9EHCL5/gFzt+f4qjTwMX0mEoC2FH4VHomnY5NUO9z6GsaGu+2Fzv6uSuT2+uU2Q7MdsqsBXbQd1LZq3b/DPhknDZVACFn+1LgMee8W4BTnf1Zbj0StPVxeiYcP88Rqt3ONZ/FMVdEtbcR+CI9k8V3xDh+RLQXq3W+h50oLna2S2LUo8i5v1ek8s56fotkz0ev3xn4OfAJrGloJzDD2X8/8GXPtmsaOgl4IUEdxnu2bwQe7k89R9I9S/UZTfAbi+d3FeA/gf9M9b4OxieVh+mPWI+MDmA38OkYZbbT0zHOwtoDVwJnRpWbD7ztfLcSuNjZPxV4ynkA1tNjDy8D/uScbz1wr7O/FHgGO2n2a2zH5l7//cAm7Kj36wna9SJQBbQ47brQ2X/AaavBetr830xvL1ZYtTmf/XHuofclO925/jvA9KhyF3ra+yaw0Nm/APiX06Z1wGeStHUq1h69Brgz6vox701UPT7m3KeVno8rOD/lHNvhnD/WPM9Iau+nsJ3UFuJ3bt/APo/e9o5N4f1N+HwQRxA42+c5v8karH0/6Oy/nx5BkAU857T3xhjnf8A5fjW2Yx3fn3qOwHuW9BmNVw6rhbzsnHct8CAeL6Lh+OjKYkVRlFGOrixWFEUZ5aggUBRFGeWoIFAURRnlqCBQFEUZ5aggUBRFGeWoIFAURRnlqCBQFA/iCfE9kDKKkkmoIFAURRnlqCBQMh4RmSI2hPL9IrJJbCjl80XkZbHJXhaJSImIPCY2EchrInK8c2y8EN+IyMdE5A0ndsx/OwHCUqnLuyLya+ecz4hIjvPdZ8SGF18lNtx4rrP/fhH5pVOvbWJDQ9/nnOd+z7mXiA2L/LaI/EWGO5mJctSggkA5WpiBjWw5x/lcgw2nfDPwH8DtwDvGmOOd/3/vHPdt4CVjzLHYgG6TAETkGGxEyNONMQuwwdg+mmJdZgL3OOesoyeg2N+MMScbY+Zjw2Z/2nNMMXAqNh7P49gQ6McCx4nNxlaGDTNxvjHmRGxI8ZtSrI+iJGSow1ArymDxnjFmDYCIrAOeNcYYEVmDDSI4GadDNsY852gCBcQP8X0eNqDam0544hxs8ptU67LS2X7LuT7APLEJToqwSVae9hzzv576HohqyxRsRM65wMtOfbKxcXAUZcCoIFCOFryhlLs9/3djn/OOIzyfAP9jjPnaAOvShRUiYIO1fdAYs0pEPoEN+BZ9jLfu7v8B5zzLjDG9sq0pSjpQ05AyWngRx7QjIucA1camC/wX1oyEiFyMNdGADXF9lYiMdb4rEZHJA6xDGJsMJYvUzUwurwGni8gMpz55IjJrgPVRFEA1AmX0cBtwn4isBpqBa539twN/dEwwr2Bj8GOMWS8i38BmlfNhNYobsCHA+8s3gdexYYlfxwqGlDDGVDlaxB+lJ1fxN7ChmxVlQGgYakVRlFGOmoYURVFGOWoaUpR+ICKl2HmEaM4zxhwa6vooykBQ05CiKMooR01DiqIooxwVBIqiKKMcFQSKoiijHBUEiqIooxwVBIqiKKOc/w97qOTOIuYzxgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dfp.sort_values((\"loss\",\"valid\"))[\"loss\"].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### There is corellation Loss on valid & test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xy=dfp.sort_values((\"loss\",\"valid\"))\n",
+    "ax = plt.plot(xy[\"loss\"]['valid'], xy[\"loss\"]['test'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/cKQfBgMj1ucvmfrUpOgX09Z4RUcT9pTA3tGKKNm0I31rzcCBnpGinvvxXafw293B+/cI0W+NRfLaO3ak79hvODH+GvTCSjYnfkbuz144hhvueRpnR5IF33csaX6uWgWLfmeXOf10JFH6JL6kPNSk6BfY4BKAKbinBhNFfbmdpIpcnBVSCEvbGwEUn8xN6ZyRnBW88p8fx4+ezb48F313WgPYOyLSF4vHzo4kcf6nHxn3hLCCIn1xnwl0S471x2EwPN/XfMSrwN7Z2tUHQEb6lURNin5ILS7SB8afzE3aidzCPP1ISMHclnrUh9WiRV8zjCxxiqd0nBiI4xM/3ZG1/8BoEgoBzXXhvOI76qpE6bF6Ff1i2/GijlVQzGD0ifT0z46Yr7MY0bPtnQqt3knphm1tjSal6FcKNSf6msGBpma5cQ5UGQ8prfCSzZTOCKsKFIWwuK34Ch5NZwyMafjx1iP4i//aCgAYjJsngaa67Hk6/WMpNNeHoSqUt6xw1FXPr5TMXy+iDcO4nzM4vcPmld9oESM1RaSvTVYL03Hy8vFBjKV0REOKvZ5DMvWpOdE3DC5IcAVtTVHMbIyMO9IXrZULOe8krEgfAJa2N3pG+meGE3mjraRuYGAsiW3dA3hibw8MgzE4Zt6nuS6ctX/faAot9WEoRHm9dbenX6qou5AgWJkET7/XsvtGioh0bdGv0Eh/q+XnX7x4RlEnPcnkUHOir1l1+sWwYvb42zEIe6cQXUrpBiKWJbWkrRHH+seyBP6t9z6Drz22L+fjaDojpTMGx1LQDUb/WCp3pD+axLSGCFQlv3iL40mLfmlC/eJ674zvOQtB5HjGY+9UaiK3s6sP57Q2YPHMmIz0K4iaEf2/ue8F3LNlPwwej+g3Yc/JoXGtoBT2TiERcNIV6QPAQVcFT89wAsfzNGMTVxnCkugdTtgef2M0W/QHxtKRfr7XLKo33IPnx1vCWcyVwkSW6Yvy2dEiqlfs6p0KjPSZGZ2Hz2LDgumIRVWMlKBSSzIxBBJ9IrqKiPYQ0X4iuj3HfjcSERPRBse2NUT0ByLaSUQ7iKiuFAdeCPGUjod2nMDzh/ugjyfS72hGQjPQ1VtcySSQFt5Cvh4p3UDYqusXou/29TWdc0ZbzGzXg4tVsz3DCQyO5Yr0U2hpCIMC2DtjLntHKZm9M3VX5Gq6YVc4jcveqUBPv6t3FGeGk9iwsBWxaAgGp7vBSopj36khPLWv/B1X84o+EakA7gZwNYBVAN5KRKs89msC8BEAzzq2hQD8EMAHmPk8AJsAFF7bNk52nRhESmeMJPSiSzaB9ECV8SRzU7a9U1ykv2BGg2cPnqRuYDjuLzzOSF340L3DSQxa92ny8PQH4yk014UD2TtC9ET1oRhSXoz+Zrw3Bdy/Lmy+R8WUTxZD32jKfn3jSuRWYKQv6vNfsXA6YhEzYJAWT3GMJjV88Ve7cfW//Q6f/cXOss/MCBLpXwRgPzMfZOYkgPsAXO+x3+cAfAmAs+PWGwBsZ+ZtAMDMvcw84Rmf7VZfkNGUXvTiLMCMslWFxpXMTVr2TiFimNQNhC1PPxpSsWBGZgWPGcUbOb90TmHps2bunskT6SdSBurCChSiAJ6+Ze+49itO9NM/v+3bzwYWkzkt9QDSglRunGs2hKev6QY++sC2QKt0xypa9PvQ0hDGkrZGxCxrsBiLq5ZhZvz6pRO44qtP4JtPHMCbL5yL+963EUqxUWlAgqjfXABHHb93W9tsiGgdgPnM/JDrvucCYCJ6hIieJ6K/H9fRFsm2bvMLOJbUzJLNIt/UurCKxTNj2FWCSN9PROMpHS8c6cvYltTSiVzATOY6I33dYDDnjrRSDgtBPHXvcNIW/bDH2oWkbl5hEBEMzn11MuJanCVeXzE+r/vEcSbPfOIXjvQhoaUF59lDZyfEX+4dSR+XOOmdGkrgwee68cEfPoe+PAv5xirY3tlq+fmKQohFVAAy0i+Ew70jePd/bcUHfvg8muvDeOADr8SXb16LGY3Rsj/3uBO5RKQAuAvAbR43hwC8GsDbrf/fTESv83iM9xFRJxF19vT0jPeQsrAj/aQOg4sr2RSYA1WKj/RtT99Hk37+4jHc8I3f4+jZUXubEF/BkvYYDp0ZscVCePU5I32PCpHekYRdveMWSU03oBuMiKqma+59jlk32PZzRdAqdi0miHWfEHNFwmdHkrjhG7/HL7adsLedGIjjiOP9K5b/eHwfnnedgJ2IhDiQPumJ3MaJgTg+9uD2nCefeKoyE7m9wwkc7BnBhoWtAJCO9OUCrbzEUzq+9thevP5fn8TWQ2fxqWtW4pd//Wq8wnovJ4Igon8MwHzH7/OsbYImAOcD2EJEXQA2AthsJXO7ATzJzGeYeRTAwwDWuZ+Ame9l5g3MvKGtra24V+LDUDxlWyGjSX1ckT5g+vrdfWO2WBYCM+OYNSXJ73sufOJnDvba25wlmwCwtK0RKZ1xtM+s1hFloMNx/woKr0EdPUNJu07ffS/xmJGQYncl9bs6cX7ZRZRuR/pF1Gy6DzWXtTSW0sGcXhkrcL5/xfLVR/fihnt+73u7sHemN4TtSF/49BfMb8Fju07lPPlUaiK387B5ItywYDoAIBaVkX4Qtuw5jSu/9iS+9tg+vGHVLDx+2ya859LFRXUIGA9Bnm0rgGVEtIiIIgBuAbBZ3MjMA8w8k5kXMvNCAM8AuI6ZOwE8AmA1ETVYSd3LALxc8leRgx3HBsAMLJ/VhNGkVvTiLIFI5u4tol7/t7tPY5vl9fqJoRCCrQ5fOqkZGfaLuwePsG40R8TtJl+k7xZW0eQtGlLsk6TficqZxBT2jni4kkT6OerYdfsqxxT/pmgIMxsjeOZg+X393uEEiIC50+vtRLawbC5f0Q4A2HfKf/W0uCqotEi/s+ssIiEFq+dNA5CO9GXTNW+O94/hgz98Du/67laoRPjhX16Mr79tHWZPm/BCRgCm/ZITZtaI6FaYAq4C+A4z7ySiOwF0MvPmHPftI6K7YJ44GMDDHr5/WRHWzsWLW7Hn1BBSulFwP30nK6yBKrtODtmXt0H5+v/tx6KZMVPYfb7n4pJfNLICTPvGae8sbjNF/9CZYQCzMvz6obiGurCa9bhewnlmOGFX9biDaSH6kZCCuJbdQdOJc2GS28svxlt3P0+uNQLiymIkoZnWnUq4ePEMPHOwF8xc0LCcQukdSWJ6QwRN0bCdxBRCvnquKYj7e4ZxBWZ53l+8r5W2OKvzcB/WzpuGaMj8OxPVO8WUrVYzKd3Ad546hH97fB8MZnzsyuV4z6WL7Pdtssgr+gDAzA/DtGac2+7w2XeT6/cfwizbnBS2d/fjnNYGzLUqO0aSmudCpKDMmVaHproQdp8o3Nc/PZjAK5fMwB8Pnc2ZyAWAQ2dGcHoojvamuoySTcCMZoF0hC0WfAHmJXZbU3YyyMve6R1O+lo3Ca0Qeycd4dknEev3oiJ916FqObpQiucbTWpgNhPSGxfPwEPbT+DI2VEsmBEr/AAQ7GR1diSJ1lgEsaiKEwOmbSc+v/bmKNqaojmb46Uj/cqxd8aSOl46NoD3XLrY3paO9KXoC5492It//PlL2HtqGFesnIVP/8kqzG9tmOzDAhBQ9CuZbUcHcOE5LWiwKgyG4hpa6iNFPx4RYWWR7RhEDx2F/MvPncPFtx7qwzVrOjJKNgFAUQhEaSsl6Yj0/Wr1vaLJ0aSOU4OmFz7mqjMXjxkNKY5FT97H7Pyyj7oWabnFUzcYZL0GPwqJ9MW+wwkdYZUQCSnYuMi8Anv24NlxiH7+fXot0W+IhOzXLeyd+rCKpW3efZIA87MTJ9ZKsne2dfcjpTNesXC6vU14+rL/jtld9p8f3oWfvHAM86bX49vv2IArVnlf6U0WVd2G4cxwAsf6x7B2XgvqrUvQobg27jrYlR1N2H1iMMNWCUJCMzsSihJIL8ZSut1CWfj6Sc1ANJT5UalEtrXhjBT9kmkph4gD6T41QqRGXF/YhGUzRVTF3tdPfEcdJ6pPb94JwN/Tf8XnH8PlX93i+TgCd8lmLlEUxzSS0EwbzJpyNiMWGVcyN0j7iIRmoC6s2m0IgPRJuz6iYml7Iw6cHva8anDmXirJ3hFrINafk7Y2I6qCkEI1ncjVDcb3/9CFy7+6Bb/Yfhy3vnYpHv3by6ac4ANVLvrbrfr8NfOm2bXEQ/HUuBK5APCqpTMxktQzkq1BEJE+kb99EE/paIyGsG5BC549ZD6+sw2DQFXIFkO3veOF2HemVQc801UP7C63syP9sJK3e6XXohy/SP/sSBJdvbnLKd2Rfq4VihmibyW8iQgbHb5+MQRp5aAbBlRCZqRv/V8XMkV/KKHhtMc6gzHHibLQ4GEy2drVh3NnNWJaQ3oFNxEhFg3Z7bVrjReP9uP6u5/CHT/fibXzWvDrv3kNPnrlctRHJte796OqRX/b0QEoBJw/d5r9AaT04vrpO7l02UxEQgoe3xV89CAzWxG7CoK/fTCWMlAXUfGKha3YfXIQA2OpLE8fMEXf095JeJeSirLAGY2mtdXhqhxwV17YiVxVtXsV+do7Hgk88fqK0dxsTz+/vTOS1MyTY8g81o2LW3F8II6jZ3M3oQt6DF7ohjkMJxZRMZI0y2XHrCskEekD3pPOnKI/ngZ+E4luMJ4/3OdZwBCLqBiuseqd/tEkPv6THXjzPU+jZyiBr7/tQvzgLy/CEqvQYqpS3aLf3Y9l7U2IRUNoiKTTF+ON9BsiIbxqyQw8vutU4EjS7ZHnKtmsCym4aFErmM3Lac3grBWzGZF+Dk//0ZdP4dRg3F7AJSL8Wc2Zop8V6TsSufmap3lFeGl7J32foOJWUPWOHenrGbmPjYtnACi+Xj9IpG8YDFUBGqIhMJtCLsQ8GlJyir6wgYgqx9Pfe2oIQwktw88XxKKhmlqc1TOUwBV3PYn7O4/iLy9ZhMdv24Rr18wpa7VYqaha0WdmbO8ewBqrlrjBcalVbJdNJ69b0Y6u3lEcPBOs46az7t1MwnrvF0/pqI+ouHD+dIRVwtP7TdHKFelnlGw6BPjkQBzv/X4nfrz1qO37z4hlR/qzm+uyI31dt59X/CH72SxOT18gTmrOuwRthOYW+UCRfkLLWM9g+/qHihP9IJ6+brXpFtbhSEI3P7+wCiJCe1MUTdGQd6Rv2UCNkVDF2DvCz9+wwCPSj4ZqytP//YEzODOcwPfefRE+de2qcVUETjRVK/rdfWM4O5LEmvktAEov+pevNBM0j+86FWj/hFZIpK+iPqJi9dxpeHq/2Wo14or0QwEi/Sf2mvZTUjPsZKHo7RGLhuzSz46WuqwozZnIzWfveHv65v/O1xl0qLxbb3MNDhevy0zkGo5ENeHixa149mBxfXg4gA4b1uhNcRU5mtQwltRtK5GIsMRn0pmI9BvrQhVj72zt6sPs5jrMm16fdZszmV0LbDs6gLqwgo2LJ659QqmoWtEXi7LW2pF++kxcCtGf21KPlR3NeCygr++0SwB/AR1LpUXjFYvMBWXO+wmcnS+TPoncLXvMPkaawba9NNPy9MMKZfj77uqdzEQurGP2WZyV1LLeUyG0ztcZVPTdUXauQNhenJXUkXCtXN64eAaO9Y+hu69wX7+gSD+ajvTHrEhfsLS9Efs9ZhqLRXiN0ZBtvU11OrvOYsPC6Z4WRsyRzK4Ftnf347w50ya8hUIpqLwj9mEkoeEHf+iym6Ft6+5HRFXsgebOTPp4E7mCK1a247nDfegfzS9m6UhfzTlzNm61MwaAixwJMy9PX/eK9C3RT+mGPZBBN9KRvvD0w6pi/zy7uR5Jzch4HHtxlpq2dz71s5c8j3k0oWe1ZhYvzxllBxX97IZr/qrvvGlgLJVR5SR8/d8fKHwwRRBPX9NN0c+I9FO6/fkBpuj3DCWyrC3h/TfVhXJeyUwVjvWP4fhA3O6346aW7B1NN/DS8bR1XGlUjeindAP/+POd+L3lgW872o+Vc5rtCNlp74w3kSu4fEU7dIPxxN78nUGdkX7Oks2kbrdR2LCg1a6Rd9s7Xonc+rBq2zsvHOm3/X3dSAuniO5DqmL/PHuaKf5Om8aZgxArcn+72/uqxmuVc7oNQ3rb7/b5v09vuWOpAAAgAElEQVT9o0nssK7O3LmDIG0YzMdIZZwcl7U3Yn5rPTZvO+57fz+CDLIwmKGSI9JP6og77B3AbI4HZCdzx2x7J1wRdfq2n+/TeiQWVWsm0t93ehjxlIG181om+1CKompEv7kujJBCdj+Zl44N2NYOYEa2QjhLNaRg7bwWzGyMBrJ4RL/3tKfvTVxLi/60hjCWzzIbvIUDJHJbYxE72npi72moCiEaUqAbhm0hzG6uQ2ssgvmt9WhvqkNzXQjN1tQsZ+mlsw1DvpkzY0ndQ/TN/50R838/e8R+TDff+t1BvP3bz1j3ybwtZyLXyLS2nCdHIsJN6+bj9wd60d1XWKvlIDa7GL1pR/oJzdPeAYADLtEXnn5TNOTZImOq0dnVh8ZoyG446CYWqZ1I37n+pxKpGtFXLI/6zHACB3uGMZLUscZ1JhYRWKkifUUhXL6iDVv2nM5bgeGO9L3sA003xdkpGhdbLQVyR/rm/9NjYfuLt2VPD9ad04LGaAg6s12nH4uG8MdPvA7XrO7A+y9bjHvevh4NHv3Qky47KhcjSS3D3klqRlZffSdej3aiP25Hv8WUbArcJ5Qb15vzfv73uWMohCCevsFsDRERDcd0y95Jf37zWxsQCSlZvn48w96Z+pH+1q6zuPCcFl8POxYNZVmE1cq27gE01YWwsMgWH5NN1Yg+YPrVZ4aT9qi6ta4zsSitK0UiV/C6lbMwFNfyrs51evp+g8bj1j5OT/gVluh7tWEQ4ii+aNMbIhiOazg9FMfO44PYtLzd9v5TlrCEVELI8unnTW/Aq5fNzCg5FGSepHK/X6NJ3W66BZiRv5e9I/ASht6RpL2vWwR7hhJ2FZMbtzi7cx/zpjfgVUtm4MHnjxY0ezTXvqNJDUfPjpqRPhEa7N4zVvVOOLNSbPHMWLa9I0o2rUTuREz6KpaBsRT2nBrKOeijlkYmbu/ux5p508o+1rBcVJ3o9w4nsL17AI3RkN2CWFBfBtF/9dKZiKgKfpvH4hH2TiSkgOAd6ccdzboEr1sxCx/ctMQWf4GqkO0FO0V/KKHhyb2mQF52bptZ2qmnI/2wh1fT4NEaN6nrUBUy/zlE30ucRhKaHe0CwGgq92W+wdnC3juScLRuyNz/B88cxju/80fPiNgtzl5jH29ePx9Hz47ZbS2CkCuR+80tB3DDN34PzRB1+ul+8vGUnrX83qtsc8xRsmk+X+ZzJDVjwga85+P5I31gBjZ4LMoS2CMTq3yBVjylY/eJoSwXoZKoKtE37Z0ktnf34/y5zVniLsStlKIfi4bwyiUz8LhPklOQMZTE5+lF9Bd1iH59RMU/XLUiyzNXFWekb/7fGjMj/S17TmNmYxSrOpqhKGTZO+lIP/s1WJGqK9K3cyCOu3j566NJPSNRPpbU887LEieqrzyyBy8dG0DvcNIWPnf0PjiWMstOPQbEuI9HtGFwcuV5s9EUDeGB545m3eZHLsvlwJkR9I8mrRW5ZA2PT1fv1LvmGSxta8TRvtGMDqrxlNlaQ9hR7qufr/xmD6781ycz7jNZdHadhaoQLpjvL3TpSD+36Kd0A393/4v4jNWYr9LYdWIQmsFZLkIlUVWi39YYRc9QArtODHlm1hvKEOkDZunmoTMj9lhGLxIZK3LJM5IUVwNu0fDCWbIpxLClIYyxlI4n9/bgsnPboCiEkG3vWJG+RyTsFeknHP1+nPaO12Qut72Tq4rj0mUzzWPWDSQ1A1//v/24/u6n0WuVczKzb29/L9F37+vOfQDmifPatR341Y6TgZONuZygkwNmWwtRp09kRvsjCR1jST1riM3S9kYwAwd70qu3RbsNkV9yn7wO947g5GAcD3QGP1GVi61dfTh/TnPGWhc3QUYmGgbjYw9sw0+eP4Yte4L3rZpKiPU/MtKfIsxsjJpiohueH4ot+iXujxFkdW7C1cvGyz0YSwpPP5joO0s2wyqhyarCGYxr2LTcnDWsWPvZkb7HCa/RNZQFyGzn7Ix6vYR3NKllWBpjPtHp7OY6vN5qNZvSDLuMVHdE8cz+fro4KTrJSuT6JBpvWj8fYykdD20PVr6Zy945aQ1McTbva4iqGE1qiGtGlr1j9+BxBAVi5W7Istt0V9lm34hp7fznkwcndYZuUjOw7Wh/3ilxsUj235ATZsYnf/YSfvbiccyZVofTQ4kpncfwY1t3P2Y2RrMaFlYS1SX6TenhKF7lVCJSKXUCZm5LPVbMbspZupmRyIV3pB8vJNJ3JXLDqmK3VVAoHVGHrNJOIRxeVzkN9orSzOodEel7VfU4f0/pbHu6gEjkZh+zQukrjZTOGS2hBQazb5TtdZXhFn13aatg3TktWNwWwwOd3d4P7nEcntsNxqnBuP3c4hwTi4QwFDf7/7g/v0UzY1Aos1Y/rpk2kLDb3GWbfaNJTG8Io7tvDL/cfiLQMZeDl44PIKEZnk3WnIgrPa9In5nx+Yd24X/+eAQf2rQE73zVQowm9Yos8dx2tB9r502riMZqflSX6FsrTGfEIp79QUpdsunkipWzcq7OzV6clb2P3Ys9nP9jUTISueawd5EUvGB+C1oazBOgqijQrOqdsEqef6wN4ezJRwk9LfpOX9kdbYsTgvPS3y/aIyKH6Bue9ekMfz896Yp4v/XkQRx29eb3sq/Ec9+8fj46D/fhYA4bTuB3DGdGEhlWjOqI9HtHzL75btGvC6uY39qQUasvbCAR6bsXaPWNpnDlebNx7qxGfGPLgYIqj0qJPTTFo8mak5hH2a/ga4/tw7efOoR3vWohPnblcrQ3m99TrzkDU5mheAoHz4xUtLUDVJnoz4iZf0xrfM7E5SjZFLxuZe7Vuc7FWX6iL8Q1iL0TciRyk5ZAiy/epuXt9n6qYgqYphu2wGQ9lqogGlIyq3cciVynXeOO9EXPHuHpmvt7R3CqQnabhKTuXdNtsH/5omgCBwCD8RQ+//CurNW2EY9EteCGdXOhEPDgc/mjfT/nQVg7AnHV2BAJ4cywecKv8xie4R6dKOr5RaTvbDXBzOgfNUcxfnDTEuw5NeS7GrrcbO3qw6KZMc+5y07Snn7mCf/eJw/g3x7fh5vXz8Md164CEWFWk2mNiCumSmHHsQEwA2vnV24SFwgo+kR0FRHtIaL9RHR7jv1uJCImog2u7ecQ0TARfXS8B5wLEUGs9akyqC9D9Y7AXJ0b8bV40kNJFBC8u2yOFSD6Tk9fs+yd5bOasHxWE65d05Hej6xErs6elTuCWDSUYe8kNMOuIsq4AnCJ/phPpO/1+hRKe+4p3fBsP8DsvzDKGenHPWbSAv6RPmDOELjs3Db85PljeRdE+d3uFn1x1RiLqOgd9o70AdPXP3RmxLbZEinTBrITuY73YiihQTMY0xsiuHbNHMxtqcc9W/ZPuAfOzOjsOov1Pv12nKTLVtN/Q88d7sMXHt6Na9Z04Is3rrFPkOJ72jNFIn1mxuHe/C3SqyGJCwQQfSJSAdwN4GoAqwC8lYhWeezXBOAjAJ71eJi7APxqfIean5mNUdz9tnV416sWet5erkQuYEZ8r13e7rs6N2FFzopCUBTv6pCEY+pSPjLbMJhDVmZPq8Mjf/uajPUJZLV80AwjpyA2RFRXyaaOqLX/G1enTyJu0RcLuhoiKs6faza3cw9ZFyiKw97ROMuuAfJ4+o4rDiH2Qvyb603R8Wrx4OTmDfNxcjCOp3wWezmPw4uTgz6RfjSEvlEz+eplzy1pb0RSN3Dk7Kh9/HVhxV7h6rSM+qxKpumxCMKqgvdfthjPH+nHHwtYZ1AKDvSMoG80ldfPB2DNEMgs2RStL/72inMzAq12a4DP6cGpIfrf+t1BXPblLdhzcijnftu7+zG/tR6tsUjO/aY6QSL9iwDsZ+aDzJwEcB+A6z32+xyALwHI+FYQ0ZsAHAIwIYW516zpsP1sN+Uq2RSI1bmdXX1ZtzkTowTyjNrsSD+PcAGZg9GTHjN0BaK5m2b5/n7EIqFse8c6jnNnNeFH770YgGmr/NfTh+wTzogj0v/phy4xX4ef6BPZiVY/eydX9Y7zJCHeKzHARfQPynViA0wbrqUhnLcU0k/0T7gifRFAOBPZfpE+kE7mihba6ZLN9GsTJ4/p1hzam9fPx4xYBN944kDOYy41zx3O3WTNiaIQGsLeIxPdf3ZN0RDqwsqUsHf2nRrCV36zF4CZpM3FtqMDFR/lA8FEfy4A5zek29pmQ0TrAMxn5odc2xsB/AOAz+Z6AiJ6HxF1ElFnT0/+jpXFUo7FWU4uXWauzvUq3UxoumPAh3ekb6/IDRDpZyRyNf8oXszjFVcDfjS4uiQm9cy5vNGQeUxf+tVufOYXL+Phl8yKEnF1EIuqCKsKwiqZQuxbvWNVq/jYO4ZHnb59TI6rDNGPXtgw0+qDiX40pOJNF8zFb14+hYFR/xWvflcbp9yi7/D0BTlF30oix4Wn72Hv9FnFACJ4qY+o+ItXL8KWPT3YeXwg5+srJVu7+tAai2DxzGA9ZtwjE8Xn6M6vmVPF6iY9kavpBj76wDbEIirqwypePjHou2/vcALH+scqelGWYNyJXCJSYNo3t3nc/BkA/8rMOcslmPleZt7AzBva2trGe0i+lDvSj0VD2OizOvfMUBLTrMhNWC5u0pF+YYlcUbLphZjSpRlGbk8/Esou2VSdom/+LHzYIauF84jL068Pqzkjfaen723v+PvpTmvJ/Ryi4VvEY0Wum5vWz0NSM7B5m38TNucxONshuCN9UafvTGR7JXKb68Job4rakb4YqxjOYe84bYQ/27gAjdEQvvnEwbyvr1R0dp3FhgXeQ1O8cPfUF+dur6/brObopEf6//nkQWzrHsDn3nQ+Vs1pzin61eLnA8FE/xiA+Y7f51nbBE0AzgewhYi6AGwEsNlK5l4M4F+s7X8D4BNEdGsJjrsoyi36gP/q3K7eETtiUnz66Yul+UHWESjORK5VjumFmMeb0o2c9k5DRM1K2EYd3rS7XYAQxTFX9U5DJJTb3nGWbHrU3YP9o+zMSD/zOZoDRvoAcP7caVjZ0YwHclTxOK821n72N/bPJwfjGSdDcSLNF+kDZrQvyjZFyaZqR/r+9g5gXsm8feM5eGj7cXQFnMs8Hk4PxdHVO5qzyZobd0998RaSR1/V9qa6SU3k7j45iK89thfXrO7AtWvmYGVHE3adGPRNlm/r7geR+bdT6QQR/a0AlhHRIiKKALgFwGZxIzMPMPNMZl7IzAsBPAPgOmbuZOZLHdu/BuALzPz10r+MYNj2ThkXVly+wiyXdFo8hsHo6h2xW7EKy8WNWJofhJAjkZvMae+YkX4+eycW9fD0PSJ9caKxB5JbX/KGsBXpR1SMprx77yhKWpSTGnv28clZsulYI5Al+pan77ci181N6+dhe/eAb/LOq8U9M+PkQBxzHWtAlICePmCJfs8ImNlcuZtRspkZ6SuUfk2Cv7xkEUKqgnt/V/5o/zkrL7U+QBJX4O6pn7Z3svdtb45Omr2Tsmyd5row7rz+PADAqo5pGIprvqM1t3cPYGlbY0UNQPcj7zeEmTUAtwJ4BMAuAPcz804iupOIriv3AZaScnTZdDNvegNWzG7C447SzVNDccRTBhbakb53yaZXh0Y/nInclMt/z8BaE6Dpue2d7OqdzMcUPwvvWUT6olpDHLdp7/jU6RPZ9ovb3hGiaTD7l2xq2YlcgbB3/FbkunnTBXMQUgj/88cjnrd7HcPgmNlQzbnwT3VU7wj8PsOl7Y0YTmg4enYMusEZbRjcnn5LQyTriq+9uQ43rZ+HBzu7cbrM1sjWrj5EQwrOnxM8snV7+uIVeYp+Ux2GE9qkDFO/5/8O4KVjg/inN52PGdaCzpUd5nAYL4uHma12ypVv7QABPX1mfpiZz2XmJcz8eWvbHcy82WPfTczc6bH9M8z8lfEfcvFMhL0DmBUinYf77EThIavR1iJL9IXl4sY9gCMXikJ2v5ZcUbxCsEo2i4j0naJve8/mgYvAdCSpI6IqGWMpR33aMLhX5DqFTiQtGf72TsIjkSsQq5GDRvozGqN404Vz8f0/dHkuqPMa5HJi0IwC57Y4RN+O9NOi7/cZitGJL1nJ2GhI8WzD0D+aQktDOPsBALz/NYuhGQb+39OHcr6+8fLc4bO4YH5L3hJYJ+ZaD8fJWNg7Hqo/a5JW5e48PoD/+O0+XLd2Dq52lCKvmN0Mhcwumm6OD8RxZjhZ8YuyBFW1Ijcfi2bGcM3qjkCLTcbD61bOgm4wtuw1o/1D1sKPhbboeydyTXsnmOiHlMxI38+vF+WhKd3w7KUviEVCiKcMO4JP6IZdsQOk2z0LQRbW0mhSs3v3AKb37NcHPrP3TmbJpkhaGsw5Gq55R/qqQvZldxBPX/DZ687DubOacOuPns9qzeA+hnhKtwe7z2pON9tK1+kHs3cAc2UnYF4RhD0arp0dSaLVp+x4wYwYrlkzB//9zJGy9dsfTWp46fhgQX4+YF6tedk7Xn+a7ZOwKjepGbjt/m2YHovgs9edl3FbfUTFwpkxvHw8W/S3HxXjEWso0q8W6sIq7n77OsxvbSjr81zgWp3bdWYE0ZCCDkssTE/fO5HrVfnhheJorZzSDV9bg2x7J9+K3PT0J2b2jfQF4gs9mtQzotzpsYhdfeJGdSzOSursKfrM/jXyfolclQjTLZF0VtHkIxYN4Vvv2ICwquA93+/EYDwtou7zTjyl21duMx0tCZwN1wR+SfW2piia6kJ4SYi+M5GbUaef9F1rAgAfvGwJhhMafvCHrryvsRhePNIP3eCcQ1O8iEVDGYuzbHvHK5E7CZH+13+7D7tPDuELb16N6R4LrFZ1NGPXyWzR39Y9gLBKtgVU6dSU6E8UYnXuE9bq3ENnRrFwRsyOChWfcYljBSZy06LPvraGGMKeMth3ving6Kmf0G2v3Tmi0WzWlt5fZ0ek7zhRtcYiODua9MxZkLNkU0sPaxf3AyxPP0CkH3dF+leeNxv//Z6LMW96YSf0+a0NuOft63CkdxQf/p8X7Od2H0NcM9BvRdZtjU7RN1+PWCcgXqcXRISl7Y226NeFVfsE4Uzk9o+m0BrztncAYNWcZmxa3obvPt3lWyk1HrZ29YEIWFfgFXEsomIkqdtXSbkSuaL/TrlzE4Id3QO4e8sB3LBurt3e283KjmYcPTuWcfIHzJW4K2Y3Z1z5VjJS9MvE61a2Y9BandvVO4KFM9Ni5DcYPVFAIleh7H76Xojn0nQD4VwrckV75aSW0Sco/TiU8bu9IjeROTVrekME8ZThKUYqkT3Zym3vzLb6kzP7NztL+tTpqwohElJwydKZvq8vFxsXz8Bnrz8PW/b04F9+vds6jmx7p9+K9NscLbyFp+/nwbtZ2tZol2Sa1TuZiVxmxtnRpH3l4seHNi1F70gS95dhyErn4bNYPqspq3ooH6Lhn7De7JJNjz+75voQIiFlQiL9hKbjtgdexMzGCD597Xm++62aY7YR2X0iXdFlGIwd3QOerdorFSn6ZeLSZW2IqAp+8/JJHOkdtf18IB19uxkrwNPP7L2Tu69OEHtHRPqjCT2jDbQT5+9iHzPST1sbIkLt82gx7SzZNBO55mN88YbVdpI7Z/WOnt17B/D2jAvl7RcvwJ9vXID/fPIgfvJ8d9YxJFIG+seSiIQUNEbTYijedmeknwvh6wPIWJErToBjKfP9z2XvAMArFk7H+gXTce+TBz3bWRSLpht4/nBfwX4+kBZ9UZGTy94hIsxqjk5IpP9vj+3D3lPD+OINa+wFkl6s6jBF/2XHqudDvSMYSmiek/gqFSn6ZUKszv3f57qR1A0smuFYyu4T6cdT2VOX/MhM5Oaq3hH2jpHT3hElkyNJLWO0oxPn5W3CFn09w0cXEWqvh6+vENkil9QZSSu6vXp1hy0Lpwbjvp5+/2jKnlfgrN4pVTXWHX+yChsXt+L2n+zAC0cy+7DENdPTb6kPZ5w8RZ1+0KqrTNFPV+8IO+msvRo390mEiPChTUtwrH8Mv9gWbBpYEHafHMJIUi/YzwecV4si0ve3dwAzmXuqzE3XXjzaj28+cQBv2TAPr13RnnPf9qYoZsQi2OWI9Ld3W0ncKqncAaTol5UrLIsHSJdrAqZQeFWoiM6LQchK5AZouJbL3mlwDMHwi/SdJwGn6GdG+qboeyVzFSLbJnLaOxFVscXzxm/8wbZR3Pxy+wlccOejALI9/VIQVhXc8/b1aG+K4v89lVkSKeydloZwRhVUoc/tFP36SDqRm7I+S/Ha80X6APDa5e1YPqsJ33yidENW/s9qIVJUpO9qr5xuw+D9HrU3RXF6qHyRfjyl47b7X8Ts5jp86tqsxsBZEBFWdmS2Y9h2dAD1YdUut60GpOiXkcsdkYVT9OdMq8PxgXjWZXm8gDr9UJbo5264Zi7OChDpOxK5uewdsTp2JKFlRvqW6J8dyRZuoY9hlcwZudbzhFSz3bQgyPL8THundOsuWmMRfPudGzLyFEDa3mmpj2RG+gWK/rzpDfb7WB9Ol2yK90LYYkHa9yoK4YOblmDvqWHPfk+Fcv/Wo7jrsb14zbltmNOSPXkuH+6RiXak77P/rObyNl2769G9ONAzgi/dtCZwfmLVnGbsOTVkfx7buvtx/tzmnN+dSqN6XskURKzOjUXUjMlDq+Y0I6kZWf15ChF9hQiG1YY4v72THpfoh2ekr+aI9FPpSL8+7Ij0G4ToZ3+ZhTiHQ4q1ItcUhZBCGb5vvvpzZi5LpC9YMbsZ/37LhRnbRKQ/rSHT3nGuj3jrRefgvZcuyvnYqkJ2Dybn5Cy3vTM9YGL42jUdmDd9/ENWvv+HLvz9/27Hpcva8J9/tr6ox3CPTDRyJHIBs4R1KK6VpQLpucNn8a3fHcTbLj4Hly4L3sRxZUcTkpqBg2dGkNINvHx8sGrq8wVS9MvMh1+3DB+4bElGGV86YZS+jNR0s4QxyFB0IC10CR8rRpCu0/cflwhkRvq2p+86loxErm6AmTGSzIz0m+vDUAg462HRiKg4rCpI6lZFkTW31ykMfnOGBcMJrWyRvuCKVbPwpxvSfQbjmmXv1LvsHcdz//MNq/HJa/LbCMLicc7IFeWrhdg7gDnq8v2vWYwXjvTj2SKHrHzryYO44+c78fpVs/Ctd6wPnFdyI/6GRE/9dBsG789HLHIrtcUzltTx0Qe2Y860enzijSsLuu9K67u568Qg9pwcQkIzqqpyB5CiX3beuLoDf/26ZRnbFrc1oi6sYKdD9OOW0Ab19IXoi4jXP4o3rwgCV+8kNdu6yRXpM8wTDnNmh0lVIbQ0RHwifdiPq1mevrhCcQp3vkh/MK6VJZHrxnmOtO0dV6RfqL0DAKvnTkNDREVDxBnpm69HRPotAauBAHMa2MzGCL6xpbAhK8yMf398Hz7/8C5cs6YD97x93bhq0e1I323v+CZyzavfUidzv/zIHhw6M4Iv37Sm4AZpS9oaEVEVvHx80G6nXE2VO4AU/UlBVQjLZzdnRPr2AJUCI/24JdB+UbxI5KbyjEuMhMwBKCPJYCWb4HTCzr0KdnpDGH2enr6I9MlK5LKn6PfnEf2B0VRZ7R0BuU5E8ZRZShkaRyIXAN51yUL8+iOvQVhVHCWbItJPorkuVJCHXBdW8e5LFuGJvT32wq98MDO+/Mge3PXoXty4bh7+/ZYLC2ph4UW2p29uz+XpA6WN9J892Ivv/v4Q3vHKBXhVEes2wqqCZbMa8fKJQWzv7se0+jAWzCjvCv6JRor+JHHenGbsPD5gR0PC13RbKn6E7EjfFGi/NgxCk/KNSwTMiH00kfb03SWbGYuzmO3e6c5IHzCTkMMe3ROd9k7KasMgrlCc0eBgPtEfS2UtzioHzocVs3GzIv0irKVoSMU5lpAQkTXkXiRyU54tAvIhhqwEGanIzLjzly/jni0H8LaLz8GXb1pTkvdQ2Dvi70Ksys5VvQOUblbuaFLDxx7cjvnTG/APV60o+nFWdTRj14lBbLMWZQUdIlMpSNGfJFZ1NGMwruFYv9m5UVgqQSN98UUSVkjMx4clmBO2tDxtGACgMRoyI32f6h3npT+zc2qWO9L3Fq10pK/YM3LTkX56Py97J+OkEE8hrun2CaNc8xGcYiUag7XURzJOnqUQy5BjIE5fgNW4XkyrD+PPNi7Ar3acwKEcQ1YMg/HJn72E7z7dhXdfshCff9P5RVlUXoRUBdGQYl8B5kvktjSEEVEVnCpRpP+lX+3GkbOj+PJNa+yrjmJY2dGMM8NJ7D45WHV+PiBFf9I4z1ryLXz9saTw9Auzd0R5o1+JH1HaOshVpw+Y4j2SK9IPZUb6oo2uW/T9jsUu2bSqd5x5Bmc0lfKYnetsaCYifbEKtlSilX286cc9OZCO9MmxyCzf1VMQQo55x6boF9b+QPAXr15oDll50jva13QDH31wG3707BF8aNMS3HHtqpJHsc4W3fnq9IkIbU1R9JQg0v/9/jP43h8O492XLMTFi2eM67FEOwbm6vPzASn6k4bo3y18/XiBkX5Q0VeIbBHPF+k3WJG+X0VQxJXITY9KzIyq/OwJEZFHLE8/6Yj080mP88QyMJpCQjPsEYnlKqHOjPTN91mcaMTJqhSVQyFVsUs2+0aKs3cAc4Xrzevn4X+fO5bVsjilG/jIj1/ET54/httefy7+/qoVZbEtYlHVDga8mu5lHXNzdNyR/nDCtHUWzYzh768s3tYRrJzdbP+8dr4UfUmJqI+oWNzWaEf6IjFZaPVOPtEHwbZrctXpA6ZF5PT0c1bvWOWagEek72NPkMPeSWmmpy+ew9lB09w3875iSAqQTvyJBTfls3fSP4vnFI3VRNlmKewdkdgGird3BO8TQ1YcK4oTmo4P/vB5PLT9BD75xpVZ1WSlxDkyMVfDNcGsprpxe/pfeHgXjg+M4Ss3rym63NTJtIYw5rbUY1ZzNLHUAbUAABguSURBVGN2QrUgRX8SEQkjIB01F2zvDJti5GvvIN3MK0giN2j1DnN6EU4sEjDSt+4uPH2nveNO/LojaGfp3UlX1F02e0fJtpxE/bwq8gkl+Aaplr0TT+kYTepF2zuAOWTl2jVz8N/PHMbAqGmDved7nXhs1yl87vrz8N7XLB7/AefAOTJRFCnkuhoa76zcJ/f24EfPHsF7L12M9QsKbx3hx9suPgd/vnFByR5vKlH5U34rmPPmNGPztuPoG0k66vSDz8gFzEi/LqxkVdAInPZOvvmxsahqrsgNkMjN8PSjbk/fW7SciVy3vTPk6mGuEsE5Xt15YhHWhRD9ckX67ocNKWQnzEXZZknsHUWBZrC9MKtYe0fwgcuWYPO24/jGEwfw4tE+PHvoLP7lpjV4i2OxWbmIRUN2Ij5fySZgVvCY5bDBV6MLBuMp3P6/27GkLYa/e/25RR6xN3/12qUlfbypRKA4hYiuIqI9RLSfiG7Psd+NRMREtMH6/fVE9BwR7bD+v7xUB14NiITRrhODiFuRfuDB6A57Z0Ys6rufmci1RD/HilzAivQdK3Ld9o7b0x+17R1XpJ/H3omEyG64Jo5pOK659s28rzNvIES/ud7cNhGJXCCdxAXSVlmuVc5BCalmyabouzMeewcw/65eu7wN33ziALZ29eFrf3rBhAg+kLYIAWf1Tq5I37RPgvRbcvP5X+7CycE4vnLz2oJPGLVM3r9YIlIB3A3gagCrALyViLLWmhNRE4CPAHjWsfkMgD9h5tUA3gngB6U46GpBtGPYeXzQTuQGnZyVtncSmJ6jDa9p71g9boJ4+taK3EhIyfqyRjOqd9L12O7ks5/V5LR3UrrV+dMaqvKG82YDAC5a1Jrx+gSNjquJiYr03ecSZ898O5FbAntHVO/0jZRG9AHgI1eci7kt9bj7betw/QVzx/14QTGHo4t++v4zcgXpVbmFJXP/b89p/LjzKN5/2RJceE55Z15XG0H+ZC8CsJ+ZDzJzEsB9AK732O9zAL4EwP70mPkFZhbNvncCqCci/7C0xpjRGMXs5jq8fGLQ9vSLifRbc0T6zmg1SPXOaFJHImUg6rGvO5FrNltTswTar6NhRp2+ZlgD3c3HPH/uNHR98Rqss77AWZ6+I5ErFqS5K2lKjfsYnGJc2kSuYkX6wt4p3tMXXDC/BU/ffjmuOn/2uB+rEMTIRMCZyPV/j9KrcoNH+gOjpq1z7qxG/M0V5UtKVytBRH8uAOdMtm5rmw0RrQMwn5kfyvE4NwJ4npknbhJyBSBW5gohK2RyFmAKYGuuxJ/j+5avTl/41QNjKc8Gbu5Errutsv08vquDMz19r+6gTZa4uw/Va7GNOLmUo+EakC1WzpGIoRIuDBOJ3FLZO5OJiPSZOVDXz/Sq3OCR/md/uRNnhpP46s0XVM3c2olk3BenRKQAuAvAbTn2OQ/mVcD7fW5/HxF1ElFnT0/PeA+polg1pxkHekbsUXxB/Wmn2OSK9J0ti4NE+oBZNuhemAVkevwMzhqgYj+Pz2sQ4hxR055+JOS+SvD26Rs9nse2d8rk6bsFfVq9YzauSOSWYnGWaiZyhb0TdN7uVCQWDUEz2GzGh/yjLKc3RBBWCacCRvqPvnwKP3n+GP5q0xKsrsLVshNBENE/BsCZBZpnbRM0ATgfwBYi6gKwEcBmRzJ3HoCfAngHM3suFWTme5l5AzNvaGsL3vu6GjhvTjN0g7HtaH9gPx/IFLpco/WcuhXE0wfMqVdekX7UsYbAMMxI312jD+QSffN/Z+8ddyK0yaf23ivSt0s2J8jTd4pxKVtAhBSy7Z1YRK3o6NXZf8dgzrsATFEIbY3RQLX6fSNJfOKnO7BidhNuvVzaOsUSRGW2AlhGRIuIKALgFgCbxY3MPMDMM5l5ITMvBPAMgOuYuZOIWgA8BOB2Zn66DMdf8azqMKOVl44NFrSwJFP0c3n66Z+DVO8AZtMvT3tHdfTeAWMspXuKvl/kbTdcC4neO/72jlssnJ6+oOwrcl2vw9nuWJzY1BLkE0IKIWXZO+Mt15xsnMPRmfOvtAaAtua6QJ02P/OLnegbSeKrb1nrOz9Ckp+87xwzawBuBfAIgF0A7mfmnUR0JxFdl+futwJYCuAOInrR+pd7OnGNMb+1Hk3REJK6UVDZWeBIP8Peyf0VbHTYO/k8fcP29LPF2C+6y/b0s2f72pG+6+m9+qKX295xv4xMT99K5JYg0g9bbRjGuxp3KmCLflKz7J3878+spvyR/q9fOoGfv3gcf335Mpw3R9o64yHQ4ixmfhjAw65td/jsu8nx8z8B+KdxHF/VQ0RYOacZfzx0NnASFwge6Tu/c/naMIhFVkNxLatGH3A1YLNKNp1jIPORHqJCYDYTxu6rm3QiN7+901x2e8fl6Turd+wVuaVK5Jr2TiX7+UBmpG8EDPXbm6PY2uU/9at3OIFP/vQlnDenGR967ZJSHWrNIq+RpgCiXr+uaHsnl6fviPTz2DvOVa9evnJWl82kltWCIRfOSB8w++1cs7ojYx9f0fd4b2IRs1x0IvrpA257p3QrcsMq2YncIAPRpzLOsZsIaO/MaqpD32jKbi/u5o7NOzEYT+Grb1k77kEvEin6UwLRZrmQRK4StHqngESu05/3TOS6V+Qm9ILyEIZVwie+uGvmTcP6BZkLa4S94z4/ua2vsEoIqQoiqjIh/fQBl72jlDrSrzJ7JxHc3mlvNv9+vVbl/nL7cTy0/QT+5opzscLR/VJSPFL0pwCiHUMhAipEhyhzpagb51cuX5TktFDye/pmyWYhwyrEsnxRx//uSxZmJ2yj3pG+u/uoOAkUUuZaKFmi7yjZTLdWHv/zhFQFcU3HUFyrfHsnkh6ZaBics8OmoL3JXKDlnpXbM5TAP/7sJaydNw3vL3OjuFpCNlybAixrb0JYpaI8/ekNkZzRZsaK3ABDVAT5In3d8K/e8UMs1rlsWRvee+kiXLN6TtY+qkJojIayondn5RCQbv3QXB/ynRo2XsTbtWBGA64+vwPzptfbt4VUBapCJelJH1YIZ/K1yK4QxGK90aTZLi/Iu5OO9NMVPMyMT/1sB0aSOr5y89qCZgZLciNFfwoQCSm4bu1cO+IPQlr0c0eGmYnc3F+caMgUMt1gz8VZTp/fHopegKcv7J1zZjTgk9dktW+yaaoLZUWIzpNQLKLakf69f74BMxrLI5TiCmLOtHrcfnXmcI6wQiWzlVRFsVsXtFSJvTNslWwGsneaslsxbN52HI/sPIWPX70Cy2Y1ledgaxQp+lOEr75lbUH7C9HP1WETyIy08nn6RISGiIqhuOYp+s31YURCZt8cv7bKuTDyr8oHkE7mOnEez7T6sB3pr+won8+bK4oPqUpJmq0BmVVVfgNoKgUROIwmg1fvzIiZV6ui6drpwTju+PlOXHhOC95zqbR1So28ZqpQRASVrzlXIdU7QDpy9yrZnFYfxhMf24RNy9vsSL8Qe8cI0IsFMJO57gjRGek314cLqnQqllxuWFgtXaTvPBlXuqdPZM4cEEFBkEjfuSqXmfGJn+5APGXaOuWqzKplZKRfoQh/PlflDlBYnT6Qjtz9Vjx2TKtHSCF7VGJ9uAB7J2Co3xqLQHPs+8bVszMi/fmtDSVJoOZDCJbXrFdVoZIlkJ0n40pfkQukm67VR9RAiVwAmNUcxamhBH7y/DE8tus0PnXNSixpayzvgdYoUvQrlLS9k1sknCtyg9Q425F+zvJRsq2aIJF+NKQgoRmB7Z1PvHElxpI63vjvvwMA3PP29Rm331WgFVYsuTT99atm+7aQLhRngr3S7R3AEv2khrqwGiiRCwBtTXXYeXwAn/nFTrxi4XS8+5JFZT3GWkaKfoViJ3LziX4BdfpAWsRzNf1yimGQMtO06AdT/UUzYzlvbyqR2OZDWGNeh33ZuW247NzSNAcU/XuiIaUkg70nG2HvtMY48OK19uYoHtsVR31YxZdvkrZOOZGefoUyq7kO73jlArx+5ayc+xXScA1IV1/kivSd32P31CwvRKVNUNGfKpRr0Zcb8blUermmQNg7Bmf3L/JjllXBc/vVK7Awz0lfMj5kpF+hqArhzuvPz7ufiFYVCtb73Rb9HFaQM3oLFOlbC6sMI++uU4pSVefkQ1yBVXq5piAWDeHsyKh1hRRM9W9YNxexqIo/37igrMcmkaJf9YivXNDFLWKhU9BIP5inX5mRfjqRW15CAddcVArmrGUdAAdOuM9vbZDlmROEtHeqHBHp5xuVKBA99b3q9N2PCQSzd8RjBU3kThVKsdo2COKEXA2VO4DD3jGC2zuSiUOKfpUjvnSBI/08JZtA5gV70EQugEAzU6cSE5VLrLpI36reYXBG9ZhkaiDtnSpHfOWC1OgDwSJ9xZEn8PP+idJVLyKRq49T9GMRFbOn1Y3rMQqhXH363dhrLqrF04+EEE8Z0PTg9o5k4pCiX+UI4QqyGhcIGOlbX+SGSMjXAgkr5khEoHh753t/cVHGgq4dn7lyQu0CW7DKfIEirsKqJ5Fr/g0NJ7QJs8gkwZGiX+Wk7Z3CIn13V0sn4kSSa7yjqhBgzcQotmTTXQdfrhbKflCOFbmlxLZ38rTUqBScTdckUw/p6Vc5aXunsOqdaDi/p5+rcse5ylSIfqV5+hNVp28ncqsm0k+L/kSVvUqCIz+Sase2d4IJ2PLZTZjfWo8FrQ05HtJ8rFyVO84ri6XtZg+VdedM99t9SjJRgiXyLVUj+lYwMBzXZCJ3ChLoz5qIriKiPUS0n4huz7HfjUTERLTBse3j1v32ENGVpThoSXAU294JpmCL2xrxu7+/HO3N/glTEQDnqtxRHYp54fwWbPnoJvzlqyurn8pE+dHrzpmON6yahWWzqqPBmIj0hxKaLNmcguT19IlIBXA3gNcD6AawlYg2M/PLrv2aAHwEwLOObasA3ALgPABzADxGROcys/cEZEnJEZFW0OqdIIgTSc5I33FlEQ4pFbm0XsnRe6eUzG9twL3v2JB/xwrBHpkY1+zxl5KpQ5Dw7yIA+5n5IDMnAdwH4HqP/T4H4EsA4o5t1wO4j5kTzHwIwH7r8SQThJ3ILWESVJxIcnn6zoZZQfMJUw1ZblgconpnLKVLc2cKEuTbOBfAUcfv3dY2GyJaB2A+Mz9U6H2t+7+PiDqJqLOnpyfQgUuCUai9E+gxrYfKNcjEeWUxO4dVNJWZqDr9aiPmjO7lWzjlGLcSEJEC4C4AtxX7GMx8LzNvYOYNbW2laVcrMbHbMJTQ3hGP2ZCvZBPAb/72NRO6oKqUSM0vDqfoyxPn1COI4XYMwHzH7/OsbYImAOcD2GKJwWwAm4nougD3lUwQQRdnBUF8jXMlcsXzVVqTNScT1XCt2nAGA1Lypx5BlGArgGVEtIiIIjATs5vFjcw8wMwzmXkhMy8E8AyA65i509rvFiKKEtEiAMsA/LHkr0Lii1KGSF88Zk7Rt55P0ytXMsXVSqWtL5hsFIXsfI+M9KceeSN9ZtaI6FYAjwBQAXyHmXcS0Z0AOpl5c4777iSi+wG8DEAD8FeycmdiEd+5UiZTqYDqHa3SWms6kHJVPLFoCKNJXVpkU5BA9VTM/DCAh13b7vDZd5Pr988D+HyRxycZJ4X20w+CiN6CVO/olTY5xYEUrOKJRVTIkoypSWXW0kkCY9s7Zag/rI/4xwziJJOqYHtHUPmvYOIRyVxp70w9pOhXOYU2XAuCEqQNgx3pS8msRcQCLan5Uw8p+jVCKe2ddGvl/PZOJXv60tUvHrFAS4r+1EOKfpVT6LjEIARpwyB66Feypy+QxTuFI+2dqYtsjFHllGNFLgUo2fz8m1ejvXkfLl0mF9vVIra9M8nHIclGin6Vk67eKeWKXPP/XJH+rOY6fOHNq0v2nJOBeJ0y0C8ce1WujPSnHNLeqXLS9k4pV+TmL9msBqRcFY/w9GXTuqmHFP0qRylL9Y75f65xiZLaRkT6UvOnHlL0qx27DcPEVu9IahsxPUsORp96SNGvcmxPv6TVO/kTuVWFLN8pmHT1ziQfiCQLKfpVjhDoUlbvNERCqAsrqAtVt+jLKLV40vaOfA+nGrJ6p8pJN1wr3Zfvzzaeg03L26DUSBgn4/zCESWbUvOnHlL0q5y0vVO6SL+pLoyVHeGSPZ6k+pDVO1MXae9UOWl7R377CmVOiznx65rVHZN8JJWHtHemLjLSr3bKYO/UCu1NdXj5zitzLkKTeGOLvvyzm3JI0a9yymHv1BINOdpHS/yJyclZUxapBFVOOQajSyT5kJH+1EWKfpWj2PaO/KglE0dYVRAJKbLsdQoilaDKSQ9RkR+1ZGKJRVSZxp2CBFICIrqKiPYQ0X4iut3j9g8Q0Q4iepGIniKiVdb2MBF9z7ptFxF9vNQvQJIbUT1RjnGJEkkuYtGQtHemIHlFn4hUAHcDuBrAKgBvFaLu4EfMvJqZLwDwLwDusrbfDCDKzKsBrAfwfiJaWKJjlwRARvqSySIWCclIfwoSpDThIgD7mfkgABDRfQCuB/Cy2IGZBx37x5BexMgAYkQUAlAPIAnAua+kzJCs05dMEh967RI01cnqp6lGkE9kLoCjjt+7AVzs3omI/grA3wGIALjc2vwgzBPECQANAP6Wmc+O54AlhSGkvpT99CWSIFx/wdzJPgSJByVTAma+m5mXAPgHAJ+yNl8EQAcwB8AiALcR0WL3fYnofUTUSUSdPT09pTokCeSKXIlEkkkQ0T8GYL7j93nWNj/uA/Am6+e3Afg1M6eY+TSApwFscN+Bme9l5g3MvKGtTc5ULSWXLJ2BD1y2BMvaGyf7UCQSyRQgiOhvBbCMiBYRUQTALQA2O3cgomWOX68BsM/6+Qgsq4eIYgA2Atg93oOWBKelIYLbr14hE7kSiQRAAE+fmTUiuhXAIwBUAN9h5p1EdCeATmbeDOBWIroCQApAH4B3Wne/G8B3iWgnTHv5u8y8vRwvRCKRSCT5IZ5iU4E2bNjAnZ2dk30YEolEUlEQ0XPMnGWfu5HX/BKJRFJDSNGXSCSSGkKKvkQikdQQUvQlEomkhpCiL5FIJDWEFH2JRCL5/+3dQYhVVRzH8e8vpI0YCUMQJY6FEhEtKqJliwghmAIhplUuCoLMTUQtXNWqFrnJTUXQJl24iIkWEkREkZCQFRqZWpGt1MxWlcqvxX3BTebNu9Obuec65/eBgblvDu/++XHefy7n3plTkcE9sinpLPBz6TpaZoBzpYsYsOQzXrJZWvIZ7/9ks9n2xH9pMLimPzSSjnR59rVWyWe8ZLO05DPeamaT5Z2IiIqk6UdEVCRNf7I3SxcwcMlnvGSztOQz3qplkzX9iIiK5Eo/IqIiafojkrZL+l7SSUkvLfLzZyR9K+mopM8W2Rx+zZqUTWvcDkmWVNUTGR3mzk5JZ0dz56ikp0rUWUqX+SPpcUnHJR2T9F7fNZbSYe7sbc2bE5J+n/qktqv/otkn4BRwG80ev18Dd1415obW93M0O4IVr30I2YzGbQA+BQ4D95Wue0j5ADuBN0rXOuB8tgJfARtHxzeVrnso2Vw1/jma/UymOm+u9Bv3Aydtn7b9N82Wj4+2B9j+o3W4HqjlZsjEbEZeAV4F/uyzuAHomk+tuuTzNLDP9gUAN1ur1mC5c+cJYP+0J03Tb9wC/NI6PjN67T8kPSvpFPAasLun2kqbmI2ke4BNtj/ss7CB6DR3gB2SvpF0UNKmRX6+VnXJZxuwTdLnkg5L2t5bdWV1nTtI2gxsAT6e9qRp+stge5/t24EXgT2l6xkCSdcBrwPPl65lwD4AZm3fDXwEvFu4nqFZR7PE8yDN1exbkm4sWtHwzAMHbV+Z9o3S9Bu/Au2rr1tHr41zAHhsVSsajknZbADuAj6R9BPwALBQ0c3ciXPH9nnbf40O3wbu7am2Iejy2ToDLNi+ZPtH4ATNL4G1bjl9Z54VWNqBNP1/fQlslbRF0vU0AS+0B0hqT8JHgB96rK+kJbOxfdH2jO1Z27M0N3LnbNey0XGXuXNz63AO+K7H+kqbmA/wPs1VPpJmaJZ7TvdZZCFdskHSHcBG4IuVOOm6lXiTa53ty5J2AYdo7qi/Y/uYpJeBI7YXgF2SHgIuAReAJ8tV3J+O2VSrYz67Jc0Bl4HfaJ7mqULHfA4BD0s6DlwBXrB9vlzV/VjGZ2seOODRIzzTyl/kRkRUJMs7EREVSdOPiKhImn5EREXS9CMiKpKmHxFRkTT9iIiKpOlHRFQkTT8ioiL/AJQFGdXloz36AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xy=dfp.sort_values((\"loss\",\"valid\"))\n",
+    "ax = plt.plot(xy[\"loss\"]['valid'], xy[\"F1_score\"]['valid'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xy=dfp.sort_values((\"accuracy\",\"valid\"))\n",
+    "ax = plt.plot(xy[\"accuracy\"]['valid'], xy[\"accuracy\"]['test'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xy=dfp.sort_values((\"accuracy\",\"valid\"))\n",
+    "ax = plt.plot(xy[\"accuracy\"]['valid'], xy[\"F1_score\"]['test'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xy=dfp.sort_values((\"accuracy\",\"valid\"))\n",
+    "ax = plt.plot(xy[\"accuracy\"]['valid'], xy[\"F1_score\"]['valid'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xy=dfp.sort_values((\"loss\",\"valid\"))\n",
+    "ax = plt.plot(xy[\"loss\"]['valid'], xy[\"F1_score\"]['test'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xy=dfp.sort_values((\"F1_score\",\"valid\"))\n",
+    "ax = plt.plot(xy[\"F1_score\"]['valid'], xy[\"F1_score\"]['test'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">F1_score</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">loss</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">accuracy</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dataset</th>\n",
+       "      <th>test</th>\n",
+       "      <th>valid</th>\n",
+       "      <th>test</th>\n",
+       "      <th>valid</th>\n",
+       "      <th>test</th>\n",
+       "      <th>valid</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_name</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>0-noft-20</th>\n",
+       "      <td>0.595156</td>\n",
+       "      <td>0.482759</td>\n",
+       "      <td>0.415361</td>\n",
+       "      <td>0.392013</td>\n",
+       "      <td>0.883</td>\n",
+       "      <td>0.8934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1-ft-seed1-17</th>\n",
+       "      <td>0.568093</td>\n",
+       "      <td>0.484472</td>\n",
+       "      <td>0.561745</td>\n",
+       "      <td>0.414461</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>0.9017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0-ft-seed1-6</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.496454</td>\n",
+       "      <td>0.606656</td>\n",
+       "      <td>0.451102</td>\n",
+       "      <td>0.884</td>\n",
+       "      <td>0.9159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0-ft-seed0-19</th>\n",
+       "      <td>0.513043</td>\n",
+       "      <td>0.496732</td>\n",
+       "      <td>0.548660</td>\n",
+       "      <td>0.432850</td>\n",
+       "      <td>0.888</td>\n",
+       "      <td>0.9088</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1-ft-seed1-8</th>\n",
+       "      <td>0.636704</td>\n",
+       "      <td>0.517647</td>\n",
+       "      <td>0.297993</td>\n",
+       "      <td>0.286143</td>\n",
+       "      <td>0.903</td>\n",
+       "      <td>0.9028</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               F1_score                loss           accuracy        \n",
+       "dataset            test     valid      test     valid     test   valid\n",
+       "model_name                                                            \n",
+       "0-noft-20      0.595156  0.482759  0.415361  0.392013    0.883  0.8934\n",
+       "1-ft-seed1-17  0.568093  0.484472  0.561745  0.414461    0.889  0.9017\n",
+       "0-ft-seed1-6   0.500000  0.496454  0.606656  0.451102    0.884  0.9159\n",
+       "0-ft-seed0-19  0.513043  0.496732  0.548660  0.432850    0.888  0.9088\n",
+       "1-ft-seed1-8   0.636704  0.517647  0.297993  0.286143    0.903  0.9028"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfp.sort_values((\"F1_score\",\"valid\")).tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:fastaiv1]",
+   "language": "python",
+   "name": "conda-env-fastaiv1-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/experiments/poleval19/poleval-noearly.csv b/experiments/poleval19/poleval-noearly.csv
new file mode 100644
index 0000000..764ac16
--- /dev/null
+++ b/experiments/poleval19/poleval-noearly.csv
@@ -0,0 +1,39 @@
+,model_dir_parent,model_name,test Accuracy,test F1 score bin,test Loss,test Precision,test Recall,valid Accuracy,valid F1 score bin,valid Kappa,valid Kappa Linear,valid Kappa quadratic,valid Loss,valid Matthews Correff,valid Precision,valid Recall
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diff --git a/experiments/poleval19/poleval-review.ipynb b/experiments/poleval19/poleval-review.ipynb
new file mode 100644
index 0000000..03bc4b1
--- /dev/null
+++ b/experiments/poleval19/poleval-review.ipynb
@@ -0,0 +1,13417 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastai.text import *\n",
+    "import IPython.display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model_dir_parent</th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.870</td>\n",
+       "      <td>0.577922</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.456889</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.511494</td>\n",
+       "      <td>0.664179</td>\n",
+       "      <td>0.876617</td>\n",
+       "      <td>0.526718</td>\n",
+       "      <td>0.465655</td>\n",
+       "      <td>0.429593</td>\n",
+       "      <td>0.507218</td>\n",
+       "      <td>0.389830</td>\n",
+       "      <td>0.811765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.858</td>\n",
+       "      <td>0.572289</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.426693</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.479798</td>\n",
+       "      <td>0.708955</td>\n",
+       "      <td>0.860696</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.433233</td>\n",
+       "      <td>0.434975</td>\n",
+       "      <td>0.483857</td>\n",
+       "      <td>0.358974</td>\n",
+       "      <td>0.823529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.853</td>\n",
+       "      <td>0.573913</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.465223</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.469194</td>\n",
+       "      <td>0.738806</td>\n",
+       "      <td>0.849751</td>\n",
+       "      <td>0.488136</td>\n",
+       "      <td>0.418061</td>\n",
+       "      <td>0.523841</td>\n",
+       "      <td>0.477067</td>\n",
+       "      <td>0.342857</td>\n",
+       "      <td>0.847059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.855</td>\n",
+       "      <td>0.577259</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.483052</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.738806</td>\n",
+       "      <td>0.841791</td>\n",
+       "      <td>0.461017</td>\n",
+       "      <td>0.387230</td>\n",
+       "      <td>0.530552</td>\n",
+       "      <td>0.441885</td>\n",
+       "      <td>0.323810</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.862</td>\n",
+       "      <td>0.576687</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.491995</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.489583</td>\n",
+       "      <td>0.701493</td>\n",
+       "      <td>0.851741</td>\n",
+       "      <td>0.487973</td>\n",
+       "      <td>0.418319</td>\n",
+       "      <td>0.488528</td>\n",
+       "      <td>0.474608</td>\n",
+       "      <td>0.344660</td>\n",
+       "      <td>0.835294</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      model_dir_parent  \\\n",
+       "0  data/hate/pl-10-reddit/models/sp25k   \n",
+       "1  data/hate/pl-10-reddit/models/sp25k   \n",
+       "2  data/hate/pl-10-reddit/models/sp25k   \n",
+       "3  data/hate/pl-10-reddit/models/sp25k   \n",
+       "4  data/hate/pl-10-reddit/models/sp25k   \n",
+       "\n",
+       "                                          model_name  test Accuracy  \\\n",
+       "0  qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.870   \n",
+       "1  qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.858   \n",
+       "2  qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.853   \n",
+       "3  qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.855   \n",
+       "4  qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.862   \n",
+       "\n",
+       "   test F1 score bin  test Kappa Linear  test Loss  test Matthews Correff  \\\n",
+       "0           0.577922                NaN   0.456889                    NaN   \n",
+       "1           0.572289                NaN   0.426693                    NaN   \n",
+       "2           0.573913                NaN   0.465223                    NaN   \n",
+       "3           0.577259                NaN   0.483052                    NaN   \n",
+       "4           0.576687                NaN   0.491995                    NaN   \n",
+       "\n",
+       "   test Precision  test Recall  valid Accuracy  valid F1 score bin  \\\n",
+       "0        0.511494     0.664179        0.876617            0.526718   \n",
+       "1        0.479798     0.708955        0.860696            0.500000   \n",
+       "2        0.469194     0.738806        0.849751            0.488136   \n",
+       "3        0.473684     0.738806        0.841791            0.461017   \n",
+       "4        0.489583     0.701493        0.851741            0.487973   \n",
+       "\n",
+       "   valid Kappa Linear  valid Loss  valid Matthews Correff  valid Precision  \\\n",
+       "0            0.465655    0.429593                0.507218         0.389830   \n",
+       "1            0.433233    0.434975                0.483857         0.358974   \n",
+       "2            0.418061    0.523841                0.477067         0.342857   \n",
+       "3            0.387230    0.530552                0.441885         0.323810   \n",
+       "4            0.418319    0.488528                0.474608         0.344660   \n",
+       "\n",
+       "   valid Recall  \n",
+       "0      0.811765  \n",
+       "1      0.823529  \n",
+       "2      0.847059  \n",
+       "3      0.800000  \n",
+       "4      0.835294  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = pd.read_csv(\"./poleval.csv\", index_col=0)\n",
+    "df1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model_dir_parent</th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>bwdlstm_ft20_cl8_lmseed-0-ftseed-0-clsweightse...</td>\n",
+       "      <td>0.896</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.498822</td>\n",
+       "      <td>0.650000</td>\n",
+       "      <td>0.485075</td>\n",
+       "      <td>0.930348</td>\n",
+       "      <td>0.627660</td>\n",
+       "      <td>0.358982</td>\n",
+       "      <td>0.572816</td>\n",
+       "      <td>0.694118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>bwdlstm_ft20_cl8_lmseed-0-ftseed-0-clsweightse...</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>0.519480</td>\n",
+       "      <td>0.466780</td>\n",
+       "      <td>0.618557</td>\n",
+       "      <td>0.447761</td>\n",
+       "      <td>0.921393</td>\n",
+       "      <td>0.594872</td>\n",
+       "      <td>0.351481</td>\n",
+       "      <td>0.527273</td>\n",
+       "      <td>0.682353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>bwdlstm_ft20_cl8_lmseed-0-ftseed-0-clsweightse...</td>\n",
+       "      <td>0.886</td>\n",
+       "      <td>0.491071</td>\n",
+       "      <td>0.452916</td>\n",
+       "      <td>0.611111</td>\n",
+       "      <td>0.410448</td>\n",
+       "      <td>0.920398</td>\n",
+       "      <td>0.578947</td>\n",
+       "      <td>0.289560</td>\n",
+       "      <td>0.523810</td>\n",
+       "      <td>0.647059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>bwdlstm_ft20_cl8_lmseed-0-ftseed-0-clsweightse...</td>\n",
+       "      <td>0.903</td>\n",
+       "      <td>0.587234</td>\n",
+       "      <td>0.510721</td>\n",
+       "      <td>0.683168</td>\n",
+       "      <td>0.514925</td>\n",
+       "      <td>0.927363</td>\n",
+       "      <td>0.609626</td>\n",
+       "      <td>0.393536</td>\n",
+       "      <td>0.558824</td>\n",
+       "      <td>0.670588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>bwdlstm_ft20_cl8_lmseed-0-ftseed-0-clsweightse...</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.443047</td>\n",
+       "      <td>0.680412</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.322416</td>\n",
+       "      <td>0.542857</td>\n",
+       "      <td>0.670588</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      model_dir_parent  \\\n",
+       "0  data/hate/pl-10-reddit/models/sp25k   \n",
+       "1  data/hate/pl-10-reddit/models/sp25k   \n",
+       "2  data/hate/pl-10-reddit/models/sp25k   \n",
+       "3  data/hate/pl-10-reddit/models/sp25k   \n",
+       "4  data/hate/pl-10-reddit/models/sp25k   \n",
+       "\n",
+       "                                          model_name  test Accuracy  \\\n",
+       "0  bwdlstm_ft20_cl8_lmseed-0-ftseed-0-clsweightse...          0.896   \n",
+       "1  bwdlstm_ft20_cl8_lmseed-0-ftseed-0-clsweightse...          0.889   \n",
+       "2  bwdlstm_ft20_cl8_lmseed-0-ftseed-0-clsweightse...          0.886   \n",
+       "3  bwdlstm_ft20_cl8_lmseed-0-ftseed-0-clsweightse...          0.903   \n",
+       "4  bwdlstm_ft20_cl8_lmseed-0-ftseed-0-clsweightse...          0.901   \n",
+       "\n",
+       "   test F1 score bin  test Loss  test Precision  test Recall  valid Accuracy  \\\n",
+       "0           0.555556   0.498822        0.650000     0.485075        0.930348   \n",
+       "1           0.519480   0.466780        0.618557     0.447761        0.921393   \n",
+       "2           0.491071   0.452916        0.611111     0.410448        0.920398   \n",
+       "3           0.587234   0.510721        0.683168     0.514925        0.927363   \n",
+       "4           0.571429   0.443047        0.680412     0.492537        0.924378   \n",
+       "\n",
+       "   valid F1 score bin  valid Loss  valid Precision  valid Recall  \n",
+       "0            0.627660    0.358982         0.572816      0.694118  \n",
+       "1            0.594872    0.351481         0.527273      0.682353  \n",
+       "2            0.578947    0.289560         0.523810      0.647059  \n",
+       "3            0.609626    0.393536         0.558824      0.670588  \n",
+       "4            0.600000    0.322416         0.542857      0.670588  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = pd.read_csv(\"./all-results-fixed.csv\", index_col=0) # marcins results\n",
+    "df2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model_dir_parent</th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.870</td>\n",
+       "      <td>0.577922</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.456889</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.511494</td>\n",
+       "      <td>0.664179</td>\n",
+       "      <td>0.876617</td>\n",
+       "      <td>0.526718</td>\n",
+       "      <td>0.465655</td>\n",
+       "      <td>0.429593</td>\n",
+       "      <td>0.507218</td>\n",
+       "      <td>0.389830</td>\n",
+       "      <td>0.811765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.858</td>\n",
+       "      <td>0.572289</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.426693</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.479798</td>\n",
+       "      <td>0.708955</td>\n",
+       "      <td>0.860696</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.433233</td>\n",
+       "      <td>0.434975</td>\n",
+       "      <td>0.483857</td>\n",
+       "      <td>0.358974</td>\n",
+       "      <td>0.823529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.853</td>\n",
+       "      <td>0.573913</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.465223</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.469194</td>\n",
+       "      <td>0.738806</td>\n",
+       "      <td>0.849751</td>\n",
+       "      <td>0.488136</td>\n",
+       "      <td>0.418061</td>\n",
+       "      <td>0.523841</td>\n",
+       "      <td>0.477067</td>\n",
+       "      <td>0.342857</td>\n",
+       "      <td>0.847059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.855</td>\n",
+       "      <td>0.577259</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.483052</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.738806</td>\n",
+       "      <td>0.841791</td>\n",
+       "      <td>0.461017</td>\n",
+       "      <td>0.387230</td>\n",
+       "      <td>0.530552</td>\n",
+       "      <td>0.441885</td>\n",
+       "      <td>0.323810</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.862</td>\n",
+       "      <td>0.576687</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.491995</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.489583</td>\n",
+       "      <td>0.701493</td>\n",
+       "      <td>0.851741</td>\n",
+       "      <td>0.487973</td>\n",
+       "      <td>0.418319</td>\n",
+       "      <td>0.488528</td>\n",
+       "      <td>0.474608</td>\n",
+       "      <td>0.344660</td>\n",
+       "      <td>0.835294</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      model_dir_parent  \\\n",
+       "0  data/hate/pl-10-reddit/models/sp25k   \n",
+       "1  data/hate/pl-10-reddit/models/sp25k   \n",
+       "2  data/hate/pl-10-reddit/models/sp25k   \n",
+       "3  data/hate/pl-10-reddit/models/sp25k   \n",
+       "4  data/hate/pl-10-reddit/models/sp25k   \n",
+       "\n",
+       "                                          model_name  test Accuracy  \\\n",
+       "0  qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.870   \n",
+       "1  qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.858   \n",
+       "2  qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.853   \n",
+       "3  qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.855   \n",
+       "4  qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.862   \n",
+       "\n",
+       "   test F1 score bin  test Kappa Linear  test Loss  test Matthews Correff  \\\n",
+       "0           0.577922                NaN   0.456889                    NaN   \n",
+       "1           0.572289                NaN   0.426693                    NaN   \n",
+       "2           0.573913                NaN   0.465223                    NaN   \n",
+       "3           0.577259                NaN   0.483052                    NaN   \n",
+       "4           0.576687                NaN   0.491995                    NaN   \n",
+       "\n",
+       "   test Precision  test Recall  valid Accuracy  valid F1 score bin  \\\n",
+       "0        0.511494     0.664179        0.876617            0.526718   \n",
+       "1        0.479798     0.708955        0.860696            0.500000   \n",
+       "2        0.469194     0.738806        0.849751            0.488136   \n",
+       "3        0.473684     0.738806        0.841791            0.461017   \n",
+       "4        0.489583     0.701493        0.851741            0.487973   \n",
+       "\n",
+       "   valid Kappa Linear  valid Loss  valid Matthews Correff  valid Precision  \\\n",
+       "0            0.465655    0.429593                0.507218         0.389830   \n",
+       "1            0.433233    0.434975                0.483857         0.358974   \n",
+       "2            0.418061    0.523841                0.477067         0.342857   \n",
+       "3            0.387230    0.530552                0.441885         0.323810   \n",
+       "4            0.418319    0.488528                0.474608         0.344660   \n",
+       "\n",
+       "   valid Recall  \n",
+       "0      0.811765  \n",
+       "1      0.823529  \n",
+       "2      0.847059  \n",
+       "3      0.800000  \n",
+       "4      0.835294  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.concat([df1,df2], sort=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df[~df[\"model_name\"].str.contains(\"bwdlstm\")] # remove bwdlstm as it is wrong model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['qrnn_ft0_cl8', 'qrnn_ft20_cl8', 'qrnn_ft6_cl8', 'lstm_ft0_cl8', 'lstm_ft20_cl8', 'lstm_ft6_cl8',\n",
+       "       'lstm_noearly_stop_ft6_cl8', 'lstm_nowce_ft6_cl8', 'lstm_small_ft6_el20', 'lstm_tmp-100', 'lstm_dp05_ft20_cl8',\n",
+       "       'lstm_dp05_ft6_cl8'], dtype=object)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"arch\"] = df[\"model_name\"].str.extract(\"(.*?)_lmseed.*\")\n",
+    "df[\"arch\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['reddit', 'wiki'], dtype=object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"ds\"] = df[\"model_dir_parent\"].str.extract(\".*pl-10-(.*)/models.*\")\n",
+    "df[\"ds\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df[~(df[\"arch\"] == \"lstm_tmp-100\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>584.000000</td>\n",
+       "      <td>584.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>584.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>584.000000</td>\n",
+       "      <td>584.000000</td>\n",
+       "      <td>584.000000</td>\n",
+       "      <td>584.000000</td>\n",
+       "      <td>324.000000</td>\n",
+       "      <td>584.000000</td>\n",
+       "      <td>324.000000</td>\n",
+       "      <td>584.000000</td>\n",
+       "      <td>584.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.884046</td>\n",
+       "      <td>0.541107</td>\n",
+       "      <td>0.386346</td>\n",
+       "      <td>0.468251</td>\n",
+       "      <td>0.413414</td>\n",
+       "      <td>0.592322</td>\n",
+       "      <td>0.517430</td>\n",
+       "      <td>0.907761</td>\n",
+       "      <td>0.559696</td>\n",
+       "      <td>0.484952</td>\n",
+       "      <td>0.363650</td>\n",
+       "      <td>0.505940</td>\n",
+       "      <td>0.488150</td>\n",
+       "      <td>0.681628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.013672</td>\n",
+       "      <td>0.044664</td>\n",
+       "      <td>0.052320</td>\n",
+       "      <td>0.058604</td>\n",
+       "      <td>0.039129</td>\n",
+       "      <td>0.066598</td>\n",
+       "      <td>0.100605</td>\n",
+       "      <td>0.022296</td>\n",
+       "      <td>0.037469</td>\n",
+       "      <td>0.041443</td>\n",
+       "      <td>0.058652</td>\n",
+       "      <td>0.030642</td>\n",
+       "      <td>0.073917</td>\n",
+       "      <td>0.082774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.786000</td>\n",
+       "      <td>0.340909</td>\n",
+       "      <td>0.294158</td>\n",
+       "      <td>0.306861</td>\n",
+       "      <td>0.330020</td>\n",
+       "      <td>0.369281</td>\n",
+       "      <td>0.223881</td>\n",
+       "      <td>0.790050</td>\n",
+       "      <td>0.418733</td>\n",
+       "      <td>0.332226</td>\n",
+       "      <td>0.218546</td>\n",
+       "      <td>0.390519</td>\n",
+       "      <td>0.273381</td>\n",
+       "      <td>0.447059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.878000</td>\n",
+       "      <td>0.517763</td>\n",
+       "      <td>0.341244</td>\n",
+       "      <td>0.430748</td>\n",
+       "      <td>0.392833</td>\n",
+       "      <td>0.543268</td>\n",
+       "      <td>0.447761</td>\n",
+       "      <td>0.894279</td>\n",
+       "      <td>0.537962</td>\n",
+       "      <td>0.461222</td>\n",
+       "      <td>0.329288</td>\n",
+       "      <td>0.487962</td>\n",
+       "      <td>0.427035</td>\n",
+       "      <td>0.623529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.886000</td>\n",
+       "      <td>0.548852</td>\n",
+       "      <td>0.391300</td>\n",
+       "      <td>0.464669</td>\n",
+       "      <td>0.412878</td>\n",
+       "      <td>0.601868</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.916418</td>\n",
+       "      <td>0.562657</td>\n",
+       "      <td>0.490675</td>\n",
+       "      <td>0.358859</td>\n",
+       "      <td>0.507218</td>\n",
+       "      <td>0.504137</td>\n",
+       "      <td>0.670588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.893000</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.429274</td>\n",
+       "      <td>0.497233</td>\n",
+       "      <td>0.442910</td>\n",
+       "      <td>0.641304</td>\n",
+       "      <td>0.597015</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>0.587191</td>\n",
+       "      <td>0.513207</td>\n",
+       "      <td>0.394423</td>\n",
+       "      <td>0.526609</td>\n",
+       "      <td>0.540816</td>\n",
+       "      <td>0.741176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.912000</td>\n",
+       "      <td>0.622222</td>\n",
+       "      <td>0.487822</td>\n",
+       "      <td>0.743211</td>\n",
+       "      <td>0.507263</td>\n",
+       "      <td>0.756098</td>\n",
+       "      <td>0.843284</td>\n",
+       "      <td>0.942289</td>\n",
+       "      <td>0.648936</td>\n",
+       "      <td>0.605920</td>\n",
+       "      <td>0.708696</td>\n",
+       "      <td>0.605933</td>\n",
+       "      <td>0.764706</td>\n",
+       "      <td>0.894118</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Kappa Linear   test Loss  \\\n",
+       "count     584.000000         584.000000          38.000000  584.000000   \n",
+       "mean        0.884046           0.541107           0.386346    0.468251   \n",
+       "std         0.013672           0.044664           0.052320    0.058604   \n",
+       "min         0.786000           0.340909           0.294158    0.306861   \n",
+       "25%         0.878000           0.517763           0.341244    0.430748   \n",
+       "50%         0.886000           0.548852           0.391300    0.464669   \n",
+       "75%         0.893000           0.571429           0.429274    0.497233   \n",
+       "max         0.912000           0.622222           0.487822    0.743211   \n",
+       "\n",
+       "       test Matthews Correff  test Precision  test Recall  valid Accuracy  \\\n",
+       "count              38.000000      584.000000   584.000000      584.000000   \n",
+       "mean                0.413414        0.592322     0.517430        0.907761   \n",
+       "std                 0.039129        0.066598     0.100605        0.022296   \n",
+       "min                 0.330020        0.369281     0.223881        0.790050   \n",
+       "25%                 0.392833        0.543268     0.447761        0.894279   \n",
+       "50%                 0.412878        0.601868     0.500000        0.916418   \n",
+       "75%                 0.442910        0.641304     0.597015        0.923383   \n",
+       "max                 0.507263        0.756098     0.843284        0.942289   \n",
+       "\n",
+       "       valid F1 score bin  valid Kappa Linear  valid Loss  \\\n",
+       "count          584.000000          324.000000  584.000000   \n",
+       "mean             0.559696            0.484952    0.363650   \n",
+       "std              0.037469            0.041443    0.058652   \n",
+       "min              0.418733            0.332226    0.218546   \n",
+       "25%              0.537962            0.461222    0.329288   \n",
+       "50%              0.562657            0.490675    0.358859   \n",
+       "75%              0.587191            0.513207    0.394423   \n",
+       "max              0.648936            0.605920    0.708696   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count              324.000000       584.000000    584.000000  \n",
+       "mean                 0.505940         0.488150      0.681628  \n",
+       "std                  0.030642         0.073917      0.082774  \n",
+       "min                  0.390519         0.273381      0.447059  \n",
+       "25%                  0.487962         0.427035      0.623529  \n",
+       "50%                  0.507218         0.504137      0.670588  \n",
+       "75%                  0.526609         0.540816      0.741176  \n",
+       "max                  0.605933         0.764706      0.894118  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LSTM on wiki or on reddit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>210.000000</td>\n",
+       "      <td>210.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>210.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>210.000000</td>\n",
+       "      <td>210.000000</td>\n",
+       "      <td>210.000000</td>\n",
+       "      <td>210.000000</td>\n",
+       "      <td>210.000000</td>\n",
+       "      <td>210.000000</td>\n",
+       "      <td>210.000000</td>\n",
+       "      <td>210.000000</td>\n",
+       "      <td>210.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.880810</td>\n",
+       "      <td>0.514146</td>\n",
+       "      <td>0.386346</td>\n",
+       "      <td>0.464212</td>\n",
+       "      <td>0.413414</td>\n",
+       "      <td>0.581972</td>\n",
+       "      <td>0.480881</td>\n",
+       "      <td>0.906833</td>\n",
+       "      <td>0.542377</td>\n",
+       "      <td>0.493213</td>\n",
+       "      <td>0.364210</td>\n",
+       "      <td>0.505140</td>\n",
+       "      <td>0.485381</td>\n",
+       "      <td>0.642577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.011998</td>\n",
+       "      <td>0.052149</td>\n",
+       "      <td>0.052320</td>\n",
+       "      <td>0.065367</td>\n",
+       "      <td>0.039129</td>\n",
+       "      <td>0.061509</td>\n",
+       "      <td>0.105518</td>\n",
+       "      <td>0.022386</td>\n",
+       "      <td>0.033830</td>\n",
+       "      <td>0.042047</td>\n",
+       "      <td>0.061358</td>\n",
+       "      <td>0.032287</td>\n",
+       "      <td>0.079529</td>\n",
+       "      <td>0.082740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.786000</td>\n",
+       "      <td>0.340909</td>\n",
+       "      <td>0.294158</td>\n",
+       "      <td>0.306861</td>\n",
+       "      <td>0.330020</td>\n",
+       "      <td>0.369281</td>\n",
+       "      <td>0.223881</td>\n",
+       "      <td>0.790050</td>\n",
+       "      <td>0.418733</td>\n",
+       "      <td>0.332226</td>\n",
+       "      <td>0.218546</td>\n",
+       "      <td>0.390519</td>\n",
+       "      <td>0.273381</td>\n",
+       "      <td>0.447059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.877000</td>\n",
+       "      <td>0.495575</td>\n",
+       "      <td>0.341244</td>\n",
+       "      <td>0.424022</td>\n",
+       "      <td>0.392833</td>\n",
+       "      <td>0.544839</td>\n",
+       "      <td>0.417910</td>\n",
+       "      <td>0.894527</td>\n",
+       "      <td>0.521739</td>\n",
+       "      <td>0.466806</td>\n",
+       "      <td>0.335349</td>\n",
+       "      <td>0.486658</td>\n",
+       "      <td>0.421900</td>\n",
+       "      <td>0.588235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.883000</td>\n",
+       "      <td>0.522800</td>\n",
+       "      <td>0.391300</td>\n",
+       "      <td>0.458297</td>\n",
+       "      <td>0.412878</td>\n",
+       "      <td>0.579193</td>\n",
+       "      <td>0.481343</td>\n",
+       "      <td>0.912935</td>\n",
+       "      <td>0.546625</td>\n",
+       "      <td>0.499253</td>\n",
+       "      <td>0.364061</td>\n",
+       "      <td>0.507686</td>\n",
+       "      <td>0.489378</td>\n",
+       "      <td>0.635294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.888000</td>\n",
+       "      <td>0.550000</td>\n",
+       "      <td>0.429274</td>\n",
+       "      <td>0.492554</td>\n",
+       "      <td>0.442910</td>\n",
+       "      <td>0.616329</td>\n",
+       "      <td>0.557836</td>\n",
+       "      <td>0.922139</td>\n",
+       "      <td>0.563532</td>\n",
+       "      <td>0.521648</td>\n",
+       "      <td>0.396743</td>\n",
+       "      <td>0.525712</td>\n",
+       "      <td>0.537937</td>\n",
+       "      <td>0.705882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.901000</td>\n",
+       "      <td>0.608392</td>\n",
+       "      <td>0.487822</td>\n",
+       "      <td>0.743211</td>\n",
+       "      <td>0.507263</td>\n",
+       "      <td>0.756098</td>\n",
+       "      <td>0.843284</td>\n",
+       "      <td>0.942289</td>\n",
+       "      <td>0.639053</td>\n",
+       "      <td>0.605920</td>\n",
+       "      <td>0.708696</td>\n",
+       "      <td>0.605933</td>\n",
+       "      <td>0.764706</td>\n",
+       "      <td>0.894118</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Kappa Linear   test Loss  \\\n",
+       "count     210.000000         210.000000          38.000000  210.000000   \n",
+       "mean        0.880810           0.514146           0.386346    0.464212   \n",
+       "std         0.011998           0.052149           0.052320    0.065367   \n",
+       "min         0.786000           0.340909           0.294158    0.306861   \n",
+       "25%         0.877000           0.495575           0.341244    0.424022   \n",
+       "50%         0.883000           0.522800           0.391300    0.458297   \n",
+       "75%         0.888000           0.550000           0.429274    0.492554   \n",
+       "max         0.901000           0.608392           0.487822    0.743211   \n",
+       "\n",
+       "       test Matthews Correff  test Precision  test Recall  valid Accuracy  \\\n",
+       "count              38.000000      210.000000   210.000000      210.000000   \n",
