ai-text-detection-bert-model.ipynb

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     "status": "completed"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f245c91",
   "metadata": {
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     "start_time": "2023-12-19T22:08:07.233605",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Dataset Loading"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "231878f2",
   "metadata": {
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    "tags": []
   },
   "source": [
    "#### Loading the datasets from the direct path is important orelse it threw me an error if I downloaded the kaggle datasets and then used them here."
   ]
  },
  {
   "cell_type": "code",
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     "status": "completed"
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   },
   "outputs": [
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       "            id  prompt_id                                               text  \\\n",
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train_essay = pd.read_csv(\"/kaggle/input/llm-detect-ai-generated-text/train_essays.csv\")\n",
    "train_essay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "17cd3790",
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     "start_time": "2023-12-19T22:08:07.466677",
     "status": "completed"
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    "tags": []
   },
   "outputs": [
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   "source": [
    "test_essay = pd.read_csv(\"/kaggle/input/llm-detect-ai-generated-text/test_essays.csv\")\n",
    "test_essay"
   ]
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  {
   "cell_type": "code",
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     "status": "completed"
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   "outputs": [
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       "         id  prompt_id          text\n",
       "0  0000aaaa          2  Aaa bbb ccc.\n",
       "1  1111bbbb          3  Bbb ccc ddd.\n",
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "sample_sub = pd.read_csv(\"//kaggle/input/llm-detect-ai-generated-text/test_essays.csv\")\n",
    "sample_sub"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fbc92fe",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Data Analysis and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "362ff2a8",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1378 entries, 0 to 1377\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   id         1378 non-null   object\n",
      " 1   prompt_id  1378 non-null   int64 \n",
      " 2   text       1378 non-null   object\n",
      " 3   generated  1378 non-null   int64 \n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 43.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_essay.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5bcf9560",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "prompt_id\n",
       "0    708\n",
       "1    670\n",
       "Name: count, dtype: int64"
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "train_essay['prompt_id'].value_counts()"
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  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [
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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": 8,
   "id": "83edcbda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:08.107567Z",
     "iopub.status.busy": "2023-12-19T22:08:08.107142Z",
     "iopub.status.idle": "2023-12-19T22:08:08.289370Z",
     "shell.execute_reply": "2023-12-19T22:08:08.287993Z"
    },
    "papermill": {
     "duration": 0.206644,
     "end_time": "2023-12-19T22:08:08.293219",
     "exception": false,
     "start_time": "2023-12-19T22:08:08.086575",
     "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": 9,
   "id": "42cd05c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:08.372253Z",
     "iopub.status.busy": "2023-12-19T22:08:08.371879Z",
     "iopub.status.idle": "2023-12-19T22:08:08.513672Z",
     "shell.execute_reply": "2023-12-19T22:08:08.512440Z"
    },
    "papermill": {
     "duration": 0.17993,
     "end_time": "2023-12-19T22:08:08.516178",
     "exception": false,
     "start_time": "2023-12-19T22:08:08.336248",
     "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%%\") # to see what to take as base\n",
    "plt.title(\"Target label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d77da9fe",
   "metadata": {
    "papermill": {
     "duration": 0.019265,
     "end_time": "2023-12-19T22:08:08.554920",
     "exception": false,
