BERT_Medical_Diagnosis.ipynb

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        "\n",
        "### The Use of BERT for Medical Diagnosis with Data Augmentation\n",
        "\n",
        "The integration of BERT for diagnosis, combined with data augmentation techniques such as backtranslation and paraphrasing, enhances the performance and robustness of the model. The approach involves the following steps:\n",
        "\n",
        "#### 1. **Data Augmentation**\n",
        "Data augmentation techniques are crucial for improving model generalization by creating diverse training examples.\n",
        "\n",
        "- **Backtranslation**:\n",
        "  Backtranslation is a process where text is first translated into another language (e.g., English to German) and then translated back to English. This generates alternative expressions of the same content while maintaining semantic meaning. By introducing linguistic variations, backtranslation diversifies the training data, making the model more resilient to different phrasings and reducing overfitting.\n",
        "\n",
        "- **Paraphrasing**:\n",
        "  Paraphrasing further enriches the dataset by generating new sentences that express the same meaning using different words and structures. This increases the variation in input data and helps the model learn to generalize better across different linguistic patterns.\n",
        "\n",
        "#### 2. **BERT-Based Model**\n",
        "BERT (Bidirectional Encoder Representations from Transformers) is a powerful language model that captures contextual relationships in text. It is fine-tuned for medical diagnosis classification tasks, where it predicts disease labels based on symptom descriptions provided in the input text. BERT’s ability to process bidirectional context helps in understanding complex medical terminology and nuances in symptoms, making it highly suitable for medical NLP tasks.\n",
        "\n",
        "#### 3. **Workflow**\n",
        "\n",
        "- **Dataset Preparation**:\n",
        "  Symptom descriptions and corresponding disease labels are collected and preprocessed to ensure consistency and quality.\n",
        "\n",
        "- **Augmentation**:\n",
        "  Backtranslation and paraphrasing techniques are applied to expand the dataset, addressing issues such as data scarcity and improving the model's ability to generalize.\n",
        "\n",
        "- **Fine-Tuning**:\n",
        "  The augmented dataset is used to fine-tune the BERT model, optimizing performance on classification metrics such as accuracy and F1-score.\n",
        "\n",
        "#### 4. **Applications**\n",
        "This methodology enhances diagnostic accuracy by leveraging diverse linguistic inputs, making it particularly valuable in medical NLP. It supports the identification of diseases based on symptom descriptions, improving the model’s capability to make accurate predictions in a clinical setting.\n",
        "\n",
        "---\n",
        "\n"
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        "import requests\n",
        "\n",
        "# URL of the file\n",
        "url = \"https://raw.githubusercontent.com/mohammad95labbaf/BERT_Medical_Diagnosis/main/Symptom2Disease.csv\"\n",
        "\n",
        "# Send a GET request to download the file\n",
        "response = requests.get(url)\n",
        "\n",
        "# Save the file to the local system\n",
        "with open(\"Symptom2Disease.csv\", \"wb\") as file:\n",
        "    file.write(response.content)\n",
        "\n",
        "print(\"File downloaded successfully!\")\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "df = pd.read_csv('Symptom2Disease.csv')\n",
        "\n",
        "# Display the DataFrame\n",
        "df.head(20)"
      ],
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          "text": [
            "File downloaded successfully!\n"
          ]
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        "### Data and Dataset Description\n",
        "\n",
        "The dataset used in this study focuses on the relationship between symptoms and corresponding medical conditions, facilitating diagnostic predictions. It is designed to train and evaluate machine learning models, particularly for Natural Language Processing (NLP)-based medical applications.\n",
        "\n",
        "#### **Dataset Overview**\n",
        "- **Source**: The dataset is derived from public repositories or curated medical databases, ensuring a wide variety of symptom descriptions linked to specific diseases.\n",
        "- **Structure**: Each data point typically consists of two columns:\n",
        "  1. **Symptom Description (Text)**: A free-text description of symptoms experienced by patients, often written in natural language.\n",
        "  2. **Disease Label**: The corresponding diagnosis, represented as categorical labels.\n",
        "\n",
        "#### **Key Characteristics**\n",
        "1. **Data Size**:\n",
        "   - The dataset contains **1,200 samples** before augmentation, with symptom descriptions ranging from brief phrases to detailed accounts of medical conditions.\n",
        "   - Post-augmentation, the dataset size doubles or triples, depending on the number of backtranslation and paraphrasing operations applied.\n",
        "\n",
        "2. **Class Distribution**:\n",
        "   - Includes **24 unique disease categories**, covering a diverse set of medical conditions such as pneumonia, psoriasis, typhoid, dengue, and more.\n",
        "   - Initial data imbalance is addressed through data augmentation and oversampling techniques to ensure equal representation across all classes.\n",
        "\n",
        "3. **Text Features**:\n",
        "   - Symptom descriptions vary in complexity, typically consisting of 60 to 317 characters, with an average length of approximately 171 characters.\n",
        "   - Descriptions include symptoms like fever, rash, fatigue, and pain, often requiring contextual understanding for accurate classification.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "iKkbgw-XRhRq"
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    {
      "cell_type": "markdown",
      "source": [
        "# Data Cleaning\n",
        "\n",
        "### **Data Cleaning (X)**\n",
        "\n",
        "To prepare the input text data (`X`) for training, a thorough cleaning process was applied to ensure consistency, quality, and relevance. The steps involved in data cleaning are as follows:\n",
        "\n",
        "---\n",
        "\n",
        "#### **1. Removing Duplicates**\n",
        "Duplicate symptom descriptions were removed to avoid redundancy and ensure each entry contributed unique information to the training dataset. This step also helped prevent potential biases during model training.\n",
        "\n",
        "---\n",
        "\n",
        "#### **2. Handling Missing Values**\n",
        "Rows with missing or incomplete symptom descriptions were excluded from the dataset. This ensured that the training data was comprehensive and meaningful, as incomplete entries could negatively impact model performance.\n",
        "\n",
        "---\n",
        "\n",
        "#### **3. Standardizing Text**\n",
        "Text data was standardized to maintain consistency across the dataset:\n",
        "- All text was converted to lowercase to ensure uniformity.\n",
        "- Punctuation marks and special characters that were irrelevant to the analysis were removed.\n",
        "- This preprocessing step helped eliminate noise and prepared the text for tokenization.\n",
        "\n",
        "---\n",
        "\n",
        "#### **4. Text Length Analysis**\n",
        "The length of each text entry was analyzed to ensure that very short or excessively long entries did not skew the model's performance. Descriptive statistics, such as minimum, maximum, and average text lengths, were calculated to gain insights into the dataset's distribution.\n",
        "\n",
        "---\n",
        "\n",
        "#### **5. Filtering Short Texts**\n",
        "Entries with extremely short descriptions (e.g., less than 10 characters) were excluded. These entries often lacked sufficient information for meaningful analysis or accurate predictions.\n",
        "\n",
        "---\n",
        "\n",
        "#### **6. Shuffling the Data**\n",
        "The dataset was shuffled to ensure a random distribution of samples. This step prevented unintended patterns or biases in the ordering of data, which could have influenced model training.\n",
        "\n",
        "---\n",
        "\n",
        "By performing these cleaning steps, the input text data was refined to improve its quality and relevance. This ensured a robust foundation for subsequent augmentation, feature extraction, and model training."
      ],
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        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1200 entries, 0 to 1199\n",
            "Data columns (total 3 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   Unnamed: 0  1200 non-null   int64 \n",
            " 1   label       1200 non-null   object\n",
            " 2   text        1200 non-null   object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 28.2+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "df = df.drop('Unnamed: 0', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "99b8612f",
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        "tags": [],
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Psoriasis', 'Varicose Veins', 'Typhoid', 'Chicken pox',\n",
              "       'Impetigo', 'Dengue', 'Fungal infection', 'Common Cold',\n",
              "       'Pneumonia', 'Dimorphic Hemorrhoids', 'Arthritis', 'Acne',\n",
              "       'Bronchial Asthma', 'Hypertension', 'Migraine',\n",
              "       'Cervical spondylosis', 'Jaundice', 'Malaria',\n",
              "       'urinary tract infection', 'allergy',\n",
              "       'gastroesophageal reflux disease', 'drug reaction',\n",
              "       'peptic ulcer disease', 'diabetes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df['label'].unique()"
      ]
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        {
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            "text/plain": [
              "24"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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      "source": [
        "len(df['label'].unique())"
      ]
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      "execution_count": null,
      "id": "54e84508",
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          "status": "completed"
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        "tags": [],
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          "base_uri": "https://localhost:8080/"
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 1200 data in this dataset\n",
            "Number of Duplicates: 47\n",
            "Number of Missing Values by column:\n",
            " label    0\n",
            "text     0\n",
            "dtype: int64\n",
            "Number of Missing Values: 0\n"
          ]
        }
      ],
      "source": [
        "# How many reviews do we have?\n",
        "print('There are', df.shape[0], 'data in this dataset')\n",
        "\n",
        "# Do we have duplicates?\n",
        "print('Number of Duplicates:', len(df[df.duplicated()]))\n",
        "\n",
        "# Do we have missing values?\n",
        "missing_values = df.isnull().sum()\n",
        "print('Number of Missing Values by column:\\n',missing_values)\n",
        "\n",
        "print('Number of Missing Values:', df.isnull().sum().sum())"
      ]
    },
    {
      "cell_type": "code",
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      "id": "a881ec1d",
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      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Number of Missing Values and Empty Spaces by column:\n",
            " label    0\n",
            "text     0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "df.replace(\"\", np.nan, inplace=True)\n",
        "missing_values = df.isnull().sum()\n",
        "print('Number of Missing Values and Empty Spaces by column:\\n',missing_values)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3f081b0c",
      "metadata": {
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          "status": "completed"
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      },
      "source": [
        "**Review Duplicates**\n",
        "\n",
        "Next, we identify and display duplicate rows in the DataFrame. The code first selects all duplicate rows and then sorts the DataFrame to ensure duplicates are adjacent. It then displays the top 5 pairs of duplicates."