+       "mean                0.413414        0.581972     0.480881        0.906833   \n",
+       "std                 0.039129        0.061509     0.105518        0.022386   \n",
+       "min                 0.330020        0.369281     0.223881        0.790050   \n",
+       "25%                 0.392833        0.544839     0.417910        0.894527   \n",
+       "50%                 0.412878        0.579193     0.481343        0.912935   \n",
+       "75%                 0.442910        0.616329     0.557836        0.922139   \n",
+       "max                 0.507263        0.756098     0.843284        0.942289   \n",
+       "\n",
+       "       valid F1 score bin  valid Kappa Linear  valid Loss  \\\n",
+       "count          210.000000          210.000000  210.000000   \n",
+       "mean             0.542377            0.493213    0.364210   \n",
+       "std              0.033830            0.042047    0.061358   \n",
+       "min              0.418733            0.332226    0.218546   \n",
+       "25%              0.521739            0.466806    0.335349   \n",
+       "50%              0.546625            0.499253    0.364061   \n",
+       "75%              0.563532            0.521648    0.396743   \n",
+       "max              0.639053            0.605920    0.708696   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count              210.000000       210.000000    210.000000  \n",
+       "mean                 0.505140         0.485381      0.642577  \n",
+       "std                  0.032287         0.079529      0.082740  \n",
+       "min                  0.390519         0.273381      0.447059  \n",
+       "25%                  0.486658         0.421900      0.588235  \n",
+       "50%                  0.507686         0.489378      0.635294  \n",
+       "75%                  0.525712         0.537937      0.705882  \n",
+       "max                  0.605933         0.764706      0.894118  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "focus=[\"test F1 score bin\", \"valid F1 score bin\"]\n",
+    "wiki = df[df[\"ds\"] == \"wiki\"]\n",
+    "wiki.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>184.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.895304</td>\n",
+       "      <td>0.557343</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.463688</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.645122</td>\n",
+       "      <td>0.493592</td>\n",
+       "      <td>0.920674</td>\n",
+       "      <td>0.587429</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.337478</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.527694</td>\n",
+       "      <td>0.667008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.005932</td>\n",
+       "      <td>0.029341</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.045555</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.036094</td>\n",
+       "      <td>0.045885</td>\n",
+       "      <td>0.007117</td>\n",
+       "      <td>0.020043</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.037845</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.033589</td>\n",
+       "      <td>0.044828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.873000</td>\n",
+       "      <td>0.479638</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.308932</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.523810</td>\n",
+       "      <td>0.388060</td>\n",
+       "      <td>0.890547</td>\n",
+       "      <td>0.531250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.227242</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.421384</td>\n",
+       "      <td>0.576471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.891750</td>\n",
+       "      <td>0.538375</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.445491</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.622321</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>0.574359</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.317254</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.508698</td>\n",
+       "      <td>0.635294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.896000</td>\n",
+       "      <td>0.560000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.469791</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.642940</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.921393</td>\n",
+       "      <td>0.587629</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.338125</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.530000</td>\n",
+       "      <td>0.658824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.899000</td>\n",
+       "      <td>0.579652</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.494402</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.670330</td>\n",
+       "      <td>0.529851</td>\n",
+       "      <td>0.925373</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.361941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.547632</td>\n",
+       "      <td>0.694118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.912000</td>\n",
+       "      <td>0.622222</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.554716</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.744681</td>\n",
+       "      <td>0.626866</td>\n",
+       "      <td>0.936318</td>\n",
+       "      <td>0.648936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.474846</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.617977</td>\n",
+       "      <td>0.835294</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Kappa Linear   test Loss  \\\n",
+       "count     184.000000         184.000000                0.0  184.000000   \n",
+       "mean        0.895304           0.557343                NaN    0.463688   \n",
+       "std         0.005932           0.029341                NaN    0.045555   \n",
+       "min         0.873000           0.479638                NaN    0.308932   \n",
+       "25%         0.891750           0.538375                NaN    0.445491   \n",
+       "50%         0.896000           0.560000                NaN    0.469791   \n",
+       "75%         0.899000           0.579652                NaN    0.494402   \n",
+       "max         0.912000           0.622222                NaN    0.554716   \n",
+       "\n",
+       "       test Matthews Correff  test Precision  test Recall  valid Accuracy  \\\n",
+       "count                    0.0      184.000000   184.000000      184.000000   \n",
+       "mean                     NaN        0.645122     0.493592        0.920674   \n",
+       "std                      NaN        0.036094     0.045885        0.007117   \n",
+       "min                      NaN        0.523810     0.388060        0.890547   \n",
+       "25%                      NaN        0.622321     0.455224        0.917413   \n",
+       "50%                      NaN        0.642940     0.492537        0.921393   \n",
+       "75%                      NaN        0.670330     0.529851        0.925373   \n",
+       "max                      NaN        0.744681     0.626866        0.936318   \n",
+       "\n",
+       "       valid F1 score bin  valid Kappa Linear  valid Loss  \\\n",
+       "count          184.000000                 0.0  184.000000   \n",
+       "mean             0.587429                 NaN    0.337478   \n",
+       "std              0.020043                 NaN    0.037845   \n",
+       "min              0.531250                 NaN    0.227242   \n",
+       "25%              0.574359                 NaN    0.317254   \n",
+       "50%              0.587629                 NaN    0.338125   \n",
+       "75%              0.600000                 NaN    0.361941   \n",
+       "max              0.648936                 NaN    0.474846   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count                     0.0       184.000000    184.000000  \n",
+       "mean                      NaN         0.527694      0.667008  \n",
+       "std                       NaN         0.033589      0.044828  \n",
+       "min                       NaN         0.421384      0.576471  \n",
+       "25%                       NaN         0.508698      0.635294  \n",
+       "50%                       NaN         0.530000      0.658824  \n",
+       "75%                       NaN         0.547632      0.694118  \n",
+       "max                       NaN         0.617977      0.835294  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "focus=[\"test F1 score bin\", \"valid F1 score bin\"]\n",
+    "reddit = df[df[\"arch\"].str.startswith(\"lstm_ft\") & (df[\"ds\"] == \"reddit\")]\n",
+    "reddit.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\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>model_dir_parent</th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>ds</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.492891</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.500168</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.675325</td>\n",
+       "      <td>0.388060</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>0.569832</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.336440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.542553</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.561702</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.496962</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.653465</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.930348</td>\n",
+       "      <td>0.627660</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.358982</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.572816</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>0.519480</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.463098</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.618557</td>\n",
+       "      <td>0.447761</td>\n",
+       "      <td>0.921393</td>\n",
+       "      <td>0.594872</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.351481</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.527273</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.886</td>\n",
+       "      <td>0.495575</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.449501</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.608696</td>\n",
+       "      <td>0.417910</td>\n",
+       "      <td>0.920398</td>\n",
+       "      <td>0.578947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.289560</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.523810</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.903</td>\n",
+       "      <td>0.587234</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.509811</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.683168</td>\n",
+       "      <td>0.514925</td>\n",
+       "      <td>0.927363</td>\n",
+       "      <td>0.609626</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.393536</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.558824</td>\n",
+       "      <td>0.670588</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.899</td>\n",
+       "      <td>0.558952</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.444723</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.673684</td>\n",
+       "      <td>0.477612</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.322416</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.542857</td>\n",
+       "      <td>0.670588</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.896</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.473386</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.650000</td>\n",
+       "      <td>0.485075</td>\n",
+       "      <td>0.927363</td>\n",
+       "      <td>0.617801</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.381014</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.556604</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.546218</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.457810</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.485075</td>\n",
+       "      <td>0.928358</td>\n",
+       "      <td>0.617021</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.314275</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.563107</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.542222</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.534131</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.670330</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>0.585106</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.375518</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.533981</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.898</td>\n",
+       "      <td>0.552632</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.439562</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.670213</td>\n",
+       "      <td>0.470149</td>\n",
+       "      <td>0.936318</td>\n",
+       "      <td>0.632184</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.259310</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.617977</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.898</td>\n",
+       "      <td>0.544643</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.465242</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.677778</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.920398</td>\n",
+       "      <td>0.587629</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.364343</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.522936</td>\n",
+       "      <td>0.670588</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.906</td>\n",
+       "      <td>0.584071</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.447657</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.717391</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>0.579235</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.327854</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.540816</td>\n",
+       "      <td>0.623529</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.541485</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.481187</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.652632</td>\n",
+       "      <td>0.462687</td>\n",
+       "      <td>0.925373</td>\n",
+       "      <td>0.590164</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.334556</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.551020</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.506912</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.506958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.662651</td>\n",
+       "      <td>0.410448</td>\n",
+       "      <td>0.925373</td>\n",
+       "      <td>0.585635</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.353671</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.552083</td>\n",
+       "      <td>0.623529</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.543779</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.434235</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.710843</td>\n",
+       "      <td>0.440298</td>\n",
+       "      <td>0.931343</td>\n",
+       "      <td>0.614525</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.305361</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.585106</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.898</td>\n",
+       "      <td>0.595238</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.507020</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.635593</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.919403</td>\n",
+       "      <td>0.588832</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.394445</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.517857</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.891</td>\n",
+       "      <td>0.506787</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.460268</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.643678</td>\n",
+       "      <td>0.417910</td>\n",
+       "      <td>0.920398</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.354573</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.526316</td>\n",
+       "      <td>0.588235</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.896</td>\n",
+       "      <td>0.518519</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.453971</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.682927</td>\n",
+       "      <td>0.417910</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>0.583784</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.324631</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.540000</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.891</td>\n",
+       "      <td>0.515556</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.519612</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.637363</td>\n",
+       "      <td>0.432836</td>\n",
+       "      <td>0.926368</td>\n",
+       "      <td>0.593407</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.347545</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.556701</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.908</td>\n",
+       "      <td>0.613445</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.444260</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.701923</td>\n",
+       "      <td>0.544776</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>0.574359</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.354521</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.509091</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.608059</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.512180</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.597122</td>\n",
+       "      <td>0.619403</td>\n",
+       "      <td>0.897512</td>\n",
+       "      <td>0.546256</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.474846</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.436620</td>\n",
+       "      <td>0.729412</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.556962</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.519183</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.640777</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.921393</td>\n",
+       "      <td>0.590674</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.382168</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.527778</td>\n",
+       "      <td>0.670588</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.564516</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.528106</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.614035</td>\n",
+       "      <td>0.522388</td>\n",
+       "      <td>0.915423</td>\n",
+       "      <td>0.581281</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.360472</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.888</td>\n",
+       "      <td>0.513043</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.464349</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.614583</td>\n",
+       "      <td>0.440298</td>\n",
+       "      <td>0.926368</td>\n",
+       "      <td>0.579545</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.261940</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.560440</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.575107</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.435515</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.676768</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>0.585106</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.344719</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.533981</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.902</td>\n",
+       "      <td>0.601626</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.512516</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.660714</td>\n",
+       "      <td>0.552239</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>0.599034</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.362585</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.508197</td>\n",
+       "      <td>0.729412</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.530973</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.515985</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.652174</td>\n",
+       "      <td>0.447761</td>\n",
+       "      <td>0.928358</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.293562</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.568421</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.899</td>\n",
+       "      <td>0.562771</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.452660</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.670103</td>\n",
+       "      <td>0.485075</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>0.595745</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.321803</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.543689</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>0.488479</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.503330</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.638554</td>\n",
+       "      <td>0.395522</td>\n",
+       "      <td>0.931343</td>\n",
+       "      <td>0.596491</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.286096</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.593023</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.896</td>\n",
+       "      <td>0.547826</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.465411</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.656250</td>\n",
+       "      <td>0.470149</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>0.579235</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.317424</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.540816</td>\n",
+       "      <td>0.623529</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</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>306</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-5...</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.595918</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.453026</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.657658</td>\n",
+       "      <td>0.544776</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>0.592593</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.369372</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.538462</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-5...</td>\n",
+       "      <td>0.890</td>\n",
+       "      <td>0.576923</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.412375</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.595238</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.901493</td>\n",
+       "      <td>0.543779</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.341758</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.446970</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.576271</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.458157</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.507463</td>\n",
+       "      <td>0.920398</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.343866</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.523364</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.552301</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.526710</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.628571</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>0.579235</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.376918</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.540816</td>\n",
+       "      <td>0.623529</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.542222</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.378023</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.670330</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.928358</td>\n",
+       "      <td>0.612903</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.245958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.564356</td>\n",
+       "      <td>0.670588</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.549356</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.502734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.646465</td>\n",
+       "      <td>0.477612</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>0.569948</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.366041</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.509259</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.899</td>\n",
+       "      <td>0.542986</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.484061</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.689655</td>\n",
+       "      <td>0.447761</td>\n",
+       "      <td>0.928358</td>\n",
+       "      <td>0.586207</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.292909</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.573034</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>0.561265</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.478323</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.596639</td>\n",
+       "      <td>0.529851</td>\n",
+       "      <td>0.912438</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.371474</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.486726</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.559671</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.460062</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.623853</td>\n",
+       "      <td>0.507463</td>\n",
+       "      <td>0.916418</td>\n",
+       "      <td>0.575758</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.314330</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.504425</td>\n",
+       "      <td>0.670588</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.891</td>\n",
+       "      <td>0.532189</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.520550</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.626263</td>\n",
+       "      <td>0.462687</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>0.586957</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.342852</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.545455</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.548043</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.401640</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.523810</td>\n",
+       "      <td>0.574627</td>\n",
+       "      <td>0.890547</td>\n",
+       "      <td>0.549180</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.359008</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.421384</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.891</td>\n",
+       "      <td>0.528139</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.489808</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.628866</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>0.556150</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.327826</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.509804</td>\n",
+       "      <td>0.611765</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.568965</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.445748</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.673469</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>0.580645</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.303262</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.534653</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.567901</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.542561</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.633027</td>\n",
+       "      <td>0.514925</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>0.589474</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.334571</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.886</td>\n",
+       "      <td>0.508621</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.528722</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.602041</td>\n",
+       "      <td>0.440298</td>\n",
+       "      <td>0.921393</td>\n",
+       "      <td>0.586387</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.341839</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.528302</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.590038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.384665</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.606299</td>\n",
+       "      <td>0.574627</td>\n",
+       "      <td>0.906468</td>\n",
+       "      <td>0.572727</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.314057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.466667</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.534483</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.502705</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.632653</td>\n",
+       "      <td>0.462687</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>0.561798</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.337562</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.537634</td>\n",
+       "      <td>0.588235</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.904</td>\n",
+       "      <td>0.586207</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.488476</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.693878</td>\n",
+       "      <td>0.507463</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>0.589474</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.342792</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.558333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.466521</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.632075</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>0.592593</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.303226</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.538462</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.902</td>\n",
+       "      <td>0.566372</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.447245</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.695652</td>\n",
+       "      <td>0.477612</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>0.546448</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.347164</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.510204</td>\n",
+       "      <td>0.588235</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.592593</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.429450</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.660550</td>\n",
+       "      <td>0.537313</td>\n",
+       "      <td>0.915423</td>\n",
+       "      <td>0.554974</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.313667</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.623529</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.888</td>\n",
+       "      <td>0.521367</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.500936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.610000</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>0.583784</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.378845</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.540000</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.602410</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.490258</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.652174</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.914428</td>\n",
+       "      <td>0.574257</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.392229</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.495726</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.887</td>\n",
+       "      <td>0.502203</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.516663</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.612903</td>\n",
+       "      <td>0.425373</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>0.580645</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.355745</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.534653</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.548523</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.473883</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.631068</td>\n",
+       "      <td>0.485075</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>0.604167</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.329670</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.542056</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.588235</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.490045</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.619835</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.914428</td>\n",
+       "      <td>0.590476</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.358677</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.496000</td>\n",
+       "      <td>0.729412</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.549356</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.458278</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.646465</td>\n",
+       "      <td>0.477612</td>\n",
+       "      <td>0.916418</td>\n",
+       "      <td>0.584158</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.332558</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.504274</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.558333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.485095</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.632075</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.928358</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.338581</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.568421</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.899</td>\n",
+       "      <td>0.607004</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.308932</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.634146</td>\n",
+       "      <td>0.582090</td>\n",
+       "      <td>0.919403</td>\n",
+       "      <td>0.608696</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.237768</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.516393</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.906</td>\n",
+       "      <td>0.614754</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.471272</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.681818</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>0.574359</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.372592</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.509091</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>184 rows × 18 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        model_dir_parent  \\\n",
+       "76   data/hate/pl-10-reddit/models/sp25k   \n",
+       "77   data/hate/pl-10-reddit/models/sp25k   \n",
+       "78   data/hate/pl-10-reddit/models/sp25k   \n",
+       "79   data/hate/pl-10-reddit/models/sp25k   \n",
+       "80   data/hate/pl-10-reddit/models/sp25k   \n",
+       "81   data/hate/pl-10-reddit/models/sp25k   \n",
+       "82   data/hate/pl-10-reddit/models/sp25k   \n",
+       "83   data/hate/pl-10-reddit/models/sp25k   \n",
+       "84   data/hate/pl-10-reddit/models/sp25k   \n",
+       "85   data/hate/pl-10-reddit/models/sp25k   \n",
+       "86   data/hate/pl-10-reddit/models/sp25k   \n",
+       "87   data/hate/pl-10-reddit/models/sp25k   \n",
+       "88   data/hate/pl-10-reddit/models/sp25k   \n",
+       "89   data/hate/pl-10-reddit/models/sp25k   \n",
+       "90   data/hate/pl-10-reddit/models/sp25k   \n",
+       "91   data/hate/pl-10-reddit/models/sp25k   \n",
+       "92   data/hate/pl-10-reddit/models/sp25k   \n",
+       "93   data/hate/pl-10-reddit/models/sp25k   \n",
+       "94   data/hate/pl-10-reddit/models/sp25k   \n",
+       "95   data/hate/pl-10-reddit/models/sp25k   \n",
+       "96   data/hate/pl-10-reddit/models/sp25k   \n",
+       "97   data/hate/pl-10-reddit/models/sp25k   \n",
+       "98   data/hate/pl-10-reddit/models/sp25k   \n",
+       "99   data/hate/pl-10-reddit/models/sp25k   \n",
+       "100  data/hate/pl-10-reddit/models/sp25k   \n",
+       "101  data/hate/pl-10-reddit/models/sp25k   \n",
+       "102  data/hate/pl-10-reddit/models/sp25k   \n",
+       "103  data/hate/pl-10-reddit/models/sp25k   \n",
+       "104  data/hate/pl-10-reddit/models/sp25k   \n",
+       "105  data/hate/pl-10-reddit/models/sp25k   \n",
+       "..                                   ...   \n",
+       "306  data/hate/pl-10-reddit/models/sp25k   \n",
+       "307  data/hate/pl-10-reddit/models/sp25k   \n",
+       "308  data/hate/pl-10-reddit/models/sp25k   \n",
+       "309  data/hate/pl-10-reddit/models/sp25k   \n",
+       "310  data/hate/pl-10-reddit/models/sp25k   \n",
+       "311  data/hate/pl-10-reddit/models/sp25k   \n",
+       "312  data/hate/pl-10-reddit/models/sp25k   \n",
+       "313  data/hate/pl-10-reddit/models/sp25k   \n",
+       "314  data/hate/pl-10-reddit/models/sp25k   \n",
+       "315  data/hate/pl-10-reddit/models/sp25k   \n",
+       "316  data/hate/pl-10-reddit/models/sp25k   \n",
+       "317  data/hate/pl-10-reddit/models/sp25k   \n",
+       "318  data/hate/pl-10-reddit/models/sp25k   \n",
+       "319  data/hate/pl-10-reddit/models/sp25k   \n",
+       "320  data/hate/pl-10-reddit/models/sp25k   \n",
+       "321  data/hate/pl-10-reddit/models/sp25k   \n",
+       "322  data/hate/pl-10-reddit/models/sp25k   \n",
+       "323  data/hate/pl-10-reddit/models/sp25k   \n",
+       "324  data/hate/pl-10-reddit/models/sp25k   \n",
+       "325  data/hate/pl-10-reddit/models/sp25k   \n",
+       "326  data/hate/pl-10-reddit/models/sp25k   \n",
+       "327  data/hate/pl-10-reddit/models/sp25k   \n",
+       "328  data/hate/pl-10-reddit/models/sp25k   \n",
+       "329  data/hate/pl-10-reddit/models/sp25k   \n",
+       "330  data/hate/pl-10-reddit/models/sp25k   \n",
+       "331  data/hate/pl-10-reddit/models/sp25k   \n",
+       "332  data/hate/pl-10-reddit/models/sp25k   \n",
+       "333  data/hate/pl-10-reddit/models/sp25k   \n",
+       "334  data/hate/pl-10-reddit/models/sp25k   \n",
+       "335  data/hate/pl-10-reddit/models/sp25k   \n",
+       "\n",
+       "                                            model_name  test Accuracy  \\\n",
+       "76   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.893   \n",
+       "77   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.897   \n",
+       "78   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.889   \n",
+       "79   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.886   \n",
+       "80   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.903   \n",
+       "81   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.899   \n",
+       "82   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.896   \n",
+       "83   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.892   \n",
+       "84   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.897   \n",
+       "85   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.898   \n",
+       "86   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.898   \n",
+       "87   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.906   \n",
+       "88   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.895   \n",
+       "89   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.893   \n",
+       "90   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.901   \n",
+       "91   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.898   \n",
+       "92   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.891   \n",
+       "93   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.896   \n",
+       "94   lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...          0.891   \n",
+       "95   lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.908   \n",
+       "96   lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.893   \n",
+       "97   lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.895   \n",
+       "98   lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.892   \n",
+       "99   lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.888   \n",
+       "100  lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.901   \n",
+       "101  lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.902   \n",
+       "102  lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.894   \n",
+       "103  lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.899   \n",
+       "104  lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.889   \n",
+       "105  lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.896   \n",
+       "..                                                 ...            ...   \n",
+       "306  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-5...          0.901   \n",
+       "307  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-5...          0.890   \n",
+       "308  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.900   \n",
+       "309  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.893   \n",
+       "310  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.897   \n",
+       "311  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.895   \n",
+       "312  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.899   \n",
+       "313  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.889   \n",
+       "314  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.893   \n",
+       "315  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.891   \n",
+       "316  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.873   \n",
+       "317  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.891   \n",
+       "318  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.900   \n",
+       "319  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.895   \n",
+       "320  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.886   \n",
+       "321  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.893   \n",
+       "322  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.892   \n",
+       "323  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.904   \n",
+       "324  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.894   \n",
+       "325  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.902   \n",
+       "326  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.901   \n",
+       "327  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.888   \n",
+       "328  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.901   \n",
+       "329  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.887   \n",
+       "330  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.893   \n",
+       "331  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.895   \n",
+       "332  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.895   \n",
+       "333  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.894   \n",
+       "334  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.899   \n",
+       "335  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.906   \n",
+       "\n",
+       "     test F1 score bin  test Kappa Linear  test Loss  test Matthews Correff  \\\n",
+       "76            0.492891                NaN   0.500168                    NaN   \n",
+       "77            0.561702                NaN   0.496962                    NaN   \n",
+       "78            0.519480                NaN   0.463098                    NaN   \n",
+       "79            0.495575                NaN   0.449501                    NaN   \n",
+       "80            0.587234                NaN   0.509811                    NaN   \n",
+       "81            0.558952                NaN   0.444723                    NaN   \n",
+       "82            0.555556                NaN   0.473386                    NaN   \n",
+       "83            0.546218                NaN   0.457810                    NaN   \n",
+       "84            0.542222                NaN   0.534131                    NaN   \n",
+       "85            0.552632                NaN   0.439562                    NaN   \n",
+       "86            0.544643                NaN   0.465242                    NaN   \n",
+       "87            0.584071                NaN   0.447657                    NaN   \n",
+       "88            0.541485                NaN   0.481187                    NaN   \n",
+       "89            0.506912                NaN   0.506958                    NaN   \n",
+       "90            0.543779                NaN   0.434235                    NaN   \n",
+       "91            0.595238                NaN   0.507020                    NaN   \n",
+       "92            0.506787                NaN   0.460268                    NaN   \n",
+       "93            0.518519                NaN   0.453971                    NaN   \n",
+       "94            0.515556                NaN   0.519612                    NaN   \n",
+       "95            0.613445                NaN   0.444260                    NaN   \n",
+       "96            0.608059                NaN   0.512180                    NaN   \n",
+       "97            0.556962                NaN   0.519183                    NaN   \n",
+       "98            0.564516                NaN   0.528106                    NaN   \n",
+       "99            0.513043                NaN   0.464349                    NaN   \n",
+       "100           0.575107                NaN   0.435515                    NaN   \n",
+       "101           0.601626                NaN   0.512516                    NaN   \n",
+       "102           0.530973                NaN   0.515985                    NaN   \n",
+       "103           0.562771                NaN   0.452660                    NaN   \n",
+       "104           0.488479                NaN   0.503330                    NaN   \n",
+       "105           0.547826                NaN   0.465411                    NaN   \n",
+       "..                 ...                ...        ...                    ...   \n",
+       "306           0.595918                NaN   0.453026                    NaN   \n",
+       "307           0.576923                NaN   0.412375                    NaN   \n",
+       "308           0.576271                NaN   0.458157                    NaN   \n",
+       "309           0.552301                NaN   0.526710                    NaN   \n",
+       "310           0.542222                NaN   0.378023                    NaN   \n",
+       "311           0.549356                NaN   0.502734                    NaN   \n",
+       "312           0.542986                NaN   0.484061                    NaN   \n",
+       "313           0.561265                NaN   0.478323                    NaN   \n",
+       "314           0.559671                NaN   0.460062                    NaN   \n",
+       "315           0.532189                NaN   0.520550                    NaN   \n",
+       "316           0.548043                NaN   0.401640                    NaN   \n",
+       "317           0.528139                NaN   0.489808                    NaN   \n",
+       "318           0.568965                NaN   0.445748                    NaN   \n",
+       "319           0.567901                NaN   0.542561                    NaN   \n",
+       "320           0.508621                NaN   0.528722                    NaN   \n",
+       "321           0.590038                NaN   0.384665                    NaN   \n",
+       "322           0.534483                NaN   0.502705                    NaN   \n",
+       "323           0.586207                NaN   0.488476                    NaN   \n",
+       "324           0.558333                NaN   0.466521                    NaN   \n",
+       "325           0.566372                NaN   0.447245                    NaN   \n",
+       "326           0.592593                NaN   0.429450                    NaN   \n",
+       "327           0.521367                NaN   0.500936                    NaN   \n",
+       "328           0.602410                NaN   0.490258                    NaN   \n",
+       "329           0.502203                NaN   0.516663                    NaN   \n",
+       "330           0.548523                NaN   0.473883                    NaN   \n",
+       "331           0.588235                NaN   0.490045                    NaN   \n",
+       "332           0.549356                NaN   0.458278                    NaN   \n",
+       "333           0.558333                NaN   0.485095                    NaN   \n",
+       "334           0.607004                NaN   0.308932                    NaN   \n",
+       "335           0.614754                NaN   0.471272                    NaN   \n",
+       "\n",
+       "     test Precision  test Recall  valid Accuracy  valid F1 score bin  \\\n",
+       "76         0.675325     0.388060        0.923383            0.569832   \n",
+       "77         0.653465     0.492537        0.930348            0.627660   \n",
+       "78         0.618557     0.447761        0.921393            0.594872   \n",
+       "79         0.608696     0.417910        0.920398            0.578947   \n",
+       "80         0.683168     0.514925        0.927363            0.609626   \n",
+       "81         0.673684     0.477612        0.924378            0.600000   \n",
+       "82         0.650000     0.485075        0.927363            0.617801   \n",
+       "83         0.625000     0.485075        0.928358            0.617021   \n",
+       "84         0.670330     0.455224        0.922388            0.585106   \n",
+       "85         0.670213     0.470149        0.936318            0.632184   \n",
+       "86         0.677778     0.455224        0.920398            0.587629   \n",
+       "87         0.717391     0.492537        0.923383            0.579235   \n",
+       "88         0.652632     0.462687        0.925373            0.590164   \n",
+       "89         0.662651     0.410448        0.925373            0.585635   \n",
+       "90         0.710843     0.440298        0.931343            0.614525   \n",
+       "91         0.635593     0.559702        0.919403            0.588832   \n",
+       "92         0.643678     0.417910        0.920398            0.555556   \n",
+       "93         0.682927     0.417910        0.923383            0.583784   \n",
+       "94         0.637363     0.432836        0.926368            0.593407   \n",
+       "95         0.701923     0.544776        0.917413            0.574359   \n",
+       "96         0.597122     0.619403        0.897512            0.546256   \n",
+       "97         0.640777     0.492537        0.921393            0.590674   \n",
+       "98         0.614035     0.522388        0.915423            0.581281   \n",
+       "99         0.614583     0.440298        0.926368            0.579545   \n",
+       "100        0.676768     0.500000        0.922388            0.585106   \n",
+       "101        0.660714     0.552239        0.917413            0.599034   \n",
+       "102        0.652174     0.447761        0.928358            0.600000   \n",
+       "103        0.670103     0.485075        0.924378            0.595745   \n",
+       "104        0.638554     0.395522        0.931343            0.596491   \n",
+       "105        0.656250     0.470149        0.923383            0.579235   \n",
+       "..              ...          ...             ...                 ...   \n",
+       "306        0.657658     0.544776        0.923383            0.592593   \n",
+       "307        0.595238     0.559702        0.901493            0.543779   \n",
+       "308        0.666667     0.507463        0.920398            0.583333   \n",
+       "309        0.628571     0.492537        0.923383            0.579235   \n",
+       "310        0.670330     0.455224        0.928358            0.612903   \n",
+       "311        0.646465     0.477612        0.917413            0.569948   \n",
+       "312        0.689655     0.447761        0.928358            0.586207   \n",
+       "313        0.596639     0.529851        0.912438            0.555556   \n",
+       "314        0.623853     0.507463        0.916418            0.575758   \n",
+       "315        0.626263     0.462687        0.924378            0.586957   \n",
+       "316        0.523810     0.574627        0.890547            0.549180   \n",
+       "317        0.628866     0.455224        0.917413            0.556150   \n",
+       "318        0.673469     0.492537        0.922388            0.580645   \n",
+       "319        0.633027     0.514925        0.922388            0.589474   \n",
+       "320        0.602041     0.440298        0.921393            0.586387   \n",
+       "321        0.606299     0.574627        0.906468            0.572727   \n",
+       "322        0.632653     0.462687        0.922388            0.561798   \n",
+       "323        0.693878     0.507463        0.922388            0.589474   \n",
+       "324        0.632075     0.500000        0.923383            0.592593   \n",
+       "325        0.695652     0.477612        0.917413            0.546448   \n",
+       "326        0.660550     0.537313        0.915423            0.554974   \n",
+       "327        0.610000     0.455224        0.923383            0.583784   \n",
+       "328        0.652174     0.559702        0.914428            0.574257   \n",
+       "329        0.612903     0.425373        0.922388            0.580645   \n",
+       "330        0.631068     0.485075        0.924378            0.604167   \n",
+       "331        0.619835     0.559702        0.914428            0.590476   \n",
+       "332        0.646465     0.477612        0.916418            0.584158   \n",
+       "333        0.632075     0.500000        0.928358            0.600000   \n",
+       "334        0.634146     0.582090        0.919403            0.608696   \n",
+       "335        0.681818     0.559702        0.917413            0.574359   \n",
+       "\n",
+       "     valid Kappa Linear  valid Loss  valid Matthews Correff  valid Precision  \\\n",
+       "76                  NaN    0.336440                     NaN         0.542553   \n",
+       "77                  NaN    0.358982                     NaN         0.572816   \n",
+       "78                  NaN    0.351481                     NaN         0.527273   \n",
+       "79                  NaN    0.289560                     NaN         0.523810   \n",
+       "80                  NaN    0.393536                     NaN         0.558824   \n",
+       "81                  NaN    0.322416                     NaN         0.542857   \n",
+       "82                  NaN    0.381014                     NaN         0.556604   \n",
+       "83                  NaN    0.314275                     NaN         0.563107   \n",
+       "84                  NaN    0.375518                     NaN         0.533981   \n",
+       "85                  NaN    0.259310                     NaN         0.617977   \n",
+       "86                  NaN    0.364343                     NaN         0.522936   \n",
+       "87                  NaN    0.327854                     NaN         0.540816   \n",
+       "88                  NaN    0.334556                     NaN         0.551020   \n",
+       "89                  NaN    0.353671                     NaN         0.552083   \n",
+       "90                  NaN    0.305361                     NaN         0.585106   \n",
+       "91                  NaN    0.394445                     NaN         0.517857   \n",
+       "92                  NaN    0.354573                     NaN         0.526316   \n",
+       "93                  NaN    0.324631                     NaN         0.540000   \n",
+       "94                  NaN    0.347545                     NaN         0.556701   \n",
+       "95                  NaN    0.354521                     NaN         0.509091   \n",
+       "96                  NaN    0.474846                     NaN         0.436620   \n",
+       "97                  NaN    0.382168                     NaN         0.527778   \n",
+       "98                  NaN    0.360472                     NaN         0.500000   \n",
+       "99                  NaN    0.261940                     NaN         0.560440   \n",
+       "100                 NaN    0.344719                     NaN         0.533981   \n",
+       "101                 NaN    0.362585                     NaN         0.508197   \n",
+       "102                 NaN    0.293562                     NaN         0.568421   \n",
+       "103                 NaN    0.321803                     NaN         0.543689   \n",
+       "104                 NaN    0.286096                     NaN         0.593023   \n",
+       "105                 NaN    0.317424                     NaN         0.540816   \n",
+       "..                  ...         ...                     ...              ...   \n",
+       "306                 NaN    0.369372                     NaN         0.538462   \n",
+       "307                 NaN    0.341758                     NaN         0.446970   \n",
+       "308                 NaN    0.343866                     NaN         0.523364   \n",
+       "309                 NaN    0.376918                     NaN         0.540816   \n",
+       "310                 NaN    0.245958                     NaN         0.564356   \n",
+       "311                 NaN    0.366041                     NaN         0.509259   \n",
+       "312                 NaN    0.292909                     NaN         0.573034   \n",
+       "313                 NaN    0.371474                     NaN         0.486726   \n",
+       "314                 NaN    0.314330                     NaN         0.504425   \n",
+       "315                 NaN    0.342852                     NaN         0.545455   \n",
+       "316                 NaN    0.359008                     NaN         0.421384   \n",
+       "317                 NaN    0.327826                     NaN         0.509804   \n",
+       "318                 NaN    0.303262                     NaN         0.534653   \n",
+       "319                 NaN    0.334571                     NaN         0.533333   \n",
+       "320                 NaN    0.341839                     NaN         0.528302   \n",
+       "321                 NaN    0.314057                     NaN         0.466667   \n",
+       "322                 NaN    0.337562                     NaN         0.537634   \n",
+       "323                 NaN    0.342792                     NaN         0.533333   \n",
+       "324                 NaN    0.303226                     NaN         0.538462   \n",
+       "325                 NaN    0.347164                     NaN         0.510204   \n",
+       "326                 NaN    0.313667                     NaN         0.500000   \n",
+       "327                 NaN    0.378845                     NaN         0.540000   \n",
+       "328                 NaN    0.392229                     NaN         0.495726   \n",
+       "329                 NaN    0.355745                     NaN         0.534653   \n",
+       "330                 NaN    0.329670                     NaN         0.542056   \n",
+       "331                 NaN    0.358677                     NaN         0.496000   \n",
+       "332                 NaN    0.332558                     NaN         0.504274   \n",
+       "333                 NaN    0.338581                     NaN         0.568421   \n",
+       "334                 NaN    0.237768                     NaN         0.516393   \n",
+       "335                 NaN    0.372592                     NaN         0.509091   \n",
+       "\n",
+       "     valid Recall           arch      ds  \n",
+       "76       0.600000  lstm_ft20_cl8  reddit  \n",
+       "77       0.694118  lstm_ft20_cl8  reddit  \n",
+       "78       0.682353  lstm_ft20_cl8  reddit  \n",
+       "79       0.647059  lstm_ft20_cl8  reddit  \n",
+       "80       0.670588  lstm_ft20_cl8  reddit  \n",
+       "81       0.670588  lstm_ft20_cl8  reddit  \n",
+       "82       0.694118  lstm_ft20_cl8  reddit  \n",
+       "83       0.682353  lstm_ft20_cl8  reddit  \n",
+       "84       0.647059  lstm_ft20_cl8  reddit  \n",
