resume-classification-using-bert.ipynb

{
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   },
   "source": [
    "## Importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
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   "id": "09f924d9",
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    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/updated-resume-dataset/UpdatedResumeDataSet.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8281f56e",
   "metadata": {
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting wget\r\n",
      "  Downloading wget-3.2.zip (10 kB)\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hBuilding wheels for collected packages: wget\r\n",
      "  Building wheel for wget (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9675 sha256=d2113fa040bd346ddcad0a756370a5f9f809d4d3f0c7c82551e91d2a30766992\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/a1/b6/7c/0e63e34eb06634181c63adacca38b79ff8f35c37e3c13e3c02\r\n",
      "Successfully built wget\r\n",
      "Installing collected packages: wget\r\n",
      "Successfully installed wget-3.2\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c03cc073",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-01-12T19:27:32.300722Z"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-12 19:27:23.074433: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:23.075590: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:23.076865: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:23.077731: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:23.078562: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:23.079382: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:23.083165: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-01-12 19:27:23.357866: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:23.358880: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:23.359773: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:23.360662: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:23.361491: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:23.362322: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:31.083100: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:31.084059: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:31.084978: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:31.085736: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:31.086471: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:31.087130: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13351 MB memory:  -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\n",
      "2023-01-12 19:27:31.090495: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-01-12 19:27:31.091224: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13351 MB memory:  -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\n",
      "[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings, wget\n",
    "warnings.filterwarnings('ignore')\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense, Dropout, Input, Flatten, BatchNormalization\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.metrics import SparseCategoricalAccuracy\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from transformers import AutoTokenizer, TFDistilBertForSequenceClassification\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from wordcloud import WordCloud\n",
    "wget.download(\"https://raw.githubusercontent.com/yogawicaksana/helper_prabowo/main/helper_prabowo_ml.py\",out=\"helper_prabowo_ml.py\")\n",
    "from helper_prabowo_ml import clean_html, remove_links, remove_special_characters, removeStopWords, remove_, remove_digits, lower, email_address, non_ascii, punct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07de8271",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2023-01-12T19:27:32.332422Z",
     "shell.execute_reply": "2023-01-12T19:27:32.331329Z"
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     "exception": false,
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (12,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54970341",
   "metadata": {
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     "duration": 0.009638,
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     "start_time": "2023-01-12T19:27:32.344584",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e317ed4",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "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>Category</th>\n",
       "      <th>Resume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Skills * Programming Languages: Python (pandas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Education Details \\r\\nMay 2013 to May 2017 B.E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Areas of Interest Deep Learning, Control Syste...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Skills â¢ R â¢ Python â¢ SAP HANA â¢ Table...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Education Details \\r\\n MCA   YMCAUST,  Faridab...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Category                                             Resume\n",
       "0  Data Science  Skills * Programming Languages: Python (pandas...\n",
       "1  Data Science  Education Details \\r\\nMay 2013 to May 2017 B.E...\n",
       "2  Data Science  Areas of Interest Deep Learning, Control Syste...\n",
       "3  Data Science  Skills â¢ R â¢ Python â¢ SAP HANA â¢ Table...\n",
       "4  Data Science  Education Details \\r\\n MCA   YMCAUST,  Faridab..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/kaggle/input/updated-resume-dataset/UpdatedResumeDataSet.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "158f1142",
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     "start_time": "2023-01-12T19:27:32.476924",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
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   "id": "2c2233d3",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(962, 2)"
      ]
     },
     "execution_count": 6,
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a8f85e0",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 962 entries, 0 to 961\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Category  962 non-null    object\n",
      " 1   Resume    962 non-null    object\n",
      "dtypes: object(2)\n",
      "memory usage: 15.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category    0\n",
       "Resume      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.Category)\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d4bc3db1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:33.217190Z",
     "iopub.status.busy": "2023-01-12T19:27:33.216819Z",
     "iopub.status.idle": "2023-01-12T19:27:33.223642Z",
     "shell.execute_reply": "2023-01-12T19:27:33.222652Z"
    },
    "papermill": {
     "duration": 0.021851,
     "end_time": "2023-01-12T19:27:33.226253",
     "exception": false,
     "start_time": "2023-01-12T19:27:33.204402",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of available jobs: 25\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of available jobs:\", df.Category.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d0e7380d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:33.249957Z",
     "iopub.status.busy": "2023-01-12T19:27:33.249651Z",
     "iopub.status.idle": "2023-01-12T19:27:33.599320Z",
     "shell.execute_reply": "2023-01-12T19:27:33.598320Z"
    },
    "papermill": {
     "duration": 0.36417,
     "end_time": "2023-01-12T19:27:33.601790",
     "exception": false,
     "start_time": "2023-01-12T19:27:33.237620",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['resume_len'] = df.Resume.apply(len)\n",
    "sns.distplot(df.resume_len);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fd1d18b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:33.628996Z",
     "iopub.status.busy": "2023-01-12T19:27:33.627938Z",
     "iopub.status.idle": "2023-01-12T19:27:33.645749Z",
     "shell.execute_reply": "2023-01-12T19:27:33.644363Z"
    },
    "papermill": {
     "duration": 0.034599,
     "end_time": "2023-01-12T19:27:33.648943",
     "exception": false,
     "start_time": "2023-01-12T19:27:33.614344",
     "status": "completed"
    },
    "tags": []
   },
   "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>resume_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>962.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3160.364865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2886.528521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1217.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2355.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4073.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14816.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         resume_len\n",
       "count    962.000000\n",
       "mean    3160.364865\n",
       "std     2886.528521\n",
       "min      142.000000\n",
       "25%     1217.250000\n",
       "50%     2355.000000\n",
       "75%     4073.750000\n",
       "max    14816.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "228f9f4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:33.675149Z",
     "iopub.status.busy": "2023-01-12T19:27:33.674795Z",
     "iopub.status.idle": "2023-01-12T19:27:33.679728Z",
     "shell.execute_reply": "2023-01-12T19:27:33.678477Z"
    },
    "papermill": {
     "duration": 0.020697,
     "end_time": "2023-01-12T19:27:33.682525",
     "exception": false,
     "start_time": "2023-01-12T19:27:33.661828",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "max_resume_len = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "074f768a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:33.708556Z",
     "iopub.status.busy": "2023-01-12T19:27:33.708110Z",
     "iopub.status.idle": "2023-01-12T19:27:36.110501Z",
     "shell.execute_reply": "2023-01-12T19:27:36.109493Z"
    },
    "papermill": {
     "duration": 2.422399,
     "end_time": "2023-01-12T19:27:36.116981",
     "exception": false,
     "start_time": "2023-01-12T19:27:33.694582",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wc = WordCloud(width=600,height=300,random_state=101).generate(' '.join(df.Resume))\n",
    "plt.title(label='Word Cloud of Resume Sections',pad=20,fontsize=25,fontweight='bold',color='sienna')\n",
    "plt.imshow(wc);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ab4194e",
   "metadata": {
    "papermill": {
     "duration": 0.016491,
     "end_time": "2023-01-12T19:27:36.150244",
     "exception": false,
     "start_time": "2023-01-12T19:27:36.133753",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Encoding the target labels i.e. all available jobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "53a688d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:36.185170Z",
     "iopub.status.busy": "2023-01-12T19:27:36.184769Z",
     "iopub.status.idle": "2023-01-12T19:27:36.194213Z",
     "shell.execute_reply": "2023-01-12T19:27:36.193090Z"
    },
    "papermill": {
     "duration": 0.029779,
     "end_time": "2023-01-12T19:27:36.196595",
     "exception": false,
     "start_time": "2023-01-12T19:27:36.166816",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Data Science': 0,\n",
       " 'HR': 1,\n",
       " 'Advocate': 2,\n",
       " 'Arts': 3,\n",
       " 'Web Designing': 4,\n",
       " 'Mechanical Engineer': 5,\n",
       " 'Sales': 6,\n",
       " 'Health and fitness': 7,\n",
       " 'Civil Engineer': 8,\n",
       " 'Java Developer': 9,\n",
       " 'Business Analyst': 10,\n",
       " 'SAP Developer': 11,\n",
       " 'Automation Testing': 12,\n",
       " 'Electrical Engineering': 13,\n",
       " 'Operations Manager': 14,\n",
       " 'Python Developer': 15,\n",
       " 'DevOps Engineer': 16,\n",
       " 'Network Security Engineer': 17,\n",
       " 'PMO': 18,\n",
       " 'Database': 19,\n",
       " 'Hadoop': 20,\n",
       " 'ETL Developer': 21,\n",
       " 'DotNet Developer': 22,\n",
       " 'Blockchain': 23,\n",
       " 'Testing': 24}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_dict = {}\n",
    "\n",
    "for idx, label in enumerate(df.Category.unique()):\n",
    "    labels_dict[label] = idx\n",
    "\n",
    "labels_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ed168b9d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:36.232309Z",
     "iopub.status.busy": "2023-01-12T19:27:36.231237Z",
     "iopub.status.idle": "2023-01-12T19:27:36.239402Z",
     "shell.execute_reply": "2023-01-12T19:27:36.238221Z"
    },
    "papermill": {
     "duration": 0.028263,
     "end_time": "2023-01-12T19:27:36.241893",
     "exception": false,
     "start_time": "2023-01-12T19:27:36.213630",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.Category = df.Category.apply(func=lambda x: labels_dict[x])\n",
    "df.Category = df.Category.astype(np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef9fe52",
   "metadata": {
    "papermill": {
     "duration": 0.016465,
     "end_time": "2023-01-12T19:27:36.275182",
     "exception": false,
     "start_time": "2023-01-12T19:27:36.258717",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Text Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "01d332a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:36.310087Z",
     "iopub.status.busy": "2023-01-12T19:27:36.309018Z",
     "iopub.status.idle": "2023-01-12T19:27:36.317866Z",
     "shell.execute_reply": "2023-01-12T19:27:36.316792Z"
    },
    "papermill": {
     "duration": 0.028581,
     "end_time": "2023-01-12T19:27:36.320142",
     "exception": false,
     "start_time": "2023-01-12T19:27:36.291561",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def text_preprocess(data,col):\n",
    "    data[col] = data[col].apply(func=clean_html)\n",
    "    data[col] = data[col].apply(func=remove_)\n",
    "    data[col] = data[col].apply(func=removeStopWords)\n",
    "    data[col] = data[col].apply(func=remove_digits)\n",
    "    data[col] = data[col].apply(func=remove_links)\n",
    "    data[col] = data[col].apply(func=remove_special_characters)\n",
    "    data[col] = data[col].apply(func=punct)\n",
    "    data[col] = data[col].apply(func=non_ascii)\n",
    "    data[col] = data[col].apply(func=email_address)\n",
    "    data[col] = data[col].apply(func=lower)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42f30f99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:36.355576Z",
     "iopub.status.busy": "2023-01-12T19:27:36.354581Z",
     "iopub.status.idle": "2023-01-12T19:27:37.337654Z",
     "shell.execute_reply": "2023-01-12T19:27:37.336527Z"
    },
    "papermill": {
     "duration": 1.003364,
     "end_time": "2023-01-12T19:27:37.339981",
     "exception": false,
     "start_time": "2023-01-12T19:27:36.336617",
     "status": "completed"
    },
    "tags": []
   },
   "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>Category</th>\n",
       "      <th>Resume</th>\n",
       "      <th>resume_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>skills programming languages python pandas num...</td>\n",
       "      <td>4786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>education details may may b e uitrgpv data sci...</td>\n",
       "      <td>1268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>areas interest deep learning control system de...</td>\n",
       "      <td>1871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>skills r python sap hana tableau sap hana sql ...</td>\n",
       "      <td>6995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>education details mca ymcaust faridabad haryan...</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Category                                             Resume  resume_len\n",
       "0         0  skills programming languages python pandas num...        4786\n",
       "1         0  education details may may b e uitrgpv data sci...        1268\n",
       "2         0  areas interest deep learning control system de...        1871\n",
       "3         0  skills r python sap hana tableau sap hana sql ...        6995\n",
       "4         0  education details mca ymcaust faridabad haryan...         452"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_df = text_preprocess(df,'Resume')\n",
    "preprocessed_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f7c75cf",
   "metadata": {
    "papermill": {
     "duration": 0.016073,
     "end_time": "2023-01-12T19:27:37.372864",
     "exception": false,
     "start_time": "2023-01-12T19:27:37.356791",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Downloading the pretrained tokenizer and DistilBert model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "62ec1931",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:37.408122Z",
     "iopub.status.busy": "2023-01-12T19:27:37.407153Z",
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     "shell.execute_reply": "2023-01-12T19:27:52.825460Z"
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     "exception": false,
     "start_time": "2023-01-12T19:27:37.389360",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "769e4872c7dd441c87f89c6af4f7b023",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/258 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b8897b0dd584021b72720472ed3576d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/0.98k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b88e764d9a2a4f22a14c05d727a5bcaa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "192d236b605140989ee1f29c04ad3e78",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/112 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "437dc3a15e4849599e6595fd08d73226",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/255M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-12 19:27:50.060214: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n",
      "All PyTorch model weights were used when initializing TFDistilBertForSequenceClassification.\n",
      "\n",
      "All the weights of TFDistilBertForSequenceClassification were initialized from the PyTorch model.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertForSequenceClassification for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"manishiitg/distilbert-resume-parts-classify\")\n",
    "bert_model = TFDistilBertForSequenceClassification.from_pretrained(\"manishiitg/distilbert-resume-parts-classify\",from_pt=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5746500",
   "metadata": {
    "papermill": {
     "duration": 0.017649,
     "end_time": "2023-01-12T19:27:52.864437",
     "exception": false,
     "start_time": "2023-01-12T19:27:52.846788",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Splitting the preprocessed dataset into training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "74ef9fbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:52.901519Z",
     "iopub.status.busy": "2023-01-12T19:27:52.901092Z",
     "iopub.status.idle": "2023-01-12T19:27:52.909183Z",
     "shell.execute_reply": "2023-01-12T19:27:52.908145Z"
    },
    "papermill": {
     "duration": 0.029366,
     "end_time": "2023-01-12T19:27:52.911559",
     "exception": false,
     "start_time": "2023-01-12T19:27:52.882193",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df, test_df = train_test_split(preprocessed_df,test_size=0.3,shuffle=True,random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6793198",
   "metadata": {
    "papermill": {
     "duration": 0.017382,
     "end_time": "2023-01-12T19:27:52.946578",
     "exception": false,
     "start_time": "2023-01-12T19:27:52.929196",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Text Tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9453e892",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:52.983734Z",
     "iopub.status.busy": "2023-01-12T19:27:52.983329Z",
     "iopub.status.idle": "2023-01-12T19:27:54.084563Z",
     "shell.execute_reply": "2023-01-12T19:27:54.083362Z"
    },
    "papermill": {
     "duration": 1.124034,
     "end_time": "2023-01-12T19:27:54.087437",
     "exception": false,
     "start_time": "2023-01-12T19:27:52.963403",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train = tokenizer(text=train_df.Resume.tolist(),\n",
    "                   add_special_tokens=True,\n",
    "                   padding=True,\n",
    "                   truncation=True,\n",
    "                   max_length=max_resume_len,\n",
    "                   return_tensors='tf',\n",
    "                   return_attention_mask=True,\n",
    "                   return_token_type_ids=False,\n",
    "                   verbose=1)\n",
    "\n",
    "X_test = tokenizer(text=test_df.Resume.tolist(),\n",
    "                  add_special_tokens=True,\n",
    "                  padding=True,\n",
    "                  truncation=True,\n",
    "                  max_length=max_resume_len,\n",
    "                  return_tensors='tf',\n",
    "                  return_attention_mask=True,\n",
