Financial_Document_Classification.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### **Required Libraries**"
      ],
      "metadata": {
        "id": "SRKSLi5UJIyE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ytt9tA8duY7G"
      },
      "outputs": [],
      "source": [
        "!pip install transformers datasets tensorflow bs4 python-dotenv\n",
        "!pip install sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import requests\n",
        "import glob\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from bs4 import BeautifulSoup\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, pipeline\n",
        "from huggingface_hub import login\n",
        "from dotenv import load_dotenv\n",
        "from warnings import filterwarnings\n",
        "filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "WVTw1kifwmT7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Data Collection**"
      ],
      "metadata": {
        "id": "miaTVXs6JPfw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset download from Kaggle\n",
        "!kaggle datasets download -d gopiashokan/financial-document-classification-dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xujoItf2mCDq",
        "outputId": "e2278007-7483-48f7-cb4f-c5f31b31512c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/gopiashokan/financial-document-classification-dataset\n",
            "License(s): MIT\n",
            "Downloading financial-document-classification-dataset.zip to /content\n",
            "  0% 0.00/2.75M [00:00<?, ?B/s]\n",
            "100% 2.75M/2.75M [00:00<00:00, 95.7MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the Data from Zip File\n",
        "!unzip financial-document-classification-dataset.zip"
      ],
      "metadata": {
        "id": "8kBSZFrNmJ_9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Data Preprocessing**"
      ],
      "metadata": {
        "id": "YoEdDL1WJTC7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def text_extract_from_html(html_file):\n",
        "\n",
        "    # Open and Read the HTML File\n",
        "    with open(file=html_file, mode='r', encoding='utf-8') as file:\n",
        "        html_content = file.read()\n",
        "\n",
        "    # Parse the HTML Content\n",
        "    soup = BeautifulSoup(html_content, 'html.parser')\n",
        "\n",
        "    # Extract the Text\n",
        "    text = soup.get_text()\n",
        "\n",
        "    # Split the Text and Remove Unwanted Space\n",
        "    result = [i.strip() for i in text.split()]\n",
        "\n",
        "    return result"
      ],
      "metadata": {
        "id": "fjI5Cx8OEszx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract Text from All HTML Documents\n",
        "\n",
        "dataset_path = '/content/data'\n",
        "\n",
        "file_name = []\n",
        "extracted_text = []\n",
        "target = []\n",
        "\n",
        "for folder_name in os.listdir(dataset_path):\n",
        "\n",
        "    # Make a Target_Folder Path\n",
        "    path = os.path.join(dataset_path, folder_name)\n",
        "\n",
        "    for html_file in os.listdir(path):\n",
        "\n",
        "        # Verify the File as HTML_File or not\n",
        "        if html_file.endswith('.html'):\n",
        "\n",
        "            # Make a HTML File Path\n",
        "            html_file_path = os.path.join(dataset_path, folder_name, html_file)\n",
        "\n",
        "            # Extract the Text from HTML Document\n",
        "            result = text_extract_from_html(html_file_path)\n",
        "\n",
        "            # Append the Data into List\n",
        "            file_name.append(html_file)\n",
        "            extracted_text.append(result)\n",
        "            target.append(folder_name)"
      ],
      "metadata": {
        "id": "NjGCITwfP-5V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of Total Documents\n",
        "len(file_name), len(extracted_text), len(target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "odnFwmLyVHUa",
        "outputId": "598b7453-ee8e-4014-be5e-ffbe796b9172"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2525, 2525, 2525)"
            ]
          },
          "metadata": {},
          "execution_count": 7