+       "85       0.647059  lstm_ft20_cl8  reddit  \n",
+       "86       0.670588  lstm_ft20_cl8  reddit  \n",
+       "87       0.623529  lstm_ft20_cl8  reddit  \n",
+       "88       0.635294  lstm_ft20_cl8  reddit  \n",
+       "89       0.623529  lstm_ft20_cl8  reddit  \n",
+       "90       0.647059  lstm_ft20_cl8  reddit  \n",
+       "91       0.682353  lstm_ft20_cl8  reddit  \n",
+       "92       0.588235  lstm_ft20_cl8  reddit  \n",
+       "93       0.635294  lstm_ft20_cl8  reddit  \n",
+       "94       0.635294  lstm_ft20_cl8  reddit  \n",
+       "95       0.658824  lstm_ft20_cl8  reddit  \n",
+       "96       0.729412  lstm_ft20_cl8  reddit  \n",
+       "97       0.670588  lstm_ft20_cl8  reddit  \n",
+       "98       0.694118  lstm_ft20_cl8  reddit  \n",
+       "99       0.600000  lstm_ft20_cl8  reddit  \n",
+       "100      0.647059  lstm_ft20_cl8  reddit  \n",
+       "101      0.729412  lstm_ft20_cl8  reddit  \n",
+       "102      0.635294  lstm_ft20_cl8  reddit  \n",
+       "103      0.658824  lstm_ft20_cl8  reddit  \n",
+       "104      0.600000  lstm_ft20_cl8  reddit  \n",
+       "105      0.623529  lstm_ft20_cl8  reddit  \n",
+       "..            ...            ...     ...  \n",
+       "306      0.658824   lstm_ft6_cl8  reddit  \n",
+       "307      0.694118   lstm_ft6_cl8  reddit  \n",
+       "308      0.658824   lstm_ft6_cl8  reddit  \n",
+       "309      0.623529   lstm_ft6_cl8  reddit  \n",
+       "310      0.670588   lstm_ft6_cl8  reddit  \n",
+       "311      0.647059   lstm_ft6_cl8  reddit  \n",
+       "312      0.600000   lstm_ft6_cl8  reddit  \n",
+       "313      0.647059   lstm_ft6_cl8  reddit  \n",
+       "314      0.670588   lstm_ft6_cl8  reddit  \n",
+       "315      0.635294   lstm_ft6_cl8  reddit  \n",
+       "316      0.788235   lstm_ft6_cl8  reddit  \n",
+       "317      0.611765   lstm_ft6_cl8  reddit  \n",
+       "318      0.635294   lstm_ft6_cl8  reddit  \n",
+       "319      0.658824   lstm_ft6_cl8  reddit  \n",
+       "320      0.658824   lstm_ft6_cl8  reddit  \n",
+       "321      0.741176   lstm_ft6_cl8  reddit  \n",
+       "322      0.588235   lstm_ft6_cl8  reddit  \n",
+       "323      0.658824   lstm_ft6_cl8  reddit  \n",
+       "324      0.658824   lstm_ft6_cl8  reddit  \n",
+       "325      0.588235   lstm_ft6_cl8  reddit  \n",
+       "326      0.623529   lstm_ft6_cl8  reddit  \n",
+       "327      0.635294   lstm_ft6_cl8  reddit  \n",
+       "328      0.682353   lstm_ft6_cl8  reddit  \n",
+       "329      0.635294   lstm_ft6_cl8  reddit  \n",
+       "330      0.682353   lstm_ft6_cl8  reddit  \n",
+       "331      0.729412   lstm_ft6_cl8  reddit  \n",
+       "332      0.694118   lstm_ft6_cl8  reddit  \n",
+       "333      0.635294   lstm_ft6_cl8  reddit  \n",
+       "334      0.741176   lstm_ft6_cl8  reddit  \n",
+       "335      0.658824   lstm_ft6_cl8  reddit  \n",
+       "\n",
+       "[184 rows x 18 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reddit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>wiki test F1</th>\n",
+       "      <th>reddit test F1</th>\n",
+       "      <th>diff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>210.000000</td>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>-26.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.514146</td>\n",
+       "      <td>0.557343</td>\n",
+       "      <td>0.043197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.052149</td>\n",
+       "      <td>0.029341</td>\n",
+       "      <td>-0.022808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.340909</td>\n",
+       "      <td>0.479638</td>\n",
+       "      <td>0.138729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.495575</td>\n",
+       "      <td>0.538375</td>\n",
+       "      <td>0.042800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.522800</td>\n",
+       "      <td>0.560000</td>\n",
+       "      <td>0.037200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.550000</td>\n",
+       "      <td>0.579652</td>\n",
+       "      <td>0.029652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.608392</td>\n",
+       "      <td>0.622222</td>\n",
+       "      <td>0.013831</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       wiki test F1  reddit test F1       diff\n",
+       "count    210.000000      184.000000 -26.000000\n",
+       "mean       0.514146        0.557343   0.043197\n",
+       "std        0.052149        0.029341  -0.022808\n",
+       "min        0.340909        0.479638   0.138729\n",
+       "25%        0.495575        0.538375   0.042800\n",
+       "50%        0.522800        0.560000   0.037200\n",
+       "75%        0.550000        0.579652   0.029652\n",
+       "max        0.608392        0.622222   0.013831"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wikivsreddit = pd.DataFrame({\"wiki test F1\":wiki.describe()[\"test F1 score bin\"], \n",
+    "              \"reddit test F1\": reddit.describe()[\"test F1 score bin\"]})\n",
+    "wikivsreddit[\"diff\"] = wikivsreddit[\"reddit test F1\"] - wikivsreddit[\"wiki test F1\"]\n",
+    "wikivsreddit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dropout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>76.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.888961</td>\n",
+       "      <td>0.528825</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.524034</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.615529</td>\n",
+       "      <td>0.467400</td>\n",
+       "      <td>0.921406</td>\n",
+       "      <td>0.585514</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.351811</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.534038</td>\n",
+       "      <td>0.654489</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.005927</td>\n",
+       "      <td>0.031651</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.063405</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.036916</td>\n",
+       "      <td>0.052513</td>\n",
+       "      <td>0.009106</td>\n",
+       "      <td>0.025376</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.048383</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.045318</td>\n",
+       "      <td>0.048253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.874000</td>\n",
+       "      <td>0.457944</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.337015</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.530303</td>\n",
+       "      <td>0.365672</td>\n",
+       "      <td>0.892537</td>\n",
+       "      <td>0.526316</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.247777</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.419580</td>\n",
+       "      <td>0.541176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.885000</td>\n",
+       "      <td>0.509484</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.492292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.594943</td>\n",
+       "      <td>0.432836</td>\n",
+       "      <td>0.916418</td>\n",
+       "      <td>0.568252</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.316945</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.504505</td>\n",
+       "      <td>0.620588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.890000</td>\n",
+       "      <td>0.526988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.541785</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.614966</td>\n",
+       "      <td>0.462687</td>\n",
+       "      <td>0.921393</td>\n",
+       "      <td>0.583935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.346929</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.528846</td>\n",
+       "      <td>0.652941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.892250</td>\n",
+       "      <td>0.548926</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.562234</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.638811</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.928358</td>\n",
+       "      <td>0.604476</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.388405</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.569490</td>\n",
+       "      <td>0.682353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.902000</td>\n",
+       "      <td>0.602151</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.641866</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.725000</td>\n",
+       "      <td>0.626866</td>\n",
+       "      <td>0.938308</td>\n",
+       "      <td>0.643564</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.453879</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.632184</td>\n",
+       "      <td>0.776471</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Kappa Linear  test Loss  \\\n",
+       "count      76.000000          76.000000                0.0  76.000000   \n",
+       "mean        0.888961           0.528825                NaN   0.524034   \n",
+       "std         0.005927           0.031651                NaN   0.063405   \n",
+       "min         0.874000           0.457944                NaN   0.337015   \n",
+       "25%         0.885000           0.509484                NaN   0.492292   \n",
+       "50%         0.890000           0.526988                NaN   0.541785   \n",
+       "75%         0.892250           0.548926                NaN   0.562234   \n",
+       "max         0.902000           0.602151                NaN   0.641866   \n",
+       "\n",
+       "       test Matthews Correff  test Precision  test Recall  valid Accuracy  \\\n",
+       "count                    0.0       76.000000    76.000000       76.000000   \n",
+       "mean                     NaN        0.615529     0.467400        0.921406   \n",
+       "std                      NaN        0.036916     0.052513        0.009106   \n",
+       "min                      NaN        0.530303     0.365672        0.892537   \n",
+       "25%                      NaN        0.594943     0.432836        0.916418   \n",
+       "50%                      NaN        0.614966     0.462687        0.921393   \n",
+       "75%                      NaN        0.638811     0.500000        0.928358   \n",
+       "max                      NaN        0.725000     0.626866        0.938308   \n",
+       "\n",
+       "       valid F1 score bin  valid Kappa Linear  valid Loss  \\\n",
+       "count           76.000000                 0.0   76.000000   \n",
+       "mean             0.585514                 NaN    0.351811   \n",
+       "std              0.025376                 NaN    0.048383   \n",
+       "min              0.526316                 NaN    0.247777   \n",
+       "25%              0.568252                 NaN    0.316945   \n",
+       "50%              0.583935                 NaN    0.346929   \n",
+       "75%              0.604476                 NaN    0.388405   \n",
+       "max              0.643564                 NaN    0.453879   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count                     0.0        76.000000     76.000000  \n",
+       "mean                      NaN         0.534038      0.654489  \n",
+       "std                       NaN         0.045318      0.048253  \n",
+       "min                       NaN         0.419580      0.541176  \n",
+       "25%                       NaN         0.504505      0.620588  \n",
+       "50%                       NaN         0.528846      0.652941  \n",
+       "75%                       NaN         0.569490      0.682353  \n",
+       "max                       NaN         0.632184      0.776471  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"model_name\"].str.startswith(\"lstm_dp\") & df[\"model_dir_parent\"].str.contains(\"reddit\")].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Select submission\n",
+    "\n",
+    "- `X` Select Using F1\n",
+    "- `X` Select Using Accuracy\n",
+    "   - [ ] Retrain the best 10 models, using \"accuracy early stopping\"\n",
+    "- [x] Use half test for validation \n",
+    "   - `V` select using F1\n",
+    "   - `V` select using Accuracy\n",
+    "- [x] Deduplicate validation\n",
+    "   - `X` Select using F1\n",
+    "   - [ ] Select using Accuracy\n",
+    "   \n",
+    "## Raw - Accuracy "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": false
+   },
+   "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>test F1 score bin</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Recall</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.617801</td>\n",
+       "      <td>0.896</td>\n",
+       "      <td>0.485075</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>0.650000</td>\n",
+       "      <td>0.556604</td>\n",
+       "      <td>0.617801</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>0.567686</td>\n",
+       "      <td>0.618557</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.485075</td>\n",
+       "      <td>0.705882</td>\n",
+       "      <td>0.684211</td>\n",
+       "      <td>0.550459</td>\n",
+       "      <td>0.618557</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-9...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>0.537037</td>\n",
+       "      <td>0.619883</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.432836</td>\n",
+       "      <td>0.623529</td>\n",
+       "      <td>0.707317</td>\n",
+       "      <td>0.616279</td>\n",
+       "      <td>0.619883</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>0.549550</td>\n",
+       "      <td>0.620690</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>0.693182</td>\n",
+       "      <td>0.606742</td>\n",
+       "      <td>0.620690</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>0.513514</td>\n",
+       "      <td>0.621053</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.425373</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>0.647727</td>\n",
+       "      <td>0.561905</td>\n",
+       "      <td>0.621053</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-4...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>0.531532</td>\n",
+       "      <td>0.625698</td>\n",
+       "      <td>0.896</td>\n",
+       "      <td>0.440298</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>0.670455</td>\n",
+       "      <td>0.595745</td>\n",
+       "      <td>0.625698</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-5...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>0.561702</td>\n",
+       "      <td>0.627660</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>0.653465</td>\n",
+       "      <td>0.572816</td>\n",
+       "      <td>0.627660</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>0.561702</td>\n",
+       "      <td>0.631579</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.705882</td>\n",
+       "      <td>0.653465</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.631579</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-4...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>0.552632</td>\n",
+       "      <td>0.632184</td>\n",
+       "      <td>0.898</td>\n",
+       "      <td>0.470149</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>0.670213</td>\n",
+       "      <td>0.617977</td>\n",
+       "      <td>0.632184</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>0.565217</td>\n",
+       "      <td>0.648936</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.485075</td>\n",
+       "      <td>0.717647</td>\n",
+       "      <td>0.677083</td>\n",
+       "      <td>0.592233</td>\n",
+       "      <td>0.648936</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-5...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     test F1 score bin  valid F1 score bin  test Accuracy  test Recall  \\\n",
+       "82            0.555556            0.617801          0.896     0.485075   \n",
+       "257           0.567686            0.618557          0.901     0.485075   \n",
+       "107           0.537037            0.619883          0.900     0.432836   \n",
+       "113           0.549550            0.620690          0.900     0.455224   \n",
+       "297           0.513514            0.621053          0.892     0.425373   \n",
+       "228           0.531532            0.625698          0.896     0.440298   \n",
+       "77            0.561702            0.627660          0.897     0.492537   \n",
+       "227           0.561702            0.631579          0.897     0.492537   \n",
+       "85            0.552632            0.632184          0.898     0.470149   \n",
+       "230           0.565217            0.648936          0.900     0.485075   \n",
+       "\n",
+       "     valid Recall  test Precision  valid Precision  valid F1 score bin  \\\n",
+       "82       0.694118        0.650000         0.556604            0.617801   \n",
+       "257      0.705882        0.684211         0.550459            0.618557   \n",
+       "107      0.623529        0.707317         0.616279            0.619883   \n",
+       "113      0.635294        0.693182         0.606742            0.620690   \n",
+       "297      0.694118        0.647727         0.561905            0.621053   \n",
+       "228      0.658824        0.670455         0.595745            0.625698   \n",
+       "77       0.694118        0.653465         0.572816            0.627660   \n",
+       "227      0.705882        0.653465         0.571429            0.631579   \n",
+       "85       0.647059        0.670213         0.617977            0.632184   \n",
+       "230      0.717647        0.677083         0.592233            0.648936   \n",
+       "\n",
+       "              arch      ds                                         model_name  \n",
+       "82   lstm_ft20_cl8  reddit  lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...  \n",
+       "257   lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-9...  \n",
+       "107  lstm_ft20_cl8  reddit  lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...  \n",
+       "113  lstm_ft20_cl8  reddit  lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...  \n",
+       "297   lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-4...  \n",
+       "228   lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-5...  \n",
+       "77   lstm_ft20_cl8  reddit  lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...  \n",
+       "227   lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-4...  \n",
+       "85   lstm_ft20_cl8  reddit  lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...  \n",
+       "230   lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-5...  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "order=\"valid F1 score bin\"\n",
+    "focus=[\"test F1 score bin\", \"valid F1 score bin\", \n",
+    "       \"test Accuracy\", \"test Recall\", \"valid Recall\",\n",
+    "       \"test Precision\", \"valid Precision\", order, \"arch\", \"ds\", \"model_name\"]\n",
+    "reddit.sort_values([order])[focus].tail(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": false
+   },
+   "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>test F1 score bin</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Recall</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>0.602511</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.905</td>\n",
+       "      <td>0.537313</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>0.685714</td>\n",
+       "      <td>0.491525</td>\n",
+       "      <td>0.602511</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>0.605042</td>\n",
+       "      <td>0.587629</td>\n",
+       "      <td>0.906</td>\n",
+       "      <td>0.537313</td>\n",
+       "      <td>0.670588</td>\n",
+       "      <td>0.692308</td>\n",
+       "      <td>0.522936</td>\n",
+       "      <td>0.605042</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-6...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>0.605578</td>\n",
+       "      <td>0.594059</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.567164</td>\n",
+       "      <td>0.705882</td>\n",
+       "      <td>0.649573</td>\n",
+       "      <td>0.512821</td>\n",
+       "      <td>0.605578</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>0.607004</td>\n",
+       "      <td>0.608696</td>\n",
+       "      <td>0.899</td>\n",
+       "      <td>0.582090</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>0.634146</td>\n",
+       "      <td>0.516393</td>\n",
+       "      <td>0.607004</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>0.608059</td>\n",
+       "      <td>0.546256</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.619403</td>\n",
+       "      <td>0.729412</td>\n",
+       "      <td>0.597122</td>\n",
+       "      <td>0.436620</td>\n",
+       "      <td>0.608059</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>0.613445</td>\n",
+       "      <td>0.574359</td>\n",
+       "      <td>0.908</td>\n",
+       "      <td>0.544776</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>0.701923</td>\n",
+       "      <td>0.509091</td>\n",
+       "      <td>0.613445</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>0.614035</td>\n",
+       "      <td>0.572973</td>\n",
+       "      <td>0.912</td>\n",
+       "      <td>0.522388</td>\n",
+       "      <td>0.623529</td>\n",
+       "      <td>0.744681</td>\n",
+       "      <td>0.530000</td>\n",
+       "      <td>0.614035</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>0.614232</td>\n",
+       "      <td>0.591667</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.611940</td>\n",
+       "      <td>0.835294</td>\n",
+       "      <td>0.616541</td>\n",
+       "      <td>0.458065</td>\n",
+       "      <td>0.614232</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>0.614754</td>\n",
+       "      <td>0.574359</td>\n",
+       "      <td>0.906</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>0.681818</td>\n",
+       "      <td>0.509091</td>\n",
+       "      <td>0.614754</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>0.622222</td>\n",
+       "      <td>0.579439</td>\n",
+       "      <td>0.898</td>\n",
+       "      <td>0.626866</td>\n",
+       "      <td>0.729412</td>\n",
+       "      <td>0.617647</td>\n",
+       "      <td>0.480620</td>\n",
+       "      <td>0.622222</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-1...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     test F1 score bin  valid F1 score bin  test Accuracy  test Recall  \\\n",
+       "190           0.602511            0.571429          0.905     0.537313   \n",
+       "236           0.605042            0.587629          0.906     0.537313   \n",
+       "273           0.605578            0.594059          0.901     0.567164   \n",
+       "334           0.607004            0.608696          0.899     0.582090   \n",
+       "96            0.608059            0.546256          0.893     0.619403   \n",
+       "95            0.613445            0.574359          0.908     0.544776   \n",
+       "290           0.614035            0.572973          0.912     0.522388   \n",
+       "213           0.614232            0.591667          0.897     0.611940   \n",
+       "335           0.614754            0.574359          0.906     0.559702   \n",
+       "275           0.622222            0.579439          0.898     0.626866   \n",
+       "\n",
+       "     valid Recall  test Precision  valid Precision  test F1 score bin  \\\n",
+       "190      0.682353        0.685714         0.491525           0.602511   \n",
+       "236      0.670588        0.692308         0.522936           0.605042   \n",
+       "273      0.705882        0.649573         0.512821           0.605578   \n",
+       "334      0.741176        0.634146         0.516393           0.607004   \n",
+       "96       0.729412        0.597122         0.436620           0.608059   \n",
+       "95       0.658824        0.701923         0.509091           0.613445   \n",
+       "290      0.623529        0.744681         0.530000           0.614035   \n",
+       "213      0.835294        0.616541         0.458065           0.614232   \n",
+       "335      0.658824        0.681818         0.509091           0.614754   \n",
+       "275      0.729412        0.617647         0.480620           0.622222   \n",
+       "\n",
+       "              arch      ds                                         model_name  \n",
+       "190   lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...  \n",
+       "236   lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-6...  \n",
+       "273   lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-1...  \n",
+       "334   lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...  \n",
+       "96   lstm_ft20_cl8  reddit  lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...  \n",
+       "95   lstm_ft20_cl8  reddit  lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...  \n",
+       "290   lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-3...  \n",
+       "213   lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-2...  \n",
+       "335   lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...  \n",
+       "275   lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-1...  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "order=\"test F1 score bin\"\n",
+    "focus=[\"test F1 score bin\", \"valid F1 score bin\", \n",
+    "       \"test Accuracy\", \"test Recall\", \"valid Recall\",\n",
+    "       \"test Precision\", \"valid Precision\", order, \"arch\", \"ds\", \"model_name\"]\n",
+    "reddit.sort_values([order])[focus].tail(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>test F1 score bin</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Recall</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>0.607004</td>\n",
+       "      <td>0.608696</td>\n",
+       "      <td>0.899</td>\n",
+       "      <td>0.582090</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>0.634146</td>\n",
+       "      <td>0.516393</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>0.538462</td>\n",
+       "      <td>0.565022</td>\n",
+       "      <td>0.880</td>\n",
+       "      <td>0.522388</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.456522</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>0.588710</td>\n",
+       "      <td>0.611650</td>\n",
+       "      <td>0.898</td>\n",
+       "      <td>0.544776</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>0.640351</td>\n",
+       "      <td>0.520661</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-4...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>0.596899</td>\n",
+       "      <td>0.580357</td>\n",
+       "      <td>0.896</td>\n",
+       "      <td>0.574627</td>\n",
+       "      <td>0.764706</td>\n",
+       "      <td>0.620968</td>\n",
+       "      <td>0.467626</td>\n",
+       "      <td>0.764706</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>0.510460</td>\n",
+       "      <td>0.602740</td>\n",
+       "      <td>0.883</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.776471</td>\n",
+       "      <td>0.580952</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.776471</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>0.548043</td>\n",
+       "      <td>0.549180</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.574627</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>0.523810</td>\n",
+       "      <td>0.421384</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>0.553030</td>\n",
+       "      <td>0.572650</td>\n",
+       "      <td>0.882</td>\n",
+       "      <td>0.544776</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>0.561538</td>\n",
+       "      <td>0.449664</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-6...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>0.592308</td>\n",
+       "      <td>0.617512</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.574627</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>0.611111</td>\n",
+       "      <td>0.507576</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-9...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>0.585185</td>\n",
+       "      <td>0.593220</td>\n",
+       "      <td>0.888</td>\n",
+       "      <td>0.589552</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>0.580882</td>\n",
+       "      <td>0.463576</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>0.614232</td>\n",
+       "      <td>0.591667</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.611940</td>\n",
+       "      <td>0.835294</td>\n",
+       "      <td>0.616541</td>\n",
+       "      <td>0.458065</td>\n",
+       "      <td>0.835294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-2...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     test F1 score bin  valid F1 score bin  test Accuracy  test Recall  \\\n",
+       "334           0.607004            0.608696          0.899     0.582090   \n",
+       "288           0.538462            0.565022          0.880     0.522388   \n",
+       "225           0.588710            0.611650          0.898     0.544776   \n",
+       "261           0.596899            0.580357          0.896     0.574627   \n",
+       "218           0.510460            0.602740          0.883     0.455224   \n",
+       "316           0.548043            0.549180          0.873     0.574627   \n",
+       "238           0.553030            0.572650          0.882     0.544776   \n",
+       "259           0.592308            0.617512          0.894     0.574627   \n",
+       "204           0.585185            0.593220          0.888     0.589552   \n",
+       "213           0.614232            0.591667          0.897     0.611940   \n",
+       "\n",
+       "     valid Recall  test Precision  valid Precision  valid Recall  \\\n",
+       "334      0.741176        0.634146         0.516393      0.741176   \n",
+       "288      0.741176        0.555556         0.456522      0.741176   \n",
+       "225      0.741176        0.640351         0.520661      0.741176   \n",
+       "261      0.764706        0.620968         0.467626      0.764706   \n",
+       "218      0.776471        0.580952         0.492537      0.776471   \n",
+       "316      0.788235        0.523810         0.421384      0.788235   \n",
+       "238      0.788235        0.561538         0.449664      0.788235   \n",
+       "259      0.788235        0.611111         0.507576      0.788235   \n",
+       "204      0.823529        0.580882         0.463576      0.823529   \n",
+       "213      0.835294        0.616541         0.458065      0.835294   \n",
+       "\n",
+       "             arch      ds                                         model_name  \n",
+       "334  lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...  \n",
+       "288  lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-3...  \n",
+       "225  lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-4...  \n",
+       "261  lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-0...  \n",
+       "218  lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-3...  \n",
+       "316  lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...  \n",
+       "238  lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-6...  \n",
+       "259  lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-9...  \n",
+       "204  lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-1...  \n",
+       "213  lstm_ft6_cl8  reddit  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-2...  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "order=\"valid Recall\"\n",
+    "focus=[\"test F1 score bin\", \"valid F1 score bin\", \n",
+    "       \"test Accuracy\", \"test Recall\", \"valid Recall\",\n",
+    "       \"test Precision\", \"valid Precision\", order, \"arch\", \"ds\", \"model_name\"]\n",
+    "reddit.sort_values([order])[focus].tail(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# kappa\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>0.534799</td>\n",
+       "      <td>0.508929</td>\n",
+       "      <td>0.890547</td>\n",
+       "      <td>0.451338</td>\n",
+       "      <td>0.468657</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>0.570423</td>\n",
+       "      <td>0.510288</td>\n",
+       "      <td>0.881592</td>\n",
+       "      <td>0.449771</td>\n",
+       "      <td>0.477813</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>0.581315</td>\n",
+       "      <td>0.510288</td>\n",
+       "      <td>0.881592</td>\n",
+       "      <td>0.449771</td>\n",
+       "      <td>0.477813</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>0.566802</td>\n",
+       "      <td>0.511013</td>\n",
+       "      <td>0.889552</td>\n",
+       "      <td>0.453148</td>\n",
+       "      <td>0.472147</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>0.560606</td>\n",
+       "      <td>0.511013</td>\n",
+       "      <td>0.889552</td>\n",
+       "      <td>0.453148</td>\n",
+       "      <td>0.472147</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>0.545994</td>\n",
+       "      <td>0.513011</td>\n",
+       "      <td>0.869652</td>\n",
+       "      <td>0.449292</td>\n",
+       "      <td>0.494118</td>\n",
+       "      <td>qrnn_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>qrnn_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>0.545994</td>\n",
+       "      <td>0.513011</td>\n",
+       "      <td>0.869652</td>\n",
+       "      <td>0.449292</td>\n",
+       "      <td>0.494118</td>\n",
+       "      <td>qrnn_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>qrnn_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>0.554795</td>\n",
+       "      <td>0.514286</td>\n",
+       "      <td>0.881592</td>\n",
+       "      <td>0.453966</td>\n",
+       "      <td>0.483499</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>0.549383</td>\n",
+       "      <td>0.516364</td>\n",
+       "      <td>0.867662</td>\n",
+       "      <td>0.452361</td>\n",
+       "      <td>0.501671</td>\n",
+       "      <td>qrnn_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>0.549383</td>\n",
+       "      <td>0.516364</td>\n",
+       "      <td>0.867662</td>\n",
+       "      <td>0.452361</td>\n",
+       "      <td>0.501671</td>\n",
+       "      <td>qrnn_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>qrnn_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     test F1 score bin  valid F1 score bin  valid Accuracy  \\\n",
+       "263           0.534799            0.508929        0.890547   \n",
+       "256           0.570423            0.510288        0.881592   \n",
+       "218           0.581315            0.510288        0.881592   \n",
+       "264           0.566802            0.511013        0.889552   \n",
+       "226           0.560606            0.511013        0.889552   \n",
+       "101           0.545994            0.513011        0.869652   \n",
+       "82            0.545994            0.513011        0.869652   \n",
+       "251           0.554795            0.514286        0.881592   \n",
+       "60            0.549383            0.516364        0.867662   \n",
+       "41            0.549383            0.516364        0.867662   \n",
+       "\n",
+       "     valid Kappa Linear  valid Matthews Correff                       arch  \\\n",
+       "263            0.451338                0.468657  lstm_noearly_stop_ft6_cl8   \n",
+       "256            0.449771                0.477813  lstm_noearly_stop_ft6_cl8   \n",
+       "218            0.449771                0.477813               lstm_ft6_cl8   \n",
+       "264            0.453148                0.472147  lstm_noearly_stop_ft6_cl8   \n",
+       "226            0.453148                0.472147               lstm_ft6_cl8   \n",
+       "101            0.449292                0.494118               qrnn_ft6_cl8   \n",
+       "82             0.449292                0.494118               qrnn_ft6_cl8   \n",
+       "251            0.453966                0.483499  lstm_noearly_stop_ft6_cl8   \n",
+       "60             0.452361                0.501671              qrnn_ft20_cl8   \n",
+       "41             0.452361                0.501671              qrnn_ft20_cl8   \n",
+       "\n",
+       "         ds                                         model_name  \n",
+       "263    wiki  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "256    wiki  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "218    wiki  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-0...  \n",
+       "264    wiki  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "226    wiki  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...  \n",
+       "101  reddit  qrnn_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-0...  \n",
+       "82   reddit  qrnn_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...  \n",
+       "251    wiki  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "60   reddit  qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...  \n",
+       "41   reddit  qrnn_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "order=\"valid Kappa Linear\"\n",
+    "focus=[\"test F1 score bin\", \"valid F1 score bin\", \"valid Accuracy\", order, \n",
+    "       \"valid Matthews Correff\", \"arch\", \"ds\", \"model_name\"]\n",
+    "q=df[~df[order].isnull() & ~df[\"arch\"].str.contains(\"ft0\")]\n",
+    "#q=nedf\n",
+    "q.sort_values([order])[focus].head(int(len(q) * 0.1)).sort_values([\"valid F1 score bin\"]).tail(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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>test F1 score bin</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>0.544681</td>\n",
+       "      <td>0.594595</td>\n",
+       "      <td>0.925373</td>\n",
+       "      <td>0.553796</td>\n",
+       "      <td>0.556015</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>0.544681</td>\n",
+       "      <td>0.594595</td>\n",
+       "      <td>0.925373</td>\n",
+       "      <td>0.553796</td>\n",
+       "      <td>0.556015</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>0.491071</td>\n",
+       "      <td>0.595506</td>\n",
+       "      <td>0.928358</td>\n",
+       "      <td>0.556291</td>\n",
+       "      <td>0.556969</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>0.527132</td>\n",
+       "      <td>0.595745</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>0.554454</td>\n",
+       "      <td>0.557567</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-7...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>0.532787</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>0.558752</td>\n",
+       "      <td>0.562558</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>0.539419</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>0.558752</td>\n",
+       "      <td>0.562558</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>0.534483</td>\n",
+       "      <td>0.602273</td>\n",
+       "      <td>0.930348</td>\n",
+       "      <td>0.564153</td>\n",
+       "      <td>0.564547</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>0.530973</td>\n",
+       "      <td>0.602273</td>\n",
+       "      <td>0.930348</td>\n",
+       "      <td>0.564153</td>\n",
+       "      <td>0.564547</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>0.504274</td>\n",
+       "      <td>0.605128</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>0.563475</td>\n",
+       "      <td>0.569221</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>0.515284</td>\n",
+       "      <td>0.639053</td>\n",
+       "      <td>0.939303</td>\n",
+       "      <td>0.605920</td>\n",
+       "      <td>0.605933</td>\n",
+       "      <td>lstm_ft20_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     test F1 score bin  valid F1 score bin  valid Accuracy  \\\n",
+       "191           0.544681            0.594595        0.925373   \n",
+       "229           0.544681            0.594595        0.925373   \n",
+       "152           0.491071            0.595506        0.928358   \n",
+       "206           0.527132            0.595745        0.924378   \n",
+       "195           0.532787            0.600000        0.924378   \n",
+       "233           0.539419            0.600000        0.924378   \n",
+       "202           0.534483            0.602273        0.930348   \n",
+       "240           0.530973            0.602273        0.930348   \n",
+       "158           0.504274            0.605128        0.923383   \n",
+       "164           0.515284            0.639053        0.939303   \n",
+       "\n",
+       "     valid Kappa Linear  valid Matthews Correff                       arch  \\\n",
+       "191            0.553796                0.556015               lstm_ft6_cl8   \n",
+       "229            0.553796                0.556015  lstm_noearly_stop_ft6_cl8   \n",
+       "152            0.556291                0.556969              lstm_ft20_cl8   \n",
+       "206            0.554454                0.557567               lstm_ft6_cl8   \n",
+       "195            0.558752                0.562558               lstm_ft6_cl8   \n",
+       "233            0.558752                0.562558  lstm_noearly_stop_ft6_cl8   \n",
+       "202            0.564153                0.564547               lstm_ft6_cl8   \n",
+       "240            0.564153                0.564547  lstm_noearly_stop_ft6_cl8   \n",
+       "158            0.563475                0.569221              lstm_ft20_cl8   \n",
+       "164            0.605920                0.605933              lstm_ft20_cl8   \n",
+       "\n",
+       "       ds                                         model_name  \n",
+       "191  wiki  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...  \n",
+       "229  wiki  lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...  \n",
+       "152  wiki  lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...  \n",
+       "206  wiki  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-7...  \n",
+       "195  wiki  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...  \n",
+       "233  wiki  lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...  \n",
+       "202  wiki  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-3...  \n",
+       "240  wiki  lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...  \n",
+       "158  wiki  lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...  \n",
+       "164  wiki  lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-...  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q.sort_values([\"valid F1 score bin\"])[focus].tail(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>248.000000</td>\n",
+       "      <td>248.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>248.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>248.000000</td>\n",
+       "      <td>248.000000</td>\n",
+       "      <td>248.000000</td>\n",
+       "      <td>248.000000</td>\n",
+       "      <td>248.000000</td>\n",
+       "      <td>248.000000</td>\n",
+       "      <td>248.000000</td>\n",
+       "      <td>248.000000</td>\n",
+       "      <td>248.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.879641</td>\n",
+       "      <td>0.530643</td>\n",
+       "      <td>0.386346</td>\n",
+       "      <td>0.457431</td>\n",
+       "      <td>0.413414</td>\n",
+       "      <td>0.570750</td>\n",
+       "      <td>0.521004</td>\n",
+       "      <td>0.903499</td>\n",
+       "      <td>0.546103</td>\n",
+       "      <td>0.495688</td>\n",
+       "      <td>0.366369</td>\n",
+       "      <td>0.512618</td>\n",
+       "      <td>0.473181</td>\n",
+       "      <td>0.679602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.011587</td>\n",
+       "      <td>0.055190</td>\n",
+       "      <td>0.052320</td>\n",
+       "      <td>0.063028</td>\n",
+       "      <td>0.039129</td>\n",
+       "      <td>0.059511</td>\n",
+       "      <td>0.118153</td>\n",
+       "      <td>0.020746</td>\n",
+       "      <td>0.027297</td>\n",
+       "      <td>0.034083</td>\n",
+       "      <td>0.057716</td>\n",
+       "      <td>0.026121</td>\n",
+       "      <td>0.075373</td>\n",
+       "      <td>0.099951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.786000</td>\n",
+       "      <td>0.340909</td>\n",
+       "      <td>0.294158</td>\n",
+       "      <td>0.306861</td>\n",
+       "      <td>0.330020</td>\n",
+       "      <td>0.369281</td>\n",
+       "      <td>0.223881</td>\n",
+       "      <td>0.790050</td>\n",
+       "      <td>0.418733</td>\n",
+       "      <td>0.332226</td>\n",
+       "      <td>0.218546</td>\n",
+       "      <td>0.419594</td>\n",
+       "      <td>0.273381</td>\n",
+       "      <td>0.447059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.502193</td>\n",
+       "      <td>0.341244</td>\n",
+       "      <td>0.418202</td>\n",
+       "      <td>0.392833</td>\n",
+       "      <td>0.529791</td>\n",
+       "      <td>0.432836</td>\n",
+       "      <td>0.889303</td>\n",
+       "      <td>0.527197</td>\n",
+       "      <td>0.472108</td>\n",
+       "      <td>0.338949</td>\n",
+       "      <td>0.495752</td>\n",
+       "      <td>0.413740</td>\n",
+       "      <td>0.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.881000</td>\n",
+       "      <td>0.544039</td>\n",
+       "      <td>0.391300</td>\n",
+       "      <td>0.447006</td>\n",
+       "      <td>0.412878</td>\n",
+       "      <td>0.559381</td>\n",
+       "      <td>0.526119</td>\n",
+       "      <td>0.906468</td>\n",
+       "      <td>0.546516</td>\n",
+       "      <td>0.497312</td>\n",
+       "      <td>0.366850</td>\n",
+       "      <td>0.512162</td>\n",
+       "      <td>0.463526</td>\n",
+       "      <td>0.694118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.886000</td>\n",
+       "      <td>0.570470</td>\n",
+       "      <td>0.429274</td>\n",
+       "      <td>0.485430</td>\n",
+       "      <td>0.442910</td>\n",
+       "      <td>0.607477</td>\n",
+       "      <td>0.619403</td>\n",
+       "      <td>0.920398</td>\n",
+       "      <td>0.563876</td>\n",
+       "      <td>0.518347</td>\n",
+       "      <td>0.396206</td>\n",
+       "      <td>0.529294</td>\n",
+       "      <td>0.525253</td>\n",
+       "      <td>0.764706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.901000</td>\n",
+       "      <td>0.608392</td>\n",
+       "      <td>0.487822</td>\n",
+       "      <td>0.743211</td>\n",
+       "      <td>0.507263</td>\n",
+       "      <td>0.756098</td>\n",
+       "      <td>0.843284</td>\n",
+       "      <td>0.942289</td>\n",
+       "      <td>0.639053</td>\n",
+       "      <td>0.605920</td>\n",
+       "      <td>0.708696</td>\n",
+       "      <td>0.605933</td>\n",
+       "      <td>0.764706</td>\n",
+       "      <td>0.894118</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Kappa Linear   test Loss  \\\n",
+       "count     248.000000         248.000000          38.000000  248.000000   \n",
+       "mean        0.879641           0.530643           0.386346    0.457431   \n",
+       "std         0.011587           0.055190           0.052320    0.063028   \n",
+       "min         0.786000           0.340909           0.294158    0.306861   \n",
+       "25%         0.875000           0.502193           0.341244    0.418202   \n",
+       "50%         0.881000           0.544039           0.391300    0.447006   \n",
+       "75%         0.886000           0.570470           0.429274    0.485430   \n",
+       "max         0.901000           0.608392           0.487822    0.743211   \n",
+       "\n",
+       "       test Matthews Correff  test Precision  test Recall  valid Accuracy  \\\n",
+       "count              38.000000      248.000000   248.000000      248.000000   \n",
+       "mean                0.413414        0.570750     0.521004        0.903499   \n",
+       "std                 0.039129        0.059511     0.118153        0.020746   \n",
+       "min                 0.330020        0.369281     0.223881        0.790050   \n",
+       "25%                 0.392833        0.529791     0.432836        0.889303   \n",
+       "50%                 0.412878        0.559381     0.526119        0.906468   \n",
+       "75%                 0.442910        0.607477     0.619403        0.920398   \n",
+       "max                 0.507263        0.756098     0.843284        0.942289   \n",
+       "\n",
+       "       valid F1 score bin  valid Kappa Linear  valid Loss  \\\n",
+       "count          248.000000          248.000000  248.000000   \n",
+       "mean             0.546103            0.495688    0.366369   \n",
+       "std              0.027297            0.034083    0.057716   \n",
+       "min              0.418733            0.332226    0.218546   \n",
+       "25%              0.527197            0.472108    0.338949   \n",
+       "50%              0.546516            0.497312    0.366850   \n",
+       "75%              0.563876            0.518347    0.396206   \n",
+       "max              0.639053            0.605920    0.708696   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count              248.000000       248.000000    248.000000  \n",
+       "mean                 0.512618         0.473181      0.679602  \n",
+       "std                  0.026121         0.075373      0.099951  \n",
+       "min                  0.419594         0.273381      0.447059  \n",
+       "25%                  0.495752         0.413740      0.600000  \n",
+       "50%                  0.512162         0.463526      0.694118  \n",
+       "75%                  0.529294         0.525253      0.764706  \n",
+       "max                  0.605933         0.764706      0.894118  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>test F1 score bin</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.529631</td>\n",
+       "      <td>0.524965</td>\n",
+       "      <td>0.903483</td>\n",
+       "      <td>0.485005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.046230</td>\n",
+       "      <td>0.006772</td>\n",
+       "      <td>0.015004</td>\n",
+       "      <td>0.006333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.460094</td>\n",
+       "      <td>0.514523</td>\n",
+       "      <td>0.882587</td>\n",
+       "      <td>0.468980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.488976</td>\n",
+       "      <td>0.520200</td>\n",
+       "      <td>0.890547</td>\n",
+       "      <td>0.481969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.538836</td>\n",
+       "      <td>0.524252</td>\n",
+       "      <td>0.906965</td>\n",
+       "      <td>0.486925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.572976</td>\n",
+       "      <td>0.531253</td>\n",
+       "      <td>0.917662</td>\n",
+       "      <td>0.491206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.581818</td>\n",
+       "      <td>0.534884</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>0.491477</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test F1 score bin  valid F1 score bin  valid Accuracy  \\\n",
+       "count          20.000000           20.000000       20.000000   \n",
+       "mean            0.529631            0.524965        0.903483   \n",
+       "std             0.046230            0.006772        0.015004   \n",
+       "min             0.460094            0.514523        0.882587   \n",
+       "25%             0.488976            0.520200        0.890547   \n",
+       "50%             0.538836            0.524252        0.906965   \n",
+       "75%             0.572976            0.531253        0.917662   \n",
+       "max             0.581818            0.534884        0.922388   \n",
+       "\n",
+       "       valid Matthews Correff  \n",
+       "count               20.000000  \n",
+       "mean                 0.485005  \n",
+       "std                  0.006333  \n",
+       "min                  0.468980  \n",
+       "25%                  0.481969  \n",
+       "50%                  0.486925  \n",
+       "75%                  0.491206  \n",
+       "max                  0.491477  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "order=\"valid Matthews Correff\"\n",
+    "focus=[\"test F1 score bin\", \"valid F1 score bin\", \"valid Accuracy\", order, \"arch\", \"ds\", \"model_name\"]\n",
+    "q=df[~df[order].isnull() & ~df[\"arch\"].str.contains(\"ft0\")]\n",
+    "#q=nedf\n",
+    "q.sort_values([order])[focus].head(int(len(q) * 0.2)).sort_values([\"valid F1 score bin\"]).tail(20).describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deduplicate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array(['redditdedup'], dtype=object),\n",
+       " array(['lstm_ft6_cl20', 'lstm_ft6_cl6'], dtype=object))"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf = pd.read_csv(\"./all-results-dedup.csv\", index_col=0)\n",
+    "ddf[\"ds\"] = ddf[\"model_dir_parent\"].str.extract(\".*pl-10-(.*)/models.*\")\n",
+    "ddf[\"arch\"] = ddf[\"model_name\"].str.extract(\"(.*?)_lmseed.*\")\n",
+    "ddf[\"ds\"].unique(), ddf[\"arch\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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>test F1 score bin</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.555102</td>\n",
+       "      <td>0.502994</td>\n",
+       "      <td>0.891</td>\n",
+       "      <td>lstm_ft6_cl20</td>\n",
+       "      <td>redditdedup</td>\n",
+       "      <td>lstm_ft6_cl20_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>0.516667</td>\n",
+       "      <td>0.503311</td>\n",
+       "      <td>0.884</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>redditdedup</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>0.533898</td>\n",
+       "      <td>0.503497</td>\n",
+       "      <td>0.890</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>redditdedup</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>0.540323</td>\n",
+       "      <td>0.503597</td>\n",
+       "      <td>0.886</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>redditdedup</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>0.566802</td>\n",
+       "      <td>0.509317</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>redditdedup</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>0.596078</td>\n",
+       "      <td>0.509317</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>redditdedup</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.510822</td>\n",
+       "      <td>0.529412</td>\n",
+       "      <td>0.887</td>\n",
+       "      <td>lstm_ft6_cl20</td>\n",
+       "      <td>redditdedup</td>\n",
+       "      <td>lstm_ft6_cl20_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>0.542056</td>\n",
+       "      <td>0.529915</td>\n",
+       "      <td>0.902</td>\n",
+       "      <td>lstm_ft6_cl20</td>\n",
+       "      <td>redditdedup</td>\n",
+       "      <td>lstm_ft6_cl20_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>0.542056</td>\n",
+       "      <td>0.532374</td>\n",
+       "      <td>0.902</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>redditdedup</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.525822</td>\n",
+       "      <td>0.559441</td>\n",
+       "      <td>0.899</td>\n",
+       "      <td>lstm_ft6_cl20</td>\n",
+       "      <td>redditdedup</td>\n",
+       "      <td>lstm_ft6_cl20_lmseed-0-ftseed-0-clsweightseed-...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    test F1 score bin  valid F1 score bin  test Accuracy           arch  \\\n",
+       "24           0.555102            0.502994          0.891  lstm_ft6_cl20   \n",
+       "49           0.516667            0.503311          0.884   lstm_ft6_cl6   \n",
+       "41           0.533898            0.503497          0.890   lstm_ft6_cl6   \n",
+       "58           0.540323            0.503597          0.886   lstm_ft6_cl6   \n",
+       "68           0.566802            0.509317          0.893   lstm_ft6_cl6   \n",
+       "39           0.596078            0.509317          0.897   lstm_ft6_cl6   \n",
+       "15           0.510822            0.529412          0.887  lstm_ft6_cl20   \n",
+       "31           0.542056            0.529915          0.902  lstm_ft6_cl20   \n",
+       "50           0.542056            0.532374          0.902   lstm_ft6_cl6   \n",
+       "2            0.525822            0.559441          0.899  lstm_ft6_cl20   \n",
+       "\n",
+       "             ds                                         model_name  \n",
+       "24  redditdedup  lstm_ft6_cl20_lmseed-1-ftseed-0-clsweightseed-...  \n",
+       "49  redditdedup  lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-2...  \n",
+       "41  redditdedup  lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-0...  \n",
+       "58  redditdedup  lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0...  \n",
+       "68  redditdedup  lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-2...  \n",
+       "39  redditdedup  lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-0...  \n",
+       "15  redditdedup  lstm_ft6_cl20_lmseed-0-ftseed-0-clsweightseed-...  \n",
+       "31  redditdedup  lstm_ft6_cl20_lmseed-1-ftseed-0-clsweightseed-...  \n",
+       "50  redditdedup  lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-3...  \n",
+       "2   redditdedup  lstm_ft6_cl20_lmseed-0-ftseed-0-clsweightseed-...  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "focus=[\"test F1 score bin\", \"valid F1 score bin\", \"test Accuracy\", \"arch\", \"ds\", \"model_name\"]\n",
+    "ddf.sort_values([\"valid F1 score bin\"])[focus].tail(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Halftest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array(['reddithalftest'], dtype=object),\n",
+       " array(['lstm_ft6_cl6'], dtype=object))"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hdf = pd.read_csv(\"./all-results-halftest.csv\", index_col=0)\n",
+    "hdf[\"ds\"] = hdf[\"model_dir_parent\"].str.extract(\".*pl-10-(.*)/models.*\")\n",
+    "hdf[\"arch\"] = hdf[\"model_name\"].str.extract(\"(.*?)_lmseed.*\")\n",
+    "hdf[\"ds\"].unique(), hdf[\"arch\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": false
+   },
+   "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>test F1 score bin</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>0.557377</td>\n",
+       "      <td>0.604651</td>\n",
+       "      <td>0.838</td>\n",
+       "      <td>0.604651</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>reddithalftest</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.569231</td>\n",
+       "      <td>0.604651</td>\n",
+       "      <td>0.888</td>\n",
+       "      <td>0.604651</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>reddithalftest</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.622222</td>\n",
+       "      <td>0.604651</td>\n",
+       "      <td>0.898</td>\n",
+       "      <td>0.604651</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>reddithalftest</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>0.549618</td>\n",
+       "      <td>0.604651</td>\n",
+       "      <td>0.882</td>\n",
+       "      <td>0.604651</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>reddithalftest</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>0.609375</td>\n",
+       "      <td>0.606061</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.606061</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>reddithalftest</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.554745</td>\n",
+       "      <td>0.607407</td>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.607407</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>reddithalftest</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.583942</td>\n",
+       "      <td>0.611940</td>\n",
+       "      <td>0.886</td>\n",
+       "      <td>0.611940</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>reddithalftest</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-7...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.598425</td>\n",
+       "      <td>0.615385</td>\n",
+       "      <td>0.898</td>\n",
+       "      <td>0.615385</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>reddithalftest</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.580645</td>\n",