    "                  return_token_type_ids=False,\n",
    "                  verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e74ec9a",
   "metadata": {
    "papermill": {
     "duration": 0.017055,
     "end_time": "2023-01-12T19:27:54.122585",
     "exception": false,
     "start_time": "2023-01-12T19:27:54.105530",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Defining the model architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6ae03b26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:54.158315Z",
     "iopub.status.busy": "2023-01-12T19:27:54.157924Z",
     "iopub.status.idle": "2023-01-12T19:27:54.167540Z",
     "shell.execute_reply": "2023-01-12T19:27:54.166346Z"
    },
    "papermill": {
     "duration": 0.030873,
     "end_time": "2023-01-12T19:27:54.170595",
     "exception": false,
     "start_time": "2023-01-12T19:27:54.139722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "input_ids = Input(shape=(max_resume_len,),dtype=tf.int32,name='input_ids')\n",
    "attention_masks = Input(shape=(max_resume_len,),dtype=tf.int32,name='attention_mask')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c046583a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:54.207921Z",
     "iopub.status.busy": "2023-01-12T19:27:54.207583Z",
     "iopub.status.idle": "2023-01-12T19:27:59.878878Z",
     "shell.execute_reply": "2023-01-12T19:27:59.877878Z"
    },
    "papermill": {
     "duration": 5.692077,
     "end_time": "2023-01-12T19:27:59.881148",
     "exception": false,
     "start_time": "2023-01-12T19:27:54.189071",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<keras.engine.input_layer.InputLayer at 0x7f5dd6398310>,\n",
       " <keras.engine.input_layer.InputLayer at 0x7f5dd6398710>,\n",
       " <transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertForSequenceClassification at 0x7f5dd6392b10>,\n",
       " <keras.layers.core.Flatten at 0x7f5a13cfbfd0>,\n",
       " <keras.layers.core.Dense at 0x7f5dd63d6f90>,\n",
       " <keras.layers.normalization.batch_normalization.BatchNormalization at 0x7f5a13e2bc50>,\n",
       " <keras.layers.core.Dropout at 0x7f5a13c9b510>,\n",
       " <keras.layers.core.Dense at 0x7f5a13c9b050>,\n",
       " <keras.layers.core.Dropout at 0x7f5a13c9b110>,\n",
       " <keras.layers.core.Dense at 0x7f5eb9684910>,\n",
       " <keras.layers.normalization.batch_normalization.BatchNormalization at 0x7f5eb96848d0>,\n",
       " <keras.layers.core.Dropout at 0x7f5e1c0a2790>,\n",
       " <keras.layers.core.Dense at 0x7f5a12e39d50>,\n",
       " <keras.layers.core.Dropout at 0x7f5a12e39f10>,\n",
       " <keras.layers.core.Dense at 0x7f5a12e39150>,\n",
       " <keras.layers.core.Dense at 0x7f5a13848610>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_embeddings = bert_model(input_ids,attention_mask=attention_masks)[0] # 0 --> final hidden state, 1 --> pooling output\n",
    "\n",
    "output = Flatten()(word_embeddings)\n",
    "output = Dense(units=1024,activation='relu')(output)\n",
    "output = BatchNormalization()(output)\n",
    "output = Dropout(0.25)(output)\n",
    "output = Dense(units=512,activation='relu')(output)\n",
    "output = Dropout(0.25)(output)\n",
    "output = Dense(units=256,activation='relu')(output)\n",
    "output = BatchNormalization()(output)\n",
    "output = Dropout(0.25)(output)\n",
    "output = Dense(units=128,activation='relu')(output)\n",
    "output = Dropout(0.25)(output)\n",
    "output = Dense(units=64,activation='relu')(output)\n",
    "output = Dense(units=25,activation='softmax')(output)\n",
    "\n",
    "model = Model(inputs=[input_ids,attention_masks],outputs=output)\n",
    "model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d740b934",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:59.915441Z",
     "iopub.status.busy": "2023-01-12T19:27:59.914469Z",
     "iopub.status.idle": "2023-01-12T19:27:59.923447Z",
     "shell.execute_reply": "2023-01-12T19:27:59.922516Z"
    },
    "papermill": {
     "duration": 0.028188,
     "end_time": "2023-01-12T19:27:59.925394",
     "exception": false,
     "start_time": "2023-01-12T19:27:59.897206",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.layers[2].trainable = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d00ada02",
   "metadata": {
    "papermill": {
     "duration": 0.015961,
     "end_time": "2023-01-12T19:27:59.957136",
     "exception": false,
     "start_time": "2023-01-12T19:27:59.941175",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Visualizing the model architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6fa9e2e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:27:59.992938Z",
     "iopub.status.busy": "2023-01-12T19:27:59.992081Z",
     "iopub.status.idle": "2023-01-12T19:28:00.005644Z",
     "shell.execute_reply": "2023-01-12T19:28:00.004419Z"
    },
    "papermill": {
     "duration": 0.036475,
     "end_time": "2023-01-12T19:28:00.009735",
     "exception": false,
     "start_time": "2023-01-12T19:27:59.973260",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_ids (InputLayer)          [(None, 200)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "attention_mask (InputLayer)     [(None, 200)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "tf_distil_bert_for_sequence_cla TFSequenceClassifier 66962700    input_ids[0][0]                  \n",
      "                                                                 attention_mask[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 12)           0           tf_distil_bert_for_sequence_class\n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 1024)         13312       flatten[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 1024)         4096        dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_20 (Dropout)            (None, 1024)         0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 512)          524800      dropout_20[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_21 (Dropout)            (None, 512)          0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 256)          131328      dropout_21[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 256)          1024        dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_22 (Dropout)            (None, 256)          0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 128)          32896       dropout_22[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_23 (Dropout)            (None, 128)          0           dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 64)           8256        dropout_23[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 25)           1625        dense_4[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 67,680,037\n",
      "Trainable params: 67,677,477\n",
      "Non-trainable params: 2,560\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1e38367c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:28:00.046110Z",
     "iopub.status.busy": "2023-01-12T19:28:00.045741Z",
     "iopub.status.idle": "2023-01-12T19:28:01.489645Z",
     "shell.execute_reply": "2023-01-12T19:28:01.488342Z"
    },
    "papermill": {
     "duration": 1.466584,
     "end_time": "2023-01-12T19:28:01.493641",
     "exception": false,
     "start_time": "2023-01-12T19:28:00.027057",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "data": {
      "image/png": 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mzpypSZMmacyYMS5jfvrpJ61atUpFRUXav3+/OnTooMTERCUlJbl8BumXs6MLCwu1a9cuFRUV6dVXX9WqVavUp08flZWVOfVr3bq1pH/NIQAAAICGi6IzAAAAAAAeNmnSJPXp00cWi0UDBw7U0KFDlZWVpeLiYpfxVqtVy5Yts/fp2bOn3n33XV26dEnJycl1nu+VK1fsBWlPsxU8g4KCqhTfokULZWRkqEmTJpoyZYqOHj16w/hZs2bp+++/15IlS5SQkKDAwEB16tRJ6enpCg8PV1JSkgoLC536VXVOZ82apePHj2vx4sUaMmSI/P39FRERoffee0+maVZrRXZVlJSUKD4+Xg8//LCWL1/uMmb37t1655131KNHD1ksFnXu3FnvvPOOevXqpaVLl2rfvn0O8bYV/L6+vlq9erV+/etfKzg4WE888YRmzZql3Nxc/eUvf3G6T2BgoAzDsM8hAAAAgIaLojMAAAAAAB4WExPj8Oe2bdtKkk6fPu0y3mKx2LcmtunWrZtatWqlnJycOi/iXb0S19NsBc8mTZpUuc/999+vRYsWyWq1avTo0bpw4cJ1Yzds2CBJGjp0qEO7j4+PBgwYoAsXLrjcHrqqc7px40Y1atRICQkJDu1hYWGKiIjQgQMHdPLkySo/241YrVbFxcXp3nvv1dq1a9W4ceNq9R81apQkafPmzQ7tttX0AwcOdNqK/ZFHHpF0/S20vby8bvj9AwAAAGgYKDoDAAAAAOBh167S9fb2lvTLimJXgoODXbaHhoZKkoqKimoxu/qtadOmkqTLly9Xq19SUpLGjh2rQ4cOaerUqS5jLl68qLKyMjVt2lQBAQFO11u2bCnply24r1WVObWNf+XKFQUFBTmcCW0Yhv2s5G+//bZaz+ZKZWWlRo8erdatW+vtt9+udsFZksLDwyU5v1/t27eXJIWEhDj1sb2TZ86cuW5evr6+1c4FAAAAQP1C0RkAAAAAgAampKTE5dbWtmKgrdAn/XKO8aVLl5xiz549e93xDcOohSxvDVsh1NWZwTeTlpamzp07a9WqVVqzZo3TdR8fHwUFBamiokLl5eVO123baoeFhVX73rbxg4OD5eXlpcuXL9u3LL/2079/f7fGv9qUKVN08eJFZWRkOKxGvvvuu6t8NrdtlfbV75ck9e3bV5JcrrC3vZO2Av3Vzp07J9M07XMIAAAAoOGi6AwAAAAAQANTUVGhrKwsh7avv/5ap0+fVmRkpEMRLzw8XKdOnXKILSgo0IkTJ647vp+fn0OhunPnzlq5cmUtZV+7unbtKklubUHt7++vDz74QBaLRcuWLXMZM2LECEnS1q1bHdovXryo7du3y9fXV3FxcdW+t83IkSNVWVmpPXv2OF1buHCh2rVrp8rKSrfHl6Q5c+bo8OHD2rRpk3x8fG4Ym5aWpujoaKd20zSVkZEh6V9bZtsMGTJErVu31rZt2+zbndvYtuIePny405i299I2hwAAAAAaLorOAAAAAAA0MEFBQXrhhReUmZkpq9Wq7OxsTZgwQd7e3kpNTXWIHTRokE6fPq033nhD58+fV15enpKTk51Wq16tR48eys3N1Y8//qjMzEwdO3ZM/fr1s1+PjY1VSEhIlVfI1qXIyEiFhoYqJyfHrf4RERFasWLFda8vWLBAHTp0UEpKirZs2aLy8nLl5ubqscceU35+vlJTU12u4q2qBQsWqGPHjpo4caI+/vhjlZWVqbS0VCtWrNArr7yiRYsWOaxMnjBhggzD0Pfff1+l8VevXq2XX35Z+/btU0BAgNMW3nl5eU59vvzySz399NP67rvvVFFRoW+++UaPP/64Dhw4oGnTpql3794O8T4+PkpLS1NJSYnGjRunb7/9VmfPntWaNWu0YMEC9e7dW0lJSU73OXjwoKRf3lEAAAAADRtFZwAAAAAA6sjGjRtlGIY2bdokSfL19dWECRO0d+9ep/bZs2dL+mVr64ULF0qSoqKilJCQ4DSuv7+/li5dqpdfflnh4eF68MEH1axZM+3YsUMPPfSQQ+zcuXM1adIkzZ8/X6GhoXryySc1Y8YMhYWFqaSkRIZhaObMmQ59lixZovvuu09dunTR2LFjlZqaqi5dutivV1ZW2rd+9jTDMDRp0iTt27fPvv2zJBUXF8swDMXExKisrEyGYahnz54ux0hMTNRTTz3l8lpYWJiysrI0fvx4JSUlKSQkRL169ZLVatWnn36qyZMnS5LbcxoaGqr9+/dr+PDhmjp1qu666y795je/0YcffqhNmzZpzJgxDvnk5+fL399f7dq1q9L3s379+irF2Tz++ONat26d8vPzFR8fr+DgYPXu3VunTp1Senq6/vrXv7rsFx8fr88//1wVFRWKiYlRy5YtNX/+fM2YMUM7d+50eW7zhg0b1Lp1aw0dOrRaOQIAAACofwyzPvwNEQAAALiDZWRkaOzYsSorK1NgYKCn0wHueIcPH1bXrl31xRdfqE+fPp5Ox0n37t1VXFzs1nbS9V1cXJwCAwO1bt26avUrKytTRESEEhIStHz58jrKzvPOnj2rVq1aKTExUW+99Zan06mRnJwcRUVFKT09XePGjXNrjMWLF2vOnDk6d+5cLWcHAAAAoJrSWOkMAAAAAAAatKCgIG3evFnr16/Xm2++6el06oRpmkpKSlJgYKBeffVVT6dTI8eOHdPIkSM1a9YstwvOAAAAAOoXis4AAADAbeK9996zn9HZtGlTT6fToP30009avny5YmNj1bx5c/n6+uqee+5RYmKiy3NjqxvvDn9/f6ezWBs1aqRmzZopMjJSf/jDH3TgwIFauRfQEEVFRSk7O1sff/zxbbnytbCwUMeOHdP27dsVFhbm6XRqZMWKFZo3b57mzZvn6VQAAAAA1BKKzgAAAMBtYty4cTJNUwMGDPB0Kg3ejBkzNG3aNA0bNkxHjhxRSUmJVq1apYMHDyo6OlobN26sUbw7zp8/r3/84x+SpGHDhsk0TV2+fFlHjx7VK6+8oqNHj6pnz576t3/7N/3f//1fje+H+mfRokUyDEM5OTk6deqUDMOwnxmMX7Rv315btmy5LY8qCAsL0+7duxUREeHpVGps4cKFrHAGAAAAbjMUnQEAAADcVvz9/dW3b98ajzNx4kQlJycrLCxMfn5+6tevn9LT0/Xzzz/r+eefr3F8bWjcuLFatmypYcOGaceOHXr++ee1evVqjR8/XqZp1sk967vamv/6aPr06TJN0+Ezd+5cT6cFAAAAAIC8PJ0AAAAAANQ3aWlpLtsjIyPl6+urvLw8maYpwzDciq8r//Ef/6Fdu3bpo48+0nvvvafx48fX6f0AAAAAAAAkVjoDAAAAQJVZrVZduHBBXbt2rVIBubrxNWUYhqZOnSpJWrZsWZ3fDwAAAAAAQKLoDAAAADRYR48e1fDhwxUUFCSLxaJ+/fpp9+7dLmM3btwowzDsn2+++UZjxoxRSEiIva24uFiSVFJSomeffVYdO3aUt7e3mjVrpsGDB2vnzp0OY9rOlzUMQ23atFFWVpYGDBiggIAA+fn5qX///tqzZ49TLlUdf+7cufbxr94uedu2bfb2Fi1aOOVjtVq1Z88ee4yXV+1t8LRu3TpJ0osvvlgn8bXB9l3t3btXly9flsT8AwAAAACAukXRGQAAAGiAvvvuO/Xp00fZ2dlav369CgsLtWzZMr366qvKy8tzih8+fLhM09SwYcMkSVOmTNEf/vAH/fjjj9q7d68aN24sSSooKFBMTIzS09OVmpqq4uJi7du3T35+fhowYIDDNtK282UjIyN19uxZJScna+7cuSooKNDnn3+u0tJSxcbGateuXfY+1Rl/9uzZMk1TFovF4Vni4+Nlmqaio6Md2m35WCwWPfDAA/YzbysrK2v+hUsqLCzUzJkzNWnSJI0ZM6ZW4mNjYxUSEqK9e/fWSo6SFBYWJkmqrKy0F5KZfwAAAAAAUJcoOgMAAAAN0AsvvKCzZ88qNTVVv/vd7+Tv769u3brpv/7rv5Sfn3/T/v/v//0/Pfzww/Lz81Pv3r1VWVmpFi1aaNasWfr++++1ZMkSJSQkKDAwUJ06dVJ6errCw8OVlJSkwsJCp/GsVquWLVumPn36yGKxqGfPnnr33Xd16dIlJScn2+PcHd/TSkpKFB8fr4cffljLly+vtfgrV67Yi6O1pSpjMf8AAAAAAKA2sc8YAAAA0ABt27ZNkhQXF+fQ3qpVK3Xq1Em5ubk37N+rVy+X7Rs2bJAkDR061KHdx8dHAwYM0Jo1a/TJJ5/oiSeecLhusVjUvXt3h7Zu3bqpVatWysnJUX5+vsLDw90e35OsVqvi4uJ077336p133rGvCq6N+M8++6yWs5X9Pzpo0qSJw/bTV2P+qyYlJUXBwcGeTuOO8o9//EONGjVy+mcb4Mrx48d15coVT6cBAAAAQBSdAQAAgAbn4sWLKi8vV9OmTeXv7+90PTQ09KZF52u3LLaNW1ZWpqZNmyogIMDpesuWLSX9skXyta5XmAsNDdXp06dVVFSk5s2buz2+p1RWVmr06NFq3bq13n777ZsWnKsbXxds53r36dNHTZo0cRnD/FeNxWJRYGCgp9O4ozRu3FheXl5876gSHx8fT6cAAAAA4J8oOgMAAAANjI+PjwICAlReXq7z5887FZ5LS0vdHjcoKEhlZWUqLy93Kgzatj22nRl8tZKSEpmmKcMwHNqLiook/VJ8dHf8Ro0a6dKlS073PHv2rMvnuDaHmpgyZYouXryoDRs2yMvrX399uvvuu/Xuu+/q/vvvr1F8bbty5YrefPNNSdLTTz9drb7Mv7N58+apT58+tTYebi4uLk6BgYFat26dp1NBA7B48WLNmTPH02kAAAAAEGc6AwAAAA3S4MGDJf1rm22b4uJiffPNN26PO2LECEnS1q1bHdovXryo7du3y9fX1+W2txUVFcrKynJo+/rrr3X69GlFRkYqPDzc7fHDw8N16tQph7aCggKdOHHC5TP4+fk5FCk7d+6slStXXveZr2fOnDk6fPiwNm3aVKXVdNWNrwuzZs3S/v37NWLECI0ePbra/Zl/AAAAAADgDorOAAAAQAM0f/58NW/eXCkpKfrf//1fnT9/XkeOHNGECRNcbrldVQsWLFCHDh2UkpKiLVu2qLy8XLm5uXrssceUn5+v1NRU+zbIVwsKCtILL7ygzMxMWa1WZWdna8KECfL29lZqamqNxh80aJBOnz6tN954Q+fPn1deXp6Sk5MVGhrq8hl69Oih3Nxc/fjjj8rMzNSxY8fUr1+/an0Pq1ev1ssvv6x9+/YpICBAhmE4fPLy8moUbxMbG6uQkBDt3bu3WvnZXLlyRUVFRdq0aZMGDBig1157TRMnTtTatWvdWvHL/AMAAAAAAHdQdAYAAAAaoI4dOyozM1MxMTEaNWqUQkND9eSTT2ratGnq1q2bLl68KMMwNGnSJEnS3r17ZRiGNm3aJEny9fV1WZQMCwtTVlaWxo8fr6SkJIWEhKhXr16yWq369NNPNXnyZJf5+Pv7a+nSpXr55ZcVHh6uBx98UM2aNdOOHTv00EMP1Wj8uXPnatKkSZo/f779OWfMmKGwsDCVlJTIMAzNnDnTHr9kyRLdd9996tKli8aOHavU1FR16dKlWt/v+vXr6zTeprKyUqZpyjTNm8b6+/srKipKkrRp0yYZhiEvLy916tRJL730kjp37qwDBw7oP//zP+Xr6+vQl/mv3vwDAAAAAIDqMcyq/NsNAAAAAHUmIyNDY8eOVVlZmQIDAz2dTrV1795dxcXFOnnypKdTgQfcjvN/+PBhde3aVV988QVnOt9inOmM6rCd6Xzu3DlPpwIAAADc6dJY6QwAAAAAAAAAAAAAcBtFZwAAAAAAcFs4fvy4Hn30UZ07d07FxcUOZ6tHRUWpoqLCqc+1cYZhqGfPnh7Ivm789NNPWr58uWJjY9W8eXP5+vrqnnvuUWJionJyclz26du3r9N3YvukpKRc916XL1/W66+/rujoaAUEBCg0NFSDBw/W5s2bHY4RmDlzpt5///1af1YAAAAAnkPRGQAAAIBbFi1aJMMwlJOTo1OnTskwDM2ePdvTad3Q9YooV3/mzJnj6TQbhIY4/7i9HTx4UD179tSgQYMUGBioFi1ayDRNZWVl2a+7Kpja4jIzMxUSEiLTNJWdnX2r068zM2bM0LRp0zRs2DAdOXJEJSUlWrVqlQ4ePKjo6Ght3LixVu5jtVoVGxur1atX6/XXX1dRUZGys7Pl7++vRx99VIcPH7bHTp48WbNmzdJLL71UK/cGAAAA4Hlenk4AAAAAQMM0ffp0TZ8+3dNpVMvVK+1QMw1x/m8H/v7+6t69u3bv3n1H53Ctc+fO6ZFHHtHvf/97TZ061em6j4+P/P39tWLFCj300EMaP368B7L0nIkTJyo5Odn+5379+ik9PV3du3fX888/r+HDhzv1ycrKqtaK7xkzZuirr75Sbm6uWrZsKUlq166dVq9erc2bNzvEduzYURs2bFBUVJS6deumMWPGuPlkAAAAAOoLVjoDAAAAAIAG7bXXXlNBQYH++Mc/urzetGlTrV27Vo0aNdKUKVOUm5t7izP0nLS0NK1YscKpPTIyUr6+vsrLy6vxf5BTWFiolStXKjEx0V5wtrFYLKqoqFDXrl2d7j9q1Cg999xzqqysrNH9AQAAAHgeRWcAAAAAANBgmaaptLQ09e7dW61atbpuXFxcnGbPnq3y8nKNHj3a5fnOdxKr1aoLFy6oa9euMgyjRmN99NFH+vnnn9W3b99q9RsxYoROnjyprVu31uj+AAAAADyPojMAAAAAALWkpKREzz77rDp27Chvb281a9ZMgwcP1s6dO+0xc+fOtZ8hfnWRbtu2bfb2Fi1aOIxrO0PbarVqz5499jgvLy+H64ZhqE2bNsrKytKAAQMUEBAgPz8/9e/fX3v27KnTHDwlJydHhYWFioyMvGnsn/70Jw0aNEhfffWVpk2bVuV7VGVeN27c6HA+/A8//KCxY8cqODhYISEhSkhIUF5ensvxz5w5o6SkJLVv317e3t666667NHLkSB08eLDKOVbXunXrJEkvvviiy+tr1qxR9+7dZbFYFBQUZN+S25Uvv/xSktSsWTM999xzatu2rby9vfWrX/1KSUlJKi0tddmve/fukqRPPvmkpo8DAAAAwMMoOgMAAAAAUAsKCgoUExOj9PR0paamqri4WPv27ZOfn58GDBigtLQ0SdLs2bNlmqYsFotD//j4eJmmqejoaKexp0+fbu/zwAMPyDRNmaZp35bYdj0yMlJnz55VcnKy5s6dq4KCAn3++ecqLS1VbGysdu3aVWc52MTGxiokJER79+51/8ushkOHDkmS2rRpc9PYRo0aae3atWrbtq3S0tK0du3am/ap6rwOHz5cpmlq2LBhkqSUlBSlpKTo1KlTev/997Vjxw6XZ0nn5+crJiZGGRkZWrZsmUpLS/XZZ5+ptLRUffr0UWZmZnW+jiopLCzUzJkzNWnSpOuep/zTTz9p1apVKioq0v79+9WhQwclJiYqKSnJ5TNIv5wdXVhYqF27dqmoqEivvvqqVq1apT59+qisrMypX+vWrSX9aw4BAAAANFwUnQEAAAAAqAWzZs3S999/ryVLlighIUGBgYHq1KmT0tPTFR4erqSkJBUWFtZ5HlarVcuWLVOfPn1ksVjUs2dPvfvuu7p06ZKSk5Pr/P5XrlyxF6RvBVvBMygoqErxLVq0UEZGhpo0aaIpU6bo6NGjN4x3d14nTZpkn4OBAwdq6NChysrKUnFxsdP4x48f1+LFizVkyBD5+/srIiJC7733nkzTrNaK7KooKSlRfHy8Hn74YS1fvtxlzO7du/XOO++oR48eslgs6ty5s9555x316tVLS5cu1b59+xzibVuV+/r6avXq1fr1r3+t4OBgPfHEE5o1a5Zyc3P1l7/8xek+gYGBMgzDPocAAAAAGi6KzgAAAAAA1IINGzZIkoYOHerQ7uPjowEDBujChQu3ZBthi8Vi37bYplu3bmrVqpVycnLqvMB39SrdW8FW8GzSpEmV+9x///1atGiRrFarRo8erQsXLlw31t15jYmJcfhz27ZtJUmnT592aN+4caMaNWqkhIQEh/awsDBFRETowIEDOnnyZJWf7UasVqvi4uJ07733au3atWrcuHG1+o8aNUqStHnzZod224r5gQMHOm23/sgjj0i6/hbaXl5eN/z+AQAAADQMFJ0BAAAAAKihixcvqqysTE2bNlVAQIDT9ZYtW0r6ZavmuhYcHOyyPTQ0VJJUVFRU5zncSk2bNpUkXb58uVr9kpKSNHbsWB06dEhTp051GVOTeb125bW3t7ekX1aCXzv+lStXFBQU5HAmtGEY9rOSv/3222o9myuVlZUaPXq0WrdurbfffrvaBWdJCg8Pl+T8DrVv316SFBIS4tTH9t6dOXPmunn5+vpWOxcAAAAA9QtFZwAAAAAAasjHx0dBQUGqqKhQeXm503Xb9sthYWH2tkaNGunSpUtOsWfPnr3ufQzDuGkuJSUlLre2thUKbUXAuszhVrIVQl2dGXwzaWlp6ty5s1atWqU1a9Y4XXdnXqvDx8dHwcHB8vLy0uXLl+3bkl/76d+/v1vjX23KlCm6ePGiMjIyHFYj33333VU+f9u2Svvqd0iS+vbtK0kuV9Hb3jtbgf5q586dk2ma9jkEAAAA0HBRdAYAAAAAoBaMGDFCkrR161aH9osXL2r79u3y9fVVXFycvT08PFynTp1yiC0oKNCJEyeuew8/Pz+HInHnzp21cuVKh5iKigplZWU5tH399dc6ffq0IiMjHQp8dZXDrdS1a1dJcmsLan9/f33wwQeyWCxatmyZy5jqzmt1jRw5UpWVldqzZ4/TtYULF6pdu3aqrKx0e3xJmjNnjg4fPqxNmzbJx8fnhrFpaWmKjo52ajdNUxkZGZL+tWW2zZAhQ9S6dWtt27bNvt25jW0r7uHDhzuNaXv3bHMIAAAAoOGi6AwAAAAAQC1YsGCBOnTooJSUFG3ZskXl5eXKzc3VY489pvz8fKWmpjqs9hw0aJBOnz6tN954Q+fPn1deXp6Sk5OdVpFerUePHsrNzdWPP/6ozMxMHTt2TP369XPWnJOzAAAgAElEQVSICQoK0gsvvKDMzExZrVZlZ2drwoQJ8vb2VmpqqkNsXeQQGxurkJCQKq+eranIyEiFhoYqJyfHrf4RERFasWLFda9Xd16ra8GCBerYsaMmTpyojz/+WGVlZSotLdWKFSv0yiuvaNGiRQ4rkydMmCDDMPT9999XafzVq1fr5Zdf1r59+xQQEOC0hXdeXp5Tny+//FJPP/20vvvuO1VUVOibb77R448/rgMHDmjatGnq3bu3Q7yPj4/S0tJUUlKicePG6dtvv9XZs2e1Zs0aLViwQL1791ZSUpLTfQ4ePCjpl/cQAAAAQMNG0RkAAAAAgFoQFhamrKwsjR8/XklJSQoJCVGvXr1ktVr16aefavLkyQ7xc+fO1aRJkzR//nyFhobqySef1IwZMxQWFqaSkhIZhqGZM2c69FmyZInuu+8+denSRWPHjlVqaqq6dOniEOPv76+lS5fq5ZdfVnh4uB588EE1a9ZMO3bs0EMPPVTnOVRWVtq3hb4VDMPQpEmTtG/fPvv2z5JUXFwswzAUExOjsrIyGYahnj17uhwjMTFRTz31lMtrVZ3XvXv3yjAMbdq0SZLk6+ur2bNn23NcuHChJCkqKkoJCQn28UNDQ7V//34NHz5cU6dO1V133aXf/OY3+vDDD7Vp0yaNGTPGIZ/8/Hz5+/urXbt2Vfp+1q9fX6U4m8cff1zr1q1Tfn6+4uPjFRwcrN69e+vUqVNKT0/XX//6V5f94uPj9fnnn6uiokIxMTFq2bKl5s+frxkzZmjnzp0uz23esGGDWrduraFDh1YrRwAAAAD1j2Heqr8FAgAAAHApIyNDY8eOVVlZmQIDAz2dDnD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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(model,to_file='model.png',dpi=100,show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1162528f",
   "metadata": {
    "papermill": {
     "duration": 0.020287,
     "end_time": "2023-01-12T19:28:01.535058",
     "exception": false,
     "start_time": "2023-01-12T19:28:01.514771",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Compiling the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3b30d5db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:28:01.581666Z",
     "iopub.status.busy": "2023-01-12T19:28:01.580089Z",
     "iopub.status.idle": "2023-01-12T19:28:01.603251Z",
     "shell.execute_reply": "2023-01-12T19:28:01.602324Z"
    },
    "papermill": {
     "duration": 0.049075,
     "end_time": "2023-01-12T19:28:01.605499",
     "exception": false,
     "start_time": "2023-01-12T19:28:01.556424",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "adam = Adam(learning_rate=5e-5,\n",
    "           epsilon=2e-8,\n",
    "           decay=0.01,\n",
    "           clipnorm=1.0)\n",
    "\n",
    "model.compile(loss='sparse_categorical_crossentropy',optimizer=adam,metrics=SparseCategoricalAccuracy('balanced_accuracy'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6209d135",
   "metadata": {
    "papermill": {
     "duration": 0.018885,
     "end_time": "2023-01-12T19:28:01.643891",
     "exception": false,
     "start_time": "2023-01-12T19:28:01.625006",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Training the fine-tuned Distil-Bert model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4b033b17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T19:28:01.685905Z",
     "iopub.status.busy": "2023-01-12T19:28:01.684890Z",
     "iopub.status.idle": "2023-01-12T21:40:35.920584Z",
     "shell.execute_reply": "2023-01-12T21:40:35.919431Z"
    },
    "papermill": {
     "duration": 7954.259925,
     "end_time": "2023-01-12T21:40:35.923291",
     "exception": false,
     "start_time": "2023-01-12T19:28:01.663366",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-12 19:28:02.071426: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "22/22 [==============================] - 28s 747ms/step - loss: 3.6338 - balanced_accuracy: 0.0461 - val_loss: 3.2273 - val_balanced_accuracy: 0.0554\n",
      "\n",
      "Epoch 00001: val_balanced_accuracy improved from -inf to 0.05536, saving model to resume_parser.h5\n",
      "Epoch 2/500\n",
      "22/22 [==============================] - 15s 661ms/step - loss: 3.5043 - balanced_accuracy: 0.0446 - val_loss: 3.2301 - val_balanced_accuracy: 0.0484\n",
      "\n",
      "Epoch 00002: val_balanced_accuracy did not improve from 0.05536\n",
      "Epoch 3/500\n",
      "22/22 [==============================] - 15s 671ms/step - loss: 3.4589 - balanced_accuracy: 0.0357 - val_loss: 3.2419 - val_balanced_accuracy: 0.0415\n",
      "\n",
      "Epoch 00003: val_balanced_accuracy did not improve from 0.05536\n",
      "Epoch 4/500\n",
      "22/22 [==============================] - 15s 674ms/step - loss: 3.4318 - balanced_accuracy: 0.0475 - val_loss: 3.2464 - val_balanced_accuracy: 0.0450\n",
      "\n",
      "Epoch 00004: val_balanced_accuracy did not improve from 0.05536\n",
      "Epoch 5/500\n",
      "22/22 [==============================] - 15s 691ms/step - loss: 3.3762 - balanced_accuracy: 0.0579 - val_loss: 3.2536 - val_balanced_accuracy: 0.0484\n",
      "\n",
      "Epoch 00005: val_balanced_accuracy did not improve from 0.05536\n",
      "Epoch 6/500\n",
