bert-model-for-ai-text-detection.ipynb

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    "# 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"
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    {
     "name": "stderr",
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     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import tensorflow_text as text  # Registers the ops.\n",
    "import tensorflow_hub as hub\n",
    "import matplotlib.pyplot as plt"
   ]
  },
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   "source": [
    "#!pip install numpy==1.22.4"
   ]
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    "train_essay = pd.read_csv(\"/kaggle/input/llm-detect-ai-generated-text/train_essays.csv\")\n",
    "train_essay"
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      "dtypes: int64(2), object(2)\n",
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=train_essay['prompt_id'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fd4857c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T08:35:20.961242Z",
     "iopub.status.busy": "2023-12-18T08:35:20.960968Z",
     "iopub.status.idle": "2023-12-18T08:35:21.084856Z",
     "shell.execute_reply": "2023-12-18T08:35:21.083872Z"
    },
    "papermill": {
     "duration": 0.138901,
     "end_time": "2023-12-18T08:35:21.086952",
     "exception": false,
     "start_time": "2023-12-18T08:35:20.948051",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_essay['prompt_id'].value_counts().plot(kind=\"pie\",autopct=\"%.1f%%\")\n",
    "plt.title(\"Prompt ID\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "496d6b3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T08:35:21.114487Z",
     "iopub.status.busy": "2023-12-18T08:35:21.113921Z",
     "iopub.status.idle": "2023-12-18T08:35:21.224429Z",
     "shell.execute_reply": "2023-12-18T08:35:21.223460Z"
    },
    "papermill": {
     "duration": 0.126428,
     "end_time": "2023-12-18T08:35:21.226633",
     "exception": false,
     "start_time": "2023-12-18T08:35:21.100205",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_essay['generated'].value_counts().plot(kind=\"pie\",autopct=\"%.1f%%\")\n",
    "plt.title(\"Target label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e86b542",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T08:35:21.255707Z",
     "iopub.status.busy": "2023-12-18T08:35:21.255407Z",
     "iopub.status.idle": "2023-12-18T08:35:21.268016Z",
     "shell.execute_reply": "2023-12-18T08:35:21.266915Z"
    },
    "papermill": {
     "duration": 0.029725,
     "end_time": "2023-12-18T08:35:21.270261",
     "exception": false,
     "start_time": "2023-12-18T08:35:21.240536",
     "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>id</th>\n",
       "      <th>prompt_id</th>\n",
       "      <th>text</th>\n",
       "      <th>generated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0059830c</td>\n",
       "      <td>0</td>\n",
       "      <td>Cars. Cars have been around since they became ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>005db917</td>\n",
       "      <td>0</td>\n",
       "      <td>Transportation is a large necessity in most co...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>008f63e3</td>\n",
       "      <td>0</td>\n",
       "      <td>\"America's love affair with it's vehicles seem...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00940276</td>\n",
       "      <td>0</td>\n",
       "      <td>How often do you ride in a car? Do you drive a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00c39458</td>\n",
       "      <td>0</td>\n",
       "      <td>Cars are a wonderful thing. They are perhaps o...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  prompt_id                                               text  \\\n",
       "0  0059830c          0  Cars. Cars have been around since they became ...   \n",
       "1  005db917          0  Transportation is a large necessity in most co...   \n",
       "2  008f63e3          0  \"America's love affair with it's vehicles seem...   \n",
       "3  00940276          0  How often do you ride in a car? Do you drive a...   \n",
       "4  00c39458          0  Cars are a wonderful thing. They are perhaps o...   \n",
       "\n",
       "   generated  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_essay.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b729bcbb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T08:35:21.344101Z",
     "iopub.status.busy": "2023-12-18T08:35:21.343346Z",
     "iopub.status.idle": "2023-12-18T08:35:21.354275Z",
     "shell.execute_reply": "2023-12-18T08:35:21.353435Z"
    },
    "papermill": {
     "duration": 0.027604,
     "end_time": "2023-12-18T08:35:21.356354",
     "exception": false,
