ANN_MODEL_ON_EDA_(1).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/09aryan/MEME_CLASSIFICATION_USING_SENTIMENT_ANALYSIS/blob/main/ANN_MODEL_ON_EDA_(1).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa389d7e",
      "metadata": {
        "id": "aa389d7e",
        "outputId": "1c45d50a-7e81-4818-c6ea-9e62743a0b7b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu\n",
            "  Using cached tensorflow-gpu-2.12.0.tar.gz (2.6 kB)\n",
            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'error'\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  error: subprocess-exited-with-error\n",
            "  \n",
            "  python setup.py egg_info did not run successfully.\n",
            "  exit code: 1\n",
            "  \n",
            "  [44 lines of output]\n",
            "  Traceback (most recent call last):\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\requirements.py\", line 35, in __init__\n",
            "      parsed = _parse_requirement(requirement_string)\n",
            "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\_parser.py\", line 64, in parse_requirement\n",
            "      return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES))\n",
            "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\_parser.py\", line 82, in _parse_requirement\n",
            "      url, specifier, marker = _parse_requirement_details(tokenizer)\n",
            "                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\_parser.py\", line 126, in _parse_requirement_details\n",
            "      marker = _parse_requirement_marker(\n",
            "               ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\_parser.py\", line 147, in _parse_requirement_marker\n",
            "      tokenizer.raise_syntax_error(\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\_tokenizer.py\", line 165, in raise_syntax_error\n",
            "      raise ParserSyntaxError(\n",
            "  setuptools.extern.packaging._tokenizer.ParserSyntaxError: Expected end or semicolon (after name and no valid version specifier)\n",
            "      python_version>\"3.7\"\n",
            "                    ^\n",
            "  \n",
            "  The above exception was the direct cause of the following exception:\n",
            "  \n",
            "  Traceback (most recent call last):\n",
            "    File \"<string>\", line 2, in <module>\n",
            "    File \"<pip-setuptools-caller>\", line 34, in <module>\n",
            "    File \"C:\\Users\\TANUSH\\AppData\\Local\\Temp\\pip-install-d0gvozds\\tensorflow-gpu_0cb953026b0e4cb49f313b2c894fdb91\\setup.py\", line 40, in <module>\n",
            "      setuptools.setup()\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\__init__.py\", line 106, in setup\n",
            "      _install_setup_requires(attrs)\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\__init__.py\", line 77, in _install_setup_requires\n",
            "      dist.parse_config_files(ignore_option_errors=True)\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\dist.py\", line 900, in parse_config_files\n",
            "      self._finalize_requires()\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\dist.py\", line 597, in _finalize_requires\n",
            "      self._move_install_requirements_markers()\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\dist.py\", line 637, in _move_install_requirements_markers\n",
            "      inst_reqs = list(_reqs.parse(spec_inst_reqs))\n",
            "                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\requirements.py\", line 37, in __init__\n",
            "      raise InvalidRequirement(str(e)) from e\n",
            "  setuptools.extern.packaging.requirements.InvalidRequirement: Expected end or semicolon (after name and no valid version specifier)\n",
            "      python_version>\"3.7\"\n",
            "                    ^\n",
            "  [end of output]\n",
            "  \n",
            "  note: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "error: metadata-generation-failed\n",
            "\n",
            "Encountered error while generating package metadata.\n",
            "\n",
            "See above for output.\n",
            "\n",
            "note: This is an issue with the package mentioned above, not pip.\n",
            "hint: See above for details.\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow-gpu\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e466f7ca",
      "metadata": {
        "id": "e466f7ca",
        "outputId": "03b8292e-cba4-4dcf-ba90-7e5ae76e3a95"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu==2.12.0Note: you may need to restart the kernel to use updated packages.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  error: subprocess-exited-with-error\n",
            "  \n",
            "  python setup.py egg_info did not run successfully.\n",
            "  exit code: 1\n",
            "  \n",
            "  [44 lines of output]\n",
            "  Traceback (most recent call last):\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\requirements.py\", line 35, in __init__\n",
            "      parsed = _parse_requirement(requirement_string)\n",
            "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\_parser.py\", line 64, in parse_requirement\n",
            "      return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES))\n",
            "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\_parser.py\", line 82, in _parse_requirement\n",
            "      url, specifier, marker = _parse_requirement_details(tokenizer)\n",
            "                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\_parser.py\", line 126, in _parse_requirement_details\n",
            "      marker = _parse_requirement_marker(\n",
            "               ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\_parser.py\", line 147, in _parse_requirement_marker\n",
            "      tokenizer.raise_syntax_error(\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\_tokenizer.py\", line 165, in raise_syntax_error\n",
            "      raise ParserSyntaxError(\n",
            "  setuptools.extern.packaging._tokenizer.ParserSyntaxError: Expected end or semicolon (after name and no valid version specifier)\n",
            "      python_version>\"3.7\"\n",
            "                    ^\n",
            "  \n",
            "  The above exception was the direct cause of the following exception:\n",
            "  \n",
            "  Traceback (most recent call last):\n",
            "    File \"<string>\", line 2, in <module>\n",
            "    File \"<pip-setuptools-caller>\", line 34, in <module>\n",
            "    File \"C:\\Users\\TANUSH\\AppData\\Local\\Temp\\pip-install-nceyn8c5\\tensorflow-gpu_d035c8b3c9184d8c886fce5f940a67e2\\setup.py\", line 40, in <module>\n",
            "      setuptools.setup()\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\__init__.py\", line 106, in setup\n",
            "      _install_setup_requires(attrs)\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\__init__.py\", line 77, in _install_setup_requires\n",
            "      dist.parse_config_files(ignore_option_errors=True)\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\dist.py\", line 900, in parse_config_files\n",
            "      self._finalize_requires()\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\dist.py\", line 597, in _finalize_requires\n",
            "      self._move_install_requirements_markers()\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\dist.py\", line 637, in _move_install_requirements_markers\n",
            "      inst_reqs = list(_reqs.parse(spec_inst_reqs))\n",
            "                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "    File \"C:\\Users\\TANUSH\\anaconda3\\Lib\\site-packages\\setuptools\\_vendor\\packaging\\requirements.py\", line 37, in __init__\n",
            "      raise InvalidRequirement(str(e)) from e\n",
            "  setuptools.extern.packaging.requirements.InvalidRequirement: Expected end or semicolon (after name and no valid version specifier)\n",
            "      python_version>\"3.7\"\n",
            "                    ^\n",
            "  [end of output]\n",
            "  \n",
            "  note: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "error: metadata-generation-failed\n",
            "\n",
            "Encountered error while generating package metadata.\n",
            "\n",
            "See above for output.\n",
            "\n",
            "note: This is an issue with the package mentioned above, not pip.\n",
            "hint: See above for details.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  Using cached tensorflow-gpu-2.12.0.tar.gz (2.6 kB)\n",
            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'error'\n"
          ]
        }
