diff --git a/sentence_quality_classifier.ipynb b/sentence_quality_classifier.ipynb
index 846803b..8f03f61 100644
--- a/sentence_quality_classifier.ipynb
+++ b/sentence_quality_classifier.ipynb
@@ -11,18 +11,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "%load_ext autoreload\n",
@@ -57,10 +48,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "### save config file (don't change)\n",
@@ -75,10 +64,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from tokenizer import get_tokenizer\n",
@@ -87,10 +74,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# load training data\n",
@@ -102,26 +87,134 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8007\n",
       "8005\n",
-      "['SENTENCE', 'LOW_QUALITY', 'TITLE', 'REFERENCE', 'FOREIGN']\n"
+      "8005\n",
+      "['LOW_QUALITY', 'SENTENCE', 'REFERENCE', 'FOREIGN', 'TITLE']\n"
      ]
     },
     {
      "data": {
-      "image/png": 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+      "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>Satz</th>\n",
+       "      <th>Tokens</th>\n",
+       "      <th>Klassierung</th>\n",
+       "      <th>Bemerkungen</th>\n",
+       "      <th>label</th>\n",
+       "      <th>input</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Synonyme  sind:  Aspartyl-Glukosaminidase-Mang...</td>\n",
+       "      <td>['synonyme', 'sind', 'aspartyl-glukosaminidase...</td>\n",
+       "      <td>LOW_QUALITY</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>LOW_QUALITY</td>\n",
+       "      <td>[[0.0, 0.04761905, 0.0, 0.0952381, 0.0, 0.0, 0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Es wird also die bisherige Vermeidung aufgegeb...</td>\n",
+       "      <td>['es', 'wird', 'also', 'die', 'bisherige', 've...</td>\n",
+       "      <td>SENTENCE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SENTENCE</td>\n",
+       "      <td>[[0.04, 0.04, 0.04, 0.08, 0.04, 0.04, 0.04, 0....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>übersicht  Wien Klin Wochenschr (2009) 121: 54...</td>\n",
+       "      <td>['übersicht', 'wien', 'klin', 'wochenschr', '2...</td>\n",
+       "      <td>REFERENCE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>REFERENCE</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0153846...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Aschoff4 · L. Kinzl1 1 Zentrum für Chirurgie, ...</td>\n",
+       "      <td>['aschoff4', '', 'l', 'kinzl1', '1', 'zentrum'...</td>\n",
+       "      <td>LOW_QUALITY</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>LOW_QUALITY</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.014285714, 0.0, 0.0, 0.0, 0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Morgenland – Abendland.</td>\n",
+       "      <td>['morgenland', 'abendland']</td>\n",
+       "      <td>LOW_QUALITY</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>LOW_QUALITY</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe4b1e6c080>"
+       "                                                Satz  \\\n",
+       "0  Synonyme  sind:  Aspartyl-Glukosaminidase-Mang...   \n",