     "start_time": "2023-12-18T08:35:21.328750",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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    "stopwords_text = \"\"\"\"i me my myself we our ours ourselves you you're you've you'll you'd your yours yourself yourselves he him his himself she she's her hers herself it it's its itself they them their theirs themselves what which who whom this that that'll these those am is are was were be been being have has had having do does did doing a an the and but if or because as until while of at by for with about against between into through during before after above below to from up down in out on off over under again further then once here there when where why how all any both each few more most other some such only own same so than too very s t can will just don don't should should've now d ll m o re ve y ain aren aren't couldn couldn't didn didn't doesn doesn't hadn hadn't hasn hasn't haven haven't isn isn't ma mightn mightn't mustn mustn't needn needn't shan shan't shouldn shouldn't wasn wasn't weren weren't won won't wouldn wouldn't\"\n",
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    "df = pd.read_csv(\"/kaggle/input/dataset-4/Training_Essay_Data.csv\")"
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    "df2 = df2[['text','label']]\n",
    "df2.columns = ['text','generated']\n",
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    "train_data"
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    "# Dataset\n",
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    "d1"
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       "                                         generated_text  \\\n",
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       "4     I think we should totally switch to renewable ...   \n",
       "...                                                 ...   \n",
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       "                                         writing_prompt  \n",
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       "[1172 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Dataset\n",
    "d2 = pd.read_csv(\"/kaggle/input/dataset-3/llama_70b_v1.csv\")\n",
    "d2"
   ]
  },
  {
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    "data = pd.concat([d1,d2],axis=0,ignore_index=True)\n",
    "data['generated'] = 1\n",
    "data.columns = ['text','writing_prompt','generated']\n",
    "data = data[['text','generated']]\n",
    "data"
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     "execution_count": 27,
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    "Train_Data = pd.concat([train_data,data],axis=0,ignore_index=True)\n",
    "Train_Data"
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  },
  {
   "cell_type": "code",
   "execution_count": 28,
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     "execution_count": 28,
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   ],
   "source": [
    "d = pd.read_csv(\"/kaggle/input/dataset-3/LLM_generated_essay_PaLM.csv\")\n",
    "d"
   ]
  },
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   "execution_count": 30,
   "id": "ddc0cfdc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T08:35:26.862245Z",
     "iopub.status.busy": "2023-12-18T08:35:26.861997Z",
     "iopub.status.idle": "2023-12-18T08:35:26.872976Z",
     "shell.execute_reply": "2023-12-18T08:35:26.872135Z"
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     "exception": false,
     "start_time": "2023-12-18T08:35:26.845250",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>## The Advantages of Limiting Car Usage\\n\\nIn ...</td>\n",
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       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>Dear Senator,\\n\\nI am writing to you today to ...</td>\n",
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       "    <tr>\n",
       "      <th>1381</th>\n",
       "      <td>Dear Senator,\\n\\nI am writing to you today to ...</td>\n",
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       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>Dear Senator,\\n\\nI am writing to you today to ...</td>\n",
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       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>Dear Senator,\\n\\nI am writing to you today to ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1384 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  generated\n",
       "0     ## The Advantages of Limiting Car Usage\\n\\nIn ...          1\n",
       "1     The United States is a car-dependent nation, w...          1\n",
       "2     In recent years, there has been a growing move...          1\n",
       "3     In recent years, there has been a growing move...          1\n",
       "4     In the past few decades, the United States has...          1\n",
       "...                                                 ...        ...\n",
       "1379  Dear Senator,\\n\\nI am writing to you today to ...          1\n",
       "1380  Dear Senator,\\n\\nI am writing to you today to ...          1\n",
       "1381  Dear Senator,\\n\\nI am writing to you today to ...          1\n",
       "1382  Dear Senator,\\n\\nI am writing to you today to ...          1\n",
       "1383  Dear Senator,\\n\\nI am writing to you today to ...          1\n",
       "\n",
       "[1384 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd = d[['text','generated']]\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d9d4b699",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T08:35:26.910646Z",
     "iopub.status.busy": "2023-12-18T08:35:26.910126Z",
     "iopub.status.idle": "2023-12-18T08:35:26.921450Z",
     "shell.execute_reply": "2023-12-18T08:35:26.920659Z"
    },