      "22/22 [==============================] - 15s 706ms/step - loss: 3.2909 - balanced_accuracy: 0.0550 - val_loss: 3.2426 - val_balanced_accuracy: 0.0588\n",
      "\n",
      "Epoch 00006: val_balanced_accuracy improved from 0.05536 to 0.05882, saving model to resume_parser.h5\n",
      "Epoch 7/500\n",
      "22/22 [==============================] - 16s 736ms/step - loss: 3.3006 - balanced_accuracy: 0.0669 - val_loss: 3.2402 - val_balanced_accuracy: 0.0692\n",
      "\n",
      "Epoch 00007: val_balanced_accuracy improved from 0.05882 to 0.06920, saving model to resume_parser.h5\n",
      "Epoch 8/500\n",
      "22/22 [==============================] - 16s 715ms/step - loss: 3.1668 - balanced_accuracy: 0.1114 - val_loss: 3.2283 - val_balanced_accuracy: 0.0761\n",
      "\n",
      "Epoch 00008: val_balanced_accuracy improved from 0.06920 to 0.07612, saving model to resume_parser.h5\n",
      "Epoch 9/500\n",
      "22/22 [==============================] - 16s 707ms/step - loss: 3.2013 - balanced_accuracy: 0.0788 - val_loss: 3.1785 - val_balanced_accuracy: 0.1246\n",
      "\n",
      "Epoch 00009: val_balanced_accuracy improved from 0.07612 to 0.12457, saving model to resume_parser.h5\n",
      "Epoch 10/500\n",
      "22/22 [==============================] - 16s 707ms/step - loss: 3.1195 - balanced_accuracy: 0.0921 - val_loss: 3.1550 - val_balanced_accuracy: 0.1280\n",
      "\n",
      "Epoch 00010: val_balanced_accuracy improved from 0.12457 to 0.12803, saving model to resume_parser.h5\n",
      "Epoch 11/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 3.0761 - balanced_accuracy: 0.1174 - val_loss: 3.1200 - val_balanced_accuracy: 0.1246\n",
      "\n",
      "Epoch 00011: val_balanced_accuracy did not improve from 0.12803\n",
      "Epoch 12/500\n",
      "22/22 [==============================] - 16s 716ms/step - loss: 2.9858 - balanced_accuracy: 0.1382 - val_loss: 3.1048 - val_balanced_accuracy: 0.1315\n",
      "\n",
      "Epoch 00012: val_balanced_accuracy improved from 0.12803 to 0.13149, saving model to resume_parser.h5\n",
      "Epoch 13/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 2.9672 - balanced_accuracy: 0.1055 - val_loss: 3.0573 - val_balanced_accuracy: 0.1557\n",
      "\n",
      "Epoch 00013: val_balanced_accuracy improved from 0.13149 to 0.15571, saving model to resume_parser.h5\n",
      "Epoch 14/500\n",
      "22/22 [==============================] - 16s 706ms/step - loss: 2.8952 - balanced_accuracy: 0.1560 - val_loss: 3.0211 - val_balanced_accuracy: 0.1626\n",
      "\n",
      "Epoch 00014: val_balanced_accuracy improved from 0.15571 to 0.16263, saving model to resume_parser.h5\n",
      "Epoch 15/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 2.8861 - balanced_accuracy: 0.1456 - val_loss: 2.9742 - val_balanced_accuracy: 0.2042\n",
      "\n",
      "Epoch 00015: val_balanced_accuracy improved from 0.16263 to 0.20415, saving model to resume_parser.h5\n",
      "Epoch 16/500\n",
      "22/22 [==============================] - 16s 719ms/step - loss: 2.7905 - balanced_accuracy: 0.1664 - val_loss: 2.8774 - val_balanced_accuracy: 0.2457\n",
      "\n",
      "Epoch 00016: val_balanced_accuracy improved from 0.20415 to 0.24567, saving model to resume_parser.h5\n",
      "Epoch 17/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 2.8236 - balanced_accuracy: 0.1813 - val_loss: 2.8279 - val_balanced_accuracy: 0.2664\n",
      "\n",
      "Epoch 00017: val_balanced_accuracy improved from 0.24567 to 0.26644, saving model to resume_parser.h5\n",
      "Epoch 18/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 2.7576 - balanced_accuracy: 0.1947 - val_loss: 2.7557 - val_balanced_accuracy: 0.2837\n",
      "\n",
      "Epoch 00018: val_balanced_accuracy improved from 0.26644 to 0.28374, saving model to resume_parser.h5\n",
      "Epoch 19/500\n",
      "22/22 [==============================] - 16s 706ms/step - loss: 2.6764 - balanced_accuracy: 0.2199 - val_loss: 2.7169 - val_balanced_accuracy: 0.2699\n",
      "\n",
      "Epoch 00019: val_balanced_accuracy did not improve from 0.28374\n",
      "Epoch 20/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 2.6713 - balanced_accuracy: 0.2065 - val_loss: 2.6316 - val_balanced_accuracy: 0.2907\n",
      "\n",
      "Epoch 00020: val_balanced_accuracy improved from 0.28374 to 0.29066, saving model to resume_parser.h5\n",
      "Epoch 21/500\n",
      "22/22 [==============================] - 16s 715ms/step - loss: 2.6195 - balanced_accuracy: 0.2125 - val_loss: 2.6011 - val_balanced_accuracy: 0.3218\n",
      "\n",
      "Epoch 00021: val_balanced_accuracy improved from 0.29066 to 0.32180, saving model to resume_parser.h5\n",
      "Epoch 22/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 2.5593 - balanced_accuracy: 0.2437 - val_loss: 2.4822 - val_balanced_accuracy: 0.4048\n",
      "\n",
      "Epoch 00022: val_balanced_accuracy improved from 0.32180 to 0.40484, saving model to resume_parser.h5\n",
      "Epoch 23/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 2.5314 - balanced_accuracy: 0.2704 - val_loss: 2.4350 - val_balanced_accuracy: 0.3841\n",
      "\n",
      "Epoch 00023: val_balanced_accuracy did not improve from 0.40484\n",
      "Epoch 24/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 2.5487 - balanced_accuracy: 0.2704 - val_loss: 2.3846 - val_balanced_accuracy: 0.4048\n",
      "\n",
      "Epoch 00024: val_balanced_accuracy did not improve from 0.40484\n",
      "Epoch 25/500\n",
      "22/22 [==============================] - 16s 738ms/step - loss: 2.4693 - balanced_accuracy: 0.2749 - val_loss: 2.3001 - val_balanced_accuracy: 0.4187\n",
      "\n",
      "Epoch 00025: val_balanced_accuracy improved from 0.40484 to 0.41869, saving model to resume_parser.h5\n",
      "Epoch 26/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 2.4071 - balanced_accuracy: 0.3135 - val_loss: 2.2618 - val_balanced_accuracy: 0.4637\n",
      "\n",
      "Epoch 00026: val_balanced_accuracy improved from 0.41869 to 0.46367, saving model to resume_parser.h5\n",
      "Epoch 27/500\n",
      "22/22 [==============================] - 16s 715ms/step - loss: 2.3935 - balanced_accuracy: 0.3224 - val_loss: 2.1771 - val_balanced_accuracy: 0.5363\n",
      "\n",
      "Epoch 00027: val_balanced_accuracy improved from 0.46367 to 0.53633, saving model to resume_parser.h5\n",
      "Epoch 28/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 2.3401 - balanced_accuracy: 0.3269 - val_loss: 2.1238 - val_balanced_accuracy: 0.4740\n",
      "\n",
      "Epoch 00028: val_balanced_accuracy did not improve from 0.53633\n",
      "Epoch 29/500\n",
      "22/22 [==============================] - 16s 716ms/step - loss: 2.3441 - balanced_accuracy: 0.3254 - val_loss: 2.0609 - val_balanced_accuracy: 0.5398\n",
      "\n",
      "Epoch 00029: val_balanced_accuracy improved from 0.53633 to 0.53979, saving model to resume_parser.h5\n",
      "Epoch 30/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 2.2891 - balanced_accuracy: 0.3299 - val_loss: 2.0141 - val_balanced_accuracy: 0.5121\n",
      "\n",
      "Epoch 00030: val_balanced_accuracy did not improve from 0.53979\n",
      "Epoch 31/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 2.2222 - balanced_accuracy: 0.3759 - val_loss: 1.9784 - val_balanced_accuracy: 0.5848\n",
      "\n",
      "Epoch 00031: val_balanced_accuracy improved from 0.53979 to 0.58478, saving model to resume_parser.h5\n",
      "Epoch 32/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 2.2513 - balanced_accuracy: 0.3685 - val_loss: 1.9349 - val_balanced_accuracy: 0.5952\n",
      "\n",
      "Epoch 00032: val_balanced_accuracy improved from 0.58478 to 0.59516, saving model to resume_parser.h5\n",
      "Epoch 33/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 2.2295 - balanced_accuracy: 0.3700 - val_loss: 1.9711 - val_balanced_accuracy: 0.5744\n",
      "\n",
      "Epoch 00033: val_balanced_accuracy did not improve from 0.59516\n",
      "Epoch 34/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 2.1661 - balanced_accuracy: 0.3774 - val_loss: 1.8678 - val_balanced_accuracy: 0.5779\n",
      "\n",
      "Epoch 00034: val_balanced_accuracy did not improve from 0.59516\n",
      "Epoch 35/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 2.1453 - balanced_accuracy: 0.3952 - val_loss: 1.8774 - val_balanced_accuracy: 0.6367\n",
      "\n",
      "Epoch 00035: val_balanced_accuracy improved from 0.59516 to 0.63668, saving model to resume_parser.h5\n",
      "Epoch 36/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 2.1631 - balanced_accuracy: 0.3952 - val_loss: 1.8056 - val_balanced_accuracy: 0.6125\n",
      "\n",
      "Epoch 00036: val_balanced_accuracy did not improve from 0.63668\n",
      "Epoch 37/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 2.1364 - balanced_accuracy: 0.4012 - val_loss: 1.7207 - val_balanced_accuracy: 0.6747\n",
      "\n",
      "Epoch 00037: val_balanced_accuracy improved from 0.63668 to 0.67474, saving model to resume_parser.h5\n",
      "Epoch 38/500\n",
      "22/22 [==============================] - 16s 738ms/step - loss: 2.0702 - balanced_accuracy: 0.4220 - val_loss: 1.6814 - val_balanced_accuracy: 0.6505\n",
      "\n",
      "Epoch 00038: val_balanced_accuracy did not improve from 0.67474\n",
      "Epoch 39/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 2.0106 - balanced_accuracy: 0.4354 - val_loss: 1.6852 - val_balanced_accuracy: 0.7059\n",
      "\n",
      "Epoch 00039: val_balanced_accuracy improved from 0.67474 to 0.70588, saving model to resume_parser.h5\n",
      "Epoch 40/500\n",
      "22/22 [==============================] - 16s 715ms/step - loss: 2.0389 - balanced_accuracy: 0.4428 - val_loss: 1.6560 - val_balanced_accuracy: 0.7024\n",
      "\n",
      "Epoch 00040: val_balanced_accuracy did not improve from 0.70588\n",
      "Epoch 41/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.9991 - balanced_accuracy: 0.4487 - val_loss: 1.6212 - val_balanced_accuracy: 0.6644\n",
      "\n",
      "Epoch 00041: val_balanced_accuracy did not improve from 0.70588\n",
      "Epoch 42/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 2.0030 - balanced_accuracy: 0.4383 - val_loss: 1.5989 - val_balanced_accuracy: 0.7128\n",
      "\n",
      "Epoch 00042: val_balanced_accuracy improved from 0.70588 to 0.71280, saving model to resume_parser.h5\n",
      "Epoch 43/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.9808 - balanced_accuracy: 0.4517 - val_loss: 1.5625 - val_balanced_accuracy: 0.7301\n",
      "\n",
      "Epoch 00043: val_balanced_accuracy improved from 0.71280 to 0.73010, saving model to resume_parser.h5\n",
      "Epoch 44/500\n",
      "22/22 [==============================] - 16s 715ms/step - loss: 1.9459 - balanced_accuracy: 0.4859 - val_loss: 1.5739 - val_balanced_accuracy: 0.7266\n",
      "\n",
      "Epoch 00044: val_balanced_accuracy did not improve from 0.73010\n",
      "Epoch 45/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.9087 - balanced_accuracy: 0.4695 - val_loss: 1.5248 - val_balanced_accuracy: 0.7093\n",
      "\n",
      "Epoch 00045: val_balanced_accuracy did not improve from 0.73010\n",
      "Epoch 46/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.9223 - balanced_accuracy: 0.4651 - val_loss: 1.4936 - val_balanced_accuracy: 0.7439\n",
      "\n",
      "Epoch 00046: val_balanced_accuracy improved from 0.73010 to 0.74394, saving model to resume_parser.h5\n",
      "Epoch 47/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 1.8941 - balanced_accuracy: 0.5186 - val_loss: 1.4682 - val_balanced_accuracy: 0.7439\n",
      "\n",
      "Epoch 00047: val_balanced_accuracy did not improve from 0.74394\n",
      "Epoch 48/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 1.8188 - balanced_accuracy: 0.5111 - val_loss: 1.4665 - val_balanced_accuracy: 0.7163\n",
      "\n",
      "Epoch 00048: val_balanced_accuracy did not improve from 0.74394\n",
      "Epoch 49/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.8629 - balanced_accuracy: 0.5007 - val_loss: 1.4254 - val_balanced_accuracy: 0.7612\n",
      "\n",
      "Epoch 00049: val_balanced_accuracy improved from 0.74394 to 0.76125, saving model to resume_parser.h5\n",
      "Epoch 50/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.8187 - balanced_accuracy: 0.5037 - val_loss: 1.4259 - val_balanced_accuracy: 0.7543\n",
      "\n",
      "Epoch 00050: val_balanced_accuracy did not improve from 0.76125\n",
      "Epoch 51/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.8413 - balanced_accuracy: 0.5052 - val_loss: 1.3847 - val_balanced_accuracy: 0.7474\n",
      "\n",
      "Epoch 00051: val_balanced_accuracy did not improve from 0.76125\n",
      "Epoch 52/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 1.8036 - balanced_accuracy: 0.5141 - val_loss: 1.3578 - val_balanced_accuracy: 0.7578\n",
      "\n",
      "Epoch 00052: val_balanced_accuracy did not improve from 0.76125\n",
      "Epoch 53/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.7592 - balanced_accuracy: 0.5483 - val_loss: 1.3127 - val_balanced_accuracy: 0.7647\n",
      "\n",
      "Epoch 00053: val_balanced_accuracy improved from 0.76125 to 0.76471, saving model to resume_parser.h5\n",
      "Epoch 54/500\n",
      "22/22 [==============================] - 16s 716ms/step - loss: 1.7532 - balanced_accuracy: 0.5290 - val_loss: 1.3244 - val_balanced_accuracy: 0.7474\n",
      "\n",
      "Epoch 00054: val_balanced_accuracy did not improve from 0.76471\n",
      "Epoch 55/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.7263 - balanced_accuracy: 0.5557 - val_loss: 1.3138 - val_balanced_accuracy: 0.7751\n",
      "\n",
      "Epoch 00055: val_balanced_accuracy improved from 0.76471 to 0.77509, saving model to resume_parser.h5\n",
      "Epoch 56/500\n",
      "22/22 [==============================] - 16s 716ms/step - loss: 1.7323 - balanced_accuracy: 0.5617 - val_loss: 1.2943 - val_balanced_accuracy: 0.7647\n",
      "\n",
      "Epoch 00056: val_balanced_accuracy did not improve from 0.77509\n",
      "Epoch 57/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.6742 - balanced_accuracy: 0.5572 - val_loss: 1.2719 - val_balanced_accuracy: 0.7716\n",
      "\n",
      "Epoch 00057: val_balanced_accuracy did not improve from 0.77509\n",
      "Epoch 58/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 1.7225 - balanced_accuracy: 0.5498 - val_loss: 1.2432 - val_balanced_accuracy: 0.7716\n",
      "\n",
      "Epoch 00058: val_balanced_accuracy did not improve from 0.77509\n",
      "Epoch 59/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.6778 - balanced_accuracy: 0.5884 - val_loss: 1.2421 - val_balanced_accuracy: 0.7578\n",
      "\n",
      "Epoch 00059: val_balanced_accuracy did not improve from 0.77509\n",
      "Epoch 60/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.7150 - balanced_accuracy: 0.5379 - val_loss: 1.2200 - val_balanced_accuracy: 0.7716\n",
      "\n",
      "Epoch 00060: val_balanced_accuracy did not improve from 0.77509\n",
      "Epoch 61/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.6830 - balanced_accuracy: 0.5587 - val_loss: 1.2324 - val_balanced_accuracy: 0.7785\n",
      "\n",
      "Epoch 00061: val_balanced_accuracy improved from 0.77509 to 0.77855, saving model to resume_parser.h5\n",
      "Epoch 62/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.6552 - balanced_accuracy: 0.5750 - val_loss: 1.2027 - val_balanced_accuracy: 0.8028\n",
      "\n",
      "Epoch 00062: val_balanced_accuracy improved from 0.77855 to 0.80277, saving model to resume_parser.h5\n",
      "Epoch 63/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 1.6126 - balanced_accuracy: 0.5765 - val_loss: 1.1836 - val_balanced_accuracy: 0.7958\n",
      "\n",
      "Epoch 00063: val_balanced_accuracy did not improve from 0.80277\n",
      "Epoch 64/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 1.6460 - balanced_accuracy: 0.5483 - val_loss: 1.1752 - val_balanced_accuracy: 0.7958\n",
      "\n",
      "Epoch 00064: val_balanced_accuracy did not improve from 0.80277\n",
      "Epoch 65/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.5791 - balanced_accuracy: 0.6092 - val_loss: 1.1599 - val_balanced_accuracy: 0.8062\n",
      "\n",
      "Epoch 00065: val_balanced_accuracy improved from 0.80277 to 0.80623, saving model to resume_parser.h5\n",
      "Epoch 66/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.6323 - balanced_accuracy: 0.5587 - val_loss: 1.1581 - val_balanced_accuracy: 0.8028\n",
      "\n",
      "Epoch 00066: val_balanced_accuracy did not improve from 0.80623\n",
      "Epoch 67/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.5769 - balanced_accuracy: 0.6077 - val_loss: 1.1472 - val_balanced_accuracy: 0.7682\n",
      "\n",
      "Epoch 00067: val_balanced_accuracy did not improve from 0.80623\n",
      "Epoch 68/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 1.6001 - balanced_accuracy: 0.5944 - val_loss: 1.1186 - val_balanced_accuracy: 0.8131\n",
      "\n",
      "Epoch 00068: val_balanced_accuracy improved from 0.80623 to 0.81315, saving model to resume_parser.h5\n",
      "Epoch 69/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.5762 - balanced_accuracy: 0.5840 - val_loss: 1.1130 - val_balanced_accuracy: 0.8304\n",
      "\n",
      "Epoch 00069: val_balanced_accuracy improved from 0.81315 to 0.83045, saving model to resume_parser.h5\n",
      "Epoch 70/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.5279 - balanced_accuracy: 0.6181 - val_loss: 1.1116 - val_balanced_accuracy: 0.7958\n",
      "\n",
      "Epoch 00070: val_balanced_accuracy did not improve from 0.83045\n",
      "Epoch 71/500\n",
      "22/22 [==============================] - 16s 715ms/step - loss: 1.5341 - balanced_accuracy: 0.6270 - val_loss: 1.0860 - val_balanced_accuracy: 0.8131\n",
      "\n",
      "Epoch 00071: val_balanced_accuracy did not improve from 0.83045\n",
      "Epoch 72/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 1.5481 - balanced_accuracy: 0.6077 - val_loss: 1.0820 - val_balanced_accuracy: 0.7751\n",
      "\n",
      "Epoch 00072: val_balanced_accuracy did not improve from 0.83045\n",
      "Epoch 73/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 1.5481 - balanced_accuracy: 0.6092 - val_loss: 1.0675 - val_balanced_accuracy: 0.8097\n",
      "\n",
      "Epoch 00073: val_balanced_accuracy did not improve from 0.83045\n",
      "Epoch 74/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.5062 - balanced_accuracy: 0.6211 - val_loss: 1.0620 - val_balanced_accuracy: 0.7820\n",
      "\n",
      "Epoch 00074: val_balanced_accuracy did not improve from 0.83045\n",
      "Epoch 75/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.4973 - balanced_accuracy: 0.6449 - val_loss: 1.0507 - val_balanced_accuracy: 0.8235\n",
      "\n",
      "Epoch 00075: val_balanced_accuracy did not improve from 0.83045\n",
      "Epoch 76/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.4747 - balanced_accuracy: 0.6360 - val_loss: 1.0373 - val_balanced_accuracy: 0.7958\n",
      "\n",
      "Epoch 00076: val_balanced_accuracy did not improve from 0.83045\n",
      "Epoch 77/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.4895 - balanced_accuracy: 0.6315 - val_loss: 1.0434 - val_balanced_accuracy: 0.8339\n",
      "\n",
      "Epoch 00077: val_balanced_accuracy improved from 0.83045 to 0.83391, saving model to resume_parser.h5\n",
      "Epoch 78/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.4853 - balanced_accuracy: 0.6360 - val_loss: 1.0063 - val_balanced_accuracy: 0.8547\n",
      "\n",
      "Epoch 00078: val_balanced_accuracy improved from 0.83391 to 0.85467, saving model to resume_parser.h5\n",
      "Epoch 79/500\n",
      "22/22 [==============================] - 16s 718ms/step - loss: 1.4736 - balanced_accuracy: 0.6345 - val_loss: 1.0034 - val_balanced_accuracy: 0.8028\n",
      "\n",
      "Epoch 00079: val_balanced_accuracy did not improve from 0.85467\n",
      "Epoch 80/500\n",
      "22/22 [==============================] - 16s 715ms/step - loss: 1.4763 - balanced_accuracy: 0.6374 - val_loss: 1.0059 - val_balanced_accuracy: 0.7785\n",
      "\n",
      "Epoch 00080: val_balanced_accuracy did not improve from 0.85467\n",
      "Epoch 81/500\n",
      "22/22 [==============================] - 16s 716ms/step - loss: 1.4442 - balanced_accuracy: 0.6508 - val_loss: 0.9917 - val_balanced_accuracy: 0.8304\n",
      "\n",
      "Epoch 00081: val_balanced_accuracy did not improve from 0.85467\n",
      "Epoch 82/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.4444 - balanced_accuracy: 0.6582 - val_loss: 0.9648 - val_balanced_accuracy: 0.8408\n",
      "\n",
      "Epoch 00082: val_balanced_accuracy did not improve from 0.85467\n",
      "Epoch 83/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.4564 - balanced_accuracy: 0.6419 - val_loss: 0.9645 - val_balanced_accuracy: 0.8685\n",