      ]
    },
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              "                    label                                               text\n",
              "556                  Acne  A nasty rash has just appeared on my skin. Bla...\n",
              "563                  Acne  A nasty rash has just appeared on my skin. Bla...\n",
              "560                  Acne  A skin rash with several pus-filled pimples an...\n",
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              "570                  Acne  I've been dealing with a really nasty rash on ...\n",
              "574                  Acne  I've been dealing with a really nasty rash on ...\n",
              "524             Arthritis  I've been experiencing stiffness and weakness ...\n",
              "526             Arthritis  I've been experiencing stiffness and weakness ...\n",
              "514             Arthritis  I've been feeling really weak in my muscles an...\n",
              "520             Arthritis  I've been feeling really weak in my muscles an...\n",
              "527             Arthritis  I've been feeling really weak in my muscles an...\n",
              "515             Arthritis  My muscles have been feeling really weak, and ...\n",
              "521             Arthritis  My muscles have been feeling really weak, and ...\n",
              "614      Bronchial Asthma  I've been struggling with fatigue and a consta...\n",
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              "773  Cervical spondylosis  Back pain, a persistent cough, and numbness in...\n",
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              "        document.querySelector('#df-a3810497-c99b-4590-a0b3-6faa71d8b502 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-a3810497-c99b-4590-a0b3-6faa71d8b502');\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",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
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              "  }\n",
              "\n",
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              "    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",
              "    }\n",
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              "    }\n",
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              "      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-37b285fb-40c5-483d-80a6-87e1afb64337 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_4012fdf9-ac90-420f-b9a7-4d1abff42f91\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border-radius: 50%;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('top_5_duplicate_pairs')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
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              "        document.querySelector('#id_4012fdf9-ac90-420f-b9a7-4d1abff42f91 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('top_5_duplicate_pairs');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "top_5_duplicate_pairs",
              "summary": "{\n  \"name\": \"top_5_duplicate_pairs\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Arthritis\",\n          \"Chicken pox\",\n          \"Bronchial Asthma\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Back pain, a persistent cough, and numbness in my arms and legs have been bothering me. I've had trouble staying balanced and experiencing dizziness, and my neck hurts.\",\n          \"A skin rash with several pus-filled pimples and blackheads has been bothering me lately. Additionally, my skin has been scurring a lot.\",\n          \"I've been feeling really weak in my muscles and my neck has been really stiff. My joints have been swelling up and it's hard for me to move around without feeling stiff. Walking has been really painful too.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# First, get all duplicate rows (keep=False ensures all duplicates are kept)\n",
        "duplicate_rows = df[df.duplicated(keep=False)]\n",
        "\n",
        "# Then sort the dataframe on all columns to ensure duplicates are adjacent\n",
        "sorted_duplicates = duplicate_rows.sort_values(by=list(duplicate_rows.columns))\n",
        "\n",
        "# Now, if we want to see 5 pairs of duplicates (10 rows), we can simply:\n",
        "top_5_duplicate_pairs = sorted_duplicates.head(20)\n",
        "\n",
        "top_5_duplicate_pairs"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b69751a6",
      "metadata": {
        "id": "b69751a6",
        "papermill": {
          "duration": 0.090381,
          "end_time": "2023-07-13T15:57:49.344637",
          "exception": false,
          "start_time": "2023-07-13T15:57:49.254256",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "**Drop Duplicates**\n",
        "\n",
        "After identifying duplicate rows, we remove them from the DataFrame. This helps ensure data integrity and avoids duplicate entries in our analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5a61bca4",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-07-13T15:57:49.528277Z",
          "iopub.status.busy": "2023-07-13T15:57:49.527414Z",
          "iopub.status.idle": "2023-07-13T15:57:49.537729Z",
          "shell.execute_reply": "2023-07-13T15:57:49.536591Z"
        },
        "id": "5a61bca4",
        "outputId": "2e714de0-2521-41a9-cced-e4eafa2471e4",
        "papermill": {
          "duration": 0.105637,
          "end_time": "2023-07-13T15:57:49.540501",
          "exception": false,
          "start_time": "2023-07-13T15:57:49.434864",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Duplicates: 0\n"
          ]
        }
      ],
      "source": [
        "df = df.drop_duplicates()\n",
        "print('Number of Duplicates:', len(df[df.duplicated()]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8c6793ec",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-07-13T15:58:02.706573Z",
          "iopub.status.busy": "2023-07-13T15:58:02.705316Z",
          "iopub.status.idle": "2023-07-13T15:58:02.717599Z",
          "shell.execute_reply": "2023-07-13T15:58:02.716028Z"
        },
        "id": "8c6793ec",
        "outputId": "a3a13f03-8e8d-4699-f287-1445f14121d3",
        "papermill": {
          "duration": 0.117075,
          "end_time": "2023-07-13T15:58:02.720162",
          "exception": false,
          "start_time": "2023-07-13T15:58:02.603087",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-8dafa28022a8>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['text_length'] = df['text'].apply(len)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1153.000000\n",
              "mean      171.112749\n",
              "std        35.500946\n",
              "min        60.000000\n",
              "25%       147.000000\n",
              "50%       169.000000\n",
              "75%       192.000000\n",
              "max       317.000000\n",
              "Name: text_length, 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",
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              "    }\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>text_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1153.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>171.112749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>35.500946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>60.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>147.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>169.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>192.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>317.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Calculate text length\n",
        "df['text_length'] = df['text'].apply(len)\n",
        "\n",
        "# Display the DataFrame\n",
        "df.text_length.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2847a3de",
      "metadata": {
        "id": "2847a3de",
        "papermill": {
          "duration": 0.094176,
          "end_time": "2023-07-13T15:58:02.909150",
          "exception": false,
          "start_time": "2023-07-13T15:58:02.814974",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "**Visualize text with low word frequency**\n",
        "\n",
        "A count plot is generated to visualize the distribution of texts with less than 10 words, filtering the data based on the 'text_length' column."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df[df['text_length'] < 10])\n",
        "filtered_df = df[df['text_length'] < 10]\n",
        "if filtered_df.empty:\n",
        "    print(\"No text entries with less than 10 words.\")\n",
        "else:\n",
        "    plt.figure(figsize=(7, 5))\n",
        "    ax = sns.countplot(x='text_length', data=filtered_df, palette='mako')\n",
        "    plt.title('Training text with less than 10 words')\n",
        "    plt.yticks([])\n",
        "    ax.bar_label(ax.containers[0])\n",
        "    plt.ylabel('Count')\n",
        "    plt.xlabel('')\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hgX76Rvah70p",
        "outputId": "d6204533-4bef-4f2f-abc4-a1313b5e73c7"
      },
      "id": "hgX76Rvah70p",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: [label, text, text_length]\n",
            "Index: []\n",
            "No text entries with less than 10 words.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5fc72ece",
      "metadata": {
        "id": "5fc72ece",
        "papermill": {
          "duration": 0.095944,
          "end_time": "2023-07-13T15:58:05.577248",
          "exception": false,
          "start_time": "2023-07-13T15:58:05.481304",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "**Label Encoding**\n",
        "\n",
        "The following code block shows how to encode categorical labels using the LabelEncoder from scikit-learn. LabelEncoder is used to convert categorical labels into numerical values. This encoding is important for training machine learning models that require numerical inputs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "40fd44ec",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-07-13T15:58:05.769465Z",
          "iopub.status.busy": "2023-07-13T15:58:05.769070Z",
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          "shell.execute_reply": "2023-07-13T15:58:05.775954Z"
        },
        "id": "40fd44ec",
        "papermill": {
          "duration": 0.107013,
          "end_time": "2023-07-13T15:58:05.779612",
          "exception": false,
          "start_time": "2023-07-13T15:58:05.672599",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Define LabelEncoder\n",
        "le = LabelEncoder()\n",
        "\n",
        "# Fit and transform the data\n",
        "df['label_encoded'] = le.fit_transform(df['label'])\n",
        "label_mapping = dict(zip(le.classes_, le.transform(le.classes_)))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "83009949",
      "metadata": {
        "id": "83009949",
        "papermill": {
          "duration": 0.095996,
          "end_time": "2023-07-13T15:58:05.970641",
          "exception": false,
          "start_time": "2023-07-13T15:58:05.874645",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "In the next code block, a DataFrame is created to store the mapping between the original labels and their corresponding encoded values. This mapping can be useful for reference and interpretation of the encoded labels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "89ea3191",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-07-13T15:58:06.165488Z",
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          "status": "completed"
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          "height": 801
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      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                              Label  Label Encoded\n",
              "0                              Acne              0\n",
              "1                         Arthritis              1\n",
              "2                  Bronchial Asthma              2\n",
              "3              Cervical spondylosis              3\n",
              "4                       Chicken pox              4\n",
              "5                       Common Cold              5\n",
              "6                            Dengue              6\n",
              "7             Dimorphic Hemorrhoids              7\n",
              "8                  Fungal infection              8\n",
              "9                      Hypertension              9\n",
              "10                         Impetigo             10\n",
              "11                         Jaundice             11\n",
              "12                          Malaria             12\n",
              "13                         Migraine             13\n",
              "14                        Pneumonia             14\n",
              "15                        Psoriasis             15\n",
              "16                          Typhoid             16\n",
              "17                   Varicose Veins             17\n",
              "18                          allergy             18\n",
              "19                         diabetes             19\n",
              "20                    drug reaction             20\n",
              "21  gastroesophageal reflux disease             21\n",
              "22             peptic ulcer disease             22\n",
              "23          urinary tract infection             23"
            ],
            "text/html": [
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              "      <th></th>\n",
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              "      <th>Label Encoded</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acne</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Arthritis</td>\n",
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              "      <th>2</th>\n",
              "      <td>Bronchial Asthma</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cervical spondylosis</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Chicken pox</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Common Cold</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dengue</td>\n",
              "      <td>6</td>\n",
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              "      <th>7</th>\n",
              "      <td>Dimorphic Hemorrhoids</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Fungal infection</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Hypertension</td>\n",