    "papermill": {
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     "exception": false,
     "start_time": "2023-12-18T08:35:26.892762",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cars. Cars have been around since they became ...</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>\"America's love affair with it's vehicles seem...</td>\n",
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       "      <td>Cars are a wonderful thing. They are perhaps o...</td>\n",
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       "    <tr>\n",
       "      <th>69068</th>\n",
       "      <td>Dear Senator,\\n\\nI am writing to you today to ...</td>\n",
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       "    <tr>\n",
       "      <th>69069</th>\n",
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       "    <tr>\n",
       "      <th>69070</th>\n",
       "      <td>Dear Senator,\\n\\nI am writing to you today to ...</td>\n",
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       "    <tr>\n",
       "      <th>69071</th>\n",
       "      <td>Dear Senator,\\n\\nI am writing to you today to ...</td>\n",
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       "    <tr>\n",
       "      <th>69072</th>\n",
       "      <td>Dear Senator,\\n\\nI am writing to you today to ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69073 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  generated\n",
       "0      Cars. Cars have been around since they became ...          0\n",
       "1      Transportation is a large necessity in most co...          0\n",
       "2      \"America's love affair with it's vehicles seem...          0\n",
       "3      How often do you ride in a car? Do you drive a...          0\n",
       "4      Cars are a wonderful thing. They are perhaps o...          0\n",
       "...                                                  ...        ...\n",
       "69068  Dear Senator,\\n\\nI am writing to you today to ...          1\n",
       "69069  Dear Senator,\\n\\nI am writing to you today to ...          1\n",
       "69070  Dear Senator,\\n\\nI am writing to you today to ...          1\n",
       "69071  Dear Senator,\\n\\nI am writing to you today to ...          1\n",
       "69072  Dear Senator,\\n\\nI am writing to you today to ...          1\n",
       "\n",
       "[69073 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_Data  =pd.concat([Train_Data,dd],axis=0,ignore_index=True)\n",
    "Train_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "abb11a26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T08:35:26.958079Z",
     "iopub.status.busy": "2023-12-18T08:35:26.957837Z",
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     "shell.execute_reply": "2023-12-18T08:35:27.055555Z"
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     "exception": false,
     "start_time": "2023-12-18T08:35:26.939859",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Train_Data.generated.value_counts().plot(kind='pie',autopct=\"%.1f%%\")\n",
    "plt.title(\"Target Column Distributions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5f0f15d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T08:35:27.148308Z",
     "iopub.status.busy": "2023-12-18T08:35:27.147828Z",
     "iopub.status.idle": "2023-12-18T08:35:27.166882Z",
     "shell.execute_reply": "2023-12-18T08:35:27.165974Z"
    },
    "papermill": {
     "duration": 0.065607,
     "end_time": "2023-12-18T08:35:27.168890",
     "exception": false,
     "start_time": "2023-12-18T08:35:27.103283",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(68658,)\n",
      "(415,)\n",
      "(68658,)\n",
      "(415,)\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(Train_Data.text,Train_Data.generated,test_size=0.006,shuffle=True)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "260650e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T08:35:27.207074Z",
     "iopub.status.busy": "2023-12-18T08:35:27.206403Z",
     "iopub.status.idle": "2023-12-18T08:35:27.213277Z",
     "shell.execute_reply": "2023-12-18T08:35:27.212418Z"
    },
    "papermill": {
     "duration": 0.028681,
     "end_time": "2023-12-18T08:35:27.215365",
     "exception": false,
     "start_time": "2023-12-18T08:35:27.186684",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42613     Students and their families should carefully ...\n",
       "47380    Many students become participated in extracurr...\n",
       "5448     \"The Challenge of Exploring Venus\" talks about...\n",
       "6944     In the story The Challenge of Exploring Venus ...\n",
       "64816    Life is full of unexpected events, and no matt...\n",
       "                               ...                        \n",
       "36581    I think schools shuldnt have meditation and mi...\n",
       "28807    Hey there!  So, like, you know how school menu...\n",
       "25486    Working in groups in the classroom has numerou...\n",
       "17362    Luke's point of view on convincing people to g...\n",
       "26955    Children deserve their own free time. Making e...\n",
       "Name: text, Length: 68658, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "721f503d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T08:35:27.251514Z",