     "start_time": "2023-12-19T22:08:08.535655",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### We observe that wrt 'generated' the data is highly imbalanced so we would be using 'prompt_id'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "58ed4abd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:08.596115Z",
     "iopub.status.busy": "2023-12-19T22:08:08.594829Z",
     "iopub.status.idle": "2023-12-19T22:08:08.606152Z",
     "shell.execute_reply": "2023-12-19T22:08:08.605150Z"
    },
    "papermill": {
     "duration": 0.034272,
     "end_time": "2023-12-19T22:08:08.608600",
     "exception": false,
     "start_time": "2023-12-19T22:08:08.574328",
     "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_essay.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0fc1571",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:08.649797Z",
     "iopub.status.busy": "2023-12-19T22:08:08.649426Z",
     "iopub.status.idle": "2023-12-19T22:08:08.660111Z",
     "shell.execute_reply": "2023-12-19T22:08:08.659247Z"
    },
    "papermill": {
     "duration": 0.032771,
     "end_time": "2023-12-19T22:08:08.662268",
     "exception": false,
     "start_time": "2023-12-19T22:08:08.629497",
     "status": "completed"
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    "tags": []
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   "outputs": [
    {
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       "\n",
       "      generated  \n",
       "704           1  \n",
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       "1262          1  "
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     "execution_count": 11,
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    "ai_df = train_essay[train_essay['generated']==1]\n",
    "ai_df"
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  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d991fd6",
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     "status": "completed"
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   "outputs": [
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       "            id  prompt_id                                               text  \\\n",
       "0     0059830c          0  Cars. Cars have been around since they became ...   \n",
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       "...        ...        ...                                                ...   \n",
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_essay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d78b01af",
   "metadata": {
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     "duration": 0.020238,
     "end_time": "2023-12-19T22:08:08.761423",
     "exception": false,
     "start_time": "2023-12-19T22:08:08.741185",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e960695d",
   "metadata": {
    "papermill": {
     "duration": 0.019941,
     "end_time": "2023-12-19T22:08:08.801741",
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     "start_time": "2023-12-19T22:08:08.781800",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### we would be using the stopwords_text and then we would be splitting them and all these words would be removed from the essays we had earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c2d10412",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2023-12-19T22:08:08.842090Z",
     "iopub.status.idle": "2023-12-19T22:08:08.847635Z",
     "shell.execute_reply": "2023-12-19T22:08:08.846790Z"
    },
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     "duration": 0.028097,
     "end_time": "2023-12-19T22:08:08.849722",
     "exception": false,
     "start_time": "2023-12-19T22:08:08.821625",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "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",
    "\"\"\"\n",
    "stopwords_list = stopwords_text.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd9bbcdc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:08.890924Z",
     "iopub.status.busy": "2023-12-19T22:08:08.890579Z",
     "iopub.status.idle": "2023-12-19T22:08:08.896730Z",
     "shell.execute_reply": "2023-12-19T22:08:08.895742Z"
    },
    "papermill": {
     "duration": 0.028926,
     "end_time": "2023-12-19T22:08:08.898851",
     "exception": false,
     "start_time": "2023-12-19T22:08:08.869925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "176"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(stopwords_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bfa2025",
   "metadata": {
    "papermill": {
     "duration": 0.02049,
     "end_time": "2023-12-19T22:08:08.940129",
     "exception": false,
     "start_time": "2023-12-19T22:08:08.919639",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Using custom datasets to feed in more data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57cf37eb",
   "metadata": {
    "papermill": {
     "duration": 0.020723,
     "end_time": "2023-12-19T22:08:08.987046",
