diff --git a/nlp/4_4_Classification.ipynb b/nlp/4_4_Classification.ipynb
index 1ff8ec8..a3c8a0a 100644
--- a/nlp/4_4_Classification.ipynb
+++ b/nlp/4_4_Classification.ipynb
@@ -74,9 +74,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.datasets import fetch_20newsgroups\n",
     "\n",
@@ -90,9 +98,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    }
+   ],
    "source": [
     "#Number of categories\n",
     "print(len(newsgroups_train.target_names))"
@@ -100,9 +116,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Category id 4 comp.sys.mac.hardware\n",
+      "Doc A fair number of brave souls who upgraded their SI clock oscillator have\n",
+      "shared their experiences for this poll. Please send a brief message detailing\n",
+      "your experiences with the procedure. Top speed attained, CPU rated speed,\n",
+      "add on cards and adapters, heat sinks, hour of usage per day, floppy disk\n",
+      "functionality with 800 and 1.4 m floppies are especially requested.\n",
+      "\n",
+      "I will be summarizing in the next two days, so please add to the network\n",
+      "knowledge base if you have done the clock upgrade and haven't answered this\n",
+      "poll. Thanks.\n"
+     ]
+    }
+   ],
    "source": [
     "# Show a document\n",
     "docid = 1\n",
@@ -115,9 +148,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11314,)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Number of files\n",
     "newsgroups_train.filenames.shape"
@@ -125,9 +169,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11314, 101322)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Obtain a vector\n",
     "\n",
@@ -141,9 +196,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "66.802987449178"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# The tf-idf vectors are very sparse with an average of 66 non zero components in 101.323 dimensions (.06%)\n",
     "vectors_train.nnz / float(vectors_train.shape[0])"
@@ -165,9 +231,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.695453607190013"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.naive_bayes import MultinomialNB\n",
     "\n",
@@ -195,29 +272,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alt.atheism: islam atheists say just religion atheism think don people god\n",
+      "comp.graphics: looking format 3d know program file files thanks image graphics\n",
+      "comp.os.ms-windows.misc: card problem thanks driver drivers use files dos file windows\n",
+      "comp.sys.ibm.pc.hardware: monitor disk thanks pc ide controller bus card scsi drive\n",
+      "comp.sys.mac.hardware: know monitor does quadra simms thanks problem drive apple mac\n",
+      "comp.windows.x: using windows x11r5 use application thanks widget server motif window\n",
+      "misc.forsale: asking email sell price condition new shipping offer 00 sale\n",
+      "rec.autos: don ford new good dealer just engine like cars car\n",
+      "rec.motorcycles: don just helmet riding like motorcycle ride bikes dod bike\n",
+      "rec.sport.baseball: braves players pitching hit runs games game baseball team year\n",
+      "rec.sport.hockey: league year nhl games season players play hockey team game\n",
+      "sci.crypt: people use escrow nsa keys government chip clipper encryption key\n",
+      "sci.electronics: don thanks voltage used know does like circuit power use\n",
+      "sci.med: skepticism cadre dsl banks chastity n3jxp pitt gordon geb msg\n",
+      "sci.space: just lunar earth shuttle like moon launch orbit nasa space\n",
+      "soc.religion.christian: believe faith christian christ bible people christians church jesus god\n",
+      "talk.politics.guns: just law firearms government fbi don weapons people guns gun\n",
+      "talk.politics.mideast: said arabs arab turkish people armenians armenian jews israeli israel\n",
+      "talk.politics.misc: know state clinton president just think tax don government people\n",