      ],
      "source": [
        " pip install tensorflow-gpu==2.12.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b17c6742",
      "metadata": {
        "id": "b17c6742",
        "outputId": "e3fed6f3-6bb2-4f10-fce3-9ad5c0ccb3fc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.13.0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "df2525cb",
      "metadata": {
        "id": "df2525cb",
        "outputId": "b0c0d534-59a1-46ff-cdbc-2c7b746efe4d"
      },
      "outputs": [
        {
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              "                                            text_ocr  \\\n",
              "0  LOOK THERE MY FRIEND LIGHTYEAR NOW ALL SOHALIK...   \n",
              "1  The best of #10 YearChallenge! Completed in le...   \n",
              "2  Sam Thorne @Strippin ( Follow Follow Saw every...   \n",
              "3              10 Year Challenge - Sweet Dee Edition   \n",
              "4  10 YEAR CHALLENGE WITH NO FILTER 47 Hilarious ...   \n",
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          "execution_count": 12,
          "metadata": {},
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      "source": [
        "dataset.head()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e5e6c607",
      "metadata": {
        "id": "e5e6c607",
        "outputId": "cf868afc-bc58-456b-c963-4e41dc894d1f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.13.0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "26570a3e",
      "metadata": {
        "id": "26570a3e",
        "outputId": "ea231fc7-c9f1-4853-bd8a-d815283ea465"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.13.0\n"
          ]
        }
      ],
      "source": [
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "64071771",
      "metadata": {
        "id": "64071771",
        "outputId": "0ba2aa4c-0a04-41b0-8526-74babcf804b5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow in c:\\users\\tanush\\anaconda3\\lib\\site-packages (2.13.0)\n",
            "Requirement already satisfied: tensorflow-intel==2.13.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow) (2.13.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (2.0.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.1.21 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (23.5.26)\n",
            "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (0.4.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (3.7.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (16.0.6)\n",
            "Requirement already satisfied: numpy<=1.24.3,>=1.22 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (1.24.3)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (3.3.0)\n",
            "Requirement already satisfied: packaging in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (23.0)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (4.24.3)\n",
            "Requirement already satisfied: setuptools in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (68.0.0)\n",
            "Requirement already satisfied: six>=1.12.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (2.3.0)\n",
            "Requirement already satisfied: typing-extensions<4.6.0,>=3.6.6 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (4.5.0)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (1.14.1)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (1.58.0)\n",
            "Requirement already satisfied: tensorboard<2.14,>=2.13 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (2.13.0)\n",
            "Requirement already satisfied: tensorflow-estimator<2.14,>=2.13.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (2.13.0)\n",
            "Requirement already satisfied: keras<2.14,>=2.13.1 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (2.13.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.13.0->tensorflow) (0.31.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.13.0->tensorflow) (0.38.4)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (2.23.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (1.0.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (3.4.1)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (2.31.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (0.7.1)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (2.2.3)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (5.3.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (4.9)\n",
            "Requirement already satisfied: urllib3<2.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (1.26.16)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (1.3.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (2.0.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (2023.7.22)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (2.1.1)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\tanush\\anaconda3\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (3.2.2)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "pip install tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "67054ef3",
      "metadata": {
        "id": "67054ef3",
        "outputId": "a89e5644-176a-456c-8a4d-73026278e6f0"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>image_name</th>\n",
              "      <th>text_ocr</th>\n",
              "      <th>text_corrected</th>\n",
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              "      <th>sarcasm</th>\n",
              "      <th>offensive</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>image_1.jpg</td>\n",
              "      <td>LOOK THERE MY FRIEND LIGHTYEAR NOW ALL SOHALIK...</td>\n",
              "      <td>LOOK THERE MY FRIEND LIGHTYEAR NOW ALL SOHALIK...</td>\n",
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              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>image_4.png</td>\n",
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              "      <td>4</td>\n",
              "      <td>image_5.png</td>\n",
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              "   Unnamed: 0    image_name  \\\n",
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              "3           3   image_4.png   \n",
              "4           4   image_5.png   \n",
              "\n",
              "                                            text_ocr  \\\n",
              "0  LOOK THERE MY FRIEND LIGHTYEAR NOW ALL SOHALIK...   \n",
              "1  The best of #10 YearChallenge! Completed in le...   \n",
              "2  Sam Thorne @Strippin ( Follow Follow Saw every...   \n",
              "3              10 Year Challenge - Sweet Dee Edition   \n",
              "4  10 YEAR CHALLENGE WITH NO FILTER 47 Hilarious ...   \n",
              "\n",
              "                                      text_corrected      humour  \\\n",
              "0  LOOK THERE MY FRIEND LIGHTYEAR NOW ALL SOHALIK...   hilarious   \n",
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              "\n",
              "           sarcasm       offensive      motivational overall_sentiment  \n",
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          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ce2dce52",
      "metadata": {
        "id": "ce2dce52"
      },
      "outputs": [],
      "source": [
        "X=dataset.iloc[:,4:8]\n",
        "Y = dataset.iloc[:,8]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e64ae6e0",
      "metadata": {
        "id": "e64ae6e0",
        "outputId": "f8080855-4c7e-48bf-b4bd-7717f281079c"
      },
      "outputs": [
        {
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              "       humour          sarcasm       offensive      motivational\n",
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    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0f25881b",
      "metadata": {
        "id": "0f25881b"
      },
      "outputs": [],
      "source": [
        "Humour = pd.get_dummies(X['humour'],drop_first=True)\n",
        "Sarcasm = pd.get_dummies(X['sarcasm'],drop_first=True)\n",
        "Offensive = pd.get_dummies(X['offensive'],drop_first=True)\n",
        "Motivational = pd.get_dummies(X['motivational'],drop_first=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e72ae20e",
      "metadata": {
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    {
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      "execution_count": null,
      "id": "d4c27a3a",
      "metadata": {
        "id": "d4c27a3a"
      },
      "outputs": [],
      "source": [
        "X = X.drop(['humour','sarcasm','offensive','motivational'],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bbc03322",
      "metadata": {
        "id": "bbc03322"
      },
      "outputs": [],
      "source": [
        "X = pd.concat([X,Humour,Sarcasm,Offensive,Motivational],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2b3e4a15",
      "metadata": {
        "id": "2b3e4a15",
        "outputId": "12622b59-2802-459a-8ae9-d6cc58218de4"