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              "    </tr>\n",
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              "      <th>10</th>\n",
              "      <td>Impetigo</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Jaundice</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Malaria</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Migraine</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Pneumonia</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Psoriasis</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Typhoid</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Varicose Veins</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>allergy</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>diabetes</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>drug reaction</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>gastroesophageal reflux disease</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>peptic ulcer disease</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>urinary tract infection</td>\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dr')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dr');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dr",
              "summary": "{\n  \"name\": \"dr\",\n  \"rows\": 24,\n  \"fields\": [\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"Fungal infection\",\n          \"Typhoid\",\n          \"Acne\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label Encoded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 0,\n        \"max\": 23,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          8,\n          16,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Define data\n",
        "data = {\n",
        "    'Label': list(label_mapping.keys()),\n",
        "    'Label Encoded': list(label_mapping.values())\n",
        "}\n",
        "\n",
        "# Create DataFrame\n",
        "dr = pd.DataFrame(data)\n",
        "\n",
        "# Print DataFrame\n",
        "dr"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9a366cf5",
      "metadata": {
        "id": "9a366cf5",
        "papermill": {
          "duration": 0.100621,
          "end_time": "2023-07-13T15:58:06.385866",
          "exception": false,
          "start_time": "2023-07-13T15:58:06.285245",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "class_names are a list of the original labels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7cb4dc86",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-07-13T15:58:06.588121Z",
          "iopub.status.busy": "2023-07-13T15:58:06.587676Z",
          "iopub.status.idle": "2023-07-13T15:58:06.594865Z",
          "shell.execute_reply": "2023-07-13T15:58:06.593883Z"
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          "start_time": "2023-07-13T15:58:06.485330",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Acne',\n",
              " 'Arthritis',\n",
              " 'Bronchial Asthma',\n",
              " 'Cervical spondylosis',\n",
              " 'Chicken pox',\n",
              " 'Common Cold',\n",
              " 'Dengue',\n",
              " 'Dimorphic Hemorrhoids',\n",
              " 'Fungal infection',\n",
              " 'Hypertension',\n",
              " 'Impetigo',\n",
              " 'Jaundice',\n",
              " 'Malaria',\n",
              " 'Migraine',\n",
              " 'Pneumonia',\n",
              " 'Psoriasis',\n",
              " 'Typhoid',\n",
              " 'Varicose Veins',\n",
              " 'allergy',\n",
              " 'diabetes',\n",
              " 'drug reaction',\n",
              " 'gastroesophageal reflux disease',\n",
              " 'peptic ulcer disease',\n",
              " 'urinary tract infection']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "class_names=dr.Label.to_list()\n",
        "class_names"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Back Translation\n",
        "\n",
        "This code snippet demonstrates a **data augmentation** technique for natural language processing (NLP) tasks using **back-translation**. Below is a description of the key steps:\n",
        "\n",
        "---\n",
        "\n",
        "### **1. Back-Translation**\n",
        "Back-translation is an effective data augmentation technique where text is translated to another language (e.g., English → German) and then back to the original language (German → English). This introduces variations in the text while preserving its original meaning.\n",
        "\n",
        "---\n",
        "\n",
        "### **2. Workflow**\n",
        "#### **Loading Models and Tokenizers**\n",
        "- Two pre-trained models from the `Helsinki-NLP/opus-mt` series are loaded:\n",
        "  - `opus-mt-en-de`: Translates English to German.\n",
        "  - `opus-mt-de-en`: Translates German back to English.\n",
        "- The models are moved to the GPU (`device`) if available for faster processing.\n",
        "\n",
        "#### **Back-Translation Function**\n",
        "- **`back_translate_single`**:\n",
        "  - Takes a single text as input.\n",
        "  - Translates it to German using the English-to-German model.\n",
        "  - Translates the German text back to English using the German-to-English model.\n",
        "  - If an error occurs during translation, it returns the original text as a fallback.\n",
        "\n",
        "#### **Batch Processing**\n",
        "- Processes the dataset (`X` and `y`) in **batches** (default size: 16) to handle large datasets efficiently.\n",
        "- For each text-label pair in the batch:\n",
        "  - Back-translates the text.\n",
        "  - Appends the augmented text and the corresponding label to separate lists.\n",
        "\n",
        "---\n",
        "\n",
        "### **3. Dataset Creation**\n",
        "#### **Augmented Dataset**\n",
        "- A new dataset containing only back-translated texts and labels is created and saved as `augmented_texts.csv`.\n",
        "\n",
        "#### **Combined Dataset**\n",
        "- Combines the original dataset and the augmented dataset:\n",
        "  - Texts from the original dataset (`X`).\n",
        "  - Back-translated texts (`augmented_texts`).\n",
        "  - Corresponding labels (`y` and `augmented_labels`).\n",
        "- The combined dataset is shuffled to mix original and augmented samples randomly.\n",
        "- The final dataset is saved as `combined_dataset.csv`.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "NTgtz5ghCGQb"
      },
      "id": "NTgtz5ghCGQb"
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract features (X) and labels (y)\n",
        "X = df['text'].to_numpy()\n",
        "y = df['label_encoded'].to_numpy()\n",
        "X"
      ],
      "metadata": {
        "id": "4oiRx6Fkkf1w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f48b5466-6f02-4a05-ee85-c12bf3815363"
      },
      "id": "4oiRx6Fkkf1w",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['I have been experiencing a skin rash on my arms, legs, and torso for the past few weeks. It is red, itchy, and covered in dry, scaly patches.',\n",
              "       'My skin has been peeling, especially on my knees, elbows, and scalp. This peeling is often accompanied by a burning or stinging sensation.',\n",
              "       'I have been experiencing joint pain in my fingers, wrists, and knees. The pain is often achy and throbbing, and it gets worse when I move my joints.',\n",
              "       ...,\n",
              "       \"I regularly experience these intense urges and the want to urinate. I frequently feel drowsy and lost. I've also significantly lost my vision.\",\n",
              "       'I have trouble breathing, especially outside. I start to feel hot and start to sweat. I frequently have urinary tract infections and yeast infections.',\n",
              "       \"I constantly sneeze and have a dry cough. My infections don't seem to be healing, and I have palpitations. My throat does ache occasionally, but it usually gets better.\"],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from transformers import MarianMTModel, MarianTokenizer\n",
        "import torch\n",
        "from tqdm import tqdm  # For progress bar\n",
        "\n",
        "# Load models and tokenizers\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "model_name_en_de = 'Helsinki-NLP/opus-mt-en-de'\n",
        "tokenizer_en_de = MarianTokenizer.from_pretrained(model_name_en_de)\n",
        "model_en_de = MarianMTModel.from_pretrained(model_name_en_de).to(device)\n",
        "\n",
        "model_name_de_en = 'Helsinki-NLP/opus-mt-de-en'\n",
        "tokenizer_de_en = MarianTokenizer.from_pretrained(model_name_de_en)\n",
        "model_de_en = MarianMTModel.from_pretrained(model_name_de_en).to(device)\n",
        "\n",
        "def back_translate_single(text):\n",
        "    \"\"\"Translate a single text to German and back to English.\"\"\"\n",
        "    try:\n",
        "        # Translate to German\n",
        "        inputs = tokenizer_en_de(text, return_tensors=\"pt\", padding=True, truncation=True).to(device)\n",
        "        german_translation = model_en_de.generate(**inputs, num_beams=1)\n",
        "        german_text = tokenizer_en_de.decode(german_translation[0], skip_special_tokens=True)\n",
        "\n",
        "        # Translate back to English\n",
        "        inputs_back = tokenizer_de_en(german_text, return_tensors=\"pt\", padding=True, truncation=True).to(device)\n",
        "        english_translation = model_de_en.generate(**inputs_back, num_beams=1)\n",
        "        back_translated_text = tokenizer_de_en.decode(english_translation[0], skip_special_tokens=True)\n",
        "\n",
        "        return back_translated_text\n",
        "    except Exception as e:\n",
        "        return text  # Return original text in case of failure\n",
        "\n",
        "# Process the dataset in batches\n",
        "# batch_size = 32\n",
        "batch_size = 16\n",
        "augmented_texts = []\n",
        "augmented_labels = []\n",
        "\n",
        "for i in tqdm(range(0, len(X), batch_size), desc=\"Processing batches\"):\n",
        "    batch_texts = X[i:i + batch_size]\n",
        "    batch_labels = y[i:i + batch_size]\n",
        "\n",
        "    # Back-translate each text in the batch\n",
        "    for text, label in zip(batch_texts, batch_labels):\n",
        "        augmented_text = back_translate_single(text)\n",
        "        augmented_texts.append(augmented_text)\n",
        "        augmented_labels.append(label)\n",
        "\n",
        "# Save augmented texts to a separate file\n",
        "augmented_only_df = pd.DataFrame({\n",
        "    'text': augmented_texts,\n",
        "    'label_encoded': augmented_labels\n",
        "})\n",
        "augmented_only_df.to_csv(\"augmented_texts.csv\", index=False)\n",
        "\n",
        "# Combine original and augmented data\n",
        "augmented_df = pd.DataFrame({\n",
        "    'text': list(X) + augmented_texts,\n",
        "    'label_encoded': list(y) + augmented_labels\n",
        "})\n",
        "\n",
        "# Shuffle the dataset\n",
        "augmented_df = augmented_df.sample(frac=1).reset_index(drop=True)\n",
        "\n",
        "# Save the combined dataset\n",
        "augmented_df.to_csv(\"combined_dataset.csv\", index=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIOnugReOgyM",
        "outputId": "f9813ea8-d0bb-46d9-c4ff-bf6bc074d005"
      },
      "id": "wIOnugReOgyM",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n",
            "Processing batches: 100%|██████████| 73/73 [13:40<00:00, 11.24s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Randomly sample 40 rows from the dataframe\n",
        "sampled_data = augmented_df.sample(n=40, random_state=42)  # Set random_state for reproducibility\n",
        "\n",
        "# Display the sampled data\n",
        "sampled_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "INHseEvlrdrK",
        "outputId": "a89a24f4-d499-46a7-9a31-ccceb69d1139"
      },
      "id": "INHseEvlrdrK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                                                                                                                                         text  \\\n",
              "665                                                                                                                                                                                                Paraphrase   \n",
              "410   My eyes are constantly red and runny. A persistent tightness in my sinuses has disturbed me too. I have a lot of difficulty breathing and constantly feel tired. In addition, I have a lot of cervic...   \n",
              "2686  I sneeze over and over again, and my eyes don't stop dripping. It's incredibly hard for me to breathe because it feels like something's trapped in my throat. I often feel exhausted, and lately I'v...   \n",
              "2222                                                    I often have headaches, blurred vision, excessive appetite, sore throat, anxiety, irritability and indigestion including indigestion and acid reflux.   \n",
              "2609                My sinuses feel sticky, and my eyes were pretty red. I just have no energy, and my neck was really scratchy. Along with the swelling in my lymph nodes, I also have a lot of mucus cough.   \n",
              "578                                                            Long durations of standing or walking have caused severe discomfort in my legs. It's a burning ache that gets worse the longer I'm on my feet.   \n",
              "602                                                                      I feel tired most of the time, I don't want to eat, I get high fever and chills every night, and I've been vomiting since yesterday.   \n",
              "192                                                                                                                                                                                               Paraphrase:   \n",
              "3311                      I get frequent heartburn and indigestion, especially eating spicy foods or fatty foods. I get chest pain that gets worse when I lie down or bend over. I also often have chest pain   \n",
              "299                                                                                                              Paraphrase: My nails have small dents on them. Even my joints have started to pain severely.   \n",
