diff --git a/l4/TM-Lab4.ipynb b/l4/TM-Lab4.ipynb
index 94fcda6f3c6511341ca695df30a4664738a63327..62b4f9c3fb7de1d27944e9f2ca810127b4ae0dea 100644
--- a/l4/TM-Lab4.ipynb
+++ b/l4/TM-Lab4.ipynb
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 190,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 191,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 192,
    "metadata": {},
    "outputs": [
     {
@@ -200,7 +200,7 @@
        "4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         i loved these movies , and i cant wiat for the third one ! very funny , not suitable for chilren  "
       ]
      },
-     "execution_count": 45,
+     "execution_count": 192,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -239,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 193,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -334,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 194,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -379,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 195,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -424,12 +424,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 196,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -459,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 197,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -518,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 198,
    "metadata": {
     "tags": [
      "solution"
@@ -529,9 +529,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Cluster 0: product, use, software, does, program, great, used, work, easy, just\n",
-      "Cluster 1: camera, lens, pictures, canon, digital, use, flash, battery, quality, great\n",
-      "Cluster 2: book, movie, like, album, cd, just, music, great, good, quot\n"
+      "Cluster 0: movie, cd, like, album, great, just, good, music, film, songs\n",
+      "Cluster 1: camera, product, use, lens, software, pictures, great, easy, does, used\n",
+      "Cluster 2: book, read, books, author, reading, story, like, quot, just, good\n"
      ]
     }
    ],
@@ -580,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 205,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -600,15 +600,14 @@
    },
    "outputs": [
     {
-     "ename": "NotImplementedError",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[52], line 6\u001b[0m\n\u001b[1;32m      3\u001b[0m sns\u001b[39m.\u001b[39mset()\n\u001b[1;32m      5\u001b[0m \u001b[39m# YOUR CODE HERE\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mNotImplementedError\u001b[39;00m()\n",
-      "\u001b[0;31mNotImplementedError\u001b[0m: "
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -616,8 +615,31 @@
     "import seaborn as sns\n",
     "sns.set()\n",
     "\n",
-    "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "data = df[:1000]\n",
+    "\n",
+    "reviews = vectorizer.fit_transform(data['text'])\n",
+    "feature_names = vectorizer.get_feature_names_out()\n",
+    "\n",
+    "rand_scores = []\n",
+    "adjusted_rand_scores = []\n",
+    "v_measure_scores = []\n",
+    "\n",
+    "for k in range(1, 11):\n",
+    "    kmeans = fit_kmeans(reviews, k)\n",
+    "\n",
+    "    labels_pred = kmeans.labels_#[feature_names[i] for i in kmeans.labels_]\n",
+    "    labels_true = data['category']\n",
+    "\n",
+    "    rand_scores.append(rand_score(labels_true, labels_pred))\n",
+    "    adjusted_rand_scores.append(adjusted_rand_score(labels_true, labels_pred))\n",
+    "    v_measure_scores.append(v_measure_score(labels_true, labels_pred))\n",
+    "\n",
+    "plt.plot(range(1, 11), rand_scores)\n",
+    "plt.plot(range(1, 11), adjusted_rand_scores)\n",
+    "plt.plot(range(1, 11), v_measure_scores)\n",
+    "plt.show()\n",
+    "\n",
+    "\n"
    ]
   },
   {
@@ -643,9 +665,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 200,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mr speaker mr vice president members of the 94th congress and distinguished guests\n",
+      "twenty six years ago a freshman congressman a young fellow with lots of idealism who was out to change the world stood before sam rayburn in the well of the house and solemnly swore to the same oath that all of you took yesterday an unforgettable experience and i congratulate you all\n",