+       "      <td>0.626866</td>\n",
+       "      <td>0.896</td>\n",
+       "      <td>0.626866</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>reddithalftest</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>0.627737</td>\n",
+       "      <td>0.629371</td>\n",
+       "      <td>0.898</td>\n",
+       "      <td>0.629371</td>\n",
+       "      <td>lstm_ft6_cl6</td>\n",
+       "      <td>reddithalftest</td>\n",
+       "      <td>lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    test F1 score bin  valid F1 score bin  test Accuracy  valid F1 score bin  \\\n",
+       "27           0.557377            0.604651          0.838            0.604651   \n",
+       "7            0.569231            0.604651          0.888            0.604651   \n",
+       "4            0.622222            0.604651          0.898            0.604651   \n",
+       "23           0.549618            0.604651          0.882            0.604651   \n",
+       "26           0.609375            0.606061          0.900            0.606061   \n",
+       "9            0.554745            0.607407          0.878            0.607407   \n",
+       "16           0.583942            0.611940          0.886            0.611940   \n",
+       "12           0.598425            0.615385          0.898            0.615385   \n",
+       "1            0.580645            0.626866          0.896            0.626866   \n",
+       "37           0.627737            0.629371          0.898            0.629371   \n",
+       "\n",
+       "            arch              ds  \\\n",
+       "27  lstm_ft6_cl6  reddithalftest   \n",
+       "7   lstm_ft6_cl6  reddithalftest   \n",
+       "4   lstm_ft6_cl6  reddithalftest   \n",
+       "23  lstm_ft6_cl6  reddithalftest   \n",
+       "26  lstm_ft6_cl6  reddithalftest   \n",
+       "9   lstm_ft6_cl6  reddithalftest   \n",
+       "16  lstm_ft6_cl6  reddithalftest   \n",
+       "12  lstm_ft6_cl6  reddithalftest   \n",
+       "1   lstm_ft6_cl6  reddithalftest   \n",
+       "37  lstm_ft6_cl6  reddithalftest   \n",
+       "\n",
+       "                                           model_name  \n",
+       "27  lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0...  \n",
+       "7   lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-0...  \n",
+       "4   lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-0...  \n",
+       "23  lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0...  \n",
+       "26  lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0...  \n",
+       "9   lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-0...  \n",
+       "16  lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-7...  \n",
+       "12  lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-3...  \n",
+       "1   lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-0...  \n",
+       "37  lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-9...  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "order=\"valid F1 score bin\"\n",
+    "#order=\"valid Accuracy\"\n",
+    "focus=[\"test F1 score bin\", \"valid F1 score bin\", \"test Accuracy\", order, \"arch\", \"ds\", \"model_name\"]\n",
+    "hdf.sort_values([order])[focus].tail(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## No early"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array(['wiki'], dtype=object),\n",
+       " array(['lstm_noearly_stop_ft6_cl8'], dtype=object))"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nedf = pd.read_csv(\"./poleval-noearly.csv\", index_col=0)\n",
+    "nedf[\"ds\"] = nedf[\"model_dir_parent\"].str.extract(\".*pl-10-(.*)/models.*\")\n",
+    "nedf[\"arch\"] = nedf[\"model_name\"].str.extract(\"(.*?)_lmseed.*\")\n",
+    "nedf[\"ds\"].unique(), nedf[\"arch\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Kappa quadratic</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.884026</td>\n",
+       "      <td>0.540362</td>\n",
+       "      <td>0.451621</td>\n",
+       "      <td>0.582396</td>\n",
+       "      <td>0.514336</td>\n",
+       "      <td>0.903561</td>\n",
+       "      <td>0.541631</td>\n",
+       "      <td>0.490324</td>\n",
+       "      <td>0.490324</td>\n",
+       "      <td>0.490324</td>\n",
+       "      <td>0.378578</td>\n",
+       "      <td>0.504238</td>\n",
+       "      <td>0.465598</td>\n",
+       "      <td>0.666873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.006953</td>\n",
+       "      <td>0.036469</td>\n",
+       "      <td>0.036495</td>\n",
+       "      <td>0.042296</td>\n",
+       "      <td>0.078603</td>\n",
+       "      <td>0.017295</td>\n",
+       "      <td>0.028827</td>\n",
+       "      <td>0.036302</td>\n",
+       "      <td>0.036302</td>\n",
+       "      <td>0.036302</td>\n",
+       "      <td>0.035378</td>\n",
+       "      <td>0.028528</td>\n",
+       "      <td>0.063967</td>\n",
+       "      <td>0.068605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.870000</td>\n",
+       "      <td>0.453704</td>\n",
+       "      <td>0.383624</td>\n",
+       "      <td>0.512658</td>\n",
+       "      <td>0.365672</td>\n",
+       "      <td>0.879602</td>\n",
+       "      <td>0.497238</td>\n",
+       "      <td>0.441865</td>\n",
+       "      <td>0.441865</td>\n",
+       "      <td>0.441865</td>\n",
+       "      <td>0.310931</td>\n",
+       "      <td>0.448687</td>\n",
+       "      <td>0.392405</td>\n",
+       "      <td>0.529412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.878250</td>\n",
+       "      <td>0.517938</td>\n",
+       "      <td>0.418870</td>\n",
+       "      <td>0.546561</td>\n",
+       "      <td>0.436567</td>\n",
+       "      <td>0.888060</td>\n",
+       "      <td>0.515848</td>\n",
+       "      <td>0.455593</td>\n",
+       "      <td>0.455593</td>\n",
+       "      <td>0.455593</td>\n",
+       "      <td>0.348865</td>\n",
+       "      <td>0.483228</td>\n",
+       "      <td>0.408611</td>\n",
+       "      <td>0.611765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.885000</td>\n",
+       "      <td>0.549590</td>\n",
+       "      <td>0.449618</td>\n",
+       "      <td>0.583582</td>\n",
+       "      <td>0.526119</td>\n",
+       "      <td>0.902985</td>\n",
+       "      <td>0.536872</td>\n",
+       "      <td>0.488905</td>\n",
+       "      <td>0.488905</td>\n",
+       "      <td>0.488905</td>\n",
+       "      <td>0.371432</td>\n",
+       "      <td>0.496245</td>\n",
+       "      <td>0.452722</td>\n",
+       "      <td>0.676471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.889000</td>\n",
+       "      <td>0.566180</td>\n",
+       "      <td>0.478240</td>\n",
+       "      <td>0.611950</td>\n",
+       "      <td>0.582090</td>\n",
+       "      <td>0.921144</td>\n",
+       "      <td>0.561875</td>\n",
+       "      <td>0.518846</td>\n",
+       "      <td>0.518846</td>\n",
+       "      <td>0.518846</td>\n",
+       "      <td>0.409666</td>\n",
+       "      <td>0.521986</td>\n",
+       "      <td>0.531640</td>\n",
+       "      <td>0.717647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.895000</td>\n",
+       "      <td>0.608392</td>\n",
+       "      <td>0.517178</td>\n",
+       "      <td>0.662791</td>\n",
+       "      <td>0.649254</td>\n",
+       "      <td>0.930348</td>\n",
+       "      <td>0.602273</td>\n",
+       "      <td>0.564153</td>\n",
+       "      <td>0.564153</td>\n",
+       "      <td>0.564153</td>\n",
+       "      <td>0.449702</td>\n",
+       "      <td>0.564547</td>\n",
+       "      <td>0.582418</td>\n",
+       "      <td>0.776471</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Loss  test Precision  \\\n",
+       "count      38.000000          38.000000  38.000000       38.000000   \n",
+       "mean        0.884026           0.540362   0.451621        0.582396   \n",
+       "std         0.006953           0.036469   0.036495        0.042296   \n",
+       "min         0.870000           0.453704   0.383624        0.512658   \n",
+       "25%         0.878250           0.517938   0.418870        0.546561   \n",
+       "50%         0.885000           0.549590   0.449618        0.583582   \n",
+       "75%         0.889000           0.566180   0.478240        0.611950   \n",
+       "max         0.895000           0.608392   0.517178        0.662791   \n",
+       "\n",
+       "       test Recall  valid Accuracy  valid F1 score bin  valid Kappa  \\\n",
+       "count    38.000000       38.000000           38.000000    36.000000   \n",
+       "mean      0.514336        0.903561            0.541631     0.490324   \n",
+       "std       0.078603        0.017295            0.028827     0.036302   \n",
+       "min       0.365672        0.879602            0.497238     0.441865   \n",
+       "25%       0.436567        0.888060            0.515848     0.455593   \n",
+       "50%       0.526119        0.902985            0.536872     0.488905   \n",
+       "75%       0.582090        0.921144            0.561875     0.518846   \n",
+       "max       0.649254        0.930348            0.602273     0.564153   \n",
+       "\n",
+       "       valid Kappa Linear  valid Kappa quadratic  valid Loss  \\\n",
+       "count           36.000000              36.000000   38.000000   \n",
+       "mean             0.490324               0.490324    0.378578   \n",
+       "std              0.036302               0.036302    0.035378   \n",
+       "min              0.441865               0.441865    0.310931   \n",
+       "25%              0.455593               0.455593    0.348865   \n",
+       "50%              0.488905               0.488905    0.371432   \n",
+       "75%              0.518846               0.518846    0.409666   \n",
+       "max              0.564153               0.564153    0.449702   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count               36.000000        38.000000     38.000000  \n",
+       "mean                 0.504238         0.465598      0.666873  \n",
+       "std                  0.028528         0.063967      0.068605  \n",
+       "min                  0.448687         0.392405      0.529412  \n",
+       "25%                  0.483228         0.408611      0.611765  \n",
+       "50%                  0.496245         0.452722      0.676471  \n",
+       "75%                  0.521986         0.531640      0.717647  \n",
+       "max                  0.564547         0.582418      0.776471  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nedf.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>valid Kappa</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.515837</td>\n",
+       "      <td>0.497238</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.447685</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0.560000</td>\n",
+       "      <td>0.502128</td>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.441865</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>0.534799</td>\n",
+       "      <td>0.508929</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.451338</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0.570423</td>\n",
+       "      <td>0.510288</td>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.449771</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>0.566802</td>\n",
+       "      <td>0.511013</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.453148</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>0.583026</td>\n",
+       "      <td>0.512820</td>\n",
+       "      <td>0.887</td>\n",
+       "      <td>0.454013</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>0.573529</td>\n",
+       "      <td>0.513274</td>\n",
+       "      <td>0.884</td>\n",
+       "      <td>0.455848</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>0.608392</td>\n",
+       "      <td>0.514286</td>\n",
+       "      <td>0.888</td>\n",
+       "      <td>0.453966</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>0.554795</td>\n",
+       "      <td>0.514286</td>\n",
+       "      <td>0.870</td>\n",
+       "      <td>0.453966</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>0.581818</td>\n",
+       "      <td>0.514523</td>\n",
+       "      <td>0.885</td>\n",
+       "      <td>0.454830</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    test F1 score bin  valid F1 score bin  test Accuracy  valid Kappa  \\\n",
+       "9            0.515837            0.497238          0.893     0.447685   \n",
+       "29           0.560000            0.502128          0.879     0.441865   \n",
+       "35           0.534799            0.508929          0.873     0.451338   \n",
+       "28           0.570423            0.510288          0.878     0.449771   \n",
+       "36           0.566802            0.511013          0.893     0.453148   \n",
+       "33           0.583026            0.512820          0.887     0.454013   \n",
+       "32           0.573529            0.513274          0.884     0.455848   \n",
+       "26           0.608392            0.514286          0.888     0.453966   \n",
+       "23           0.554795            0.514286          0.870     0.453966   \n",
+       "31           0.581818            0.514523          0.885     0.454830   \n",
+       "\n",
+       "                         arch    ds  \\\n",
+       "9   lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "29  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "35  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "28  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "36  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "33  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "32  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "26  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "23  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "31  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "\n",
+       "                                           model_name  \n",
+       "9   lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...  \n",
+       "29  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "35  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "28  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "36  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "33  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "32  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "26  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "23  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "31  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "order=\"valid Kappa\"\n",
+    "focus=[\"test F1 score bin\", \"valid F1 score bin\", \"test Accuracy\", order, \"arch\", \"ds\", \"model_name\"]\n",
+    "nedf.sort_values([order])[focus].head(10).sort_values([\"valid F1 score bin\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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>test F1 score bin</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.515837</td>\n",
+       "      <td>0.497238</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.448687</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0.560000</td>\n",
+       "      <td>0.502128</td>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.464773</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>0.534799</td>\n",
+       "      <td>0.508929</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.468657</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0.570423</td>\n",
+       "      <td>0.510288</td>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.477813</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>0.566802</td>\n",
+       "      <td>0.511013</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.472147</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>0.583026</td>\n",
+       "      <td>0.512820</td>\n",
+       "      <td>0.887</td>\n",
+       "      <td>0.476972</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>0.573529</td>\n",
+       "      <td>0.513274</td>\n",
+       "      <td>0.884</td>\n",
+       "      <td>0.474415</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>0.608392</td>\n",
+       "      <td>0.514286</td>\n",
+       "      <td>0.888</td>\n",
+       "      <td>0.483499</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>0.581818</td>\n",
+       "      <td>0.514523</td>\n",
+       "      <td>0.885</td>\n",
+       "      <td>0.481969</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.519824</td>\n",
+       "      <td>0.880</td>\n",
+       "      <td>0.482413</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    test F1 score bin  valid F1 score bin  test Accuracy  \\\n",
+       "9            0.515837            0.497238          0.893   \n",
+       "29           0.560000            0.502128          0.879   \n",
+       "35           0.534799            0.508929          0.873   \n",
+       "28           0.570423            0.510288          0.878   \n",
+       "36           0.566802            0.511013          0.893   \n",
+       "33           0.583026            0.512820          0.887   \n",
+       "32           0.573529            0.513274          0.884   \n",
+       "26           0.608392            0.514286          0.888   \n",
+       "31           0.581818            0.514523          0.885   \n",
+       "34           0.571429            0.519824          0.880   \n",
+       "\n",
+       "    valid Matthews Correff                       arch    ds  \\\n",
+       "9                 0.448687  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "29                0.464773  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "35                0.468657  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "28                0.477813  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "36                0.472147  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "33                0.476972  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "32                0.474415  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "26                0.483499  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "31                0.481969  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "34                0.482413  lstm_noearly_stop_ft6_cl8  wiki   \n",
+       "\n",
+       "                                           model_name  \n",
+       "9   lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...  \n",
+       "29  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "35  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "28  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "36  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "33  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "32  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "26  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "31  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  \n",
+       "34  lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-cl...  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "order=\"valid Matthews Correff\"\n",
+    "focus=[\"test F1 score bin\", \"valid F1 score bin\", \"test Accuracy\", order, \"arch\", \"ds\", \"model_name\"]\n",
+    "nedf.sort_values([order])[focus].head(10).sort_values([\"valid F1 score bin\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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>model_dir_parent</th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Kappa quadratic</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>arch</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...</td>\n",
+       "      <td>0.883</td>\n",
+       "      <td>0.489083</td>\n",
+       "      <td>0.471327</td>\n",
+       "      <td>0.589474</td>\n",
+       "      <td>0.417910</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>0.560000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.350842</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.544444</td>\n",
+       "      <td>0.576471</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.544681</td>\n",
+       "      <td>0.446630</td>\n",
+       "      <td>0.633663</td>\n",
+       "      <td>0.477612</td>\n",
+       "      <td>0.925373</td>\n",
+       "      <td>0.594595</td>\n",
+       "      <td>0.553796</td>\n",
+       "      <td>0.553796</td>\n",
+       "      <td>0.553796</td>\n",
+       "      <td>0.335345</td>\n",
+       "      <td>0.556015</td>\n",
+       "      <td>0.550000</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...</td>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.497959</td>\n",
+       "      <td>0.510850</td>\n",
+       "      <td>0.549550</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.911443</td>\n",
+       "      <td>0.557214</td>\n",
+       "      <td>0.509312</td>\n",
+       "      <td>0.509312</td>\n",
+       "      <td>0.509312</td>\n",
+       "      <td>0.366872</td>\n",
+       "      <td>0.516918</td>\n",
+       "      <td>0.482759</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>0.554217</td>\n",
+       "      <td>0.454685</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.514925</td>\n",
+       "      <td>0.909453</td>\n",
+       "      <td>0.547264</td>\n",
+       "      <td>0.498286</td>\n",
+       "      <td>0.498286</td>\n",
+       "      <td>0.498286</td>\n",
+       "      <td>0.409838</td>\n",
+       "      <td>0.505727</td>\n",
+       "      <td>0.474138</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...</td>\n",
+       "      <td>0.886</td>\n",
+       "      <td>0.547619</td>\n",
+       "      <td>0.472040</td>\n",
+       "      <td>0.584746</td>\n",
+       "      <td>0.514925</td>\n",
+       "      <td>0.918408</td>\n",
+       "      <td>0.590000</td>\n",
+       "      <td>0.545825</td>\n",
+       "      <td>0.545825</td>\n",
+       "      <td>0.545825</td>\n",
+       "      <td>0.357952</td>\n",
+       "      <td>0.553520</td>\n",
+       "      <td>0.513043</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm_noearly_stop_ft6_cl8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    model_dir_parent  \\\n",
+       "0  data/hate/pl-10-wiki/models/sp25k   \n",
+       "1  data/hate/pl-10-wiki/models/sp25k   \n",
+       "2  data/hate/pl-10-wiki/models/sp25k   \n",
+       "3  data/hate/pl-10-wiki/models/sp25k   \n",
+       "4  data/hate/pl-10-wiki/models/sp25k   \n",
+       "\n",
+       "                                          model_name  test Accuracy  \\\n",
+       "0  lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...          0.883   \n",
+       "1  lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...          0.893   \n",
+       "2  lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...          0.877   \n",
+       "3  lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...          0.889   \n",
+       "4  lstm_noearly_stop_ft6_cl8_lmseed-0-ftseed-0-cl...          0.886   \n",
+       "\n",
+       "   test F1 score bin  test Loss  test Precision  test Recall  valid Accuracy  \\\n",
+       "0           0.489083   0.471327        0.589474     0.417910        0.923383   \n",
+       "1           0.544681   0.446630        0.633663     0.477612        0.925373   \n",
+       "2           0.497959   0.510850        0.549550     0.455224        0.911443   \n",
+       "3           0.554217   0.454685        0.600000     0.514925        0.909453   \n",
+       "4           0.547619   0.472040        0.584746     0.514925        0.918408   \n",
+       "\n",
+       "   valid F1 score bin  valid Kappa  valid Kappa Linear  valid Kappa quadratic  \\\n",
+       "0            0.560000          NaN                 NaN                    NaN   \n",
+       "1            0.594595     0.553796            0.553796               0.553796   \n",
+       "2            0.557214     0.509312            0.509312               0.509312   \n",
+       "3            0.547264     0.498286            0.498286               0.498286   \n",
+       "4            0.590000     0.545825            0.545825               0.545825   \n",
+       "\n",
+       "   valid Loss  valid Matthews Correff  valid Precision  valid Recall    ds  \\\n",
+       "0    0.350842                     NaN         0.544444      0.576471  wiki   \n",
+       "1    0.335345                0.556015         0.550000      0.647059  wiki   \n",
+       "2    0.366872                0.516918         0.482759      0.658824  wiki   \n",
+       "3    0.409838                0.505727         0.474138      0.647059  wiki   \n",
+       "4    0.357952                0.553520         0.513043      0.694118  wiki   \n",
+       "\n",
+       "                        arch  \n",
+       "0  lstm_noearly_stop_ft6_cl8  \n",
+       "1  lstm_noearly_stop_ft6_cl8  \n",
+       "2  lstm_noearly_stop_ft6_cl8  \n",
+       "3  lstm_noearly_stop_ft6_cl8  \n",
+       "4  lstm_noearly_stop_ft6_cl8  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nedf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Weighted corss entropy\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['qrnn_ft0_cl8', 'qrnn_ft20_cl8', 'qrnn_ft6_cl8', 'lstm_ft0_cl8', 'lstm_ft20_cl8', 'lstm_ft6_cl8',\n",
+       "       'lstm_noearly_stop_ft6_cl8', 'lstm_nowce_ft6_cl8', 'lstm_small_ft6_el20', 'lstm_dp05_ft20_cl8',\n",
+       "       'lstm_dp05_ft6_cl8'], dtype=object)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"arch\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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>test F1 score bin</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.442618</td>\n",
+       "      <td>0.543644</td>\n",
+       "      <td>0.885605</td>\n",
+       "      <td>0.506515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.056835</td>\n",
+       "      <td>0.017263</td>\n",
+       "      <td>0.005514</td>\n",
+       "      <td>0.022520</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.340909</td>\n",
+       "      <td>0.509317</td>\n",
+       "      <td>0.876000</td>\n",
+       "      <td>0.463182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.392634</td>\n",
+       "      <td>0.531996</td>\n",
+       "      <td>0.882000</td>\n",
+       "      <td>0.491514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.457767</td>\n",
+       "      <td>0.545455</td>\n",
+       "      <td>0.885500</td>\n",
+       "      <td>0.505817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.490414</td>\n",
+       "      <td>0.555947</td>\n",
+       "      <td>0.888000</td>\n",
+       "      <td>0.517761</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.539535</td>\n",
+       "      <td>0.581081</td>\n",
+       "      <td>0.901000</td>\n",
+       "      <td>0.565151</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test F1 score bin  valid F1 score bin  test Accuracy  \\\n",
+       "count          38.000000           38.000000      38.000000   \n",
+       "mean            0.442618            0.543644       0.885605   \n",
+       "std             0.056835            0.017263       0.005514   \n",
+       "min             0.340909            0.509317       0.876000   \n",
+       "25%             0.392634            0.531996       0.882000   \n",
+       "50%             0.457767            0.545455       0.885500   \n",
+       "75%             0.490414            0.555947       0.888000   \n",
+       "max             0.539535            0.581081       0.901000   \n",
+       "\n",
+       "       valid Matthews Correff  \n",
+       "count               38.000000  \n",
+       "mean                 0.506515  \n",
+       "std                  0.022520  \n",
+       "min                  0.463182  \n",
+       "25%                  0.491514  \n",
+       "50%                  0.505817  \n",
+       "75%                  0.517761  \n",
+       "max                  0.565151  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"arch\"] == \"lstm_nowce_ft6_cl8\"][focus].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "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>test F1 score bin</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.547691</td>\n",
+       "      <td>0.542830</td>\n",
+       "      <td>0.878553</td>\n",
+       "      <td>0.508073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.030312</td>\n",
+       "      <td>0.035475</td>\n",
+       "      <td>0.016858</td>\n",
+       "      <td>0.030323</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.451613</td>\n",
+       "      <td>0.418733</td>\n",
+       "      <td>0.786000</td>\n",
+       "      <td>0.419594</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.533211</td>\n",
+       "      <td>0.520081</td>\n",
+       "      <td>0.875250</td>\n",
+       "      <td>0.491206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.552581</td>\n",
+       "      <td>0.544979</td>\n",
+       "      <td>0.880000</td>\n",
+       "      <td>0.509452</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.566869</td>\n",
+       "      <td>0.567013</td>\n",
+       "      <td>0.886000</td>\n",
+       "      <td>0.526846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.608392</td>\n",
+       "      <td>0.602273</td>\n",
+       "      <td>0.895000</td>\n",
+       "      <td>0.564547</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test F1 score bin  valid F1 score bin  test Accuracy  \\\n",
+       "count          38.000000           38.000000      38.000000   \n",
+       "mean            0.547691            0.542830       0.878553   \n",
+       "std             0.030312            0.035475       0.016858   \n",
+       "min             0.451613            0.418733       0.786000   \n",
+       "25%             0.533211            0.520081       0.875250   \n",
+       "50%             0.552581            0.544979       0.880000   \n",
+       "75%             0.566869            0.567013       0.886000   \n",
+       "max             0.608392            0.602273       0.895000   \n",
+       "\n",
+       "       valid Matthews Correff  \n",
+       "count               38.000000  \n",
+       "mean                 0.508073  \n",
+       "std                  0.030323  \n",
+       "min                  0.419594  \n",
+       "25%                  0.491206  \n",
+       "50%                  0.509452  \n",
+       "75%                  0.526846  \n",
+       "max                  0.564547  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(df[\"arch\"] == \"lstm_ft6_cl8\") & (df[\"ds\"]==\"wiki\")][focus].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Weight's Model selection based on the 1k pretraining"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "scrolled": false
+   },
+   "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>seed1</th>\n",
+       "      <th>seed0</th>\n",
+       "      <th>diff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.525926</td>\n",
+       "      <td>0.469891</td>\n",
+       "      <td>0.056035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.024596</td>\n",
+       "      <td>0.038780</td>\n",
+       "      <td>-0.014185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.495798</td>\n",
+       "      <td>0.394231</td>\n",
+       "      <td>0.101568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.504658</td>\n",
+       "      <td>0.443662</td>\n",
+       "      <td>0.060996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.526767</td>\n",
+       "      <td>0.478060</td>\n",
+       "      <td>0.048707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.539330</td>\n",
+       "      <td>0.490383</td>\n",
+       "      <td>0.048947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.564885</td>\n",
+       "      <td>0.523809</td>\n",
+       "      <td>0.041076</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           seed1      seed0      diff\n",
+       "count  10.000000  10.000000  0.000000\n",
+       "mean    0.525926   0.469891  0.056035\n",
+       "std     0.024596   0.038780 -0.014185\n",
+       "min     0.495798   0.394231  0.101568\n",
+       "25%     0.504658   0.443662  0.060996\n",
+       "50%     0.526767   0.478060  0.048707\n",
+       "75%     0.539330   0.490383  0.048947\n",
+       "max     0.564885   0.523809  0.041076"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "focus = ['test F1 score bin',\n",
+    " 'test Accuracy',\n",
+    " 'test Recall',\n",
+    " 'test Precision']\n",
+    "arch=\"lstm_small_ft6_el20\"\n",
+    "seed1 = df[(df[\"arch\"] == arch)&df[\"model_name\"].str.contains(\"lmseed-1\")&(df[\"ds\"]==\"wiki\")][focus]\n",
+    "seed0 = df[(df[\"arch\"] == arch)&df[\"model_name\"].str.contains(\"lmseed-0\")&(df[\"ds\"]==\"wiki\")][focus]\n",
+    "res = pd.DataFrame({\"seed1\":seed1.describe()['test F1 score bin'], \"seed0\":seed0.describe()['test F1 score bin']})\n",
+    "res[\"diff\"] = res[\"seed1\"]-res[\"seed0\"]\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.style.use('seaborn')\n",
+    "plt.hist([\n",
+    "         seed0[\"test F1 score bin\"],\n",
+    "         seed1[\"test F1 score bin\"]\n",
+    "          \n",
+    "         ],\n",
+    "    bins=10,\n",
+    "        label=[\"Language model, seed=0, 1 epoch\", \"Language model, seed=1, 1 epoch\"], histtype='bar', stacked=False,\n",
+    "        range=[0.3,0.7])\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"test F1 score\");\n",
+    "plt.ylabel(\"count\");\n",
+    "plt.savefig(\"1k.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<a href='1k.pdf' target='_blank'>1k.pdf</a><br>"
+      ],
+      "text/plain": [
+       "/home/pczapla/workspace/ulmfit-multilingual/experiments/poleval19/1k.pdf"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "IPython.display.FileLink(\"1k.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "scrolled": false
+   },
+   "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>seed1</th>\n",
+       "      <th>seed0</th>\n",
+       "      <th>diff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>151.000000</td>\n",
+       "      <td>147.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.555511</td>\n",
+       "      <td>0.539647</td>\n",
+       "      <td>0.015865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.028428</td>\n",
+       "      <td>0.033603</td>\n",
+       "      <td>-0.005174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.488479</td>\n",
+       "      <td>0.451613</td>\n",
+       "      <td>0.036866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.536181</td>\n",
+       "      <td>0.515420</td>\n",
+       "      <td>0.020761</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.558333</td>\n",
+       "      <td>0.541485</td>\n",
+       "      <td>0.016849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.576201</td>\n",
+       "      <td>0.563492</td>\n",
+       "      <td>0.012709</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.622222</td>\n",
+       "      <td>0.614232</td>\n",
+       "      <td>0.007990</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            seed1       seed0      diff\n",
+       "count  151.000000  147.000000  4.000000\n",
+       "mean     0.555511    0.539647  0.015865\n",
+       "std      0.028428    0.033603 -0.005174\n",
+       "min      0.488479    0.451613  0.036866\n",
+       "25%      0.536181    0.515420  0.020761\n",
+       "50%      0.558333    0.541485  0.016849\n",
+       "75%      0.576201    0.563492  0.012709\n",
+       "max      0.622222    0.614232  0.007990"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "focus = ['test F1 score bin',\n",
+    " 'test Accuracy',\n",
+    " 'test Recall',\n",
+    " 'test Precision']\n",
+    "arch=\"lstm_ft6_cl8\"\n",
+    "seed1 = df[df[\"model_name\"].str.contains(\"lmseed-1\") & df[\"model_name\"].str.contains(\"lstm_ft\")][focus]\n",
+    "seed0 = df[df[\"model_name\"].str.contains(\"lmseed-0\") & df[\"model_name\"].str.contains(\"lstm_ft\")][focus]\n",
+    "res = pd.DataFrame({\"seed1\":seed1.describe()['test F1 score bin'], \"seed0\":seed0.describe()['test F1 score bin']})\n",
+    "res[\"diff\"] = res[\"seed1\"]-res[\"seed0\"]\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.style.use('seaborn')\n",
+    "ax = plt.hist([\n",
+    "         seed0[\"test F1 score bin\"],\n",
+    "         seed1[\"test F1 score bin\"]\n",
+    "          \n",
+    "         ],\n",
+    "    bins=10,\n",
+    "        label=[\"Language model, seed=0, 10 epochs\", \"Language model, seed=1, 10 epochs\"], histtype='bar', stacked=False,\n",
+    "        range=[0.3,0.7])\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"test F1 score\");\n",
+    "plt.ylabel(\"count\");\n",
+    "plt.savefig(\"10k.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<a href='10k.pdf' target='_blank'>10k.pdf</a><br>"
+      ],
+      "text/plain": [
+       "/home/pczapla/workspace/ulmfit-multilingual/experiments/poleval19/10k.pdf"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "IPython.display.FileLink(\"10k.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#pd.DataFrame({\"seed1\":seed1['test F1 score bin'], \"seed0\":seed0['test F1 score bin']})\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ablation studies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <td>584.0</td>\n",
+       "      <td>0.541107</td>\n",
+       "      <td>0.044664</td>\n",
+       "      <td>0.340909</td>\n",
+       "      <td>0.517763</td>\n",
+       "      <td>0.548852</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.622222</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   count      mean       std       min       25%       50%  \\\n",
+       "test F1 score bin  584.0  0.541107  0.044664  0.340909  0.517763  0.548852   \n",
+       "\n",
+       "                        75%       max  \n",
+       "test F1 score bin  0.571429  0.622222  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[[\"test F1 score bin\"]].describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"lmseed\"] = df[\"model_name\"].str.extract(\".*lmseed-([0-9]).*\")\n",
+    "df[\"fine_tune\"] = df[\"model_name\"].str.extract(\".*ft([0-9]+).*\") != \"0\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[[\"model\", \"exp_type\"]] = df[\"model_name\"].str.extract(\"(qrnn|lstm)_?(.*?)_ft[0-9]+_.l[0-9]+.*\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[df[\"exp_type\"]==\"reg\",\"exp_type\"] = \"_reg\"\n",
+    "df.loc[df[\"exp_type\"]==\"\",\"exp_type\"] = \"_reg\"\n",
+    "df.loc[df[\"exp_type\"]==\"_reg\",\"exp_type\"] = \"_orig\"\n",
+    "df.loc[df[\"exp_type\"]==\"noearly_stop\",\"exp_type\"] = \"wo early stopping\"\n",
+    "df.loc[df[\"exp_type\"]==\"nowce\",\"exp_type\"] = \"cross entropy wo wieghts\"\n",
+    "df.loc[df[\"exp_type\"]==\"small\",\"exp_type\"] = \"1 epoch pretraining\"\n",
+    "df.loc[df[\"exp_type\"]==\"dp05\",\"exp_type\"] = \"dropmul = 0.5\"\n",
+    "ft=df[\"fine_tune\"]==True\n",
+    "df.loc[ft, \"fine_tune\"] = \"yes\"\n",
+    "df.loc[~ft,\"fine_tune\"] = \"no\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "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>ds</th>\n",
+       "      <th>model</th>\n",
+       "      <th>exp_type</th>\n",
+       "      <th>lmseed</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>max</th>\n",
+       "      <th>75%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.553660</td>\n",
+       "      <td>0.029906</td>\n",
+       "      <td>0.614232</td>\n",
+       "      <td>0.575757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.560869</td>\n",
+       "      <td>0.028504</td>\n",
+       "      <td>0.622222</td>\n",
+       "      <td>0.580522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>dropmul = 0.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.519352</td>\n",
+       "      <td>0.030414</td>\n",
+       "      <td>0.589552</td>\n",
+       "      <td>0.533757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>reddit</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>dropmul = 0.5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.538298</td>\n",
+       "      <td>0.030352</td>\n",
+       "      <td>0.602151</td>\n",
+       "      <td>0.557069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>reddit</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.572165</td>\n",
+       "      <td>0.018227</td>\n",
+       "      <td>0.603279</td>\n",
+       "      <td>0.587732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>reddit</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.572165</td>\n",
+       "      <td>0.018227</td>\n",
+       "      <td>0.603279</td>\n",
+       "      <td>0.587732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>1 epoch pretraining</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.469891</td>\n",
+       "      <td>0.038780</td>\n",
+       "      <td>0.523809</td>\n",
+       "      <td>0.490383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>1 epoch pretraining</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.525926</td>\n",
+       "      <td>0.024596</td>\n",
+       "      <td>0.564885</td>\n",
+       "      <td>0.539330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.523124</td>\n",
+       "      <td>0.027906</td>\n",
+       "      <td>0.570470</td>\n",
+       "      <td>0.540592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.550656</td>\n",
+       "      <td>0.027349</td>\n",
+       "      <td>0.608392</td>\n",
+       "      <td>0.573604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>cross entropy wo wieghts</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.433285</td>\n",
+       "      <td>0.062494</td>\n",
+       "      <td>0.521739</td>\n",
+       "      <td>0.487437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>cross entropy wo wieghts</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.451950</td>\n",
+       "      <td>0.050503</td>\n",
+       "      <td>0.539535</td>\n",
+       "      <td>0.490566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>wo early stopping</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.516319</td>\n",
+       "      <td>0.033725</td>\n",
+       "      <td>0.564315</td>\n",
+       "      <td>0.546150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>wiki</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>wo early stopping</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.564405</td>\n",
+       "      <td>0.019395</td>\n",
+       "      <td>0.608392</td>\n",
+       "      <td>0.574534</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        ds model                  exp_type lmseed      mean       std  \\\n",
+       "0   reddit  lstm                     _orig      0  0.553660  0.029906   \n",
+       "1   reddit  lstm                     _orig      1  0.560869  0.028504   \n",
+       "2   reddit  lstm             dropmul = 0.5      0  0.519352  0.030414   \n",
+       "3   reddit  lstm             dropmul = 0.5      1  0.538298  0.030352   \n",
+       "4   reddit  qrnn                     _orig      0  0.572165  0.018227   \n",
+       "5   reddit  qrnn                     _orig      1  0.572165  0.018227   \n",
+       "8     wiki  lstm       1 epoch pretraining      0  0.469891  0.038780   \n",
+       "9     wiki  lstm       1 epoch pretraining      1  0.525926  0.024596   \n",
+       "11    wiki  lstm                     _orig      0  0.523124  0.027906   \n",
+       "13    wiki  lstm                     _orig      1  0.550656  0.027349   \n",
+       "14    wiki  lstm  cross entropy wo wieghts      0  0.433285  0.062494   \n",
+       "15    wiki  lstm  cross entropy wo wieghts      1  0.451950  0.050503   \n",
+       "16    wiki  lstm         wo early stopping      0  0.516319  0.033725   \n",
+       "17    wiki  lstm         wo early stopping      1  0.564405  0.019395   \n",
+       "\n",
+       "         max       75%  \n",
+       "0   0.614232  0.575757  \n",
+       "1   0.622222  0.580522  \n",
+       "2   0.589552  0.533757  \n",
+       "3   0.602151  0.557069  \n",
+       "4   0.603279  0.587732  \n",
+       "5   0.603279  0.587732  \n",
+       "8   0.523809  0.490383  \n",
+       "9   0.564885  0.539330  \n",
+       "11  0.570470  0.540592  \n",
+       "13  0.608392  0.573604  \n",
+       "14  0.521739  0.487437  \n",
+       "15  0.539535  0.490566  \n",
+       "16  0.564315  0.546150  \n",
+       "17  0.608392  0.574534  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = df.pivot_table(index=\"model_name\", columns=[\"ds\", \"model\", \"exp_type\", \"lmseed\", \"fine_tune\"], \n",
+    "               values=[\"test F1 score bin\"]).describe().T.sort_values([\"ds\",\"model\", \"exp_type\",\"mean\"]).reset_index()\n",
+    "del results[\"level_0\"]\n",
+    "results[results[\"fine_tune\"] == \"yes\"][[\"ds\",\"model\", \"exp_type\", \"lmseed\", \"mean\", \"std\", \"max\", \"75%\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "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>ds</th>\n",
+       "      <th>fine_tune</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>max</th>\n",
+       "      <th>75%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>wiki</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0.522515</td>\n",
+       "      <td>0.026661</td>\n",
+       "      <td>0.582781</td>\n",
+       "      <td>0.541998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>wiki</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>0.536890</td>\n",
+       "      <td>0.030744</td>\n",
+       "      <td>0.608392</td>\n",
+       "      <td>0.558897</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>reddit</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>0.561675</td>\n",
+       "      <td>0.027365</td>\n",
+       "      <td>0.622222</td>\n",
+       "      <td>0.581680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>reddit</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0.573931</td>\n",
+       "      <td>0.016832</td>\n",
+       "      <td>0.603390</td>\n",
+       "      <td>0.581451</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ds fine_tune      mean       std       max       75%\n",
+       "0    wiki        no  0.522515  0.026661  0.582781  0.541998\n",
+       "1    wiki       yes  0.536890  0.030744  0.608392  0.558897\n",
+       "2  reddit       yes  0.561675  0.027365  0.622222  0.581680\n",
+       "3  reddit        no  0.573931  0.016832  0.603390  0.581451"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = df[df[\"exp_type\"]==\"_orig\"].pivot_table(index=\"model_name\", columns=[\"ds\", \"fine_tune\"], \n",
+    "               values=[\"test F1 score bin\"]).describe().T.sort_values([\"mean\"]).reset_index()\n",
+    "del results[\"level_0\"]\n",
+    "results[[\"ds\", \"fine_tune\", \"mean\", \"std\", \"max\", \"75%\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "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>model_dir_parent</th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>...</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>lmseed</th>\n",
+       "      <th>fine_tune</th>\n",
+       "      <th>model</th>\n",
+       "      <th>exp_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.870</td>\n",
+       "      <td>0.577922</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.456889</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.511494</td>\n",
+       "      <td>0.664179</td>\n",
+       "      <td>0.876617</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.429593</td>\n",
+       "      <td>0.507218</td>\n",
+       "      <td>0.389830</td>\n",
+       "      <td>0.811765</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.858</td>\n",
+       "      <td>0.572289</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.426693</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.479798</td>\n",
+       "      <td>0.708955</td>\n",
+       "      <td>0.860696</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.434975</td>\n",
+       "      <td>0.483857</td>\n",
+       "      <td>0.358974</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.853</td>\n",
+       "      <td>0.573913</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.465223</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.469194</td>\n",
+       "      <td>0.738806</td>\n",
+       "      <td>0.849751</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.523841</td>\n",
+       "      <td>0.477067</td>\n",
+       "      <td>0.342857</td>\n",
+       "      <td>0.847059</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.855</td>\n",
+       "      <td>0.577259</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.483052</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.738806</td>\n",
+       "      <td>0.841791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.530552</td>\n",
+       "      <td>0.441885</td>\n",
+       "      <td>0.323810</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.862</td>\n",
+       "      <td>0.576687</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.491995</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.489583</td>\n",
+       "      <td>0.701493</td>\n",
+       "      <td>0.851741</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.488528</td>\n",
+       "      <td>0.474608</td>\n",
+       "      <td>0.344660</td>\n",
+       "      <td>0.835294</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.854</td>\n",
+       "      <td>0.570588</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.483143</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.470874</td>\n",
+       "      <td>0.723881</td>\n",
+       "      <td>0.829851</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.537976</td>\n",
+       "      <td>0.435847</td>\n",
+       "      <td>0.309735</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.850</td>\n",
+       "      <td>0.561404</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.519333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.461538</td>\n",
+       "      <td>0.716418</td>\n",
+       "      <td>0.842786</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.541371</td>\n",
+       "      <td>0.454872</td>\n",
+       "      <td>0.328638</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.865</td>\n",
+       "      <td>0.597015</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.457041</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.497512</td>\n",
+       "      <td>0.746269</td>\n",
+       "      <td>0.857711</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.452747</td>\n",
+       "      <td>0.473131</td>\n",
+       "      <td>0.352041</td>\n",
+       "      <td>0.811765</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.871</td>\n",
+       "      <td>0.582524</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.418411</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.514286</td>\n",
+       "      <td>0.671642</td>\n",
+       "      <td>0.874627</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.378810</td>\n",
+       "      <td>0.492229</td>\n",
+       "      <td>0.382857</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.849</td>\n",
+       "      <td>0.557185</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.482558</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.458937</td>\n",
+       "      <td>0.708955</td>\n",
+       "      <td>0.866667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.471542</td>\n",
+       "      <td>0.494311</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-1...</td>\n",
+       "      <td>0.876</td>\n",
+       "      <td>0.578231</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.419756</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.531250</td>\n",
+       "      <td>0.634328</td>\n",
+       "      <td>0.891542</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.345693</td>\n",
+       "      <td>0.532369</td>\n",
+       "      <td>0.425000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-2...</td>\n",
+       "      <td>0.864</td>\n",
+       "      <td>0.569620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.458027</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.494505</td>\n",
+       "      <td>0.671642</td>\n",
+       "      <td>0.873632</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.425387</td>\n",
+       "      <td>0.484692</td>\n",
+       "      <td>0.379310</td>\n",
+       "      <td>0.776471</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-3...</td>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.574924</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.477018</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.487047</td>\n",
+       "      <td>0.701493</td>\n",
+       "      <td>0.876617</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.449001</td>\n",
+       "      <td>0.518262</td>\n",
+       "      <td>0.392265</td>\n",
+       "      <td>0.835294</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-4...</td>\n",
+       "      <td>0.883</td>\n",
+       "      <td>0.603390</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.419202</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.552795</td>\n",
+       "      <td>0.664179</td>\n",
+       "      <td>0.881592</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.407950</td>\n",
+       "      <td>0.511490</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-5...</td>\n",
+       "      <td>0.876</td>\n",
+       "      <td>0.594771</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.427007</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.529070</td>\n",
+       "      <td>0.679105</td>\n",
+       "      <td>0.881592</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.408002</td>\n",
+       "      <td>0.500378</td>\n",
+       "      <td>0.397590</td>\n",
+       "      <td>0.776471</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.848</td>\n",
+       "      <td>0.530864</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.488566</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.452632</td>\n",
+       "      <td>0.641791</td>\n",
+       "      <td>0.869652</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.478236</td>\n",
+       "      <td>0.505266</td>\n",
+       "      <td>0.377660</td>\n",
+       "      <td>0.835294</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.856</td>\n",
+       "      <td>0.563636</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.437123</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.474490</td>\n",
+       "      <td>0.694030</td>\n",
+       "      <td>0.877612</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.403486</td>\n",
+       "      <td>0.525650</td>\n",
+       "      <td>0.395604</td>\n",
+       "      <td>0.847059</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.591640</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.448317</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.519774</td>\n",
+       "      <td>0.686567</td>\n",
+       "      <td>0.884577</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.427392</td>\n",
+       "      <td>0.539378</td>\n",
+       "      <td>0.411429</td>\n",
+       "      <td>0.847059</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.863</td>\n",
+       "      <td>0.550820</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.450464</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.491228</td>\n",
+       "      <td>0.626866</td>\n",
+       "      <td>0.892537</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.404172</td>\n",
+       "      <td>0.539953</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.811765</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.880</td>\n",
+       "      <td>0.580420</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.419509</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.546053</td>\n",
+       "      <td>0.619403</td>\n",
+       "      <td>0.890547</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.378666</td>\n",
+       "      <td>0.491477</td>\n",
+       "      <td>0.414966</td>\n",
+       "      <td>0.717647</td>\n",
+       "      <td>qrnn_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.865</td>\n",
+       "      <td>0.545455</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.453675</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.496933</td>\n",
+       "      <td>0.604478</td>\n",
+       "      <td>0.880597</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.435547</td>\n",
+       "      <td>0.503941</td>\n",
+       "      <td>0.396450</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>qrnn_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.881</td>\n",
+       "      <td>0.593857</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.423210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.547170</td>\n",
+       "      <td>0.649254</td>\n",
+       "      <td>0.891542</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.410248</td>\n",
+       "      <td>0.526933</td>\n",
+       "      <td>0.424051</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>qrnn_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.854</td>\n",
+       "      <td>0.549383</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.510561</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.468421</td>\n",
+       "      <td>0.664179</td>\n",
+       "      <td>0.867662</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.508237</td>\n",
+       "      <td>0.501671</td>\n",
+       "      <td>0.373684</td>\n",
+       "      <td>0.835294</td>\n",
+       "      <td>qrnn_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.876</td>\n",
+       "      <td>0.563380</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.362120</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.597015</td>\n",
+       "      <td>0.885572</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.317093</td>\n",
+       "      <td>0.525116</td>\n",
+       "      <td>0.410714</td>\n",
+       "      <td>0.811765</td>\n",
+       "      <td>qrnn_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.867</td>\n",
+       "      <td>0.558140</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.366643</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.502994</td>\n",
+       "      <td>0.626866</td>\n",
+       "      <td>0.887562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.317464</td>\n",
+       "      <td>0.496125</td>\n",
+       "      <td>0.409091</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>qrnn_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.868</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.486303</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.505747</td>\n",
+       "      <td>0.656716</td>\n",
+       "      <td>0.867662</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.441740</td>\n",
+       "      <td>0.479220</td>\n",
+       "      <td>0.368132</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>qrnn_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.587838</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.450848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.537037</td>\n",
+       "      <td>0.649254</td>\n",
+       "      <td>0.889552</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.413494</td>\n",
+       "      <td>0.522595</td>\n",
+       "      <td>0.418750</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>qrnn_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.578073</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.442141</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.520958</td>\n",
+       "      <td>0.649254</td>\n",
+       "      <td>0.891542</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.373358</td>\n",
+       "      <td>0.526933</td>\n",
+       "      <td>0.424051</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>qrnn_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.885</td>\n",
+       "      <td>0.590747</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.456181</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.564626</td>\n",