              "51                                                                                              My skin only developed an uninherited rash, it has a lot of pus and trophies, and my skin was very sensitive.   \n",
              "2328                                                                                                                                                                                              Paraphrase:   \n",
              "1662                                                       I have noticed a sudden peeling of skin at different parts of my body, mainly arms, legs and back. Also, I face severe joint pain and skin rashes.   \n",
              "3415                                      Paraphrase: The itch, the nausea, and the weariness have been bothering me. I developed a fever and lost weight. My pee is black, and I've developed a yellow rash.   \n",
              "2104                          phrase: I feel really sick and uncomfortable like something is wrong inside. I don't know what it could be. I noticed small red spots on my arms, which itches if I touch them.   \n",
              "1763  I have a lot of itching on my whole skin, and sometimes it gets really red and bumpy. There are also some stains that are different colors than the rest of my skin, and sometimes I get these littl...   \n",
              "1684                                               phrase: I've had a nagging cough that won't go away, and my breathing has become labored. My fever is also very high, and I'm feeling very weak and tired.   \n",
              "2897                                                                                                                                                                                               Paraphrase   \n",
              "3441                        The headaches I have experienced are heavy and accompanied by a feeling of pressure in my head. I have a slight fever along with headaches. There are small red spots on my back.   \n",
              "411                                                            I've been struggling with back pain, a chronic cough, and limb weakness. I've been feeling disoriented and weak on my feet, and my neck hurts.   \n",
              "3364                      Recently, the pain in my calves has become constant and worse when I stand or walk for a long time. My legs have started to swell and some blood vessels are also quite noticeable.   \n",
              "2310                                                    My throat often feels like it's on fire, especially after eating. I occasionally get an aftertaste that is sour or bitter and makes me feel nauseous.   \n",
              "759   I felt really sick and had a lot of fatigue. I had a really bad cough and my throat was really sore. I had a lot of sinus pressure and my nose was really overloaded. I just feel really run down an...   \n",
              "2757                                                                                                                                                                                               Paraphrase   \n",
              "2229                                         phrase: I've been experiencing intense itchiness, nausea, and exhaustion. I also had a high fever and lost weight. Both my skin and my urine have become yellow.   \n",
              "2664  I've been suffering with chills, tiredness, and a cough recently. I've been sweating tremendously and feeling overall unwell and weak. I've also had some thick, black phlegm. My chest aches, and m...   \n",
              "3381                  I often get heartburn and indigestion, especially when eating spicy foods or fatty foods. I get chest pain that gets worse when I lie down or bend over. I also often have chest pains.   \n",
              "2147                                                           I had a lot of sleepiness, a cough that refused to leave and difficulty breathing. I coughed a lot of thick, slimy saliva and have high fever.   \n",
              "322                                                          I have swollen lymph nodes and red spots all over the body, and they cause discomfort. I also have a mild fever and feel tired most of the time.   \n",
              "2233                                                         I have difficulty breathing and can easily get out of my breath. Face and trunk are swollen, my eyes are aching and I have a dry, hacking cough.   \n",
              "1507                                  When I woke up this morning, I saw a terrible rash on my skin. Blackheads and pus-filled pimples are all over the place. Additionally, my skin has been shifting a lot.   \n",
              "2844                                : I have a bitter acidic taste in my mouth. I have frequent belching and burping. I have a feeling of pressure or fullness in my upper abdomen that last for a few hours.   \n",
              "203                                               The blood vessels on my legs are quite visible and give me a lot of pain. They're large and protrude from my skin. It is unusual and I am worried about it.   \n",
              "32                              I have been suffering from a high fever for the last couple of days. Sores are developing near my nose. The sores are painful and I am feeling very uncomfortable these days.   \n",
              "1075                                                                                      I have a sluggish wound and reduced healing rate. My hands and feet are both numb and have this tingling sensation.   \n",
              "2789                                                                  I lose weight unintentionally and find it difficult to acquire weight. I use antacids to relieve pain and discomfort that I experience.   \n",
              "680                                                                  I have a burning sensation in my upper abdomen, ofetn between or at night. I have heartburn and indigestion and often feel very nauseous   \n",
              "2409                           I'm suffering from intense itching, chills, vomiting, and a high fever. I've also been sweating a lot and have a headache. Nausea and muscle pain have also been bothering me.   \n",
              "478                                   Initially, I developed small red sores near my nose and neck. Now, I am having a high fever and the sores have become more painful and inflamed over the past few days.   \n",
              "756   I've been having a lot of problems with itching, and it's been accompanied by a rash that seems to be getting worse over time. There are also some patches of skin that are different colors than th...   \n",
              "\n",
              "      label_encoded  \n",
              "665              17  \n",
              "410               5  \n",
              "2686              5  \n",
              "2222             13  \n",
              "2609              5  \n",
              "578              17  \n",
              "602              16  \n",
              "192               4  \n",
              "3311             21  \n",
              "299              15  \n",
              "51                0  \n",
              "2328             19  \n",
              "1662             15  \n",
              "3415             11  \n",
              "2104              4  \n",
              "1763              8  \n",
              "1684              2  \n",
              "2897              9  \n",
              "3441              6  \n",
              "411               3  \n",
              "3364             17  \n",
              "2310             21  \n",
              "759               5  \n",
              "2757             15  \n",
              "2229             11  \n",
              "2664             14  \n",
              "3381             21  \n",
              "2147              2  \n",
              "322               4  \n",
              "2233             18  \n",
              "1507              0  \n",
              "2844             22  \n",
              "203              17  \n",
              "32               10  \n",
              "1075             19  \n",
              "2789             22  \n",
              "680              22  \n",
              "2409             12  \n",
              "478              10  \n",
              "756               8  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d2e494d1-1a07-4ce4-a174-871c874156e2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label_encoded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>665</th>\n",
              "      <td>Paraphrase</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>My eyes are constantly red and runny. A persistent tightness in my sinuses has disturbed me too. I have a lot of difficulty breathing and constantly feel tired. In addition, I have a lot of cervic...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2686</th>\n",
              "      <td>I sneeze over and over again, and my eyes don't stop dripping. It's incredibly hard for me to breathe because it feels like something's trapped in my throat. I often feel exhausted, and lately I'v...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2222</th>\n",
              "      <td>I often have headaches, blurred vision, excessive appetite, sore throat, anxiety, irritability and indigestion including indigestion and acid reflux.</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2609</th>\n",
              "      <td>My sinuses feel sticky, and my eyes were pretty red. I just have no energy, and my neck was really scratchy. Along with the swelling in my lymph nodes, I also have a lot of mucus cough.</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>578</th>\n",
              "      <td>Long durations of standing or walking have caused severe discomfort in my legs. It's a burning ache that gets worse the longer I'm on my feet.</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602</th>\n",
              "      <td>I feel tired most of the time, I don't want to eat, I get high fever and chills every night, and I've been vomiting since yesterday.</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>Paraphrase:</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3311</th>\n",
              "      <td>I get frequent heartburn and indigestion, especially eating spicy foods or fatty foods. I get chest pain that gets worse when I lie down or bend over. I also often have chest pain</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>Paraphrase: My nails have small dents on them. Even my joints have started to pain severely.</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>My skin only developed an uninherited rash, it has a lot of pus and trophies, and my skin was very sensitive.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2328</th>\n",
              "      <td>Paraphrase:</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1662</th>\n",
              "      <td>I have noticed a sudden peeling of skin at different parts of my body, mainly arms, legs and back. Also, I face severe joint pain and skin rashes.</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3415</th>\n",
              "      <td>Paraphrase: The itch, the nausea, and the weariness have been bothering me. I developed a fever and lost weight. My pee is black, and I've developed a yellow rash.</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2104</th>\n",
              "      <td>phrase: I feel really sick and uncomfortable like something is wrong inside. I don't know what it could be. I noticed small red spots on my arms, which itches if I touch them.</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1763</th>\n",
              "      <td>I have a lot of itching on my whole skin, and sometimes it gets really red and bumpy. There are also some stains that are different colors than the rest of my skin, and sometimes I get these littl...</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1684</th>\n",
              "      <td>phrase: I've had a nagging cough that won't go away, and my breathing has become labored. My fever is also very high, and I'm feeling very weak and tired.</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2897</th>\n",
              "      <td>Paraphrase</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3441</th>\n",
              "      <td>The headaches I have experienced are heavy and accompanied by a feeling of pressure in my head. I have a slight fever along with headaches. There are small red spots on my back.</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>411</th>\n",
              "      <td>I've been struggling with back pain, a chronic cough, and limb weakness. I've been feeling disoriented and weak on my feet, and my neck hurts.</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3364</th>\n",
              "      <td>Recently, the pain in my calves has become constant and worse when I stand or walk for a long time. My legs have started to swell and some blood vessels are also quite noticeable.</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2310</th>\n",
              "      <td>My throat often feels like it's on fire, especially after eating. I occasionally get an aftertaste that is sour or bitter and makes me feel nauseous.</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>759</th>\n",
              "      <td>I felt really sick and had a lot of fatigue. I had a really bad cough and my throat was really sore. I had a lot of sinus pressure and my nose was really overloaded. I just feel really run down an...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2757</th>\n",
              "      <td>Paraphrase</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2229</th>\n",
              "      <td>phrase: I've been experiencing intense itchiness, nausea, and exhaustion. I also had a high fever and lost weight. Both my skin and my urine have become yellow.</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2664</th>\n",
              "      <td>I've been suffering with chills, tiredness, and a cough recently. I've been sweating tremendously and feeling overall unwell and weak. I've also had some thick, black phlegm. My chest aches, and m...</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3381</th>\n",