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              "</table>\n",
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            ],
            "text/plain": [
              "   hilarious  not_funny  very_funny  not_sarcastic  twisted_meaning  \\\n",
              "0          1          0           0              0                0   \n",
              "1          0          1           0              0                0   \n",
              "2          0          0           1              1                0   \n",
              "3          0          0           1              0                1   \n",
              "4          1          0           0              0                0   \n",
              "\n",
              "   very_twisted  not_offensive  slight  very_offensive  not_motivational  \n",
              "0             0              1       0               0                 1  \n",
              "1             0              1       0               0                 0  \n",
              "2             0              1       0               0                 1  \n",
              "3             0              0       0               1                 0  \n",
              "4             1              0       0               1                 1  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0cfbc30c",
      "metadata": {
        "id": "0cfbc30c",
        "outputId": "c960cc0a-5e53-44c6-d1ae-5dce73ee5baf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    very_positive\n",
              "1    very_positive\n",
              "2         positive\n",
              "3         positive\n",
              "4          neutral\n",
              "Name: overall_sentiment, dtype: object"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "95840743",
      "metadata": {
        "id": "95840743",
        "outputId": "629d52eb-1151-434d-8c5f-cc09b80db577",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-6b61da5a0e97>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mOverall_sentiment\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dummies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'overall_sentiment'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
          ]
        }
      ],
      "source": [
        "Overall_sentiment = pd.get_dummies(Y['overall_sentiment'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f5958dfe",
      "metadata": {
        "id": "f5958dfe",
        "outputId": "4ea1e9ec-cfae-494f-f818-746d71b223f1"
      },
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "'Series' object has no attribute 'columns'",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[21], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(Y\u001b[38;5;241m.\u001b[39mcolumns)\n",
            "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:5902\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5895\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   5896\u001b[0m     name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[0;32m   5897\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[0;32m   5898\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[0;32m   5899\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[0;32m   5900\u001b[0m ):\n\u001b[0;32m   5901\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[1;32m-> 5902\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mobject\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getattribute__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)\n",
            "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'columns'"
          ]
        }
      ],
      "source": [
        "print(Y.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b3aa6044",
      "metadata": {
        "id": "b3aa6044",
        "outputId": "f29fa2e8-176e-42aa-9509-80b3c4a7f01f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['hilarious', 'not_funny', 'very_funny', 'not_sarcastic',\n",
            "       'twisted_meaning', 'very_twisted', 'not_offensive', 'slight',\n",
            "       'very_offensive', 'not_motivational'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print(X.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dd2593d3",
      "metadata": {
        "id": "dd2593d3"
      },
      "outputs": [],
      "source": [
        "Y = pd.DataFrame(Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "678ad4d3",
      "metadata": {
        "id": "678ad4d3",
        "outputId": "2d9efe13-bcdf-470c-a1cb-bc91180c4a6f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['overall_sentiment'], dtype='object')"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aef4d493",
      "metadata": {
        "id": "aef4d493",
        "outputId": "26ea0125-0d0e-4359-d452-4b75342fb4ab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0     very_positive\n",
              "1     very_positive\n",
              "2          positive\n",
              "3          positive\n",
              "4           neutral\n",
              "          ...      \n",
              "95         positive\n",
              "96         positive\n",
              "97         positive\n",
              "98         negative\n",
              "99          neutral\n",
              "Name: overall_sentiment, Length: 100, dtype: object"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y.head(100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b7601af9",
      "metadata": {
        "id": "b7601af9",
        "outputId": "8a1e9407-4253-4882-a798-4782dea047e6"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'Y' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m Y\u001b[38;5;241m.\u001b[39mhead()\n",
            "\u001b[1;31mNameError\u001b[0m: name 'Y' is not defined"
          ]
        }
      ],
      "source": [
        "Y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d386b2e5",
      "metadata": {
        "id": "d386b2e5",
        "outputId": "25edff4e-7a17-4122-9f7c-7bbf4eb3db09"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'pd' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m Overall_sentiment \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mget_dummies(Y[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moverall_sentiment\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
            "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
          ]
        }
      ],
      "source": [
        "Overall_sentiment = pd.get_dummies(Y['overall_sentiment'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a8368150",
      "metadata": {
        "id": "a8368150",
        "outputId": "7500e306-36c6-43af-8bcd-b557a1b66d34"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>overall_sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>very_positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>very_positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6987</th>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6988</th>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6989</th>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6990</th>\n",
              "      <td>very_positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6991</th>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6992 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     overall_sentiment\n",
              "0        very_positive\n",
              "1        very_positive\n",
              "2             positive\n",
              "3             positive\n",
              "4              neutral\n",
              "...                ...\n",
              "6987           neutral\n",
              "6988           neutral\n",
              "6989          positive\n",
              "6990     very_positive\n",
              "6991          positive\n",
              "\n",
              "[6992 rows x 1 columns]"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b1cbc900",
      "metadata": {
        "id": "b1cbc900"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2,random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e7f0667c",
      "metadata": {
        "id": "e7f0667c"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train= sc.fit_transform(X_train)\n",
        "X_test= sc.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ff0d4b7a",
      "metadata": {
        "id": "ff0d4b7a",
        "outputId": "60fa5b38-a17a-475b-a06d-b69d477974ac"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(5593, 10)"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "26a93b89",
      "metadata": {
        "id": "26a93b89"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "Y_train_encoded = label_encoder.fit_transform(Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c1fac092",
      "metadata": {
        "id": "c1fac092"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LeakyReLU, PReLU, ELU, ReLU\n",
        "from tensorflow.keras.layers import Dropout"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "76e40eae",