+       "      <td>0.619403</td>\n",
+       "      <td>0.888557</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.400573</td>\n",
+       "      <td>0.503877</td>\n",
+       "      <td>0.412903</td>\n",
+       "      <td>0.752941</td>\n",
+       "      <td>qrnn_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...</td>\n",
+       "      <td>0.880</td>\n",
+       "      <td>0.577465</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.413595</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.546667</td>\n",
+       "      <td>0.611940</td>\n",
+       "      <td>0.899503</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.342127</td>\n",
+       "      <td>0.528914</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.752941</td>\n",
+       "      <td>qrnn_ft20_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</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>306</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-5...</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.595918</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.453026</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.657658</td>\n",
+       "      <td>0.544776</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.369372</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.538462</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-5...</td>\n",
+       "      <td>0.890</td>\n",
+       "      <td>0.576923</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.412375</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.595238</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.901493</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.341758</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.446970</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.576271</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.458157</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.507463</td>\n",
+       "      <td>0.920398</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.343866</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.523364</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.552301</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.526710</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.628571</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.376918</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.540816</td>\n",
+       "      <td>0.623529</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.542222</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.378023</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.670330</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.928358</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.245958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.564356</td>\n",
+       "      <td>0.670588</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.549356</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.502734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.646465</td>\n",
+       "      <td>0.477612</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.366041</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.509259</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.899</td>\n",
+       "      <td>0.542986</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.484061</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.689655</td>\n",
+       "      <td>0.447761</td>\n",
+       "      <td>0.928358</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.292909</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.573034</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>0.561265</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.478323</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.596639</td>\n",
+       "      <td>0.529851</td>\n",
+       "      <td>0.912438</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.371474</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.486726</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.559671</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.460062</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.623853</td>\n",
+       "      <td>0.507463</td>\n",
+       "      <td>0.916418</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.314330</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.504425</td>\n",
+       "      <td>0.670588</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.891</td>\n",
+       "      <td>0.532189</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.520550</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.626263</td>\n",
+       "      <td>0.462687</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.342852</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.545455</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.548043</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.401640</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.523810</td>\n",
+       "      <td>0.574627</td>\n",
+       "      <td>0.890547</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.359008</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.421384</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.891</td>\n",
+       "      <td>0.528139</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.489808</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.628866</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.327826</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.509804</td>\n",
+       "      <td>0.611765</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.568965</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.445748</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.673469</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.303262</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.534653</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.567901</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.542561</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.633027</td>\n",
+       "      <td>0.514925</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.334571</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.886</td>\n",
+       "      <td>0.508621</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.528722</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.602041</td>\n",
+       "      <td>0.440298</td>\n",
+       "      <td>0.921393</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.341839</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.528302</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.590038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.384665</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.606299</td>\n",
+       "      <td>0.574627</td>\n",
+       "      <td>0.906468</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.314057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.466667</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.534483</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.502705</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.632653</td>\n",
+       "      <td>0.462687</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.337562</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.537634</td>\n",
+       "      <td>0.588235</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.904</td>\n",
+       "      <td>0.586207</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.488476</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.693878</td>\n",
+       "      <td>0.507463</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.342792</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.558333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.466521</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.632075</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.303226</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.538462</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.902</td>\n",
+       "      <td>0.566372</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.447245</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.695652</td>\n",
+       "      <td>0.477612</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.347164</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.510204</td>\n",
+       "      <td>0.588235</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.592593</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.429450</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.660550</td>\n",
+       "      <td>0.537313</td>\n",
+       "      <td>0.915423</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.313667</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.623529</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.888</td>\n",
+       "      <td>0.521367</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.500936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.610000</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.378845</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.540000</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.602410</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.490258</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.652174</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.914428</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.392229</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.495726</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.887</td>\n",
+       "      <td>0.502203</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.516663</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.612903</td>\n",
+       "      <td>0.425373</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.355745</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.534653</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.548523</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.473883</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.631068</td>\n",
+       "      <td>0.485075</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.329670</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.542056</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.588235</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.490045</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.619835</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.914428</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.358677</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.496000</td>\n",
+       "      <td>0.729412</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.549356</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.458278</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.646465</td>\n",
+       "      <td>0.477612</td>\n",
+       "      <td>0.916418</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.332558</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.504274</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.558333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.485095</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.632075</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.928358</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.338581</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.568421</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.899</td>\n",
+       "      <td>0.607004</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.308932</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.634146</td>\n",
+       "      <td>0.582090</td>\n",
+       "      <td>0.919403</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.237768</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.516393</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.906</td>\n",
+       "      <td>0.614754</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.471272</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.681818</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.372592</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.509091</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>189 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        model_dir_parent  \\\n",
+       "19   data/hate/pl-10-reddit/models/sp25k   \n",
+       "20   data/hate/pl-10-reddit/models/sp25k   \n",
+       "21   data/hate/pl-10-reddit/models/sp25k   \n",
+       "22   data/hate/pl-10-reddit/models/sp25k   \n",
+       "23   data/hate/pl-10-reddit/models/sp25k   \n",
+       "24   data/hate/pl-10-reddit/models/sp25k   \n",
+       "25   data/hate/pl-10-reddit/models/sp25k   \n",
+       "26   data/hate/pl-10-reddit/models/sp25k   \n",
+       "27   data/hate/pl-10-reddit/models/sp25k   \n",
+       "28   data/hate/pl-10-reddit/models/sp25k   \n",
+       "29   data/hate/pl-10-reddit/models/sp25k   \n",
+       "30   data/hate/pl-10-reddit/models/sp25k   \n",
+       "31   data/hate/pl-10-reddit/models/sp25k   \n",
+       "32   data/hate/pl-10-reddit/models/sp25k   \n",
+       "33   data/hate/pl-10-reddit/models/sp25k   \n",
+       "34   data/hate/pl-10-reddit/models/sp25k   \n",
+       "35   data/hate/pl-10-reddit/models/sp25k   \n",
+       "36   data/hate/pl-10-reddit/models/sp25k   \n",
+       "37   data/hate/pl-10-reddit/models/sp25k   \n",
+       "57   data/hate/pl-10-reddit/models/sp25k   \n",
+       "58   data/hate/pl-10-reddit/models/sp25k   \n",
+       "59   data/hate/pl-10-reddit/models/sp25k   \n",
+       "60   data/hate/pl-10-reddit/models/sp25k   \n",
+       "61   data/hate/pl-10-reddit/models/sp25k   \n",
+       "62   data/hate/pl-10-reddit/models/sp25k   \n",
+       "63   data/hate/pl-10-reddit/models/sp25k   \n",
+       "64   data/hate/pl-10-reddit/models/sp25k   \n",
+       "65   data/hate/pl-10-reddit/models/sp25k   \n",
+       "66   data/hate/pl-10-reddit/models/sp25k   \n",
+       "67   data/hate/pl-10-reddit/models/sp25k   \n",
+       "..                                   ...   \n",
+       "306  data/hate/pl-10-reddit/models/sp25k   \n",
+       "307  data/hate/pl-10-reddit/models/sp25k   \n",
+       "308  data/hate/pl-10-reddit/models/sp25k   \n",
+       "309  data/hate/pl-10-reddit/models/sp25k   \n",
+       "310  data/hate/pl-10-reddit/models/sp25k   \n",
+       "311  data/hate/pl-10-reddit/models/sp25k   \n",
+       "312  data/hate/pl-10-reddit/models/sp25k   \n",
+       "313  data/hate/pl-10-reddit/models/sp25k   \n",
+       "314  data/hate/pl-10-reddit/models/sp25k   \n",
+       "315  data/hate/pl-10-reddit/models/sp25k   \n",
+       "316  data/hate/pl-10-reddit/models/sp25k   \n",
+       "317  data/hate/pl-10-reddit/models/sp25k   \n",
+       "318  data/hate/pl-10-reddit/models/sp25k   \n",
+       "319  data/hate/pl-10-reddit/models/sp25k   \n",
+       "320  data/hate/pl-10-reddit/models/sp25k   \n",
+       "321  data/hate/pl-10-reddit/models/sp25k   \n",
+       "322  data/hate/pl-10-reddit/models/sp25k   \n",
+       "323  data/hate/pl-10-reddit/models/sp25k   \n",
+       "324  data/hate/pl-10-reddit/models/sp25k   \n",
+       "325  data/hate/pl-10-reddit/models/sp25k   \n",
+       "326  data/hate/pl-10-reddit/models/sp25k   \n",
+       "327  data/hate/pl-10-reddit/models/sp25k   \n",
+       "328  data/hate/pl-10-reddit/models/sp25k   \n",
+       "329  data/hate/pl-10-reddit/models/sp25k   \n",
+       "330  data/hate/pl-10-reddit/models/sp25k   \n",
+       "331  data/hate/pl-10-reddit/models/sp25k   \n",
+       "332  data/hate/pl-10-reddit/models/sp25k   \n",
+       "333  data/hate/pl-10-reddit/models/sp25k   \n",
+       "334  data/hate/pl-10-reddit/models/sp25k   \n",
+       "335  data/hate/pl-10-reddit/models/sp25k   \n",
+       "\n",
+       "                                            model_name  test Accuracy  \\\n",
+       "19   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.870   \n",
+       "20   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.858   \n",
+       "21   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.853   \n",
+       "22   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.855   \n",
+       "23   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.862   \n",
+       "24   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.854   \n",
+       "25   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.850   \n",
+       "26   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.865   \n",
+       "27   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.871   \n",
+       "28   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.849   \n",
+       "29   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-1...          0.876   \n",
+       "30   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-2...          0.864   \n",
+       "31   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-3...          0.861   \n",
+       "32   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-4...          0.883   \n",
+       "33   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-5...          0.876   \n",
+       "34   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.848   \n",
+       "35   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.856   \n",
+       "36   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.873   \n",
+       "37   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.863   \n",
+       "57   qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.880   \n",
+       "58   qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.865   \n",
+       "59   qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.881   \n",
+       "60   qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.854   \n",
+       "61   qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.876   \n",
+       "62   qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.867   \n",
+       "63   qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.868   \n",
+       "64   qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.878   \n",
+       "65   qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.873   \n",
+       "66   qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.885   \n",
+       "67   qrnn_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-...          0.880   \n",
+       "..                                                 ...            ...   \n",
+       "306  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-5...          0.901   \n",
+       "307  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-5...          0.890   \n",
+       "308  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.900   \n",
+       "309  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.893   \n",
+       "310  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.897   \n",
+       "311  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.895   \n",
+       "312  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.899   \n",
+       "313  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.889   \n",
+       "314  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.893   \n",
+       "315  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.891   \n",
+       "316  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.873   \n",
+       "317  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.891   \n",
+       "318  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.900   \n",
+       "319  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.895   \n",
+       "320  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.886   \n",
+       "321  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.893   \n",
+       "322  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.892   \n",
+       "323  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.904   \n",
+       "324  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.894   \n",
+       "325  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.902   \n",
+       "326  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.901   \n",
+       "327  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.888   \n",
+       "328  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.901   \n",
+       "329  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.887   \n",
+       "330  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.893   \n",
+       "331  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.895   \n",
+       "332  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.895   \n",
+       "333  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.894   \n",
+       "334  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.899   \n",
+       "335  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.906   \n",
+       "\n",
+       "     test F1 score bin  test Kappa Linear  test Loss  test Matthews Correff  \\\n",
+       "19            0.577922                NaN   0.456889                    NaN   \n",
+       "20            0.572289                NaN   0.426693                    NaN   \n",
+       "21            0.573913                NaN   0.465223                    NaN   \n",
+       "22            0.577259                NaN   0.483052                    NaN   \n",
+       "23            0.576687                NaN   0.491995                    NaN   \n",
+       "24            0.570588                NaN   0.483143                    NaN   \n",
+       "25            0.561404                NaN   0.519333                    NaN   \n",
+       "26            0.597015                NaN   0.457041                    NaN   \n",
+       "27            0.582524                NaN   0.418411                    NaN   \n",
+       "28            0.557185                NaN   0.482558                    NaN   \n",
+       "29            0.578231                NaN   0.419756                    NaN   \n",
+       "30            0.569620                NaN   0.458027                    NaN   \n",
+       "31            0.574924                NaN   0.477018                    NaN   \n",
+       "32            0.603390                NaN   0.419202                    NaN   \n",
+       "33            0.594771                NaN   0.427007                    NaN   \n",
+       "34            0.530864                NaN   0.488566                    NaN   \n",
+       "35            0.563636                NaN   0.437123                    NaN   \n",
+       "36            0.591640                NaN   0.448317                    NaN   \n",
+       "37            0.550820                NaN   0.450464                    NaN   \n",
+       "57            0.580420                NaN   0.419509                    NaN   \n",
+       "58            0.545455                NaN   0.453675                    NaN   \n",
+       "59            0.593857                NaN   0.423210                    NaN   \n",
+       "60            0.549383                NaN   0.510561                    NaN   \n",
+       "61            0.563380                NaN   0.362120                    NaN   \n",
+       "62            0.558140                NaN   0.366643                    NaN   \n",
+       "63            0.571429                NaN   0.486303                    NaN   \n",
+       "64            0.587838                NaN   0.450848                    NaN   \n",
+       "65            0.578073                NaN   0.442141                    NaN   \n",
+       "66            0.590747                NaN   0.456181                    NaN   \n",
+       "67            0.577465                NaN   0.413595                    NaN   \n",
+       "..                 ...                ...        ...                    ...   \n",
+       "306           0.595918                NaN   0.453026                    NaN   \n",
+       "307           0.576923                NaN   0.412375                    NaN   \n",
+       "308           0.576271                NaN   0.458157                    NaN   \n",
+       "309           0.552301                NaN   0.526710                    NaN   \n",
+       "310           0.542222                NaN   0.378023                    NaN   \n",
+       "311           0.549356                NaN   0.502734                    NaN   \n",
+       "312           0.542986                NaN   0.484061                    NaN   \n",
+       "313           0.561265                NaN   0.478323                    NaN   \n",
+       "314           0.559671                NaN   0.460062                    NaN   \n",
+       "315           0.532189                NaN   0.520550                    NaN   \n",
+       "316           0.548043                NaN   0.401640                    NaN   \n",
+       "317           0.528139                NaN   0.489808                    NaN   \n",
+       "318           0.568965                NaN   0.445748                    NaN   \n",
+       "319           0.567901                NaN   0.542561                    NaN   \n",
+       "320           0.508621                NaN   0.528722                    NaN   \n",
+       "321           0.590038                NaN   0.384665                    NaN   \n",
+       "322           0.534483                NaN   0.502705                    NaN   \n",
+       "323           0.586207                NaN   0.488476                    NaN   \n",
+       "324           0.558333                NaN   0.466521                    NaN   \n",
+       "325           0.566372                NaN   0.447245                    NaN   \n",
+       "326           0.592593                NaN   0.429450                    NaN   \n",
+       "327           0.521367                NaN   0.500936                    NaN   \n",
+       "328           0.602410                NaN   0.490258                    NaN   \n",
+       "329           0.502203                NaN   0.516663                    NaN   \n",
+       "330           0.548523                NaN   0.473883                    NaN   \n",
+       "331           0.588235                NaN   0.490045                    NaN   \n",
+       "332           0.549356                NaN   0.458278                    NaN   \n",
+       "333           0.558333                NaN   0.485095                    NaN   \n",
+       "334           0.607004                NaN   0.308932                    NaN   \n",
+       "335           0.614754                NaN   0.471272                    NaN   \n",
+       "\n",
+       "     test Precision  test Recall  valid Accuracy  ...  valid Loss  \\\n",
+       "19         0.511494     0.664179        0.876617  ...    0.429593   \n",
+       "20         0.479798     0.708955        0.860696  ...    0.434975   \n",
+       "21         0.469194     0.738806        0.849751  ...    0.523841   \n",
+       "22         0.473684     0.738806        0.841791  ...    0.530552   \n",
+       "23         0.489583     0.701493        0.851741  ...    0.488528   \n",
+       "24         0.470874     0.723881        0.829851  ...    0.537976   \n",
+       "25         0.461538     0.716418        0.842786  ...    0.541371   \n",
+       "26         0.497512     0.746269        0.857711  ...    0.452747   \n",
+       "27         0.514286     0.671642        0.874627  ...    0.378810   \n",
+       "28         0.458937     0.708955        0.866667  ...    0.471542   \n",
+       "29         0.531250     0.634328        0.891542  ...    0.345693   \n",
+       "30         0.494505     0.671642        0.873632  ...    0.425387   \n",
+       "31         0.487047     0.701493        0.876617  ...    0.449001   \n",
+       "32         0.552795     0.664179        0.881592  ...    0.407950   \n",
+       "33         0.529070     0.679105        0.881592  ...    0.408002   \n",
+       "34         0.452632     0.641791        0.869652  ...    0.478236   \n",
+       "35         0.474490     0.694030        0.877612  ...    0.403486   \n",
+       "36         0.519774     0.686567        0.884577  ...    0.427392   \n",
+       "37         0.491228     0.626866        0.892537  ...    0.404172   \n",
+       "57         0.546053     0.619403        0.890547  ...    0.378666   \n",
+       "58         0.496933     0.604478        0.880597  ...    0.435547   \n",
+       "59         0.547170     0.649254        0.891542  ...    0.410248   \n",
+       "60         0.468421     0.664179        0.867662  ...    0.508237   \n",
+       "61         0.533333     0.597015        0.885572  ...    0.317093   \n",
+       "62         0.502994     0.626866        0.887562  ...    0.317464   \n",
+       "63         0.505747     0.656716        0.867662  ...    0.441740   \n",
+       "64         0.537037     0.649254        0.889552  ...    0.413494   \n",
+       "65         0.520958     0.649254        0.891542  ...    0.373358   \n",
+       "66         0.564626     0.619403        0.888557  ...    0.400573   \n",
+       "67         0.546667     0.611940        0.899503  ...    0.342127   \n",
+       "..              ...          ...             ...  ...         ...   \n",
+       "306        0.657658     0.544776        0.923383  ...    0.369372   \n",
+       "307        0.595238     0.559702        0.901493  ...    0.341758   \n",
+       "308        0.666667     0.507463        0.920398  ...    0.343866   \n",
+       "309        0.628571     0.492537        0.923383  ...    0.376918   \n",
+       "310        0.670330     0.455224        0.928358  ...    0.245958   \n",
+       "311        0.646465     0.477612        0.917413  ...    0.366041   \n",
+       "312        0.689655     0.447761        0.928358  ...    0.292909   \n",
+       "313        0.596639     0.529851        0.912438  ...    0.371474   \n",
+       "314        0.623853     0.507463        0.916418  ...    0.314330   \n",
+       "315        0.626263     0.462687        0.924378  ...    0.342852   \n",
+       "316        0.523810     0.574627        0.890547  ...    0.359008   \n",
+       "317        0.628866     0.455224        0.917413  ...    0.327826   \n",
+       "318        0.673469     0.492537        0.922388  ...    0.303262   \n",
+       "319        0.633027     0.514925        0.922388  ...    0.334571   \n",
+       "320        0.602041     0.440298        0.921393  ...    0.341839   \n",
+       "321        0.606299     0.574627        0.906468  ...    0.314057   \n",
+       "322        0.632653     0.462687        0.922388  ...    0.337562   \n",
+       "323        0.693878     0.507463        0.922388  ...    0.342792   \n",
+       "324        0.632075     0.500000        0.923383  ...    0.303226   \n",
+       "325        0.695652     0.477612        0.917413  ...    0.347164   \n",
+       "326        0.660550     0.537313        0.915423  ...    0.313667   \n",
+       "327        0.610000     0.455224        0.923383  ...    0.378845   \n",
+       "328        0.652174     0.559702        0.914428  ...    0.392229   \n",
+       "329        0.612903     0.425373        0.922388  ...    0.355745   \n",
+       "330        0.631068     0.485075        0.924378  ...    0.329670   \n",
+       "331        0.619835     0.559702        0.914428  ...    0.358677   \n",
+       "332        0.646465     0.477612        0.916418  ...    0.332558   \n",
+       "333        0.632075     0.500000        0.928358  ...    0.338581   \n",
+       "334        0.634146     0.582090        0.919403  ...    0.237768   \n",
+       "335        0.681818     0.559702        0.917413  ...    0.372592   \n",
+       "\n",
+       "     valid Matthews Correff  valid Precision  valid Recall           arch  \\\n",
+       "19                 0.507218         0.389830      0.811765   qrnn_ft0_cl8   \n",
+       "20                 0.483857         0.358974      0.823529   qrnn_ft0_cl8   \n",
+       "21                 0.477067         0.342857      0.847059   qrnn_ft0_cl8   \n",
+       "22                 0.441885         0.323810      0.800000   qrnn_ft0_cl8   \n",
+       "23                 0.474608         0.344660      0.835294   qrnn_ft0_cl8   \n",
+       "24                 0.435847         0.309735      0.823529   qrnn_ft0_cl8   \n",
+       "25                 0.454872         0.328638      0.823529   qrnn_ft0_cl8   \n",
+       "26                 0.473131         0.352041      0.811765   qrnn_ft0_cl8   \n",
+       "27                 0.492229         0.382857      0.788235   qrnn_ft0_cl8   \n",
+       "28                 0.494311         0.370370      0.823529   qrnn_ft0_cl8   \n",
+       "29                 0.532369         0.425000      0.800000   qrnn_ft0_cl8   \n",
+       "30                 0.484692         0.379310      0.776471   qrnn_ft0_cl8   \n",
+       "31                 0.518262         0.392265      0.835294   qrnn_ft0_cl8   \n",
+       "32                 0.511490         0.400000      0.800000   qrnn_ft0_cl8   \n",
+       "33                 0.500378         0.397590      0.776471   qrnn_ft0_cl8   \n",
+       "34                 0.505266         0.377660      0.835294   qrnn_ft0_cl8   \n",
+       "35                 0.525650         0.395604      0.847059   qrnn_ft0_cl8   \n",
+       "36                 0.539378         0.411429      0.847059   qrnn_ft0_cl8   \n",
+       "37                 0.539953         0.428571      0.811765   qrnn_ft0_cl8   \n",
+       "57                 0.491477         0.414966      0.717647  qrnn_ft20_cl8   \n",
+       "58                 0.503941         0.396450      0.788235  qrnn_ft20_cl8   \n",
+       "59                 0.526933         0.424051      0.788235  qrnn_ft20_cl8   \n",
+       "60                 0.501671         0.373684      0.835294  qrnn_ft20_cl8   \n",
+       "61                 0.525116         0.410714      0.811765  qrnn_ft20_cl8   \n",
+       "62                 0.496125         0.409091      0.741176  qrnn_ft20_cl8   \n",
+       "63                 0.479220         0.368132      0.788235  qrnn_ft20_cl8   \n",
+       "64                 0.522595         0.418750      0.788235  qrnn_ft20_cl8   \n",
+       "65                 0.526933         0.424051      0.788235  qrnn_ft20_cl8   \n",
+       "66                 0.503877         0.412903      0.752941  qrnn_ft20_cl8   \n",
+       "67                 0.528914         0.444444      0.752941  qrnn_ft20_cl8   \n",
+       "..                      ...              ...           ...            ...   \n",
+       "306                     NaN         0.538462      0.658824   lstm_ft6_cl8   \n",
+       "307                     NaN         0.446970      0.694118   lstm_ft6_cl8   \n",
+       "308                     NaN         0.523364      0.658824   lstm_ft6_cl8   \n",
+       "309                     NaN         0.540816      0.623529   lstm_ft6_cl8   \n",
+       "310                     NaN         0.564356      0.670588   lstm_ft6_cl8   \n",
+       "311                     NaN         0.509259      0.647059   lstm_ft6_cl8   \n",
+       "312                     NaN         0.573034      0.600000   lstm_ft6_cl8   \n",
+       "313                     NaN         0.486726      0.647059   lstm_ft6_cl8   \n",
+       "314                     NaN         0.504425      0.670588   lstm_ft6_cl8   \n",
+       "315                     NaN         0.545455      0.635294   lstm_ft6_cl8   \n",
+       "316                     NaN         0.421384      0.788235   lstm_ft6_cl8   \n",
+       "317                     NaN         0.509804      0.611765   lstm_ft6_cl8   \n",
+       "318                     NaN         0.534653      0.635294   lstm_ft6_cl8   \n",
+       "319                     NaN         0.533333      0.658824   lstm_ft6_cl8   \n",
+       "320                     NaN         0.528302      0.658824   lstm_ft6_cl8   \n",
+       "321                     NaN         0.466667      0.741176   lstm_ft6_cl8   \n",
+       "322                     NaN         0.537634      0.588235   lstm_ft6_cl8   \n",
+       "323                     NaN         0.533333      0.658824   lstm_ft6_cl8   \n",
+       "324                     NaN         0.538462      0.658824   lstm_ft6_cl8   \n",
+       "325                     NaN         0.510204      0.588235   lstm_ft6_cl8   \n",
+       "326                     NaN         0.500000      0.623529   lstm_ft6_cl8   \n",
+       "327                     NaN         0.540000      0.635294   lstm_ft6_cl8   \n",
+       "328                     NaN         0.495726      0.682353   lstm_ft6_cl8   \n",
+       "329                     NaN         0.534653      0.635294   lstm_ft6_cl8   \n",
+       "330                     NaN         0.542056      0.682353   lstm_ft6_cl8   \n",
+       "331                     NaN         0.496000      0.729412   lstm_ft6_cl8   \n",
+       "332                     NaN         0.504274      0.694118   lstm_ft6_cl8   \n",
+       "333                     NaN         0.568421      0.635294   lstm_ft6_cl8   \n",
+       "334                     NaN         0.516393      0.741176   lstm_ft6_cl8   \n",
+       "335                     NaN         0.509091      0.658824   lstm_ft6_cl8   \n",
+       "\n",
+       "         ds lmseed fine_tune model exp_type  \n",
+       "19   reddit      1        no  qrnn    _orig  \n",
+       "20   reddit      1        no  qrnn    _orig  \n",
+       "21   reddit      1        no  qrnn    _orig  \n",
+       "22   reddit      1        no  qrnn    _orig  \n",
+       "23   reddit      1        no  qrnn    _orig  \n",
+       "24   reddit      1        no  qrnn    _orig  \n",
+       "25   reddit      1        no  qrnn    _orig  \n",
+       "26   reddit      1        no  qrnn    _orig  \n",
+       "27   reddit      1        no  qrnn    _orig  \n",
+       "28   reddit      1        no  qrnn    _orig  \n",
+       "29   reddit      1        no  qrnn    _orig  \n",
+       "30   reddit      1        no  qrnn    _orig  \n",
+       "31   reddit      1        no  qrnn    _orig  \n",
+       "32   reddit      1        no  qrnn    _orig  \n",
+       "33   reddit      1        no  qrnn    _orig  \n",
+       "34   reddit      1        no  qrnn    _orig  \n",
+       "35   reddit      1        no  qrnn    _orig  \n",
+       "36   reddit      1        no  qrnn    _orig  \n",
+       "37   reddit      1        no  qrnn    _orig  \n",
+       "57   reddit      1       yes  qrnn    _orig  \n",
+       "58   reddit      1       yes  qrnn    _orig  \n",
+       "59   reddit      1       yes  qrnn    _orig  \n",
+       "60   reddit      1       yes  qrnn    _orig  \n",
+       "61   reddit      1       yes  qrnn    _orig  \n",
+       "62   reddit      1       yes  qrnn    _orig  \n",
+       "63   reddit      1       yes  qrnn    _orig  \n",
+       "64   reddit      1       yes  qrnn    _orig  \n",
+       "65   reddit      1       yes  qrnn    _orig  \n",
+       "66   reddit      1       yes  qrnn    _orig  \n",
+       "67   reddit      1       yes  qrnn    _orig  \n",
+       "..      ...    ...       ...   ...      ...  \n",
+       "306  reddit      1       yes  lstm    _orig  \n",
+       "307  reddit      1       yes  lstm    _orig  \n",
+       "308  reddit      1       yes  lstm    _orig  \n",
+       "309  reddit      1       yes  lstm    _orig  \n",
+       "310  reddit      1       yes  lstm    _orig  \n",
+       "311  reddit      1       yes  lstm    _orig  \n",
+       "312  reddit      1       yes  lstm    _orig  \n",
+       "313  reddit      1       yes  lstm    _orig  \n",
+       "314  reddit      1       yes  lstm    _orig  \n",
+       "315  reddit      1       yes  lstm    _orig  \n",
+       "316  reddit      1       yes  lstm    _orig  \n",
+       "317  reddit      1       yes  lstm    _orig  \n",
+       "318  reddit      1       yes  lstm    _orig  \n",
+       "319  reddit      1       yes  lstm    _orig  \n",
+       "320  reddit      1       yes  lstm    _orig  \n",
+       "321  reddit      1       yes  lstm    _orig  \n",
+       "322  reddit      1       yes  lstm    _orig  \n",
+       "323  reddit      1       yes  lstm    _orig  \n",
+       "324  reddit      1       yes  lstm    _orig  \n",
+       "325  reddit      1       yes  lstm    _orig  \n",
+       "326  reddit      1       yes  lstm    _orig  \n",
+       "327  reddit      1       yes  lstm    _orig  \n",
+       "328  reddit      1       yes  lstm    _orig  \n",
+       "329  reddit      1       yes  lstm    _orig  \n",
+       "330  reddit      1       yes  lstm    _orig  \n",
+       "331  reddit      1       yes  lstm    _orig  \n",
+       "332  reddit      1       yes  lstm    _orig  \n",
+       "333  reddit      1       yes  lstm    _orig  \n",
+       "334  reddit      1       yes  lstm    _orig  \n",
+       "335  reddit      1       yes  lstm    _orig  \n",
+       "\n",
+       "[189 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(df[\"ds\"]==\"reddit\")&(df[\"model_name\"].str.contains(\"lmseed-1\"))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "      <td>1-ftseed-0-clsweightseed-0-clstrainseed-0.m</td>\n",
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+       "      <td>1-ftseed-0-clsweightseed-9-clstrainseed-0.m</td>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               0\n",
+       "304  0-ftseed-0-clsweightseed-0-clstrainseed-0.m\n",
+       "305  0-ftseed-0-clsweightseed-1-clstrainseed-0.m\n",
+       "306  0-ftseed-0-clsweightseed-2-clstrainseed-0.m\n",
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+       "313  0-ftseed-0-clsweightseed-9-clstrainseed-0.m\n",
+       "314  1-ftseed-0-clsweightseed-0-clstrainseed-0.m\n",
+       "315  1-ftseed-0-clsweightseed-1-clstrainseed-0.m\n",
+       "316  1-ftseed-0-clsweightseed-2-clstrainseed-0.m\n",
+       "317  1-ftseed-0-clsweightseed-3-clstrainseed-0.m\n",
+       "318  1-ftseed-0-clsweightseed-4-clstrainseed-0.m\n",
+       "319  1-ftseed-0-clsweightseed-5-clstrainseed-0.m\n",
+       "320  1-ftseed-0-clsweightseed-6-clstrainseed-0.m\n",
+       "321  1-ftseed-0-clsweightseed-7-clstrainseed-0.m\n",
+       "322  1-ftseed-0-clsweightseed-8-clstrainseed-0.m\n",
+       "323  1-ftseed-0-clsweightseed-9-clstrainseed-0.m"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(df[\"arch\"] == \"lstm_small_ft6_el20\") & (df[\"ds\"]==\"wiki\")][\"model_name\"].str.extract(\"lmseed-(.*)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-0-clstrainseed-0.m',\n",
+       "       'lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-0-clstrainseed-1.m',\n",
+       "       'lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-0-clstrainseed-2.m',\n",
+       "       'lstm_ft20_cl8_lmseed-0-ftseed-0-clsweightseed-0-clstrainseed-3.m', ...,\n",
+       "       'lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9-clstrainseed-3.m',\n",
+       "       'lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9-clstrainseed-4.m',\n",
+       "       'lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9-clstrainseed-5.m',\n",
+       "       'lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9-clstrainseed-6.m'], dtype=object)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"model_name\"].str.contains(\"lstm\") & df[\"model_dir_parent\"].str.contains(\"reddit\")][\"model_name\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "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>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.862474</td>\n",
+       "      <td>0.573931</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.458411</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.492615</td>\n",
+       "      <td>0.690495</td>\n",
+       "      <td>0.867452</td>\n",
+       "      <td>0.512282</td>\n",
+       "      <td>0.448106</td>\n",
+       "      <td>0.449435</td>\n",
+       "      <td>0.494340</td>\n",
+       "      <td>0.374274</td>\n",
+       "      <td>0.816718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.010040</td>\n",
+       "      <td>0.016832</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.028654</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.027161</td>\n",
+       "      <td>0.034733</td>\n",
+       "      <td>0.017477</td>\n",
+       "      <td>0.031288</td>\n",
+       "      <td>0.037504</td>\n",
+       "      <td>0.055156</td>\n",
+       "      <td>0.030134</td>\n",
+       "      <td>0.033402</td>\n",
+       "      <td>0.021990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.848000</td>\n",
+       "      <td>0.530864</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.418411</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.452632</td>\n",
+       "      <td>0.626866</td>\n",
+       "      <td>0.829851</td>\n",
+       "      <td>0.450161</td>\n",
+       "      <td>0.373101</td>\n",
+       "      <td>0.345693</td>\n",
+       "      <td>0.435847</td>\n",
+       "      <td>0.309735</td>\n",
+       "      <td>0.776471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.854250</td>\n",
+       "      <td>0.565132</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.429536</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.471576</td>\n",
+       "      <td>0.666045</td>\n",
+       "      <td>0.853234</td>\n",
+       "      <td>0.488877</td>\n",
+       "      <td>0.419496</td>\n",
+       "      <td>0.407963</td>\n",
+       "      <td>0.475223</td>\n",
+       "      <td>0.346505</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.862000</td>\n",
+       "      <td>0.574924</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.457041</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.489583</td>\n",
+       "      <td>0.694030</td>\n",
+       "      <td>0.873632</td>\n",
+       "      <td>0.515385</td>\n",
+       "      <td>0.453120</td>\n",
+       "      <td>0.434975</td>\n",
+       "      <td>0.494311</td>\n",
+       "      <td>0.379310</td>\n",
+       "      <td>0.823529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.870750</td>\n",
+       "      <td>0.581451</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.482928</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.513588</td>\n",
+       "      <td>0.714552</td>\n",
+       "      <td>0.880597</td>\n",
+       "      <td>0.533709</td>\n",
+       "      <td>0.473814</td>\n",
+       "      <td>0.485955</td>\n",
+       "      <td>0.516569</td>\n",
+       "      <td>0.397094</td>\n",
+       "      <td>0.835294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.883000</td>\n",
+       "      <td>0.603390</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.519333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.552795</td>\n",
+       "      <td>0.746269</td>\n",
+       "      <td>0.892537</td>\n",
+       "      <td>0.560976</td>\n",
+       "      <td>0.506322</td>\n",
+       "      <td>0.541371</td>\n",
+       "      <td>0.539953</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.847059</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Kappa Linear  test Loss  \\\n",
+       "count      38.000000          38.000000                0.0  38.000000   \n",
+       "mean        0.862474           0.573931                NaN   0.458411   \n",
+       "std         0.010040           0.016832                NaN   0.028654   \n",
+       "min         0.848000           0.530864                NaN   0.418411   \n",
+       "25%         0.854250           0.565132                NaN   0.429536   \n",
+       "50%         0.862000           0.574924                NaN   0.457041   \n",
+       "75%         0.870750           0.581451                NaN   0.482928   \n",
+       "max         0.883000           0.603390                NaN   0.519333   \n",
+       "\n",
+       "       test Matthews Correff  test Precision  test Recall  valid Accuracy  \\\n",
+       "count                    0.0       38.000000    38.000000       38.000000   \n",
+       "mean                     NaN        0.492615     0.690495        0.867452   \n",
+       "std                      NaN        0.027161     0.034733        0.017477   \n",
+       "min                      NaN        0.452632     0.626866        0.829851   \n",
+       "25%                      NaN        0.471576     0.666045        0.853234   \n",
+       "50%                      NaN        0.489583     0.694030        0.873632   \n",
+       "75%                      NaN        0.513588     0.714552        0.880597   \n",
+       "max                      NaN        0.552795     0.746269        0.892537   \n",
+       "\n",
+       "       valid F1 score bin  valid Kappa Linear  valid Loss  \\\n",
+       "count           38.000000           38.000000   38.000000   \n",
+       "mean             0.512282            0.448106    0.449435   \n",
+       "std              0.031288            0.037504    0.055156   \n",
+       "min              0.450161            0.373101    0.345693   \n",
+       "25%              0.488877            0.419496    0.407963   \n",
+       "50%              0.515385            0.453120    0.434975   \n",
+       "75%              0.533709            0.473814    0.485955   \n",
+       "max              0.560976            0.506322    0.541371   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count               38.000000        38.000000     38.000000  \n",
+       "mean                 0.494340         0.374274      0.816718  \n",
+       "std                  0.030134         0.033402      0.021990  \n",
+       "min                  0.435847         0.309735      0.776471  \n",
+       "25%                  0.475223         0.346505      0.800000  \n",
+       "50%                  0.494311         0.379310      0.823529  \n",
+       "75%                  0.516569         0.397094      0.835294  \n",
+       "max                  0.539953         0.428571      0.847059  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"model_name\"].str.contains(\"ft0\") & df[\"model_dir_parent\"].str.contains(\"reddit\")].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "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>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>114.000000</td>\n",
+       "      <td>114.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>114.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>114.000000</td>\n",
+       "      <td>114.000000</td>\n",
+       "      <td>114.000000</td>\n",
+       "      <td>114.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>114.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>114.000000</td>\n",
+       "      <td>114.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.886325</td>\n",
+       "      <td>0.549485</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.477472</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.598312</td>\n",
+       "      <td>0.522912</td>\n",
+       "      <td>0.912272</td>\n",
+       "      <td>0.575503</td>\n",
+       "      <td>0.488324</td>\n",
+       "      <td>0.356876</td>\n",
+       "      <td>0.518006</td>\n",
+       "      <td>0.502310</td>\n",
+       "      <td>0.693808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.010419</td>\n",
+       "      <td>0.034398</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.060049</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.059474</td>\n",
+       "      <td>0.087491</td>\n",
+       "      <td>0.017925</td>\n",
+       "      <td>0.031314</td>\n",
+       "      <td>0.025352</td>\n",
+       "      <td>0.049796</td>\n",
+       "      <td>0.022445</td>\n",
+       "      <td>0.068440</td>\n",
+       "      <td>0.070335</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.854000</td>\n",
+       "      <td>0.457944</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.337015</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.468421</td>\n",
+       "      <td>0.365672</td>\n",
+       "      <td>0.867662</td>\n",
+       "      <td>0.501873</td>\n",
+       "      <td>0.436951</td>\n",
+       "      <td>0.259310</td>\n",
+       "      <td>0.479220</td>\n",
+       "      <td>0.368132</td>\n",
+       "      <td>0.576471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.880000</td>\n",
+       "      <td>0.519692</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.434011</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.547170</td>\n",
+       "      <td>0.447761</td>\n",
+       "      <td>0.896517</td>\n",
+       "      <td>0.551511</td>\n",
+       "      <td>0.469048</td>\n",
+       "      <td>0.321651</td>\n",
+       "      <td>0.500217</td>\n",
+       "      <td>0.437528</td>\n",
+       "      <td>0.635294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.888000</td>\n",
+       "      <td>0.553019</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.471294</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.608696</td>\n",
+       "      <td>0.496269</td>\n",
+       "      <td>0.918905</td>\n",
+       "      <td>0.579091</td>\n",
+       "      <td>0.488143</td>\n",
+       "      <td>0.354547</td>\n",
+       "      <td>0.522595</td>\n",
+       "      <td>0.515685</td>\n",
+       "      <td>0.682353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.893750</td>\n",
+       "      <td>0.578073</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.521337</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.643650</td>\n",
+       "      <td>0.619403</td>\n",
+       "      <td>0.926368</td>\n",
+       "      <td>0.598915</td>\n",
+       "      <td>0.504911</td>\n",
+       "      <td>0.385994</td>\n",
+       "      <td>0.528724</td>\n",
+       "      <td>0.556342</td>\n",
+       "      <td>0.761765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.908000</td>\n",
+       "      <td>0.613445</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.621682</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.717391</td>\n",
+       "      <td>0.664179</td>\n",
+       "      <td>0.938308</td>\n",
+       "      <td>0.643564</td>\n",
+       "      <td>0.539513</td>\n",
+       "      <td>0.508237</td>\n",
+       "      <td>0.564766</td>\n",
+       "      <td>0.632184</td>\n",
+       "      <td>0.835294</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Kappa Linear   test Loss  \\\n",
+       "count     114.000000         114.000000                0.0  114.000000   \n",
+       "mean        0.886325           0.549485                NaN    0.477472   \n",
+       "std         0.010419           0.034398                NaN    0.060049   \n",
+       "min         0.854000           0.457944                NaN    0.337015   \n",
+       "25%         0.880000           0.519692                NaN    0.434011   \n",
+       "50%         0.888000           0.553019                NaN    0.471294   \n",
+       "75%         0.893750           0.578073                NaN    0.521337   \n",
+       "max         0.908000           0.613445                NaN    0.621682   \n",
+       "\n",
+       "       test Matthews Correff  test Precision  test Recall  valid Accuracy  \\\n",
+       "count                    0.0      114.000000   114.000000      114.000000   \n",
+       "mean                     NaN        0.598312     0.522912        0.912272   \n",
+       "std                      NaN        0.059474     0.087491        0.017925   \n",
+       "min                      NaN        0.468421     0.365672        0.867662   \n",
+       "25%                      NaN        0.547170     0.447761        0.896517   \n",
+       "50%                      NaN        0.608696     0.496269        0.918905   \n",
+       "75%                      NaN        0.643650     0.619403        0.926368   \n",
+       "max                      NaN        0.717391     0.664179        0.938308   \n",
+       "\n",
+       "       valid F1 score bin  valid Kappa Linear  valid Loss  \\\n",
+       "count          114.000000           38.000000  114.000000   \n",
+       "mean             0.575503            0.488324    0.356876   \n",
+       "std              0.031314            0.025352    0.049796   \n",
+       "min              0.501873            0.436951    0.259310   \n",
+       "25%              0.551511            0.469048    0.321651   \n",
+       "50%              0.579091            0.488143    0.354547   \n",
+       "75%              0.598915            0.504911    0.385994   \n",
+       "max              0.643564            0.539513    0.508237   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count               38.000000       114.000000    114.000000  \n",
+       "mean                 0.518006         0.502310      0.693808  \n",
+       "std                  0.022445         0.068440      0.070335  \n",
+       "min                  0.479220         0.368132      0.576471  \n",
+       "25%                  0.500217         0.437528      0.635294  \n",
+       "50%                  0.522595         0.515685      0.682353  \n",
+       "75%                  0.528724         0.556342      0.761765  \n",
+       "max                  0.564766         0.632184      0.835294  "
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"model_name\"].str.contains(\"ft20\")&df[\"model_dir_parent\"].str.contains(\"reddit\")].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.889631</td>\n",
+       "      <td>0.556689</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.469021</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.616103</td>\n",
+       "      <td>0.519564</td>\n",
+       "      <td>0.913222</td>\n",
+       "      <td>0.576077</td>\n",
+       "      <td>0.472772</td>\n",
+       "      <td>0.351914</td>\n",
+       "      <td>0.509898</td>\n",
+       "      <td>0.502990</td>\n",
+       "      <td>0.689189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.012674</td>\n",
+       "      <td>0.029699</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.054419</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.061584</td>\n",
+       "      <td>0.074937</td>\n",
+       "      <td>0.017361</td>\n",
+       "      <td>0.029764</td>\n",
+       "      <td>0.031813</td>\n",
+       "      <td>0.048060</td>\n",
+       "      <td>0.024269</td>\n",
+       "      <td>0.057346</td>\n",
+       "      <td>0.065336</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.830000</td>\n",
+       "      <td>0.458716</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.308932</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.424370</td>\n",
+       "      <td>0.373134</td>\n",
+       "      <td>0.841791</td>\n",
+       "      <td>0.481752</td>\n",
+       "      <td>0.409904</td>\n",
+       "      <td>0.227242</td>\n",
+       "      <td>0.457706</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.541176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.886000</td>\n",
+       "      <td>0.538203</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.442533</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.594122</td>\n",
+       "      <td>0.470149</td>\n",
+       "      <td>0.910448</td>\n",
+       "      <td>0.559786</td>\n",
+       "      <td>0.452652</td>\n",
+       "      <td>0.324985</td>\n",
+       "      <td>0.495162</td>\n",
+       "      <td>0.478974</td>\n",
+       "      <td>0.647059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.893000</td>\n",
+       "      <td>0.561120</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.468689</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.628719</td>\n",
+       "      <td>0.507463</td>\n",
+       "      <td>0.918905</td>\n",
+       "      <td>0.581117</td>\n",
+       "      <td>0.482824</td>\n",
+       "      <td>0.345211</td>\n",
+       "      <td>0.513778</td>\n",
+       "      <td>0.515478</td>\n",
+       "      <td>0.676471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.897000</td>\n",
+       "      <td>0.580073</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.495938</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.654746</td>\n",
+       "      <td>0.544776</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>0.594856</td>\n",
+       "      <td>0.496302</td>\n",
+       "      <td>0.375990</td>\n",
+       "      <td>0.531811</td>\n",
+       "      <td>0.540000</td>\n",
+       "      <td>0.717647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.912000</td>\n",