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    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({'file_name':file_name, 'text':extracted_text, 'label':target})\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "TzLJ5YH-Wb5V",
        "outputId": "7ff8610a-c9a3-43f0-c95f-fa42cb2e56cf"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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              "2523  Income Statement  \n",
              "2524  Income Statement  \n",
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              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d0560ce-a7a8-4ce9-85ce-6590024988d0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert: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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert: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",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4d0560ce-a7a8-4ce9-85ce-6590024988d0 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-4d0560ce-a7a8-4ce9-85ce-6590024988d0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-74d20145-59c7-4de7-a6e2-6d12a8728503\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-74d20145-59c7-4de7-a6e2-6d12a8728503')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-74d20145-59c7-4de7-a6e2-6d12a8728503 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9ad48788-5003-47bf-8022-a5cf0bc0907f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        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('df')\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9ad48788-5003-47bf-8022-a5cf0bc0907f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2525,\n  \"fields\": [\n    {\n      \"column\": \"file_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2525,\n        \"samples\": [\n          \"19058356_table_93.html\",\n          \"18646822_14.html\",\n          \"18944587_1.html\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Balance Sheets\",\n          \"Income Statement\",\n          \"Notes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Create a dictionary that Map each unique label to a unique index\n",
        "label_mapping = {label: idx for idx, label in enumerate(df['label'].unique())}\n",
        "print(label_mapping)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UXEA_d1dveeV",
        "outputId": "0f76c90b-5262-4498-cec4-83be5d84f839"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Others': 0, 'Balance Sheets': 1, 'Notes': 2, 'Cash Flow': 3, 'Income Statement': 4}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Map values in 'label' column based on the 'label_mapping' dictionary\n",
        "df['label'] = df['label'].map(label_mapping)\n",
        "df.tail(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "5fTWVcNlzg2N",
        "outputId": "2b2493f9-5610-4bac-a310-436de6392651"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             file_name                                               text  \\\n",
              "2522  18460658_74.html  [Particulars, Quarter, Ended, Mar-18, Dec-17, ...   \n",
              "2523   18849925_5.html  [Standalone, Consolidated, Quarter, ended, Yea...   \n",
              "2524   18671329_3.html  [SL., Standalone, Consolidated, No., Quarter, ...   \n",
              "\n",
              "      label  \n",
              "2522      4  \n",
              "2523      4  \n",
              "2524      4  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1b9f024-0539-4d6f-9fbf-cd97f7fd77da\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file_name</th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2522</th>\n",
              "      <td>18460658_74.html</td>\n",
              "      <td>[Particulars, Quarter, Ended, Mar-18, Dec-17, ...</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>2523</th>\n",
              "      <td>18849925_5.html</td>\n",
              "      <td>[Standalone, Consolidated, Quarter, ended, Yea...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2524</th>\n",
              "      <td>18671329_3.html</td>\n",