      "metadata": {
        "id": "76e40eae"
      },
      "outputs": [],
      "source": [
        "classifier = Sequential()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "92e7570d",
      "metadata": {
        "id": "92e7570d"
      },
      "outputs": [],
      "source": [
        "classifier.add(Dense(units=10,activation='relu'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "705c0f89",
      "metadata": {
        "id": "705c0f89"
      },
      "outputs": [],
      "source": [
        "classifier.add(Dense(units=8,activation='relu'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2841d087",
      "metadata": {
        "id": "2841d087"
      },
      "outputs": [],
      "source": [
        "classifier.add(Dense(units=6,activation='relu'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5a91e7cd",
      "metadata": {
        "id": "5a91e7cd",
        "outputId": "e2c78fc4-2343-49a8-e130-6c2cacd66a69"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(6992, 1)"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "397d07eb",
      "metadata": {
        "id": "397d07eb"
      },
      "outputs": [],
      "source": [
        "classifier.add(Dense(5,activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "81c35596",
      "metadata": {
        "id": "81c35596"
      },
      "outputs": [],
      "source": [
        "classifier.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c7c520f0",
      "metadata": {
        "id": "c7c520f0"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    min_delta=0.00005,\n",
        "    patience=20,\n",
        "    verbose=1,\n",
        "    mode=\"auto\",\n",
        "    baseline=None,\n",
        "    restore_best_weights=False,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e3838c80",
      "metadata": {
        "scrolled": true,
        "id": "e3838c80",
        "outputId": "29481de1-d1b8-404e-bbc5-6e48533f268c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 1.3873 - accuracy: 0.4412 - val_loss: 1.2620 - val_accuracy: 0.4567\n",
            "Epoch 2/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.2566 - accuracy: 0.4620 - val_loss: 1.2378 - val_accuracy: 0.4794\n",
            "Epoch 3/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.2375 - accuracy: 0.4790 - val_loss: 1.2311 - val_accuracy: 0.4881\n",
            "Epoch 4/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.2272 - accuracy: 0.4884 - val_loss: 1.2258 - val_accuracy: 0.4897\n",
            "Epoch 5/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.2195 - accuracy: 0.4903 - val_loss: 1.2224 - val_accuracy: 0.4875\n",
            "Epoch 6/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.2142 - accuracy: 0.4841 - val_loss: 1.2200 - val_accuracy: 0.4897\n",
            "Epoch 7/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.2104 - accuracy: 0.4873 - val_loss: 1.2183 - val_accuracy: 0.4870\n",
            "Epoch 8/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.2070 - accuracy: 0.4865 - val_loss: 1.2146 - val_accuracy: 0.4908\n",
            "Epoch 9/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.2039 - accuracy: 0.4903 - val_loss: 1.2129 - val_accuracy: 0.4902\n",
            "Epoch 10/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.2016 - accuracy: 0.4836 - val_loss: 1.2104 - val_accuracy: 0.4886\n",
            "Epoch 11/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1988 - accuracy: 0.4871 - val_loss: 1.2085 - val_accuracy: 0.4892\n",
            "Epoch 12/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1959 - accuracy: 0.4921 - val_loss: 1.2068 - val_accuracy: 0.4935\n",
            "Epoch 13/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1947 - accuracy: 0.4892 - val_loss: 1.2073 - val_accuracy: 0.4935\n",
            "Epoch 14/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1930 - accuracy: 0.4932 - val_loss: 1.2062 - val_accuracy: 0.4902\n",
            "Epoch 15/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1921 - accuracy: 0.4921 - val_loss: 1.2047 - val_accuracy: 0.4892\n",
            "Epoch 16/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1915 - accuracy: 0.4887 - val_loss: 1.2045 - val_accuracy: 0.4919\n",
            "Epoch 17/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1911 - accuracy: 0.4871 - val_loss: 1.2037 - val_accuracy: 0.4935\n",
            "Epoch 18/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1895 - accuracy: 0.4913 - val_loss: 1.2055 - val_accuracy: 0.4881\n",
            "Epoch 19/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1884 - accuracy: 0.4937 - val_loss: 1.2041 - val_accuracy: 0.4940\n",
            "Epoch 20/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1885 - accuracy: 0.4916 - val_loss: 1.2074 - val_accuracy: 0.4924\n",
            "Epoch 21/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1876 - accuracy: 0.4953 - val_loss: 1.2023 - val_accuracy: 0.4892\n",
            "Epoch 22/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1876 - accuracy: 0.4937 - val_loss: 1.2047 - val_accuracy: 0.4881\n",
            "Epoch 23/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1852 - accuracy: 0.4892 - val_loss: 1.2047 - val_accuracy: 0.4881\n",
            "Epoch 24/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1870 - accuracy: 0.4956 - val_loss: 1.2043 - val_accuracy: 0.4865\n",
            "Epoch 25/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1856 - accuracy: 0.4887 - val_loss: 1.2061 - val_accuracy: 0.4881\n",
            "Epoch 26/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1848 - accuracy: 0.4959 - val_loss: 1.2077 - val_accuracy: 0.4919\n",
            "Epoch 27/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1857 - accuracy: 0.4921 - val_loss: 1.2033 - val_accuracy: 0.4892\n",
            "Epoch 28/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1846 - accuracy: 0.4959 - val_loss: 1.2025 - val_accuracy: 0.4870\n",
            "Epoch 29/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1838 - accuracy: 0.4959 - val_loss: 1.2062 - val_accuracy: 0.4924\n",
            "Epoch 30/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1832 - accuracy: 0.4964 - val_loss: 1.2023 - val_accuracy: 0.4870\n",
            "Epoch 31/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1831 - accuracy: 0.4977 - val_loss: 1.2027 - val_accuracy: 0.4897\n",
            "Epoch 32/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1840 - accuracy: 0.4953 - val_loss: 1.2064 - val_accuracy: 0.4924\n",
            "Epoch 33/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1827 - accuracy: 0.4948 - val_loss: 1.2049 - val_accuracy: 0.4870\n",
            "Epoch 34/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1834 - accuracy: 0.4972 - val_loss: 1.2024 - val_accuracy: 0.4875\n",
            "Epoch 35/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1826 - accuracy: 0.4977 - val_loss: 1.2067 - val_accuracy: 0.4886\n",
            "Epoch 36/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1817 - accuracy: 0.4959 - val_loss: 1.2013 - val_accuracy: 0.4870\n",
            "Epoch 37/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1817 - accuracy: 0.4948 - val_loss: 1.2004 - val_accuracy: 0.4865\n",
            "Epoch 38/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1811 - accuracy: 0.4967 - val_loss: 1.2019 - val_accuracy: 0.4875\n",
            "Epoch 39/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1807 - accuracy: 0.4964 - val_loss: 1.2022 - val_accuracy: 0.4875\n",
            "Epoch 40/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1803 - accuracy: 0.4937 - val_loss: 1.1999 - val_accuracy: 0.4870\n",
            "Epoch 41/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1797 - accuracy: 0.4980 - val_loss: 1.2032 - val_accuracy: 0.4897\n",
            "Epoch 42/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1794 - accuracy: 0.5001 - val_loss: 1.2069 - val_accuracy: 0.4892\n",
            "Epoch 43/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1798 - accuracy: 0.4996 - val_loss: 1.2040 - val_accuracy: 0.4908\n",
            "Epoch 44/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1794 - accuracy: 0.4983 - val_loss: 1.2031 - val_accuracy: 0.4886\n",
            "Epoch 45/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1788 - accuracy: 0.4943 - val_loss: 1.2005 - val_accuracy: 0.4902\n",
            "Epoch 46/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1788 - accuracy: 0.4972 - val_loss: 1.2043 - val_accuracy: 0.4967\n",
            "Epoch 47/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1786 - accuracy: 0.5001 - val_loss: 1.2004 - val_accuracy: 0.4913\n",
            "Epoch 48/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1789 - accuracy: 0.4967 - val_loss: 1.2026 - val_accuracy: 0.4897\n",
            "Epoch 49/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1777 - accuracy: 0.5004 - val_loss: 1.2038 - val_accuracy: 0.4930\n",