     "exception": false,
     "start_time": "2023-12-19T22:08:08.966323",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### this was done to make the model train better with wide variety of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b8f6e733",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:09.029668Z",
     "iopub.status.busy": "2023-12-19T22:08:09.029288Z",
     "iopub.status.idle": "2023-12-19T22:08:10.429367Z",
     "shell.execute_reply": "2023-12-19T22:08:10.428371Z"
    },
    "papermill": {
     "duration": 1.424386,
     "end_time": "2023-12-19T22:08:10.431904",
     "exception": false,
     "start_time": "2023-12-19T22:08:09.007518",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/kaggle/input/dataset-4/Training_Essay_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d84e07c9",
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    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:10.476615Z",
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     "shell.execute_reply": "2023-12-19T22:08:10.486508Z"
    },
    "papermill": {
     "duration": 0.036638,
     "end_time": "2023-12-19T22:08:10.489686",
     "exception": false,
     "start_time": "2023-12-19T22:08:10.453048",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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      "text/plain": [
       "                                                    text  generated\n",
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       "29144  Cars have been around since the 1800's and hav...          0\n",
       "\n",
       "[29145 rows x 2 columns]"
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df"
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "47eb2b8e",
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     "iopub.status.busy": "2023-12-19T22:08:10.533781Z",
     "iopub.status.idle": "2023-12-19T22:08:12.894226Z",
     "shell.execute_reply": "2023-12-19T22:08:12.893281Z"
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    "papermill": {
     "duration": 2.384571,
     "end_time": "2023-12-19T22:08:12.896792",
     "exception": false,
     "start_time": "2023-12-19T22:08:10.512221",
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   },
   "outputs": [
    {
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    "df2 = pd.read_csv(\"/kaggle/input/dataset-2/train_drcat_04.csv\")\n",
    "df2 = df2[['text','label']]\n",
    "df2.columns = ['text','generated']\n",
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       "<p>1055 rows × 2 columns</p>\n",
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       "                                         writing_prompt  \n",
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     "execution_count": 24,
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   "source": [
    "# Dataset\n",
    "d1 = pd.read_csv(\"/kaggle/input/dataset-3/falcon_180b_v1.csv\")\n",
    "d1"
   ]
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    "# Dataset\n",
    "d2 = pd.read_csv(\"/kaggle/input/dataset-3/llama_70b_v1.csv\")\n",
    "d2"
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     "execution_count": 28,
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    "d = pd.read_csv(\"/kaggle/input/dataset-3/LLM_generated_essay_PaLM.csv\")\n",
    "d"
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       "69072  Dear Senator,\\n\\nI am writing to you today to ...          1\n",
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    "Train_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": 36,
   "id": "4afc6d00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:16.040654Z",
     "iopub.status.busy": "2023-12-19T22:08:16.040032Z",
     "iopub.status.idle": "2023-12-19T22:08:16.223423Z",
     "shell.execute_reply": "2023-12-19T22:08:16.222460Z"
    },
    "papermill": {
     "duration": 0.210986,
     "end_time": "2023-12-19T22:08:16.225671",
     "exception": false,
     "start_time": "2023-12-19T22:08:16.014685",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=Train_Data['generated'])\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86eb3d4e",
   "metadata": {
    "papermill": {
     "duration": 0.025018,
     "end_time": "2023-12-19T22:08:16.274992",
     "exception": false,
     "start_time": "2023-12-19T22:08:16.249974",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Balancing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c84619e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:16.330191Z",
     "iopub.status.busy": "2023-12-19T22:08:16.329824Z",
     "iopub.status.idle": "2023-12-19T22:08:16.336960Z",
     "shell.execute_reply": "2023-12-19T22:08:16.335899Z"
    },
    "papermill": {
     "duration": 0.037791,
     "end_time": "2023-12-19T22:08:16.339145",
     "exception": false,