+      "two days later that same freshman stood at the back of this great chamber over there someplace as president truman all charged up by his single handed election victory reported as the constitution requires on the state of the union\n",
+      "when the bipartisan applause stopped president truman said i am happy to report to this 81st congress that the state of the union is good our nation is better able than ever before to meet the needs of the american people and to give them their fair chance in the pursuit of happiness it is foremost among the nations of the world in the search for peace\n",
+      "today that freshman member from michigan stands where mr truman stood and i must say to you that the state of the union is not good\n"
+     ]
+    }
+   ],
    "source": [
     "from itertools import islice\n",
     "\n",
@@ -685,9 +719,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 201,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "OSError",
+     "evalue": "[E050] Can't find model 'en_core_web_sm'. It doesn't seem to be a Python package or a valid path to a data directory.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[201], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mspacy\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m nlp \u001b[39m=\u001b[39m spacy\u001b[39m.\u001b[39;49mload(\u001b[39m\"\u001b[39;49m\u001b[39men_core_web_sm\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
+      "File \u001b[0;32m~/projects/text-mining/l4/.venv/lib/python3.10/site-packages/spacy/__init__.py:51\u001b[0m, in \u001b[0;36mload\u001b[0;34m(name, vocab, disable, enable, exclude, config)\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mload\u001b[39m(\n\u001b[1;32m     28\u001b[0m     name: Union[\u001b[39mstr\u001b[39m, Path],\n\u001b[1;32m     29\u001b[0m     \u001b[39m*\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     34\u001b[0m     config: Union[Dict[\u001b[39mstr\u001b[39m, Any], Config] \u001b[39m=\u001b[39m util\u001b[39m.\u001b[39mSimpleFrozenDict(),\n\u001b[1;32m     35\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Language:\n\u001b[1;32m     36\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Load a spaCy model from an installed package or a local path.\u001b[39;00m\n\u001b[1;32m     37\u001b[0m \n\u001b[1;32m     38\u001b[0m \u001b[39m    name (str): Package name or model path.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[39m    RETURNS (Language): The loaded nlp object.\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 51\u001b[0m     \u001b[39mreturn\u001b[39;00m util\u001b[39m.\u001b[39;49mload_model(\n\u001b[1;32m     52\u001b[0m         name,\n\u001b[1;32m     53\u001b[0m         vocab\u001b[39m=\u001b[39;49mvocab,\n\u001b[1;32m     54\u001b[0m         disable\u001b[39m=\u001b[39;49mdisable,\n\u001b[1;32m     55\u001b[0m         enable\u001b[39m=\u001b[39;49menable,\n\u001b[1;32m     56\u001b[0m         exclude\u001b[39m=\u001b[39;49mexclude,\n\u001b[1;32m     57\u001b[0m         config\u001b[39m=\u001b[39;49mconfig,\n\u001b[1;32m     58\u001b[0m     )\n",
+      "File \u001b[0;32m~/projects/text-mining/l4/.venv/lib/python3.10/site-packages/spacy/util.py:472\u001b[0m, in \u001b[0;36mload_model\u001b[0;34m(name, vocab, disable, enable, exclude, config)\u001b[0m\n\u001b[1;32m    470\u001b[0m \u001b[39mif\u001b[39;00m name \u001b[39min\u001b[39;00m OLD_MODEL_SHORTCUTS:\n\u001b[1;32m    471\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mIOError\u001b[39;00m(Errors\u001b[39m.\u001b[39mE941\u001b[39m.\u001b[39mformat(name\u001b[39m=\u001b[39mname, full\u001b[39m=\u001b[39mOLD_MODEL_SHORTCUTS[name]))  \u001b[39m# type: ignore[index]\u001b[39;00m\n\u001b[0;32m--> 472\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mIOError\u001b[39;00m(Errors\u001b[39m.\u001b[39mE050\u001b[39m.\u001b[39mformat(name\u001b[39m=\u001b[39mname))\n",
+      "\u001b[0;31mOSError\u001b[0m: [E050] Can't find model 'en_core_web_sm'. It doesn't seem to be a Python package or a valid path to a data directory."
+     ]
+    }
+   ],
    "source": [
     "import spacy\n",
     "nlp = spacy.load(\"en_core_web_sm\")"