+       "1  Es wird also die bisherige Vermeidung aufgegeb...   \n",
+       "2  übersicht  Wien Klin Wochenschr (2009) 121: 54...   \n",
+       "3  Aschoff4 · L. Kinzl1 1 Zentrum für Chirurgie, ...   \n",
+       "4                            Morgenland – Abendland.   \n",
+       "\n",
+       "                                              Tokens  Klassierung Bemerkungen  \\\n",
+       "0  ['synonyme', 'sind', 'aspartyl-glukosaminidase...  LOW_QUALITY         NaN   \n",
+       "1  ['es', 'wird', 'also', 'die', 'bisherige', 've...     SENTENCE         NaN   \n",
+       "2  ['übersicht', 'wien', 'klin', 'wochenschr', '2...    REFERENCE         NaN   \n",
+       "3  ['aschoff4', '', 'l', 'kinzl1', '1', 'zentrum'...  LOW_QUALITY         NaN   \n",
+       "4                        ['morgenland', 'abendland']  LOW_QUALITY         NaN   \n",
+       "\n",
+       "         label                                              input  \n",
+       "0  LOW_QUALITY  [[0.0, 0.04761905, 0.0, 0.0952381, 0.0, 0.0, 0...  \n",
+       "1     SENTENCE  [[0.04, 0.04, 0.04, 0.08, 0.04, 0.04, 0.04, 0....  \n",
+       "2    REFERENCE  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0153846...  \n",
+       "3  LOW_QUALITY  [[0.0, 0.0, 0.0, 0.014285714, 0.0, 0.0, 0.0, 0...  \n",
+       "4  LOW_QUALITY  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  "
       ]
      },
+     "execution_count": 11,
      "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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AERENlQIQEdFQKQAREQ2VAhAR0VApABERDZUCEBHRUCkAERENlQIQEdFQKQAREQ2VAhAR0VApABERDTVmAZB0gaSHJN3REttV0mpJG8rfOSUuSWdL2ijpNkkHtKyzvLTfIGn5ltmciIjoVjdHABcCS9pipwDX2F4AXFPmAY6kGgd4AbACOA+qgkE1lOTBVAPIf3SwaERERD3GLAC2vwVsbgsvBS4q0xcBR7fEL3blBmB2GUD+CGC17c22HwFWM7yoRERED030GsDuth8AKH93K/G5wH0t7TaV2EjxYSStkLRW0tqBgYEJphcREWOZ6ovA6hDzKPHhQXul7X7b/X19fVOaXEREDJloAXiwnNqh/H2oxDcB81vazQPuHyUeERE1mWgBWAUM9uRZDlzREn9H6Q20CHi0nCK6GlgsaU65+Lu4xCIioibbjNVA0t8AhwIvlrSJqjfPx4HLJJ0A3AscU5pfBRwFbASeAI4HsL1Z0hnAmtLudNvtF5YjIqKHxiwAto8dYdHhHdoaOGmE97kAuGBc2UVExBaTO4EjIhoqBSAioqFSACIiGmrMawARM9GrLnpV3SkAcPvy2+tOIRosRwAREQ2VAhAR0VApABERDZUCEBHRUCkAERENlQIQEdFQKQAREQ2VAhAR0VApABERDZUCEBHRUCkAERENNakCIOlHkm6XtE7S2hLbVdJqSRvK3zklLklnS9oo6TZJB0zFBkRExMRMxRHAb9teaLu/zJ8CXGN7AXBNmQc4ElhQXiuA86bgsyMiYoK2xCmgpcBFZfoi4OiW+MWu3ADMHhxYPiIiem+yBcDANyTdLGlFie1eBoKn/N2txOcC97Wsu6nEnkPSCklrJa0dGBiYZHoRETGSyY4HcIjt+yXtBqyW9P1R2qpDzMMC9kpgJUB/f/+w5RERMTUmdQRg+/7y9yHgq8BBwIODp3bK34dK803A/JbV5wH3T+bzIyJi4iZcACTtKGnnwWlgMXAHsApYXpotB64o06uAd5TeQIuARwdPFUVERO9N5hTQ7sBXJQ2+z5ds/4OkNcBlkk4A7gWOKe2vAo4CNgJPAMdP4rMjImKSJlwAbN8NvLpD/N+AwzvEDZw00c+LiNjSzjnx2rpTAOCkvzqsJ5+TO4EjIhoqBSAioqFSACIiGioFICKioVIAIiIaKgUgIqKhUgAiIhoqBSAioqFSACIiGioFICKioVIAIiIaKgUgIqKhUgAiIhoqBSAioqFSACIiGqrnBUDSEkk/kLRR0im9/vyIiKj0tABImgWcAxwJ7AscK2nfXuYQERGVyQwJOREHARvLaGJIuhRYCty5pT5wz1P+35Z663H50cdfX3cKERHPoWqkxh59mPRWYIntPyjzbwcOtn1yS5sVwIoy+0rgBz1LcGQvBh6uO4mtRPbFkOyLIdkXQ7aGffFS231jNer1EYA6xJ5TgWyvBFb2Jp3uSFpru7/uPLYG2RdDsi+GZF8MmU77otcXgTcB81vm5wH39ziHiIig9wVgDbBA0l6StgOWAat6nENERNDjU0C2n5J0MnA1MAu4wPb6XuYwQVvVKamaZV8Myb4Ykn0xZNrsi55eBI6IiK1H7gSOiGioFICIiIZKAYiIaKgUgBiRpEV15xAxHUnq9T1WE5ICEKM5T9JnJc2uO5G6Sdq7ZXr7tmWNKZTZD0MkXd8yfUnb4pt6nM6EpAC0kXSdpGtHeF1Td3499hrgLuCm8tiOJvtSy/R325ad28tEapb9MGTHlun92pZ1eurBVmdaHKb02Ps7xBYBHwQe6nEutbL9DPBpSd8AvivpXKpHd6ha7F1qTbC3NMJ0p/mZLPthyGh96KdF//oUgDa2bx6clvRbwJ8A2wMn2v56bYnVRNIJwCnA/wbOcXNvHPEI053mZ7LshyGzJb2J6kzKbElvLnEBL6wvre6lAHQg6QiqL/5fAGfavq7mlGoh6TvAj4DftP2TmtOp2zxJZ1P94x6cpszPrS+tnst+GPJPwBtbpn+vZdm3ep/O+OVO4DaS1gB9wCcZfo4T27f0PKmaSPoj25+pO4+tgaTloy23fVGvcqlT9kN3JL3F9uV15zGWFIA2kr7J0KHs4PnuQbZ9WM+TqomkW2wfUHceWwNJOwA72x5oi+8GPGb7F/Vk1lvZD92RdK/tl9Sdx1hyCqiN7UPrziG2SmcD/wB8pS3+u8DrgD/seUb1yH7ozrS4IJ4jgDaSdgF2t72hzB8DPL8svtr2g7Ul12OSngKe6LSIhvUCknSn7Y7jV0tab7u9G+CMlP3QnRwBTF9/DnwH2FDm/wz4OlUReC1wYk151eF22/vXncRWYrRfdE26nyb7oZB0O517PgnYvcfpTEgKwHAHAu9umf+Z7T+C5975F43zkKSDbD/nDk9JBwIDI6wzE2U/DHlD3QlMVgrAcNu09XVvvQO2aY9E+Lu6E9iKfAC4TNKFwOC9Iv3AO6hGtmuK7Ichn7O9uO4kJiMFYLhnJP2nwX7vtu8AkDQXeKbWzHpvj5Z+3sPY/h+9TKZOtm+SdDDw34H/VsLrgYNtN+YO8eyH5+irO4HJykXgNpKOA94DvA+4tYQPoLo2cLbt9oc+zVhj9Pm27Yt7lkzEVkbS3XR+dAwAttt7Sm11cgTQxvYXJD0M/ClDD3i6A/hI0x4FMdpNPZL+vJe51E3SdYz8qAPbPryX+dQl++E5Xkh1HaDThXEzvKvsVidHADEh06Wb21SR9JoO4WcfEmj7wB6nVIvshyEz4UbJHAG0kfQZRnmoVZPOe49hWtzoMlXykMBK9sNzTPt/AykAw62tO4GthaRdR1rEDPiff7zykMBK9sOzjqs7gcnKKaBxkLSN7afqzqNXJN3D8OchPcv2Xr3NqD55SGAl+2GIpJ8DT3daxDS5Uz4FoI2k622/rkxfYvvtLcum/Tm/mJi2hwS2a8xDArMfhki6dbrfKZ9TQMNN+2HetiRJL6e64edY279Wdz69kocEVrIfnmPa/3pu1LM7ujTth3mbapL2kPReSTdR3fSzDXBszWn1lKQPtkwf07bs//Q+o3pkPzzHbpL+10ivupPrRgrAcLMlvUnSW8r0m8vrLUyTYd6miqR3SbqWarSjFwN/ADxg+2O2b683u55rfczBqW3LlvQykZplPwyZBewE7DzCa6uXU0DDTfth3qbQOVQX+v6r7bUAkhp5FEQGQx+U/TDkAdun153EZKQAtLF9fN05bEV+BTgG+JSk3YHLgG3rTak2GQy9kv0wZNoXvPQC6kDSLGCO7YfL/HZUD776n7b3qTO3ukiaR7n4C7wA+KrtD9ebVe9Iehr4OdU/+uczNFCOgB1sN6IwZj8MkbSr7c115zEZKQBtJC0DPkv1P/kG4DTgEmANcEaT+jmPRNIrgWW2P1Z3LtFbkra1/cu684ipkQLQRtIdwNG2N0o6gOoc+DLbX605tZ6T9Oa2kIGHgXW2f1ZDSlsdSbOBk2yfWXcuvZB7YWaWXAMY7j9sb4TqrkZJ9zTxy7/4vQ6xXYFfl3SC7Wt7nVBdJM2nevzBrwB/D3wJOINqIJQv1Zhar037894xJAVguN3a+vDu1Dpv+1M15FSLkS6IS3op1QXhg3ubUa0upuoVdjlVd8cbqO6JeNXg4EEN0TdaH/cm/fuYCVIAhvscz+3D2z7feLZ/LKkxF/uKXW2fVqavlvQgcKDtJ2vMqQ6Dfd9zJDADpAC0yYXNsZWLwE374kPSHIa++H4CvEDSjgDTvTfIOEz7vu8xJAWgA0lHUt3luC/Vhc87gU/YvqrWxHpM0tcY3rd7V2APZsCjcMfphVSDoLf+8h3sEWbgZT3PqB755T+DpBdQG0nvAt5NNcLR4NgA/cDHgb+2vbKu3HqtDPjRysC/ARts/0cNKUXNJM2x/UjdecTUSAFoI+lO4HXth/SSXgRc38QbwSQtAAa3+xbbm+rMpw6SjrP9hTJ9iO1vtyw72fb/rS+73pH0M4aOCgePBkx1NmE72zmrMI3kYXDDqdP5XNv/VkcydZI0W9LfA1dT3Ql9PPBPkj6rSpMe/tXa8+Uzbcve2ctE6mR7Z9u7lNfOVN1iz6S6JvKX9WYX45VqPdxjkl5t+3utQUmvBpp289NngHXAm20/AyBJwB8DXwNeCSyoL72eykPQWpQb4N7L0H0QBzbxR9J0lwIw3PuAVZI+T3XRz8CBwHKad+FzUeuIaFAN+QScIekh4JB60qpFHoIGSHox1b+R3wcuAPa3/Wi9WcVE5RpAB+XJlydRjQgmqht+zmnYDT9I2mj7FSMs22C7Kb/+kfQEsJHq/4eXl2nK/Mts7zjSujNJGQd3APg8HY6IcyPY9JIjgDaSXmL7XuAjdeeyFfi2pI9QPQTv2V8Kkv6YDgOCz3CNu/g/gk8ydMSTGySnuRwBtGl92JWky22/pe6c6iJpF+B84ACqawEG9gduBd5p+7Ea0+spSYts31B3HhFTKb2Ahmu9oNeUm3s6sv2Y7WOAxcCFVM/DWWz7ra1f/pL2qynFXjp3cEJS045+niXpspbpT7Qt+0bvM4rJSAEYbrSLfY1k+4e2v2Z7le0fdmhySc+T6r3WHwY71JZF/Vqv+/xu27K+XiYSk5drAMO9WtJjlBGPyjRl3rZ3qS+1rVYTukE+rzwL6Hkt089ud4OeBTTaj6L8YJpmUgDa2J5Vdw7TUBP+4bc/C6h1ZLgmPQvoBZL2pyqEzy+DJsHQEJExjeQicExaRolqDknXURW81sdAPLvY9m/3PquYqFwDiKkw4x8MJ+m4lulD2pad3PuMavMh4G22f7t82V8EPA7cATS2x9x0lQIQI5J0haQPSDpE0nYjtbO9qJd51STPAqr8FWUsCEn/GfgzqiLwKNCYJ+XOFLkGEKP5HPBaqod9/bqk7wPfBr4DfMf2g3Um12N5FlBlVssF798HVtq+HLhc0roa84oJSAGIEdm+ErgSQNIsqpvADqW6G3QvquEBmyLPAqrMkrSN7aeAw4EVLcvyfTLN5D9YjKo8/Ou15bWIqg/8P9K8R0HsLek2yrOAyjRlvik9gAD+huqR4A8D/w78M4CkV1CdBoppJL2AYkSSNlD9o74cuAFYY/vxerOqh6SXjrbc9o97lUvdJC2iGhb0G7Z/XmK/Cuxk+5ZRV46tSgpAjEjSqVS/+ucC/0L1q/+7wK22n64zt61FOTW2zPYX684lYrxSAKIr5Rfea4HfAH4TGLDdPmbwjFUejHcSVTFcBawGTgbeD6yzvbTG9CImJNcAYkySXgYcBBxMdUTQB9xda1K9dwnwCNUR0B8AHwC2A5baTu+XmJZyBBAjkvRVqi/8x6i6fn6bqvvnnbUmVgNJt9t+VZmeBTwMvMR204YJjRkkRwAxms8D77L9cN2JbAV+OThh+2lJ9+TLP6a7HAHEqCTtxtDwmAbuBM5t2E1gSHoa+PngLNWDz54gT4mNaSyPgogRlWferCmzFwNfKNM3tj8PZ6azPcv2LuW1s+1tWqbz5R/TUo4AYkSSbgD+0PatbfGFwGdtH1xPZhExFXIEEKPZpf3LH6D0esmA4BHTXApAjEZl5Kv24K7k/52IaS//iGM0ZwHfkPRbknYur0OBrwOfrje1iJisXAOIUUl6A/BBntsL6JO2v1ZrYhExaSkAMSGS3ms7RwER01gKQEyIpHttv6TuPCJi4nINICaqSaNgRcxIKQAxUTl0jJjm8iygGJGkn9H5i37wUQgRMY3lGkBEREPlFFBEREOlAERENFQKQMwYkh5vmT5K0gZJL5F0mqT3T+HnfGeq3iuiTikAMeNIOhz4DLDE9r1T/f62XzuOXCQp/85iq5T/MWNGkfSbwOeA19v+YYfl75K0RtL3JF0u6QUlfoykO0r8WyW2n6SbJK2TdJukBSXeeqTxgfJ+t0n6WIntKekuSecCtwDz29Z5q6QLy/SFks6W9B1Jd0t6a4k/T9K5ktZLulLSVYPLIqZKCkDMJNsDVwBH2/7+CG2+YvtA268G7gJOKPGPAEeU+BtL7ETgL20vBPqBTa1vJGkxsAA4CFgIvEbSfy6LXwlcbHt/2z8eI+89gNcBbwA+XmJvBvYEXkU1CP1vjPEeEeOWAhAzyS+pBq8/YZQ2vybpnyXdDryN6iF3UA3CKdOvAAABmUlEQVR4f6GkdwGzSuy7wIclfQh4qe1/b3uvxeV1K9Uv/b2pCgLAj23f0GXef2/7Gdt3AruX2OuAvyvxnwDXdfleEV1LAYiZ5BngvwAHSvrwCG0uBE62/SrgY8AOALZPBP4YmA+sk/Qi21+iOhr4d+BqSYe1vZeAP7O9sLxeYfv8suznbW1bb7jZoW3Zk23v2fo3YotJAYgZxfYTVKdS3iap05HAzsADkralOgIAQNLLbd9o+yPAw1Tn7V8G3G37bGAV8Ott73U18E5JO5X3mCtptxFSe1DSPuWC8Ju62JTrgbeUawG7A4d2sU7EuORREDHj2N4saQnwLUkPty3+E+BG4MfA7QwNbfnJcpFXwDXA94BTgOMk/RL4CXB62+d8Q9I+wHclATwOHAc83SGtU4ArgfuAO4CdxtiMy4HDS9t/KTk/OsY6EeOSR0FEbKUk7WT7cUkvAm4CDinXAyKmRI4AIrZeV0qaDWwHnJEv/5hqOQKIiGioXASOiGioFICIiIZKAYiIaKgUgIiIhkoBiIhoqBSAiIiG+v9og67exTz5QAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -137,53 +230,89 @@
     "\n",
     "# set label / classes\n",
     "classes = list(data['label'].unique())\n",
-    "data.head()\n",
-    "print(classes)"
+    "print(classes)\n",
+    "data.head()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /home/joel/nltk_data...\n",
+      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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dF94/nadmrkh2WCLSim5PNmY20sxeM7OPzGy+mV0bygeZ2Stmtjj8HBjKzczu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\nSMIp2YiISMIp2YiISML9/3S4pBodlVXEAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe4b2c786d8>"
+       "True"
       ]
      },
+     "execution_count": 7,
      "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.download('punkt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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"
     },
     {
      "data": {