              "      <td>I often get heartburn and indigestion, especially when eating spicy foods or fatty foods. I get chest pain that gets worse when I lie down or bend over. I also often have chest pains.</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2147</th>\n",
              "      <td>I had a lot of sleepiness, a cough that refused to leave and difficulty breathing. I coughed a lot of thick, slimy saliva and have high fever.</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>322</th>\n",
              "      <td>I have swollen lymph nodes and red spots all over the body, and they cause discomfort. I also have a mild fever and feel tired most of the time.</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2233</th>\n",
              "      <td>I have difficulty breathing and can easily get out of my breath. Face and trunk are swollen, my eyes are aching and I have a dry, hacking cough.</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1507</th>\n",
              "      <td>When I woke up this morning, I saw a terrible rash on my skin. Blackheads and pus-filled pimples are all over the place. Additionally, my skin has been shifting a lot.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2844</th>\n",
              "      <td>: I have a bitter acidic taste in my mouth. I have frequent belching and burping. I have a feeling of pressure or fullness in my upper abdomen that last for a few hours.</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>The blood vessels on my legs are quite visible and give me a lot of pain. They're large and protrude from my skin. It is unusual and I am worried about it.</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>I have been suffering from a high fever for the last couple of days. Sores are developing near my nose. The sores are painful and I am feeling very uncomfortable these days.</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1075</th>\n",
              "      <td>I have a sluggish wound and reduced healing rate. My hands and feet are both numb and have this tingling sensation.</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2789</th>\n",
              "      <td>I lose weight unintentionally and find it difficult to acquire weight. I use antacids to relieve pain and discomfort that I experience.</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>680</th>\n",
              "      <td>I have a burning sensation in my upper abdomen, ofetn between or at night. I have heartburn and indigestion and often feel very nauseous</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2409</th>\n",
              "      <td>I'm suffering from intense itching, chills, vomiting, and a high fever. I've also been sweating a lot and have a headache. Nausea and muscle pain have also been bothering me.</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>478</th>\n",
              "      <td>Initially, I developed small red sores near my nose and neck. Now, I am having a high fever and the sores have become more painful and inflamed over the past few days.</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>756</th>\n",
              "      <td>I've been having a lot of problems with itching, and it's been accompanied by a rash that seems to be getting worse over time. There are also some patches of skin that are different colors than th...</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "variable_name": "sampled_data",
              "summary": "{\n  \"name\": \"sampled_data\",\n  \"rows\": 40,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"I've been struggling with back pain, a chronic cough, and limb weakness. I've been feeling disoriented and weak on my feet, and my neck hurts.\",\n          \"phrase: I feel really sick and uncomfortable like something is wrong inside. I don't know what it could be. I noticed small red spots on my arms, which itches if I touch them.\",\n          \"My sinuses feel sticky, and my eyes were pretty red. I just have no energy, and my neck was really scratchy. Along with the swelling in my lymph nodes, I also have a lot of mucus cough.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label_encoded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 0,\n        \"max\": 22,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          17,\n          22,\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 84
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    {
      "cell_type": "markdown",
      "source": [
        "# Transformer\n",
        "\n",
        "The code snippet demonstrates how to preprocess text data and labels for NLP tasks with a BERT-based model. It includes tokenization, data splitting, conversion to PyTorch tensors, and dataset creation for use in training and evaluation pipelines.\n",
        "\n",
        "---\n",
        "\n",
        "### **1. Data Preparation**\n",
        "\n",
        "#### **Tokenization**\n",
        "- The `AutoTokenizer` from the Hugging Face `transformers` library is used to tokenize text data with the `bert-base-uncased` model.\n",
        "- Key options:\n",
        "  - `padding=True`: Ensures all tokenized sequences are of equal length by padding shorter sequences.\n",
        "  - `truncation=True`: Truncates sequences exceeding the model's maximum length.\n",
        "  - `return_tensors=\"pt\"`: Returns PyTorch tensors for integration with PyTorch models.\n",
        "- The tokenized output, `X_encoded`, contains:\n",
        "  - `input_ids`: Numerical representations of tokens.\n",
        "  - `attention_mask`: Masks indicating which tokens are actual input (1) versus padding (0).\n",
        "\n",
        "---\n",
        "\n",
        "### **2. Data Splitting**\n",
        "The tokenized data is split into training and validation sets using `train_test_split`:\n",
        "- Inputs:\n",
        "  - `input_ids` and `attention_mask` (converted to NumPy arrays).\n",
        "  - Labels `y`.\n",
        "- Outputs:\n",
        "  - `X_train_ids`, `X_val_ids`: Input IDs for training and validation sets.\n",
        "  - `X_train_mask`, `X_val_mask`: Attention masks for training and validation sets.\n",
        "  - `y_train`, `y_val`: Labels for training and validation sets.\n",
        "- `test_size=0.2`: Reserves 20% of the data for validation.\n",
        "- `random_state=42`: Ensures reproducibility of the data split.\n",
        "\n",
        "---\n",
        "\n",
        "### **3. Conversion to PyTorch Tensors**\n",
        "The split data (IDs, masks, and labels) is converted back to PyTorch tensors for compatibility with PyTorch models and data loaders:\n",
        "- `torch.tensor`: Converts NumPy arrays to PyTorch tensors.\n",
        "- Ensures seamless integration into downstream tasks like training and validation.\n",
        "\n",
        "---\n",
        "\n",
        "### **4. Dataset Creation**\n",
        "\n",
        "#### **`TensorDataset`**\n",
        "- The `TensorDataset` combines the tensors (`input_ids`, `attention_mask`, `labels`) into a single dataset for easy access during training.\n",
        "\n",
        "#### **`DictDataset`**\n",
        "- A custom dataset class inheriting from PyTorch's `Dataset`.\n",
        "- Structure:\n",
        "  - Stores `input_ids`, `attention_mask`, and `labels`.\n",
        "  - Implements `__len__`: Returns the number of samples.\n",
        "  - Implements `__getitem__`: Returns a dictionary with:\n",
        "    - `\"input_ids\"`\n",
        "    - `\"attention_mask\"`\n",
        "    - `\"labels\"`\n",
        "- The `DictDataset` format is useful when working with models that expect dictionary inputs, such as Hugging Face models.\n",
        "\n",
        "---\n",
        "\n",
        "### **5. Final Datasets**\n",
        "- **`train_dataset`** and **`val_dataset`**:\n",
        "  - Training and validation datasets in `DictDataset` format.\n",
        "  - Can be used with PyTorch `DataLoader` for batching, shuffling, and parallel processing.\n",
        "\n",
        "---\n"
      ],
      "metadata": {
        "id": "28IGCzOwEA7V"
      },
      "id": "28IGCzOwEA7V"
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    {
      "cell_type": "code",
      "source": [
        "X = augmented_df['text'].to_numpy()\n",
        "y = augmented_df['label_encoded'].to_numpy()\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from transformers import AutoTokenizer\n",
        "import torch\n",
        "from torch.utils.data import TensorDataset\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "# Tokenize the text data\n",
        "tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n",
        "X_encoded = tokenizer(list(X), padding=True, truncation=True, return_tensors=\"pt\")\n",
        "\n",
        "# Ensure `X_encoded` is formatted for splitting\n",
        "input_ids = X_encoded['input_ids']  # Extract input_ids\n",
        "attention_mask = X_encoded['attention_mask']  # Extract attention_mask\n",
        "\n",
        "# Split data (convert to numpy if necessary)\n",
        "X_train_ids, X_val_ids, X_train_mask, X_val_mask, y_train, y_val = train_test_split(\n",
        "    input_ids.numpy(), attention_mask.numpy(), y, test_size=0.2, random_state=42\n",
        ")\n",
        "\n",
        "# Convert to PyTorch tensors\n",
        "X_train_ids = torch.tensor(X_train_ids)\n",
        "X_train_mask = torch.tensor(X_train_mask)\n",
        "y_train = torch.tensor(y_train)\n",
        "\n",
        "X_val_ids = torch.tensor(X_val_ids)\n",
        "X_val_mask = torch.tensor(X_val_mask)\n",
        "y_val = torch.tensor(y_val)\n",
        "\n",
        "# Create TensorDataset\n",
        "train_dataset = TensorDataset(X_train_ids, X_train_mask, y_train)\n",
        "val_dataset = TensorDataset(X_val_ids, X_val_mask, y_val)\n",
        "\n",
        "\n",
        "class DictDataset(Dataset):\n",
        "    def __init__(self, input_ids, attention_mask, labels):\n",
        "        self.input_ids = input_ids\n",
        "        self.attention_mask = attention_mask\n",
        "        self.labels = labels\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return {\n",
        "            \"input_ids\": self.input_ids[idx],\n",
        "            \"attention_mask\": self.attention_mask[idx],\n",
        "            \"labels\": self.labels[idx],\n",
        "        }\n",
        "\n",
        "# Convert datasets\n",
        "train_dataset = DictDataset(X_train_ids, X_train_mask, y_train)\n",
        "val_dataset = DictDataset(X_val_ids, X_val_mask, y_val)"
      ],
      "metadata": {
        "id": "dx9v76h_zWY0"
      },
      "id": "dx9v76h_zWY0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training\n",
        "\n",
        "The code trains a multi-class text classification model using Hugging Face's Transformers library with the following setup and results:\n",
        "\n",
        "---\n",
        "\n",
        "### **1. Code Setup**\n",
        "\n",
        "#### **Model**\n",
        "- **Architecture:** BERT-based sequence classification (`bert-base-uncased`).\n",
        "- **Task:** Multi-class classification with 24 classes.\n",
        "\n",
        "#### **Training Configuration**\n",
        "- **Hyperparameters:**\n",
        "  - Learning Rate: \\(2 \\times 10^{-5}\\)\n",
        "  - Batch Size: 16\n",
        "  - Weight Decay: \\(0.01\\)\n",
        "  - Epochs: 5\n",
        "- **Evaluation:**\n",
        "  - Validation at the end of each epoch.\n",
        "  - Metric: `eval_loss` to select the best model.\n",
        "- **Logging and Checkpoints:**\n",
        "  - Logs every 100 steps.\n",
        "  - Saves model checkpoints at each epoch.\n",
        "\n",
        "#### **Metrics**\n",
        "- **Accuracy:** Measures the percentage of correctly classified samples.\n",
        "- **ROC-AUC:** Evaluates the area under the ROC curve for multi-class classification (OneVsRest strategy).\n",
        "- **F1-Score:** Harmonic mean of precision and recall (macro-averaged for multi-class).\n",
        "\n",
        "---\n",
        "\n",
        "### **2. Results**\n",
        "\n",
        "| **Epoch** | **Training Loss** | **Validation Loss** | **Accuracy** | **ROC-AUC** | **F1-Score** |\n",
        "|-----------|--------------------|---------------------|--------------|-------------|--------------|\n",
        "| 1         | 2.758600          | 1.967651            | 0.722944     | 0.982034    | 0.693938     |\n",
        "| 2         | 1.695100          | 1.050036            | 0.896104     | 0.998032    | 0.890821     |\n",
        "| 3         | 1.015600          | 0.598317            | 0.961039     | 0.999277    | 0.962996     |\n",
        "| 4         | 0.616400          | 0.388860            | 0.982684     | 0.999708    | 0.983624     |\n",
        "| 5         | 0.413700          | 0.330307            | 0.980519     | 0.999798    | 0.981702     |\n",
        "\n",
        "#### **Final Training Summary**\n",
        "- **Global Steps:** 580\n",
        "- **Training Loss:** \\(1.1681\\)\n",
        "- **Runtime:** ~376 seconds\n",
        "- **Training Speed:** ~24.53 samples/second.\n",
        "\n",
        "---\n",
        "\n",
        "### **3. Observations**\n",
        "1. **Validation Loss**: Steadily decreases across epochs, indicating the model is learning effectively.\n",
        "2. **Accuracy & F1-Score**: Both metrics improve consistently and plateau by the 4th epoch, suggesting convergence.\n",
        "3. **ROC-AUC**: Very high throughout, showing excellent model confidence and separation across all classes.\n",
        "4. **Training Speed**: Achieved ~1.54 steps/second, typical for BERT-based models on medium-sized datasets.\n",
        "\n",
        "---\n",
        "\n",
        "### **4. Recommendations**\n",
        "1. **Early Stopping**: Performance improvement diminishes after the 4th epoch. Future training might benefit from early stopping to save computational resources.\n",
        "2. **Learning Rate Tuning**: The high final accuracy suggests the learning rate is suitable, but a slight increase might reduce training time further.\n",
        "3. **Class Imbalance**: If the dataset is imbalanced, ensure proper sampling or weighting to avoid overfitting to dominant classes.\n",
        "4. **Further Fine-tuning**: Experiment with techniques like layer freezing for additional speedups or domain adaptation.\n",
        "\n",
        "This setup achieves strong performance metrics and demonstrates effective training with Hugging Face's Trainer."