     "start_time": "2023-12-19T22:08:16.301354",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# function to balance label_counts\n",
    "\n",
    "def balance_labels(df, label_column='generated'):\n",
    "    label_counts=df[label_column].value_counts()\n",
    "    max_count=label_counts.max()\n",
    "    \n",
    "    balanced_df=pd.DataFrame()\n",
    "    \n",
    "    for label, count in label_counts.items():\n",
    "        label_subset = df[df[label_column]==label]\n",
    "        oversampled_subset= label_subset.sample(n=(max_count-count), replace=True, random_state=42)\n",
    "        balanced_df = pd.concat([balanced_df, label_subset, oversampled_subset], ignore_index=True)\n",
    "        \n",
    "    return balanced_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5ef25ea2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:16.394344Z",
     "iopub.status.busy": "2023-12-19T22:08:16.393555Z",
     "iopub.status.idle": "2023-12-19T22:08:16.416423Z",
     "shell.execute_reply": "2023-12-19T22:08:16.415525Z"
    },
    "papermill": {
     "duration": 0.052879,
     "end_time": "2023-12-19T22:08:16.419045",
     "exception": false,
     "start_time": "2023-12-19T22:08:16.366166",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Balance labels in the dataframe\n",
    "Train_Data=balance_labels(Train_Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50d29357",
   "metadata": {
    "papermill": {
     "duration": 0.026367,
     "end_time": "2023-12-19T22:08:16.473384",
     "exception": false,
     "start_time": "2023-12-19T22:08:16.447017",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4ddd9413",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:16.527494Z",
     "iopub.status.busy": "2023-12-19T22:08:16.526714Z",
     "iopub.status.idle": "2023-12-19T22:08:16.660109Z",
     "shell.execute_reply": "2023-12-19T22:08:16.658793Z"
    },
    "papermill": {
     "duration": 0.164146,
     "end_time": "2023-12-19T22:08:16.664018",
     "exception": false,
     "start_time": "2023-12-19T22:08:16.499872",
     "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_Data.generated.value_counts().plot(kind='pie',autopct=\"%.1f%%\")\n",
    "plt.title(\"Target Column Distributions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb2a81dc",
   "metadata": {
    "papermill": {
     "duration": 0.032605,
     "end_time": "2023-12-19T22:08:16.757167",
     "exception": false,
     "start_time": "2023-12-19T22:08:16.724562",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05d2ec8c",
   "metadata": {
    "papermill": {
     "duration": 0.025049,
     "end_time": "2023-12-19T22:08:16.808225",
     "exception": false,
     "start_time": "2023-12-19T22:08:16.783176",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### first we would be segregating the training dataset we have into train and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "295cf741",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:16.859370Z",
     "iopub.status.busy": "2023-12-19T22:08:16.858961Z",
     "iopub.status.idle": "2023-12-19T22:08:16.874538Z",
     "shell.execute_reply": "2023-12-19T22:08:16.873409Z"
    },
    "papermill": {
     "duration": 0.044379,
     "end_time": "2023-12-19T22:08:16.876845",
     "exception": false,
     "start_time": "2023-12-19T22:08:16.832466",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(81771,)\n",
      "(743,)\n",
      "(81771,)\n",
      "(743,)\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(Train_Data.text,Train_Data.generated,test_size=0.009,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": 41,
   "id": "8b7f1241",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:16.928179Z",
     "iopub.status.busy": "2023-12-19T22:08:16.927829Z",
     "iopub.status.idle": "2023-12-19T22:08:16.935193Z",
     "shell.execute_reply": "2023-12-19T22:08:16.934271Z"
    },
    "papermill": {
     "duration": 0.035769,
     "end_time": "2023-12-19T22:08:16.937546",
     "exception": false,
     "start_time": "2023-12-19T22:08:16.901777",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27790    Paint a picture in your head, imagine if stude...\n",
       "7153     Technology has been used for decades in educat...\n",
       "68715     The current requirement for high school stude...\n",
       "35155    The Seagoing Cowboys were great heroes to othe...\n",
       "61616    Freedom of choice is a fundamental human right...\n",
       "                               ...                        \n",
       "62417    As the cost of higher education continues to r...\n",
       "26536    Avantages for limiting car usage is a real eye...\n",
       "22904    Have you ever thought about participating in t...\n",
       "63157    The importance of setting goals in life is oft...\n",