     "iopub.status.busy": "2023-12-18T08:35:27.251256Z",
     "iopub.status.idle": "2023-12-18T08:35:46.872937Z",
     "shell.execute_reply": "2023-12-18T08:35:46.871969Z"
    },
    "papermill": {
     "duration": 19.644673,
     "end_time": "2023-12-18T08:35:46.877609",
     "exception": false,
     "start_time": "2023-12-18T08:35:27.232936",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input (InputLayer)          [(None,)]                    0         []                            \n",
      "                                                                                                  \n",
      " keras_layer (KerasLayer)    {'input_word_ids': (None,    0         ['input[0][0]']               \n",
      "                             128),                                                                \n",
      "                              'input_type_ids': (None,                                            \n",
      "                             128),                                                                \n",
      "                              'input_mask': (None, 128)                                           \n",
      "                             }                                                                    \n",
      "                                                                                                  \n",
      " keras_layer_1 (KerasLayer)  {'sequence_output': (None,   1094822   ['keras_layer[0][0]',         \n",
      "                              128, 768),                  41         'keras_layer[0][1]',         \n",
      "                              'pooled_output': (None, 7              'keras_layer[0][2]']         \n",
      "                             68),                                                                 \n",
      "                              'encoder_outputs': [(None                                           \n",
      "                             , 128, 768),                                                         \n",
      "                              (None, 128, 768),                                                   \n",
      "                              (None, 128, 768),                                                   \n",
      "                              (None, 128, 768),                                                   \n",
      "                              (None, 128, 768),                                                   \n",
      "                              (None, 128, 768),                                                   \n",
      "                              (None, 128, 768),                                                   \n",
      "                              (None, 128, 768),                                                   \n",
      "                              (None, 128, 768),                                                   \n",
      "                              (None, 128, 768),                                                   \n",
      "                              (None, 128, 768),                                                   \n",
      "                              (None, 128, 768)],                                                  \n",
      "                              'default': (None, 768)}                                             \n",
      "                                                                                                  \n",
      " dropout (Dropout)           (None, 768)                  0         ['keras_layer_1[0][13]']      \n",
      "                                                                                                  \n",
      " output (Dense)              (None, 1)                    769       ['dropout[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 109483010 (417.64 MB)\n",
      "Trainable params: 769 (3.00 KB)\n",
      "Non-trainable params: 109482241 (417.64 MB)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "path =\"/kaggle/input/bert/tensorflow2/bert-en-uncased-l-12-h-768-a-12/2\"\n",
    "\n",
    "## Create Model\n",
    "## BERT Preprocess Data\n",
    "text_input = tf.keras.layers.Input(shape=(), dtype=tf.string , name='input')\n",
    "bert_preprocess = hub.KerasLayer(\"/kaggle/input/bert/tensorflow2/en-uncased-preprocess/3\")\n",
    "preprocessed_text = bert_preprocess(text_input)\n",
    "\n",
    "## Use BERT Model\n",
    "bert_encoder = hub.KerasLayer(\"/kaggle/input/bert/tensorflow2/bert-en-uncased-l-12-h-768-a-12/2\")\n",
    "outputs = bert_encoder(preprocessed_text)\n",
    "net = outputs['pooled_output']\n",
    "\n",
    "l = tf.keras.layers.Dropout(0.1 , name=\"dropout\")(net)\n",
    "d = tf.keras.layers.Dense(1 , activation='sigmoid' , name='output')(l)\n",
    "\n",
    "model = tf.keras.Model(inputs=[text_input] , outputs=[d])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4e540da1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T08:35:46.920111Z",
     "iopub.status.busy": "2023-12-18T08:35:46.919823Z",
     "iopub.status.idle": "2023-12-18T10:59:14.217031Z",
     "shell.execute_reply": "2023-12-18T10:59:14.216235Z"
    },
    "papermill": {
     "duration": 8607.320742,
     "end_time": "2023-12-18T10:59:14.219082",
     "exception": false,