              "      <td>[SL., Standalone, Consolidated, No., Quarter, ...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1b9f024-0539-4d6f-9fbf-cd97f7fd77da')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert: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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert: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",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a1b9f024-0539-4d6f-9fbf-cd97f7fd77da 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-a1b9f024-0539-4d6f-9fbf-cd97f7fd77da');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f67903d6-3893-462c-9564-7abc0e0b6fd7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f67903d6-3893-462c-9564-7abc0e0b6fd7')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f67903d6-3893-462c-9564-7abc0e0b6fd7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"file_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"18460658_74.html\",\n          \"18849925_5.html\",\n          \"18671329_3.html\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 4,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split into train/test\n",
        "train_texts, test_texts, train_labels, test_labels = train_test_split(\n",
        "    df[\"text\"].tolist(), df[\"label\"].tolist(), test_size=0.2, random_state=42\n",
        ")"
      ],
      "metadata": {
        "id": "N-DLgm6rziQq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load FinBERT tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"yiyanghkust/finbert-pretrain\")\n",
        "tokenizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379,
          "referenced_widgets": [
            "59e9625108b2488b93cb33570c734970",
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        },
        "id": "waIWfZWO01Vu",
        "outputId": "2306904b-ab8b-407d-d4de-2902863ab6a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/359 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "59e9625108b2488b93cb33570c734970"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/226k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5af02f587b2c4809a3c4967d764083f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertTokenizerFast(name_or_path='yiyanghkust/finbert-pretrain', vocab_size=30873, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True, added_tokens_decoder={\n",
              "\t0: AddedToken(\"[PAD]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t2: AddedToken(\"[UNK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t3: AddedToken(\"[CLS]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t4: AddedToken(\"[SEP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t5: AddedToken(\"[MASK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "}\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenize function\n",
        "def tokenize_data(texts):\n",
        "    texts = [\" \".join(text) for text in texts]\n",
        "    return tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors=\"tf\")"
      ],
      "metadata": {
        "id": "gUFmj14E1PSx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenize train/test data\n",
        "train_encodings = tokenize_data(train_texts)\n",
        "train_encodings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zt4qc2ir1YSI",
        "outputId": "29b4ef48-ba47-413f-fe41-66583a487d05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': <tf.Tensor: shape=(2020, 512), dtype=int32, numpy=\n",
              "array([[   3,  643,   63, ...,    0,    0,    0],\n",
              "       [   3,  643,   63, ...,    0,    0,    0],\n",
              "       [   3,  643,   63, ...,    0,    0,    0],\n",
              "       ...,\n",
              "       [   3, 2514, 6744, ...,    0,    0,    0],\n",
              "       [   3, 6622,  206, ...,  727,   48,    4],\n",
              "       [   3,  643,   63, ...,    0,    0,    0]], dtype=int32)>, 'token_type_ids': <tf.Tensor: shape=(2020, 512), dtype=int32, numpy=\n",
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(2020, 512), dtype=int32, numpy=\n",
              "array([[1, 1, 1, ..., 0, 0, 0],\n",
              "       [1, 1, 1, ..., 0, 0, 0],\n",
              "       [1, 1, 1, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [1, 1, 1, ..., 0, 0, 0],\n",
              "       [1, 1, 1, ..., 1, 1, 1],\n",
              "       [1, 1, 1, ..., 0, 0, 0]], dtype=int32)>}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_encodings = tokenize_data(test_texts)\n",
        "test_encodings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jr6FU6rN1cUi",