      "\n",
      "Epoch 00083: val_balanced_accuracy improved from 0.85467 to 0.86851, saving model to resume_parser.h5\n",
      "Epoch 84/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 1.4249 - balanced_accuracy: 0.6523 - val_loss: 0.9616 - val_balanced_accuracy: 0.8789\n",
      "\n",
      "Epoch 00084: val_balanced_accuracy improved from 0.86851 to 0.87889, saving model to resume_parser.h5\n",
      "Epoch 85/500\n",
      "22/22 [==============================] - 16s 719ms/step - loss: 1.4485 - balanced_accuracy: 0.6360 - val_loss: 0.9456 - val_balanced_accuracy: 0.8927\n",
      "\n",
      "Epoch 00085: val_balanced_accuracy improved from 0.87889 to 0.89273, saving model to resume_parser.h5\n",
      "Epoch 86/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 1.3781 - balanced_accuracy: 0.6627 - val_loss: 0.9282 - val_balanced_accuracy: 0.8927\n",
      "\n",
      "Epoch 00086: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 87/500\n",
      "22/22 [==============================] - 16s 716ms/step - loss: 1.4046 - balanced_accuracy: 0.6582 - val_loss: 0.9357 - val_balanced_accuracy: 0.8858\n",
      "\n",
      "Epoch 00087: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 88/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.4386 - balanced_accuracy: 0.6464 - val_loss: 0.9052 - val_balanced_accuracy: 0.8927\n",
      "\n",
      "Epoch 00088: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 89/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.3642 - balanced_accuracy: 0.6790 - val_loss: 0.9079 - val_balanced_accuracy: 0.8893\n",
      "\n",
      "Epoch 00089: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 90/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.3866 - balanced_accuracy: 0.6642 - val_loss: 0.9003 - val_balanced_accuracy: 0.8304\n",
      "\n",
      "Epoch 00090: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 91/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.3685 - balanced_accuracy: 0.6538 - val_loss: 0.8939 - val_balanced_accuracy: 0.8720\n",
      "\n",
      "Epoch 00091: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 92/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.3822 - balanced_accuracy: 0.6716 - val_loss: 0.8898 - val_balanced_accuracy: 0.8824\n",
      "\n",
      "Epoch 00092: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 93/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.3813 - balanced_accuracy: 0.6701 - val_loss: 0.8751 - val_balanced_accuracy: 0.8927\n",
      "\n",
      "Epoch 00093: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 94/500\n",
      "22/22 [==============================] - 16s 716ms/step - loss: 1.2931 - balanced_accuracy: 0.7251 - val_loss: 0.8652 - val_balanced_accuracy: 0.8824\n",
      "\n",
      "Epoch 00094: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 95/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 1.3107 - balanced_accuracy: 0.7251 - val_loss: 0.8603 - val_balanced_accuracy: 0.8616\n",
      "\n",
      "Epoch 00095: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 96/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.3513 - balanced_accuracy: 0.6790 - val_loss: 0.8680 - val_balanced_accuracy: 0.8824\n",
      "\n",
      "Epoch 00096: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 97/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.3617 - balanced_accuracy: 0.6672 - val_loss: 0.8616 - val_balanced_accuracy: 0.8789\n",
      "\n",
      "Epoch 00097: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 98/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.3274 - balanced_accuracy: 0.6835 - val_loss: 0.8472 - val_balanced_accuracy: 0.8893\n",
      "\n",
      "Epoch 00098: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 99/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.3436 - balanced_accuracy: 0.6880 - val_loss: 0.8399 - val_balanced_accuracy: 0.8927\n",
      "\n",
      "Epoch 00099: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 100/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 1.2844 - balanced_accuracy: 0.7266 - val_loss: 0.8331 - val_balanced_accuracy: 0.8443\n",
      "\n",
      "Epoch 00100: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 101/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.2828 - balanced_accuracy: 0.7162 - val_loss: 0.8297 - val_balanced_accuracy: 0.8547\n",
      "\n",
      "Epoch 00101: val_balanced_accuracy did not improve from 0.89273\n",
      "Epoch 102/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 1.3029 - balanced_accuracy: 0.6909 - val_loss: 0.8263 - val_balanced_accuracy: 0.8962\n",
      "\n",
      "Epoch 00102: val_balanced_accuracy improved from 0.89273 to 0.89619, saving model to resume_parser.h5\n",
      "Epoch 103/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.2701 - balanced_accuracy: 0.6880 - val_loss: 0.8085 - val_balanced_accuracy: 0.8893\n",
      "\n",
      "Epoch 00103: val_balanced_accuracy did not improve from 0.89619\n",
      "Epoch 104/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.2837 - balanced_accuracy: 0.7058 - val_loss: 0.8201 - val_balanced_accuracy: 0.8997\n",
      "\n",
      "Epoch 00104: val_balanced_accuracy improved from 0.89619 to 0.89965, saving model to resume_parser.h5\n",
      "Epoch 105/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.3070 - balanced_accuracy: 0.7088 - val_loss: 0.8139 - val_balanced_accuracy: 0.8927\n",
      "\n",
      "Epoch 00105: val_balanced_accuracy did not improve from 0.89965\n",
      "Epoch 106/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.2949 - balanced_accuracy: 0.7028 - val_loss: 0.8011 - val_balanced_accuracy: 0.8997\n",
      "\n",
      "Epoch 00106: val_balanced_accuracy did not improve from 0.89965\n",
      "Epoch 107/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.2647 - balanced_accuracy: 0.7147 - val_loss: 0.7924 - val_balanced_accuracy: 0.8858\n",
      "\n",
      "Epoch 00107: val_balanced_accuracy did not improve from 0.89965\n",
      "Epoch 108/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.2459 - balanced_accuracy: 0.7281 - val_loss: 0.7799 - val_balanced_accuracy: 0.8997\n",
      "\n",
      "Epoch 00108: val_balanced_accuracy did not improve from 0.89965\n",
      "Epoch 109/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.2401 - balanced_accuracy: 0.7474 - val_loss: 0.7714 - val_balanced_accuracy: 0.9066\n",
      "\n",
      "Epoch 00109: val_balanced_accuracy improved from 0.89965 to 0.90657, saving model to resume_parser.h5\n",
      "Epoch 110/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.2474 - balanced_accuracy: 0.7192 - val_loss: 0.7644 - val_balanced_accuracy: 0.9100\n",
      "\n",
      "Epoch 00110: val_balanced_accuracy improved from 0.90657 to 0.91003, saving model to resume_parser.h5\n",
      "Epoch 111/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 1.2012 - balanced_accuracy: 0.7489 - val_loss: 0.7569 - val_balanced_accuracy: 0.9066\n",
      "\n",
      "Epoch 00111: val_balanced_accuracy did not improve from 0.91003\n",
      "Epoch 112/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 1.2241 - balanced_accuracy: 0.7073 - val_loss: 0.7552 - val_balanced_accuracy: 0.8893\n",
      "\n",
      "Epoch 00112: val_balanced_accuracy did not improve from 0.91003\n",
      "Epoch 113/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.2623 - balanced_accuracy: 0.7132 - val_loss: 0.7579 - val_balanced_accuracy: 0.9066\n",
      "\n",
      "Epoch 00113: val_balanced_accuracy did not improve from 0.91003\n",
      "Epoch 114/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.2197 - balanced_accuracy: 0.7236 - val_loss: 0.7453 - val_balanced_accuracy: 0.9100\n",
      "\n",
      "Epoch 00114: val_balanced_accuracy did not improve from 0.91003\n",
      "Epoch 115/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.1959 - balanced_accuracy: 0.7474 - val_loss: 0.7416 - val_balanced_accuracy: 0.9100\n",
      "\n",
      "Epoch 00115: val_balanced_accuracy did not improve from 0.91003\n",
      "Epoch 116/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 1.2264 - balanced_accuracy: 0.7325 - val_loss: 0.7339 - val_balanced_accuracy: 0.8997\n",
      "\n",
      "Epoch 00116: val_balanced_accuracy did not improve from 0.91003\n",
      "Epoch 117/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.1931 - balanced_accuracy: 0.7117 - val_loss: 0.7174 - val_balanced_accuracy: 0.9066\n",
      "\n",
      "Epoch 00117: val_balanced_accuracy did not improve from 0.91003\n",
      "Epoch 118/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.2100 - balanced_accuracy: 0.7355 - val_loss: 0.7128 - val_balanced_accuracy: 0.9100\n",
      "\n",
      "Epoch 00118: val_balanced_accuracy did not improve from 0.91003\n",
      "Epoch 119/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.2052 - balanced_accuracy: 0.7311 - val_loss: 0.7109 - val_balanced_accuracy: 0.9170\n",
      "\n",
      "Epoch 00119: val_balanced_accuracy improved from 0.91003 to 0.91695, saving model to resume_parser.h5\n",
      "Epoch 120/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.1803 - balanced_accuracy: 0.7415 - val_loss: 0.7080 - val_balanced_accuracy: 0.9170\n",
      "\n",
      "Epoch 00120: val_balanced_accuracy did not improve from 0.91695\n",
      "Epoch 121/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 1.1769 - balanced_accuracy: 0.7340 - val_loss: 0.7110 - val_balanced_accuracy: 0.9100\n",
      "\n",
      "Epoch 00121: val_balanced_accuracy did not improve from 0.91695\n",
      "Epoch 122/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.1910 - balanced_accuracy: 0.7608 - val_loss: 0.7070 - val_balanced_accuracy: 0.9031\n",
      "\n",
      "Epoch 00122: val_balanced_accuracy did not improve from 0.91695\n",
      "Epoch 123/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 1.1483 - balanced_accuracy: 0.7593 - val_loss: 0.7060 - val_balanced_accuracy: 0.9066\n",
      "\n",
      "Epoch 00123: val_balanced_accuracy did not improve from 0.91695\n",
      "Epoch 124/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 1.1887 - balanced_accuracy: 0.7385 - val_loss: 0.6990 - val_balanced_accuracy: 0.9135\n",
      "\n",
      "Epoch 00124: val_balanced_accuracy did not improve from 0.91695\n",
      "Epoch 125/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 1.1620 - balanced_accuracy: 0.7697 - val_loss: 0.6841 - val_balanced_accuracy: 0.9066\n",
      "\n",
      "Epoch 00125: val_balanced_accuracy did not improve from 0.91695\n",
      "Epoch 126/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.1698 - balanced_accuracy: 0.7400 - val_loss: 0.6802 - val_balanced_accuracy: 0.9066\n",
      "\n",
      "Epoch 00126: val_balanced_accuracy did not improve from 0.91695\n",
      "Epoch 127/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.1753 - balanced_accuracy: 0.7325 - val_loss: 0.6759 - val_balanced_accuracy: 0.8962\n",
      "\n",
      "Epoch 00127: val_balanced_accuracy did not improve from 0.91695\n",
      "Epoch 128/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.1501 - balanced_accuracy: 0.7623 - val_loss: 0.6700 - val_balanced_accuracy: 0.9135\n",
      "\n",
      "Epoch 00128: val_balanced_accuracy did not improve from 0.91695\n",
      "Epoch 129/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.1410 - balanced_accuracy: 0.7578 - val_loss: 0.6557 - val_balanced_accuracy: 0.9135\n",
      "\n",
      "Epoch 00129: val_balanced_accuracy did not improve from 0.91695\n",
      "Epoch 130/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.1449 - balanced_accuracy: 0.7474 - val_loss: 0.6563 - val_balanced_accuracy: 0.9100\n",
      "\n",
      "Epoch 00130: val_balanced_accuracy did not improve from 0.91695\n",
      "Epoch 131/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.1219 - balanced_accuracy: 0.7519 - val_loss: 0.6494 - val_balanced_accuracy: 0.9343\n",
      "\n",
      "Epoch 00131: val_balanced_accuracy improved from 0.91695 to 0.93426, saving model to resume_parser.h5\n",
      "Epoch 132/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 1.1304 - balanced_accuracy: 0.7727 - val_loss: 0.6522 - val_balanced_accuracy: 0.9308\n",
      "\n",
      "Epoch 00132: val_balanced_accuracy did not improve from 0.93426\n",
      "Epoch 133/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.0991 - balanced_accuracy: 0.7637 - val_loss: 0.6398 - val_balanced_accuracy: 0.9481\n",
      "\n",
      "Epoch 00133: val_balanced_accuracy improved from 0.93426 to 0.94810, saving model to resume_parser.h5\n",
      "Epoch 134/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.1345 - balanced_accuracy: 0.7637 - val_loss: 0.6359 - val_balanced_accuracy: 0.9446\n",
      "\n",
      "Epoch 00134: val_balanced_accuracy did not improve from 0.94810\n",
      "Epoch 135/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 1.1061 - balanced_accuracy: 0.7756 - val_loss: 0.6239 - val_balanced_accuracy: 0.9516\n",
      "\n",
      "Epoch 00135: val_balanced_accuracy improved from 0.94810 to 0.95156, saving model to resume_parser.h5\n",
      "Epoch 136/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.1089 - balanced_accuracy: 0.7801 - val_loss: 0.6165 - val_balanced_accuracy: 0.9550\n",
      "\n",
      "Epoch 00136: val_balanced_accuracy improved from 0.95156 to 0.95502, saving model to resume_parser.h5\n",
      "Epoch 137/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.1187 - balanced_accuracy: 0.7741 - val_loss: 0.6179 - val_balanced_accuracy: 0.9308\n",
      "\n",
      "Epoch 00137: val_balanced_accuracy did not improve from 0.95502\n",
      "Epoch 138/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.0677 - balanced_accuracy: 0.7935 - val_loss: 0.6131 - val_balanced_accuracy: 0.9619\n",
      "\n",
      "Epoch 00138: val_balanced_accuracy improved from 0.95502 to 0.96194, saving model to resume_parser.h5\n",
      "Epoch 139/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.0897 - balanced_accuracy: 0.7712 - val_loss: 0.6098 - val_balanced_accuracy: 0.9654\n",
      "\n",
      "Epoch 00139: val_balanced_accuracy improved from 0.96194 to 0.96540, saving model to resume_parser.h5\n",
      "Epoch 140/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.0916 - balanced_accuracy: 0.7667 - val_loss: 0.6045 - val_balanced_accuracy: 0.9654\n",
      "\n",
      "Epoch 00140: val_balanced_accuracy did not improve from 0.96540\n",
      "Epoch 141/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 1.0888 - balanced_accuracy: 0.7652 - val_loss: 0.6020 - val_balanced_accuracy: 0.9412\n",
      "\n",
      "Epoch 00141: val_balanced_accuracy did not improve from 0.96540\n",
      "Epoch 142/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.0945 - balanced_accuracy: 0.7637 - val_loss: 0.5825 - val_balanced_accuracy: 0.9585\n",
      "\n",
      "Epoch 00142: val_balanced_accuracy did not improve from 0.96540\n",
      "Epoch 143/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.1049 - balanced_accuracy: 0.7712 - val_loss: 0.5812 - val_balanced_accuracy: 0.9585\n",
      "\n",
      "Epoch 00143: val_balanced_accuracy did not improve from 0.96540\n",
      "Epoch 144/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.0664 - balanced_accuracy: 0.7949 - val_loss: 0.5792 - val_balanced_accuracy: 0.9654\n",
      "\n",
      "Epoch 00144: val_balanced_accuracy did not improve from 0.96540\n",
      "Epoch 145/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.0888 - balanced_accuracy: 0.7682 - val_loss: 0.5786 - val_balanced_accuracy: 0.9273\n",
      "\n",
      "Epoch 00145: val_balanced_accuracy did not improve from 0.96540\n",
      "Epoch 146/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.0352 - balanced_accuracy: 0.7890 - val_loss: 0.5809 - val_balanced_accuracy: 0.9273\n",
      "\n",
      "Epoch 00146: val_balanced_accuracy did not improve from 0.96540\n",
      "Epoch 147/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 1.0416 - balanced_accuracy: 0.7831 - val_loss: 0.5675 - val_balanced_accuracy: 0.9377\n",
      "\n",
      "Epoch 00147: val_balanced_accuracy did not improve from 0.96540\n",
      "Epoch 148/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.0310 - balanced_accuracy: 0.7964 - val_loss: 0.5674 - val_balanced_accuracy: 0.9308\n",
      "\n",
      "Epoch 00148: val_balanced_accuracy did not improve from 0.96540\n",
      "Epoch 149/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.0612 - balanced_accuracy: 0.7890 - val_loss: 0.5586 - val_balanced_accuracy: 0.9619\n",
      "\n",
      "Epoch 00149: val_balanced_accuracy did not improve from 0.96540\n",
      "Epoch 150/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.0435 - balanced_accuracy: 0.7831 - val_loss: 0.5534 - val_balanced_accuracy: 0.9654\n",
      "\n",
      "Epoch 00150: val_balanced_accuracy did not improve from 0.96540\n",
      "Epoch 151/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.0360 - balanced_accuracy: 0.7771 - val_loss: 0.5511 - val_balanced_accuracy: 0.9654\n",
      "\n",
      "Epoch 00151: val_balanced_accuracy did not improve from 0.96540\n",
      "Epoch 152/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.0636 - balanced_accuracy: 0.7741 - val_loss: 0.5469 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00152: val_balanced_accuracy improved from 0.96540 to 0.96886, saving model to resume_parser.h5\n",
      "Epoch 153/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.0401 - balanced_accuracy: 0.7890 - val_loss: 0.5471 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00153: val_balanced_accuracy did not improve from 0.96886\n",
      "Epoch 154/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.0016 - balanced_accuracy: 0.8098 - val_loss: 0.5436 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00154: val_balanced_accuracy did not improve from 0.96886\n",
      "Epoch 155/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.0406 - balanced_accuracy: 0.7816 - val_loss: 0.5394 - val_balanced_accuracy: 0.9619\n",
      "\n",
      "Epoch 00155: val_balanced_accuracy did not improve from 0.96886\n",
      "Epoch 156/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.0367 - balanced_accuracy: 0.7845 - val_loss: 0.5307 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00156: val_balanced_accuracy did not improve from 0.96886\n",
      "Epoch 157/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 1.0249 - balanced_accuracy: 0.7905 - val_loss: 0.5290 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00157: val_balanced_accuracy did not improve from 0.96886\n",
      "Epoch 158/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.9952 - balanced_accuracy: 0.7935 - val_loss: 0.5258 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00158: val_balanced_accuracy did not improve from 0.96886\n",
      "Epoch 159/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 1.0294 - balanced_accuracy: 0.7964 - val_loss: 0.5263 - val_balanced_accuracy: 0.9654\n",
      "\n",
      "Epoch 00159: val_balanced_accuracy did not improve from 0.96886\n",
      "Epoch 160/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 1.0089 - balanced_accuracy: 0.8128 - val_loss: 0.5203 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00160: val_balanced_accuracy did not improve from 0.96886\n",
      "Epoch 161/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 1.0255 - balanced_accuracy: 0.7756 - val_loss: 0.5197 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00161: val_balanced_accuracy did not improve from 0.96886\n",
      "Epoch 162/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.9956 - balanced_accuracy: 0.7949 - val_loss: 0.5143 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00162: val_balanced_accuracy did not improve from 0.96886\n",
      "Epoch 163/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.9897 - balanced_accuracy: 0.8068 - val_loss: 0.5104 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00163: val_balanced_accuracy did not improve from 0.96886\n",
      "Epoch 164/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.9641 - balanced_accuracy: 0.8172 - val_loss: 0.5109 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00164: val_balanced_accuracy did not improve from 0.96886\n",
      "Epoch 165/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.9838 - balanced_accuracy: 0.8202 - val_loss: 0.5024 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00165: val_balanced_accuracy did not improve from 0.96886\n",
      "Epoch 166/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.9786 - balanced_accuracy: 0.8068 - val_loss: 0.5018 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00166: val_balanced_accuracy improved from 0.96886 to 0.97924, saving model to resume_parser.h5\n",
      "Epoch 167/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 0.9406 - balanced_accuracy: 0.8068 - val_loss: 0.4921 - val_balanced_accuracy: 0.9654\n",