+       "      <td>0.622222</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.641866</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.744681</td>\n",
+       "      <td>0.753731</td>\n",
+       "      <td>0.935323</td>\n",
+       "      <td>0.648936</td>\n",
+       "      <td>0.516319</td>\n",
+       "      <td>0.510704</td>\n",
+       "      <td>0.545763</td>\n",
+       "      <td>0.619048</td>\n",
+       "      <td>0.870588</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Kappa Linear   test Loss  \\\n",
+       "count     222.000000         222.000000                0.0  222.000000   \n",
+       "mean        0.889631           0.556689                NaN    0.469021   \n",
+       "std         0.012674           0.029699                NaN    0.054419   \n",
+       "min         0.830000           0.458716                NaN    0.308932   \n",
+       "25%         0.886000           0.538203                NaN    0.442533   \n",
+       "50%         0.893000           0.561120                NaN    0.468689   \n",
+       "75%         0.897000           0.580073                NaN    0.495938   \n",
+       "max         0.912000           0.622222                NaN    0.641866   \n",
+       "\n",
+       "       test Matthews Correff  test Precision  test Recall  valid Accuracy  \\\n",
+       "count                    0.0      222.000000   222.000000      222.000000   \n",
+       "mean                     NaN        0.616103     0.519564        0.913222   \n",
+       "std                      NaN        0.061584     0.074937        0.017361   \n",
+       "min                      NaN        0.424370     0.373134        0.841791   \n",
+       "25%                      NaN        0.594122     0.470149        0.910448   \n",
+       "50%                      NaN        0.628719     0.507463        0.918905   \n",
+       "75%                      NaN        0.654746     0.544776        0.923383   \n",
+       "max                      NaN        0.744681     0.753731        0.935323   \n",
+       "\n",
+       "       valid F1 score bin  valid Kappa Linear  valid Loss  \\\n",
+       "count          222.000000           38.000000  222.000000   \n",
+       "mean             0.576077            0.472772    0.351914   \n",
+       "std              0.029764            0.031813    0.048060   \n",
+       "min              0.481752            0.409904    0.227242   \n",
+       "25%              0.559786            0.452652    0.324985   \n",
+       "50%              0.581117            0.482824    0.345211   \n",
+       "75%              0.594856            0.496302    0.375990   \n",
+       "max              0.648936            0.516319    0.510704   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count               38.000000       222.000000    222.000000  \n",
+       "mean                 0.509898         0.502990      0.689189  \n",
+       "std                  0.024269         0.057346      0.065336  \n",
+       "min                  0.457706         0.333333      0.541176  \n",
+       "25%                  0.495162         0.478974      0.647059  \n",
+       "50%                  0.513778         0.515478      0.676471  \n",
+       "75%                  0.531811         0.540000      0.717647  \n",
+       "max                  0.545763         0.619048      0.870588  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"model_name\"].str.contains(\"ft6\")&df[\"model_dir_parent\"].str.contains(\"reddit\")].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.877648</td>\n",
+       "      <td>0.530767</td>\n",
+       "      <td>0.394214</td>\n",
+       "      <td>0.440682</td>\n",
+       "      <td>0.416614</td>\n",
+       "      <td>0.559141</td>\n",
+       "      <td>0.526155</td>\n",
+       "      <td>0.895949</td>\n",
+       "      <td>0.527250</td>\n",
+       "      <td>0.473292</td>\n",
+       "      <td>0.378256</td>\n",
+       "      <td>0.492101</td>\n",
+       "      <td>0.446751</td>\n",
+       "      <td>0.675854</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.011742</td>\n",
+       "      <td>0.049111</td>\n",
+       "      <td>0.048942</td>\n",
+       "      <td>0.049949</td>\n",
+       "      <td>0.042219</td>\n",
+       "      <td>0.058934</td>\n",
+       "      <td>0.103923</td>\n",
+       "      <td>0.022894</td>\n",
+       "      <td>0.032346</td>\n",
+       "      <td>0.040935</td>\n",
+       "      <td>0.065082</td>\n",
+       "      <td>0.030763</td>\n",
+       "      <td>0.077313</td>\n",
+       "      <td>0.084773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.841000</td>\n",
+       "      <td>0.349206</td>\n",
+       "      <td>0.294158</td>\n",
+       "      <td>0.306861</td>\n",
+       "      <td>0.330020</td>\n",
+       "      <td>0.439394</td>\n",
+       "      <td>0.246269</td>\n",
+       "      <td>0.827861</td>\n",
+       "      <td>0.427536</td>\n",
+       "      <td>0.351639</td>\n",
+       "      <td>0.218546</td>\n",
+       "      <td>0.390519</td>\n",
+       "      <td>0.303571</td>\n",
+       "      <td>0.470588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.873000</td>\n",
+       "      <td>0.510067</td>\n",
+       "      <td>0.378241</td>\n",
+       "      <td>0.414953</td>\n",
+       "      <td>0.398873</td>\n",
+       "      <td>0.523810</td>\n",
+       "      <td>0.462687</td>\n",
+       "      <td>0.881592</td>\n",
+       "      <td>0.511013</td>\n",
+       "      <td>0.453966</td>\n",
+       "      <td>0.340201</td>\n",
+       "      <td>0.476972</td>\n",
+       "      <td>0.393750</td>\n",
+       "      <td>0.623529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.879000</td>\n",
+       "      <td>0.540541</td>\n",
+       "      <td>0.389634</td>\n",
+       "      <td>0.432840</td>\n",
+       "      <td>0.416495</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.552239</td>\n",
+       "      <td>0.897512</td>\n",
+       "      <td>0.529148</td>\n",
+       "      <td>0.474097</td>\n",
+       "      <td>0.383505</td>\n",
+       "      <td>0.496001</td>\n",
+       "      <td>0.434109</td>\n",
+       "      <td>0.694118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.885000</td>\n",
+       "      <td>0.564885</td>\n",
+       "      <td>0.429435</td>\n",
+       "      <td>0.468542</td>\n",
+       "      <td>0.441779</td>\n",
+       "      <td>0.590909</td>\n",
+       "      <td>0.597015</td>\n",
+       "      <td>0.912438</td>\n",
+       "      <td>0.551282</td>\n",
+       "      <td>0.505464</td>\n",
+       "      <td>0.416792</td>\n",
+       "      <td>0.511805</td>\n",
+       "      <td>0.488189</td>\n",
+       "      <td>0.729412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.901000</td>\n",
+       "      <td>0.608392</td>\n",
+       "      <td>0.487822</td>\n",
+       "      <td>0.616851</td>\n",
+       "      <td>0.507263</td>\n",
+       "      <td>0.728571</td>\n",
+       "      <td>0.716418</td>\n",
+       "      <td>0.938308</td>\n",
+       "      <td>0.584906</td>\n",
+       "      <td>0.548576</td>\n",
+       "      <td>0.538841</td>\n",
+       "      <td>0.555753</td>\n",
+       "      <td>0.682540</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Kappa Linear   test Loss  \\\n",
+       "count     105.000000         105.000000          19.000000  105.000000   \n",
+       "mean        0.877648           0.530767           0.394214    0.440682   \n",
+       "std         0.011742           0.049111           0.048942    0.049949   \n",
+       "min         0.841000           0.349206           0.294158    0.306861   \n",
+       "25%         0.873000           0.510067           0.378241    0.414953   \n",
+       "50%         0.879000           0.540541           0.389634    0.432840   \n",
+       "75%         0.885000           0.564885           0.429435    0.468542   \n",
+       "max         0.901000           0.608392           0.487822    0.616851   \n",
+       "\n",
+       "       test Matthews Correff  test Precision  test Recall  valid Accuracy  \\\n",
+       "count              19.000000      105.000000   105.000000      105.000000   \n",
+       "mean                0.416614        0.559141     0.526155        0.895949   \n",
+       "std                 0.042219        0.058934     0.103923        0.022894   \n",
+       "min                 0.330020        0.439394     0.246269        0.827861   \n",
+       "25%                 0.398873        0.523810     0.462687        0.881592   \n",
+       "50%                 0.416495        0.555556     0.552239        0.897512   \n",
+       "75%                 0.441779        0.590909     0.597015        0.912438   \n",
+       "max                 0.507263        0.728571     0.716418        0.938308   \n",
+       "\n",
+       "       valid F1 score bin  valid Kappa Linear  valid Loss  \\\n",
+       "count          105.000000          105.000000  105.000000   \n",
+       "mean             0.527250            0.473292    0.378256   \n",
+       "std              0.032346            0.040935    0.065082   \n",
+       "min              0.427536            0.351639    0.218546   \n",
+       "25%              0.511013            0.453966    0.340201   \n",
+       "50%              0.529148            0.474097    0.383505   \n",
+       "75%              0.551282            0.505464    0.416792   \n",
+       "max              0.584906            0.548576    0.538841   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count              105.000000       105.000000    105.000000  \n",
+       "mean                 0.492101         0.446751      0.675854  \n",
+       "std                  0.030763         0.077313      0.084773  \n",
+       "min                  0.390519         0.303571      0.470588  \n",
+       "25%                  0.476972         0.393750      0.623529  \n",
+       "50%                  0.496001         0.434109      0.694118  \n",
+       "75%                  0.511805         0.488189      0.729412  \n",
+       "max                  0.555753         0.682540      0.800000  "
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"model_name\"].str.contains(\"lmseed-1\")&df[\"model_dir_parent\"].str.contains(\"wiki\")].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "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>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.883971</td>\n",
+       "      <td>0.497524</td>\n",
+       "      <td>0.378477</td>\n",
+       "      <td>0.487743</td>\n",
+       "      <td>0.410213</td>\n",
+       "      <td>0.604804</td>\n",
+       "      <td>0.435608</td>\n",
+       "      <td>0.917716</td>\n",
+       "      <td>0.557504</td>\n",
+       "      <td>0.513135</td>\n",
+       "      <td>0.350163</td>\n",
+       "      <td>0.518178</td>\n",
+       "      <td>0.524011</td>\n",
+       "      <td>0.609300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.011456</td>\n",
+       "      <td>0.049955</td>\n",
+       "      <td>0.055686</td>\n",
+       "      <td>0.070524</td>\n",
+       "      <td>0.036650</td>\n",
+       "      <td>0.055474</td>\n",
+       "      <td>0.086233</td>\n",
+       "      <td>0.015613</td>\n",
+       "      <td>0.028135</td>\n",
+       "      <td>0.032800</td>\n",
+       "      <td>0.054146</td>\n",
+       "      <td>0.028378</td>\n",
+       "      <td>0.060992</td>\n",
+       "      <td>0.065841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.786000</td>\n",
+       "      <td>0.340909</td>\n",
+       "      <td>0.295877</td>\n",
+       "      <td>0.310529</td>\n",
+       "      <td>0.351113</td>\n",
+       "      <td>0.369281</td>\n",
+       "      <td>0.223881</td>\n",
+       "      <td>0.790050</td>\n",
+       "      <td>0.418733</td>\n",
+       "      <td>0.332226</td>\n",
+       "      <td>0.227039</td>\n",
+       "      <td>0.419594</td>\n",
+       "      <td>0.273381</td>\n",
+       "      <td>0.447059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.880000</td>\n",
+       "      <td>0.480349</td>\n",
+       "      <td>0.331747</td>\n",
+       "      <td>0.452680</td>\n",
+       "      <td>0.384954</td>\n",
+       "      <td>0.568421</td>\n",
+       "      <td>0.402985</td>\n",
+       "      <td>0.913433</td>\n",
+       "      <td>0.543590</td>\n",
+       "      <td>0.496241</td>\n",
+       "      <td>0.331412</td>\n",
+       "      <td>0.501775</td>\n",
+       "      <td>0.490566</td>\n",
+       "      <td>0.576471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.886000</td>\n",
+       "      <td>0.504274</td>\n",
+       "      <td>0.392966</td>\n",
+       "      <td>0.480935</td>\n",
+       "      <td>0.402936</td>\n",
+       "      <td>0.606383</td>\n",
+       "      <td>0.432836</td>\n",
+       "      <td>0.919403</td>\n",
+       "      <td>0.556818</td>\n",
+       "      <td>0.511386</td>\n",
+       "      <td>0.348879</td>\n",
+       "      <td>0.516180</td>\n",
+       "      <td>0.519608</td>\n",
+       "      <td>0.611765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.889000</td>\n",
+       "      <td>0.531532</td>\n",
+       "      <td>0.426830</td>\n",
+       "      <td>0.510850</td>\n",
+       "      <td>0.443213</td>\n",
+       "      <td>0.633663</td>\n",
+       "      <td>0.485075</td>\n",
+       "      <td>0.925373</td>\n",
+       "      <td>0.574586</td>\n",
+       "      <td>0.531980</td>\n",
+       "      <td>0.374401</td>\n",
+       "      <td>0.535929</td>\n",
+       "      <td>0.550562</td>\n",
+       "      <td>0.647059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.896000</td>\n",
+       "      <td>0.570470</td>\n",
+       "      <td>0.452756</td>\n",
+       "      <td>0.743211</td>\n",
+       "      <td>0.461858</td>\n",
+       "      <td>0.756098</td>\n",
+       "      <td>0.843284</td>\n",
+       "      <td>0.942289</td>\n",
+       "      <td>0.639053</td>\n",
+       "      <td>0.605920</td>\n",
+       "      <td>0.708696</td>\n",
+       "      <td>0.605933</td>\n",
+       "      <td>0.764706</td>\n",
+       "      <td>0.894118</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Kappa Linear   test Loss  \\\n",
+       "count     105.000000         105.000000          19.000000  105.000000   \n",
+       "mean        0.883971           0.497524           0.378477    0.487743   \n",
+       "std         0.011456           0.049955           0.055686    0.070524   \n",
+       "min         0.786000           0.340909           0.295877    0.310529   \n",
+       "25%         0.880000           0.480349           0.331747    0.452680   \n",
+       "50%         0.886000           0.504274           0.392966    0.480935   \n",
+       "75%         0.889000           0.531532           0.426830    0.510850   \n",
+       "max         0.896000           0.570470           0.452756    0.743211   \n",
+       "\n",
+       "       test Matthews Correff  test Precision  test Recall  valid Accuracy  \\\n",
+       "count              19.000000      105.000000   105.000000      105.000000   \n",
+       "mean                0.410213        0.604804     0.435608        0.917716   \n",
+       "std                 0.036650        0.055474     0.086233        0.015613   \n",
+       "min                 0.351113        0.369281     0.223881        0.790050   \n",
+       "25%                 0.384954        0.568421     0.402985        0.913433   \n",
+       "50%                 0.402936        0.606383     0.432836        0.919403   \n",
+       "75%                 0.443213        0.633663     0.485075        0.925373   \n",
+       "max                 0.461858        0.756098     0.843284        0.942289   \n",
+       "\n",
+       "       valid F1 score bin  valid Kappa Linear  valid Loss  \\\n",
+       "count          105.000000          105.000000  105.000000   \n",
+       "mean             0.557504            0.513135    0.350163   \n",
+       "std              0.028135            0.032800    0.054146   \n",
+       "min              0.418733            0.332226    0.227039   \n",
+       "25%              0.543590            0.496241    0.331412   \n",
+       "50%              0.556818            0.511386    0.348879   \n",
+       "75%              0.574586            0.531980    0.374401   \n",
+       "max              0.639053            0.605920    0.708696   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count              105.000000       105.000000    105.000000  \n",
+       "mean                 0.518178         0.524011      0.609300  \n",
+       "std                  0.028378         0.060992      0.065841  \n",
+       "min                  0.419594         0.273381      0.447059  \n",
+       "25%                  0.501775         0.490566      0.576471  \n",
+       "50%                  0.516180         0.519608      0.611765  \n",
+       "75%                  0.535929         0.550562      0.647059  \n",
+       "max                  0.605933         0.764706      0.894118  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"model_name\"].str.contains(\"lmseed-0\")&df[\"model_dir_parent\"].str.contains(\"wiki\")].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>134.000000</td>\n",
+       "      <td>134.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>134.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>134.000000</td>\n",
+       "      <td>134.000000</td>\n",
+       "      <td>134.000000</td>\n",
+       "      <td>134.000000</td>\n",
+       "      <td>134.000000</td>\n",
+       "      <td>134.000000</td>\n",
+       "      <td>134.000000</td>\n",
+       "      <td>134.000000</td>\n",
+       "      <td>134.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.882701</td>\n",
+       "      <td>0.508385</td>\n",
+       "      <td>0.386346</td>\n",
+       "      <td>0.469366</td>\n",
+       "      <td>0.413414</td>\n",
+       "      <td>0.594190</td>\n",
+       "      <td>0.465805</td>\n",
+       "      <td>0.910322</td>\n",
+       "      <td>0.544230</td>\n",
+       "      <td>0.496651</td>\n",
+       "      <td>0.355789</td>\n",
+       "      <td>0.506987</td>\n",
+       "      <td>0.499340</td>\n",
+       "      <td>0.626427</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.010649</td>\n",
+       "      <td>0.061408</td>\n",
+       "      <td>0.052320</td>\n",
+       "      <td>0.077210</td>\n",
+       "      <td>0.039129</td>\n",
+       "      <td>0.059123</td>\n",
+       "      <td>0.113975</td>\n",
+       "      <td>0.019958</td>\n",
+       "      <td>0.026982</td>\n",
+       "      <td>0.033789</td>\n",
+       "      <td>0.063274</td>\n",
+       "      <td>0.026100</td>\n",
+       "      <td>0.079239</td>\n",
+       "      <td>0.089218</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.786000</td>\n",
+       "      <td>0.340909</td>\n",
+       "      <td>0.294158</td>\n",
+       "      <td>0.306861</td>\n",
+       "      <td>0.330020</td>\n",
+       "      <td>0.369281</td>\n",
+       "      <td>0.223881</td>\n",
+       "      <td>0.790050</td>\n",
+       "      <td>0.418733</td>\n",
+       "      <td>0.332226</td>\n",
+       "      <td>0.218546</td>\n",
+       "      <td>0.419594</td>\n",
+       "      <td>0.273381</td>\n",
+       "      <td>0.447059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.878250</td>\n",
+       "      <td>0.475739</td>\n",
+       "      <td>0.341244</td>\n",
+       "      <td>0.418694</td>\n",
+       "      <td>0.392833</td>\n",
+       "      <td>0.553692</td>\n",
+       "      <td>0.389925</td>\n",
+       "      <td>0.895522</td>\n",
+       "      <td>0.522606</td>\n",
+       "      <td>0.472869</td>\n",
+       "      <td>0.324373</td>\n",
+       "      <td>0.491255</td>\n",
+       "      <td>0.428529</td>\n",
+       "      <td>0.564706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.883000</td>\n",
+       "      <td>0.522408</td>\n",
+       "      <td>0.391300</td>\n",
+       "      <td>0.460637</td>\n",
+       "      <td>0.412878</td>\n",
+       "      <td>0.586261</td>\n",
+       "      <td>0.462687</td>\n",
+       "      <td>0.916418</td>\n",
+       "      <td>0.545455</td>\n",
+       "      <td>0.498272</td>\n",
+       "      <td>0.366168</td>\n",
+       "      <td>0.507061</td>\n",
+       "      <td>0.504717</td>\n",
+       "      <td>0.623529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.888000</td>\n",
+       "      <td>0.555996</td>\n",
+       "      <td>0.429274</td>\n",
+       "      <td>0.499096</td>\n",
+       "      <td>0.442910</td>\n",
+       "      <td>0.629365</td>\n",
+       "      <td>0.557836</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>0.559594</td>\n",
+       "      <td>0.520867</td>\n",
+       "      <td>0.393097</td>\n",
+       "      <td>0.522013</td>\n",
+       "      <td>0.547654</td>\n",
+       "      <td>0.694118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.901000</td>\n",
+       "      <td>0.608392</td>\n",
+       "      <td>0.487822</td>\n",
+       "      <td>0.743211</td>\n",
+       "      <td>0.507263</td>\n",
+       "      <td>0.756098</td>\n",
+       "      <td>0.843284</td>\n",
+       "      <td>0.942289</td>\n",
+       "      <td>0.602273</td>\n",
+       "      <td>0.564153</td>\n",
+       "      <td>0.708696</td>\n",
+       "      <td>0.565151</td>\n",
+       "      <td>0.764706</td>\n",
+       "      <td>0.894118</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Kappa Linear   test Loss  \\\n",
+       "count     134.000000         134.000000          38.000000  134.000000   \n",
+       "mean        0.882701           0.508385           0.386346    0.469366   \n",
+       "std         0.010649           0.061408           0.052320    0.077210   \n",
+       "min         0.786000           0.340909           0.294158    0.306861   \n",
+       "25%         0.878250           0.475739           0.341244    0.418694   \n",
+       "50%         0.883000           0.522408           0.391300    0.460637   \n",
+       "75%         0.888000           0.555996           0.429274    0.499096   \n",
+       "max         0.901000           0.608392           0.487822    0.743211   \n",
+       "\n",
+       "       test Matthews Correff  test Precision  test Recall  valid Accuracy  \\\n",
+       "count              38.000000      134.000000   134.000000      134.000000   \n",
+       "mean                0.413414        0.594190     0.465805        0.910322   \n",
+       "std                 0.039129        0.059123     0.113975        0.019958   \n",
+       "min                 0.330020        0.369281     0.223881        0.790050   \n",
+       "25%                 0.392833        0.553692     0.389925        0.895522   \n",
+       "50%                 0.412878        0.586261     0.462687        0.916418   \n",
+       "75%                 0.442910        0.629365     0.557836        0.924378   \n",
+       "max                 0.507263        0.756098     0.843284        0.942289   \n",
+       "\n",
+       "       valid F1 score bin  valid Kappa Linear  valid Loss  \\\n",
+       "count          134.000000          134.000000  134.000000   \n",
+       "mean             0.544230            0.496651    0.355789   \n",
+       "std              0.026982            0.033789    0.063274   \n",
+       "min              0.418733            0.332226    0.218546   \n",
+       "25%              0.522606            0.472869    0.324373   \n",
+       "50%              0.545455            0.498272    0.366168   \n",
+       "75%              0.559594            0.520867    0.393097   \n",
+       "max              0.602273            0.564153    0.708696   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count              134.000000       134.000000    134.000000  \n",
+       "mean                 0.506987         0.499340      0.626427  \n",
+       "std                  0.026100         0.079239      0.089218  \n",
+       "min                  0.419594         0.273381      0.447059  \n",
+       "25%                  0.491255         0.428529      0.564706  \n",
+       "50%                  0.507061         0.504717      0.623529  \n",
+       "75%                  0.522013         0.547654      0.694118  \n",
+       "max                  0.565151         0.764706      0.894118  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"model_name\"].str.contains(\"ft6\")&df[\"model_dir_parent\"].str.contains(\"wiki\")].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "      <td>222.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.889631</td>\n",
+       "      <td>0.556689</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.469021</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.616103</td>\n",
+       "      <td>0.519564</td>\n",
+       "      <td>0.913222</td>\n",
+       "      <td>0.576077</td>\n",
+       "      <td>0.472772</td>\n",
+       "      <td>0.351914</td>\n",
+       "      <td>0.509898</td>\n",
+       "      <td>0.502990</td>\n",
+       "      <td>0.689189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.012674</td>\n",
+       "      <td>0.029699</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.054419</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.061584</td>\n",
+       "      <td>0.074937</td>\n",
+       "      <td>0.017361</td>\n",
+       "      <td>0.029764</td>\n",
+       "      <td>0.031813</td>\n",
+       "      <td>0.048060</td>\n",
+       "      <td>0.024269</td>\n",
+       "      <td>0.057346</td>\n",
+       "      <td>0.065336</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.830000</td>\n",
+       "      <td>0.458716</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.308932</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.424370</td>\n",
+       "      <td>0.373134</td>\n",
+       "      <td>0.841791</td>\n",
+       "      <td>0.481752</td>\n",
+       "      <td>0.409904</td>\n",
+       "      <td>0.227242</td>\n",
+       "      <td>0.457706</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.541176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.886000</td>\n",
+       "      <td>0.538203</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.442533</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.594122</td>\n",
+       "      <td>0.470149</td>\n",
+       "      <td>0.910448</td>\n",
+       "      <td>0.559786</td>\n",
+       "      <td>0.452652</td>\n",
+       "      <td>0.324985</td>\n",
+       "      <td>0.495162</td>\n",
+       "      <td>0.478974</td>\n",
+       "      <td>0.647059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.893000</td>\n",
+       "      <td>0.561120</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.468689</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.628719</td>\n",
+       "      <td>0.507463</td>\n",
+       "      <td>0.918905</td>\n",
+       "      <td>0.581117</td>\n",
+       "      <td>0.482824</td>\n",
+       "      <td>0.345211</td>\n",
+       "      <td>0.513778</td>\n",
+       "      <td>0.515478</td>\n",
+       "      <td>0.676471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.897000</td>\n",
+       "      <td>0.580073</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.495938</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.654746</td>\n",
+       "      <td>0.544776</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>0.594856</td>\n",
+       "      <td>0.496302</td>\n",
+       "      <td>0.375990</td>\n",
+       "      <td>0.531811</td>\n",
+       "      <td>0.540000</td>\n",
+       "      <td>0.717647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.912000</td>\n",
+       "      <td>0.622222</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.641866</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.744681</td>\n",
+       "      <td>0.753731</td>\n",
+       "      <td>0.935323</td>\n",
+       "      <td>0.648936</td>\n",
+       "      <td>0.516319</td>\n",
+       "      <td>0.510704</td>\n",
+       "      <td>0.545763</td>\n",
+       "      <td>0.619048</td>\n",
+       "      <td>0.870588</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Kappa Linear   test Loss  \\\n",
+       "count     222.000000         222.000000                0.0  222.000000   \n",
+       "mean        0.889631           0.556689                NaN    0.469021   \n",
+       "std         0.012674           0.029699                NaN    0.054419   \n",
+       "min         0.830000           0.458716                NaN    0.308932   \n",
+       "25%         0.886000           0.538203                NaN    0.442533   \n",
+       "50%         0.893000           0.561120                NaN    0.468689   \n",
+       "75%         0.897000           0.580073                NaN    0.495938   \n",
+       "max         0.912000           0.622222                NaN    0.641866   \n",
+       "\n",
+       "       test Matthews Correff  test Precision  test Recall  valid Accuracy  \\\n",
+       "count                    0.0      222.000000   222.000000      222.000000   \n",
+       "mean                     NaN        0.616103     0.519564        0.913222   \n",
+       "std                      NaN        0.061584     0.074937        0.017361   \n",
+       "min                      NaN        0.424370     0.373134        0.841791   \n",
+       "25%                      NaN        0.594122     0.470149        0.910448   \n",
+       "50%                      NaN        0.628719     0.507463        0.918905   \n",
+       "75%                      NaN        0.654746     0.544776        0.923383   \n",
+       "max                      NaN        0.744681     0.753731        0.935323   \n",
+       "\n",
+       "       valid F1 score bin  valid Kappa Linear  valid Loss  \\\n",
+       "count          222.000000           38.000000  222.000000   \n",
+       "mean             0.576077            0.472772    0.351914   \n",
+       "std              0.029764            0.031813    0.048060   \n",
+       "min              0.481752            0.409904    0.227242   \n",
+       "25%              0.559786            0.452652    0.324985   \n",
+       "50%              0.581117            0.482824    0.345211   \n",
+       "75%              0.594856            0.496302    0.375990   \n",
+       "max              0.648936            0.516319    0.510704   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count               38.000000       222.000000    222.000000  \n",
+       "mean                 0.509898         0.502990      0.689189  \n",
+       "std                  0.024269         0.057346      0.065336  \n",
+       "min                  0.457706         0.333333      0.541176  \n",
+       "25%                  0.495162         0.478974      0.647059  \n",
+       "50%                  0.513778         0.515478      0.676471  \n",
+       "75%                  0.531811         0.540000      0.717647  \n",
+       "max                  0.545763         0.619048      0.870588  "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"model_name\"].str.contains(\"ft6\")&df[\"model_dir_parent\"].str.contains(\"reddit\")].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "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>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.881105</td>\n",
+       "      <td>0.527548</td>\n",
+       "      <td>0.394214</td>\n",
+       "      <td>0.435930</td>\n",
+       "      <td>0.416614</td>\n",
+       "      <td>0.575084</td>\n",
+       "      <td>0.510867</td>\n",
+       "      <td>0.899921</td>\n",
+       "      <td>0.530734</td>\n",
+       "      <td>0.478477</td>\n",
+       "      <td>0.358723</td>\n",
+       "      <td>0.495977</td>\n",
+       "      <td>0.460817</td>\n",
+       "      <td>0.663571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.006837</td>\n",
+       "      <td>0.062993</td>\n",
+       "      <td>0.048942</td>\n",
+       "      <td>0.060446</td>\n",
+       "      <td>0.042219</td>\n",
+       "      <td>0.054312</td>\n",
+       "      <td>0.118727</td>\n",
+       "      <td>0.019059</td>\n",
+       "      <td>0.018979</td>\n",
+       "      <td>0.026522</td>\n",
+       "      <td>0.066003</td>\n",
+       "      <td>0.019224</td>\n",
+       "      <td>0.082383</td>\n",
+       "      <td>0.096653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.869000</td>\n",
+       "      <td>0.349206</td>\n",
+       "      <td>0.294158</td>\n",
+       "      <td>0.306861</td>\n",
+       "      <td>0.330020</td>\n",
+       "      <td>0.509317</td>\n",
+       "      <td>0.246269</td>\n",
+       "      <td>0.875622</td>\n",
+       "      <td>0.502128</td>\n",
+       "      <td>0.441865</td>\n",
+       "      <td>0.218546</td>\n",
+       "      <td>0.464773</td>\n",
+       "      <td>0.386364</td>\n",
+       "      <td>0.470588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.876000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.378241</td>\n",
+       "      <td>0.410202</td>\n",
+       "      <td>0.398873</td>\n",
+       "      <td>0.537931</td>\n",
+       "      <td>0.395522</td>\n",
+       "      <td>0.883582</td>\n",
+       "      <td>0.514286</td>\n",
+       "      <td>0.454830</td>\n",
+       "      <td>0.308024</td>\n",
+       "      <td>0.481969</td>\n",
+       "      <td>0.397436</td>\n",
+       "      <td>0.564706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.881000</td>\n",
+       "      <td>0.554795</td>\n",
+       "      <td>0.389634</td>\n",
+       "      <td>0.421994</td>\n",
+       "      <td>0.416495</td>\n",
+       "      <td>0.559441</td>\n",
+       "      <td>0.567164</td>\n",
+       "      <td>0.891542</td>\n",
+       "      <td>0.525140</td>\n",
+       "      <td>0.473170</td>\n",
+       "      <td>0.377337</td>\n",
+       "      <td>0.495008</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>0.705882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.885000</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.429435</td>\n",
+       "      <td>0.445094</td>\n",
+       "      <td>0.441779</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.597015</td>\n",
+       "      <td>0.919403</td>\n",
+       "      <td>0.546584</td>\n",
+       "      <td>0.499536</td>\n",
+       "      <td>0.415756</td>\n",
+       "      <td>0.509516</td>\n",
+       "      <td>0.520833</td>\n",
+       "      <td>0.741176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.901000</td>\n",
+       "      <td>0.608392</td>\n",
+       "      <td>0.487822</td>\n",
+       "      <td>0.616851</td>\n",
+       "      <td>0.507263</td>\n",
+       "      <td>0.728571</td>\n",
+       "      <td>0.649254</td>\n",
+       "      <td>0.938308</td>\n",
+       "      <td>0.581081</td>\n",
+       "      <td>0.548576</td>\n",
+       "      <td>0.449702</td>\n",
+       "      <td>0.555753</td>\n",
+       "      <td>0.682540</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Kappa Linear  test Loss  \\\n",
+       "count      57.000000          57.000000          19.000000  57.000000   \n",
+       "mean        0.881105           0.527548           0.394214   0.435930   \n",
+       "std         0.006837           0.062993           0.048942   0.060446   \n",
+       "min         0.869000           0.349206           0.294158   0.306861   \n",
+       "25%         0.876000           0.500000           0.378241   0.410202   \n",
+       "50%         0.881000           0.554795           0.389634   0.421994   \n",
+       "75%         0.885000           0.571429           0.429435   0.445094   \n",
+       "max         0.901000           0.608392           0.487822   0.616851   \n",
+       "\n",
+       "       test Matthews Correff  test Precision  test Recall  valid Accuracy  \\\n",
+       "count              19.000000       57.000000    57.000000       57.000000   \n",
+       "mean                0.416614        0.575084     0.510867        0.899921   \n",
+       "std                 0.042219        0.054312     0.118727        0.019059   \n",
+       "min                 0.330020        0.509317     0.246269        0.875622   \n",
+       "25%                 0.398873        0.537931     0.395522        0.883582   \n",
+       "50%                 0.416495        0.559441     0.567164        0.891542   \n",
+       "75%                 0.441779        0.600000     0.597015        0.919403   \n",
+       "max                 0.507263        0.728571     0.649254        0.938308   \n",
+       "\n",
+       "       valid F1 score bin  valid Kappa Linear  valid Loss  \\\n",
+       "count           57.000000           57.000000   57.000000   \n",
+       "mean             0.530734            0.478477    0.358723   \n",
+       "std              0.018979            0.026522    0.066003   \n",
+       "min              0.502128            0.441865    0.218546   \n",
+       "25%              0.514286            0.454830    0.308024   \n",
+       "50%              0.525140            0.473170    0.377337   \n",
+       "75%              0.546584            0.499536    0.415756   \n",
+       "max              0.581081            0.548576    0.449702   \n",
+       "\n",
+       "       valid Matthews Correff  valid Precision  valid Recall  \n",
+       "count               57.000000        57.000000     57.000000  \n",
+       "mean                 0.495977         0.460817      0.663571  \n",
+       "std                  0.019224         0.082383      0.096653  \n",
+       "min                  0.464773         0.386364      0.470588  \n",
+       "25%                  0.481969         0.397436      0.564706  \n",
+       "50%                  0.495008         0.416667      0.705882  \n",
+       "75%                  0.509516         0.520833      0.741176  \n",
+       "max                  0.555753         0.682540      0.800000  "
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"model_name\"].str.contains(\"lmseed-1\")&\n",
+    "   df[\"model_name\"].str.contains(\"ft6_cl8\")&df[\"model_dir_parent\"].str.contains(\"wiki\")].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-1.m\n",
+      "lstm_noearly_stop_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-7.m\n",
+      "lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-7.m\n",
+      "lstm_ft20_cl8_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-0.m\n",
+      "lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-3-clstrainseed-3.m\n",
+      "lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-2-clstrainseed-6.m\n",
+      "lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9-clstrainseed-6.m\n",
+      "lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-1-clstrainseed-4.m\n"
+     ]
+    }
+   ],
+   "source": [
+    "for model_name in df.sort_values(\"test F1 score bin\").tail(8)[\"model_name\"]:\n",
+    "    print(model_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lstm_nowce_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-4-clstrainseed-0.m\n",
+      "lstm_nowce_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-5.m\n",
+      "lstm_nowce_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0-clstrainseed-9.m\n",
+      "lstm_nowce_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-1-clstrainseed-0.m\n",
+      "lstm_nowce_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-7.m\n",
+      "lstm_nowce_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0-clstrainseed-6.m\n",
+      "lstm_nowce_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0-clstrainseed-5.m\n",
+      "lstm_nowce_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0-clstrainseed-1.m\n"
+     ]
+    }
+   ],
+   "source": [
+    "for model_name in df.sort_values(\"test F1 score bin\").head(8)[\"model_name\"]:\n",
+    "    print(model_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "orderby=\"valid F1 score bin\"\n",
+    "ycolumn=\"test F1 score bin\"\n",
+    "xy = df.sort_values(orderby)\n",
+    "plt.plot(xy[orderby], xy[ycolumn], 'b.')\n",
+    "# plt.plot(xy[orderby], xy[\"test Precision\"], 'r.')\n",
+    "# plt.plot(xy[orderby], xy[\"test Recall\"], 'g.')\n",
+    "plt.xlabel(orderby)\n",
+    "plt.ylabel(ycolumn);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "orderby=\"valid Recall\"\n",
+    "ycolumn=\"test F1 score bin\"\n",
+    "xy = df.sort_values(orderby)\n",
+    "plt.plot(xy[orderby], xy[ycolumn], 'b.')\n",
+    "plt.xlabel(orderby)\n",
+    "plt.ylabel(ycolumn);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "orderby=\"valid Accuracy\"\n",
+    "ycolumn=\"test Accuracy\"\n",
+    "xy = df.sort_values(orderby)\n",
+    "plt.plot(xy[orderby], xy[ycolumn], 'b.-')\n",
+    "plt.xlabel(orderby)\n",
+    "plt.ylabel(ycolumn);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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t0PM5ncKV/PKRk1bWYBDxzW+q2e0eWJtHj3ZKjsKDUcxH66JAcrjmHgH0vOapN3rs17XWp7S0eMkiKampDtTUXJJ8TnJygkwudRFDh4oQBFeFtZQUJ8aPd0iu82ux5u6rgIwe1t4HDnTCZBJRX2+4siu/Z/r2daKtTX4sazCI6N9fxMWLAux214bL/PwOxVwIcu9/KP9Pya25M4kNEV1TejJtLn/pnIDGxqtBp64uBnV1MSgosOGDD2I8v/SXLFHOT+4vpWQ1rvO1Y9262Cu546W/nKSmOlFXF1jwlHrN+nrDlcvjeu7cOQPOnQu8Re3tvtvhdApoabl6jJpkR77e/8LCwNscKE7LE5GudU0/e9110pOVvpKO+HcpmOvSK6W1WDV8FSTxtXHP/bxHHukNUQSGDZPuc2qqEzU1lxAbxL1+cXGurHajRkVOmdRePax184tf9JLdmxHujZNKGNyJSLfcl8J1XitvaZH+tTdunCvRidSavN3u3wg0GJvnpNoxZw48Aaa+Xvocx44Zuj1PLmGNe1OXLYh7u266yRXU1SThCZUO6XxBiux2QXZvRiRsnPQlMlpBRKQBpTSznW3fbkRycoJsSdXERKfq5CwOB/wuUNN1hmHFCunnL1zYG2lp8X7vbHdf6ia1qWv06OCNso8dMyAtLR4APBvJlN8zbbd+jR7tREGB7Uqq3MAoJbEJ9cZJOQzuRKRbvtLMdtXSYvCM8KVcvCigoaEVgopBvCjK76aX4s8MgzsVqr87xC0WQXapILijbKHT+rNrij4meAniemTcONcmx2DsK3DPmITrEje1GNyJSLeUC5+o5x6R+TPK7Txz4Ovaen9mGJTIffnwNaI0m+1eyX6MxuCkj12xwjUb4ZBJ7S4IIhYtCu7nJDVDceiQP98uXMmOYmKk++9evw/XJW5qMbgTkW7JFWfpCZPJ9cven1Gue+ZA6dp6f2YYlNxzj3Qk9TWirKgwes0cuPYYBD7KVZoNEUUB69df3e8QDNnZ3RPL+LMObjQCDQ2tsssendfv1SSxCRdNg/v+/fuRlZWFzMxMbNy4sdvjjY2NyM/PR15eHnJzc7Fv3z4AQEdHB5588knk5uYiOzsbZWVlWjaTiCJYIOUy5dLPdh1xuUaqvrmLj3QdsSUmKidZkdtZ7V4/l9uNLwhXS6pK6bwPwN2W/ftjMHSoE9/8phMGg6vPBoOIF1+M7fbeud9buX0GysROf3pu794YhZKx6l9fqkiMP+vg7mPldvsHc3+CljQL7g6HAyUlJSgvL0dVVRUqKytx8uRJr2NeffVVZGdnY+fOnfj5z3+OZ555BgDwxz/+ETabDb///e+xY8cOvPPOO/jiiy+0aioRRahglMuUSj/bdcT10EPKI/zOo2v38x96qOPK2rjvUa78yNH3bnxRvFpSVcrq1a6+lZVdht3uWqd3OAQ0NhrwxReu68ytVsGTfa7ze9f5vQ2sVGswRvmBloy9Suq99me2xT3DEelr6ko0C+61tbUYPnw4kpOTERsbi5ycHFRXV3sdIwgCWltdVXEuXryIwYMHe+5vb2+H3W7H5cuX0atXLyQkJGjVVCKKUKG6lrjrCF+K1Lqw0lq5e/1bzcix8yjc15qvK0mM9/qu3Pskxf3e+fMcqfMPGxa6Eaw/a/JS73Xn2ZbOsxnDhrlmOKTWzCN9TV2RqJH33ntPLC4u9tyuqKgQn3nmGa9jLBaLOG3aNHHixInid77zHfHTTz8VRVEUbTabuHTpUvHOO+8Ub731VvHtt99WPF9Hhz24HSCisIuJEUWg+x+jUf1rbN0qimPGuF5rzBjXban7Fi0Sxd69pc8HuB7vSu5Y9x9BuNoGpWMBUczKUvfaat8nuT+9e7va5s9z3H+2bu3ZOQP5s2iR/+3rqvPn27u39OepJ2FNP1tVVQWz2Yz58+fj6NGjWL58OSorK1FbWwuDwYADBw7gwoULmDt3LsaNG4fk5GTZ12ppkc4JHQ2utdze0UyP/YrkPqWkSOeBT0lxoLlZ/v+8u09dU4R++ikwZ473sVL3XSV68ow//bQVzc3ej8bFyeWcdxk61Inm5jZkZABlZUaFVLDArl1Aerod77zTLvvacXEimpu964DLvU9yLl/29ago2b64OBEvvXQZGRl2NDf7f061jEYRKSnObql7b77ZiJKSOMmEPAaDiNGjXce629eZ+1JDt8uXgfXrgfZ23+V/eyoScstrNi1vMpnQ1NTkuW2xWGAymbyO2bZtG7KzswEAY8eOhdVqRUtLCyorKzFx4kT06tUL119/PW6//XZ8+umnWjWViCKUmnXPrslfOl9b3vOpZ5e4OHjW6Ttzn1Npl/vZs4JnE6B7nb6szGdkRU2NK2DK7fSXuj+Y16nLXUr30kuXvYrSnD+vTfU4h8M1Bn/55cvddqB/9ZX0OUePdvrcrS63fBLMSxAjjWbBfcyYMTh9+jQaGhpgs9lQVVWF9PR0r2OSkpJw+PBhAMCpU6dgtVoxcOBAJCUl4cMPPwQAXLp0CZ988gm+9a1vadVUIopQSuueUslfystjsXix6/l//3tgv+KkgnfXc/omX1JWbm3ffQmW1E7/RYukC5l0fp/827kuetahr57/ap8Mhu7vuXs2pLFRm/AhivKb/+RmSZQudZP7EhbMSxAjjaYlX/ft24fS0lI4HA7MnDkTP/zhD7Fu3TrcfPPNyMjIwMmTJ/HUU0/h0qVLEAQBTzzxBCZMmIC2tjb8+Mc/xqlTpyCKImbMmIGCggKf54rUaUU1InlatKf02CdAn/2K5j7JlWLt3Rs4c+aij1Kt6sTFiWho8J4C7+lrDhvmxHXXiZ7p5ro66el5QRBhsbR2fwGo+6zkStpKcfdP7jmJiU5cuiTAanVd/92vn3ilelpoar67y/Aq9UmuXK+7JKvcey31+QZDJEzLa7rmnpaWhrS0NK/7lixZ4vn3iBEj8HbnMkdX9O3bFy+99JKWTSMiHZAbebnXlAMtiCI1Bd7T0d7ZswacPev6t69AJZeERq2lS22q67i7+yc3w9E5Ba7dDq+yqKHgHpErjcylLk9TU88+mEmOIg0z1BFR1JK7RKr3lZwscglH+vZ1epLDCIKIyZPtnaa0XX9SUx2SU+By51RKOKO2He+80x5Q4h65pQypZD7u/vW0JKrW3Je1yV1KGBcndrs8TTkxT/f+61FYd8sTEQUiP7/Daxe02w9+4Pp7yBARdXXdn9fWdnVcI4rA3r3dfxUeOxaD4uK4bgFA7pw9KUpy6ZLQbQq+64jzahGWdhQWqntds9nebXOZ2WyXDWbBLPkaTJ0TykiNwjtv8gPUjdbd6WX1jiN3IopacullAdfa+N69gV2qJbWbWuqcvhLOqC0T6xbMxD2dryQYOjQBo0b1lZwNCG5K1Z4kuQlOQhk1V0dESklWrWm6oS6UonVDEBDdG5rk6LFPgD77pbc+db2mOTAivvxSeZQ3eHACerbJrPvrJyUlwOHo/lpGo4iODkH1Z6XmfXAHSDUjXrXcrynXD1/PCZSac4Yiy1wkbKjjyJ2Ioo7UmrR7lFpeHrwFZDVpT9XWbFdLbmTp74hTzTXc7tkANev0BoN7hsL73+69Bu7nAK4d+3JlXruWZN26FaqCrZp9CPLvURSmjw0QR+4RQG8jJ0CffQL02a9o61MwR5lKlDZdBT5L0H3kLte/srJ2FBb2Uf1ZqZlNMBpFNDYqz0z4apO/a95dn6Pm5y/Q84c6qHPkTkTkp0AKnvhaH3cXFPFnN3WgGc6kZgaCVbBEzayD2tkAtfsA5D+bwEbOas8f9cVegoi75Ykoqihd8yynoKADpaVWDBkiXWHS6fQ+To1AM5zJXWcttdu9s+LiOLz5Zi9YrfDkvle7q7+z+noD0tLisXSpzef55N5z9/PdiXnkrpc3GiGZZEYtufNL3a/03l0rOHInoqji39pz91H4qFFyz7+avlbtOrrBj9+gkyfb/Z4ZkCKXcrdrm7vu6jcaRQwc6PRKNStV512K3Hvufr7D4frbVZNd/fPVCtY+hGsJgzsRRRV/iqQUFHR4Cr+4N2TV1yv/2us83e5rI5dckRUpFouAhoZWfPllK1566TIOHYqR3BAoVQBHrm1K95eWWj3nbGxsRX19m+yXm5KSONl+BlqYRiqDnD/UFBAib5yWJ6Ko4ppybce6da6c4VLJY2JigIcfvjoylt/oJV3e1D3d7iuhjNlsl90RLsU9hSz/mt7nd0+pdx3dB1oERW6Ku2t63M797Pyeu6fg6+sNkiN1d/nVriVbAyF1/mC8rp5xt3wEiLbdymrosU+APvsViX2aPbsPampiIIqu0fE99zjwzjvt3Y5TKhwDuKax33ijl+SXAEEQJe9XKqjiLlTiTxEZtUVQpNrhNmhQP/TuLUqeUxBEvPbaZcWA58/55Qqy+HodX8+REok/f4Hibnkioi5mz+6DvXuNV4KuAFEUsHevEbNndx95KxWOca9P+5sa1r3RTWkj17hx6ofu7ilkfzYESvVPbhOeKAqKa+eAf1PsvtrKqfLIxuBORBGlpkZ6VLl3b0y3NWmlwjFKl6r95386fRZUUdrI1dQk96XBtXmt8+VYR4642u/PVL5U/0pLrT7rtiulqZW6XEwuVayvDWu87Cyycc2diCKK/EKh4BnJutekU1MdOHas+5cBd+EYpXVo97qt3K51uYIlSqNwoxGor2/z3O5pshupUXpxcZxkn93UzAx0vVxMbk+C0iicl51FLo7ciSii+LMD/dQpg+TI+6WXlF7LdZxSYFIanaq9REt+BkFdSVZ1ryV9bjU4CtcfbqiLANxQEj302K9I65N7zV0d6cIu7j4ppWANNICpTXcq3w51hWncBg3qd6X+u3Z9CrVI+/kLBm6oIyJdUXutti/vvNOOyZPtV4LY1cIkUpRSrCo97k8ZVam+qR3xyrVDTYpY9c9RNxtB1wYGdyIKCrWZ09R45512WCyu5CsWSysWLJDeIS63c9ytw/fDqneu++qb2WxHTc0lNDa2oqbmkmRwlWunUvv9e46rTUq75enawOBOREHhT+Y0f3VNpao2fat8qlkXNevTrsAeWN962n5fryU3m+HPbATpF9fcIwDXnKKHHvsVrD4Fa105GNx9UipBqrQ+rbzLPbR96/xZJSUlwOHo/n6rLeMaKfh/KvBzSeHInYiCIpjrysHSeU3cXdLVYFC/G1xpZB7OvrGYCvnC4E50DQnGhjc5wVxXDib3mnhTUysaGlrR1CS/Nt6V0nXy4ewbM8SRLwzuRNcIuU1hixcH5/WDua4cKZR2poezb7w2nXxRDO5WtaWGiCgiuUfrcpvCXn89eOfqXGLUXWpVLV+lVf21eDGCMkMhNzIvKOiIiC8tanbq07VJ8X9Peno6cnNzMXfuXNxwww2haBMRBYmatKfuIivhpFRa1R+uPgPuzX2+yqcqcR//5pu9YLW6RvL5+ZER2Il8URy5v/vuu7juuuswb948FBQUYO/evaFoFxEFgZpLtdxFVuRIjah7snbva2T+4ovSX0DWrYv1e0Qf7EvyApmN0HKPA5Evqi+FczgcqK6uRmlpKQwGAx588EE88MADiAvndtFOovlSCl4KEj2irV9K6VcBYNEi4OmnpfukdClZZ77WoJXStJpMCTJlWaVTrfpaW46US/LkZk0CWauPtp8/NdinwM8lRdWGuvb2dvz2t7/Fhg0bcMMNN+Cxxx7DP/7xD/zAXXqJiCKSrw1hXYusSJEbUUspL+8lO7L2NTLvCV/Pi4RL8oKR+IYoEIrBvaSkBJmZmTh27BjWrl2LX/3qV8jNzcWqVavw5ZdfhqKNRNRDvjaEqZliVpue1UXwrJV3DfByr+O+399UWr7aFe5L8q6O2KVnTLhHmUJB8X/usGHDUFVVhZKSEqSkpHg99qtf/UqzhhGRSyDrtoFentbThChdR9ZKCVfkRtVyJVt9tau01IqsLHgVnpk8Wb5me7BFcuIbunYoBveJEyeiV6+rP6xtbW04ceIEAGDw4MHatYyIglKMJZANYXKJUpR0HVkrJVyRG1Xfc4/D5/OkVFQYsWsXrqzhCxBFAXv3GkNWUCWSE9/QtUMxuK9YscIruPfq1QtPPvmkpo0iIpe9/6PrAAAgAElEQVRg7/z2dxZALlFK59kAKV1H1koJV+RmGN55p93zvKulX0UsXtxbtu3BXt/3VyQnvqFrh+JXWYfD4RXcY2Nj4XBIf5smouCSGwX2ZN226+5t9/XfffoATz8t/zyz2d5tZ7rZ7JrmltsFLzWylnqdzkpLrZKBz2y248iRGBw7FtOt7e7ndaa0vq+1/PwOmV3yvD6eQkfxp91oNKKhocFz+8yZM4iJifHxjKv279+PrKwsZGZmYuPGjd0eb2xsRH5+PvLy8pCbm4t9+/Z5Hquvr8fs2bORk5OD3NxcZsqja1Iwd37LjfYDyVAXqhSo/sxghLugih7T8FL0URy5FxUVYc6cOUhLSwMA7Nu3D6tWrVJ8YYfDgZKSEmzevBkmkwmzZs1Ceno6RowY4Tnm1VdfRXZ2NubOnYuTJ0+isLAQe/bsgd1uxxNPPIHnn38eo0ePRktLC4zG0KyXEfmrosKIF1+MxfHjBqSkOLF0qS1owU1uFNiTdVu578eBZqhTGpEHg68ZjK7v//jxDtTVdR+AhLKgitwsBFGoKEbMyZMn480338QHH3wAACgsLMTw4cMVX7i2thbDhw9HcnIyACAnJwfV1dVewV0QBLS2upJKXLx40bNB79ChQxg1ahRGjx4NAEhMTPSzW0ShEcy0qVKCmf40Lk46SCplqIsEcm03GtHt/a+ri8GiRUB1tcMT8JcsCd4XLqJooGo4fOONN+LGG2/064UtFguGDBniuW0ymVBbW+t1TFFRERYsWIAtW7agvb0dmzdvBgB8/vnnEAQBCxYswLlz5zB16lQmzKGI5GvzVrCCSbBGgXKzANHwX0uu7f36iWhp6X693L59QE3NpVA0jSgiKQb3f/3rX3j++edRX1/vte5dXV0d8MmrqqpgNpsxf/58HD16FMuXL0dlZSUcDgf+8pe/YNu2bejTpw8eeugh3Hzzzbj77rtlXysxMR5Go7q9AJFILoVgNNNjnwDvfh0/Ln3M8eMxEdf/118H+vRx/X35smvE/oMf4EqGushqa1dybX/lFeltQ8eOXRs/f3rBPgWfYnAvLi7G1KlTUVdXh7Vr12Lr1q2qqsOZTCY0NTV5blssFphMJq9jtm3bhnJX+SaMHTsWVqsVLS0tGDJkCO644w4MHDgQADBp0iT87W9/8xncW1qi91s6cytHj679SkmJl1zfTUlxoLk58n4mn366+874xYv7YeNGMeKrnkm1ffdu6fc/NTW6603I0eP/K/Yp8HNJUdwt39LSgu9///swGo0YO3YsVq9e7bWrXc6YMWNw+vRpNDQ0wGazoaqqCunp6V7HJCUl4fDhwwCAU6dOwWq1YuDAgZgwYQKOHz+O9vZ22O12HDlyxGutnihSKCVniXTFxXFYvx4BJckJJ7n3/8c/DnFDiCKMYnB3X+MeHx+PxsZG2O12nDt3TvGFjUYjVq5ciYKCAkydOhXZ2dkYOXIk1q1b55nSX7FiBX7zm99g+vTpWLZsGVavXg1BENC/f3889NBDmDVrFvLy8pCamop77rknsJ4SaUCrS8H8LXPaU8FOkhNqcu///fcH7xws20rRSLHk65o1a7Bw4ULU1NRg9erViI2Nxb333ovi4uJQtVGVaJ7W4bRU9AhFv5TKowZTpJRHDbZgfU5alG0NhB7/X7FPgZ9LiuLI/ZFHHsGAAQOQl5eHHTt2oLy8POICO5GeBDt9qq9ZgEgoj9oToRpNR/vMBl27fAZ3URQxe/Zsz+2hQ4d2qwxHRMEVzPSp7lmAuroYOBzdS7KGuzxqTwSjmI5awUz/SxRKPn9bCIKApKQknD9/PlTtIYoaWo0eg5k+VW4WYOHC3khLi8cddziwaBGiKlVqKEfT0TqzQaS4SychIQFmsxmTJk1CfHy85/7ly5dr2jCiSCZXhAXoXsjEX0uX2lQXY1EiP9q/OorfuhV4+unoWV8P5Wg6mOl/iUJJcZ5v5MiRmDFjBr7xjW8gPj7e84foWqbl6DGYO/DVjPafe64nrQyfUI6mWQSGopWqwjFEkai4OC4oOdd7QuvRY7CKscjNAnR27FjAp/Hw9ZkEq8BOqEfTLAJD0UgxuP/sZz+TvJ/T8hROWk6LqyFXyCTS1mJdwbMd69bF4tgxA6Que0tNDc65fH0md9zhCFqBnWAW0yHSK8Vp+c5T8TExMThw4ABaWlpC0TYiWeG+REmrXeZabNIzm+2oqbmEsjLp2q7Byubm6zMJ9uV9paVWNDS04ssvW9HQ0MrATtSF39PyCxcuxJIlSzRrEJEa4b5ESYvRo9azEZ1H8Z1Lod5/fx80Nwf88j4/k2Be3kdEyvzOadm3b180NjZq0RYi1SJhWjzYa7G+Rr7BOk+w1vKl+PpMvvUtp0yBHf8v7yMiZX6tuYuiiM8++ww33XSTpo0iUqLHS5TCPRsRKF+fSdc1d7doKbBDFG0Ug3vny95iYmIwZ84cZGZmatooIiXRvqlKaud4uGYj3n4bKCmJD3gXu/Jn0n1JQKtZBKJrnWLhmGgRzYUHWDghegSjX3KFYSZPtmPv3u7ft7W8rjqURWpCiT9/0YN9CvxcUhR3syxatAhff/2153ZLSws31BF14u8Od7md4xaLEPKEKcHexU5EkUFxWr6hoQEDBgzw3E5MTMSZM2c0bRRRtOjJDndfO8drai6FdGmBu9iJ9Enxf7DD4YDD4fDc7ujogM3GTTBEQM+utw9mYZhABaMtoSq/SkTqKQb3CRMm4LHHHsPHH3+Mjz/+GI8//jgmTpwYirYRRbye7HBfulT6y3E4do4H2pZQll8lIvUUg/uyZcuQkpKC1atXY/Xq1UhJScGyZctC0TYiAK5NX2lp8UhKSkBaWrynFnkk6EkRk2AWhgmU2WzH1q3ocVvUzFxwZE8UetwtHwG4W1RepO3m7tqvrmvubtFUOSyQz2rw4ARI5asHRHz5ZWvY3h89/p8C9Nkv9inwc0lRHLmvWrWq2275n/70p8FrGZEPkb6b+1ovCao0cxHuGgBE1yrF4P7xxx932y1/5MgRTRtF5BYNu7mv5SImSgV0oj3rHlG0UrVbviu7PXqTW1B0iaSd5dSd0syFQeY3jNz9RBQciv/FxowZg1WrVsFisaCpqQmrVq3CmDFjQtE2oojaWU7SfM1cCFLL8T7uJ6LgUAzuxcXFaGtrQ15eHmbMmIG2tjYUFxeHom1EEbWznPwnMfHn834iCg7Fa4oSEhLw3HPPed3X2NiIfv2kd+gRBZuWZUpJW5FQmpfoWqR65ctms6GyshIPPfQQvve972nZJiLSCaUNd0SkDcWR+2effYZt27bhvffeg9VqxU9/+lO89tproWgbEUW5aC/NSxStZIP7L3/5S+zYsQOXL1/GjBkzsHPnTjzwwAPIyckJZfuIKMqVlloZzIlCTDa4P/fcc7j77rtRUlKC5ORkAIDALa5EREQRTza4V1VVYfv27ZgzZw5uvPFGmM1m6CRTLRERka7Jbqi76aabsHz5cuzbtw8PPfQQdu/eja+++grLly/HgQMHQtlGIroikovoEFHkUPzNEBMTg4yMDGRkZODf//43KioqsGbNGpZ9JQqxrkV06upirtzmdf9E5M2vJJDXX389CgoKUFlZqVV7iEhGpBfRIaLIwQzPRH54+22EbVo8GoroEFFk0PS3wv79+5GVlYXMzExs3Lix2+ONjY3Iz89HXl4ecnNzsW/fvm6Pjx07Fm+88YaWzSRSpaLCiDlzXNPhDofgmRYPVYBnER0iUksxuLe2tqq6ryuHw4GSkhKUl5ejqqoKlZWVOHnypNcxr776KrKzs7Fz5078/Oc/xzPPPOP1+OrVq7m2TxEj3NPiLKJDRGopBvf8/HxV93VVW1uL4cOHIzk5GbGxscjJyUF1dbXXMYIgeL4oXLx4EYMHD/Y8tnv3bgwbNgwjR45UPBdRKIR7WpxFdIhILdn5RLvdjo6ODjidTly+fNlzjfvFixfR3t6u+MIWiwVDhgzx3DaZTKitrfU6pqioCAsWLMCWLVvQ3t6OzZs3AwDa2trw+uuvY9OmTdi0aVOPOkYUbCkpTtTVxUjeHyosokNEasgG99deew0bNmyAIAi47bbbPPcnJCTg4YcfDsrJq6qqYDabMX/+fBw9ehTLly9HZWUlNmzYgHnz5qFv376qXysxMR5GY/dfvNFi0CD9VdnTW59WrgTmzOl+/9NPx0R9X6O9/VL02CdAn/1in4JPNrgXFRWhqKgIJSUlWLlypd8vbDKZ0NTU5LltsVhgMpm8jtm2bRvKy8sBAGPHjoXVakVLSws++eQT7Nq1C2vXrsWFCxdgMBgQFxeHBx98UPZ8LS2X/G5jpBg0qB+amy+GuxlBpcc+ZWQAW7f2w7PPOnD8uAEpKU4sWWJDRoYdzc3hbl3P6fGz0mOfAH32i30K/FxSFLf5Llu2DE6nEwaDAcePH8eJEyeQmZmJ2Fjfm4jGjBmD06dPo6GhASaTCVVVVXjhhRe8jklKSsLhw4cxY8YMnDp1ClarFQMHDsRbb73lOWb9+vWIj4/3GdiJQuX++4GMjOj9IklE1wbFnUD/8z//g8uXL6O5uRkLFizAjh07VI3kjUYjVq5ciYKCAkydOhXZ2dkYOXIk1q1b59lYt2LFCvzmN7/B9OnTsWzZMqxevZrFaYiIiAKkOHIXRRHx8fGoqqrCfffdh0WLFiE3N1fVi6elpSEtLc3rviVLlnj+PWLECLz99ts+X2PRokWqzkVEREQuiiN3q9UKm82GQ4cO4e6773Y9ycCMWERERJFKMUpPnToV48ePxxdffIHbb78dzc3NiIuLC0XbiIiIqAcUp+WLioqQn5+Pfv36wWAwID4+HuvXrw9F24iIiKgHFEfuoiji/fff9+x0b2lpwdmzZzVvGBEREfWMYnB/7rnn8Oc//9mzw71v374oLS3VvGFERETUM4rB/cMPP8TatWvRu3dvAEBiYiKsVqvmDSMiIqKeUQzucXFxXteeO50sL0lERBTJFDfUpaSk4N1334Uoivjiiy+wceNGfPvb3w5F24iIiKgHFEfuK1aswEcffYTm5mbcd999cDqdeOKJJ0LRNiIiIuoBxZE7AKxatcrrtrsGOxEREUUexZF7fn6+qvuIiIgoMsiO3O12Ozo6OuB0OnH58mWIoggAuHjxItrb20PWQCIiIvKPbHB/7bXXsGHDBgiCgNtuu81zf0JCAh5++OGQNI6IiIj8JzstX1RUhPr6esyZMwf19fWePx9//DEeffTRULaRKCIVF8chOTkBgwcnIDk5AcXFrLlARJFBcUOdmtrtRNea4uI4lJfHem5brfDcLi1lkiciCi/WbiXqgTff7OXX/UREocTgTtQDchmYmZmZiCIBgztRD8TJLK/L3U9EFEoM7kQ9kJ/f4df9REShpCpDHRF5c2+ae/PNXrBaXSP2/PwObqYjoojA4E7UQ6WlVgZzIopInJYnIiLSGQZ3IiIinWFwJyIi0hkGdyIiIp1hcCciItIZBnciIiKdYXAnIiLSGQZ3IiIinWFwJyIi0hkGdyIiIp1hcCciItIZBnciIiKdYXAnIiLSGU2D+/79+5GVlYXMzExs3Lix2+ONjY3Iz89HXl4ecnNzsW/fPgDAoUOHMGPGDOTm5mLGjBk4fPiwls0kIiLSFc1KvjocDpSUlGDz5s0wmUyYNWsW0tPTMWLECM8xr776KrKzszF37lycPHkShYWF2LNnDxITE/Hqq6/CZDLh+PHjWLBgAQ4cOKBVU4mIiHRFs5F7bW0thg8fjuTkZMTGxiInJwfV1dVexwiCgNbWVgDAxYsXMXjwYABAamoqTCYTAGDkyJGwWq2w2WxaNZWIiEhXNBu5WywWDBkyxHPbZDKhtrbW65iioiIsWLAAW7ZsQXt7OzZv3tztdXbt2oXU1FTExsZq1VQiIiJd0Sy4q1FVVQWz2Yz58+fj6NGjWL58OSorK2EwuCYUTpw4gbVr12LTpk2Kr5WYGA+jMUbrJmtm0KB+4W5C0OmxT4A++8U+RQ899ot9Cj7NgrvJZEJTU5PntsVi8Uy1u23btg3l5eUAgLFjx8JqtaKlpQXXX389mpqaUFRUhDVr1uCGG25QPF9Ly6XgdiCEBg3qh+bmi+FuRlDpsU+APvvFPkUPPfaLfQr8XFI0W3MfM2YMTp8+jYaGBthsNlRVVSE9Pd3rmKSkJM9O+FOnTsFqtWLgwIG4cOECCgsL8fjjj+Pb3/62Vk0kIiLSJc1G7kajEStXrkRBQQEcDgdmzpyJkSNHYt26dbj55puRkZGBFStW4KmnnsIvfvELCIKA1atXQxAEbNmyBWfOnMHLL7+Ml19+GQCwadMmXH/99Vo1l4iISDcEURTFcDciGKJ5WofTUtFDj/1in6KHHvvFPgV+LinMUEdERKQzDO5EREQ6w+BORESkMwzuREREOsPgTkREpDMM7kRERDrD4E5ERKQzDO5EREQ6w+BORESkMwzuREREOsPgTkREpDMM7kRERDrD4E5ERKQzDO5EREQ6w+BORESkMwzuREREOsPgTkREpDMM7kRERDrD4E5ERKQzDO5EREQ6w+BORESkMwzuREREOsPgTkREpDMM7kRERDrD4E5ERKQzDO5EREQ6w+BORESkMwzuREREOsPgTkREpDMM7kRERDrD4E5ERKQzDO5EREQ6w+BORESkMwzuREREOqNpcN+/fz+ysrKQmZmJjRs3dnu8sbER+fn5yMvLQ25uLvbt2+d5rKysDJmZmcjKysKBAwe0bCYREZGuGLV6YYfDgZKSEmzevBkmkwmzZs1Ceno6RowY4Tnm1VdfRXZ2NubOnYuTJ0+isLAQe/bswcmTJ1FVVYWqqipYLBY8/PDD2LVrF2JiYrRqLhERkW5oNnKvra3F8OHDkZycjNjYWOTk5KC6utrrGEEQ0NraCgC4ePEiBg8eDACorq5GTk4OYmNjkZycjOHDh6O2tlarphIREemKZiN3i8WCIUOGeG6bTKZuAbqoqAgLFizAli1b0N7ejs2bN3uee+utt3o912KxaNVUIiIiXdEsuKtRVVUFs9mM+fPn4+jRo1i+fDkqKyt79FqJifEwGqN32n7QoH7hbkLQ6bFPgD77xT5FDz32i30KPs2Cu8lkQlNTk+e2xWKByWTyOmbbtm0oLy8HAIwdOxZWqxUtLS2qnttVS8ulILY+tAYN6ofm5ovhbkZQ6bFPgD77xT5FDz32i30K/FxSNFtzHzNmDE6fPo2GhgbYbDZUVVUhPT3d65ikpCQcPnwYAHDq1ClYrVYMHDgQ6enpqKqqgs1mQ0NDA06fPo1bbrlFq6YSERHpimYjd6PRiJUrV6KgoAAOhwMzZ87EyJEjsW7dOtx8883IyMjAihUr8NRTT+EXv/gFBEHA6tWrIQgCRo4ciezsbEydOhUxMTFYuXIld8oTERGpJIiiKIa7EcEQzdM6nJaKHnrsF/sUPfTYL/Yp8HNJYYY6IiIinWFwJyIi0hkGdyIiIp1hcCciItIZBnciIiKdYXAnIiLSGQZ3IiIinWFwJyIi0hkGdyIiIp1hcCciItIZBnciIiKdYXAnIiLSGQZ3IiIinWFwJyIi0hkGdyIiIp1hcCciItIZBnciIiKdYXAnIiLSGQZ3IiIinWFwJyIi0hkGdyIiIp1hcCciItIZBncJFRVGpKXFIykpAWlp8aioMIa7SURERKoxanVRUWHEwoV9PLfr6mKu3G6H2WwPX8OIiIhU4si9ixdfjJW8f9066fuJiIgiDYN7F8ePS78lcvcTERFFGkasLlJSnH7dT0REFGkY3LtYutQmef+SJdL3ExERRRoG9y7MZjvKytqRmuqA0SgiNdWBsjJupiMioujB3fISzGY7gzkREUUtjtyJiIh0hsGdiIhIZxjciYiIdIbBnYiISGcY3ImIiHRG093y+/fvx09/+lM4nU58//vfR2FhodfjpaWl+PDDDwEAly9fxr///W98/PHHAICf/exn2LdvH5xOJ8aPH4//9//+HwRB0LK5REREuqBZcHc4HCgpKcHmzZthMpkwa9YspKenY8SIEZ5jiouLPf9+8803cezYMQDAX//6V/z1r3/Fu+++CwCYO3cuPvroI9x5551aNZeIiEg3NJuWr62txfDhw5GcnIzY2Fjk5OSgurpa9viqqipMmzYNACAIAmw2Gzo6Ojx/f+Mb39CqqURERLqi2cjdYrFgyJAhntsmkwm1tbWSx549exZffPEF7rrrLgDA2LFjceedd2LChAkQRREPPvggbrrpJp/nS0yMh9EYE7wOhNigQf3C3YSg02OfAH32i32KHnrsF/sUfBGxoa6qqgpZWVmIiXEF53/+8584deoU9u3bh/379+PPf/6zZy1eTjQHdiIiomDSLLibTCY0NTV5blssFphMJslj//CHPyAnJ8dz+09/+hNuvfVW9O3bF3379sXEiRNx9OhRrZpKRESkK5oF9zFjxuD06dNoaGiAzWZDVVUV0tPTux136tQpXLhwAWPHjvXcN3ToUBw5cgR2ux0dHR04cuSI4rQ8ERERuWi25m40GrFy5UoUFBTA4XBg5syZGDlyJNatW4ebb74ZGRkZAFyj9qlTp3pd5paVlYU///nPyM3NhSAImDhxouQXAyIiIupOEEVRDHcjiIiIKHgiYkMdERERBQ+DOxERkc4wuGts//79yMrKQmZmJjZu3Njt8cbGRuTn5yMvLw+5ubnYt2+f57GysjJkZmYiKysLBw4cCGWzfeppnw4dOoQZM2YgNzcXM2bMwOHDh0PddFmBfE7ux8eOHYs33ngjVE1WJZB+1dfXY/bs2cjJyUFubi6sVmsomy6rp33q6OjAk08+idzcXGRnZ6OsrCzUTZel1KezZ89i3rx5yM3NRX5+vteVSBUVFZgyZQqmTJmCioqKUDZbUU/7VVdX5/Wz94c//CHUTZcVyGcFAK2trZg0aRJKSkq0bahImrHb7WJGRoZ45swZ0Wq1irm5ueKJEye8jnnqqafEX//616IoiuKJEyfEyZMne/6dm5srWq1W8cyZM2JGRoZot9tD3oeuAunT3/72N7GpqUkURVH8+9//Lk6YMCG0jZcRSJ/cFi1aJC5atEgsLy8PWbuVBNKvjo4Ocdq0aWJdXZ0oiqJ47ty5qP/5e/fdd8WlS5eKoiiKly5dEidPniw2NDSEtgMS1PRp0aJF4o4dO0RRFMUPPvhA/NGPfiSKoii2tLSI6enpYktLi/j111+L6enp4tdffx3yPkgJpF//+Mc/xM8//1wURVFsamoSx48fL54/fz6k7ZcSSJ/cnn32WXHZsmXiM888o2lbOXLXkJoUvIIgoLW1FQBw8eJFDB48GABQXV2NnJwcxMbGIjk5GcOHD5fN8BdKgfQpNTXVk+tg5MiRsFqtsNlsoe2AhED6BAC7d+/GsGHDMHLkyJC2W0kg/Tp06BBGjRqF0aNHAwASExM9SabCKZA+CYKA9vZ22O12XL58Gb169UJCQkLI+9CVmj6dOnXKk8Hzrrvu8jx+8OBBjB8/HgMGDED//v0xfvz4iJnlC6RfN954I/7jP/4DgCtnysCBA3Hu3LmQtl9KIH0CgM8++wz//ve/MX78eM3byuCuIakUvBaLxeuYoqIi/P73v8ekSZNQWFiIp556SvVzwyGQPnW2a9cupKamIjY2VvM2KwmkT21tbXj99ddRVFQU0jarEUi/Pv/8cwiCgAULFsBsNuP1118PadvlBNKnrKws9OnTBxMmTMDkyZMxf/58DBgwIKTtl6KmT6NHj8b7778PwJXkq62tDS0tLRH7ewIIrF+d1dbWoqOjAzfccIP2jVYQSJ+cTifWrFmDJ598MiRtZXAPs6qqKpjNZuzfvx8bN27E8uXL4XQ6w92sgCj16cSJE1i7dq32a05BJNenDRs2YN68eejbt2+4m9gjcv1yOBz4y1/+gueffx5vvfUWdu/eHVF7JHyR61NtbS0MBgMOHDiA6upqbNq0CQ0NDeFurirLly/HkSNHkJeXh48++ggmkykiZlICpdSvL7/8Ek888QSee+45GAzREa7k+vTWW29h0qRJXl8OtKRpPfdrnZoUvNu2bUN5eTkAV8Ecq9WKlpYWv9L3hlIgfbr++uvR1NSEoqIirFmzJiK+iQOB9emTTz7Brl27sHbtWly4cAEGgwFxcXF48MEHQ9oHKYH0a8iQIbjjjjswcOBAAMCkSZPwt7/9DXfffXfoOiAhkD5VVlZi4sSJ6NWrF66//nrcfvvt+PTTT5GcnBzSPnSlpk8mkwkbNmwA4Jotev/993HdddfBZDLho48+8nruf//3f4em4QoC6Rfg2ni2cOFCPPbYY7jttttC13AfAunT0aNH8Ze//AVbt25FW1sbOjo6EB8fjx/96EeatDU6vgpFKTUpeJOSkjwjolOnTsFqtWLgwIFIT09HVVUVbDYbGhoacPr0adxyyy3h6IaXQPp04cIFFBYW4vHHH8e3v/3tcDRfUiB9euutt7Bnzx7s2bMH8+bNw8KFCyMisAOB9WvChAk4fvy4Z436yJEjGDFiRDi64SWQPiUlJeHDDz8EAFy6dAmffPIJvvWtb4W8D12p6dO5c+c8s18bN27EzJkzAQATJkzAwYMHcf78eZw/fx4HDx7EhAkTQt4HKYH0y2az4dFHH8X3vvc93HvvvSFvu5xA+vTCCy+gpqYGe/bswZNPPom8vDzNAjsA7pbXWk1NjThlyhQxIyNDfOWVV0RRFMUXX3xR3L17tyiKrt28s2fPFnNzc8Xp06eLBw4c8Dz3lVdeETMyMsQpU6aINTU1YWm/lJ726eWXXxZvvfVWcfr06Z4/X331Vdj60Vkgn5PbSy+9FFG75UUxsH7t3LlTnDp1qpiTkyOuWbMmLO2X0tM+tba2iosWLRKnTp0qZmdni6+//nrY+tCVUp/ee+89MTMzU+mpmksAAAWfSURBVJwyZYpYXFwsWq1Wz3N/+9vfit/97nfF7373u+K2bdvC0n45Pe3Xzp07xdTUVK/fFceOHQtbPzoL5LNy2759u+a75Zl+loiISGc4LU9ERKQzDO5EREQ6w+BORESkMwzuREREOsPgTkREpDMM7kQ6lZ+fj7179wIA1q1bJ1tZa/369VizZo3s65w/fx633HILVq1apUk7iSj4GNyJrgFLlizB1KlTe/TcyspK3HrrrZ6kSqHgcDhCch4ivWJwJ4pwr7zyCkpLSz23W1pacOedd+LSpUs4fPgwZs+e7aldXlVVJfkaK1aswJYtWwC4KqUtXrwY9957L/Lz83HmzBmf59++fTseeeQRjBo1yqvClc1mw5o1azBt2jRMnz4djz76qOexsrIy5ObmYvr06bj//vvhdDqxY8cOLF682HNM59s7duzAQw89hEcffRTTpk3D8ePHsWnTJsycORN5eXmYPXs26urqPM89evQo5syZg+nTp2P69Ok4ePAg3nvvPRQWFnq1b8KECWhsbFTzNhPpCnPLE0W4vLw83HfffVi+fDmMRiMqKyuRnp6O+Ph4pKam4q233kJMTAy++uorzJgxAxMmTED//v1lX+/ll19G37598cc//hHnzp3DjBkzkJ2dLXlsfX09vv76a9x1111obm7G9u3bPcdu3LgRDQ0N2LFjB2JjYz0lOSsqKrBnzx5s3boVCQkJaGlpUVX045NPPsHvfvc7T80Bk8mE+fPnAwA++OAD/OQnP8FvfvMbfP311ygqKsL69etx++23w+FwoLW1FX379sXPfvYzNDQ0IDk5GX/4wx9w6623YujQoX6930R6wJE7UYQbOnQoRowYgX379gFwBc8ZM2YAcOWxXrx4MaZNm4YFCxbg/Pnz+Pzzz32+3ocffohZs2YBAAYOHIjMzEzZY7dt24bvfe97EAQBU6ZMQW1trafE5d69ezFv3jxP2V53kZm9e/dizpw5nlrpiYmJqvp5++23exUT+uyzz/DAAw9g2rRpeO655zwj9//7v//DTTfdhNtvvx0AEBMTg/79+8NoNGL27Nl4++23AQBvvfUWHnjgAVXnJtIbjtyJooDZbMbOnTvxzW9+ExcvXsR3vvMdAMD//u//Ij09HRs2bIAgCMjKyoLVag3KOW02GyorKxEbG4vf/e53AICOjg7s2LEDP/zhD/1+vZiYGK/Sv13b2blsrs1mw5IlS7Blyxb813/9FywWCyZNmqR4jvvuuw9msxnp6em4cOFC2KvYEYULR+5EUWDKlCk4cuQINm/eDLPZDEEQALjWz4cNGwZBEHDo0CH885//VHytu+66Czt27ADgWr/fvXu35HHV1dW48cYbsX//fk/lu02bNqGiogIAMHnyZPzyl7/0bLJzT8tPnjwZW7duRWtrq+ccADB8+HD8/e9/h81mg81mw65du2TbaLPZYLfbkZSUBMA1Cne77bbbcOrUKRw9ehSAa/Pd+fPnAbhmD8aNG4dly5Zh7ty5nveJ6FrDkTtRFOjTpw8yMjKwY8cOr01tjz/+OJ555hmsX78eY8aMwahRoxRf65FHHkFxcTHuvfdeDBo0yDML0NX27duRm5vrdd/YsWPhdDrx0UcfobCwEC+88ALy8vLQq1cvDB8+HC+99BLy8vJgsVgwe/ZsGI1GxMfH49e//jVuu+023H333cjJycHgwYMxevRoNDc3S547ISEBixcvxqxZszBgwABkZWV5HhswYADWr1+P1atX49KlSzAYDHjyyScxbtw4AMCsWbPwxz/+EWazWfG9INIrVoUjIl155ZVX0NzcjJ/85CfhbgpR2HDkTkS6kZOTg5iYGLzxxhvhbgpRWHHkTkREpDPcUEdERKQzDO5EREQ6w+BORESkMwzuREREOsPgTkREpDMM7kRERDrz/wH5KptKyPMwuQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "orderby=\"valid Accuracy\"\n",
+    "ycolumn=\"test Accuracy\"\n",
+    "xy = df.sort_values(orderby)\n",
+    "plt.plot(xy[orderby], xy[ycolumn], 'bo')\n",
+    "plt.xlabel(orderby)\n",
+    "plt.ylabel(ycolumn);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "Path (<tt>../poleval.csv</tt>) doesn't exist. It may still be in the process of being generated, or you may have the incorrect path."