-      "image/png": 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LRFpRspE+75PHj+W/rzqFwTkZzF+5jS/8v4VU1jWnOiwRiaNkI/3CqZOH8+i/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\nCD/jjeH588Bz4b71bS/wQqj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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe4b21333c8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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VSwFEZA4wDfh7rOptopeSlMDwHHdLWshrtG77/kM88NL7vLWllpJdFdw9cxW/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\n8dRXRP4CDAeWAnUNbwN4Oij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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe4a43e7f98>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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YgQGYYdEdhrHyjc3M3fwKFWXhyo1hMmVleTS5sjLj8atrWxi1ajO1lWUMqyyn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\nFDhERCQv/x9xXC+uvOHd0wAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe476a3be80>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -221,9 +350,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'keras'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-9-a9f50bd6173b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# one hot encoded characters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTokenizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mtokenizer_char\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchar_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mtokenizer_char\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_on_texts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Satz'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'keras'"
+     ]
+    }
+   ],
    "source": [
     "# Keras LSTM input data\n",
     "\n",
@@ -236,25 +377,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
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-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -271,48 +412,48 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>Durch die Ausnutzung der  Schwerkraft  kann di...</td>\n",
-       "      <td>['durch', 'die', 'ausnutzung', 'der', 'schwerk...</td>\n",
-       "      <td>SENTENCE</td>\n",
+       "      <td>Synonyme  sind:  Aspartyl-Glukosaminidase-Mang...</td>\n",
+       "      <td>['synonyme', 'sind', 'aspartyl-glukosaminidase...</td>\n",
+       "      <td>LOW_QUALITY</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>SENTENCE</td>\n",
-       "      <td>[[0.0384615, 0.0769231, 0.0384615, 0.0384615, ...</td>\n",
+       "      <td>LOW_QUALITY</td>\n",
+       "      <td>[[0.0, 0.04761905, 0.0, 0.0952381, 0.0, 0.0, 0...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>Er ist vor allem mit seinen Arbeiten zum Thema...</td>\n",
-       "      <td>['er', 'ist', 'vor', 'allem', 'mit', 'seinen',...</td>\n",
+       "      <td>Es wird also die bisherige Vermeidung aufgegeb...</td>\n",
+       "      <td>['es', 'wird', 'also', 'die', 'bisherige', 've...</td>\n",
        "      <td>SENTENCE</td>\n",
        "      <td>NaN</td>\n",
        "      <td>SENTENCE</td>\n",
-       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0588235...</td>\n",