        "outputId": "9214ad7e-1540-4869-c675-4d4458d18f9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': <tf.Tensor: shape=(505, 512), dtype=int32, numpy=\n",
              "array([[   3,  912,    8, ...,    0,    0,    0],\n",
              "       [   3,   11,   48, ...,    0,    0,    0],\n",
              "       [   3,    6, 4351, ...,    0,    0,    0],\n",
              "       ...,\n",
              "       [   3,  643,   63, ...,    0,    0,    0],\n",
              "       [   3,  643,   63, ...,    0,    0,    0],\n",
              "       [   3,   47,   66, ...,    0,    0,    0]], dtype=int32)>, 'token_type_ids': <tf.Tensor: shape=(505, 512), dtype=int32, numpy=\n",
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(505, 512), dtype=int32, numpy=\n",
              "array([[1, 1, 1, ..., 0, 0, 0],\n",
              "       [1, 1, 1, ..., 0, 0, 0],\n",
              "       [1, 1, 1, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [1, 1, 1, ..., 0, 0, 0],\n",
              "       [1, 1, 1, ..., 0, 0, 0],\n",
              "       [1, 1, 1, ..., 0, 0, 0]], dtype=int32)>}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert to TensorFlow dataset\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(train_encodings), tf.convert_to_tensor(train_labels)\n",
        ")).batch(8)\n",
        "\n",
        "train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wODJgU2Y2r50",
        "outputId": "70600e5c-e1ac-4961-bbec-8392a6dce9b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_BatchDataset element_spec=({'input_ids': TensorSpec(shape=(None, 512), dtype=tf.int32, name=None), 'token_type_ids': TensorSpec(shape=(None, 512), dtype=tf.int32, name=None), 'attention_mask': TensorSpec(shape=(None, 512), dtype=tf.int32, name=None)}, TensorSpec(shape=(None,), dtype=tf.int32, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(test_encodings), tf.convert_to_tensor(test_labels)\n",
        ")).batch(8)\n",
        "\n",
        "test_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hKcEKnrL26gp",
        "outputId": "77b18c80-57b7-4dfb-b6b1-9f4c710e477c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_BatchDataset element_spec=({'input_ids': TensorSpec(shape=(None, 512), dtype=tf.int32, name=None), 'token_type_ids': TensorSpec(shape=(None, 512), dtype=tf.int32, name=None), 'attention_mask': TensorSpec(shape=(None, 512), dtype=tf.int32, name=None)}, TensorSpec(shape=(None,), dtype=tf.int32, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Model Training & Evaluation**"
      ],
      "metadata": {
        "id": "Q-LXKzSdJae8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load FinBERT for classification\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained(\n",
        "          \"yiyanghkust/finbert-pretrain\",\n",
        "          num_labels=len(label_mapping),\n",
        "          from_pt=True\n",
        ")\n",
        "\n",
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "ccb8a2d89e634c63a87ea1b98c255533",
            "dd1bce8aca8e4540a6b5170293ab0446",
            "157763189eb14bcaa8b2a23e39698848",
            "58f4648fcee94529b14b098da3029316",
            "8175f0026825411b88b129244624b00c",
            "495a51b268704ffda3db68f34c20e4fd",
            "62cce4bdc4f941f184431f5c51857ee3",
            "83c72b4785b741b392414d56872d5ad8",
            "ed0588420fcf4d24a11e8305dbe7c941",
            "435ce8dd901d45f09b466aeb4cbea578",
            "5b165f287d254367a1631384d89e4239"
          ]
        },
        "id": "ztx3RIJ93HC1",
        "outputId": "109ae373-63c0-428c-8196-8a89b31e9f9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/442M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ccb8a2d89e634c63a87ea1b98c255533"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some weights or buffers of the TF 2.0 model TFBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification at 0x7d1b5fff8150>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile model\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)\n",
        "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "\n",
        "model.compile(optimizer=optimizer, loss=loss, metrics=[\"accuracy\"])\n",
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHTCGioT3RHq",