      "\n",
      "Epoch 00167: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 168/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.9548 - balanced_accuracy: 0.8068 - val_loss: 0.4897 - val_balanced_accuracy: 0.9654\n",
      "\n",
      "Epoch 00168: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 169/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.9789 - balanced_accuracy: 0.8202 - val_loss: 0.4912 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00169: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 170/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.9411 - balanced_accuracy: 0.8113 - val_loss: 0.4875 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00170: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 171/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 1.0006 - balanced_accuracy: 0.8024 - val_loss: 0.4825 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00171: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 172/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.9844 - balanced_accuracy: 0.8158 - val_loss: 0.4844 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00172: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 173/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.9880 - balanced_accuracy: 0.8083 - val_loss: 0.4849 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00173: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 174/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.9686 - balanced_accuracy: 0.8172 - val_loss: 0.4803 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00174: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 175/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.9455 - balanced_accuracy: 0.8395 - val_loss: 0.4814 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00175: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 176/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.9410 - balanced_accuracy: 0.8276 - val_loss: 0.4663 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00176: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 177/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.9659 - balanced_accuracy: 0.8009 - val_loss: 0.4726 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00177: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 178/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.9388 - balanced_accuracy: 0.8172 - val_loss: 0.4718 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00178: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 179/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.9476 - balanced_accuracy: 0.8276 - val_loss: 0.4613 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00179: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 180/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.9364 - balanced_accuracy: 0.8143 - val_loss: 0.4550 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00180: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 181/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.9361 - balanced_accuracy: 0.8276 - val_loss: 0.4570 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00181: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 182/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.9446 - balanced_accuracy: 0.8024 - val_loss: 0.4541 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00182: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 183/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.9218 - balanced_accuracy: 0.8321 - val_loss: 0.4558 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00183: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 184/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.9186 - balanced_accuracy: 0.8366 - val_loss: 0.4462 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00184: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 185/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.9447 - balanced_accuracy: 0.8143 - val_loss: 0.4421 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00185: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 186/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.9240 - balanced_accuracy: 0.8470 - val_loss: 0.4395 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00186: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 187/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.9114 - balanced_accuracy: 0.8247 - val_loss: 0.4431 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00187: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 188/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.9066 - balanced_accuracy: 0.8202 - val_loss: 0.4369 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00188: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 189/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.8868 - balanced_accuracy: 0.8425 - val_loss: 0.4346 - val_balanced_accuracy: 0.9689\n",
      "\n",
      "Epoch 00189: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 190/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.9318 - balanced_accuracy: 0.8232 - val_loss: 0.4346 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00190: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 191/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.9342 - balanced_accuracy: 0.8202 - val_loss: 0.4333 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00191: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 192/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.9163 - balanced_accuracy: 0.8410 - val_loss: 0.4290 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00192: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 193/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.9061 - balanced_accuracy: 0.8306 - val_loss: 0.4261 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00193: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 194/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.8830 - balanced_accuracy: 0.8425 - val_loss: 0.4254 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00194: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 195/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.9068 - balanced_accuracy: 0.8306 - val_loss: 0.4199 - val_balanced_accuracy: 0.9758\n",
      "\n",
      "Epoch 00195: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 196/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.8851 - balanced_accuracy: 0.8425 - val_loss: 0.4216 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00196: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 197/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.8630 - balanced_accuracy: 0.8455 - val_loss: 0.4190 - val_balanced_accuracy: 0.9758\n",
      "\n",
      "Epoch 00197: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 198/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.9239 - balanced_accuracy: 0.8321 - val_loss: 0.4170 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00198: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 199/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.8858 - balanced_accuracy: 0.8470 - val_loss: 0.4133 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00199: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 200/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.8820 - balanced_accuracy: 0.8395 - val_loss: 0.4088 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00200: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 201/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.8953 - balanced_accuracy: 0.8366 - val_loss: 0.3991 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00201: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 202/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.8926 - balanced_accuracy: 0.8380 - val_loss: 0.3981 - val_balanced_accuracy: 0.9758\n",
      "\n",
      "Epoch 00202: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 203/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.8548 - balanced_accuracy: 0.8529 - val_loss: 0.3986 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00203: val_balanced_accuracy did not improve from 0.97924\n",
      "Epoch 204/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.8931 - balanced_accuracy: 0.8351 - val_loss: 0.3997 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00204: val_balanced_accuracy improved from 0.97924 to 0.98270, saving model to resume_parser.h5\n",
      "Epoch 205/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.8610 - balanced_accuracy: 0.8544 - val_loss: 0.3989 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00205: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 206/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.9013 - balanced_accuracy: 0.8158 - val_loss: 0.3974 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00206: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 207/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 0.8378 - balanced_accuracy: 0.8470 - val_loss: 0.3970 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00207: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 208/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.8745 - balanced_accuracy: 0.8321 - val_loss: 0.3892 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00208: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 209/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.8674 - balanced_accuracy: 0.8276 - val_loss: 0.3851 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00209: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 210/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.8112 - balanced_accuracy: 0.8603 - val_loss: 0.3824 - val_balanced_accuracy: 0.9723\n",
      "\n",
      "Epoch 00210: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 211/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.8732 - balanced_accuracy: 0.8470 - val_loss: 0.3794 - val_balanced_accuracy: 0.9723\n",
      "\n",
      "Epoch 00211: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 212/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.8688 - balanced_accuracy: 0.8648 - val_loss: 0.3814 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00212: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 213/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.8544 - balanced_accuracy: 0.8470 - val_loss: 0.3822 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00213: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 214/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.8647 - balanced_accuracy: 0.8529 - val_loss: 0.3787 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00214: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 215/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.8520 - balanced_accuracy: 0.8410 - val_loss: 0.3773 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00215: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 216/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.8347 - balanced_accuracy: 0.8544 - val_loss: 0.3742 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00216: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 217/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.8544 - balanced_accuracy: 0.8559 - val_loss: 0.3717 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00217: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 218/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.8473 - balanced_accuracy: 0.8395 - val_loss: 0.3692 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00218: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 219/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.8507 - balanced_accuracy: 0.8425 - val_loss: 0.3684 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00219: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 220/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 0.8801 - balanced_accuracy: 0.8499 - val_loss: 0.3665 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00220: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 221/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.8362 - balanced_accuracy: 0.8544 - val_loss: 0.3708 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00221: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 222/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 0.8616 - balanced_accuracy: 0.8380 - val_loss: 0.3671 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00222: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 223/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.8284 - balanced_accuracy: 0.8484 - val_loss: 0.3650 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00223: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 224/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.8601 - balanced_accuracy: 0.8262 - val_loss: 0.3656 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00224: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 225/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.8355 - balanced_accuracy: 0.8380 - val_loss: 0.3622 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00225: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 226/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.8219 - balanced_accuracy: 0.8574 - val_loss: 0.3626 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00226: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 227/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.8126 - balanced_accuracy: 0.8663 - val_loss: 0.3586 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00227: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 228/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.8480 - balanced_accuracy: 0.8529 - val_loss: 0.3566 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00228: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 229/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.8555 - balanced_accuracy: 0.8499 - val_loss: 0.3529 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00229: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 230/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.8054 - balanced_accuracy: 0.8648 - val_loss: 0.3532 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00230: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 231/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.8426 - balanced_accuracy: 0.8529 - val_loss: 0.3475 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00231: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 232/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.8081 - balanced_accuracy: 0.8618 - val_loss: 0.3419 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00232: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 233/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.7962 - balanced_accuracy: 0.8707 - val_loss: 0.3441 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00233: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 234/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.8074 - balanced_accuracy: 0.8707 - val_loss: 0.3442 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00234: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 235/500\n",
      "22/22 [==============================] - 15s 706ms/step - loss: 0.8157 - balanced_accuracy: 0.8484 - val_loss: 0.3429 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00235: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 236/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.8218 - balanced_accuracy: 0.8752 - val_loss: 0.3375 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00236: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 237/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.8414 - balanced_accuracy: 0.8484 - val_loss: 0.3362 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00237: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 238/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.8136 - balanced_accuracy: 0.8841 - val_loss: 0.3340 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00238: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 239/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.7985 - balanced_accuracy: 0.8544 - val_loss: 0.3314 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00239: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 240/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.7905 - balanced_accuracy: 0.8722 - val_loss: 0.3314 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00240: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 241/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.8171 - balanced_accuracy: 0.8380 - val_loss: 0.3346 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00241: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 242/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7925 - balanced_accuracy: 0.8633 - val_loss: 0.3356 - val_balanced_accuracy: 0.9792\n",
      "\n",
      "Epoch 00242: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 243/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.8270 - balanced_accuracy: 0.8484 - val_loss: 0.3326 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00243: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 244/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.8087 - balanced_accuracy: 0.8470 - val_loss: 0.3329 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00244: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 245/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.8059 - balanced_accuracy: 0.8544 - val_loss: 0.3344 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00245: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 246/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7960 - balanced_accuracy: 0.8588 - val_loss: 0.3283 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00246: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 247/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.8014 - balanced_accuracy: 0.8722 - val_loss: 0.3229 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00247: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 248/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.8019 - balanced_accuracy: 0.8574 - val_loss: 0.3234 - val_balanced_accuracy: 0.9723\n",
      "\n",
      "Epoch 00248: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 249/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.8167 - balanced_accuracy: 0.8588 - val_loss: 0.3255 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00249: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 250/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7585 - balanced_accuracy: 0.8678 - val_loss: 0.3240 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00250: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 251/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.7907 - balanced_accuracy: 0.8559 - val_loss: 0.3231 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00251: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 252/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.7669 - balanced_accuracy: 0.8663 - val_loss: 0.3220 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00252: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 253/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.8011 - balanced_accuracy: 0.8603 - val_loss: 0.3183 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00253: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 254/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.7278 - balanced_accuracy: 0.8886 - val_loss: 0.3162 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00254: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 255/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.8049 - balanced_accuracy: 0.8484 - val_loss: 0.3124 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00255: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 256/500\n",