+      ],
+      "text/plain": [
+       "/home/pczapla/workspace/ulmfit-multilingual/experiments/poleval.csv"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "IPython.display.FileLink(\"../poleval.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "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>model_dir_parent</th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>...</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>lmseed</th>\n",
+       "      <th>fine_tune</th>\n",
+       "      <th>model</th>\n",
+       "      <th>exp_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.870</td>\n",
+       "      <td>0.577922</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.456889</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.511494</td>\n",
+       "      <td>0.664179</td>\n",
+       "      <td>0.876617</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.429593</td>\n",
+       "      <td>0.507218</td>\n",
+       "      <td>0.389830</td>\n",
+       "      <td>0.811765</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.858</td>\n",
+       "      <td>0.572289</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.426693</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.479798</td>\n",
+       "      <td>0.708955</td>\n",
+       "      <td>0.860696</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.434975</td>\n",
+       "      <td>0.483857</td>\n",
+       "      <td>0.358974</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.853</td>\n",
+       "      <td>0.573913</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.465223</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.469194</td>\n",
+       "      <td>0.738806</td>\n",
+       "      <td>0.849751</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.523841</td>\n",
+       "      <td>0.477067</td>\n",
+       "      <td>0.342857</td>\n",
+       "      <td>0.847059</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.855</td>\n",
+       "      <td>0.577259</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.483052</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.738806</td>\n",
+       "      <td>0.841791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.530552</td>\n",
+       "      <td>0.441885</td>\n",
+       "      <td>0.323810</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.862</td>\n",
+       "      <td>0.576687</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.491995</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.489583</td>\n",
+       "      <td>0.701493</td>\n",
+       "      <td>0.851741</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.488528</td>\n",
+       "      <td>0.474608</td>\n",
+       "      <td>0.344660</td>\n",
+       "      <td>0.835294</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.854</td>\n",
+       "      <td>0.570588</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.483143</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.470874</td>\n",
+       "      <td>0.723881</td>\n",
+       "      <td>0.829851</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.537976</td>\n",
+       "      <td>0.435847</td>\n",
+       "      <td>0.309735</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.850</td>\n",
+       "      <td>0.561404</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.519333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.461538</td>\n",
+       "      <td>0.716418</td>\n",
+       "      <td>0.842786</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.541371</td>\n",
+       "      <td>0.454872</td>\n",
+       "      <td>0.328638</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.865</td>\n",
+       "      <td>0.597015</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.457041</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.497512</td>\n",
+       "      <td>0.746269</td>\n",
+       "      <td>0.857711</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.452747</td>\n",
+       "      <td>0.473131</td>\n",
+       "      <td>0.352041</td>\n",
+       "      <td>0.811765</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.871</td>\n",
+       "      <td>0.582524</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.418411</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.514286</td>\n",
+       "      <td>0.671642</td>\n",
+       "      <td>0.874627</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.378810</td>\n",
+       "      <td>0.492229</td>\n",
+       "      <td>0.382857</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.849</td>\n",
+       "      <td>0.557185</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.482558</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.458937</td>\n",
+       "      <td>0.708955</td>\n",
+       "      <td>0.866667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.471542</td>\n",
+       "      <td>0.494311</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-1...</td>\n",
+       "      <td>0.876</td>\n",
+       "      <td>0.578231</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.419756</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.531250</td>\n",
+       "      <td>0.634328</td>\n",
+       "      <td>0.891542</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.345693</td>\n",
+       "      <td>0.532369</td>\n",
+       "      <td>0.425000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-2...</td>\n",
+       "      <td>0.864</td>\n",
+       "      <td>0.569620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.458027</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.494505</td>\n",
+       "      <td>0.671642</td>\n",
+       "      <td>0.873632</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.425387</td>\n",
+       "      <td>0.484692</td>\n",
+       "      <td>0.379310</td>\n",
+       "      <td>0.776471</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-3...</td>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.574924</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.477018</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.487047</td>\n",
+       "      <td>0.701493</td>\n",
+       "      <td>0.876617</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.449001</td>\n",
+       "      <td>0.518262</td>\n",
+       "      <td>0.392265</td>\n",
+       "      <td>0.835294</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-4...</td>\n",
+       "      <td>0.883</td>\n",
+       "      <td>0.603390</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.419202</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.552795</td>\n",
+       "      <td>0.664179</td>\n",
+       "      <td>0.881592</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.407950</td>\n",
+       "      <td>0.511490</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-5...</td>\n",
+       "      <td>0.876</td>\n",
+       "      <td>0.594771</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.427007</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.529070</td>\n",
+       "      <td>0.679105</td>\n",
+       "      <td>0.881592</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.408002</td>\n",
+       "      <td>0.500378</td>\n",
+       "      <td>0.397590</td>\n",
+       "      <td>0.776471</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.848</td>\n",
+       "      <td>0.530864</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.488566</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.452632</td>\n",
+       "      <td>0.641791</td>\n",
+       "      <td>0.869652</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.478236</td>\n",
+       "      <td>0.505266</td>\n",
+       "      <td>0.377660</td>\n",
+       "      <td>0.835294</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.856</td>\n",
+       "      <td>0.563636</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.437123</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.474490</td>\n",
+       "      <td>0.694030</td>\n",
+       "      <td>0.877612</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.403486</td>\n",
+       "      <td>0.525650</td>\n",
+       "      <td>0.395604</td>\n",
+       "      <td>0.847059</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.591640</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.448317</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.519774</td>\n",
+       "      <td>0.686567</td>\n",
+       "      <td>0.884577</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.427392</td>\n",
+       "      <td>0.539378</td>\n",
+       "      <td>0.411429</td>\n",
+       "      <td>0.847059</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.863</td>\n",
+       "      <td>0.550820</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.450464</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.491228</td>\n",
+       "      <td>0.626866</td>\n",
+       "      <td>0.892537</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.404172</td>\n",
+       "      <td>0.539953</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.811765</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.870</td>\n",
+       "      <td>0.577922</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.456889</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.511494</td>\n",
+       "      <td>0.664179</td>\n",
+       "      <td>0.876617</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.429593</td>\n",
+       "      <td>0.507218</td>\n",
+       "      <td>0.389830</td>\n",
+       "      <td>0.811765</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.858</td>\n",
+       "      <td>0.572289</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.426693</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.479798</td>\n",
+       "      <td>0.708955</td>\n",
+       "      <td>0.860696</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.434975</td>\n",
+       "      <td>0.483857</td>\n",
+       "      <td>0.358974</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.853</td>\n",
+       "      <td>0.573913</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.465223</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.469194</td>\n",
+       "      <td>0.738806</td>\n",
+       "      <td>0.849751</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.523841</td>\n",
+       "      <td>0.477067</td>\n",
+       "      <td>0.342857</td>\n",
+       "      <td>0.847059</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.855</td>\n",
+       "      <td>0.577259</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.483052</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.738806</td>\n",
+       "      <td>0.841791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.530552</td>\n",
+       "      <td>0.441885</td>\n",
+       "      <td>0.323810</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.862</td>\n",
+       "      <td>0.576687</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.491995</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.489583</td>\n",
+       "      <td>0.701493</td>\n",
+       "      <td>0.851741</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.488528</td>\n",
+       "      <td>0.474608</td>\n",
+       "      <td>0.344660</td>\n",
+       "      <td>0.835294</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.854</td>\n",
+       "      <td>0.570588</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.483143</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.470874</td>\n",
+       "      <td>0.723881</td>\n",
+       "      <td>0.829851</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.537976</td>\n",
+       "      <td>0.435847</td>\n",
+       "      <td>0.309735</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.850</td>\n",
+       "      <td>0.561404</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.519333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.461538</td>\n",
+       "      <td>0.716418</td>\n",
+       "      <td>0.842786</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.541371</td>\n",
+       "      <td>0.454872</td>\n",
+       "      <td>0.328638</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.865</td>\n",
+       "      <td>0.597015</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.457041</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.497512</td>\n",
+       "      <td>0.746269</td>\n",
+       "      <td>0.857711</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.452747</td>\n",
+       "      <td>0.473131</td>\n",
+       "      <td>0.352041</td>\n",
+       "      <td>0.811765</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.871</td>\n",
+       "      <td>0.582524</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.418411</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.514286</td>\n",
+       "      <td>0.671642</td>\n",
+       "      <td>0.874627</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.378810</td>\n",
+       "      <td>0.492229</td>\n",
+       "      <td>0.382857</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.849</td>\n",
+       "      <td>0.557185</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.482558</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.458937</td>\n",
+       "      <td>0.708955</td>\n",
+       "      <td>0.866667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.471542</td>\n",
+       "      <td>0.494311</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-1...</td>\n",
+       "      <td>0.876</td>\n",
+       "      <td>0.578231</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.419756</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.531250</td>\n",
+       "      <td>0.634328</td>\n",
+       "      <td>0.891542</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.345693</td>\n",
+       "      <td>0.532369</td>\n",
+       "      <td>0.425000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>qrnn_ft0_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>qrnn</td>\n",
+       "      <td>_orig</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>306</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-5...</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.595918</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.453026</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.657658</td>\n",
+       "      <td>0.544776</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.369372</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.538462</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-5...</td>\n",
+       "      <td>0.890</td>\n",
+       "      <td>0.576923</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.412375</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.595238</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.901493</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.341758</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.446970</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.576271</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.458157</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.507463</td>\n",
+       "      <td>0.920398</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.343866</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.523364</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.552301</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.526710</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.628571</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.376918</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.540816</td>\n",
+       "      <td>0.623529</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.542222</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.378023</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.670330</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.928358</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.245958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.564356</td>\n",
+       "      <td>0.670588</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.549356</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.502734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.646465</td>\n",
+       "      <td>0.477612</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.366041</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.509259</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.899</td>\n",
+       "      <td>0.542986</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.484061</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.689655</td>\n",
+       "      <td>0.447761</td>\n",
+       "      <td>0.928358</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.292909</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.573034</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>0.561265</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.478323</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.596639</td>\n",
+       "      <td>0.529851</td>\n",
+       "      <td>0.912438</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.371474</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.486726</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.559671</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.460062</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.623853</td>\n",
+       "      <td>0.507463</td>\n",
+       "      <td>0.916418</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.314330</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.504425</td>\n",
+       "      <td>0.670588</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.891</td>\n",
+       "      <td>0.532189</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.520550</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.626263</td>\n",
+       "      <td>0.462687</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.342852</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.545455</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.548043</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.401640</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.523810</td>\n",
+       "      <td>0.574627</td>\n",
+       "      <td>0.890547</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.359008</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.421384</td>\n",
+       "      <td>0.788235</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.891</td>\n",
+       "      <td>0.528139</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.489808</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.628866</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.327826</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.509804</td>\n",
+       "      <td>0.611765</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.568965</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.445748</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.673469</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.303262</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.534653</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.567901</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.542561</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.633027</td>\n",
+       "      <td>0.514925</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.334571</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.886</td>\n",
+       "      <td>0.508621</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.528722</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.602041</td>\n",
+       "      <td>0.440298</td>\n",
+       "      <td>0.921393</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.341839</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.528302</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.590038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.384665</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.606299</td>\n",
+       "      <td>0.574627</td>\n",
+       "      <td>0.906468</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.314057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.466667</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.534483</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.502705</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.632653</td>\n",
+       "      <td>0.462687</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.337562</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.537634</td>\n",
+       "      <td>0.588235</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.904</td>\n",
+       "      <td>0.586207</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.488476</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.693878</td>\n",
+       "      <td>0.507463</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.342792</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.558333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.466521</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.632075</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.303226</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.538462</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.902</td>\n",
+       "      <td>0.566372</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.447245</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.695652</td>\n",
+       "      <td>0.477612</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.347164</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.510204</td>\n",
+       "      <td>0.588235</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.592593</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.429450</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.660550</td>\n",
+       "      <td>0.537313</td>\n",
+       "      <td>0.915423</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.313667</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.623529</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.888</td>\n",
+       "      <td>0.521367</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.500936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.610000</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.923383</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.378845</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.540000</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>0.602410</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.490258</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.652174</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.914428</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.392229</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.495726</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.887</td>\n",
+       "      <td>0.502203</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.516663</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.612903</td>\n",
+       "      <td>0.425373</td>\n",
+       "      <td>0.922388</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.355745</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.534653</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.548523</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.473883</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.631068</td>\n",
+       "      <td>0.485075</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.329670</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.542056</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.588235</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.490045</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.619835</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.914428</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.358677</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.496000</td>\n",
+       "      <td>0.729412</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.549356</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.458278</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.646465</td>\n",
+       "      <td>0.477612</td>\n",
+       "      <td>0.916418</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.332558</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.504274</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.558333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.485095</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.632075</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.928358</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.338581</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.568421</td>\n",
+       "      <td>0.635294</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.899</td>\n",
+       "      <td>0.607004</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.308932</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.634146</td>\n",
+       "      <td>0.582090</td>\n",
+       "      <td>0.919403</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.237768</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.516393</td>\n",
+       "      <td>0.741176</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>data/hate/pl-10-reddit/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...</td>\n",
+       "      <td>0.906</td>\n",
+       "      <td>0.614754</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.471272</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.681818</td>\n",
+       "      <td>0.559702</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.372592</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.509091</td>\n",
+       "      <td>0.658824</td>\n",
+       "      <td>lstm_ft6_cl8</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>1</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>lstm</td>\n",
+       "      <td>_orig</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>584 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        model_dir_parent  \\\n",
+       "0    data/hate/pl-10-reddit/models/sp25k   \n",
+       "1    data/hate/pl-10-reddit/models/sp25k   \n",
+       "2    data/hate/pl-10-reddit/models/sp25k   \n",
+       "3    data/hate/pl-10-reddit/models/sp25k   \n",
+       "4    data/hate/pl-10-reddit/models/sp25k   \n",
+       "5    data/hate/pl-10-reddit/models/sp25k   \n",
+       "6    data/hate/pl-10-reddit/models/sp25k   \n",
+       "7    data/hate/pl-10-reddit/models/sp25k   \n",
+       "8    data/hate/pl-10-reddit/models/sp25k   \n",
+       "9    data/hate/pl-10-reddit/models/sp25k   \n",
+       "10   data/hate/pl-10-reddit/models/sp25k   \n",
+       "11   data/hate/pl-10-reddit/models/sp25k   \n",
+       "12   data/hate/pl-10-reddit/models/sp25k   \n",
+       "13   data/hate/pl-10-reddit/models/sp25k   \n",
+       "14   data/hate/pl-10-reddit/models/sp25k   \n",
+       "15   data/hate/pl-10-reddit/models/sp25k   \n",
+       "16   data/hate/pl-10-reddit/models/sp25k   \n",
+       "17   data/hate/pl-10-reddit/models/sp25k   \n",
+       "18   data/hate/pl-10-reddit/models/sp25k   \n",
+       "19   data/hate/pl-10-reddit/models/sp25k   \n",
+       "20   data/hate/pl-10-reddit/models/sp25k   \n",
+       "21   data/hate/pl-10-reddit/models/sp25k   \n",
+       "22   data/hate/pl-10-reddit/models/sp25k   \n",
+       "23   data/hate/pl-10-reddit/models/sp25k   \n",
+       "24   data/hate/pl-10-reddit/models/sp25k   \n",
+       "25   data/hate/pl-10-reddit/models/sp25k   \n",
+       "26   data/hate/pl-10-reddit/models/sp25k   \n",
+       "27   data/hate/pl-10-reddit/models/sp25k   \n",
+       "28   data/hate/pl-10-reddit/models/sp25k   \n",
+       "29   data/hate/pl-10-reddit/models/sp25k   \n",
+       "..                                   ...   \n",
+       "306  data/hate/pl-10-reddit/models/sp25k   \n",
+       "307  data/hate/pl-10-reddit/models/sp25k   \n",
+       "308  data/hate/pl-10-reddit/models/sp25k   \n",
+       "309  data/hate/pl-10-reddit/models/sp25k   \n",
+       "310  data/hate/pl-10-reddit/models/sp25k   \n",
+       "311  data/hate/pl-10-reddit/models/sp25k   \n",
+       "312  data/hate/pl-10-reddit/models/sp25k   \n",
+       "313  data/hate/pl-10-reddit/models/sp25k   \n",
+       "314  data/hate/pl-10-reddit/models/sp25k   \n",
+       "315  data/hate/pl-10-reddit/models/sp25k   \n",
+       "316  data/hate/pl-10-reddit/models/sp25k   \n",
+       "317  data/hate/pl-10-reddit/models/sp25k   \n",
+       "318  data/hate/pl-10-reddit/models/sp25k   \n",
+       "319  data/hate/pl-10-reddit/models/sp25k   \n",
+       "320  data/hate/pl-10-reddit/models/sp25k   \n",
+       "321  data/hate/pl-10-reddit/models/sp25k   \n",
+       "322  data/hate/pl-10-reddit/models/sp25k   \n",
+       "323  data/hate/pl-10-reddit/models/sp25k   \n",
+       "324  data/hate/pl-10-reddit/models/sp25k   \n",
+       "325  data/hate/pl-10-reddit/models/sp25k   \n",
+       "326  data/hate/pl-10-reddit/models/sp25k   \n",
+       "327  data/hate/pl-10-reddit/models/sp25k   \n",
+       "328  data/hate/pl-10-reddit/models/sp25k   \n",
+       "329  data/hate/pl-10-reddit/models/sp25k   \n",
+       "330  data/hate/pl-10-reddit/models/sp25k   \n",
+       "331  data/hate/pl-10-reddit/models/sp25k   \n",
+       "332  data/hate/pl-10-reddit/models/sp25k   \n",
+       "333  data/hate/pl-10-reddit/models/sp25k   \n",
+       "334  data/hate/pl-10-reddit/models/sp25k   \n",
+       "335  data/hate/pl-10-reddit/models/sp25k   \n",
+       "\n",
+       "                                            model_name  test Accuracy  \\\n",
+       "0    qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.870   \n",
+       "1    qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.858   \n",
+       "2    qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.853   \n",
+       "3    qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.855   \n",
+       "4    qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.862   \n",
+       "5    qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.854   \n",
+       "6    qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.850   \n",
+       "7    qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.865   \n",
+       "8    qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.871   \n",
+       "9    qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.849   \n",
+       "10   qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-1...          0.876   \n",
+       "11   qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-2...          0.864   \n",
+       "12   qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-3...          0.861   \n",
+       "13   qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-4...          0.883   \n",
+       "14   qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-5...          0.876   \n",
+       "15   qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-6...          0.848   \n",
+       "16   qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-7...          0.856   \n",
+       "17   qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-8...          0.873   \n",
+       "18   qrnn_ft0_cl8_lmseed-0-ftseed-0-clsweightseed-9...          0.863   \n",
+       "19   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.870   \n",
+       "20   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.858   \n",
+       "21   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.853   \n",
+       "22   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.855   \n",
+       "23   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.862   \n",
+       "24   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.854   \n",
+       "25   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.850   \n",
+       "26   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.865   \n",
+       "27   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.871   \n",
+       "28   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-0...          0.849   \n",
+       "29   qrnn_ft0_cl8_lmseed-1-ftseed-0-clsweightseed-1...          0.876   \n",
+       "..                                                 ...            ...   \n",
+       "306  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-5...          0.901   \n",
+       "307  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-5...          0.890   \n",
+       "308  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.900   \n",
+       "309  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.893   \n",
+       "310  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.897   \n",
+       "311  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.895   \n",
+       "312  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.899   \n",
+       "313  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.889   \n",
+       "314  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6...          0.893   \n",
+       "315  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.891   \n",
+       "316  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.873   \n",
+       "317  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.891   \n",
+       "318  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.900   \n",
+       "319  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.895   \n",
+       "320  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.886   \n",
+       "321  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7...          0.893   \n",
+       "322  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.892   \n",
+       "323  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.904   \n",
+       "324  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.894   \n",
+       "325  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.902   \n",
+       "326  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.901   \n",
+       "327  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.888   \n",
+       "328  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8...          0.901   \n",
+       "329  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.887   \n",
+       "330  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.893   \n",
+       "331  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.895   \n",
+       "332  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.895   \n",
+       "333  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.894   \n",
+       "334  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.899   \n",
+       "335  lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9...          0.906   \n",
+       "\n",
+       "     test F1 score bin  test Kappa Linear  test Loss  test Matthews Correff  \\\n",
+       "0             0.577922                NaN   0.456889                    NaN   \n",
+       "1             0.572289                NaN   0.426693                    NaN   \n",
+       "2             0.573913                NaN   0.465223                    NaN   \n",
+       "3             0.577259                NaN   0.483052                    NaN   \n",
+       "4             0.576687                NaN   0.491995                    NaN   \n",
+       "5             0.570588                NaN   0.483143                    NaN   \n",
+       "6             0.561404                NaN   0.519333                    NaN   \n",
+       "7             0.597015                NaN   0.457041                    NaN   \n",
+       "8             0.582524                NaN   0.418411                    NaN   \n",
+       "9             0.557185                NaN   0.482558                    NaN   \n",
+       "10            0.578231                NaN   0.419756                    NaN   \n",
+       "11            0.569620                NaN   0.458027                    NaN   \n",
+       "12            0.574924                NaN   0.477018                    NaN   \n",
+       "13            0.603390                NaN   0.419202                    NaN   \n",
+       "14            0.594771                NaN   0.427007                    NaN   \n",
+       "15            0.530864                NaN   0.488566                    NaN   \n",
+       "16            0.563636                NaN   0.437123                    NaN   \n",
+       "17            0.591640                NaN   0.448317                    NaN   \n",
+       "18            0.550820                NaN   0.450464                    NaN   \n",
+       "19            0.577922                NaN   0.456889                    NaN   \n",
+       "20            0.572289                NaN   0.426693                    NaN   \n",
+       "21            0.573913                NaN   0.465223                    NaN   \n",
+       "22            0.577259                NaN   0.483052                    NaN   \n",
+       "23            0.576687                NaN   0.491995                    NaN   \n",
+       "24            0.570588                NaN   0.483143                    NaN   \n",
+       "25            0.561404                NaN   0.519333                    NaN   \n",
+       "26            0.597015                NaN   0.457041                    NaN   \n",
+       "27            0.582524                NaN   0.418411                    NaN   \n",
+       "28            0.557185                NaN   0.482558                    NaN   \n",
+       "29            0.578231                NaN   0.419756                    NaN   \n",
+       "..                 ...                ...        ...                    ...   \n",
+       "306           0.595918                NaN   0.453026                    NaN   \n",
+       "307           0.576923                NaN   0.412375                    NaN   \n",
+       "308           0.576271                NaN   0.458157                    NaN   \n",
+       "309           0.552301                NaN   0.526710                    NaN   \n",
+       "310           0.542222                NaN   0.378023                    NaN   \n",
+       "311           0.549356                NaN   0.502734                    NaN   \n",
+       "312           0.542986                NaN   0.484061                    NaN   \n",
+       "313           0.561265                NaN   0.478323                    NaN   \n",
+       "314           0.559671                NaN   0.460062                    NaN   \n",
+       "315           0.532189                NaN   0.520550                    NaN   \n",
+       "316           0.548043                NaN   0.401640                    NaN   \n",
+       "317           0.528139                NaN   0.489808                    NaN   \n",
+       "318           0.568965                NaN   0.445748                    NaN   \n",
+       "319           0.567901                NaN   0.542561                    NaN   \n",
+       "320           0.508621                NaN   0.528722                    NaN   \n",
+       "321           0.590038                NaN   0.384665                    NaN   \n",
+       "322           0.534483                NaN   0.502705                    NaN   \n",
+       "323           0.586207                NaN   0.488476                    NaN   \n",
+       "324           0.558333                NaN   0.466521                    NaN   \n",
+       "325           0.566372                NaN   0.447245                    NaN   \n",
+       "326           0.592593                NaN   0.429450                    NaN   \n",
+       "327           0.521367                NaN   0.500936                    NaN   \n",
+       "328           0.602410                NaN   0.490258                    NaN   \n",
+       "329           0.502203                NaN   0.516663                    NaN   \n",
+       "330           0.548523                NaN   0.473883                    NaN   \n",
+       "331           0.588235                NaN   0.490045                    NaN   \n",
+       "332           0.549356                NaN   0.458278                    NaN   \n",
+       "333           0.558333                NaN   0.485095                    NaN   \n",
+       "334           0.607004                NaN   0.308932                    NaN   \n",
+       "335           0.614754                NaN   0.471272                    NaN   \n",
+       "\n",
+       "     test Precision  test Recall  valid Accuracy  ...  valid Loss  \\\n",
+       "0          0.511494     0.664179        0.876617  ...    0.429593   \n",
+       "1          0.479798     0.708955        0.860696  ...    0.434975   \n",
+       "2          0.469194     0.738806        0.849751  ...    0.523841   \n",
+       "3          0.473684     0.738806        0.841791  ...    0.530552   \n",
+       "4          0.489583     0.701493        0.851741  ...    0.488528   \n",
+       "5          0.470874     0.723881        0.829851  ...    0.537976   \n",
+       "6          0.461538     0.716418        0.842786  ...    0.541371   \n",
+       "7          0.497512     0.746269        0.857711  ...    0.452747   \n",
+       "8          0.514286     0.671642        0.874627  ...    0.378810   \n",
+       "9          0.458937     0.708955        0.866667  ...    0.471542   \n",
+       "10         0.531250     0.634328        0.891542  ...    0.345693   \n",
+       "11         0.494505     0.671642        0.873632  ...    0.425387   \n",
+       "12         0.487047     0.701493        0.876617  ...    0.449001   \n",
+       "13         0.552795     0.664179        0.881592  ...    0.407950   \n",
+       "14         0.529070     0.679105        0.881592  ...    0.408002   \n",
+       "15         0.452632     0.641791        0.869652  ...    0.478236   \n",
+       "16         0.474490     0.694030        0.877612  ...    0.403486   \n",
+       "17         0.519774     0.686567        0.884577  ...    0.427392   \n",
+       "18         0.491228     0.626866        0.892537  ...    0.404172   \n",
+       "19         0.511494     0.664179        0.876617  ...    0.429593   \n",
+       "20         0.479798     0.708955        0.860696  ...    0.434975   \n",
+       "21         0.469194     0.738806        0.849751  ...    0.523841   \n",
+       "22         0.473684     0.738806        0.841791  ...    0.530552   \n",
+       "23         0.489583     0.701493        0.851741  ...    0.488528   \n",
+       "24         0.470874     0.723881        0.829851  ...    0.537976   \n",
+       "25         0.461538     0.716418        0.842786  ...    0.541371   \n",
+       "26         0.497512     0.746269        0.857711  ...    0.452747   \n",
+       "27         0.514286     0.671642        0.874627  ...    0.378810   \n",
+       "28         0.458937     0.708955        0.866667  ...    0.471542   \n",
+       "29         0.531250     0.634328        0.891542  ...    0.345693   \n",
+       "..              ...          ...             ...  ...         ...   \n",
+       "306        0.657658     0.544776        0.923383  ...    0.369372   \n",
+       "307        0.595238     0.559702        0.901493  ...    0.341758   \n",
+       "308        0.666667     0.507463        0.920398  ...    0.343866   \n",
+       "309        0.628571     0.492537        0.923383  ...    0.376918   \n",
+       "310        0.670330     0.455224        0.928358  ...    0.245958   \n",
+       "311        0.646465     0.477612        0.917413  ...    0.366041   \n",
+       "312        0.689655     0.447761        0.928358  ...    0.292909   \n",
+       "313        0.596639     0.529851        0.912438  ...    0.371474   \n",
+       "314        0.623853     0.507463        0.916418  ...    0.314330   \n",
+       "315        0.626263     0.462687        0.924378  ...    0.342852   \n",
+       "316        0.523810     0.574627        0.890547  ...    0.359008   \n",
+       "317        0.628866     0.455224        0.917413  ...    0.327826   \n",
+       "318        0.673469     0.492537        0.922388  ...    0.303262   \n",
+       "319        0.633027     0.514925        0.922388  ...    0.334571   \n",
+       "320        0.602041     0.440298        0.921393  ...    0.341839   \n",
+       "321        0.606299     0.574627        0.906468  ...    0.314057   \n",
+       "322        0.632653     0.462687        0.922388  ...    0.337562   \n",
+       "323        0.693878     0.507463        0.922388  ...    0.342792   \n",
+       "324        0.632075     0.500000        0.923383  ...    0.303226   \n",
+       "325        0.695652     0.477612        0.917413  ...    0.347164   \n",
+       "326        0.660550     0.537313        0.915423  ...    0.313667   \n",
+       "327        0.610000     0.455224        0.923383  ...    0.378845   \n",
+       "328        0.652174     0.559702        0.914428  ...    0.392229   \n",
+       "329        0.612903     0.425373        0.922388  ...    0.355745   \n",
+       "330        0.631068     0.485075        0.924378  ...    0.329670   \n",
+       "331        0.619835     0.559702        0.914428  ...    0.358677   \n",
+       "332        0.646465     0.477612        0.916418  ...    0.332558   \n",
+       "333        0.632075     0.500000        0.928358  ...    0.338581   \n",
+       "334        0.634146     0.582090        0.919403  ...    0.237768   \n",
+       "335        0.681818     0.559702        0.917413  ...    0.372592   \n",
+       "\n",
+       "     valid Matthews Correff  valid Precision  valid Recall          arch  \\\n",
+       "0                  0.507218         0.389830      0.811765  qrnn_ft0_cl8   \n",
+       "1                  0.483857         0.358974      0.823529  qrnn_ft0_cl8   \n",
+       "2                  0.477067         0.342857      0.847059  qrnn_ft0_cl8   \n",
+       "3                  0.441885         0.323810      0.800000  qrnn_ft0_cl8   \n",
+       "4                  0.474608         0.344660      0.835294  qrnn_ft0_cl8   \n",
+       "5                  0.435847         0.309735      0.823529  qrnn_ft0_cl8   \n",
+       "6                  0.454872         0.328638      0.823529  qrnn_ft0_cl8   \n",
+       "7                  0.473131         0.352041      0.811765  qrnn_ft0_cl8   \n",
+       "8                  0.492229         0.382857      0.788235  qrnn_ft0_cl8   \n",
+       "9                  0.494311         0.370370      0.823529  qrnn_ft0_cl8   \n",
+       "10                 0.532369         0.425000      0.800000  qrnn_ft0_cl8   \n",
+       "11                 0.484692         0.379310      0.776471  qrnn_ft0_cl8   \n",
+       "12                 0.518262         0.392265      0.835294  qrnn_ft0_cl8   \n",
+       "13                 0.511490         0.400000      0.800000  qrnn_ft0_cl8   \n",
+       "14                 0.500378         0.397590      0.776471  qrnn_ft0_cl8   \n",
+       "15                 0.505266         0.377660      0.835294  qrnn_ft0_cl8   \n",
+       "16                 0.525650         0.395604      0.847059  qrnn_ft0_cl8   \n",
+       "17                 0.539378         0.411429      0.847059  qrnn_ft0_cl8   \n",
+       "18                 0.539953         0.428571      0.811765  qrnn_ft0_cl8   \n",
+       "19                 0.507218         0.389830      0.811765  qrnn_ft0_cl8   \n",
+       "20                 0.483857         0.358974      0.823529  qrnn_ft0_cl8   \n",
+       "21                 0.477067         0.342857      0.847059  qrnn_ft0_cl8   \n",
+       "22                 0.441885         0.323810      0.800000  qrnn_ft0_cl8   \n",
+       "23                 0.474608         0.344660      0.835294  qrnn_ft0_cl8   \n",
+       "24                 0.435847         0.309735      0.823529  qrnn_ft0_cl8   \n",
+       "25                 0.454872         0.328638      0.823529  qrnn_ft0_cl8   \n",
+       "26                 0.473131         0.352041      0.811765  qrnn_ft0_cl8   \n",
+       "27                 0.492229         0.382857      0.788235  qrnn_ft0_cl8   \n",
+       "28                 0.494311         0.370370      0.823529  qrnn_ft0_cl8   \n",
+       "29                 0.532369         0.425000      0.800000  qrnn_ft0_cl8   \n",
+       "..                      ...              ...           ...           ...   \n",
+       "306                     NaN         0.538462      0.658824  lstm_ft6_cl8   \n",
+       "307                     NaN         0.446970      0.694118  lstm_ft6_cl8   \n",
+       "308                     NaN         0.523364      0.658824  lstm_ft6_cl8   \n",
+       "309                     NaN         0.540816      0.623529  lstm_ft6_cl8   \n",
+       "310                     NaN         0.564356      0.670588  lstm_ft6_cl8   \n",
+       "311                     NaN         0.509259      0.647059  lstm_ft6_cl8   \n",
+       "312                     NaN         0.573034      0.600000  lstm_ft6_cl8   \n",
+       "313                     NaN         0.486726      0.647059  lstm_ft6_cl8   \n",
+       "314                     NaN         0.504425      0.670588  lstm_ft6_cl8   \n",
+       "315                     NaN         0.545455      0.635294  lstm_ft6_cl8   \n",
+       "316                     NaN         0.421384      0.788235  lstm_ft6_cl8   \n",
+       "317                     NaN         0.509804      0.611765  lstm_ft6_cl8   \n",
+       "318                     NaN         0.534653      0.635294  lstm_ft6_cl8   \n",
+       "319                     NaN         0.533333      0.658824  lstm_ft6_cl8   \n",
+       "320                     NaN         0.528302      0.658824  lstm_ft6_cl8   \n",
+       "321                     NaN         0.466667      0.741176  lstm_ft6_cl8   \n",
+       "322                     NaN         0.537634      0.588235  lstm_ft6_cl8   \n",
+       "323                     NaN         0.533333      0.658824  lstm_ft6_cl8   \n",
+       "324                     NaN         0.538462      0.658824  lstm_ft6_cl8   \n",
+       "325                     NaN         0.510204      0.588235  lstm_ft6_cl8   \n",
+       "326                     NaN         0.500000      0.623529  lstm_ft6_cl8   \n",
+       "327                     NaN         0.540000      0.635294  lstm_ft6_cl8   \n",
+       "328                     NaN         0.495726      0.682353  lstm_ft6_cl8   \n",
+       "329                     NaN         0.534653      0.635294  lstm_ft6_cl8   \n",
+       "330                     NaN         0.542056      0.682353  lstm_ft6_cl8   \n",
+       "331                     NaN         0.496000      0.729412  lstm_ft6_cl8   \n",
+       "332                     NaN         0.504274      0.694118  lstm_ft6_cl8   \n",
+       "333                     NaN         0.568421      0.635294  lstm_ft6_cl8   \n",
+       "334                     NaN         0.516393      0.741176  lstm_ft6_cl8   \n",
+       "335                     NaN         0.509091      0.658824  lstm_ft6_cl8   \n",
+       "\n",
+       "         ds lmseed fine_tune model exp_type  \n",
+       "0    reddit      0        no  qrnn    _orig  \n",
+       "1    reddit      0        no  qrnn    _orig  \n",
+       "2    reddit      0        no  qrnn    _orig  \n",
+       "3    reddit      0        no  qrnn    _orig  \n",
+       "4    reddit      0        no  qrnn    _orig  \n",
+       "5    reddit      0        no  qrnn    _orig  \n",
+       "6    reddit      0        no  qrnn    _orig  \n",
+       "7    reddit      0        no  qrnn    _orig  \n",
+       "8    reddit      0        no  qrnn    _orig  \n",
+       "9    reddit      0        no  qrnn    _orig  \n",
+       "10   reddit      0        no  qrnn    _orig  \n",
+       "11   reddit      0        no  qrnn    _orig  \n",
+       "12   reddit      0        no  qrnn    _orig  \n",
+       "13   reddit      0        no  qrnn    _orig  \n",
+       "14   reddit      0        no  qrnn    _orig  \n",
+       "15   reddit      0        no  qrnn    _orig  \n",
+       "16   reddit      0        no  qrnn    _orig  \n",
+       "17   reddit      0        no  qrnn    _orig  \n",
+       "18   reddit      0        no  qrnn    _orig  \n",
+       "19   reddit      1        no  qrnn    _orig  \n",
+       "20   reddit      1        no  qrnn    _orig  \n",
+       "21   reddit      1        no  qrnn    _orig  \n",
+       "22   reddit      1        no  qrnn    _orig  \n",
+       "23   reddit      1        no  qrnn    _orig  \n",
+       "24   reddit      1        no  qrnn    _orig  \n",
+       "25   reddit      1        no  qrnn    _orig  \n",
+       "26   reddit      1        no  qrnn    _orig  \n",
+       "27   reddit      1        no  qrnn    _orig  \n",
+       "28   reddit      1        no  qrnn    _orig  \n",
+       "29   reddit      1        no  qrnn    _orig  \n",
+       "..      ...    ...       ...   ...      ...  \n",
+       "306  reddit      1       yes  lstm    _orig  \n",
+       "307  reddit      1       yes  lstm    _orig  \n",
+       "308  reddit      1       yes  lstm    _orig  \n",
+       "309  reddit      1       yes  lstm    _orig  \n",
+       "310  reddit      1       yes  lstm    _orig  \n",
+       "311  reddit      1       yes  lstm    _orig  \n",
+       "312  reddit      1       yes  lstm    _orig  \n",
+       "313  reddit      1       yes  lstm    _orig  \n",
+       "314  reddit      1       yes  lstm    _orig  \n",
+       "315  reddit      1       yes  lstm    _orig  \n",
+       "316  reddit      1       yes  lstm    _orig  \n",
+       "317  reddit      1       yes  lstm    _orig  \n",
+       "318  reddit      1       yes  lstm    _orig  \n",
+       "319  reddit      1       yes  lstm    _orig  \n",
+       "320  reddit      1       yes  lstm    _orig  \n",
+       "321  reddit      1       yes  lstm    _orig  \n",
+       "322  reddit      1       yes  lstm    _orig  \n",
+       "323  reddit      1       yes  lstm    _orig  \n",
+       "324  reddit      1       yes  lstm    _orig  \n",
+       "325  reddit      1       yes  lstm    _orig  \n",
+       "326  reddit      1       yes  lstm    _orig  \n",
+       "327  reddit      1       yes  lstm    _orig  \n",
+       "328  reddit      1       yes  lstm    _orig  \n",
+       "329  reddit      1       yes  lstm    _orig  \n",
+       "330  reddit      1       yes  lstm    _orig  \n",
+       "331  reddit      1       yes  lstm    _orig  \n",
+       "332  reddit      1       yes  lstm    _orig  \n",
+       "333  reddit      1       yes  lstm    _orig  \n",
+       "334  reddit      1       yes  lstm    _orig  \n",
+       "335  reddit      1       yes  lstm    _orig  \n",
+       "\n",
+       "[584 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:fastaiv1]",
+   "language": "python",
+   "name": "conda-env-fastaiv1-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/experiments/poleval19/poleval-small-review.ipynb b/experiments/poleval19/poleval-small-review.ipynb
new file mode 100644
index 0000000..ddc9be2
--- /dev/null
+++ b/experiments/poleval19/poleval-small-review.ipynb
@@ -0,0 +1,1206 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastai.text import *\n",
+    "import IPython.display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model_dir_parent</th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweigh...</td>\n",
+       "      <td>0.883</td>\n",
+       "      <td>0.450704</td>\n",
+       "      <td>0.552381</td>\n",
+       "      <td>0.607595</td>\n",
+       "      <td>0.358209</td>\n",
+       "      <td>0.926368</td>\n",
+       "      <td>0.593407</td>\n",
+       "      <td>0.351496</td>\n",
+       "      <td>0.556701</td>\n",
+       "      <td>0.635294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweigh...</td>\n",
+       "      <td>0.882</td>\n",
+       "      <td>0.491379</td>\n",
+       "      <td>0.511904</td>\n",
+       "      <td>0.581633</td>\n",
+       "      <td>0.425373</td>\n",
+       "      <td>0.915423</td>\n",
+       "      <td>0.545455</td>\n",
+       "      <td>0.374401</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweigh...</td>\n",
+       "      <td>0.881</td>\n",
+       "      <td>0.475771</td>\n",
+       "      <td>0.539321</td>\n",
+       "      <td>0.580645</td>\n",
+       "      <td>0.402985</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>0.556150</td>\n",
+       "      <td>0.371519</td>\n",
+       "      <td>0.509804</td>\n",
+       "      <td>0.611765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweigh...</td>\n",
+       "      <td>0.874</td>\n",
+       "      <td>0.394231</td>\n",
+       "      <td>0.581611</td>\n",
+       "      <td>0.554054</td>\n",
+       "      <td>0.305970</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>0.563218</td>\n",
+       "      <td>0.346428</td>\n",
+       "      <td>0.550562</td>\n",
+       "      <td>0.576471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweigh...</td>\n",
+       "      <td>0.880</td>\n",
+       "      <td>0.523809</td>\n",
+       "      <td>0.498890</td>\n",
+       "      <td>0.559322</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.916418</td>\n",
+       "      <td>0.567010</td>\n",
+       "      <td>0.393184</td>\n",
+       "      <td>0.504587</td>\n",
+       "      <td>0.647059</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    model_dir_parent  \\\n",
+       "0  data/hate/pl-10-wiki/models/sp25k   \n",
+       "1  data/hate/pl-10-wiki/models/sp25k   \n",
+       "2  data/hate/pl-10-wiki/models/sp25k   \n",
+       "3  data/hate/pl-10-wiki/models/sp25k   \n",
+       "4  data/hate/pl-10-wiki/models/sp25k   \n",
+       "\n",
+       "                                          model_name  test Accuracy  \\\n",
+       "0  lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweigh...          0.883   \n",
+       "1  lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweigh...          0.882   \n",
+       "2  lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweigh...          0.881   \n",
+       "3  lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweigh...          0.874   \n",
+       "4  lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweigh...          0.880   \n",
+       "\n",
+       "   test F1 score bin  test Loss  test Precision  test Recall  valid Accuracy  \\\n",
+       "0           0.450704   0.552381        0.607595     0.358209        0.926368   \n",
+       "1           0.491379   0.511904        0.581633     0.425373        0.915423   \n",
+       "2           0.475771   0.539321        0.580645     0.402985        0.917413   \n",
+       "3           0.394231   0.581611        0.554054     0.305970        0.924378   \n",
+       "4           0.523809   0.498890        0.559322     0.492537        0.916418   \n",
+       "\n",
+       "   valid F1 score bin  valid Loss  valid Precision  valid Recall  \n",
+       "0            0.593407    0.351496         0.556701      0.635294  \n",
+       "1            0.545455    0.374401         0.500000      0.600000  \n",
+       "2            0.556150    0.371519         0.509804      0.611765  \n",
+       "3            0.563218    0.346428         0.550562      0.576471  \n",
+       "4            0.567010    0.393184         0.504587      0.647059  "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sdf = pd.read_csv(\"./poleval-small.csv\", index_col=0)\n",