        "outputId": "3cf95028-fe40-4d9a-f0a9-d1683efa6eb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification at 0x7d1b5fff8150>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "history = model.fit(train_dataset, validation_data=test_dataset, epochs=3)\n",
        "history"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xW13em-93vkm",
        "outputId": "f130513a-d30e-49bd-c6b8-cc406243192a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "253/253 [==============================] - 300s 1s/step - loss: 0.3373 - accuracy: 0.8916 - val_loss: 0.2106 - val_accuracy: 0.9386\n",
            "Epoch 2/3\n",
            "253/253 [==============================] - 257s 1s/step - loss: 0.1155 - accuracy: 0.9668 - val_loss: 0.1491 - val_accuracy: 0.9584\n",
            "Epoch 3/3\n",
            "253/253 [==============================] - 260s 1s/step - loss: 0.0537 - accuracy: 0.9851 - val_loss: 0.1795 - val_accuracy: 0.9584\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf_keras.src.callbacks.History at 0x7d1b5fb41a10>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "loss, accuracy = model.evaluate(test_dataset)\n",
        "print(f\"Test Accuracy: {accuracy:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t2MIXdqm3zd-",
        "outputId": "3304ee5d-7518-4c5d-fbd8-5ef7e42d16b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64/64 [==============================] - 20s 311ms/step - loss: 0.1795 - accuracy: 0.9584\n",
            "Test Accuracy: 0.9584\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on the test dataset\n",
        "predictions = model.predict(test_dataset)\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNU-fF2tFGfv",
        "outputId": "6a181514-1d57-45e1-9c1c-3b6c1b0c2f03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64/64 [==============================] - 23s 312ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TFSequenceClassifierOutput(loss=None, logits=array([[ 2.48574   , -1.4604118 ,  3.1016219 , -3.212811  , -2.2835655 ],\n",
              "       [ 6.3067856 , -1.135364  , -0.64896816, -2.3524797 , -2.172157  ],\n",
              "       [ 6.801204  , -1.3755339 , -1.41392   , -1.5390751 , -2.0069335 ],\n",
              "       ...,\n",
              "       [ 6.8065906 , -1.0799997 , -1.7070782 , -1.6308147 , -1.9608169 ],\n",
              "       [ 6.8551707 , -0.9533801 , -1.9269737 , -1.4687493 , -2.1043599 ],\n",
              "       [ 5.742726  , -1.4233074 ,  0.17157513, -2.444131  , -2.138266  ]],\n",
              "      dtype=float32), hidden_states=None, attentions=None)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the predicted class by taking the argmax of logits\n",
        "predicted_classes = np.argmax(predictions.logits, axis=1)\n",
        "print(predicted_classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "TLEfGq8nHMjW",
        "outputId": "8b6141ef-613d-4ed6-df43-2771c48014c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 0 0 0 1 0 2 0 2 0 4 2 0 1 2 0 4 1 4 0 2 2 0 1 0 0 2 2 2 0 2 4 4 0 0 0 0\n",
            " 0 0 0 2 0 1 4 2 4 2 2 0 2 1 2 0 2 1 0 2 0 0 2 0 1 2 0 0 0 0 0 0 1 2 2 0 1\n",
            " 0 0 2 0 2 2 2 1 1 4 0 2 0 1 0 4 4 0 1 4 2 2 0 2 0 2 0 2 0 0 0 0 4 4 2 0 0\n",
            " 0 2 2 0 3 0 0 0 4 0 0 1 2 1 0 2 2 2 0 1 2 1 0 0 4 2 2 0 3 2 0 2 1 0 2 2 0\n",
            " 2 4 4 2 2 0 2 0 0 2 0 2 4 0 0 4 2 0 0 0 4 1 2 4 2 0 2 2 2 4 2 2 2 1 0 0 2\n",
            " 2 0 2 2 0 2 2 0 0 0 0 0 0 0 4 0 1 0 0 0 0 4 0 0 0 2 0 2 2 0 4 0 0 0 0 4 0\n",
            " 0 0 0 0 1 2 0 2 2 2 4 0 0 2 1 0 0 0 2 2 1 0 0 0 0 0 0 1 1 2 0 0 1 0 4 0 0\n",
            " 0 2 4 0 2 4 2 1 2 1 2 0 0 0 2 4 0 0 3 2 0 0 0 2 0 2 0 2 0 1 0 0 0 1 1 2 4\n",
            " 0 2 2 2 0 0 0 0 4 2 0 2 1 0 2 2 2 0 0 1 1 0 0 0 0 0 0 0 3 2 0 2 0 2 2 2 2\n",
            " 0 0 0 1 0 2 0 2 2 0 0 0 1 0 0 1 0 0 2 2 2 0 1 0 0 2 1 1 0 2 4 0 2 0 2 4 0\n",
            " 0 1 0 0 0 2 2 0 0 4 0 0 1 0 2 0 1 2 0 0 1 2 1 2 2 4 4 0 1 1 2 0 0 0 0 0 0\n",
            " 0 0 2 0 0 3 0 0 4 4 4 2 0 0 2 4 0 0 1 0 0 1 0 4 4 2 0 0 0 2 2 1 2 2 2 4 0\n",
            " 0 0 2 2 0 0 2 2 0 0 0 4 4 4 0 0 0 4 0 2 2 2 0 0 2 4 0 0 2 1 0 2 0 2 2 2 2\n",
            " 3 0 0 0 2 0 0 2 0 0 2 0 0 2 0 4 0 4 0 1 2 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the true labels\n",
        "true_labels = []\n",
        "for batch in test_dataset:\n",
        "    true_labels.extend(batch[1].numpy())\n",
        "\n",
        "true_labels = np.array(true_labels)\n",