            "Epoch 50/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1785 - accuracy: 0.4988 - val_loss: 1.2012 - val_accuracy: 0.4935\n",
            "Epoch 51/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1776 - accuracy: 0.4945 - val_loss: 1.2021 - val_accuracy: 0.4881\n",
            "Epoch 52/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1768 - accuracy: 0.4977 - val_loss: 1.2024 - val_accuracy: 0.4935\n",
            "Epoch 53/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1775 - accuracy: 0.4983 - val_loss: 1.2026 - val_accuracy: 0.4913\n",
            "Epoch 54/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1776 - accuracy: 0.4991 - val_loss: 1.2019 - val_accuracy: 0.4886\n",
            "Epoch 55/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1756 - accuracy: 0.4985 - val_loss: 1.2067 - val_accuracy: 0.4908\n",
            "Epoch 56/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1759 - accuracy: 0.4967 - val_loss: 1.2071 - val_accuracy: 0.4875\n",
            "Epoch 57/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1761 - accuracy: 0.4975 - val_loss: 1.2027 - val_accuracy: 0.4902\n",
            "Epoch 58/1000\n",
            "375/375 [==============================] - 1s 1ms/step - loss: 1.1758 - accuracy: 0.4956 - val_loss: 1.2018 - val_accuracy: 0.4935\n",
            "Epoch 59/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1763 - accuracy: 0.4948 - val_loss: 1.2042 - val_accuracy: 0.4940\n",
            "Epoch 60/1000\n",
            "375/375 [==============================] - 0s 1ms/step - loss: 1.1771 - accuracy: 0.4953 - val_loss: 1.2035 - val_accuracy: 0.4913\n",
            "Epoch 60: early stopping\n"
          ]
        }
      ],
      "source": [
        "model_history=classifier.fit(X_train,Y_train,validation_split=0.33,batch_size=10,epochs=1000,callbacks=early_stopping)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer, BertForSequenceClassification\n",
        "from torch.utils.data import DataLoader\n",
        "import torch\n"
      ],
      "metadata": {
        "id": "trTlRkZv-bXj"
      },
      "id": "trTlRkZv-bXj",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "kYAVRnz_Aob_",
        "outputId": "9edcc94c-7dfd-4cf6-b9a5-aa4fe1456987"
      },
      "id": "kYAVRnz_Aob_",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0    image_name  \\\n",
              "0           0   image_1.jpg   \n",
              "1           1  image_2.jpeg   \n",
              "2           2   image_3.JPG   \n",
              "3           3   image_4.png   \n",
              "4           4   image_5.png   \n",
              "\n",
              "                                            text_ocr  \\\n",
              "0  LOOK THERE MY FRIEND LIGHTYEAR NOW ALL SOHALIK...   \n",
              "1  The best of #10 YearChallenge! Completed in le...   \n",
              "2  Sam Thorne @Strippin ( Follow Follow Saw every...   \n",
              "3              10 Year Challenge - Sweet Dee Edition   \n",
              "4  10 YEAR CHALLENGE WITH NO FILTER 47 Hilarious ...   \n",
              "\n",
              "                                      text_corrected      humour  \\\n",
              "0  LOOK THERE MY FRIEND LIGHTYEAR NOW ALL SOHALIK...   hilarious   \n",
              "1  The best of #10 YearChallenge! Completed in le...   not_funny   \n",
              "2  Sam Thorne @Strippin ( Follow Follow Saw every...  very_funny   \n",
              "3              10 Year Challenge - Sweet Dee Edition  very_funny   \n",
              "4  10 YEAR CHALLENGE WITH NO FILTER 47 Hilarious ...   hilarious   \n",
              "\n",
              "           sarcasm       offensive      motivational overall_sentiment  \n",
              "0          general   not_offensive  not_motivational     very_positive  \n",
              "1          general   not_offensive      motivational     very_positive  \n",
              "2    not_sarcastic   not_offensive  not_motivational          positive  \n",
              "3  twisted_meaning  very_offensive      motivational          positive  \n",
              "4     very_twisted  very_offensive  not_motivational           neutral  "
            ],
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.columns\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjmHxoTxAvft",
        "outputId": "5237e34c-68e3-4557-a2e5-c3a63af50333"
      },
      "id": "tjmHxoTxAvft",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'image_name', 'text_ocr', 'text_corrected', 'humour',\n",
              "       'sarcasm', 'offensive', 'motivational', 'overall_sentiment'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from transformers import BertTokenizer, BertForSequenceClassification\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "import torch\n",
        "from sklearn.model_selection import train_test_split\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "U4ooglBoBk3l",
        "outputId": "b2f58221-a9ab-49d9-ba67-30efca1aa89d"
      },
      "id": "U4ooglBoBk3l",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-7db48bba7aa7>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBertTokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBertForSequenceClassification\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataLoader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTensorDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'transformers'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load your dataset\n",
        "df = pd.read_csv('labels.csv')\n",
        "\n",
        "# Keep only relevant columns\n",
        "df = df[['image_name', 'text_ocr', 'overall_sentiment']]\n",
        "\n"
      ],
      "metadata": {
        "id": "FD5rRt62Bo2U"
      },
      "id": "FD5rRt62Bo2U",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from transformers import BertTokenizer, BertForSequenceClassification\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "import torch\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from torch.cuda.amp import autocast, GradScaler"
      ],
      "metadata": {
        "id": "wuf6RKhYC-P7"
      },
      "id": "wuf6RKhYC-P7",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('labels.csv')\n",
        "\n",
        "# Keep relevant columns\n",
        "df = df[['image_name', 'text_ocr', 'overall_sentiment']]\n",
        "\n",
        "# Label Encoding for 'overall_sentiment'\n",
        "label_encoder = LabelEncoder()\n",
        "df['encoded_sentiment'] = label_encoder.fit_transform(df['overall_sentiment'])\n"
      ],
      "metadata": {
        "id": "Itgb1Va6DAuW"
      },
      "id": "Itgb1Va6DAuW",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n",
        "\n",
        "# Tokenization and Data Formatting\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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      "id": "n53V6tjRDJce",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "ef162d2e1a414fa194a50a8a0062e608"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "5d8accb326384bcd92c75b17185573a4"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "08996dd8813144c5809b5f0966159db8"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_encodings = tokenizer.batch_encode_plus(\n",
        "    train_df['text_ocr'].astype(str).tolist(),\n",
        "    add_special_tokens=True,\n",
        "    return_attention_mask=True,\n",
        "    truncation=True,  # Explicitly activate truncation\n",
        "    padding='max_length',  # Pad to max_length\n",
        "    max_length=256,\n",
        "    return_tensors='pt'\n",
        ")"
      ],
      "metadata": {
        "id": "VznmemKNDKjJ"
      },
      "id": "VznmemKNDKjJ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_encodings = tokenizer.batch_encode_plus(\n",
        "    test_df['text_ocr'].astype(str).tolist(),\n",
        "    add_special_tokens=True,\n",
        "    return_attention_mask=True,\n",
        "    truncation=True,\n",
        "    padding='max_length',\n",
        "    max_length=256,\n",
        "    return_tensors='pt'\n",
        ")"
      ],
      "metadata": {
        "id": "Qd52YecfDPeJ"
      },
      "id": "Qd52YecfDPeJ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = TensorDataset(\n",
        "    train_encodings['input_ids'],\n",
        "    train_encodings['attention_mask'],\n",
        "    torch.tensor(train_df['encoded_sentiment'].astype(int).tolist())\n",
        ")"
      ],
      "metadata": {
        "id": "64Z5ZogLDTXk"
      },
      "id": "64Z5ZogLDTXk",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = TensorDataset(\n",
        "    test_encodings['input_ids'],\n",
        "    test_encodings['attention_mask'],\n",
        "    torch.tensor(test_df['encoded_sentiment'].astype(int).tolist())\n",
        ")"
      ],
      "metadata": {
        "id": "fdLB2Y_JDUrE"
