diff --git a/word2vec.ipynb b/word2vec.ipynb
index 9131f79..8c61fe4 100644
--- a/word2vec.ipynb
+++ b/word2vec.ipynb
@@ -11,12 +11,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 18,
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "start_time": "2024-05-19T18:08:45.407869Z",
-     "end_time": "2024-05-19T18:08:45.510869Z"
+     "start_time": "2024-05-19T18:21:27.211216Z",
+     "end_time": "2024-05-19T18:21:27.318205Z"
     }
    },
    "outputs": [],
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 19,
    "outputs": [],
    "source": [
     "def load_and_filter_data(file_path):\n",
@@ -70,6 +70,13 @@
     "    data = pd.DataFrame({'text': texts})\n",
     "    return data\n",
     "\n",
+    "def load_labels(file_path):\n",
+    "    labels = []\n",
+    "    with open(file_path, 'r', encoding='utf-8') as f:\n",
+    "        for line in f:\n",
+    "            labels.append(int(line.strip()))\n",
+    "    return np.array(labels)\n",
+    "\n",
     "def clean_text(text):\n",
     "    text = text.lower()\n",
     "    text = re.sub(r'\\d+', '', text)\n",
@@ -80,8 +87,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2024-05-19T18:08:45.425869Z",
-     "end_time": "2024-05-19T18:08:45.579869Z"
+     "start_time": "2024-05-19T18:21:27.231204Z",
+     "end_time": "2024-05-19T18:21:27.377342Z"
     }
    }
   },
@@ -96,21 +103,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 20,
    "outputs": [],
    "source": [
     "train_data = load_and_filter_data('train/train.tsv.gz')\n",
     "train_data['text'] = train_data['text'].apply(clean_text)\n",
     "dev_data = load_and_filter_tsv('dev-0/in.tsv')\n",
     "dev_data['text'] = dev_data['text'].apply(clean_text)\n",
+    "dev_labels = load_labels('dev-0/expected.tsv')\n",
     "test_data = load_and_filter_tsv('test-A/in.tsv')\n",
     "test_data['text'] = test_data['text'].apply(clean_text)"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2024-05-19T18:08:45.435869Z",
-     "end_time": "2024-05-19T18:08:48.741093Z"
+     "start_time": "2024-05-19T18:21:27.241222Z",
+     "end_time": "2024-05-19T18:21:31.160229Z"
     }
    }
   },
@@ -125,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 21,
    "outputs": [],
    "source": [
     "word2vec_model = KeyedVectors.load(\"word2vec_100_3_polish.bin\")"
@@ -133,8 +141,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2024-05-19T18:08:48.743093Z",
-     "end_time": "2024-05-19T18:09:04.607384Z"
+     "start_time": "2024-05-19T18:21:31.161230Z",
+     "end_time": "2024-05-19T18:21:54.895038Z"
     }
    }
   },
@@ -149,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 22,
    "outputs": [],
    "source": [
     "def text_to_vector(text, model):\n",
@@ -160,48 +168,26 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2024-05-19T18:09:04.609383Z",
-     "end_time": "2024-05-19T18:09:04.621383Z"
+     "start_time": "2024-05-19T18:21:54.900047Z",
+     "end_time": "2024-05-19T18:21:54.909040Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 23,
    "outputs": [],
    "source": [
-    "X = np.array([text_to_vector(text, word2vec_model) for text in train_data['text']])\n",
-    "y = np.array(train_data['label'])"
+    "X_train = np.array([text_to_vector(text, word2vec_model) for text in train_data['text']])\n",
+    "y_train = np.array(train_data['label'])\n",
+    "X_dev = np.array([text_to_vector(text, word2vec_model) for text in dev_data['text']])\n",
+    "X_test = np.array([text_to_vector(text, word2vec_model) for text in test_data['text']])"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2024-05-19T18:09:04.623384Z",
-     "end_time": "2024-05-19T18:09:12.703303Z"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Dodatkowy podział danych na zbiór treningowy oraz walidacyjny"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "outputs": [],
-   "source": [
-    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2024-05-19T18:09:12.705305Z",
-     "end_time": "2024-05-19T18:09:12.749303Z"
+     "start_time": "2024-05-19T18:21:54.913039Z",
+     "end_time": "2024-05-19T18:22:03.870813Z"
     }
    }
   },
@@ -216,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 24,
    "outputs": [
     {
      "name": "stderr",
@@ -238,109 +224,109 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2024-05-19T18:09:12.750302Z",
-     "end_time": "2024-05-19T18:09:12.954821Z"