       "26482    Dear Principal,\\n\\nMy personal take on the com...\n",
       "Name: text, Length: 81771, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af9deaf7",
   "metadata": {
    "papermill": {
     "duration": 0.027375,
     "end_time": "2023-12-19T22:08:16.989312",
     "exception": false,
     "start_time": "2023-12-19T22:08:16.961937",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Using bert model for predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c9bf93da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:17.042680Z",
     "iopub.status.busy": "2023-12-19T22:08:17.042289Z",
     "iopub.status.idle": "2023-12-19T22:08:17.046929Z",
     "shell.execute_reply": "2023-12-19T22:08:17.045952Z"
    },
    "papermill": {
     "duration": 0.034978,
     "end_time": "2023-12-19T22:08:17.049074",
     "exception": false,
     "start_time": "2023-12-19T22:08:17.014096",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_path =\"/kaggle/input/bert/tensorflow2/bert-en-uncased-l-12-h-768-a-12/2\"\n",
    "preprocess_path = \"/kaggle/input/bert/tensorflow2/en-uncased-preprocess/3/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a4ba1ae2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:17.105147Z",
     "iopub.status.busy": "2023-12-19T22:08:17.104041Z",
     "iopub.status.idle": "2023-12-19T22:08:25.721946Z",
     "shell.execute_reply": "2023-12-19T22:08:25.721146Z"
    },
    "papermill": {
     "duration": 8.647977,
     "end_time": "2023-12-19T22:08:25.724288",
     "exception": false,
     "start_time": "2023-12-19T22:08:17.076311",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)\n",
    "preprocessor = hub.KerasLayer(preprocess_path)\n",
    "encoder_inputs  = preprocessor(text_input) # this is basically the preprocessed text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "95a56169",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:25.776916Z",
     "iopub.status.busy": "2023-12-19T22:08:25.776548Z",
     "iopub.status.idle": "2023-12-19T22:08:40.670210Z",
     "shell.execute_reply": "2023-12-19T22:08:40.669415Z"
    },
    "papermill": {
     "duration": 14.922876,
     "end_time": "2023-12-19T22:08:40.672791",
     "exception": false,
     "start_time": "2023-12-19T22:08:25.749915",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Use BERT Model\n",
    "encoder = hub.KerasLayer(model_path,trainable=True)\n",
    "outputs = encoder(encoder_inputs)\n",
    "pooled_output = outputs['pooled_output'] # [batch_size, 512].\n",
    "sequence_output = outputs[\"sequence_output\"] # [batch_size, seq_length, 512]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "35086ee1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:40.726554Z",
     "iopub.status.busy": "2023-12-19T22:08:40.726163Z",
     "iopub.status.idle": "2023-12-19T22:08:40.855570Z",
     "shell.execute_reply": "2023-12-19T22:08:40.854627Z"
    },
    "papermill": {
     "duration": 0.165424,
     "end_time": "2023-12-19T22:08:40.864992",
     "exception": false,
     "start_time": "2023-12-19T22:08:40.699568",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None,)]                    0         []                            \n",
      "                                                                                                  \n",
      " keras_layer (KerasLayer)    {'input_word_ids': (None,    0         ['input_1[0][0]']             \n",
      "                             128),                                                                \n",
      "                              'input_type_ids': (None,                                            \n",
      "                             128),                                                                \n",
      "                              'input_mask': (None, 128)                                           \n",
      "                             }                                                                    \n",
      "                                                                                                  \n",
      " keras_layer_1 (KerasLayer)  {'default': (None, 768),     1094822   ['keras_layer[0][0]',         \n",
      "                              'pooled_output': (None, 7   41         'keras_layer[0][1]',         \n",
      "                             68),                                    'keras_layer[0][2]']         \n",
      "                              'sequence_output': (None,                                           \n",
      "                              128, 768),                                                          \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",
      "                                                                                                  \n",