      ],
      "metadata": {
        "id": "dq6gIxPpNI-6"
      },
      "id": "dq6gIxPpNI-6"
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments, DataCollatorWithPadding\n",
        "from sklearn.metrics import accuracy_score, roc_auc_score, precision_recall_curve, f1_score\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "# Disable wandb\n",
        "os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
        "\n",
        "# Load the model\n",
        "num_classes = 24\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=num_classes)\n",
        "\n",
        "# Define training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",              # Directory to save model checkpoints\n",
        "    evaluation_strategy=\"epoch\",         # Evaluate at the end of each epoch\n",
        "    learning_rate=2e-5,                  # Learning rate\n",
        "    per_device_train_batch_size=16,      # Batch size for training\n",
        "    per_device_eval_batch_size=16,       # Batch size for evaluation\n",
        "    num_train_epochs=5,                 # Number of epochs\n",
        "    weight_decay=0.01,                   # Weight decay\n",
        "    logging_dir=\"./logs\",                # Directory for logging\n",
        "    save_strategy=\"epoch\",               # Save model after each epoch\n",
        "    logging_steps=100,                   # Log training metrics every 100 steps\n",
        "    load_best_model_at_end=True,         # Load the best model based on eval_loss\n",
        "    metric_for_best_model=\"eval_loss\",   # Metric to use for model selection (based on eval_loss)\n",
        "    report_to=\"none\",                   # Disable reporting to wandb (if necessary)\n",
        ")\n",
        "\n",
        "# Data collator\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"pt\")\n",
        "\n",
        "# Compute metrics function with additional metrics\n",
        "def compute_metrics(pred):\n",
        "    logits, labels = pred\n",
        "    predictions = logits.argmax(axis=-1)\n",
        "\n",
        "    # Accuracy\n",
        "    accuracy = accuracy_score(labels, predictions)\n",
        "\n",
        "    # Convert logits to probabilities (softmax)\n",
        "    logits_tensor = torch.tensor(logits)  # Convert to tensor\n",
        "    probs = torch.nn.functional.softmax(logits_tensor, dim=-1).numpy()  # Get class probabilities\n",
        "    labels_one_hot = np.eye(num_classes)[labels]  # One-hot encoding of true labels\n",
        "\n",
        "    # Compute ROC-AUC score (use OneVsRest strategy for multi-class)\n",
        "    try:\n",
        "        roc_auc = roc_auc_score(labels_one_hot, probs, average='macro', multi_class='ovr')\n",
        "    except ValueError:  # Handle cases where there's only one class predicted\n",
        "        roc_auc = 0.0\n",
        "\n",
        "    # Precision, Recall, F1-score\n",
        "    precision = f1_score(labels, predictions, average='macro')\n",
        "    recall = precision  # For multi-class, Precision and Recall are the same\n",
        "    f1 = precision  # For multi-class, Precision and F1 score are the same\n",
        "\n",
        "    # Return metrics\n",
        "    return {\n",
        "        \"eval_accuracy\": accuracy,\n",
        "        \"eval_roc_auc\": roc_auc,\n",
        "        \"eval_f1\": f1,\n",
        "    }\n",
        "\n",
        "# Initialize Trainer with updated compute_metrics function\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=val_dataset,\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,  # Updated metrics\n",
        ")\n",
        "\n",
        "# Train the model\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "fnau9zHIgYqO",
        "outputId": "e5ea9d9a-0b2b-49a5-f896-85be768efff6"
      },
      "id": "fnau9zHIgYqO",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1575: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "<ipython-input-42-1bcc82e45d40>:66: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
            "  trainer = Trainer(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='580' max='580' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [580/580 06:15, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Roc Auc</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.758600</td>\n",
              "      <td>1.967651</td>\n",
              "      <td>0.722944</td>\n",
              "      <td>0.982034</td>\n",
              "      <td>0.693938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.695100</td>\n",
              "      <td>1.050036</td>\n",
              "      <td>0.896104</td>\n",
              "      <td>0.998032</td>\n",
              "      <td>0.890821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.015600</td>\n",
              "      <td>0.598317</td>\n",
              "      <td>0.961039</td>\n",
              "      <td>0.999277</td>\n",
              "      <td>0.962996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.616400</td>\n",
              "      <td>0.388860</td>\n",
              "      <td>0.982684</td>\n",
              "      <td>0.999708</td>\n",
              "      <td>0.983624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.413700</td>\n",
              "      <td>0.330307</td>\n",
              "      <td>0.980519</td>\n",
              "      <td>0.999798</td>\n",
              "      <td>0.981702</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=580, training_loss=1.168056978028396, metrics={'train_runtime': 375.8034, 'train_samples_per_second': 24.534, 'train_steps_per_second': 1.543, 'total_flos': 369641255518080.0, 'train_loss': 1.168056978028396, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
        "from transformers import Trainer\n",
        "\n",
        "# Get the log history to find the best checkpoint based on evaluation loss (or accuracy)\n",
        "log_history = trainer.state.log_history\n",
        "\n",
        "best_loss = float('inf')  # Start with a very high value\n",
        "best_checkpoint = None\n",
        "\n",
        "# Iterate over the log history to find the best eval_loss\n",
        "for log in log_history:\n",
        "    if 'eval_loss' in log:\n",
        "        eval_loss = log['eval_loss']\n",
        "        if eval_loss < best_loss:\n",
        "            best_loss = eval_loss\n",
        "            # Use 'step' instead of 'checkpoint' as there isn't a 'checkpoint' key in the log entry\n",
        "            best_checkpoint = log['step']\n",
        "\n",
        "print(f\"Best step is: {best_checkpoint} with eval_loss: {best_loss}\")\n",
        "\n",
        "# Load the best model from the checkpoint corresponding to the best step\n",
        "# Check the folder where checkpoints are saved\n",
        "checkpoint_dir = f\"./results/checkpoint-{best_checkpoint}\"\n",
        "\n",
        "# Load the model from the best checkpoint\n",
        "best_model = AutoModelForSequenceClassification.from_pretrained(checkpoint_dir)\n",
        "\n",
        "# Optionally, you can also load the tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zz4nFapoTTh",
        "outputId": "ecec9fea-a8fb-4258-d1ea-f7692432a126"
      },
      "id": "_zz4nFapoTTh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best step is: 580 with eval_loss: 0.33030688762664795\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluate The Best BERT Model\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "### **Code Overview**\n",
        "\n",
        "1. **Loading the Best Model**\n",
        "   - The model is loaded from the checkpoint that performed best on the validation set during training.\n",
        "\n",
        "2. **Trainer Initialization**\n",
        "   - The `Trainer` class is initialized with the best model, training arguments, and the evaluation dataset (which can be either the validation or test dataset).\n",
        "\n",
        "3. **Evaluating the Model**\n",
        "   - The `trainer.predict()` function is used to predict labels for the dataset.\n",
        "   - The predicted labels are obtained by finding the index of the maximum value in the logits (using `argmax`).\n",
        "   - `softmax` is applied to the logits to convert them to probabilities, which are required for calculating the ROC-AUC score.\n",
        "\n",
        "4. **Calculating Evaluation Metrics**\n",
        "   - **ROC-AUC Score**: Computes the area under the receiver operating characteristic curve. It is used to evaluate multi-class models using a one-vs-rest approach.\n",
        "   - **Accuracy**: Measures the percentage of correctly classified samples.\n",
        "   - **Precision, Recall, F1-Score**: These are calculated for each class, helping to understand the performance per class.\n",
        "\n",
        "---\n",
        "\n",
        "### **Suggested Improvements and Additions**\n",
        "\n",
        "1. **Handling Multiclass F1-Score Calculation**\n",
        "   - You're using `f1_score()` without importing it. Ensure that `f1_score` is imported from `sklearn.metrics`.\n",
        "\n",
        "2. **Confusion Matrix Visualization**\n",
        "   - You can visualize the confusion matrix to better understand how well your model is performing across different classes.\n",
        "\n",
        "3. **Improve Metric Handling**\n",
        "   - If your dataset is imbalanced, consider using weighted precision, recall, and F1 scores. You can use `average='weighted'` in `precision_score`, `recall_score`, and `f1_score`.\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "MGRwDFQGpN3J"
      },
      "id": "MGRwDFQGpN3J"
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import roc_auc_score, roc_curve, confusion_matrix, accuracy_score\n",
        "from transformers import AutoModelForSequenceClassification, Trainer\n",
        "from torch.nn.functional import softmax\n",
        "import numpy as np\n",
        "import torch\n",
        "from sklearn.metrics import precision_score, recall_score\n",
        "\n",
        "# Load the best model\n",