     "start_time": "2023-12-18T08:35:46.898340",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/7\n",
      "269/269 [==============================] - 1235s 5s/step - loss: 0.4007 - accuracy: 0.8342 - val_loss: 0.3172 - val_accuracy: 0.8843\n",
      "Epoch 2/7\n",
      "269/269 [==============================] - 1229s 5s/step - loss: 0.3057 - accuracy: 0.8756 - val_loss: 0.2732 - val_accuracy: 0.9036\n",
      "Epoch 3/7\n",
      "269/269 [==============================] - 1228s 5s/step - loss: 0.2800 - accuracy: 0.8868 - val_loss: 0.2490 - val_accuracy: 0.9108\n",
      "Epoch 4/7\n",
      "269/269 [==============================] - 1228s 5s/step - loss: 0.2644 - accuracy: 0.8946 - val_loss: 0.2409 - val_accuracy: 0.9060\n",
      "Epoch 5/7\n",
      "269/269 [==============================] - 1229s 5s/step - loss: 0.2537 - accuracy: 0.8982 - val_loss: 0.2242 - val_accuracy: 0.9108\n",
      "Epoch 6/7\n",
      "269/269 [==============================] - 1228s 5s/step - loss: 0.2476 - accuracy: 0.9017 - val_loss: 0.2165 - val_accuracy: 0.9157\n",
      "Epoch 7/7\n",
      "269/269 [==============================] - 1228s 5s/step - loss: 0.2429 - accuracy: 0.9045 - val_loss: 0.2119 - val_accuracy: 0.9133\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='binary_crossentropy',metrics=[\"accuracy\"])\n",
    "history = model.fit(x_train, y_train , batch_size=256 , epochs=7 , validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "25e5d7df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T10:59:14.557474Z",
     "iopub.status.busy": "2023-12-18T10:59:14.557079Z",
     "iopub.status.idle": "2023-12-18T10:59:14.561322Z",
     "shell.execute_reply": "2023-12-18T10:59:14.560458Z"
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     "end_time": "2023-12-18T10:59:14.563139",
     "exception": false,
     "start_time": "2023-12-18T10:59:14.389714",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#loss , acc = model.evaluate(x_train, y_train)\n",
    "#print(\"Accuracy on Train data:\",acc)\n",
    "#loss , acc = model.evaluate(x_test, y_test)\n",
    "#print(\"Accuracy on Test data:\",acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "254bdf48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T10:59:14.895165Z",
     "iopub.status.busy": "2023-12-18T10:59:14.894638Z",
     "iopub.status.idle": "2023-12-18T10:59:27.472449Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.save(\"my_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a19aac30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T10:59:27.866952Z",
     "iopub.status.busy": "2023-12-18T10:59:27.866571Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>prompt_id</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000aaaa</td>\n",
       "      <td>2</td>\n",
       "      <td>Aaa bbb ccc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1111bbbb</td>\n",
       "      <td>3</td>\n",
       "      <td>Bbb ccc ddd.</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2222cccc</td>\n",
       "      <td>4</td>\n",
       "      <td>CCC ddd eee.</td>\n",
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       "</table>\n",
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       "         id  prompt_id          text\n",
       "0  0000aaaa          2  Aaa bbb ccc.\n",
       "1  1111bbbb          3  Bbb ccc ddd.\n",
       "2  2222cccc          4  CCC ddd eee."
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_essay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "20915ff9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T10:59:28.214529Z",
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    },
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     "end_time": "2023-12-18T10:59:29.133362",
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 838ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(test_essay['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4119af8d",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2023-12-18T10:59:29.302834",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.1184279 ],\n",
       "       [0.00977272],\n",
       "       [0.0170719 ]], dtype=float32)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9469fa1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T10:59:29.817792Z",
     "iopub.status.busy": "2023-12-18T10:59:29.817421Z",
     "iopub.status.idle": "2023-12-18T10:59:29.823452Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.1184279 , 0.00977272, 0.0170719 ], dtype=float32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5eeede0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T10:59:30.164022Z",
     "iopub.status.busy": "2023-12-18T10:59:30.163350Z",
     "iopub.status.idle": "2023-12-18T10:59:30.173062Z",
     "shell.execute_reply": "2023-12-18T10:59:30.172336Z"
    },
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     "end_time": "2023-12-18T10:59:30.175036",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Assuming 'test_ids' are the IDs of test samples, and 'predictions' are the predicted values\n",
    "submission = pd.DataFrame({'id': test_essay['id'] , 'generated': y_pred[:,0] })\n",
    "submission.to_csv('submission.csv', index=False)  # Save the CSV file"
   ]
  }
 ],
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