      "22/22 [==============================] - 15s 705ms/step - loss: 0.7558 - balanced_accuracy: 0.8796 - val_loss: 0.3107 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00256: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 257/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.7553 - balanced_accuracy: 0.8722 - val_loss: 0.3076 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00257: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 258/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.7693 - balanced_accuracy: 0.8663 - val_loss: 0.3055 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00258: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 259/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.7338 - balanced_accuracy: 0.8811 - val_loss: 0.3045 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00259: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 260/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.7737 - balanced_accuracy: 0.8767 - val_loss: 0.3017 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00260: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 261/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7365 - balanced_accuracy: 0.8811 - val_loss: 0.3013 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00261: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 262/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.7554 - balanced_accuracy: 0.8752 - val_loss: 0.2986 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00262: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 263/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.7775 - balanced_accuracy: 0.8752 - val_loss: 0.2969 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00263: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 264/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7412 - balanced_accuracy: 0.8722 - val_loss: 0.2976 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00264: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 265/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7419 - balanced_accuracy: 0.8618 - val_loss: 0.2964 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00265: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 266/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7608 - balanced_accuracy: 0.8796 - val_loss: 0.2966 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00266: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 267/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.7962 - balanced_accuracy: 0.8603 - val_loss: 0.2957 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00267: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 268/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.7447 - balanced_accuracy: 0.8796 - val_loss: 0.2960 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00268: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 269/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.7558 - balanced_accuracy: 0.8678 - val_loss: 0.2909 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00269: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 270/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.6883 - balanced_accuracy: 0.9064 - val_loss: 0.2887 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00270: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 271/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.7221 - balanced_accuracy: 0.8826 - val_loss: 0.2863 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00271: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 272/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7249 - balanced_accuracy: 0.8826 - val_loss: 0.2880 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00272: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 273/500\n",
      "22/22 [==============================] - 16s 715ms/step - loss: 0.7104 - balanced_accuracy: 0.8871 - val_loss: 0.2860 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00273: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 274/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7113 - balanced_accuracy: 0.8930 - val_loss: 0.2840 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00274: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 275/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.7587 - balanced_accuracy: 0.8588 - val_loss: 0.2839 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00275: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 276/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7378 - balanced_accuracy: 0.8841 - val_loss: 0.2851 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00276: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 277/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.7335 - balanced_accuracy: 0.8930 - val_loss: 0.2845 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00277: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 278/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7061 - balanced_accuracy: 0.9004 - val_loss: 0.2832 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00278: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 279/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7582 - balanced_accuracy: 0.8737 - val_loss: 0.2814 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00279: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 280/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.7241 - balanced_accuracy: 0.9019 - val_loss: 0.2809 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00280: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 281/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.7361 - balanced_accuracy: 0.8811 - val_loss: 0.2793 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00281: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 282/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.7100 - balanced_accuracy: 0.8990 - val_loss: 0.2754 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00282: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 283/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.7278 - balanced_accuracy: 0.8856 - val_loss: 0.2744 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00283: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 284/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.7067 - balanced_accuracy: 0.8945 - val_loss: 0.2736 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00284: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 285/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7064 - balanced_accuracy: 0.8915 - val_loss: 0.2737 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00285: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 286/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.7234 - balanced_accuracy: 0.8900 - val_loss: 0.2727 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00286: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 287/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.7192 - balanced_accuracy: 0.8930 - val_loss: 0.2697 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00287: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 288/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.7029 - balanced_accuracy: 0.8871 - val_loss: 0.2711 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00288: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 289/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.7039 - balanced_accuracy: 0.9108 - val_loss: 0.2717 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00289: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 290/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.7182 - balanced_accuracy: 0.8915 - val_loss: 0.2713 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00290: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 291/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6879 - balanced_accuracy: 0.9138 - val_loss: 0.2735 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00291: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 292/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.7364 - balanced_accuracy: 0.9004 - val_loss: 0.2698 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00292: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 293/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.7144 - balanced_accuracy: 0.8841 - val_loss: 0.2671 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00293: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 294/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.6890 - balanced_accuracy: 0.8900 - val_loss: 0.2666 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00294: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 295/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 0.6969 - balanced_accuracy: 0.8796 - val_loss: 0.2652 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00295: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 296/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.6845 - balanced_accuracy: 0.8915 - val_loss: 0.2612 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00296: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 297/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 0.6626 - balanced_accuracy: 0.9212 - val_loss: 0.2620 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00297: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 298/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.6982 - balanced_accuracy: 0.8990 - val_loss: 0.2623 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00298: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 299/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 0.7003 - balanced_accuracy: 0.9019 - val_loss: 0.2604 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00299: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 300/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 0.7221 - balanced_accuracy: 0.8900 - val_loss: 0.2567 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00300: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 301/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.6779 - balanced_accuracy: 0.8930 - val_loss: 0.2552 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00301: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 302/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.6917 - balanced_accuracy: 0.9004 - val_loss: 0.2543 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00302: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 303/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.6749 - balanced_accuracy: 0.9079 - val_loss: 0.2539 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00303: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 304/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6587 - balanced_accuracy: 0.9019 - val_loss: 0.2519 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00304: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 305/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6941 - balanced_accuracy: 0.8990 - val_loss: 0.2501 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00305: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 306/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.6929 - balanced_accuracy: 0.8930 - val_loss: 0.2506 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00306: val_balanced_accuracy did not improve from 0.98270\n",
      "Epoch 307/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6976 - balanced_accuracy: 0.8900 - val_loss: 0.2520 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00307: val_balanced_accuracy improved from 0.98270 to 0.99308, saving model to resume_parser.h5\n",
      "Epoch 308/500\n",
      "22/22 [==============================] - 16s 707ms/step - loss: 0.6975 - balanced_accuracy: 0.9019 - val_loss: 0.2520 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00308: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 309/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 0.7050 - balanced_accuracy: 0.8915 - val_loss: 0.2504 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00309: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 310/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.6599 - balanced_accuracy: 0.9123 - val_loss: 0.2471 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00310: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 311/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6907 - balanced_accuracy: 0.8811 - val_loss: 0.2481 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00311: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 312/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.6710 - balanced_accuracy: 0.9064 - val_loss: 0.2459 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00312: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 313/500\n",
      "22/22 [==============================] - 15s 706ms/step - loss: 0.6527 - balanced_accuracy: 0.9108 - val_loss: 0.2458 - val_balanced_accuracy: 0.9827\n",
      "\n",
      "Epoch 00313: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 314/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.6688 - balanced_accuracy: 0.9123 - val_loss: 0.2442 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00314: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 315/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6817 - balanced_accuracy: 0.8945 - val_loss: 0.2411 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00315: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 316/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 0.6521 - balanced_accuracy: 0.9079 - val_loss: 0.2411 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00316: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 317/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.6739 - balanced_accuracy: 0.9019 - val_loss: 0.2390 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00317: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 318/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.6610 - balanced_accuracy: 0.9034 - val_loss: 0.2367 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00318: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 319/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.6864 - balanced_accuracy: 0.8900 - val_loss: 0.2364 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00319: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 320/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.6510 - balanced_accuracy: 0.9079 - val_loss: 0.2350 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00320: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 321/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.6619 - balanced_accuracy: 0.8945 - val_loss: 0.2347 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00321: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 322/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.6322 - balanced_accuracy: 0.9287 - val_loss: 0.2382 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00322: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 323/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.6585 - balanced_accuracy: 0.9138 - val_loss: 0.2375 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00323: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 324/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6904 - balanced_accuracy: 0.8945 - val_loss: 0.2363 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00324: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 325/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.6517 - balanced_accuracy: 0.9034 - val_loss: 0.2352 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00325: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 326/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.6457 - balanced_accuracy: 0.9049 - val_loss: 0.2337 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00326: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 327/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6318 - balanced_accuracy: 0.9123 - val_loss: 0.2306 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00327: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 328/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6417 - balanced_accuracy: 0.9094 - val_loss: 0.2294 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00328: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 329/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.6478 - balanced_accuracy: 0.9049 - val_loss: 0.2292 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00329: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 330/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6361 - balanced_accuracy: 0.9316 - val_loss: 0.2292 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00330: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 331/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.6531 - balanced_accuracy: 0.9108 - val_loss: 0.2276 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00331: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 332/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.6727 - balanced_accuracy: 0.8945 - val_loss: 0.2276 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00332: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 333/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6445 - balanced_accuracy: 0.9168 - val_loss: 0.2256 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00333: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 334/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.6084 - balanced_accuracy: 0.9331 - val_loss: 0.2238 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00334: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 335/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.6257 - balanced_accuracy: 0.9227 - val_loss: 0.2212 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00335: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 336/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.6479 - balanced_accuracy: 0.9183 - val_loss: 0.2188 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00336: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 337/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.6349 - balanced_accuracy: 0.9287 - val_loss: 0.2183 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00337: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 338/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.6491 - balanced_accuracy: 0.9004 - val_loss: 0.2163 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00338: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 339/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.6371 - balanced_accuracy: 0.9168 - val_loss: 0.2165 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00339: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 340/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6439 - balanced_accuracy: 0.9138 - val_loss: 0.2162 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00340: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 341/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6188 - balanced_accuracy: 0.9138 - val_loss: 0.2147 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00341: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 342/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.6460 - balanced_accuracy: 0.9049 - val_loss: 0.2150 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00342: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 343/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.6451 - balanced_accuracy: 0.9183 - val_loss: 0.2128 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00343: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 344/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6398 - balanced_accuracy: 0.9049 - val_loss: 0.2148 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00344: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 345/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6373 - balanced_accuracy: 0.9108 - val_loss: 0.2146 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00345: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 346/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.6154 - balanced_accuracy: 0.9168 - val_loss: 0.2131 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00346: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 347/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.6616 - balanced_accuracy: 0.9034 - val_loss: 0.2132 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00347: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 348/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.6414 - balanced_accuracy: 0.9094 - val_loss: 0.2131 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00348: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 349/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.6074 - balanced_accuracy: 0.9227 - val_loss: 0.2109 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00349: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 350/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.6233 - balanced_accuracy: 0.9138 - val_loss: 0.2114 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00350: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 351/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.6261 - balanced_accuracy: 0.9138 - val_loss: 0.2118 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00351: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 352/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5943 - balanced_accuracy: 0.9168 - val_loss: 0.2117 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00352: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 353/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.6512 - balanced_accuracy: 0.9123 - val_loss: 0.2088 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00353: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 354/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.6029 - balanced_accuracy: 0.9257 - val_loss: 0.2092 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00354: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 355/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.6128 - balanced_accuracy: 0.9198 - val_loss: 0.2116 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00355: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 356/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 0.6132 - balanced_accuracy: 0.9257 - val_loss: 0.2117 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00356: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 357/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.6283 - balanced_accuracy: 0.9108 - val_loss: 0.2103 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00357: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 358/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.6110 - balanced_accuracy: 0.9287 - val_loss: 0.2081 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00358: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 359/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5772 - balanced_accuracy: 0.9406 - val_loss: 0.2074 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00359: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 360/500\n",