+     "start_time": "2024-05-19T18:22:03.872859Z",
+     "end_time": "2024-05-19T18:22:04.122687Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 25,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Epoch 1/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 2ms/step - accuracy: 0.8631 - loss: 0.4892 - val_accuracy: 0.9238 - val_loss: 0.2468\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 2ms/step - accuracy: 0.8769 - loss: 0.4540 - val_accuracy: 0.9310 - val_loss: 0.2222\n",
       "Epoch 2/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9240 - loss: 0.2481 - val_accuracy: 0.9367 - val_loss: 0.2040\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 2ms/step - accuracy: 0.9270 - loss: 0.2362 - val_accuracy: 0.9303 - val_loss: 0.2106\n",
       "Epoch 3/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9289 - loss: 0.2213 - val_accuracy: 0.9377 - val_loss: 0.1938\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 2ms/step - accuracy: 0.9320 - loss: 0.2191 - val_accuracy: 0.9415 - val_loss: 0.1890\n",
       "Epoch 4/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9293 - loss: 0.2195 - val_accuracy: 0.9417 - val_loss: 0.1869\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 1ms/step - accuracy: 0.9306 - loss: 0.2139 - val_accuracy: 0.9406 - val_loss: 0.1850\n",
       "Epoch 5/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9328 - loss: 0.2120 - val_accuracy: 0.9364 - val_loss: 0.1930\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 1ms/step - accuracy: 0.9322 - loss: 0.2098 - val_accuracy: 0.9395 - val_loss: 0.1883\n",
       "Epoch 6/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9302 - loss: 0.2114 - val_accuracy: 0.9384 - val_loss: 0.1898\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 2ms/step - accuracy: 0.9325 - loss: 0.2074 - val_accuracy: 0.9404 - val_loss: 0.1814\n",
       "Epoch 7/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9312 - loss: 0.2134 - val_accuracy: 0.9438 - val_loss: 0.1803\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 1ms/step - accuracy: 0.9320 - loss: 0.2093 - val_accuracy: 0.9441 - val_loss: 0.1810\n",
       "Epoch 8/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9316 - loss: 0.2091 - val_accuracy: 0.9413 - val_loss: 0.1822\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 2ms/step - accuracy: 0.9326 - loss: 0.2094 - val_accuracy: 0.9441 - val_loss: 0.1804\n",
       "Epoch 9/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9330 - loss: 0.2104 - val_accuracy: 0.9228 - val_loss: 0.2174\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 1ms/step - accuracy: 0.9327 - loss: 0.2064 - val_accuracy: 0.9400 - val_loss: 0.1807\n",
       "Epoch 10/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 2ms/step - accuracy: 0.9325 - loss: 0.2093 - val_accuracy: 0.9402 - val_loss: 0.1839\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 1ms/step - accuracy: 0.9319 - loss: 0.2073 - val_accuracy: 0.9408 - val_loss: 0.1799\n",
       "Epoch 11/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9298 - loss: 0.2123 - val_accuracy: 0.9411 - val_loss: 0.1834\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 2ms/step - accuracy: 0.9324 - loss: 0.2061 - val_accuracy: 0.9391 - val_loss: 0.1826\n",
       "Epoch 12/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9323 - loss: 0.2071 - val_accuracy: 0.9445 - val_loss: 0.1774\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 1ms/step - accuracy: 0.9320 - loss: 0.2066 - val_accuracy: 0.9433 - val_loss: 0.1814\n",
       "Epoch 13/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9326 - loss: 0.2089 - val_accuracy: 0.9439 - val_loss: 0.1786\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 1ms/step - accuracy: 0.9325 - loss: 0.2066 - val_accuracy: 0.9382 - val_loss: 0.1882\n",
       "Epoch 14/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9329 - loss: 0.2050 - val_accuracy: 0.9387 - val_loss: 0.1866\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9330 - loss: 0.2045 - val_accuracy: 0.9406 - val_loss: 0.1813\n",
       "Epoch 15/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9331 - loss: 0.2035 - val_accuracy: 0.9447 - val_loss: 0.1815\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 2ms/step - accuracy: 0.9316 - loss: 0.2106 - val_accuracy: 0.9408 - val_loss: 0.1831\n",