      },
      "id": "fdLB2Y_JDUrE",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "\n",
        "# Model Definition and Training\n",
        "NUM_CLASSES = len(df['encoded_sentiment'].unique())\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=NUM_CLASSES)\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vu11OyycDenC",
        "outputId": "0e8c807e-e508-40d3-c39e-7b7ade8517ae"
      },
      "id": "Vu11OyycDenC",
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training loop with gradient accumulation and mixed-precision training\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)\n",
        "scaler = GradScaler()\n",
        "\n",
        "accumulation_steps = 4  # Accumulate gradients over 4 batches before updating\n",
        "num_epochs = 3\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "    for i, batch in enumerate(train_loader):\n",
        "        inputs, attention_mask, labels = batch\n",
        "        inputs, attention_mask, labels = inputs.to(device), attention_mask.to(device), labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        with autocast():\n",
        "            outputs = model(inputs, attention_mask=attention_mask, labels=labels)\n",
        "            loss = outputs.loss\n",
        "\n",
        "        scaler.scale(loss).backward()\n",
        "\n",
        "        if (i + 1) % accumulation_steps == 0:\n",
        "            scaler.step(optimizer)\n",
        "            scaler.update()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "        running_loss += loss.item()\n",
        "\n",
        "        if i % 100 == 99:  # Print every 100 batches\n",
        "            print(f'Epoch {epoch + 1}, Batch {i + 1}, Loss: {running_loss / 100:.4f}')\n",
        "            running_loss = 0.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySXneAGoFQ9C",
        "outputId": "799afeaf-a61f-49c6-eda3-3eb4ec847935"
      },
      "id": "ySXneAGoFQ9C",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1, Batch 100, Loss: 1.2776\n",
            "Epoch 2, Batch 100, Loss: 1.2693\n",
            "Epoch 3, Batch 100, Loss: 1.2719\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ... (previous code)\n",
        "\n",
        "# Evaluation\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    test_loader = DataLoader(test_dataset, batch_size=8, shuffle=False)  # Reducing batch size for evaluation\n",
        "\n",
        "    for batch in test_loader:\n",
        "        inputs, attention_mask, labels = batch\n",
        "        inputs, attention_mask, labels = inputs.to(device), attention_mask.to(device), labels.to(device)\n",
        "\n",
        "        outputs = model(inputs, attention_mask=attention_mask)\n",
        "        _, predicted = torch.max(outputs.logits, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "accuracy = correct / total\n",
        "print(f'Accuracy: {accuracy * 100:.2f}%')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEgFyx0EGRFX",
        "outputId": "eb50051a-f338-43aa-f902-fc1e878c5186"
      },
      "id": "iEgFyx0EGRFX",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 43.75%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from transformers import BertTokenizer, BertForSequenceClassification, AdamW, get_linear_schedule_with_warmup\n",
        "from torch.utils.data import DataLoader, TensorDataset, random_split\n",
        "import torch\n",
        "from torch.cuda.amp import autocast, GradScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import accuracy_score\n",
        "from tqdm import tqdm\n"
      ],
      "metadata": {
        "id": "cDCQMszrJj0j"
      },
      "id": "cDCQMszrJj0j",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load your dataset\n",
        "df = pd.read_csv('labels.csv')\n",
        "\n",
        "# Keep relevant columns\n",
        "df = df[['image_name', 'text_ocr', 'overall_sentiment']]\n",
        "\n",
        "# Label Encoding for 'overall_sentiment'\n",
        "label_encoder = LabelEncoder()\n",
        "df['encoded_sentiment'] = label_encoder.fit_transform(df['overall_sentiment'])\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "FpdJQh4oJl01"
      },
      "id": "FpdJQh4oJl01",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
      ],
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        "colab": {
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          "height": 145,
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            "3d81cf676dba4d5590526c94b71cab18",
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            "4a9523411da042f68af786e864ab57b7",
            "490aa6be07464f35b12e6142a17a293f",
            "f6a82a4210194a8e8d995eda7c5c668e",
            "7f06520c3f1c4d3fbbc0c2bd20f9eea4",
            "d0100fb39a7e4218bcafa826430c1942",
            "6e5d3d46a68c4f88bd458d67232e3ad8",
            "ecc6d1ae31a14034aca151bd6a79fc25",
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            "3d789019f10d496cb549c55195e5c70e",
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        },
        "id": "6dMAWSa8JsfF",
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      },
      "id": "6dMAWSa8JsfF",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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          "metadata": {}
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          "metadata": {}
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_encodings = tokenizer.batch_encode_plus(\n",
        "    train_df['text_ocr'].astype(str).tolist(),\n",
        "    add_special_tokens=True,\n",
        "    return_attention_mask=True,\n",
        "    truncation=True,\n",
        "    padding='max_length',\n",
        "    max_length=256,\n",
        "    return_tensors='pt'\n",
        ")"
      ],
      "metadata": {
        "id": "3vCV6tM9JvqU"
      },
      "id": "3vCV6tM9JvqU",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_encodings = tokenizer.batch_encode_plus(\n",
        "    test_df['text_ocr'].astype(str).tolist(),\n",
        "    add_special_tokens=True,\n",
        "    return_attention_mask=True,\n",
        "    truncation=True,\n",
        "    padding='max_length',\n",
        "    max_length=256,\n",
        "    return_tensors='pt'\n",
        ")"
      ],
      "metadata": {
        "id": "H3LUewlMJ0Fj"
      },
      "id": "H3LUewlMJ0Fj",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = TensorDataset(\n",
        "    train_encodings['input_ids'],\n",
        "    train_encodings['attention_mask'],\n",
        "    torch.tensor(train_df['encoded_sentiment'].astype(int).tolist())\n",
        ")"
      ],
      "metadata": {
        "id": "Ll-lQ_fuJ3EO"
      },
      "id": "Ll-lQ_fuJ3EO",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = TensorDataset(\n",
        "    test_encodings['input_ids'],\n",
        "    test_encodings['attention_mask'],\n",
        "    torch.tensor(test_df['encoded_sentiment'].astype(int).tolist())\n",
        ")"
      ],
      "metadata": {
        "id": "m4GMjBfUJ4Bk"
      },
      "id": "m4GMjBfUJ4Bk",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate fine-tuning dataset\n",
        "fine_tune_df, train_df = train_test_split(train_df, test_size=0.1, random_state=42)\n",
        "\n",
        "fine_tune_encodings = tokenizer.batch_encode_plus(\n",
        "    fine_tune_df['text_ocr'].astype(str).tolist(),\n",
        "    add_special_tokens=True,\n",
        "    return_attention_mask=True,\n",
        "    truncation=True,\n",
        "    padding='max_length',\n",
        "    max_length=256,\n",
        "    return_tensors='pt'\n",
        ")\n",
        "\n",
        "fine_tune_dataset = TensorDataset(\n",
        "    fine_tune_encodings['input_ids'],\n",
        "    fine_tune_encodings['attention_mask'],\n",
        "    torch.tensor(fine_tune_df['encoded_sentiment'].astype(int).tolist())\n",
        ")\n",
        "\n",
        "# Fine-tuning DataLoader\n",
        "fine_tune_loader = DataLoader(fine_tune_dataset, batch_size=8, shuffle=True)"
      ],
      "metadata": {
        "id": "_nqc5jKdJ7HS"
      },
      "id": "_nqc5jKdJ7HS",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DS4Wqadlc_Te"
      },
      "id": "DS4Wqadlc_Te",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fine_tune_loader = DataLoader(fine_tune_dataset, batch_size=8, shuffle=True)\n"
      ],
      "metadata": {
        "id": "LZPzT0T0KD00"
      },
      "id": "LZPzT0T0KD00",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Definition and Training\n",
        "NUM_CLASSES = len(df['encoded_sentiment'].unique())\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=NUM_CLASSES)\n",
        "\n",
        "# Add dropout layers\n",
        "model.bert.encoder.layer[-1].output.dropout = torch.nn.Dropout(0.1)\n",
        "model.bert.pooler.dropout = torch.nn.Dropout(0.1)\n",