        "best_model = AutoModelForSequenceClassification.from_pretrained(checkpoint_dir)\n",
        "\n",
        "# Initialize the Trainer with the best model and the test dataset\n",
        "trainer = Trainer(\n",
        "    model=best_model,\n",
        "    args=training_args,           # Ensure previous training arguments are used\n",
        "    eval_dataset=val_dataset,     # Replace with test dataset if necessary\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,  # Assuming you have a metrics function\n",
        ")\n",
        "\n",
        "# Evaluate the model on the validation/test dataset\n",
        "test_results = trainer.predict(val_dataset)  # Replace val_dataset with test_dataset if needed\n",
        "\n",
        "# Print evaluation metrics\n",
        "print(\"Test Results:\", test_results.metrics)\n",
        "\n",
        "# Get predictions (logits -> probabilities -> predicted labels)\n",
        "predictions = test_results.predictions.argmax(axis=-1)\n",
        "print(\"Predicted Labels:\", predictions)\n",
        "\n",
        "# Get true labels from the test dataset\n",
        "true_labels = test_results.label_ids\n",
        "\n",
        "# Convert logits to probabilities for ROC-AUC calculation\n",
        "probs = softmax(torch.tensor(test_results.predictions), dim=-1).numpy()\n",
        "\n",
        "# Compute ROC-AUC score (multi-class, One-vs-Rest)\n",
        "roc_auc = roc_auc_score(true_labels, probs, average='macro', multi_class='ovr')\n",
        "accuracy = accuracy_score(true_labels, predictions)\n",
        "\n",
        "print(f\"Test Accuracy: {accuracy}\")\n",
        "print(f\"Test ROC-AUC: {roc_auc}\")\n",
        "\n",
        "\n",
        "# Compute precision and recall for each class\n",
        "precision = precision_score(true_labels, predictions, average=None)\n",
        "recall = recall_score(true_labels, predictions, average=None)\n",
        "f1 = f1_score(true_labels, predictions, average=None)\n",
        "\n",
        "# Print precision, recall, and F1-score for each class\n",
        "for i in range(len(precision)):\n",
        "    print(f\"Class {i} - Precision: {precision[i]:.4f}, Recall: {recall[i]:.4f}, F1-score: {f1[i]:.4f}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 932
        },
        "id": "rWOXdDpBsD9h",
        "outputId": "5e96526e-701b-45b2-fddd-4c675a56d6b6"
      },
      "id": "rWOXdDpBsD9h",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-44-dabfded16b17>:14: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
            "  trainer = Trainer(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Results: {'test_loss': 0.33030688762664795, 'test_model_preparation_time': 0.0037, 'test_eval_accuracy': 0.9805194805194806, 'test_eval_roc_auc': 0.9997977163552517, 'test_eval_f1': 0.981701637544341, 'test_runtime': 5.5211, 'test_samples_per_second': 83.678, 'test_steps_per_second': 5.253}\n",
            "Predicted Labels: [17 13  3 13 10  6  0 22  4  5  6 22  8 14 17 14  4  3  0  4  2 18 11 21\n",
            "  1  7 19 22 16 13 21 13 17 21 13  1 14  4 19 21  0 18  3 19 23 13  9  4\n",
            "  2  3  3 15 20 23 15  9 21  0  1  3 13  9 18  4 10 20 22  3  7  3  5 17\n",
            "  0 17 18  7 20  6  4  7 19  2 23 17 18 17 21  5 14 12  5 23 19 22 10 16\n",
            " 22 19  1 20  5 18  7  4  1 14  1  2  3 15 20 20 21 20  6 13 14 11  6 19\n",
            " 14  4  1  5 14 14  0 19 15 22 10 17 17 21  9 19  5 23  6 19 14 16 19 14\n",
            " 21 22 10  6  9 19 18  3  4  0  5  2  9 10  6 13  7 11 10  0 13 16 17  3\n",
            "  0  6 23  1 14 20 12 16 16 19 14 12 15 20 14  9 16 16 10  6 23 20 18 18\n",
            "  5 15 21  6 21  3 13 21  4 15  2  5 21 20 12  2 12  2 15  0  6 15  7  4\n",
            " 13 11 13 21 10 23 20  8 12 15 19 21  7 17  1  4 11  5 18 10 18  9 11 15\n",
            "  8 16 11  1  9 14  7  5  7  9 17 21 13  8  1  5 17 10  5  0 13  6 12  5\n",
            " 13  7 20 13 21  6  6  6  3 21  8 19  7 14  9 10  4  9  8 12 22  8 17  4\n",
            "  7 22  3 13 11  7 15 15  4  0 11 10 18 14 14 17 18  4 19  6 13  4 23 12\n",
            " 14 12 23 13  8 16  8 13  0  4  9 11  0 14 12 22 20 10 19 23 16 12 20  6\n",
            "  5  2 21 14 22 18 20  4  0 15  9  1 22  7 23 11  9 12  2 18 18 23  1 11\n",
            " 13  6 16 21  7 12  6  7 16  5 21 16  2 13  8  0 14  4 13 17  7 13  5 17\n",
            "  9 12  9  1 20 12 23  5 13  5 22  9 23 22 16  8 23  8 19 11  3 15 13  3\n",
            " 23  6 15 10 23 10 15  9  6 10 23  9  7  9 10 19  4 23  9 10  6  0 23  7\n",
            "  5 22 14 20 23  9  9  5 22 20  6 14  2  8 13 20 15  6 16 19 17 19 10  7\n",
            " 14 17  1 23  2 16]\n",
            "Test Accuracy: 0.9805194805194806\n",
            "Test ROC-AUC: 0.9997977163552517\n",
            "Class 0 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 1 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 2 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 3 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 4 - Precision: 0.9091, Recall: 1.0000, F1-score: 0.9524\n",
            "Class 5 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 6 - Precision: 1.0000, Recall: 0.9615, F1-score: 0.9804\n",
            "Class 7 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 8 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 9 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 10 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 11 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 12 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 13 - Precision: 0.9643, Recall: 0.9643, F1-score: 0.9643\n",
            "Class 14 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 15 - Precision: 1.0000, Recall: 0.9000, F1-score: 0.9474\n",
            "Class 16 - Precision: 0.8824, Recall: 0.9375, F1-score: 0.9091\n",
            "Class 17 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 18 - Precision: 1.0000, Recall: 1.0000, F1-score: 1.0000\n",
            "Class 19 - Precision: 1.0000, Recall: 0.9545, F1-score: 0.9767\n",
            "Class 20 - Precision: 0.9000, Recall: 1.0000, F1-score: 0.9474\n",
            "Class 21 - Precision: 0.9524, Recall: 1.0000, F1-score: 0.9756\n",
            "Class 22 - Precision: 1.0000, Recall: 0.9444, F1-score: 0.9714\n",
            "Class 23 - Precision: 0.9565, Recall: 0.9167, F1-score: 0.9362\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The test results indicate excellent performance from model:\n",
        "\n",
        "- **Test Accuracy**: 98.05% – This shows that model correctly classified a very high percentage of the test samples.\n",
        "- **Test ROC-AUC**: 0.9998 – This indicates an almost perfect performance in distinguishing between the classes, which is especially impressive for a multi-class classification problem.\n",
        "\n",
        "These metrics suggest that the model is not only accurate but also has a very good ability to differentiate between different classes, as shown by the high ROC-AUC score.\n"
      ],
      "metadata": {
        "id": "_gdd2qi0D8SZ"
      },
      "id": "_gdd2qi0D8SZ"
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot ROC-AUC curve for each class\n",
        "plt.figure(figsize=(12, 10), dpi=150)  # Increase figure size and DPI for higher quality\n",
        "n_classes = probs.shape[1]\n",
        "\n",
        "for i in range(n_classes):\n",
        "    fpr, tpr, _ = roc_curve(true_labels == i, probs[:, i])\n",
        "    plt.plot(fpr, tpr, label=f'Class {i} (AUC = {roc_auc:.2f})', lw=2)\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--', label='Chance', linestyle='--')\n",
        "plt.title(\"ROC Curve\", fontsize=18, fontweight='bold')\n",
        "plt.xlabel(\"False Positive Rate\", fontsize=14, fontweight='bold')\n",
        "plt.ylabel(\"True Positive Rate\", fontsize=14, fontweight='bold')\n",
        "plt.legend(loc=\"lower right\", fontsize=12)\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "\n",
        "# Save or display the ROC-AUC curve\n",
        "plt.savefig(\"roc_auc_curve_high_quality.png\", dpi=300)  # Save with high quality (300 dpi)\n",
        "plt.show()\n",
        "\n",
        "# Compute the confusion matrix\n",
        "cm = confusion_matrix(true_labels, predictions)\n",
        "\n",
        "# Plot confusion matrix with seaborn heatmap\n",
        "plt.figure(figsize=(12, 10), dpi=150)  # Increase figure size and DPI for higher quality\n",
        "sns.set(font_scale=1.5)  # Increase font size for better readability\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=True, xticklabels=np.arange(n_classes), yticklabels=np.arange(n_classes),\n",
        "            linewidths=0.5, linecolor='black', square=True)\n",
        "\n",
        "# Add axis labels and title\n",
        "plt.title(\"Confusion Matrix\", fontsize=18, fontweight='bold')\n",
        "plt.xlabel(\"Predicted Class\", fontsize=14, fontweight='bold')\n",
        "plt.ylabel(\"True Class\", fontsize=14, fontweight='bold')\n",
        "plt.xticks(rotation=45, ha='right', fontsize=12, fontweight='bold')\n",
        "plt.yticks(fontsize=12, fontweight='bold')\n",
        "\n",
        "# Save or display the confusion matrix\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"confusion_matrix_high_quality.png\", dpi=300)  # Save with high quality (300 dpi)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0KizYPjOsPE4",
        "outputId": "71288f71-dcb3-43bd-860c-041f6cdde4c5"
      },
      "id": "0KizYPjOsPE4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-45-3644b7d6277c>:9: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"k--\" (-> linestyle='--'). The keyword argument will take precedence.\n",
            "  plt.plot([0, 1], [0, 1], 'k--', label='Chance', linestyle='--')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1500 with 1 Axes>"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This code performs the following tasks:\n",
        "\n",
        "1. **Loading the Model and Tokenizer**:\n",
        "   - It loads the **BERT tokenizer** (`AutoTokenizer`) from the `\"bert-base-uncased\"` pre-trained model to prepare the text inputs for BERT.\n",