       "Epoch 16/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9326 - loss: 0.2078 - val_accuracy: 0.9352 - val_loss: 0.1954\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 1ms/step - accuracy: 0.9338 - loss: 0.2036 - val_accuracy: 0.9384 - val_loss: 0.1862\n",
       "Epoch 17/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9331 - loss: 0.2059 - val_accuracy: 0.9436 - val_loss: 0.1762\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 2ms/step - accuracy: 0.9330 - loss: 0.2063 - val_accuracy: 0.9398 - val_loss: 0.1862\n",
       "Epoch 18/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 2ms/step - accuracy: 0.9332 - loss: 0.2050 - val_accuracy: 0.9437 - val_loss: 0.1765\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 1ms/step - accuracy: 0.9320 - loss: 0.2102 - val_accuracy: 0.9408 - val_loss: 0.1802\n",
       "Epoch 19/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 2ms/step - accuracy: 0.9343 - loss: 0.2038 - val_accuracy: 0.9452 - val_loss: 0.1788\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 2ms/step - accuracy: 0.9323 - loss: 0.2059 - val_accuracy: 0.9397 - val_loss: 0.1794\n",
       "Epoch 20/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 2ms/step - accuracy: 0.9343 - loss: 0.2037 - val_accuracy: 0.9368 - val_loss: 0.1887\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 2ms/step - accuracy: 0.9338 - loss: 0.2039 - val_accuracy: 0.9431 - val_loss: 0.1728\n",
       "Epoch 21/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 2ms/step - accuracy: 0.9326 - loss: 0.2054 - val_accuracy: 0.9435 - val_loss: 0.1773\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 1ms/step - accuracy: 0.9319 - loss: 0.2102 - val_accuracy: 0.9415 - val_loss: 0.1787\n",
       "Epoch 22/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 2ms/step - accuracy: 0.9327 - loss: 0.2059 - val_accuracy: 0.9417 - val_loss: 0.1813\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 1ms/step - accuracy: 0.9351 - loss: 0.2034 - val_accuracy: 0.9433 - val_loss: 0.1780\n",
       "Epoch 23/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 2ms/step - accuracy: 0.9333 - loss: 0.2041 - val_accuracy: 0.9405 - val_loss: 0.1809\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 2ms/step - accuracy: 0.9330 - loss: 0.2059 - val_accuracy: 0.9404 - val_loss: 0.1759\n",
       "Epoch 24/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 2ms/step - accuracy: 0.9340 - loss: 0.2045 - val_accuracy: 0.9393 - val_loss: 0.1840\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 2ms/step - accuracy: 0.9335 - loss: 0.2042 - val_accuracy: 0.9409 - val_loss: 0.1789\n",
       "Epoch 25/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 2ms/step - accuracy: 0.9324 - loss: 0.2046 - val_accuracy: 0.9405 - val_loss: 0.1833\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9341 - loss: 0.2052 - val_accuracy: 0.9389 - val_loss: 0.1813\n",
       "Epoch 26/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 2ms/step - accuracy: 0.9338 - loss: 0.2030 - val_accuracy: 0.9404 - val_loss: 0.1825\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9322 - loss: 0.2078 - val_accuracy: 0.9406 - val_loss: 0.1813\n",
       "Epoch 27/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 2ms/step - accuracy: 0.9346 - loss: 0.2051 - val_accuracy: 0.9385 - val_loss: 0.1875\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9319 - loss: 0.2069 - val_accuracy: 0.9283 - val_loss: 0.2017\n",
       "Epoch 28/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 2ms/step - accuracy: 0.9306 - loss: 0.2091 - val_accuracy: 0.9431 - val_loss: 0.1784\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9324 - loss: 0.2083 - val_accuracy: 0.9409 - val_loss: 0.1883\n",
       "Epoch 29/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9352 - loss: 0.2033 - val_accuracy: 0.9396 - val_loss: 0.1877\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9326 - loss: 0.2054 - val_accuracy: 0.9411 - val_loss: 0.1791\n",
       "Epoch 30/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 2ms/step - accuracy: 0.9333 - loss: 0.2037 - val_accuracy: 0.9403 - val_loss: 0.1808\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9333 - loss: 0.2041 - val_accuracy: 0.9419 - val_loss: 0.1769\n",
       "Epoch 31/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9313 - loss: 0.2090 - val_accuracy: 0.9413 - val_loss: 0.1783\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9343 - loss: 0.2029 - val_accuracy: 0.9439 - val_loss: 0.1756\n",