      "22/22 [==============================] - 16s 716ms/step - loss: 0.6036 - balanced_accuracy: 0.9198 - val_loss: 0.2062 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00360: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 361/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6191 - balanced_accuracy: 0.9302 - val_loss: 0.2058 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00361: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 362/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.6199 - balanced_accuracy: 0.9198 - val_loss: 0.2072 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00362: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 363/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6077 - balanced_accuracy: 0.9153 - val_loss: 0.2062 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00363: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 364/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5968 - balanced_accuracy: 0.9391 - val_loss: 0.2056 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00364: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 365/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.6004 - balanced_accuracy: 0.9138 - val_loss: 0.2049 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00365: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 366/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.5702 - balanced_accuracy: 0.9361 - val_loss: 0.2033 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00366: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 367/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.6145 - balanced_accuracy: 0.9123 - val_loss: 0.2018 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00367: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 368/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.6016 - balanced_accuracy: 0.9198 - val_loss: 0.2015 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00368: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 369/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5878 - balanced_accuracy: 0.9287 - val_loss: 0.2001 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00369: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 370/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5972 - balanced_accuracy: 0.9316 - val_loss: 0.2011 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00370: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 371/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.6167 - balanced_accuracy: 0.9227 - val_loss: 0.1988 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00371: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 372/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5830 - balanced_accuracy: 0.9316 - val_loss: 0.1978 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00372: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 373/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5740 - balanced_accuracy: 0.9361 - val_loss: 0.1975 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00373: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 374/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5834 - balanced_accuracy: 0.9183 - val_loss: 0.1962 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00374: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 375/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5931 - balanced_accuracy: 0.9183 - val_loss: 0.1966 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00375: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 376/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5933 - balanced_accuracy: 0.9316 - val_loss: 0.1980 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00376: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 377/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 0.5932 - balanced_accuracy: 0.9212 - val_loss: 0.1961 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00377: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 378/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5734 - balanced_accuracy: 0.9361 - val_loss: 0.1945 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00378: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 379/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5816 - balanced_accuracy: 0.9287 - val_loss: 0.1934 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00379: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 380/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5855 - balanced_accuracy: 0.9227 - val_loss: 0.1897 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00380: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 381/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5938 - balanced_accuracy: 0.9198 - val_loss: 0.1907 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00381: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 382/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.5981 - balanced_accuracy: 0.9302 - val_loss: 0.1905 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00382: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 383/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5878 - balanced_accuracy: 0.9331 - val_loss: 0.1902 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00383: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 384/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.5571 - balanced_accuracy: 0.9257 - val_loss: 0.1892 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00384: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 385/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5960 - balanced_accuracy: 0.9064 - val_loss: 0.1866 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00385: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 386/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.5998 - balanced_accuracy: 0.9227 - val_loss: 0.1860 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00386: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 387/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.5626 - balanced_accuracy: 0.9435 - val_loss: 0.1861 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00387: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 388/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5940 - balanced_accuracy: 0.9094 - val_loss: 0.1848 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00388: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 389/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.5866 - balanced_accuracy: 0.9331 - val_loss: 0.1828 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00389: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 390/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.5767 - balanced_accuracy: 0.9346 - val_loss: 0.1822 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00390: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 391/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.5818 - balanced_accuracy: 0.9421 - val_loss: 0.1836 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00391: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 392/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.5588 - balanced_accuracy: 0.9346 - val_loss: 0.1820 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00392: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 393/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.5802 - balanced_accuracy: 0.9346 - val_loss: 0.1807 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00393: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 394/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5829 - balanced_accuracy: 0.9257 - val_loss: 0.1814 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00394: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 395/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5796 - balanced_accuracy: 0.9316 - val_loss: 0.1828 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00395: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 396/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.5914 - balanced_accuracy: 0.9153 - val_loss: 0.1819 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00396: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 397/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5603 - balanced_accuracy: 0.9287 - val_loss: 0.1801 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00397: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 398/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5991 - balanced_accuracy: 0.9079 - val_loss: 0.1783 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00398: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 399/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5482 - balanced_accuracy: 0.9406 - val_loss: 0.1802 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00399: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 400/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5780 - balanced_accuracy: 0.9227 - val_loss: 0.1795 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00400: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 401/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.5668 - balanced_accuracy: 0.9287 - val_loss: 0.1772 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00401: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 402/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5647 - balanced_accuracy: 0.9302 - val_loss: 0.1758 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00402: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 403/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 0.5854 - balanced_accuracy: 0.9198 - val_loss: 0.1739 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00403: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 404/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5915 - balanced_accuracy: 0.9212 - val_loss: 0.1736 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00404: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 405/500\n",
      "22/22 [==============================] - 16s 715ms/step - loss: 0.5480 - balanced_accuracy: 0.9421 - val_loss: 0.1743 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00405: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 406/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5499 - balanced_accuracy: 0.9406 - val_loss: 0.1739 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00406: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 407/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 0.5525 - balanced_accuracy: 0.9287 - val_loss: 0.1727 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00407: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 408/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.5745 - balanced_accuracy: 0.9421 - val_loss: 0.1732 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00408: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 409/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5654 - balanced_accuracy: 0.9227 - val_loss: 0.1726 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00409: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 410/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.5424 - balanced_accuracy: 0.9391 - val_loss: 0.1703 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00410: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 411/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5599 - balanced_accuracy: 0.9316 - val_loss: 0.1705 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00411: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 412/500\n",
      "22/22 [==============================] - 15s 706ms/step - loss: 0.5733 - balanced_accuracy: 0.9242 - val_loss: 0.1720 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00412: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 413/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.5537 - balanced_accuracy: 0.9316 - val_loss: 0.1721 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00413: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 414/500\n",
      "22/22 [==============================] - 15s 706ms/step - loss: 0.5806 - balanced_accuracy: 0.9346 - val_loss: 0.1719 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00414: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 415/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.5476 - balanced_accuracy: 0.9287 - val_loss: 0.1717 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00415: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 416/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5629 - balanced_accuracy: 0.9346 - val_loss: 0.1705 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00416: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 417/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.5514 - balanced_accuracy: 0.9376 - val_loss: 0.1715 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00417: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 418/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5608 - balanced_accuracy: 0.9331 - val_loss: 0.1704 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00418: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 419/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5499 - balanced_accuracy: 0.9287 - val_loss: 0.1708 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00419: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 420/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5576 - balanced_accuracy: 0.9331 - val_loss: 0.1689 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00420: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 421/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.5713 - balanced_accuracy: 0.9302 - val_loss: 0.1696 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00421: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 422/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5681 - balanced_accuracy: 0.9183 - val_loss: 0.1690 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00422: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 423/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5376 - balanced_accuracy: 0.9198 - val_loss: 0.1706 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00423: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 424/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5316 - balanced_accuracy: 0.9421 - val_loss: 0.1704 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00424: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 425/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5309 - balanced_accuracy: 0.9376 - val_loss: 0.1703 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00425: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 426/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5238 - balanced_accuracy: 0.9316 - val_loss: 0.1682 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00426: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 427/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5521 - balanced_accuracy: 0.9406 - val_loss: 0.1674 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00427: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 428/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5605 - balanced_accuracy: 0.9302 - val_loss: 0.1660 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00428: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 429/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.5158 - balanced_accuracy: 0.9406 - val_loss: 0.1662 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00429: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 430/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5577 - balanced_accuracy: 0.9212 - val_loss: 0.1665 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00430: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 431/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5863 - balanced_accuracy: 0.9257 - val_loss: 0.1646 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00431: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 432/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5472 - balanced_accuracy: 0.9376 - val_loss: 0.1642 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00432: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 433/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5348 - balanced_accuracy: 0.9302 - val_loss: 0.1634 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00433: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 434/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5573 - balanced_accuracy: 0.9346 - val_loss: 0.1627 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00434: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 435/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5272 - balanced_accuracy: 0.9450 - val_loss: 0.1622 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00435: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 436/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5541 - balanced_accuracy: 0.9198 - val_loss: 0.1617 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00436: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 437/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5502 - balanced_accuracy: 0.9331 - val_loss: 0.1610 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00437: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 438/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5260 - balanced_accuracy: 0.9421 - val_loss: 0.1595 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00438: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 439/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5346 - balanced_accuracy: 0.9346 - val_loss: 0.1596 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00439: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 440/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.5364 - balanced_accuracy: 0.9376 - val_loss: 0.1589 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00440: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 441/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5287 - balanced_accuracy: 0.9376 - val_loss: 0.1574 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00441: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 442/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 0.5321 - balanced_accuracy: 0.9421 - val_loss: 0.1585 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00442: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 443/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5517 - balanced_accuracy: 0.9376 - val_loss: 0.1565 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00443: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 444/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 0.5316 - balanced_accuracy: 0.9376 - val_loss: 0.1549 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00444: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 445/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.5517 - balanced_accuracy: 0.9138 - val_loss: 0.1555 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00445: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 446/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 0.5080 - balanced_accuracy: 0.9465 - val_loss: 0.1548 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00446: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 447/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.5299 - balanced_accuracy: 0.9391 - val_loss: 0.1535 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00447: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 448/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 0.5526 - balanced_accuracy: 0.9302 - val_loss: 0.1512 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00448: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 449/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5454 - balanced_accuracy: 0.9302 - val_loss: 0.1507 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00449: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 450/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 0.5296 - balanced_accuracy: 0.9495 - val_loss: 0.1517 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00450: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 451/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5274 - balanced_accuracy: 0.9346 - val_loss: 0.1524 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00451: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 452/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5165 - balanced_accuracy: 0.9302 - val_loss: 0.1523 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00452: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 453/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5331 - balanced_accuracy: 0.9361 - val_loss: 0.1511 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00453: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 454/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 0.5251 - balanced_accuracy: 0.9287 - val_loss: 0.1496 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00454: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 455/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5395 - balanced_accuracy: 0.9421 - val_loss: 0.1495 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00455: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 456/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 0.5149 - balanced_accuracy: 0.9554 - val_loss: 0.1496 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00456: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 457/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.4995 - balanced_accuracy: 0.9450 - val_loss: 0.1477 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00457: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 458/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5129 - balanced_accuracy: 0.9569 - val_loss: 0.1474 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00458: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 459/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5449 - balanced_accuracy: 0.9435 - val_loss: 0.1455 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00459: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 460/500\n",