        "model.classifier.dropout = torch.nn.Dropout(0.1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "6f0a8d5bf510479e8f68a18668c46b52",
            "1a958a6605da458487c755ec5a277eb6",
            "f7fd38286a8d4dd6b6b9839841f24258",
            "9f726fd1d1144725bd8d25303bccd86a",
            "21d734a55ca54d8e8d9103e22e021076",
            "0349a393e2364fa484cc7e8d59208752",
            "3e7ca2ef76454c68b435c97775645574",
            "71954c03187745bd867ac4767f7f0352",
            "88df20d03297406d88dcf22b27dcba55",
            "484c0004fa7d4b6b90ca5564c845443a",
            "f1db10558d214fa78f78cefc48da13b6"
          ]
        },
        "id": "NcX8FY1FKTv5",
        "outputId": "953c2af9-6cf2-4b8a-c32b-f41b8b950aa3"
      },
      "id": "NcX8FY1FKTv5",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f0a8d5bf510479e8f68a18668c46b52"
            }
          },
          "metadata": {}
        },
        {
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training loop with gradient accumulation and mixed-precision training\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5)\n",
        "scaler = GradScaler()\n",
        "\n",
        "accumulation_steps = 4  # Accumulate gradients over 4 batches before updating\n",
        "num_epochs = 5\n",
        "num_fine_tune_epochs = 3  # Adjust as needed\n",
        "\n",
        "# Learning rate scheduler\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=len(train_loader) * num_epochs)\n",
        "\n",
        "# Early stopping variables\n",
        "best_val_accuracy = 0.0\n",
        "early_stopping_counter = 0\n",
        "patience = 3  # Adjust as needed\n",
        "\n",
        "# Training loop\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "\n",
        "    for i, batch in enumerate(tqdm(train_loader, desc=f'Epoch {epoch + 1}/{num_epochs}')):\n",
        "        inputs, attention_mask, labels = batch\n",
        "        inputs, attention_mask, labels = inputs.to(device), attention_mask.to(device), labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        with autocast():\n",
        "            outputs = model(inputs, attention_mask=attention_mask, labels=labels)\n",
        "            loss = outputs.loss\n",
        "\n",
        "        scaler.scale(loss).backward()\n",
        "\n",
        "        if (i + 1) % accumulation_steps == 0:\n",
        "            scaler.step(optimizer)\n",
        "            scaler.update()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "        running_loss += loss.item()\n",
        "        scheduler.step()\n",
        "\n",
        "# Fine-tuning loop\n",
        "for fine_tune_epoch in range(num_fine_tune_epochs):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "\n",
        "    for i, batch in enumerate(tqdm(fine_tune_loader, desc=f'Fine-tune Epoch {fine_tune_epoch + 1}/{num_fine_tune_epochs}')):\n",
        "        inputs, attention_mask, labels = batch\n",
        "        inputs, attention_mask, labels = inputs.to(device), attention_mask.to(device), labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        with autocast():\n",
        "            outputs = model(inputs, attention_mask=attention_mask, labels=labels)\n",
        "            loss = outputs.loss\n",
        "\n",
        "        scaler.scale(loss).backward()\n",
        "\n",
        "        if (i + 1) % accumulation_steps == 0:\n",
        "            scaler.step(optimizer)\n",
        "            scaler.update()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "        running_loss += loss.item()\n",
        "        scheduler.step()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3bz0OvwKzjt",
        "outputId": "4467fe3d-861e-41c9-c69e-ff41d4462547"
      },
      "id": "r3bz0OvwKzjt",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 1/5:   0%|          | 0/175 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n",
            "Epoch 1/5: 100%|██████████| 175/175 [01:13<00:00,  2.38it/s]\n",
            "Epoch 2/5: 100%|██████████| 175/175 [01:13<00:00,  2.40it/s]\n",
            "Epoch 3/5: 100%|██████████| 175/175 [01:12<00:00,  2.40it/s]\n",
            "Epoch 4/5: 100%|██████████| 175/175 [01:13<00:00,  2.38it/s]\n",
            "Epoch 5/5: 100%|██████████| 175/175 [01:12<00:00,  2.41it/s]\n",
            "Fine-tune Epoch 1/3: 100%|██████████| 630/630 [01:18<00:00,  8.07it/s]\n",
            "Fine-tune Epoch 2/3: 100%|██████████| 630/630 [01:18<00:00,  8.07it/s]\n",
            "Fine-tune Epoch 3/3: 100%|██████████| 630/630 [01:18<00:00,  8.06it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torch torchvision transformers tqdm\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from transformers import BertTokenizer, BertForSequenceClassification, AdamW, get_linear_schedule_with_warmup\n",
        "from sklearn.metrics import accuracy_score\n",
        "from tqdm.notebook import tqdm\n",
        "import torch\n",
        "from torch.cuda.amp import GradScaler, autocast"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmEbTCCyV1mU",
        "outputId": "82d01a81-8426-4d0f-cebf-1119eb75585d"
      },
      "id": "jmEbTCCyV1mU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "  Downloading transformers-4.35.0-py3-none-any.whl (7.9 MB)\n",
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            "  Downloading huggingface_hub-0.19.0-py3-none-any.whl (311 kB)\n",
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            "Collecting tokenizers<0.15,>=0.14 (from transformers)\n",
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            "Installing collected packages: safetensors, huggingface-hub, tokenizers, transformers\n",
            "Successfully installed huggingface-hub-0.17.3 safetensors-0.4.0 tokenizers-0.14.1 transformers-4.35.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('labels.csv')\n",
        "\n",
        "# Keep relevant columns\n",
        "df = df[['image_name', 'text_ocr', 'overall_sentiment']]"
      ],
      "metadata": {
        "id": "WrUyVD7cWBXc"
      },
      "id": "WrUyVD7cWBXc",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "from transformers import BertTokenizer\n",
        "\n",
        "# Extract relevant columns\n",
        "text_data = df['text_ocr'].astype(str).tolist()\n",
        "label_data = df['overall_sentiment'].astype(str).tolist()\n",
        "image_name_data = df['image_name'].astype(str).tolist()\n",
        "\n",
        "# Encode string labels into numerical values\n",
        "label_encoder = LabelEncoder()\n",
        "encoded_labels = label_encoder.fit_transform(label_data)\n",
        "\n",
        "# Tokenization and encoding\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "encoded_inputs = tokenizer(text_data, padding=True, truncation=True, return_tensors='pt')\n"
      ],
      "metadata": {
        "id": "DEaCx0buWVNq"
      },
      "id": "DEaCx0buWVNq",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create datasets and dataloaders\n",
        "class YourDataset(Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: val[idx] for key, val in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx])\n",
        "        return item\n",
        "\n",
        "# Use encoded_labels instead of label_data\n",
        "labels_dataset = YourDataset(encoded_inputs, encoded_labels)\n"
      ],
      "metadata": {
        "id": "xaIkeBpmWyja"
      },
      "id": "xaIkeBpmWyja",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into train, validation, and test sets\n",
        "train_size = int(0.8 * len(labels_dataset))\n",
        "val_size = int(0.1 * len(labels_dataset))\n",
        "test_size = len(labels_dataset) - train_size - val_size\n",
        "\n",
        "train_dataset, val_dataset, test_dataset = torch.utils.data.random_split(labels_dataset, [train_size, val_size, test_size])\n",
        "\n",
        "# Create DataLoader instances\n",
        "batch_size = 32\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
      ],
      "metadata": {
        "id": "viVJpp1UW7bi"
      },
      "id": "viVJpp1UW7bi",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cell 4: Model Definition and Training Setup\n",
        "NUM_CLASSES = len(set(label_data))  # Assuming train_labels is a list or array\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=NUM_CLASSES)\n",
        "model.bert.encoder.layer[-1].output.dropout = torch.nn.Dropout(0.1)\n",
        "model.bert.pooler.dropout = torch.nn.Dropout(0.1)\n",
        "model.classifier.dropout = torch.nn.Dropout(0.1)\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5)\n",
        "scaler = GradScaler()\n",
        "accumulation_steps = 4\n",
        "num_epochs = 5\n",
        "num_fine_tune_epochs = 3\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=len(train_loader) * num_epochs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JmjrgMh4W_hN",
        "outputId": "984cf409-d9f3-49a9-da8b-8548913200a3"
      },