       "Epoch 32/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9340 - loss: 0.2063 - val_accuracy: 0.9428 - val_loss: 0.1815\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m5s\u001B[0m 1ms/step - accuracy: 0.9330 - loss: 0.2060 - val_accuracy: 0.9384 - val_loss: 0.1805\n",
       "Epoch 33/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9324 - loss: 0.2029 - val_accuracy: 0.9405 - val_loss: 0.1822\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9333 - loss: 0.2023 - val_accuracy: 0.9395 - val_loss: 0.1780\n",
       "Epoch 34/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9328 - loss: 0.2046 - val_accuracy: 0.9411 - val_loss: 0.1824\n",
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9347 - loss: 0.2025 - val_accuracy: 0.9408 - val_loss: 0.1806\n",
       "Epoch 35/35\n",
-      "\u001B[1m2454/2454\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 1ms/step - accuracy: 0.9322 - loss: 0.2063 - val_accuracy: 0.9414 - val_loss: 0.1820\n"
+      "\u001B[1m3067/3067\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m4s\u001B[0m 1ms/step - accuracy: 0.9315 - loss: 0.2038 - val_accuracy: 0.9419 - val_loss: 0.1762\n"
      ]
     },
     {
      "data": {
-      "text/plain": "<keras.src.callbacks.history.History at 0x2809ceeab60>"
+      "text/plain": "<keras.src.callbacks.history.History at 0x280fa5dcb80>"
      },
-     "execution_count": 14,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
-    "model.fit(X_train, y_train, epochs=35, batch_size=32, validation_data=(X_val, y_val))"
+    "model.fit(X_train, y_train, epochs=35, batch_size=32, validation_data=(X_dev, dev_labels))"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2024-05-19T18:09:12.957822Z",
-     "end_time": "2024-05-19T18:11:23.248486Z"
+     "start_time": "2024-05-19T18:22:04.124694Z",
+     "end_time": "2024-05-19T18:24:44.659379Z"
     }
    }
   },
@@ -355,26 +341,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 26,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[1m614/614\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 766us/step - accuracy: 0.9409 - loss: 0.1851\n",
-      "Accuracy on validation set: 0.9413533210754395\n"
+      "\u001B[1m171/171\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 814us/step - accuracy: 0.9413 - loss: 0.1863\n",
+      "Accuracy on validation set: 0.9418562054634094\n"
      ]
     }
    ],
    "source": [
-    "loss, accuracy = model.evaluate(X_val, y_val)\n",
+    "loss, accuracy = model.evaluate(X_dev, dev_labels)\n",
     "print(f'Accuracy on validation set: {accuracy}')"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2024-05-19T18:11:23.208454Z",
-     "end_time": "2024-05-19T18:11:23.753363Z"
+     "start_time": "2024-05-19T18:24:44.661382Z",
+     "end_time": "2024-05-19T18:24:44.864668Z"
     }
    }
   },
@@ -389,29 +375,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 27,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[1m171/171\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 882us/step\n",
-      "\u001B[1m171/171\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 700us/step\n"
+      "\u001B[1m171/171\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 900us/step\n",
+      "\u001B[1m171/171\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 765us/step\n"
      ]
     }
    ],
    "source": [
-    "X_dev = np.array([text_to_vector(text, word2vec_model) for text in dev_data['text']])\n",
-    "X_test = np.array([text_to_vector(text, word2vec_model) for text in test_data['text']])\n",
-    "\n",
     "dev_predictions = model.predict(X_dev)\n",
     "test_predictions = model.predict(X_test)"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2024-05-19T18:11:23.754367Z",
-     "end_time": "2024-05-19T18:11:25.114539Z"
+     "start_time": "2024-05-19T18:24:44.863671Z",
+     "end_time": "2024-05-19T18:24:45.395043Z"
     }
    }
   },
@@ -426,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 28,
    "outputs": [],
    "source": [
     "dev_predictions = (dev_predictions > 0.5).astype(int)\n",
@@ -438,8 +421,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2024-05-19T18:11:25.117540Z",
-     "end_time": "2024-05-19T18:11:25.149572Z"
+     "start_time": "2024-05-19T18:24:45.398007Z",
+     "end_time": "2024-05-19T18:24:45.438575Z"
     }
    }
   }