        "   - It uses the previously fine-tuned model (`best_model`) to make predictions.\n",
        "\n",
        "2. **Preparing the Input Data**:\n",
        "   - It takes a list of **input texts**, which seem to be descriptions of medical symptoms.\n",
        "   - Each text is **tokenized** (converted into input format that BERT understands) using the tokenizer with padding and truncation to ensure uniform input lengths. A maximum length of 128 tokens is used to limit the size of each input sequence.\n",
        "\n",
        "3. **Setting Device**:\n",
        "   - The model is loaded onto either a **GPU** (if available) or **CPU** (if no GPU is available) for computation.\n",
        "   - The tokenized inputs are moved to the same device (GPU/CPU) to ensure efficient computation.\n",
        "\n",
        "4. **Inference**:\n",
        "   - The model performs inference on the tokenized input texts using **`model(**inputs)`**, which produces the logits (raw model outputs).\n",
        "   - The logits are processed using **softmax** to convert them into probabilities (indicating the model's confidence in each class).\n",
        "   - **Predicted classes** are obtained by taking the class with the highest probability.\n",
        "\n",
        "5. **Result Interpretation**:\n",
        "   - The predicted **class numbers** and corresponding **class labels** are obtained from a label mapping, which associates class indices with human-readable labels.\n",
        "   - The class probabilities for each text are printed to show how confident the model is about its predictions for each class.\n",
        "   \n",
        "6. **Output**:\n",
        "   - For each input text, the model outputs:\n",
        "     - The **input text**.\n",
        "     - The **predicted class number** and its **human-readable label**.\n",
        "     - The **probabilities** for each class, which show the model's confidence in each possible class for that input.\n",
        "\n",
        "This approach is useful when you want to interpret the predictions of a trained classifier on individual samples, along with the model's confidence for each possible class. It's particularly helpful in cases where you need to assess whether the model's predictions are reasonable or if there is ambiguity in the predictions (i.e., the model's probabilities for different classes are close to each other).\n"
      ],
      "metadata": {
        "id": "Wuqe4BExEXYJ"
      },
      "id": "Wuqe4BExEXYJ"
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "\n",
        "# Load the tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "\n",
        "# Define your input texts\n",
        "texts = [\n",
        "    \"I'm currently experiencing a high fever, along with red spots and rashes covering my entire body. This has left me feeling extremely fatigued, with a significant loss of appetite, leading to weakness and a general sense of lethargy. I'm quite concerned about my condition.\",\n",
        "    \"I've been experiencing skin peeling in several areas, particularly on my knees, elbows, and arms. This peeling is often accompanied by a painful or burning sensation. Additionally, I've noticed small indentations forming on my nails, which is concerning.\",\n",
        "    \"I've been having difficulty using the restroom lately. It's been painful when I do go, and I've noticed buttock soreness along with bloody stools \"\n",
        "]\n",
        "\n",
        "# Tokenize and prepare the inputs\n",
        "inputs = tokenizer(texts, return_tensors=\"pt\", padding=True, truncation=True, max_length=128)\n",
        "\n",
        "# Move tensors to the same device as the model\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = best_model\n",
        "model.to(device)\n",
        "inputs = {key: val.to(device) for key, val in inputs.items()}\n",
        "\n",
        "# Perform inference\n",
        "with torch.no_grad():\n",
        "    outputs = model(**inputs)\n",
        "\n",
        "# Get probabilities and predicted classes\n",
        "probabilities = F.softmax(outputs.logits, dim=-1).cpu().numpy()\n",
        "predictions = torch.argmax(outputs.logits, dim=-1).cpu().numpy()\n",
        "\n",
        "# Get labels from the dataframe\n",
        "labels = df['label'].unique()\n",
        "label_mapping = {i: label for i, label in enumerate(labels)}\n",
        "label_names = [label_mapping[i] for i in range(len(label_mapping))]\n",
        "\n",
        "# Print results\n",
        "for text, prediction, probs in zip(texts, predictions, probabilities):\n",
        "    print(f\"Input: {text}\")\n",
        "    print(f\"Predicted Class Number: {prediction}\")\n",
        "    print(f\"Predicted Class Label: {label_names[prediction]}\")\n",
        "    print(\"Class Probabilities:\")\n",
        "    for i, prob in enumerate(probs):\n",
        "        print(f\"  Class {i} ({label_mapping[i]}): {prob:.4f}\")\n",
        "    print()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_YvZtbwsSHzv",
        "outputId": "453ae75b-127a-4b3a-f217-12ef26559019"
      },
      "id": "_YvZtbwsSHzv",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input: I'm currently experiencing a high fever, along with red spots and rashes covering my entire body. This has left me feeling extremely fatigued, with a significant loss of appetite, leading to weakness and a general sense of lethargy. I'm quite concerned about my condition.\n",
            "Predicted Class Number: 4\n",
            "Predicted Class Label: Impetigo\n",
            "Class Probabilities:\n",
            "  Class 0 (Psoriasis): 0.0100\n",
            "  Class 1 (Varicose Veins): 0.0058\n",
            "  Class 2 (Typhoid): 0.0081\n",
            "  Class 3 (Chicken pox): 0.0053\n",
            "  Class 4 (Impetigo): 0.6089\n",
            "  Class 5 (Dengue): 0.0046\n",
            "  Class 6 (Fungal infection): 0.1524\n",
            "  Class 7 (Common Cold): 0.0043\n",
            "  Class 8 (Pneumonia): 0.0135\n",
            "  Class 9 (Dimorphic Hemorrhoids): 0.0055\n",
            "  Class 10 (Arthritis): 0.0228\n",
            "  Class 11 (Acne): 0.0093\n",
            "  Class 12 (Bronchial Asthma): 0.0225\n",
            "  Class 13 (Hypertension): 0.0039\n",
            "  Class 14 (Migraine): 0.0095\n",
            "  Class 15 (Cervical spondylosis): 0.0059\n",
            "  Class 16 (Jaundice): 0.0509\n",
            "  Class 17 (Malaria): 0.0116\n",
            "  Class 18 (urinary tract infection): 0.0044\n",
            "  Class 19 (allergy): 0.0051\n",
            "  Class 20 (gastroesophageal reflux disease): 0.0044\n",
            "  Class 21 (drug reaction): 0.0165\n",
            "  Class 22 (peptic ulcer disease): 0.0066\n",
            "  Class 23 (diabetes): 0.0084\n",
            "\n",
            "Input: I've been experiencing skin peeling in several areas, particularly on my knees, elbows, and arms. This peeling is often accompanied by a painful or burning sensation. Additionally, I've noticed small indentations forming on my nails, which is concerning.\n",
            "Predicted Class Number: 15\n",
            "Predicted Class Label: Cervical spondylosis\n",
            "Class Probabilities:\n",
            "  Class 0 (Psoriasis): 0.0096\n",
            "  Class 1 (Varicose Veins): 0.0231\n",
            "  Class 2 (Typhoid): 0.0039\n",
            "  Class 3 (Chicken pox): 0.0079\n",
            "  Class 4 (Impetigo): 0.0051\n",
            "  Class 5 (Dengue): 0.0082\n",
            "  Class 6 (Fungal infection): 0.0121\n",
            "  Class 7 (Common Cold): 0.0081\n",
            "  Class 8 (Pneumonia): 0.0292\n",
            "  Class 9 (Dimorphic Hemorrhoids): 0.0041\n",
            "  Class 10 (Arthritis): 0.0178\n",
            "  Class 11 (Acne): 0.0037\n",
            "  Class 12 (Bronchial Asthma): 0.0048\n",
            "  Class 13 (Hypertension): 0.0048\n",
            "  Class 14 (Migraine): 0.0072\n",
            "  Class 15 (Cervical spondylosis): 0.7880\n",
            "  Class 16 (Jaundice): 0.0094\n",
            "  Class 17 (Malaria): 0.0047\n",
            "  Class 18 (urinary tract infection): 0.0088\n",
            "  Class 19 (allergy): 0.0053\n",
            "  Class 20 (gastroesophageal reflux disease): 0.0082\n",
            "  Class 21 (drug reaction): 0.0089\n",
            "  Class 22 (peptic ulcer disease): 0.0019\n",
            "  Class 23 (diabetes): 0.0149\n",
            "\n",
            "Input: I've been having difficulty using the restroom lately. It's been painful when I do go, and I've noticed buttock soreness along with bloody stools \n",
            "Predicted Class Number: 22\n",
            "Predicted Class Label: peptic ulcer disease\n",
            "Class Probabilities:\n",
            "  Class 0 (Psoriasis): 0.0412\n",
            "  Class 1 (Varicose Veins): 0.0359\n",
            "  Class 2 (Typhoid): 0.0119\n",
            "  Class 3 (Chicken pox): 0.0257\n",
            "  Class 4 (Impetigo): 0.0248\n",
            "  Class 5 (Dengue): 0.0104\n",
            "  Class 6 (Fungal infection): 0.0464\n",
            "  Class 7 (Common Cold): 0.0626\n",
            "  Class 8 (Pneumonia): 0.0099\n",
            "  Class 9 (Dimorphic Hemorrhoids): 0.0128\n",
            "  Class 10 (Arthritis): 0.0172\n",
            "  Class 11 (Acne): 0.0286\n",
            "  Class 12 (Bronchial Asthma): 0.0109\n",
            "  Class 13 (Hypertension): 0.0144\n",
            "  Class 14 (Migraine): 0.0088\n",
            "  Class 15 (Cervical spondylosis): 0.0713\n",
            "  Class 16 (Jaundice): 0.0426\n",
            "  Class 17 (Malaria): 0.1044\n",
            "  Class 18 (urinary tract infection): 0.0256\n",
            "  Class 19 (allergy): 0.0167\n",
            "  Class 20 (gastroesophageal reflux disease): 0.0651\n",
            "  Class 21 (drug reaction): 0.1036\n",
            "  Class 22 (peptic ulcer disease): 0.1242\n",
            "  Class 23 (diabetes): 0.0848\n",
            "\n"
          ]
        }
      ]
    }
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