+      "talk.religion.misc: think don koresh objective christians bible people christian jesus god\n"
+     ]
+    }
+   ],
    "source": [
-    "from sklearn.utils.extmath import density\n",
-    "\n",
-    "print(\"dimensionality: %d\" % model.coef_.shape[1])\n",
-    "print(\"density: %f\" % density(model.coef_))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We can review the top features per topic in Bayes (attribute coef_)\n",
+    "# We can review the top features per topic in Bayes (attribute feature_log_prob_)\n",
     "import numpy as np\n",
     "\n",
     "def show_top10(classifier, vectorizer, categories):\n",
-    "    feature_names = np.asarray(vectorizer.get_feature_names())\n",
+    "    feature_names = np.asarray(vectorizer.get_feature_names_out())\n",
     "    for i, category in enumerate(categories):\n",
-    "        top10 = np.argsort(classifier.coef_[i])[-10:]\n",
+    "        top10 = np.argsort(classifier.feature_log_prob_[i, :])[-10:]\n",
     "        print(\"%s: %s\" % (category, \" \".join(feature_names[top10])))\n",
     "\n",
     "        \n",
@@ -226,9 +318,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 2 15]\n",
+      "['comp.os.ms-windows.misc', 'soc.religion.christian']\n"
+     ]
+    }
+   ],
    "source": [
     "# We try the classifier in two new docs\n",
     "\n",
@@ -275,7 +376,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -289,7 +390,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.10.10"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/nlp/4_5_Semantic_Models.ipynb b/nlp/4_5_Semantic_Models.ipynb
index cef544f..b2e9b9a 100644
--- a/nlp/4_5_Semantic_Models.ipynb
+++ b/nlp/4_5_Semantic_Models.ipynb
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -85,7 +85,7 @@
        "(2034, 2807)"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -134,15 +134,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gensim in /home/cif/anaconda3/lib/python3.10/site-packages (4.3.1)\n",
+      "Requirement already satisfied: scipy>=1.7.0 in /home/cif/anaconda3/lib/python3.10/site-packages (from gensim) (1.10.1)\n",
+      "Requirement already satisfied: smart-open>=1.8.1 in /home/cif/anaconda3/lib/python3.10/site-packages (from gensim) (6.3.0)\n",
+      "Requirement already satisfied: numpy>=1.18.5 in /home/cif/anaconda3/lib/python3.10/site-packages (from gensim) (1.24.2)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n",
+      "Requirement already satisfied: python-Levenshtein in /home/cif/anaconda3/lib/python3.10/site-packages (0.21.0)\n",
+      "Requirement already satisfied: Levenshtein==0.21.0 in /home/cif/anaconda3/lib/python3.10/site-packages (from python-Levenshtein) (0.21.0)\n",
+      "Requirement already satisfied: rapidfuzz<4.0.0,>=2.3.0 in /home/cif/anaconda3/lib/python3.10/site-packages (from Levenshtein==0.21.0->python-Levenshtein) (3.0.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install gensim\n",
+    "%pip install python-Levenshtein"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "from gensim import matutils\n",
     "\n",
-    "vocab = vectorizer.get_feature_names()\n",
+    "vocab = vectorizer.get_feature_names_out()\n",
     "\n",
-    "dictionary = dict([(i, s) for i, s in enumerate(vectorizer.get_feature_names())])\n",
+    "dictionary = dict([(i, s) for i, s in enumerate(vectorizer.get_feature_names_out())])\n",
     "corpus_tfidf = matutils.Sparse2Corpus(vectors_train)"
    ]
   },
@@ -162,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,23 +202,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[(0,\n",
-       "  '0.011*\"baptist\" + 0.010*\"koresh\" + 0.009*\"bible\" + 0.006*\"reality\" + 0.006*\"virtual\" + 0.005*\"scarlet\" + 0.005*\"shag\" + 0.004*\"tootsie\" + 0.004*\"kinda\" + 0.004*\"captain\"'),\n",
+       "  '0.004*\"central\" + 0.004*\"assumptions\" + 0.004*\"matthew\" + 0.004*\"define\" + 0.004*\"holes\" + 0.003*\"killing\" + 0.003*\"item\" + 0.003*\"curious\" + 0.003*\"going\" + 0.003*\"presentations\"'),\n",