      "22/22 [==============================] - 16s 714ms/step - loss: 0.5242 - balanced_accuracy: 0.9346 - val_loss: 0.1461 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00460: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 461/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 0.5255 - balanced_accuracy: 0.9465 - val_loss: 0.1463 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00461: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 462/500\n",
      "22/22 [==============================] - 16s 739ms/step - loss: 0.5246 - balanced_accuracy: 0.9421 - val_loss: 0.1456 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00462: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 463/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.5110 - balanced_accuracy: 0.9406 - val_loss: 0.1440 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00463: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 464/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5038 - balanced_accuracy: 0.9554 - val_loss: 0.1454 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00464: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 465/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5072 - balanced_accuracy: 0.9435 - val_loss: 0.1461 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00465: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 466/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5055 - balanced_accuracy: 0.9316 - val_loss: 0.1467 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00466: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 467/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5295 - balanced_accuracy: 0.9346 - val_loss: 0.1473 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00467: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 468/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.5086 - balanced_accuracy: 0.9465 - val_loss: 0.1469 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00468: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 469/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.5338 - balanced_accuracy: 0.9450 - val_loss: 0.1475 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00469: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 470/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.4951 - balanced_accuracy: 0.9525 - val_loss: 0.1472 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00470: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 471/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5133 - balanced_accuracy: 0.9435 - val_loss: 0.1466 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00471: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 472/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.4944 - balanced_accuracy: 0.9554 - val_loss: 0.1447 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00472: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 473/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.5163 - balanced_accuracy: 0.9525 - val_loss: 0.1446 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00473: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 474/500\n",
      "22/22 [==============================] - 15s 707ms/step - loss: 0.5010 - balanced_accuracy: 0.9435 - val_loss: 0.1435 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00474: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 475/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5132 - balanced_accuracy: 0.9257 - val_loss: 0.1426 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00475: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 476/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.4950 - balanced_accuracy: 0.9406 - val_loss: 0.1434 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00476: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 477/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.5167 - balanced_accuracy: 0.9391 - val_loss: 0.1423 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00477: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 478/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.4887 - balanced_accuracy: 0.9525 - val_loss: 0.1421 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00478: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 479/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 0.5139 - balanced_accuracy: 0.9465 - val_loss: 0.1404 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00479: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 480/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.5017 - balanced_accuracy: 0.9406 - val_loss: 0.1390 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00480: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 481/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.4762 - balanced_accuracy: 0.9525 - val_loss: 0.1381 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00481: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 482/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5077 - balanced_accuracy: 0.9421 - val_loss: 0.1371 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00482: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 483/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5203 - balanced_accuracy: 0.9406 - val_loss: 0.1377 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00483: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 484/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.5024 - balanced_accuracy: 0.9406 - val_loss: 0.1382 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00484: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 485/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5085 - balanced_accuracy: 0.9346 - val_loss: 0.1373 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00485: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 486/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.5237 - balanced_accuracy: 0.9361 - val_loss: 0.1385 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00486: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 487/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.4862 - balanced_accuracy: 0.9495 - val_loss: 0.1396 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00487: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 488/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.4858 - balanced_accuracy: 0.9554 - val_loss: 0.1393 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00488: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 489/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.4851 - balanced_accuracy: 0.9525 - val_loss: 0.1376 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00489: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 490/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.5186 - balanced_accuracy: 0.9316 - val_loss: 0.1371 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00490: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 491/500\n",
      "22/22 [==============================] - 16s 708ms/step - loss: 0.4979 - balanced_accuracy: 0.9450 - val_loss: 0.1361 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00491: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 492/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.5089 - balanced_accuracy: 0.9450 - val_loss: 0.1373 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00492: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 493/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.5211 - balanced_accuracy: 0.9435 - val_loss: 0.1366 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00493: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 494/500\n",
      "22/22 [==============================] - 15s 708ms/step - loss: 0.5287 - balanced_accuracy: 0.9242 - val_loss: 0.1357 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00494: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 495/500\n",
      "22/22 [==============================] - 16s 711ms/step - loss: 0.5038 - balanced_accuracy: 0.9346 - val_loss: 0.1344 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00495: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 496/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.4931 - balanced_accuracy: 0.9406 - val_loss: 0.1354 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00496: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 497/500\n",
      "22/22 [==============================] - 16s 712ms/step - loss: 0.4981 - balanced_accuracy: 0.9480 - val_loss: 0.1345 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00497: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 498/500\n",
      "22/22 [==============================] - 16s 709ms/step - loss: 0.4921 - balanced_accuracy: 0.9450 - val_loss: 0.1327 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00498: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 499/500\n",
      "22/22 [==============================] - 16s 713ms/step - loss: 0.4664 - balanced_accuracy: 0.9495 - val_loss: 0.1332 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00499: val_balanced_accuracy did not improve from 0.99308\n",
      "Epoch 500/500\n",
      "22/22 [==============================] - 16s 710ms/step - loss: 0.4778 - balanced_accuracy: 0.9539 - val_loss: 0.1330 - val_balanced_accuracy: 0.9931\n",
      "\n",
      "Epoch 00500: val_balanced_accuracy did not improve from 0.99308\n"
     ]
    }
   ],
   "source": [
    "es = EarlyStopping(monitor='val_balanced_accuracy',patience=250,verbose=1,mode='max',restore_best_weights=True)\n",
    "mc = ModelCheckpoint('resume_parser.h5',monitor='val_balanced_accuracy',mode='max',verbose=1,save_best_only=True)\n",
    "\n",
    "r = model.fit(x={'input_ids': X_train['input_ids'], 'attention_mask': X_train['attention_mask']},\n",
    "             y=train_df.Category,\n",
    "             epochs=500,\n",
    "             batch_size=32,\n",
    "             callbacks=[es,mc],\n",
    "             validation_data=({'input_ids': X_test['input_ids'], 'attention_mask': X_test['attention_mask']},test_df.Category))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d1a273a",
   "metadata": {
    "papermill": {
     "duration": 0.705856,
     "end_time": "2023-01-12T21:40:37.392942",
     "exception": false,
     "start_time": "2023-01-12T21:40:36.687086",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Visualizing the model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "80c12272",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T21:40:38.879207Z",
     "iopub.status.busy": "2023-01-12T21:40:38.878799Z",
     "iopub.status.idle": "2023-01-12T21:40:39.184921Z",
     "shell.execute_reply": "2023-01-12T21:40:39.183914Z"
    },
    "papermill": {
     "duration": 1.080632,
     "end_time": "2023-01-12T21:40:39.187377",
     "exception": false,
     "start_time": "2023-01-12T21:40:38.106745",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(r.history['loss'],'r',label='train loss')\n",
    "plt.plot(r.history['val_loss'],'b',label='test loss')\n",
    "plt.xlabel('No. of Epochs')\n",
    "plt.ylabel('Categorical Crossentropy Loss')\n",
    "plt.title('Loss Graph')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d0d0dbf2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T21:40:40.715957Z",
     "iopub.status.busy": "2023-01-12T21:40:40.715566Z",
     "iopub.status.idle": "2023-01-12T21:40:41.034334Z",
     "shell.execute_reply": "2023-01-12T21:40:41.032982Z"
    },
    "papermill": {
     "duration": 1.085674,
     "end_time": "2023-01-12T21:40:41.038789",
     "exception": false,
     "start_time": "2023-01-12T21:40:39.953115",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(r.history['balanced_accuracy'],'r',label='train accuracy')\n",
    "plt.plot(r.history['val_balanced_accuracy'],'b',label='test accuracy')\n",
    "plt.xlabel('Number of Epochs')\n",
    "plt.ylabel('Balanced Categorical Accuracy')\n",
    "plt.title('Accuracy Graph')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "373c620c",
   "metadata": {
    "papermill": {
     "duration": 0.732591,
     "end_time": "2023-01-12T21:40:42.757147",
     "exception": false,
     "start_time": "2023-01-12T21:40:42.024556",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "06fe9357",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T21:40:44.242339Z",
     "iopub.status.busy": "2023-01-12T21:40:44.241930Z",
     "iopub.status.idle": "2023-01-12T21:40:46.843778Z",
     "shell.execute_reply": "2023-01-12T21:40:46.842724Z"
    },
    "papermill": {
     "duration": 3.305617,
     "end_time": "2023-01-12T21:40:46.846498",
     "exception": false,
     "start_time": "2023-01-12T21:40:43.540881",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 2s 190ms/step - loss: 0.1330 - balanced_accuracy: 0.9931\n",
      "Test Sparse Categorical Crossentropy Loss: 0.13300560414791107\n",
      "Test Balanced Categorical Accuracy: 0.9930796027183533\n"
     ]
    }
   ],
   "source": [
    "loss, acc = model.evaluate({'input_ids': X_test['input_ids'], 'attention_mask': X_test['attention_mask']},test_df.Category)\n",
    "print(\"Test Sparse Categorical Crossentropy Loss:\", loss)\n",
    "print(\"Test Balanced Categorical Accuracy:\", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8f7efe4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T21:40:48.594917Z",
     "iopub.status.busy": "2023-01-12T21:40:48.594544Z",
     "iopub.status.idle": "2023-01-12T21:40:52.037852Z",
     "shell.execute_reply": "2023-01-12T21:40:52.036427Z"
    },
    "papermill": {
     "duration": 4.215718,
     "end_time": "2023-01-12T21:40:52.041721",
     "exception": false,
     "start_time": "2023-01-12T21:40:47.826003",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 14,  0,  0, 19, 10, 24, 21, 19, 15, 18, 13, 24, 24, 14,  7,  3,\n",
       "       17, 23,  0, 13, 17,  9, 11, 14, 20,  4,  9, 17,  9, 20,  4,  4, 21,\n",
       "        6, 12,  9, 23, 12, 24, 14, 24, 10,  9,  9, 18, 10, 21, 23,  3, 14,\n",
       "       20, 14, 12,  1, 24,  6, 23, 16,  6, 12, 21, 14, 23, 24,  0, 16,  5,\n",
       "        7, 22, 10,  0,  6,  2, 23,  3,  8,  3, 15, 18, 24, 19, 17,  3,  4,\n",
       "       22, 11, 20, 14, 24,  9, 21,  0, 15,  3, 20, 20,  4, 10,  9,  9, 24,\n",
       "       17, 11, 16,  1,  5,  3,  3,  9, 23, 17, 16,  0, 18, 22, 20,  4, 14,\n",
       "       16, 14, 21, 11,  3,  5, 20,  1, 23,  5,  5,  8, 22,  6, 10, 15,  9,\n",
       "        3,  9, 18,  0, 19, 10,  6,  5,  9,  7, 19,  5,  6,  4,  3,  4, 11,\n",
       "        6,  9,  1,  5, 12, 20, 17, 13,  4, 13,  9, 16, 15,  9,  0, 24, 21,\n",
       "       24, 21, 10, 18, 11, 11, 15,  5, 21, 24,  0,  4, 20, 18, 21, 24, 14,\n",
       "        4,  6,  4,  0,  9, 21, 13,  4, 21,  4, 11, 14,  4,  5,  7, 17,  1,\n",
       "        6,  7,  9, 23, 18, 14,  0, 23, 12, 23,  3, 21,  9, 21,  3, 20,  1,\n",
       "       20, 14, 16, 20, 21, 15, 20, 21,  4, 24, 21, 18, 14, 12, 21, 22,  1,\n",
       "        9, 19,  5, 22,  5, 16, 20, 19,  9,  8,  9,  3, 17, 15, 18, 17, 18,\n",
       "        9,  9, 14,  5, 12,  3, 24,  0, 23, 16,  7, 12, 15,  7, 11,  4, 20,\n",
       "       22, 19,  7,  4, 24,  3,  6,  4, 21, 16,  8,  9, 15, 15, 19, 18,  2])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions = model.predict({'input_ids': X_test['input_ids'], 'attention_mask': X_test['attention_mask']})\n",
    "test_predictions = np.argmax(test_predictions,axis=1)\n",
    "test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "345d566a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-12T21:40:53.756052Z",
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      "Confusion Matrix:\n",
      "[[13  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
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      "   0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 16  0  0  0  0  0  0  0  0  0\n",
      "   0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0  0\n",
      "   0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 10  0  0  0  0  0  0  0\n",
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      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 12  0  0  0  0  0\n",
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      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 12\n",
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      "  17]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        13\n",
      "           1       1.00      1.00      1.00         7\n",
      "           2       1.00      0.50      0.67         4\n",
      "           3       1.00      1.00      1.00        17\n",
      "           4       1.00      1.00      1.00        19\n",
      "           5       1.00      1.00      1.00        13\n",
      "           6       1.00      1.00      1.00        11\n",
      "           7       1.00      1.00      1.00         8\n",
      "           8       1.00      1.00      1.00         4\n",
      "           9       1.00      1.00      1.00        25\n",
      "          10       1.00      1.00      1.00         8\n",
      "          11       0.78      1.00      0.88         7\n",
      "          12       1.00      1.00      1.00         9\n",
      "          13       1.00      1.00      1.00         5\n",
      "          14       1.00      1.00      1.00        16\n",
      "          15       1.00      1.00      1.00        11\n",
      "          16       1.00      1.00      1.00        10\n",
      "          17       1.00      1.00      1.00        10\n",
      "          18       1.00      1.00      1.00        12\n",
      "          19       1.00      1.00      1.00         9\n",
      "          20       1.00      1.00      1.00        16\n",
      "          21       1.00      1.00      1.00        19\n",
      "          22       1.00      1.00      1.00         7\n",
      "          23       1.00      1.00      1.00        12\n",
      "          24       1.00      1.00      1.00        17\n",
      "\n",
      "    accuracy                           0.99       289\n",
      "   macro avg       0.99      0.98      0.98       289\n",
      "weighted avg       0.99      0.99      0.99       289\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(test_df.Category,test_predictions))\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(test_df.Category,test_predictions))"
   ]
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