        "true_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Khntz1QeHcAJ",
        "outputId": "4b844ac1-9b6d-4195-ecac-7dbf9d838c04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 0, 0, 0, 1, 0, 2, 0, 2, 0, 4, 2, 0, 1, 2, 0, 4, 1, 4, 0, 2, 2,\n",
              "       0, 1, 0, 0, 2, 2, 2, 0, 2, 4, 4, 0, 0, 0, 0, 2, 0, 0, 2, 0, 1, 4,\n",
              "       2, 4, 2, 2, 0, 2, 1, 2, 0, 2, 1, 0, 2, 0, 0, 2, 4, 1, 2, 0, 0, 0,\n",
              "       0, 0, 0, 1, 2, 2, 0, 1, 0, 0, 2, 0, 2, 2, 2, 1, 1, 4, 0, 2, 0, 1,\n",
              "       3, 4, 4, 0, 1, 4, 2, 2, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 4, 4, 2, 0,\n",
              "       0, 0, 2, 2, 0, 3, 0, 0, 0, 4, 0, 0, 1, 2, 1, 0, 2, 2, 2, 0, 1, 0,\n",
              "       1, 0, 0, 4, 2, 2, 0, 3, 2, 0, 2, 1, 0, 2, 2, 0, 2, 4, 4, 2, 2, 0,\n",
              "       2, 0, 0, 2, 0, 2, 4, 0, 0, 4, 2, 0, 0, 0, 4, 1, 2, 4, 2, 0, 2, 2,\n",
              "       2, 4, 2, 2, 2, 1, 0, 0, 2, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 4, 0,\n",
              "       0, 4, 0, 1, 0, 0, 4, 0, 4, 0, 0, 0, 2, 0, 2, 2, 4, 4, 0, 0, 2, 0,\n",
              "       4, 0, 0, 0, 0, 0, 1, 2, 0, 2, 2, 2, 4, 0, 0, 2, 1, 0, 0, 0, 2, 2,\n",
              "       2, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 1, 0, 4, 0, 4, 2, 2, 4, 0, 2,\n",
              "       4, 2, 1, 0, 1, 0, 0, 0, 0, 2, 4, 0, 0, 3, 2, 0, 0, 0, 2, 0, 2, 0,\n",
              "       2, 0, 2, 0, 0, 0, 1, 1, 2, 4, 0, 2, 2, 2, 0, 0, 0, 0, 4, 2, 0, 2,\n",
              "       1, 0, 2, 0, 2, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 2, 0, 2,\n",
              "       2, 2, 2, 0, 0, 0, 1, 0, 2, 0, 2, 2, 0, 0, 0, 2, 0, 0, 1, 0, 0, 2,\n",
              "       2, 2, 0, 1, 0, 4, 2, 1, 1, 0, 0, 4, 0, 2, 0, 2, 4, 0, 0, 1, 0, 0,\n",
              "       0, 2, 2, 0, 0, 4, 0, 0, 1, 0, 2, 0, 1, 2, 0, 0, 1, 2, 1, 2, 2, 4,\n",
              "       4, 0, 1, 1, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 3, 0, 0, 4, 4, 4,\n",
              "       2, 0, 0, 2, 4, 0, 0, 1, 0, 0, 1, 0, 4, 4, 2, 0, 0, 0, 2, 2, 1, 2,\n",
              "       2, 2, 4, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 4, 4, 4, 0, 2, 0, 4,\n",
              "       0, 2, 2, 2, 0, 0, 2, 4, 0, 0, 2, 1, 0, 2, 0, 2, 2, 2, 2, 3, 0, 0,\n",
              "       0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 4, 0, 4, 0, 0, 2, 0, 0, 0],\n",
              "      dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate the confusion matrix\n",
        "cm = confusion_matrix(true_labels, predicted_classes)\n",
        "\n",
        "# Plot the confusion matrix using seaborn\n",
        "plt.figure(figsize=(4, 4))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=label_mapping.values(), yticklabels=label_mapping.values())\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "Sd4Fq5O4ITjU",
        "outputId": "7ac50357-ff5b-402a-903c-e007b6982dd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the trained model\n",
        "model.save_pretrained(\"/content/finbert_financial_classifier\")"
      ],
      "metadata": {
        "id": "sH3Di_yF_YCD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the tokenizer (this saves the vocabulary and special tokens)\n",
        "tokenizer.save_pretrained(\"/content/finbert_tokenizer\")"
      ],
      "metadata": {
        "id": "qEo-PL_D_xlB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6a78677-41c4-46b6-b94d-a144696d57a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/finbert_tokenizer/tokenizer_config.json',\n",
              " '/content/finbert_tokenizer/special_tokens_map.json',\n",
              " '/content/finbert_tokenizer/vocab.txt',\n",
              " '/content/finbert_tokenizer/added_tokens.json',\n",
              " '/content/finbert_tokenizer/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zKJNfAjbvhIA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27258a76-29b0-460b-e5f3-290192933911"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'verbose': 1, 'epochs': 3, 'steps': 253}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# Accessing Training History Parameters\n",
        "history.params"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5-cIdCbNvxjR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d03ebbb-c3e5-4262-cb90-0fa8655359f4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Accessing Keys of Training History\n",