+       "      <td>[[0.04, 0.04, 0.04, 0.08, 0.04, 0.04, 0.04, 0....</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>An anderen [[Monoamin]]transportern wirken sie...</td>\n",
-       "      <td>['an', 'anderen', 'monoamin', 'transportern', ...</td>\n",
-       "      <td>SENTENCE</td>\n",
+       "      <td>übersicht  Wien Klin Wochenschr (2009) 121: 54...</td>\n",
+       "      <td>['übersicht', 'wien', 'klin', 'wochenschr', '2...</td>\n",
+       "      <td>REFERENCE</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>SENTENCE</td>\n",
-       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>REFERENCE</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0153846...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>Etwa die Hälfte der an einem Schlaganfall-Erkr...</td>\n",
-       "      <td>['etwa', 'die', 'hälfte', 'der', 'an', 'einem'...</td>\n",
+       "      <td>Aschoff4 · L. Kinzl1 1 Zentrum für Chirurgie, ...</td>\n",
+       "      <td>['aschoff4', '', 'l', 'kinzl1', '1', 'zentrum'...</td>\n",
        "      <td>LOW_QUALITY</td>\n",
        "      <td>NaN</td>\n",
        "      <td>LOW_QUALITY</td>\n",
-       "      <td>[[0.0, 0.0294118, 0.0, 0.0294118, 0.0, 0.0, 0....</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.014285714, 0.0, 0.0, 0.0, 0...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>Schwerpunktthema: Was ist gesichert in der The...</td>\n",
-       "      <td>['schwerpunktthema', 'was', 'ist', 'gesichert'...</td>\n",
-       "      <td>TITLE</td>\n",
+       "      <td>Morgenland – Abendland.</td>\n",
+       "      <td>['morgenland', 'abendland']</td>\n",
+       "      <td>LOW_QUALITY</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>TITLE</td>\n",
-       "      <td>[[0.0, 0.0, 0.0, 0.142857, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>LOW_QUALITY</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -320,28 +461,28 @@
       ],
       "text/plain": [
        "                                                Satz  \\\n",
-       "0  Durch die Ausnutzung der  Schwerkraft  kann di...   \n",
-       "1  Er ist vor allem mit seinen Arbeiten zum Thema...   \n",
-       "2  An anderen [[Monoamin]]transportern wirken sie...   \n",
-       "3  Etwa die Hälfte der an einem Schlaganfall-Erkr...   \n",
-       "4  Schwerpunktthema: Was ist gesichert in der The...   \n",
+       "0  Synonyme  sind:  Aspartyl-Glukosaminidase-Mang...   \n",
+       "1  Es wird also die bisherige Vermeidung aufgegeb...   \n",
+       "2  übersicht  Wien Klin Wochenschr (2009) 121: 54...   \n",
+       "3  Aschoff4 · L. Kinzl1 1 Zentrum für Chirurgie, ...   \n",
+       "4                            Morgenland – Abendland.   \n",
        "\n",
        "                                              Tokens  Klassierung Bemerkungen  \\\n",
-       "0  ['durch', 'die', 'ausnutzung', 'der', 'schwerk...     SENTENCE         NaN   \n",
-       "1  ['er', 'ist', 'vor', 'allem', 'mit', 'seinen',...     SENTENCE         NaN   \n",
-       "2  ['an', 'anderen', 'monoamin', 'transportern', ...     SENTENCE         NaN   \n",
-       "3  ['etwa', 'die', 'hälfte', 'der', 'an', 'einem'...  LOW_QUALITY         NaN   \n",