        " (1,\n",
-       "  '0.010*\"targa\" + 0.008*\"thanks\" + 0.008*\"moon\" + 0.007*\"craig\" + 0.007*\"zoroastrians\" + 0.006*\"yayayay\" + 0.005*\"unfortunately\" + 0.005*\"windows\" + 0.005*\"rayshade\" + 0.004*\"tdb\"'),\n",
+       "  '0.002*\"mechanism\" + 0.002*\"led\" + 0.002*\"apple\" + 0.002*\"color\" + 0.002*\"mormons\" + 0.002*\"activity\" + 0.002*\"concepts\" + 0.002*\"frank\" + 0.002*\"platform\" + 0.002*\"fault\"'),\n",
        " (2,\n",
-       "  '0.009*\"mary\" + 0.007*\"whatever\" + 0.006*\"god\" + 0.005*\"ns\" + 0.005*\"lucky\" + 0.005*\"joseph\" + 0.005*\"ssrt\" + 0.005*\"samaritan\" + 0.005*\"crusades\" + 0.004*\"phobos\"'),\n",
+       "  '0.005*\"objects\" + 0.005*\"obtained\" + 0.003*\"manhattan\" + 0.003*\"capability\" + 0.003*\"education\" + 0.003*\"men\" + 0.003*\"photo\" + 0.003*\"decent\" + 0.003*\"environmental\" + 0.003*\"pain\"'),\n",
        " (3,\n",
-       "  '0.009*\"islam\" + 0.008*\"western\" + 0.008*\"plane\" + 0.008*\"jeff\" + 0.007*\"cheers\" + 0.007*\"kent\" + 0.007*\"joy\" + 0.007*\"khomeini\" + 0.007*\"davidian\" + 0.006*\"basically\"')]"
+       "  '0.004*\"car\" + 0.004*\"contain\" + 0.004*\"groups\" + 0.004*\"center\" + 0.004*\"evil\" + 0.004*\"maintain\" + 0.004*\"comets\" + 0.004*\"88\" + 0.004*\"density\" + 0.003*\"company\"')]"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -211,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -243,14 +269,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dictionary(10913 unique tokens: ['cel', 'ds', 'hi', 'nothing', 'prj']...)\n"
+      "Dictionary<10913 unique tokens: ['cel', 'ds', 'hi', 'nothing', 'prj']...>\n"
      ]
     }
    ],
@@ -263,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -274,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -286,14 +312,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dictionary(10913 unique tokens: ['cel', 'ds', 'hi', 'nothing', 'prj']...)\n"
+      "Dictionary<10913 unique tokens: ['cel', 'ds', 'hi', 'nothing', 'prj']...>\n"
      ]
     }
    ],
@@ -305,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -315,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -346,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -359,23 +385,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[(0,\n",
-       "  '0.009*\"whatever\" + 0.007*\"plane\" + 0.007*\"ns\" + 0.007*\"joy\" + 0.006*\"happy\" + 0.005*\"bob\" + 0.004*\"phil\" + 0.004*\"nasa\" + 0.003*\"purdue\" + 0.003*\"neie\"'),\n",
+       "  '0.011*\"mary\" + 0.007*\"ns\" + 0.006*\"joseph\" + 0.006*\"lucky\" + 0.006*\"ssrt\" + 0.005*\"god\" + 0.005*\"unfortunately\" + 0.004*\"rayshade\" + 0.004*\"phil\" + 0.004*\"nasa\"'),\n",
        " (1,\n",
-       "  '0.009*\"god\" + 0.008*\"mary\" + 0.008*\"targa\" + 0.007*\"baptist\" + 0.007*\"thanks\" + 0.007*\"koresh\" + 0.006*\"really\" + 0.006*\"bible\" + 0.005*\"lot\" + 0.005*\"lucky\"'),\n",
+       "  '0.009*\"thanks\" + 0.009*\"targa\" + 0.008*\"whatever\" + 0.008*\"baptist\" + 0.007*\"islam\" + 0.006*\"cheers\" + 0.006*\"kent\" + 0.006*\"zoroastrians\" + 0.006*\"joy\" + 0.006*\"lot\"'),\n",
        " (2,\n",
-       "  '0.010*\"moon\" + 0.007*\"phobos\" + 0.006*\"unfortunately\" + 0.006*\"martian\" + 0.006*\"russian\" + 0.005*\"rayshade\" + 0.005*\"anybody\" + 0.005*\"perturbations\" + 0.005*\"thanks\" + 0.004*\"apollo\"'),\n",
+       "  '0.008*\"moon\" + 0.008*\"really\" + 0.008*\"western\" + 0.007*\"plane\" + 0.006*\"samaritan\" + 0.006*\"crusades\" + 0.006*\"baltimore\" + 0.005*\"bob\" + 0.005*\"septuagint\" + 0.005*\"virtual\"'),\n",
        " (3,\n",
-       "  '0.008*\"islam\" + 0.008*\"western\" + 0.007*\"jeff\" + 0.007*\"zoroastrians\" + 0.006*\"davidian\" + 0.006*\"basically\" + 0.005*\"bull\" + 0.005*\"gerald\" + 0.005*\"sorry\" + 0.004*\"kent\"')]"