        "history.history.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rLDAvFU1v0M7"
      },
      "outputs": [],
      "source": [
        "# Extracting Accuracy Metrics from Training History\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "# Extracting Loss Metrics from Training History\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cNlqDZftz1PW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c7661b4-5d7a-498c-e639-d4bca3140162"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 3, 3, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "len(acc), len(val_acc), len(loss), len(val_loss)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the Acuuracy & Loss using Plots\n",
        "\n",
        "plt.figure(figsize=(12, 3))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(range(len(acc)), acc, label='Training Accuracy')\n",
        "plt.plot(range(len(val_acc)), val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training vs Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(range(len(loss)), loss, label='Training Loss')\n",
        "plt.plot(range(len(val_loss)), val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training vs Validation Loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "AmmT--jI6Cl5",
        "outputId": "73655c97-a39e-48f2-e0a8-3a6c6e7cc880"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Training vs Validation Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Model Deployment**"
      ],
      "metadata": {
        "id": "fGC_57QJBHYV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Login Hugging Face with Access Token\n",
        "login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "f82f7b92f2694729b7841f31eff13fc1",
            "29f5b666ec824a93a020781bf161cb00",
            "66703e0a525149198b24a58cf6963226",
            "0aaf2b9c9df34d9483c0e31e32bd160a",
            "b0bb5494fc1643dd8e54ca8d22c59314",
            "31ffba5dc9354e73a1014e57dd6dd0f3",
            "9a9b5080821543fcaf9421f6e8093875",
            "9e5c1e6554494eb3a3c45bce97c55a84",
            "f8a5fb8f349048c2b6f274360fbf83e3",
            "d26594336b1c420582892f669788744d",
            "a9d83dfaed48427e866e43049dddcde0",
            "48fa17c5a1ac49a8849270e18e665a92",
            "db5fce954d7e4885a9073c092311133f",
            "4cb216e0332140458d1b2f4ce4ac2791",
            "f7b9884fe8c841cc90675eb7f1b1b3f3",
            "3cfa01f8de9449f2a525e656163665b2",
            "61b947e54d454c6985eb466cfdc6b46a",
            "d9bbad75050d4f5aa798d2f6b84676a1",
            "c6a440050d82437ca01aded5d7801634",
            "0ab86a2f0346450a9331898c310e9c61"
          ]
        },
        "id": "J1hHGljyCg8M",
        "outputId": "a45ead13-bc7a-4ea6-b7c9-f730e6768058"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f82f7b92f2694729b7841f31eff13fc1"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Deploy to Hugging Face\n",
        "model.push_to_hub('gopiashokan/Financial-Document-Classification-using-Deep-Learning')\n",
        "tokenizer.push_to_hub('gopiashokan/Financial-Document-Classification-using-Deep-Learning')"
      ],
      "metadata": {
        "id": "5TP8_FJ_C-RQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Model Inference**"
      ],
      "metadata": {
        "id": "_JJxUSSo-iuq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def text_extract_from_html(html_file):\n",
        "\n",
        "    # Open and Read the HTML File\n",
        "    with open(file=html_file, mode='r', encoding='utf-8') as file:\n",
        "        html_content = file.read()\n",
        "\n",
        "    # Parse the HTML Content\n",
        "    soup = BeautifulSoup(html_content, 'html.parser')\n",
        "\n",
        "    # Extract the Text\n",
        "    text = soup.get_text()\n",
        "\n",
        "    # Split the Text and Remove Unwanted Space\n",
        "    result = [i.strip() for i in text.split()]\n",
        "    result = ' '.join(result)\n",
        "\n",
        "    return result"
      ],
      "metadata": {
        "id": "L0qx9dxD-uXv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def classify_text_with_huggingface_api(extracted_text):\n",
        "\n",
        "    # Load environment variables from .env file\n",
        "    load_dotenv()\n",