      " dropout1 (Dropout)          (None, 768)                  0         ['keras_layer_1[0][13]']      \n",
      "                                                                                                  \n",
      " dense (Dense)               (None, 64)                   49216     ['dropout1[0][0]']            \n",
      "                                                                                                  \n",
      " dropout2 (Dropout)          (None, 64)                   0         ['dense[0][0]']               \n",
      "                                                                                                  \n",
      " output (Dense)              (None, 1)                    65        ['dropout2[0][0]']            \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 109531522 (417.83 MB)\n",
      "Trainable params: 109531521 (417.83 MB)\n",
      "Non-trainable params: 1 (1.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "dropout = tf.keras.layers.Dropout(0.51 , name=\"dropout1\")(pooled_output)\n",
    "dense_2 = tf.keras.layers.Dense(64 , activation='relu')(dropout)\n",
    "dropout = tf.keras.layers.Dropout(0.3 , name=\"dropout2\")(dense_2)\n",
    "\n",
    "dense_out = tf.keras.layers.Dense(1 , activation='sigmoid', name='output')(dropout)\n",
    "\n",
    "model = tf.keras.Model(inputs=text_input, outputs=dense_out)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9a8aaff5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:40.923465Z",
     "iopub.status.busy": "2023-12-19T22:08:40.923085Z",
     "iopub.status.idle": "2023-12-19T22:08:40.927347Z",
     "shell.execute_reply": "2023-12-19T22:08:40.926420Z"
    },
    "papermill": {
     "duration": 0.035811,
     "end_time": "2023-12-19T22:08:40.929494",
     "exception": false,
     "start_time": "2023-12-19T22:08:40.893683",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model.compile(optimizer='adam', loss='binary_crossentropy',metrics=[\"accuracy\"]) # using adam gave better results as compared to rmse\n",
    "#history = model.fit(x_train, y_train , batch_size=512 , epochs=10 , validation_data=(x_test, y_test)) \n",
    "# using smaller batch size gave better predictions as it improved accuracy but the submission accuracy reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "09494eb8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:08:40.986873Z",
     "iopub.status.busy": "2023-12-19T22:08:40.986172Z",
     "iopub.status.idle": "2023-12-19T22:50:58.636655Z",
     "shell.execute_reply": "2023-12-19T22:50:58.635728Z"
    },
    "papermill": {
     "duration": 2537.682223,
     "end_time": "2023-12-19T22:50:58.639064",
     "exception": false,
     "start_time": "2023-12-19T22:08:40.956841",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10222/10222 [==============================] - 2519s 241ms/step - loss: 0.1094 - acc: 0.9585 - val_loss: 0.0917 - val_acc: 0.9771\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-6),\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=[\"acc\"])\n",
    "\n",
    "checkpoint_filepath = 'checkpoint.hdf5'\n",
    "metric = 'val_accuracy'\n",
    "callback_list = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_filepath, monitor=metric,\n",
    "                    verbose=2, save_best_only=True, mode='max')\n",
    "history = model.fit(x_train, y_train , batch_size=8, callbacks=[callback_list],epochs=1 , validation_data=(x_test, y_test))\n",
    "# model.load_weights(checkpoint_filepath)\n",
    "model.save(\"model-bert\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0babed20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T22:51:00.460608Z",
     "iopub.status.busy": "2023-12-19T22:51:00.460227Z",
     "iopub.status.idle": "2023-12-19T23:06:32.820874Z",
     "shell.execute_reply": "2023-12-19T23:06:32.819739Z"
    },
    "papermill": {
     "duration": 933.303423,
     "end_time": "2023-12-19T23:06:32.822969",
     "exception": false,
     "start_time": "2023-12-19T22:50:59.519546",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2556/2556 [==============================] - 864s 338ms/step - loss: 0.0565 - acc: 0.9810\n",
      "Accuracy on Train data: 0.9809589982032776\n",
      "24/24 [==============================] - 8s 325ms/step - loss: 0.0917 - acc: 0.9771\n",
      "Accuracy on Test data: 0.9771198034286499\n"
     ]
    }
   ],
   "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": 49,
   "id": "922343fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T23:06:35.021494Z",
     "iopub.status.busy": "2023-12-19T23:06:35.020623Z",
     "iopub.status.idle": "2023-12-19T23:06:35.030735Z",
     "shell.execute_reply": "2023-12-19T23:06:35.029752Z"
    },
    "papermill": {
     "duration": 1.101516,
     "end_time": "2023-12-19T23:06:35.032892",
     "exception": false,