+       "  '0.009*\"koresh\" + 0.008*\"bible\" + 0.008*\"jeff\" + 0.007*\"basically\" + 0.006*\"gerald\" + 0.006*\"bull\" + 0.005*\"pd\" + 0.004*\"also\" + 0.003*\"dam\" + 0.003*\"feiner\"')]"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -387,14 +413,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(0, 0.7154438), (1, 0.10569019), (2, 0.09522807), (3, 0.08363795)]\n"
+      "[(0, 0.09161347), (1, 0.1133858), (2, 0.103424065), (3, 0.69157666)]\n"
      ]
     }
    ],
@@ -406,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -427,14 +453,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(0, 0.06320839), (1, 0.80878526), (2, 0.06274223), (3, 0.065264106)]\n"
+      "[(0, 0.066217005), (1, 0.8084562), (2, 0.062542014), (3, 0.0627848)]\n"
      ]
     }
    ],
@@ -446,14 +472,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.009*\"god\" + 0.008*\"mary\" + 0.008*\"targa\" + 0.007*\"baptist\" + 0.007*\"thanks\" + 0.007*\"koresh\" + 0.006*\"really\" + 0.006*\"bible\" + 0.005*\"lot\" + 0.005*\"lucky\"\n"
+      "0.009*\"thanks\" + 0.009*\"targa\" + 0.008*\"whatever\" + 0.008*\"baptist\" + 0.007*\"islam\" + 0.006*\"cheers\" + 0.006*\"kent\" + 0.006*\"zoroastrians\" + 0.006*\"joy\" + 0.006*\"lot\"\n"
      ]
     }
    ],
@@ -464,15 +490,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(0, 0.10564032), (1, 0.67894983), (2, 0.104482815), (3, 0.11092702)]\n",
-      "0.009*\"god\" + 0.008*\"mary\" + 0.008*\"targa\" + 0.007*\"baptist\" + 0.007*\"thanks\" + 0.007*\"koresh\" + 0.006*\"really\" + 0.006*\"bible\" + 0.005*\"lot\" + 0.005*\"lucky\"\n"
+      "[(0, 0.11006463), (1, 0.6813435), (2, 0.10399808), (3, 0.10459379)]\n",
+      "0.009*\"thanks\" + 0.009*\"targa\" + 0.008*\"whatever\" + 0.008*\"baptist\" + 0.007*\"islam\" + 0.006*\"cheers\" + 0.006*\"kent\" + 0.006*\"zoroastrians\" + 0.006*\"joy\" + 0.006*\"lot\"\n"
      ]
     }
    ],
@@ -492,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -508,23 +534,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[(0,\n",
-       "  '0.769*\"god\" + 0.346*\"jesus\" + 0.235*\"bible\" + 0.204*\"christian\" + 0.148*\"christians\" + 0.107*\"christ\" + 0.090*\"well\" + 0.085*\"koresh\" + 0.081*\"kent\" + 0.080*\"christianity\"'),\n",
+       "  '-0.769*\"god\" + -0.345*\"jesus\" + -0.235*\"bible\" + -0.203*\"christian\" + -0.149*\"christians\" + -0.107*\"christ\" + -0.089*\"well\" + -0.085*\"koresh\" + -0.082*\"kent\" + -0.081*\"christianity\"'),\n",
        " (1,\n",
-       "  '-0.863*\"thanks\" + -0.255*\"please\" + -0.159*\"hello\" + -0.152*\"hi\" + 0.124*\"god\" + -0.111*\"sorry\" + -0.088*\"could\" + -0.074*\"windows\" + -0.067*\"jpeg\" + -0.063*\"gif\"'),\n",
+       "  '-0.863*\"thanks\" + -0.255*\"please\" + -0.159*\"hello\" + -0.152*\"hi\" + 0.123*\"god\" + -0.112*\"sorry\" + -0.088*\"could\" + -0.074*\"windows\" + -0.067*\"jpeg\" + -0.063*\"gif\"'),\n",
        " (2,\n",
-       "  '-0.780*\"well\" + 0.229*\"god\" + -0.165*\"yes\" + 0.154*\"thanks\" + -0.133*\"ico\" + -0.133*\"tek\" + -0.130*\"queens\" + -0.130*\"bronx\" + -0.130*\"beauchaine\" + -0.130*\"manhattan\"'),\n",
+       "  '0.779*\"well\" + -0.229*\"god\" + 0.165*\"yes\" + -0.154*\"thanks\" + 0.135*\"ico\" + 0.134*\"tek\" + 0.131*\"queens\" + 0.131*\"bronx\" + 0.131*\"beauchaine\" + 0.131*\"manhattan\"'),\n",
        " (3,\n",
-       "  '-0.338*\"well\" + 0.336*\"ico\" + 0.334*\"tek\" + 0.328*\"bronx\" + 0.328*\"beauchaine\" + 0.328*\"queens\" + 0.326*\"manhattan\" + 0.305*\"com\" + 0.305*\"bob\" + 0.072*\"god\"')]"
+       "  '-0.342*\"well\" + 0.335*\"ico\" + 0.333*\"tek\" + 0.327*\"bronx\" + 0.327*\"queens\" + 0.327*\"beauchaine\" + 0.325*\"manhattan\" + 0.305*\"bob\" + 0.304*\"com\" + 0.073*\"god\"')]"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -536,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -595,7 +621,7 @@
    "window_display": false
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -609,7 +635,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.10.10"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,