+    "sdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.882550</td>\n",
+       "      <td>0.497908</td>\n",
+       "      <td>0.510545</td>\n",
+       "      <td>0.585865</td>\n",
+       "      <td>0.439552</td>\n",
+       "      <td>0.917512</td>\n",
+       "      <td>0.552939</td>\n",
+       "      <td>0.374458</td>\n",
+       "      <td>0.513085</td>\n",
+       "      <td>0.602941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.005671</td>\n",
+       "      <td>0.042723</td>\n",
+       "      <td>0.043948</td>\n",
+       "      <td>0.035460</td>\n",
+       "      <td>0.071943</td>\n",
+       "      <td>0.006124</td>\n",
+       "      <td>0.018680</td>\n",
+       "      <td>0.022464</td>\n",
+       "      <td>0.031414</td>\n",
+       "      <td>0.040192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.873000</td>\n",
+       "      <td>0.394231</td>\n",
+       "      <td>0.419434</td>\n",
+       "      <td>0.522581</td>\n",
+       "      <td>0.305970</td>\n",
+       "      <td>0.907463</td>\n",
+       "      <td>0.508108</td>\n",
+       "      <td>0.340201</td>\n",
+       "      <td>0.463636</td>\n",
+       "      <td>0.552941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.879500</td>\n",
+       "      <td>0.479205</td>\n",
+       "      <td>0.484231</td>\n",
+       "      <td>0.558915</td>\n",
+       "      <td>0.408582</td>\n",
+       "      <td>0.913433</td>\n",
+       "      <td>0.543970</td>\n",
+       "      <td>0.358262</td>\n",
+       "      <td>0.490566</td>\n",
+       "      <td>0.576471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.881500</td>\n",
+       "      <td>0.502032</td>\n",
+       "      <td>0.507925</td>\n",
+       "      <td>0.581139</td>\n",
+       "      <td>0.436567</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>0.554117</td>\n",
+       "      <td>0.372960</td>\n",
+       "      <td>0.510110</td>\n",
+       "      <td>0.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.885250</td>\n",
+       "      <td>0.526758</td>\n",
+       "      <td>0.546827</td>\n",
+       "      <td>0.606686</td>\n",
+       "      <td>0.479478</td>\n",
+       "      <td>0.922886</td>\n",
+       "      <td>0.564166</td>\n",
+       "      <td>0.392142</td>\n",
+       "      <td>0.541209</td>\n",
+       "      <td>0.623529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.894000</td>\n",
+       "      <td>0.564885</td>\n",
+       "      <td>0.581611</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.604478</td>\n",
+       "      <td>0.926368</td>\n",
+       "      <td>0.593407</td>\n",
+       "      <td>0.410534</td>\n",
+       "      <td>0.559524</td>\n",
+       "      <td>0.729412</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Loss  test Precision  \\\n",
+       "count      20.000000          20.000000  20.000000       20.000000   \n",
+       "mean        0.882550           0.497908   0.510545        0.585865   \n",
+       "std         0.005671           0.042723   0.043948        0.035460   \n",
+       "min         0.873000           0.394231   0.419434        0.522581   \n",
+       "25%         0.879500           0.479205   0.484231        0.558915   \n",
+       "50%         0.881500           0.502032   0.507925        0.581139   \n",
+       "75%         0.885250           0.526758   0.546827        0.606686   \n",
+       "max         0.894000           0.564885   0.581611        0.666667   \n",
+       "\n",
+       "       test Recall  valid Accuracy  valid F1 score bin  valid Loss  \\\n",
+       "count    20.000000       20.000000           20.000000   20.000000   \n",
+       "mean      0.439552        0.917512            0.552939    0.374458   \n",
+       "std       0.071943        0.006124            0.018680    0.022464   \n",
+       "min       0.305970        0.907463            0.508108    0.340201   \n",
+       "25%       0.408582        0.913433            0.543970    0.358262   \n",
+       "50%       0.436567        0.917413            0.554117    0.372960   \n",
+       "75%       0.479478        0.922886            0.564166    0.392142   \n",
+       "max       0.604478        0.926368            0.593407    0.410534   \n",
+       "\n",
+       "       valid Precision  valid Recall  \n",
+       "count        20.000000     20.000000  \n",
+       "mean          0.513085      0.602941  \n",
+       "std           0.031414      0.040192  \n",
+       "min           0.463636      0.552941  \n",
+       "25%           0.490566      0.576471  \n",
+       "50%           0.510110      0.600000  \n",
+       "75%           0.541209      0.623529  \n",
+       "max           0.559524      0.729412  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sdf.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.883400</td>\n",
+       "      <td>0.525926</td>\n",
+       "      <td>0.476290</td>\n",
+       "      <td>0.582248</td>\n",
+       "      <td>0.485075</td>\n",
+       "      <td>0.913930</td>\n",
+       "      <td>0.544289</td>\n",
+       "      <td>0.380629</td>\n",
+       "      <td>0.495108</td>\n",
+       "      <td>0.608235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.006363</td>\n",
+       "      <td>0.024596</td>\n",
+       "      <td>0.029162</td>\n",
+       "      <td>0.038469</td>\n",
+       "      <td>0.058019</td>\n",
+       "      <td>0.005826</td>\n",
+       "      <td>0.018883</td>\n",
+       "      <td>0.026743</td>\n",
+       "      <td>0.028789</td>\n",
+       "      <td>0.048996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.873000</td>\n",
+       "      <td>0.495798</td>\n",
+       "      <td>0.419434</td>\n",
+       "      <td>0.522581</td>\n",
+       "      <td>0.425373</td>\n",
+       "      <td>0.907463</td>\n",
+       "      <td>0.508108</td>\n",
+       "      <td>0.340201</td>\n",
+       "      <td>0.463636</td>\n",
+       "      <td>0.552941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.878500</td>\n",
+       "      <td>0.504658</td>\n",
+       "      <td>0.469131</td>\n",
+       "      <td>0.557005</td>\n",
+       "      <td>0.440298</td>\n",
+       "      <td>0.909453</td>\n",
+       "      <td>0.539147</td>\n",
+       "      <td>0.361620</td>\n",
+       "      <td>0.470804</td>\n",
+       "      <td>0.579412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.885000</td>\n",
+       "      <td>0.526767</td>\n",
+       "      <td>0.483323</td>\n",
+       "      <td>0.582640</td>\n",
+       "      <td>0.470149</td>\n",
+       "      <td>0.913433</td>\n",
+       "      <td>0.544503</td>\n",
+       "      <td>0.389058</td>\n",
+       "      <td>0.490566</td>\n",
+       "      <td>0.605882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.886000</td>\n",
+       "      <td>0.539330</td>\n",
+       "      <td>0.493803</td>\n",
+       "      <td>0.603777</td>\n",
+       "      <td>0.513060</td>\n",
+       "      <td>0.918159</td>\n",
+       "      <td>0.551703</td>\n",
+       "      <td>0.401019</td>\n",
+       "      <td>0.515136</td>\n",
+       "      <td>0.620588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.894000</td>\n",
+       "      <td>0.564885</td>\n",
+       "      <td>0.509157</td>\n",
+       "      <td>0.655556</td>\n",
+       "      <td>0.604478</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.410534</td>\n",
+       "      <td>0.549451</td>\n",
+       "      <td>0.729412</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Loss  test Precision  \\\n",
+       "count      10.000000          10.000000  10.000000       10.000000   \n",
+       "mean        0.883400           0.525926   0.476290        0.582248   \n",
+       "std         0.006363           0.024596   0.029162        0.038469   \n",
+       "min         0.873000           0.495798   0.419434        0.522581   \n",
+       "25%         0.878500           0.504658   0.469131        0.557005   \n",
+       "50%         0.885000           0.526767   0.483323        0.582640   \n",
+       "75%         0.886000           0.539330   0.493803        0.603777   \n",
+       "max         0.894000           0.564885   0.509157        0.655556   \n",
+       "\n",
+       "       test Recall  valid Accuracy  valid F1 score bin  valid Loss  \\\n",
+       "count    10.000000       10.000000           10.000000   10.000000   \n",
+       "mean      0.485075        0.913930            0.544289    0.380629   \n",
+       "std       0.058019        0.005826            0.018883    0.026743   \n",
+       "min       0.425373        0.907463            0.508108    0.340201   \n",
+       "25%       0.440298        0.909453            0.539147    0.361620   \n",
+       "50%       0.470149        0.913433            0.544503    0.389058   \n",
+       "75%       0.513060        0.918159            0.551703    0.401019   \n",
+       "max       0.604478        0.924378            0.571429    0.410534   \n",
+       "\n",
+       "       valid Precision  valid Recall  \n",
+       "count        10.000000     10.000000  \n",
+       "mean          0.495108      0.608235  \n",
+       "std           0.028789      0.048996  \n",
+       "min           0.463636      0.552941  \n",
+       "25%           0.470804      0.579412  \n",
+       "50%           0.490566      0.605882  \n",
+       "75%           0.515136      0.620588  \n",
+       "max           0.549451      0.729412  "
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sdf[sdf[\"model_name\"].str.contains(\"lmseed-1\")&sdf[\"model_dir_parent\"].str.contains(\"wiki\")].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "scrolled": false
+   },
+   "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",
+       "        vertical-align: top;\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>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.881700</td>\n",
+       "      <td>0.469891</td>\n",
+       "      <td>0.544800</td>\n",
+       "      <td>0.589483</td>\n",
+       "      <td>0.394030</td>\n",
+       "      <td>0.921095</td>\n",
+       "      <td>0.561590</td>\n",
+       "      <td>0.368288</td>\n",
+       "      <td>0.531062</td>\n",
+       "      <td>0.597647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.005078</td>\n",
+       "      <td>0.038780</td>\n",
+       "      <td>0.024888</td>\n",
+       "      <td>0.033846</td>\n",
+       "      <td>0.054363</td>\n",
+       "      <td>0.004091</td>\n",
+       "      <td>0.014622</td>\n",
+       "      <td>0.016296</td>\n",
+       "      <td>0.023161</td>\n",
+       "      <td>0.030779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.874000</td>\n",
+       "      <td>0.394231</td>\n",
+       "      <td>0.498890</td>\n",
+       "      <td>0.554054</td>\n",
+       "      <td>0.305970</td>\n",
+       "      <td>0.915423</td>\n",
+       "      <td>0.541436</td>\n",
+       "      <td>0.343434</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.552941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.880250</td>\n",
+       "      <td>0.443662</td>\n",
+       "      <td>0.536749</td>\n",
+       "      <td>0.564228</td>\n",
+       "      <td>0.352612</td>\n",
+       "      <td>0.917413</td>\n",
+       "      <td>0.556166</td>\n",
+       "      <td>0.355985</td>\n",
+       "      <td>0.509957</td>\n",
+       "      <td>0.576471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.881000</td>\n",
+       "      <td>0.478060</td>\n",
+       "      <td>0.548678</td>\n",
+       "      <td>0.581139</td>\n",
+       "      <td>0.406716</td>\n",
+       "      <td>0.921891</td>\n",
+       "      <td>0.560018</td>\n",
+       "      <td>0.370960</td>\n",
+       "      <td>0.534231</td>\n",
+       "      <td>0.588235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.882750</td>\n",
+       "      <td>0.490383</td>\n",
+       "      <td>0.561136</td>\n",
+       "      <td>0.604430</td>\n",
+       "      <td>0.423507</td>\n",
+       "      <td>0.924378</td>\n",
+       "      <td>0.566062</td>\n",
+       "      <td>0.378969</td>\n",
+       "      <td>0.550562</td>\n",
+       "      <td>0.620588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.894000</td>\n",
+       "      <td>0.523809</td>\n",
+       "      <td>0.581611</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.492537</td>\n",
+       "      <td>0.926368</td>\n",
+       "      <td>0.593407</td>\n",
+       "      <td>0.393184</td>\n",
+       "      <td>0.559524</td>\n",
+       "      <td>0.647059</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       test Accuracy  test F1 score bin  test Loss  test Precision  \\\n",
+       "count      10.000000          10.000000  10.000000       10.000000   \n",
+       "mean        0.881700           0.469891   0.544800        0.589483   \n",
+       "std         0.005078           0.038780   0.024888        0.033846   \n",
+       "min         0.874000           0.394231   0.498890        0.554054   \n",
+       "25%         0.880250           0.443662   0.536749        0.564228   \n",
+       "50%         0.881000           0.478060   0.548678        0.581139   \n",
+       "75%         0.882750           0.490383   0.561136        0.604430   \n",
+       "max         0.894000           0.523809   0.581611        0.666667   \n",
+       "\n",
+       "       test Recall  valid Accuracy  valid F1 score bin  valid Loss  \\\n",
+       "count    10.000000       10.000000           10.000000   10.000000   \n",
+       "mean      0.394030        0.921095            0.561590    0.368288   \n",
+       "std       0.054363        0.004091            0.014622    0.016296   \n",
+       "min       0.305970        0.915423            0.541436    0.343434   \n",
+       "25%       0.352612        0.917413            0.556166    0.355985   \n",
+       "50%       0.406716        0.921891            0.560018    0.370960   \n",
+       "75%       0.423507        0.924378            0.566062    0.378969   \n",
+       "max       0.492537        0.926368            0.593407    0.393184   \n",
+       "\n",
+       "       valid Precision  valid Recall  \n",
+       "count        10.000000     10.000000  \n",
+       "mean          0.531062      0.597647  \n",
+       "std           0.023161      0.030779  \n",
+       "min           0.500000      0.552941  \n",
+       "25%           0.509957      0.576471  \n",
+       "50%           0.534231      0.588235  \n",
+       "75%           0.550562      0.620588  \n",
+       "max           0.559524      0.647059  "
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sdf[sdf[\"model_name\"].str.contains(\"lmseed-0\")&sdf[\"model_dir_parent\"].str.contains(\"wiki\")].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdf[\"key\"] = sdf[\"model_name\"].str.replace(\"lstm_small_ft6_el20_\", \"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     lmseed-0-ftseed-0-clsweightseed-0-clstrainseed...\n",
+       "1     lmseed-0-ftseed-0-clsweightseed-1-clstrainseed...\n",
+       "2     lmseed-0-ftseed-0-clsweightseed-2-clstrainseed...\n",
+       "3     lmseed-0-ftseed-0-clsweightseed-3-clstrainseed...\n",
+       "4     lmseed-0-ftseed-0-clsweightseed-4-clstrainseed...\n",
+       "5     lmseed-0-ftseed-0-clsweightseed-5-clstrainseed...\n",
+       "6     lmseed-0-ftseed-0-clsweightseed-6-clstrainseed...\n",
+       "7     lmseed-0-ftseed-0-clsweightseed-7-clstrainseed...\n",
+       "8     lmseed-0-ftseed-0-clsweightseed-8-clstrainseed...\n",
+       "9     lmseed-0-ftseed-0-clsweightseed-9-clstrainseed...\n",
+       "10    lmseed-1-ftseed-0-clsweightseed-0-clstrainseed...\n",
+       "11    lmseed-1-ftseed-0-clsweightseed-1-clstrainseed...\n",
+       "12    lmseed-1-ftseed-0-clsweightseed-2-clstrainseed...\n",
+       "13    lmseed-1-ftseed-0-clsweightseed-3-clstrainseed...\n",
+       "14    lmseed-1-ftseed-0-clsweightseed-4-clstrainseed...\n",
+       "15    lmseed-1-ftseed-0-clsweightseed-5-clstrainseed...\n",
+       "16    lmseed-1-ftseed-0-clsweightseed-6-clstrainseed...\n",
+       "17    lmseed-1-ftseed-0-clsweightseed-7-clstrainseed...\n",
+       "18    lmseed-1-ftseed-0-clsweightseed-8-clstrainseed...\n",
+       "19    lmseed-1-ftseed-0-clsweightseed-9-clstrainseed...\n",
+       "Name: key, dtype: object"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sdf[\"key\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "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>model_dir_parent</th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>test Accuracy</th>\n",
+       "      <th>test F1 score bin</th>\n",
+       "      <th>test Kappa Linear</th>\n",
+       "      <th>test Loss</th>\n",
+       "      <th>test Matthews Correff</th>\n",
+       "      <th>test Precision</th>\n",
+       "      <th>test Recall</th>\n",
+       "      <th>valid Accuracy</th>\n",
+       "      <th>valid F1 score bin</th>\n",
+       "      <th>valid Kappa Linear</th>\n",
+       "      <th>valid Loss</th>\n",
+       "      <th>valid Matthews Correff</th>\n",
+       "      <th>valid Precision</th>\n",
+       "      <th>valid Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.786</td>\n",
+       "      <td>0.513636</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.644837</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.369281</td>\n",
+       "      <td>0.843284</td>\n",
+       "      <td>0.790050</td>\n",
+       "      <td>0.418733</td>\n",
+       "      <td>0.332226</td>\n",
+       "      <td>0.708696</td>\n",
+       "      <td>0.419594</td>\n",
+       "      <td>0.273381</td>\n",
+       "      <td>0.894118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.544681</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.446630</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.633663</td>\n",
+       "      <td>0.477612</td>\n",
+       "      <td>0.925373</td>\n",
+       "      <td>0.594595</td>\n",
+       "      <td>0.553796</td>\n",
+       "      <td>0.335345</td>\n",
+       "      <td>0.556015</td>\n",
+       "      <td>0.550000</td>\n",
+       "      <td>0.647059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.497959</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.510850</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.549550</td>\n",
+       "      <td>0.455224</td>\n",
+       "      <td>0.911443</td>\n",
+       "      <td>0.557214</td>\n",
+       "      <td>0.509312</td>\n",
+       "      <td>0.366872</td>\n",
+       "      <td>0.516918</td>\n",
+       "      <td>0.482759</td>\n",
+       "      <td>0.658824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.890</td>\n",
+       "      <td>0.563492</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.439980</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.601695</td>\n",
+       "      <td>0.529851</td>\n",
+       "      <td>0.909453</td>\n",
+       "      <td>0.551724</td>\n",
+       "      <td>0.502840</td>\n",
+       "      <td>0.391641</td>\n",
+       "      <td>0.511217</td>\n",
+       "      <td>0.474576</td>\n",
+       "      <td>0.658824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>data/hate/pl-10-wiki/models/sp25k</td>\n",
+       "      <td>lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...</td>\n",
+       "      <td>0.886</td>\n",
+       "      <td>0.551181</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.473400</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>0.522388</td>\n",
+       "      <td>0.918408</td>\n",
+       "      <td>0.590000</td>\n",
+       "      <td>0.545825</td>\n",
+       "      <td>0.357952</td>\n",
+       "      <td>0.553520</td>\n",
+       "      <td>0.513043</td>\n",
+       "      <td>0.694118</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      model_dir_parent  \\\n",
+       "190  data/hate/pl-10-wiki/models/sp25k   \n",
+       "191  data/hate/pl-10-wiki/models/sp25k   \n",
+       "192  data/hate/pl-10-wiki/models/sp25k   \n",
+       "193  data/hate/pl-10-wiki/models/sp25k   \n",
+       "194  data/hate/pl-10-wiki/models/sp25k   \n",
+       "\n",
+       "                                            model_name  test Accuracy  \\\n",
+       "190  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.786   \n",
+       "191  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.893   \n",
+       "192  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.877   \n",
+       "193  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.890   \n",
+       "194  lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0...          0.886   \n",
+       "\n",
+       "     test F1 score bin  test Kappa Linear  test Loss  test Matthews Correff  \\\n",
+       "190           0.513636                NaN   0.644837                    NaN   \n",
+       "191           0.544681                NaN   0.446630                    NaN   \n",
+       "192           0.497959                NaN   0.510850                    NaN   \n",
+       "193           0.563492                NaN   0.439980                    NaN   \n",
+       "194           0.551181                NaN   0.473400                    NaN   \n",
+       "\n",
+       "     test Precision  test Recall  valid Accuracy  valid F1 score bin  \\\n",
+       "190        0.369281     0.843284        0.790050            0.418733   \n",
+       "191        0.633663     0.477612        0.925373            0.594595   \n",
+       "192        0.549550     0.455224        0.911443            0.557214   \n",
+       "193        0.601695     0.529851        0.909453            0.551724   \n",
+       "194        0.583333     0.522388        0.918408            0.590000   \n",
+       "\n",
+       "     valid Kappa Linear  valid Loss  valid Matthews Correff  valid Precision  \\\n",
+       "190            0.332226    0.708696                0.419594         0.273381   \n",
+       "191            0.553796    0.335345                0.556015         0.550000   \n",
+       "192            0.509312    0.366872                0.516918         0.482759   \n",
+       "193            0.502840    0.391641                0.511217         0.474576   \n",
+       "194            0.545825    0.357952                0.553520         0.513043   \n",
+       "\n",
+       "     valid Recall  \n",
+       "190      0.894118  \n",
+       "191      0.647059  \n",
+       "192      0.658824  \n",
+       "193      0.658824  \n",
+       "194      0.694118  "
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fdf = pd.read_csv(\"./poleval.csv\", index_col=0)\n",
+    "fdf = fdf[fdf[\"model_dir_parent\"].str.contains(\"wiki\") & fdf[\"model_name\"].str.contains(\"lstm_ft6_cl8\")]\n",
+    "fdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fdf[\"key\"] = fdf[\"model_name\"].str.replace(\"lstm_ft6_cl8_\", \"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mdf = sdf.merge(fdf, how=\"inner\", on=\"key\", suffixes=[\" small\", \" regular\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "orderby=\"valid F1 score bin small\"\n",
+    "ycolumn=\"valid F1 score bin regular\"\n",
+    "xy = mdf.sort_values(orderby)\n",
+    "plt.plot(xy[orderby], xy[ycolumn], 'b.')\n",
+    "# plt.plot(xy[orderby], xy[\"test Precision\"], 'r.')\n",
+    "# plt.plot(xy[orderby], xy[\"test Recall\"], 'g.')\n",
+    "plt.xlabel(orderby)\n",
+    "plt.ylabel(ycolumn);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "orderby=\"test F1 score bin small\"\n",
+    "ycolumn=\"test F1 score bin regular\"\n",
+    "xy = fdf.sort_values(orderby)\n",
+    "plt.plot(xy[orderby], xy[ycolumn], 'b.')\n",
+    "# plt.plot(xy[orderby], xy[\"test Precision\"], 'r.')\n",
+    "# plt.plot(xy[orderby], xy[\"test Recall\"], 'g.')\n",
+    "plt.xlabel(orderby)\n",
+    "plt.ylabel(ycolumn);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import pearsonr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.3301579878771195, 0.15512150357244367)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pearsonr(mdf[\"test F1 score bin small\"], mdf[\"test F1 score bin regular\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.10902654391320507, 0.6472724699025244)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pearsonr(mdf[\"valid F1 score bin small\"], mdf[\"valid F1 score bin regular\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "orderby=\"valid Recall\"\n",
+    "ycolumn=\"test F1 score bin\"\n",
+    "xy = fdf.sort_values(orderby)\n",
+    "plt.plot(xy[orderby], xy[ycolumn], 'b.')\n",
+    "plt.xlabel(orderby)\n",
+    "plt.ylabel(ycolumn);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orderby=\"valid Accuracy\"\n",
+    "ycolumn=\"test Accuracy\"\n",
+    "xy = df.sort_values(orderby)\n",
+    "plt.plot(xy[orderby], xy[ycolumn], 'b.-')\n",
+    "plt.xlabel(orderby)\n",
+    "plt.ylabel(ycolumn);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orderby=\"valid Accuracy\"\n",
+    "ycolumn=\"test Accuracy\"\n",
+    "xy = df.sort_values(orderby)\n",
+    "plt.plot(xy[orderby], xy[ycolumn], 'bo')\n",
+    "plt.xlabel(orderby)\n",
+    "plt.ylabel(ycolumn);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IPython.display.FileLink(\"../poleval.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:fastaiv1]",
+   "language": "python",
+   "name": "conda-env-fastaiv1-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/experiments/poleval19/poleval-small.csv b/experiments/poleval19/poleval-small.csv
new file mode 100644
index 0000000..8af7e42
--- /dev/null
+++ b/experiments/poleval19/poleval-small.csv
@@ -0,0 +1,21 @@
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diff --git a/experiments/poleval19/poleval.csv b/experiments/poleval19/poleval.csv
new file mode 100644
index 0000000..7a70955
--- /dev/null
+++ b/experiments/poleval19/poleval.csv
@@ -0,0 +1,326 @@
+,model_dir_parent,model_name,test Accuracy,test F1 score bin,test Kappa Linear,test Loss,test Matthews Correff,test Precision,test Recall,valid Accuracy,valid F1 score bin,valid Kappa Linear,valid Loss,valid Matthews Correff,valid Precision,valid Recall
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diff --git a/experiments/poleval19/valvstest-fixed.tsv b/experiments/poleval19/valvstest-fixed.tsv
new file mode 100644
index 0000000..b0b090e
--- /dev/null
+++ b/experiments/poleval19/valvstest-fixed.tsv
@@ -0,0 +1,241 @@
+index	LMseed	ft	clsseed	dataset	F1_score	loss	accuracy
+0	0	ft-seed0	2	 test	 0.5645161271095276	 0.43044528365135193	 0.8920
+120	0	ft-seed0	2	 valid	 0.4166666567325592	 0.4225634038448334	 0.8839
+6	0	ft-seed0	3	 test	 0.5925925970077515	 0.4165325462818146	 0.8900
+126	0	ft-seed0	3	 valid	 0.4606741666793823	 0.43441566824913025	 0.8863
+12	0	ft-seed0	4	 test	 0.6065574288368225	 0.4814380705356598	 0.9040
+132	0	ft-seed0	4	 valid	 0.4285714626312256	 0.5097822546958923	 0.8957
+18	0	ft-seed0	5	 test	 0.570397138595581	 0.44903358817100525	 0.8810
+138	0	ft-seed0	5	 valid	 0.4457142949104309	 0.4238314628601074	 0.8851
+24	0	ft-seed0	6	 test	 0.5592105388641357	 0.4162355363368988	 0.8660
+144	0	ft-seed0	6	 valid	 0.42105263471603394	 0.4097800552845001	 0.8697
+30	0	ft-seed0	7	 test	 0.5714285969734192	 0.4321300685405731	 0.9010
+150	0	ft-seed0	7	 valid	 0.44871795177459717	 0.4115864932537079	 0.8981
+36	0	ft-seed0	8	 test	 0.5772358179092407	 0.4818994700908661	 0.8960
+156	0	ft-seed0	8	 valid	 0.45945945382118225	 0.447449266910553	 0.9052
+42	0	ft-seed0	9	 test	 0.5	 0.45570382475852966	 0.8800
+162	0	ft-seed0	9	 valid	 0.4571428596973419	 0.327668696641922	 0.9100
+48	0	ft-seed0	10	 test	 0.5349794626235962	 0.4865730404853821	 0.8870
+168	0	ft-seed0	10	 valid	 0.4129032492637634	 0.434408038854599	 0.8922
+54	0	ft-seed0	11	 test	 0.5433071255683899	 0.4363311529159546	 0.8840
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+180	0	ft-seed0	12	 valid	 0.47191014885902405	 0.5130544304847717	 0.8886
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+192	0	ft-seed0	14	 valid	 0.4642857313156128	 0.4322844445705414	 0.8934
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+198	0	ft-seed0	15	 valid	 0.4413793385028839	 0.4142134487628937	 0.9040
+84	0	ft-seed0	16	 test	 0.5087718963623047	 0.5729814171791077	 0.8880
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+216	0	ft-seed0	18	 valid	 0.45517241954803467	 0.40583667159080505	 0.9064
+102	0	ft-seed0	19	 test	 0.5130434632301331	 0.548660397529602	 0.8880
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+228	0	ft-seed0	20	 valid	 0.43609023094177246	 0.407430499792099	 0.9111
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+235	0	ft-seed1	21	 valid	 0.4675324559211731	 0.3723865747451782	 0.9028
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+158	0	noft	8	 valid	 0.44144144654273987	 0.405771940946579	 0.8531
+44	0	noft	9	 test	 0.5108224749565125	 0.5093541145324707	 0.8870
+164	0	noft	9	 valid	 0.4413793385028839	 0.3927517235279083	 0.9040
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+170	0	noft	10	 valid	 0.447761207818985	 0.4109422266483307	 0.9123
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+176	0	noft	11	 valid	 0.4661653935909271	 0.38057011365890503	 0.9159
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+182	0	noft	12	 valid	 0.44680851697921753	 0.48734134435653687	 0.8768
+68	0	noft	13	 test	 0.5232067108154297	 0.5087217688560486	 0.8870
+188	0	noft	13	 valid	 0.45255473256111145	 0.3679719567298889	 0.9111
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+194	0	noft	14	 valid	 0.4755244553089142	 0.34815725684165955	 0.9111
+80	0	noft	15	 test	 0.49339210987091064	 0.4725494980812073	 0.8850
+200	0	noft	15	 valid	 0.4571428596973419	 0.35398390889167786	 0.9100
+86	0	noft	16	 test	 0.5022830963134766	 0.4819789230823517	 0.8910
+206	0	noft	16	 valid	 0.3999999761581421	 0.41223767399787903	 0.9040
+92	0	noft	17	 test	 0.5546218752861023	 0.48606786131858826	 0.8940
+212	0	noft	17	 valid	 0.4313725531101227	 0.4288560152053833	 0.8969
+98	0	noft	18	 test	 0.565517246723175	 0.4054659307003021	 0.8740
+218	0	noft	18	 valid	 0.4421052634716034	 0.4349640905857086	 0.8744
+104	0	noft	19	 test	 0.493506520986557	 0.5580055117607117	 0.8830
+224	0	noft	19	 valid	 0.44285714626312256	 0.44473594427108765	 0.9076
+110	0	noft	20	 test	 0.5951557159423828	 0.415360689163208	 0.8830
+230	0	noft	20	 valid	 0.48275861144065857	 0.39201340079307556	 0.8934
+116	0	noft	21	 test	 0.5362318754196167	 0.47137773036956787	 0.8720
+236	0	noft	21	 valid	 0.4550897777080536	 0.4185858368873596	 0.8922
+3	1	ft-seed0	2	 test	 0.5490196347236633	 0.5724288821220398	 0.8620
+123	1	ft-seed0	2	 valid	 0.44117647409439087	 0.6496979594230652	 0.8649
+9	1	ft-seed0	3	 test	 0.5754386186599731	 0.4020021855831146	 0.8790
+129	1	ft-seed0	3	 valid	 0.4422110617160797	 0.42443081736564636	 0.8685
+15	1	ft-seed0	4	 test	 0.6111111044883728	 0.4388059079647064	 0.9020
+135	1	ft-seed0	4	 valid	 0.44871795177459717	 0.3783545196056366	 0.8981
+21	1	ft-seed0	5	 test	 0.5679012537002563	 0.49716848134994507	 0.8950
+141	1	ft-seed0	5	 valid	 0.46357616782188416	 0.3597354590892792	 0.9040
+27	1	ft-seed0	6	 test	 0.5486725568771362	 0.43432965874671936	 0.8980
+147	1	ft-seed0	6	 valid	 0.43939390778541565	 0.3266245424747467	 0.9123
+33	1	ft-seed0	7	 test	 0.6108786463737488	 0.4717611074447632	 0.9070
+153	1	ft-seed0	7	 valid	 0.4313725531101227	 0.43972310423851013	 0.8969
+39	1	ft-seed0	8	 test	 0.5909091234207153	 0.29768291115760803	 0.8920
+159	1	ft-seed0	8	 valid	 0.4550897777080536	 0.3085547983646393	 0.8922
+45	1	ft-seed0	9	 test	 0.560669481754303	 0.4620136320590973	 0.8950
+165	1	ft-seed0	9	 valid	 0.44897958636283875	 0.37488117814064026	 0.9040
+51	1	ft-seed0	10	 test	 0.6086956858634949	 0.42837387323379517	 0.9010
+171	1	ft-seed0	10	 valid	 0.43478259444236755	 0.44988685846328735	 0.8922
+57	1	ft-seed0	11	 test	 0.5660377740859985	 0.49595555663108826	 0.8850
+177	1	ft-seed0	11	 valid	 0.4177215099334717	 0.41470322012901306	 0.8910
+63	1	ft-seed0	12	 test	 0.5942028760910034	 0.44078487157821655	 0.8880
+183	1	ft-seed0	12	 valid	 0.44692739844322205	 0.43711891770362854	 0.8827
+69	1	ft-seed0	13	 test	 0.5753424763679504	 0.5054553747177124	 0.8760
+189	1	ft-seed0	13	 valid	 0.4333333671092987	 0.42465242743492126	 0.8791
+75	1	ft-seed0	14	 test	 0.607692301273346	 0.36369848251342773	 0.8980
+195	1	ft-seed0	14	 valid	 0.4585987329483032	 0.32929185032844543	 0.8993
+81	1	ft-seed0	15	 test	 0.5620437860488892	 0.3254164457321167	 0.8800
+201	1	ft-seed0	15	 valid	 0.4497041702270508	 0.2714768946170807	 0.8898
+87	1	ft-seed0	16	 test	 0.6101694703102112	 0.3997952938079834	 0.8850
+207	1	ft-seed0	16	 valid	 0.4419889450073242	 0.36827152967453003	 0.8803
+93	1	ft-seed0	17	 test	 0.5723905563354492	 0.4410214424133301	 0.8730
+213	1	ft-seed0	17	 valid	 0.4365482032299042	 0.4674760699272156	 0.8685
+99	1	ft-seed0	18	 test	 0.5785713791847229	 0.346145898103714	 0.8820
+219	1	ft-seed0	18	 valid	 0.4134078323841095	 0.33745241165161133	 0.8756
+105	1	ft-seed0	19	 test	 0.5414847135543823	 0.505227267742157	 0.8950
+225	1	ft-seed0	19	 valid	 0.4217686951160431	 0.427654892206192	 0.8993
+111	1	ft-seed0	20	 test	 0.5779467225074768	 0.5614640712738037	 0.8890
+231	1	ft-seed0	20	 valid	 0.45000001788139343	 0.45731329917907715	 0.8957
+117	1	ft-seed0	21	 test	 0.5573770403862	 0.516575813293457	 0.8920
+237	1	ft-seed0	21	 valid	 0.4026845395565033	 0.4152399003505707	 0.8945
+4	1	ft-seed1	2	 test	 0.5914397239685059	 0.5607386231422424	 0.8950
+124	1	ft-seed1	2	 valid	 0.43589746952056885	 0.4342404901981354	 0.8957
+10	1	ft-seed1	3	 test	 0.5737704634666443	 0.518389880657196	 0.8960
+130	1	ft-seed1	3	 valid	 0.42953020334243774	 0.3975204527378082	 0.8993
+16	1	ft-seed1	4	 test	 0.5836299061775208	 0.3816649615764618	 0.8830
+136	1	ft-seed1	4	 valid	 0.37931036949157715	 0.36890846490859985	 0.8720
+22	1	ft-seed1	5	 test	 0.6038960814476013	 0.4200931787490845	 0.8780
+142	1	ft-seed1	5	 valid	 0.45000001788139343	 0.39361807703971863	 0.8697
+28	1	ft-seed1	6	 test	 0.5573770999908447	 0.4661884605884552	 0.8650
+148	1	ft-seed1	6	 valid	 0.4565217196941376	 0.38068053126335144	 0.8815
+34	1	ft-seed1	7	 test	 0.5991902351379395	 0.5403699278831482	 0.9010
+154	1	ft-seed1	7	 valid	 0.4556961953639984	 0.4898127019405365	 0.8981
+40	1	ft-seed1	8	 test	 0.63670414686203	 0.29799285531044006	 0.9030
+160	1	ft-seed1	8	 valid	 0.5176470279693604	 0.2861432135105133	 0.9028
+46	1	ft-seed1	9	 test	 0.5886792540550232	 0.392250120639801	 0.8910
+166	1	ft-seed1	9	 valid	 0.44720497727394104	 0.34671708941459656	 0.8945
+52	1	ft-seed1	10	 test	 0.5737704634666443	 0.5335988402366638	 0.8960
+172	1	ft-seed1	10	 valid	 0.4109589159488678	 0.43126046657562256	 0.8981
+58	1	ft-seed1	11	 test	 0.5517241954803467	 0.40790465474128723	 0.8830
+178	1	ft-seed1	11	 valid	 0.43113774061203003	 0.3307510316371918	 0.8874
+64	1	ft-seed1	12	 test	 0.5423728823661804	 0.5214505195617676	 0.8920
+184	1	ft-seed1	12	 valid	 0.46979862451553345	 0.40523862838745117	 0.9064
+70	1	ft-seed1	13	 test	 0.5725191235542297	 0.42663702368736267	 0.8880
+190	1	ft-seed1	13	 valid	 0.44692739844322205	 0.4057123363018036	 0.8827
+76	1	ft-seed1	14	 test	 0.6165413856506348	 0.4142192006111145	 0.8980
+196	1	ft-seed1	14	 valid	 0.4431818127632141	 0.35879600048065186	 0.8839
+82	1	ft-seed1	15	 test	 0.5925925970077515	 0.31346964836120605	 0.8900
+202	1	ft-seed1	15	 valid	 0.44171783328056335	 0.27289724349975586	 0.8922
+88	1	ft-seed1	16	 test	 0.599303126335144	 0.4012908637523651	 0.8850
+208	1	ft-seed1	16	 valid	 0.4093567132949829	 0.3552548289299011	 0.8803
+94	1	ft-seed1	17	 test	 0.5680934190750122	 0.5617448687553406	 0.8890
+214	1	ft-seed1	17	 valid	 0.48447200655937195	 0.4144614338874817	 0.9017
+100	1	ft-seed1	18	 test	 0.5746268630027771	 0.5116666555404663	 0.8860
+220	1	ft-seed1	18	 valid	 0.43023255467414856	 0.4778071343898773	 0.8839
+106	1	ft-seed1	19	 test	 0.5517240762710571	 0.6141948103904724	 0.8440
+226	1	ft-seed1	19	 valid	 0.42396309971809387	 0.6560723185539246	 0.8519
+112	1	ft-seed1	20	 test	 0.5658263564109802	 0.4831385910511017	 0.8450
+232	1	ft-seed1	20	 valid	 0.43103447556495667	 0.5641922950744629	 0.8436
+118	1	ft-seed1	21	 test	 0.5827814340591431	 0.3617390990257263	 0.8740
+238	1	ft-seed1	21	 valid	 0.3886255919933319	 0.39799734950065613	 0.8472
+5	1	noft	2	 test	 0.5281385183334351	 0.4756642282009125	 0.8910
+125	1	noft	2	 valid	 0.40287768840789795	 0.3857330083847046	 0.9017
+11	1	noft	3	 test	 0.5643153190612793	 0.4803626239299774	 0.8950
+131	1	noft	3	 valid	 0.3866666555404663	 0.47957706451416016	 0.8910
+17	1	noft	4	 test	 0.5821918249130249	 0.3984341025352478	 0.8780
+137	1	noft	4	 valid	 0.3932584226131439	 0.41079074144363403	 0.8720
+23	1	noft	5	 test	 0.5921052694320679	 0.4569582939147949	 0.8760
+143	1	noft	5	 valid	 0.46632125973701477	 0.41115298867225647	 0.8780
+29	1	noft	6	 test	 0.5925925970077515	 0.411027729511261	 0.9010
+149	1	noft	6	 valid	 0.4296296536922455	 0.3237917125225067	 0.9088
+35	1	noft	7	 test	 0.5934960246086121	 0.47999921441078186	 0.9000
+155	1	noft	7	 valid	 0.45945945382118225	 0.34085509181022644	 0.9052
+41	1	noft	8	 test	 0.5552050471305847	 0.4669162631034851	 0.8590
+161	1	noft	8	 valid	 0.44897958636283875	 0.4272935688495636	 0.8720
+47	1	noft	9	 test	 0.5791505575180054	 0.45815664529800415	 0.8910
+167	1	noft	9	 valid	 0.4125000238418579	 0.39577150344848633	 0.8886
+53	1	noft	10	 test	 0.5773195624351501	 0.44457459449768066	 0.8770
+173	1	noft	10	 valid	 0.41111111640930176	 0.4423538148403168	 0.8744
+59	1	noft	11	 test	 0.563265323638916	 0.534534752368927	 0.8930
+179	1	noft	11	 valid	 0.41134753823280334	 0.43552181124687195	 0.9017
+65	1	noft	12	 test	 0.5795053243637085	 0.5075364708900452	 0.8810
+185	1	noft	12	 valid	 0.45901644229888916	 0.4783734381198883	 0.8827
+71	1	noft	13	 test	 0.5666667222976685	 0.48282700777053833	 0.8960
+191	1	noft	13	 valid	 0.4647887349128723	 0.3755517303943634	 0.9100
+77	1	noft	14	 test	 0.579365074634552	 0.4776264429092407	 0.8940
+197	1	noft	14	 valid	 0.40522876381874084	 0.4339909851551056	 0.8922
+83	1	noft	15	 test	 0.6067415475845337	 0.28845077753067017	 0.8950
+203	1	noft	15	 valid	 0.43589746952056885	 0.28395193815231323	 0.8957
+89	1	noft	16	 test	 0.607692301273346	 0.3595740497112274	 0.8980
+209	1	noft	16	 valid	 0.4268292486667633	 0.35805508494377136	 0.8886
+95	1	noft	17	 test	 0.6209386587142944	 0.5146023035049438	 0.8950
+215	1	noft	17	 valid	 0.43023255467414856	 0.495402067899704	 0.8839
+101	1	noft	18	 test	 0.5602836608886719	 0.46523019671440125	 0.8760
+221	1	noft	18	 valid	 0.4482758641242981	 0.43405812978744507	 0.8863
+107	1	noft	19	 test	 0.5919539928436279	 0.4383227527141571	 0.8580
+227	1	noft	19	 valid	 0.4107142686843872	 0.4886990487575531	 0.8436
+113	1	noft	20	 test	 0.6204081773757935	 0.41948041319847107	 0.9070
+233	1	noft	20	 valid	 0.47887325286865234	 0.33019527792930603	 0.9123
+119	1	noft	21	 test	 0.5813148617744446	 0.5301401019096375	 0.8790
+239	1	noft	21	 valid	 0.40229886770248413	 0.5155616402626038	 0.8768
diff --git a/experiments/poleval19/valvstest.tsv b/experiments/poleval19/valvstest.tsv
new file mode 100644
index 0000000..d27d04c
--- /dev/null
+++ b/experiments/poleval19/valvstest.tsv
@@ -0,0 +1,241 @@
+index	LMseed	ft	clsseed	dataset	F1_score	loss	accuracy
+0	0	ft-seed0	2	 test	 0.5645161271095276	 0.43044528365135193	 0.5435997247695923
+120	0	ft-seed0	2	 valid	 0.4166666567325592	 0.4225634038448334	 0.40362587571144104
+6	0	ft-seed0	3	 test	 0.5925925970077515	 0.4165325462818146	 0.5603249073028564
+126	0	ft-seed0	3	 valid	 0.4606741666793823	 0.43441566824913025	 0.46482881903648376
+12	0	ft-seed0	4	 test	 0.6065574288368225	 0.4814380705356598	 0.6015315651893616
+132	0	ft-seed0	4	 valid	 0.4285714626312256	 0.5097822546958923	 0.3987082242965698
+18	0	ft-seed0	5	 test	 0.570397138595581	 0.44903358817100525	 0.5506796836853027
+138	0	ft-seed0	5	 valid	 0.4457142949104309	 0.4238314628601074	 0.4373311400413513
+24	0	ft-seed0	6	 test	 0.5592105388641357	 0.4162355363368988	 0.5435535311698914
+144	0	ft-seed0	6	 valid	 0.42105263471603394	 0.4097800552845001	 0.40014225244522095
+30	0	ft-seed0	7	 test	 0.5714285969734192	 0.4321300685405731	 0.57682865858078
+150	0	ft-seed0	7	 valid	 0.44871795177459717	 0.4115864932537079	 0.44700318574905396
+36	0	ft-seed0	8	 test	 0.5772358179092407	 0.4818994700908661	 0.5596418380737305
+156	0	ft-seed0	8	 valid	 0.45945945382118225	 0.447449266910553	 0.43708524107933044
+42	0	ft-seed0	9	 test	 0.5	 0.45570382475852966	 0.45577582716941833
+162	0	ft-seed0	9	 valid	 0.4571428596973419	 0.327668696641922	 0.45067551732063293
+48	0	ft-seed0	10	 test	 0.5349794626235962	 0.4865730404853821	 0.5182504057884216
+168	0	ft-seed0	10	 valid	 0.4129032492637634	 0.434408038854599	 0.4094066023826599
+54	0	ft-seed0	11	 test	 0.5433071255683899	 0.4363311529159546	 0.5251346230506897
+174	0	ft-seed0	11	 valid	 0.4615384638309479	 0.38903355598449707	 0.46455758810043335
+60	0	ft-seed0	12	 test	 0.5724906921386719	 0.4853847026824951	 0.5576902627944946
+180	0	ft-seed0	12	 valid	 0.47191014885902405	 0.5130544304847717	 0.47807759046554565
+66	0	ft-seed0	13	 test	 0.5742574334144592	 0.40475693345069885	 0.5624763369560242
+186	0	ft-seed0	13	 valid	 0.45771148800849915	 0.4509955048561096	 0.46506598591804504
+72	0	ft-seed0	14	 test	 0.5230769515037537	 0.44990867376327515	 0.4929400682449341
+192	0	ft-seed0	14	 valid	 0.4642857313156128	 0.4322844445705414	 0.46598318219184875
+78	0	ft-seed0	15	 test	 0.5526315569877625	 0.47065943479537964	 0.517307698726654
+198	0	ft-seed0	15	 valid	 0.4413793385028839	 0.4142134487628937	 0.4313492178916931
+84	0	ft-seed0	16	 test	 0.5087718963623047	 0.5729814171791077	 0.47096720337867737
+204	0	ft-seed0	16	 valid	 0.4383561909198761	 0.4802896976470947	 0.4158690273761749
+90	0	ft-seed0	17	 test	 0.560669481754303	 0.47273412346839905	 0.5466279983520508
+210	0	ft-seed0	17	 valid	 0.4055943787097931	 0.4395078718662262	 0.4165821969509125
+96	0	ft-seed0	18	 test	 0.5232067108154297	 0.5001763701438904	 0.48940691351890564
+216	0	ft-seed0	18	 valid	 0.45517241954803467	 0.40583667159080505	 0.44013768434524536
+102	0	ft-seed0	19	 test	 0.5130434632301331	 0.548660397529602	 0.5009177923202515
+222	0	ft-seed0	19	 valid	 0.49673202633857727	 0.4328499138355255	 0.4777645766735077
+108	0	ft-seed0	20	 test	 0.5132743716239929	 0.5187202095985413	 0.5138627886772156
+228	0	ft-seed0	20	 valid	 0.43609023094177246	 0.407430499792099	 0.42027491331100464
+114	0	ft-seed0	21	 test	 0.46153849363327026	 0.4121854901313782	 0.43544453382492065
+234	0	ft-seed0	21	 valid	 0.46268653869628906	 0.2815968096256256	 0.44372183084487915
+1	0	ft-seed1	2	 test	 0.4956521987915039	 0.5943491458892822	 0.47801804542541504
+121	0	ft-seed1	2	 valid	 0.4460431635379791	 0.5221770405769348	 0.42610034346580505
+7	0	ft-seed1	3	 test	 0.41624364256858826	 0.7639743089675903	 0.39021721482276917
+127	0	ft-seed1	3	 valid	 0.4137931168079376	 0.4463430345058441	 0.3821505904197693
+13	0	ft-seed1	4	 test	 0.5152838230133057	 0.7346494197845459	 0.4891136884689331
+133	0	ft-seed1	4	 valid	 0.4615384638309479	 0.696619987487793	 0.44672226905822754
+19	0	ft-seed1	5	 test	 0.580152690410614	 0.44208887219429016	 0.5483294129371643
+139	0	ft-seed1	5	 valid	 0.4588235318660736	 0.5001108050346375	 0.46335646510124207
+25	0	ft-seed1	6	 test	 0.5000000596046448	 0.6066558957099915	 0.494425892829895
+145	0	ft-seed1	6	 valid	 0.4964538514614105	 0.4511015713214874	 0.4882415533065796
+31	0	ft-seed1	7	 test	 0.5522388219833374	 0.6283032298088074	 0.5173004865646362
+151	0	ft-seed1	7	 valid	 0.4528301954269409	 0.6680119037628174	 0.4440968930721283
+37	0	ft-seed1	8	 test	 0.4888888895511627	 0.6613699793815613	 0.4632968306541443
+157	0	ft-seed1	8	 valid	 0.43609023094177246	 0.4850305914878845	 0.4160941541194916
+43	0	ft-seed1	9	 test	 0.5316455960273743	 0.5851835012435913	 0.5189670920372009
+163	0	ft-seed1	9	 valid	 0.4217686951160431	 0.5449877381324768	 0.41501468420028687
+49	0	ft-seed1	10	 test	 0.4651162922382355	 0.7876351475715637	 0.4422152638435364
+169	0	ft-seed1	10	 valid	 0.40875911712646484	 0.6061959862709045	 0.4077857434749603
+55	0	ft-seed1	11	 test	 0.568965494632721	 0.5870274901390076	 0.55535888671875
+175	0	ft-seed1	11	 valid	 0.46043166518211365	 0.49774301052093506	 0.4526840150356293
+61	0	ft-seed1	12	 test	 0.5461847186088562	 0.5820209383964539	 0.5405717492103577
+181	0	ft-seed1	12	 valid	 0.4458598792552948	 0.5988472104072571	 0.442074179649353
+67	0	ft-seed1	13	 test	 0.44131454825401306	 0.6617334485054016	 0.42308545112609863
+187	0	ft-seed1	13	 valid	 0.46715328097343445	 0.4917261302471161	 0.4438578188419342
+73	0	ft-seed1	14	 test	 0.582456111907959	 0.5261735916137695	 0.5650691390037537
+193	0	ft-seed1	14	 valid	 0.4687500298023224	 0.5817894339561462	 0.46887174248695374
+79	0	ft-seed1	15	 test	 0.5431034564971924	 0.5236736536026001	 0.5130492448806763
+199	0	ft-seed1	15	 valid	 0.4615384638309479	 0.4515714943408966	 0.45928627252578735
+85	0	ft-seed1	16	 test	 0.6300365924835205	 0.46109575033187866	 0.607157289981842
+205	0	ft-seed1	16	 valid	 0.43478259444236755	 0.49476781487464905	 0.4346143901348114
+91	0	ft-seed1	17	 test	 0.49122804403305054	 0.5814564228057861	 0.45914334058761597
+211	0	ft-seed1	17	 valid	 0.4142857491970062	 0.4819534420967102	 0.408165842294693
+97	0	ft-seed1	18	 test	 0.48510637879371643	 0.4999200999736786	 0.45851048827171326
+217	0	ft-seed1	18	 valid	 0.4400000274181366	 0.39687463641166687	 0.4465416967868805
+103	0	ft-seed1	19	 test	 0.5736434459686279	 0.5443088412284851	 0.5560984015464783
+223	0	ft-seed1	19	 valid	 0.44171783328056335	 0.5909873843193054	 0.4301799535751343
+109	0	ft-seed1	20	 test	 0.4864864647388458	 0.5205869674682617	 0.48399099707603455
+229	0	ft-seed1	20	 valid	 0.4117646813392639	 0.3999001383781433	 0.3924347460269928
+115	0	ft-seed1	21	 test	 0.6058091521263123	 0.4227277338504791	 0.5824961066246033
+235	0	ft-seed1	21	 valid	 0.4675324559211731	 0.3723865747451782	 0.46422505378723145
+2	0	noft	2	 test	 0.5433962941169739	 0.4767005145549774	 0.5214850902557373
+122	0	noft	2	 valid	 0.4457831382751465	 0.4171012043952942	 0.4381187856197357
+8	0	noft	3	 test	 0.5438596606254578	 0.37441006302833557	 0.5402586460113525
+128	0	noft	3	 valid	 0.4421052634716034	 0.36205780506134033	 0.44779273867607117
+14	0	noft	4	 test	 0.5405405163764954	 0.5173587799072266	 0.5371392965316772
+134	0	noft	4	 valid	 0.3813953697681427	 0.5903511047363281	 0.39343199133872986
+20	0	noft	5	 test	 0.5266667008399963	 0.4419504404067993	 0.5080550312995911
+140	0	noft	5	 valid	 0.4293193817138672	 0.4399767816066742	 0.435255229473114
+26	0	noft	6	 test	 0.5324674844741821	 0.41715914011001587	 0.5118011236190796
+146	0	noft	6	 valid	 0.44897958636283875	 0.4160920977592468	 0.4528375566005707
+32	0	noft	7	 test	 0.5679012537002563	 0.4777500629425049	 0.5508254766464233
+152	0	noft	7	 valid	 0.4142857491970062	 0.42049285769462585	 0.4008720815181732
+38	0	noft	8	 test	 0.5747800469398499	 0.3598693311214447	 0.5545601844787598
+158	0	noft	8	 valid	 0.44144144654273987	 0.405771940946579	 0.4474172294139862
+44	0	noft	9	 test	 0.5108224749565125	 0.5093541145324707	 0.48221442103385925
+164	0	noft	9	 valid	 0.4413793385028839	 0.3927517235279083	 0.426912397146225
+50	0	noft	10	 test	 0.5108224749565125	 0.5246335864067078	 0.48768067359924316
+170	0	noft	10	 valid	 0.447761207818985	 0.4109422266483307	 0.424713671207428
+56	0	noft	11	 test	 0.5603448152542114	 0.5133260488510132	 0.5482234358787537
+176	0	noft	11	 valid	 0.4661653935909271	 0.38057011365890503	 0.45219504833221436
+62	0	noft	12	 test	 0.5597269535064697	 0.49656251072883606	 0.5423819422721863
+182	0	noft	12	 valid	 0.44680851697921753	 0.48734134435653687	 0.4495908319950104
+68	0	noft	13	 test	 0.5232067108154297	 0.5087217688560486	 0.5083507895469666
+188	0	noft	13	 valid	 0.45255473256111145	 0.3679719567298889	 0.4423004686832428
+74	0	noft	14	 test	 0.536796510219574	 0.454653263092041	 0.4843226969242096
+194	0	noft	14	 valid	 0.4755244553089142	 0.34815725684165955	 0.46753519773483276
+80	0	noft	15	 test	 0.49339210987091064	 0.4725494980812073	 0.47351354360580444
+200	0	noft	15	 valid	 0.4571428596973419	 0.35398390889167786	 0.45776131749153137
+86	0	noft	16	 test	 0.5022830963134766	 0.4819789230823517	 0.46512266993522644
+206	0	noft	16	 valid	 0.3999999761581421	 0.41223767399787903	 0.38923001289367676
+92	0	noft	17	 test	 0.5546218752861023	 0.48606786131858826	 0.5333790183067322
+212	0	noft	17	 valid	 0.4313725531101227	 0.4288560152053833	 0.4281012713909149
+98	0	noft	18	 test	 0.565517246723175	 0.4054659307003021	 0.5478171706199646
+218	0	noft	18	 valid	 0.4421052634716034	 0.4349640905857086	 0.46671339869499207
+104	0	noft	19	 test	 0.493506520986557	 0.5580055117607117	 0.45603320002555847
+224	0	noft	19	 valid	 0.44285714626312256	 0.44473594427108765	 0.43967923521995544
+110	0	noft	20	 test	 0.5951557159423828	 0.415360689163208	 0.5806995630264282
+230	0	noft	20	 valid	 0.48275861144065857	 0.39201340079307556	 0.4733225107192993
+116	0	noft	21	 test	 0.5362318754196167	 0.47137773036956787	 0.5331786274909973
+236	0	noft	21	 valid	 0.4550897777080536	 0.4185858368873596	 0.4586920738220215
+3	1	ft-seed0	2	 test	 0.5490196347236633	 0.5724288821220398	 0.5155833959579468
+123	1	ft-seed0	2	 valid	 0.44117647409439087	 0.6496979594230652	 0.45821937918663025
+9	1	ft-seed0	3	 test	 0.5754386186599731	 0.4020021855831146	 0.566059410572052
+129	1	ft-seed0	3	 valid	 0.4422110617160797	 0.42443081736564636	 0.44480353593826294
+15	1	ft-seed0	4	 test	 0.6111111044883728	 0.4388059079647064	 0.5846359133720398
+135	1	ft-seed0	4	 valid	 0.44871795177459717	 0.3783545196056366	 0.4501579701900482
+21	1	ft-seed0	5	 test	 0.5679012537002563	 0.49716848134994507	 0.5324816703796387
+141	1	ft-seed0	5	 valid	 0.46357616782188416	 0.3597354590892792	 0.44924965500831604
+27	1	ft-seed0	6	 test	 0.5486725568771362	 0.43432965874671936	 0.5344046354293823
+147	1	ft-seed0	6	 valid	 0.43939390778541565	 0.3266245424747467	 0.4417502284049988
+33	1	ft-seed0	7	 test	 0.6108786463737488	 0.4717611074447632	 0.6045064926147461
+153	1	ft-seed0	7	 valid	 0.4313725531101227	 0.43972310423851013	 0.4156699478626251
+39	1	ft-seed0	8	 test	 0.5909091234207153	 0.29768291115760803	 0.5749288201332092
+159	1	ft-seed0	8	 valid	 0.4550897777080536	 0.3085547983646393	 0.4633788466453552
+45	1	ft-seed0	9	 test	 0.560669481754303	 0.4620136320590973	 0.5490953922271729
+165	1	ft-seed0	9	 valid	 0.44897958636283875	 0.37488117814064026	 0.4432413876056671
+51	1	ft-seed0	10	 test	 0.6086956858634949	 0.42837387323379517	 0.5826467275619507
+171	1	ft-seed0	10	 valid	 0.43478259444236755	 0.44988685846328735	 0.43672603368759155
+57	1	ft-seed0	11	 test	 0.5660377740859985	 0.49595555663108826	 0.5521074533462524
+177	1	ft-seed0	11	 valid	 0.4177215099334717	 0.41470322012901306	 0.420681893825531
+63	1	ft-seed0	12	 test	 0.5942028760910034	 0.44078487157821655	 0.5803970098495483
+183	1	ft-seed0	12	 valid	 0.44692739844322205	 0.43711891770362854	 0.4508112668991089
+69	1	ft-seed0	13	 test	 0.5753424763679504	 0.5054553747177124	 0.5615649223327637
+189	1	ft-seed0	13	 valid	 0.4333333671092987	 0.42465242743492126	 0.44402429461479187
+75	1	ft-seed0	14	 test	 0.607692301273346	 0.36369848251342773	 0.593707799911499
+195	1	ft-seed0	14	 valid	 0.4585987329483032	 0.32929185032844543	 0.4540887176990509
+81	1	ft-seed0	15	 test	 0.5620437860488892	 0.3254164457321167	 0.5418208837509155
+201	1	ft-seed0	15	 valid	 0.4497041702270508	 0.2714768946170807	 0.45483002066612244
+87	1	ft-seed0	16	 test	 0.6101694703102112	 0.3997952938079834	 0.5970664024353027
+207	1	ft-seed0	16	 valid	 0.4419889450073242	 0.36827152967453003	 0.44359642267227173
+93	1	ft-seed0	17	 test	 0.5723905563354492	 0.4410214424133301	 0.5432559251785278
+213	1	ft-seed0	17	 valid	 0.4365482032299042	 0.4674760699272156	 0.4319121241569519
+99	1	ft-seed0	18	 test	 0.5785713791847229	 0.346145898103714	 0.537955105304718
+219	1	ft-seed0	18	 valid	 0.4134078323841095	 0.33745241165161133	 0.4208257496356964
+105	1	ft-seed0	19	 test	 0.5414847135543823	 0.505227267742157	 0.5272364020347595
+225	1	ft-seed0	19	 valid	 0.4217686951160431	 0.427654892206192	 0.41587772965431213
+111	1	ft-seed0	20	 test	 0.5779467225074768	 0.5614640712738037	 0.5631630420684814
+231	1	ft-seed0	20	 valid	 0.45000001788139343	 0.45731329917907715	 0.44137561321258545
+117	1	ft-seed0	21	 test	 0.5573770403862	 0.516575813293457	 0.5341829657554626
+237	1	ft-seed0	21	 valid	 0.4026845395565033	 0.4152399003505707	 0.4038563370704651
+4	1	ft-seed1	2	 test	 0.5914397239685059	 0.5607386231422424	 0.5583197474479675
+124	1	ft-seed1	2	 valid	 0.43589746952056885	 0.4342404901981354	 0.4398580491542816
+10	1	ft-seed1	3	 test	 0.5737704634666443	 0.518389880657196	 0.5634279847145081
+130	1	ft-seed1	3	 valid	 0.42953020334243774	 0.3975204527378082	 0.4253101348876953
+16	1	ft-seed1	4	 test	 0.5836299061775208	 0.3816649615764618	 0.5631790161132812
+136	1	ft-seed1	4	 valid	 0.37931036949157715	 0.36890846490859985	 0.38074082136154175
+22	1	ft-seed1	5	 test	 0.6038960814476013	 0.4200931787490845	 0.5767818093299866
+142	1	ft-seed1	5	 valid	 0.45000001788139343	 0.39361807703971863	 0.4524843394756317
+28	1	ft-seed1	6	 test	 0.5573770999908447	 0.4661884605884552	 0.5439198613166809
+148	1	ft-seed1	6	 valid	 0.4565217196941376	 0.38068053126335144	 0.4747180938720703
+34	1	ft-seed1	7	 test	 0.5991902351379395	 0.5403699278831482	 0.5718095898628235
+154	1	ft-seed1	7	 valid	 0.4556961953639984	 0.4898127019405365	 0.4610288441181183
+40	1	ft-seed1	8	 test	 0.63670414686203	 0.29799285531044006	 0.6160865426063538
+160	1	ft-seed1	8	 valid	 0.5176470279693604	 0.2861432135105133	 0.5251745581626892
+46	1	ft-seed1	9	 test	 0.5886792540550232	 0.392250120639801	 0.5767463445663452
+166	1	ft-seed1	9	 valid	 0.44720497727394104	 0.34671708941459656	 0.44917047023773193
+52	1	ft-seed1	10	 test	 0.5737704634666443	 0.5335988402366638	 0.55939781665802
+172	1	ft-seed1	10	 valid	 0.4109589159488678	 0.43126046657562256	 0.4084997773170471
+58	1	ft-seed1	11	 test	 0.5517241954803467	 0.40790465474128723	 0.5228269696235657
+178	1	ft-seed1	11	 valid	 0.43113774061203003	 0.3307510316371918	 0.43079525232315063
+64	1	ft-seed1	12	 test	 0.5423728823661804	 0.5214505195617676	 0.5331392288208008
+184	1	ft-seed1	12	 valid	 0.46979862451553345	 0.40523862838745117	 0.45541146397590637
+70	1	ft-seed1	13	 test	 0.5725191235542297	 0.42663702368736267	 0.5290747284889221
+190	1	ft-seed1	13	 valid	 0.44692739844322205	 0.4057123363018036	 0.43778395652770996
+76	1	ft-seed1	14	 test	 0.6165413856506348	 0.4142192006111145	 0.6040186882019043
+196	1	ft-seed1	14	 valid	 0.4431818127632141	 0.35879600048065186	 0.4440614879131317
+82	1	ft-seed1	15	 test	 0.5925925970077515	 0.31346964836120605	 0.5623492002487183
+202	1	ft-seed1	15	 valid	 0.44171783328056335	 0.27289724349975586	 0.4450274705886841
+88	1	ft-seed1	16	 test	 0.599303126335144	 0.4012908637523651	 0.5711842179298401
+208	1	ft-seed1	16	 valid	 0.4093567132949829	 0.3552548289299011	 0.4148019850254059
+94	1	ft-seed1	17	 test	 0.5680934190750122	 0.5617448687553406	 0.5498369932174683
+214	1	ft-seed1	17	 valid	 0.48447200655937195	 0.4144614338874817	 0.4857429265975952
+100	1	ft-seed1	18	 test	 0.5746268630027771	 0.5116666555404663	 0.5738064646720886
+220	1	ft-seed1	18	 valid	 0.43023255467414856	 0.4778071343898773	 0.4372516870498657
+106	1	ft-seed1	19	 test	 0.5517240762710571	 0.6141948103904724	 0.5578478574752808
+226	1	ft-seed1	19	 valid	 0.42396309971809387	 0.6560723185539246	 0.43690624833106995
+112	1	ft-seed1	20	 test	 0.5658263564109802	 0.4831385910511017	 0.5478011965751648
+232	1	ft-seed1	20	 valid	 0.43103447556495667	 0.5641922950744629	 0.4400008022785187
+118	1	ft-seed1	21	 test	 0.5827814340591431	 0.3617390990257263	 0.5641347765922546
+238	1	ft-seed1	21	 valid	 0.3886255919933319	 0.39799734950065613	 0.39502865076065063
+5	1	noft	2	 test	 0.5281385183334351	 0.4756642282009125	 0.5024991631507874
+125	1	noft	2	 valid	 0.40287768840789795	 0.3857330083847046	 0.4048222303390503
+11	1	noft	3	 test	 0.5643153190612793	 0.4803626239299774	 0.561382532119751
+131	1	noft	3	 valid	 0.3866666555404663	 0.47957706451416016	 0.3874838650226593
+17	1	noft	4	 test	 0.5821918249130249	 0.3984341025352478	 0.5677289962768555
+137	1	noft	4	 valid	 0.3932584226131439	 0.41079074144363403	 0.41720184683799744
+23	1	noft	5	 test	 0.5921052694320679	 0.4569582939147949	 0.5805506110191345
+143	1	noft	5	 valid	 0.46632125973701477	 0.41115298867225647	 0.47134506702423096
+29	1	noft	6	 test	 0.5925925970077515	 0.411027729511261	 0.556318998336792
+149	1	noft	6	 valid	 0.4296296536922455	 0.3237917125225067	 0.43004703521728516
+35	1	noft	7	 test	 0.5934960246086121	 0.47999921441078186	 0.5628717541694641
+155	1	noft	7	 valid	 0.45945945382118225	 0.34085509181022644	 0.45467492938041687
+41	1	noft	8	 test	 0.5552050471305847	 0.4669162631034851	 0.5426645874977112
+161	1	noft	8	 valid	 0.44897958636283875	 0.4272935688495636	 0.46510761976242065
+47	1	noft	9	 test	 0.5791505575180054	 0.45815664529800415	 0.5680941939353943
+167	1	noft	9	 valid	 0.4125000238418579	 0.39577150344848633	 0.43273958563804626
+53	1	noft	10	 test	 0.5773195624351501	 0.44457459449768066	 0.5565718412399292
+173	1	noft	10	 valid	 0.41111111640930176	 0.4423538148403168	 0.42672690749168396
+59	1	noft	11	 test	 0.563265323638916	 0.534534752368927	 0.547646701335907
+179	1	noft	11	 valid	 0.41134753823280334	 0.43552181124687195	 0.40916135907173157
+65	1	noft	12	 test	 0.5795053243637085	 0.5075364708900452	 0.5634565949440002
+185	1	noft	12	 valid	 0.45901644229888916	 0.4783734381198883	 0.46437951922416687
+71	1	noft	13	 test	 0.5666667222976685	 0.48282700777053833	 0.5505626201629639
+191	1	noft	13	 valid	 0.4647887349128723	 0.3755517303943634	 0.4725068211555481
+77	1	noft	14	 test	 0.579365074634552	 0.4776264429092407	 0.5561549663543701
+197	1	noft	14	 valid	 0.40522876381874084	 0.4339909851551056	 0.40936824679374695
+83	1	noft	15	 test	 0.6067415475845337	 0.28845077753067017	 0.5883947610855103
+203	1	noft	15	 valid	 0.43589746952056885	 0.28395193815231323	 0.43057218194007874
+89	1	noft	16	 test	 0.607692301273346	 0.3595740497112274	 0.5877529382705688
+209	1	noft	16	 valid	 0.4268292486667633	 0.35805508494377136	 0.4262784719467163
+95	1	noft	17	 test	 0.6209386587142944	 0.5146023035049438	 0.6097524762153625
+215	1	noft	17	 valid	 0.43023255467414856	 0.495402067899704	 0.442810595035553
+101	1	noft	18	 test	 0.5602836608886719	 0.46523019671440125	 0.5336311459541321
+221	1	noft	18	 valid	 0.4482758641242981	 0.43405812978744507	 0.45807918906211853
+107	1	noft	19	 test	 0.5919539928436279	 0.4383227527141571	 0.5782817006111145
+227	1	noft	19	 valid	 0.4107142686843872	 0.4886990487575531	 0.4262794852256775
+113	1	noft	20	 test	 0.6204081773757935	 0.41948041319847107	 0.6076886057853699
+233	1	noft	20	 valid	 0.47887325286865234	 0.33019527792930603	 0.467338502407074
+119	1	noft	21	 test	 0.5813148617744446	 0.5301401019096375	 0.5666437745094299
+239	1	noft	21	 valid	 0.40229886770248413	 0.5155616402626038	 0.41759130358695984