        "\n",
        "    # Retrieve the Hugging Face API token from environment variables\n",
        "    hf_token = os.getenv(\"HUGGINGFACE_TOKEN\")\n",
        "\n",
        "    # Define the Hugging Face API URL for the model\n",
        "    API_URL = \"https://api-inference.huggingface.co/models/gopiashokan/Financial-Document-Classification-using-Deep-Learning\"\n",
        "\n",
        "    # Set the authorization headers with the Hugging Face token\n",
        "    HEADERS = {\"Authorization\": f\"Bearer {hf_token}\"}\n",
        "\n",
        "    # Send a POST request to the Hugging Face API with the extracted text\n",
        "    response = requests.post(API_URL, headers=HEADERS, json={\"inputs\": extracted_text})\n",
        "\n",
        "    # Parse and return the JSON response\n",
        "    if response.status_code == 200:\n",
        "        result = response.json()\n",
        "        return result[0]\n",
        "\n",
        "    else:\n",
        "        return None"
      ],
      "metadata": {
        "id": "DTfpKYzjR0H2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prediction(input_file):\n",
        "\n",
        "    # Extract text from the uploaded HTML file\n",
        "    extracted_text = text_extract_from_html(input_file)\n",
        "\n",
        "    # Limit the extracted text to the first 512 characters to avoid API input limits\n",
        "    extracted_text = extracted_text[0:512]\n",
        "\n",
        "    # Classify the extracted text using the Hugging Face API\n",
        "    result = classify_text_with_huggingface_api(extracted_text)\n",
        "\n",
        "    if result is not None:\n",
        "        # Select the prediction with the highest confidence score\n",
        "        prediction = max(result, key=lambda x: x['score'])\n",
        "\n",
        "        # Map model labels to human-readable class names\n",
        "        label_mapping = {'LABEL_0':'Others', 'LABEL_1':'Balance Sheets', 'LABEL_2':'Notes', 'LABEL_3':'Cash Flow', 'LABEL_4':'Income Statement'}\n",
        "\n",
        "        # Get the predicted class name based on the model output\n",
        "        predicted_class = label_mapping[prediction['label']]\n",
        "\n",
        "        # Convert the confidence score to a percentage\n",
        "        confidence = prediction['score'] * 100\n",
        "\n",
        "    else:\n",
        "        print('Try Again Later')\n",
        "\n",
        "    # Print the prediction results\n",
        "    print(f'Predicted Class : {predicted_class}')\n",
        "    print(f'Confidence : {confidence:.2f}%')"
      ],
      "metadata": {
        "id": "NKXQajumSBN9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction('/content/balance_Sheets_sample.html')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b2eedd3-b5a0-4be9-b672-efc3ca192295",
        "id": "t9fa6D6xD46A"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class : Balance Sheets\n",
            "Confidence : 99.55%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction('/content/cash_flow_sample.html')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05904127-4f4c-4a2d-aae8-339dd82915be",
        "id": "xoQZA0HbD46B"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class : Cash Flow\n",
            "Confidence : 90.26%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction('/content/income_statement_sample.html')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6ba4903-08a9-4f89-d1cd-bcff7196c010",
        "id": "fJpCEtZPD46C"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class : Income Statement\n",
            "Confidence : 99.63%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction('/content/notes_sample.html')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6296fcdc-4d82-4186-f5d1-d59c3d1a5b45",
        "id": "PTqo6oEKD46D"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class : Notes\n",
            "Confidence : 99.28%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction('/content/others_sample.html')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bda5d94a-88ab-4245-dc5e-b46aaaabc89b",
        "id": "w4THveOpD46D"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class : Others\n",
            "Confidence : 99.90%\n"
          ]
        }
      ]
    }
  ]
}