     "start_time": "2023-12-19T23:06:33.931376",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "         id  prompt_id          text\n",
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     "metadata": {},
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   ],
   "source": [
    "test_essay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e34fe203",
   "metadata": {
    "papermill": {
     "duration": 1.077241,
     "end_time": "2023-12-19T23:06:37.260787",
     "exception": false,
     "start_time": "2023-12-19T23:06:36.183546",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b3e2ec6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T23:06:39.559675Z",
     "iopub.status.busy": "2023-12-19T23:06:39.558905Z",
     "iopub.status.idle": "2023-12-19T23:06:40.508654Z",
     "shell.execute_reply": "2023-12-19T23:06:40.507534Z"
    },
    "papermill": {
     "duration": 2.103352,
     "end_time": "2023-12-19T23:06:40.511147",
     "exception": false,
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 860ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(test_essay['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "954087f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T23:06:42.712646Z",
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     "iopub.status.idle": "2023-12-19T23:06:42.719395Z",
     "shell.execute_reply": "2023-12-19T23:06:42.718285Z"
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    "papermill": {
     "duration": 1.130799,
     "end_time": "2023-12-19T23:06:42.721426",
     "exception": false,
     "start_time": "2023-12-19T23:06:41.590627",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.4002606 ],\n",
       "       [0.55465555],\n",
       "       [0.64684063]], dtype=float32)"
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     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fb6dd4c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T23:06:45.016689Z",
     "iopub.status.busy": "2023-12-19T23:06:45.015181Z",
     "iopub.status.idle": "2023-12-19T23:06:45.022527Z",
     "shell.execute_reply": "2023-12-19T23:06:45.021656Z"
    },
    "papermill": {
     "duration": 1.104668,
     "end_time": "2023-12-19T23:06:45.024407",
     "exception": false,
     "start_time": "2023-12-19T23:06:43.919739",
     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.4002606 , 0.55465555, 0.64684063], dtype=float32)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f336961",
   "metadata": {
    "papermill": {
     "duration": 1.135921,
     "end_time": "2023-12-19T23:06:47.329308",
     "exception": false,
     "start_time": "2023-12-19T23:06:46.193387",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Saving the output in a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "143dd88e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T23:06:49.496883Z",
     "iopub.status.busy": "2023-12-19T23:06:49.496009Z",
     "iopub.status.idle": "2023-12-19T23:06:49.509799Z",
     "shell.execute_reply": "2023-12-19T23:06:49.509000Z"
    },
    "papermill": {
     "duration": 1.096522,
     "end_time": "2023-12-19T23:06:49.511884",
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     "start_time": "2023-12-19T23:06:48.415362",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Assuming 'test_ids' are the IDs of test samples, and 'predictions' are the predicted values\n",
    "# converting to the format required for submissions\n",
    "submission = pd.DataFrame({'id': test_essay['id'] , 'generated': y_pred[:,0] })\n",
    "submission.to_csv('submission.csv', index=False)  # Save the CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8ebf8eae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-19T23:06:51.756436Z",
     "iopub.status.busy": "2023-12-19T23:06:51.755674Z",
     "iopub.status.idle": "2023-12-19T23:06:51.766449Z",
     "shell.execute_reply": "2023-12-19T23:06:51.765568Z"
    },
    "papermill": {
     "duration": 1.105301,
     "end_time": "2023-12-19T23:06:51.768708",
     "exception": false,
     "start_time": "2023-12-19T23:06:50.663407",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>generated</th>\n",
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       "      <th>0</th>\n",
       "      <td>0000aaaa</td>\n",
       "      <td>0.400261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1111bbbb</td>\n",
       "      <td>0.554656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2222cccc</td>\n",
       "      <td>0.646841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  generated\n",
       "0  0000aaaa   0.400261\n",
       "1  1111bbbb   0.554656\n",
       "2  2222cccc   0.646841"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"/kaggle/working/submission.csv\")"
   ]
  }
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