-       "4  ['schwerpunktthema', 'was', 'ist', 'gesichert'...        TITLE         NaN   \n",
+       "0  ['synonyme', 'sind', 'aspartyl-glukosaminidase...  LOW_QUALITY         NaN   \n",
+       "1  ['es', 'wird', 'also', 'die', 'bisherige', 've...     SENTENCE         NaN   \n",
+       "2  ['übersicht', 'wien', 'klin', 'wochenschr', '2...    REFERENCE         NaN   \n",
+       "3  ['aschoff4', '', 'l', 'kinzl1', '1', 'zentrum'...  LOW_QUALITY         NaN   \n",
+       "4                        ['morgenland', 'abendland']  LOW_QUALITY         NaN   \n",
        "\n",
        "         label                                              input  \n",
-       "0     SENTENCE  [[0.0384615, 0.0769231, 0.0384615, 0.0384615, ...  \n",
-       "1     SENTENCE  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0588235...  \n",
-       "2     SENTENCE  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
-       "3  LOW_QUALITY  [[0.0, 0.0294118, 0.0, 0.0294118, 0.0, 0.0, 0....  \n",
-       "4        TITLE  [[0.0, 0.0, 0.0, 0.142857, 0.0, 0.0, 0.0, 0.0,...  "
+       "0  LOW_QUALITY  [[0.0, 0.04761905, 0.0, 0.0952381, 0.0, 0.0, 0...  \n",
+       "1     SENTENCE  [[0.04, 0.04, 0.04, 0.08, 0.04, 0.04, 0.04, 0....  \n",
+       "2    REFERENCE  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0153846...  \n",
+       "3  LOW_QUALITY  [[0.0, 0.0, 0.0, 0.014285714, 0.0, 0.0, 0.0, 0...  \n",
+       "4  LOW_QUALITY  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  "
       ]
      },
-     "execution_count": 42,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -381,14 +522,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "(6003, 701)\n"
+      "/home/joel/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
+      "  from numpy.core.umath_tests import inner1d\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'char_int_encoded' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-12-87f99039f347>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m ]\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchar_int_encoded_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchar_int_encoded_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchar_int_encoded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'char_int_encoded' is not defined"
      ]
     }
    ],
@@ -908,13 +1061,7 @@
       "6003/6003 [==============================] - 0s - loss: 0.3889 - acc: 0.8517 - val_loss: 0.6230 - val_acc: 0.7747\n",
       "Epoch 121/1000\n",
       "6003/6003 [==============================] - 0s - loss: 0.3852 - acc: 0.8569 - val_loss: 0.6299 - val_acc: 0.7727\n",
-      "Epoch 122/1000\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Epoch 122/1000\n",
       "6003/6003 [==============================] - 0s - loss: 0.3862 - acc: 0.8577 - val_loss: 0.6289 - val_acc: 0.7752\n",
       "Epoch 123/1000\n",
       "6003/6003 [==============================] - 0s - loss: 0.3880 - acc: 0.8506 - val_loss: 0.6272 - val_acc: 0.7742\n",
@@ -1155,7 +1302,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.7.0"
   }
  },
  "nbformat": 4,