      "id": "JmjrgMh4W_hN",
      "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.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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cell 5.1: Training Loop Setup\n",
        "best_val_accuracy = 0.0\n",
        "early_stopping_counter = 0\n",
        "patience = 3\n"
      ],
      "metadata": {
        "id": "VKbGKjg1YH-M"
      },
      "id": "VKbGKjg1YH-M",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cell 5.2: Training Loop\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "\n",
        "    for i, batch in enumerate(tqdm(train_loader, desc=f'Epoch {epoch + 1}/{num_epochs}')):\n",
        "        inputs = {key: val.to(device) for key, val in batch.items() if key != 'labels'}\n",
        "        labels = batch['labels'].to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        with autocast():\n",
        "            outputs = model(**inputs, labels=labels)\n",
        "            loss = outputs.loss\n",
        "\n",
        "        scaler.scale(loss).backward()\n",
        "\n",
        "        if (i + 1) % accumulation_steps == 0:\n",
        "            scaler.step(optimizer)\n",
        "            scaler.update()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "        running_loss += loss.item()\n",
        "        scheduler.step()\n"
      ],
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        {
          "output_type": "display_data",
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            "text/plain": [
              "Epoch 1/5:   0%|          | 0/175 [00:00<?, ?it/s]"
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            "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
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              "Epoch 3/5:   0%|          | 0/175 [00:00<?, ?it/s]"
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              "Epoch 4/5:   0%|          | 0/175 [00:00<?, ?it/s]"
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      "cell_type": "code",
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        "df.head()"
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              "     image_name                                           text_ocr  \\\n",
              "0   image_1.jpg  LOOK THERE MY FRIEND LIGHTYEAR NOW ALL SOHALIK...   \n",
              "1  image_2.jpeg  The best of #10 YearChallenge! Completed in le...   \n",
              "2   image_3.JPG  Sam Thorne @Strippin ( Follow Follow Saw every...   \n",
              "3   image_4.png              10 Year Challenge - Sweet Dee Edition   \n",
              "4   image_5.png  10 YEAR CHALLENGE WITH NO FILTER 47 Hilarious ...   \n",
              "\n",
              "  overall_sentiment  \n",
              "0     very_positive  \n",
              "1     very_positive  \n",
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              "  .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-a2653499-11e5-4c2e-9ab5-3b939e6039a0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install scikit-learn\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xq56U95FdB9b",
        "outputId": "c25afb38-c2c7-469f-efe0-c40c6c05e9f5"
      },
      "id": "Xq56U95FdB9b",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.11.3)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Separate fine-tuning dataset\n",
        "train_df, fine_tune_df = train_test_split(df, test_size=0.1, random_state=42)\n",
        "\n",
        "# Encode string labels into numerical values\n",
        "label_encoder = LabelEncoder()\n",
        "fine_tune_encoded_labels = label_encoder.fit_transform(fine_tune_df['overall_sentiment'].astype(str))\n",
        "\n",
        "# Access the DataFrame directly\n",
        "fine_tune_encodings = tokenizer.batch_encode_plus(\n",
        "    fine_tune_df['text_ocr'].astype(str).tolist(),\n",
        "    add_special_tokens=True,\n",
        "    return_attention_mask=True,\n",
        "    truncation=True,\n",
        "    padding='max_length',\n",
        "    max_length=256,\n",
        "    return_tensors='pt'\n",
        ")\n",
        "\n",
        "fine_tune_dataset = TensorDataset(\n",
        "    fine_tune_encodings['input_ids'],\n",
        "    fine_tune_encodings['attention_mask'],\n",
        "    torch.tensor(fine_tune_encoded_labels.tolist())\n",
        ")\n"
      ],
      "metadata": {
        "id": "8ldRG8-XdUIq"
      },
      "id": "8ldRG8-XdUIq",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fine_tune_loader = DataLoader(fine_tune_dataset, batch_size=8, shuffle=True)\n"
      ],
      "metadata": {
        "id": "1BAznuHmcYoq"
      },
      "id": "1BAznuHmcYoq",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fine-tuning loop\n",
        "for fine_tune_epoch in range(num_fine_tune_epochs):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "\n",
        "    for i, batch in enumerate(tqdm(fine_tune_loader, desc=f'Fine-tune Epoch {fine_tune_epoch + 1}/{num_fine_tune_epochs}')):\n",
        "        inputs = {f'input_ids': batch[0].to(device), f'attention_mask': batch[1].to(device)}\n",
        "        labels = batch[-1].to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        with autocast():\n",
        "            outputs = model(**inputs, labels=labels)\n",
        "            loss = outputs.loss\n",
        "\n",
        "        scaler.scale(loss).backward()\n",
        "\n",
        "        if (i + 1) % accumulation_steps == 0:\n",
        "            scaler.step(optimizer)\n",
        "            scaler.update()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "        running_loss += loss.item()\n",
        "        scheduler.step()\n",
        "\n",
        "# Continue with the validation and testing loops...\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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        "outputId": "84150719-3b5d-4f20-96f3-c6d1a432e331"
      },
      "id": "FFvQ9YeqiBZS",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fine-tune Epoch 1/3:   0%|          | 0/88 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fine-tune Epoch 2/3:   0%|          | 0/88 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45eaa3e31e6c4484bca0ee42f91c892b"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fine-tune Epoch 3/3:   0%|          | 0/88 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    all_predictions = []\n",
        "    all_labels = []\n",
        "    for batch in tqdm(val_loader, desc='Validation'):\n",
        "        inputs = {'input_ids': batch['input_ids'].to(device), 'attention_mask': batch['attention_mask'].to(device)}\n",
        "        labels = batch['labels'].to(device)\n",
        "\n",
        "        outputs = model(**inputs)\n",
        "        _, predicted = torch.max(outputs.logits, 1)\n",
        "        all_predictions.extend(predicted.cpu().numpy())\n",
        "        all_labels.extend(labels.cpu().numpy())\n",
        "\n",
        "    val_accuracy = accuracy_score(all_labels, all_predictions)\n",
        "    print(f'Validation Accuracy: {val_accuracy * 100:.2f}%')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
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        "outputId": "7575c51b-c92a-482d-cf15-4f1534e1af1f"
      },
      "id": "pfMrg-nti1xi",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation:   0%|          | 0/22 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7c290a8a7ec4caa9dfccdf2cf329511"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Accuracy: 43.78%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing loop\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    all_predictions = []\n",
        "    all_labels = []\n",
        "    for batch in tqdm(test_loader, desc='Testing'):\n",
        "        inputs = {key: val.to(device) for key, val in batch.items() if key != 'labels'}\n",
        "        labels = batch['labels'].to(device)\n",
        "\n",
        "        outputs = model(**inputs)\n",
        "        _, predicted = torch.max(outputs.logits, 1)\n",
        "\n",
        "        all_predictions.extend(predicted.cpu().numpy())\n",
        "        all_labels.extend(labels.cpu().numpy())\n",
        "\n",
        "# Calculate and print test accuracy\n",
        "test_accuracy = accuracy_score(all_labels, all_predictions)\n",
        "print(f'Test Accuracy: {test_accuracy * 100:.2f}%')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
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      },
      "id": "eK0Dw53djuHb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing:   0%|          | 0/22 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "output_type": "stream",
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          "text": [
            "Test Accuracy: 45.43%\n"
          ]
        }
      ]
    }
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