Case_Study.ipynb

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        "<a href=\"https://colab.research.google.com/github/KaavyaRekanar/Subjective_Scoring_Framework_VQA/blob/main/Case_Study.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "import pandas as pd\n",
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        "!pip install -U sentence-transformers"
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          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting sentencepiece (from sentence-transformers)\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hCollecting huggingface-hub>=0.4.0 (from sentence-transformers)\n",
            "  Downloading huggingface_hub-0.17.3-py3-none-any.whl (295 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m35.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (3.12.2)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers<5.0.0,>=4.6.0->sentence-transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers<5.0.0,>=4.6.0->sentence-transformers)\n",
            "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->sentence-transformers) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (3.2.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->sentence-transformers) (9.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.6.0->sentence-transformers) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->sentence-transformers) (1.3.0)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125923 sha256=fb20871c20948934d7568c606907fde55d9fddb0e7fd9920f2a076e5d7cf0859\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: tokenizers, sentencepiece, safetensors, huggingface-hub, transformers, sentence-transformers\n",
            "Successfully installed huggingface-hub-0.17.3 safetensors-0.3.3 sentence-transformers-2.2.2 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.33.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer, InputExample, losses, evaluation, util,models\n",
        "from torch.utils.data import DataLoader\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from torch import nn\n"
      ],
      "metadata": {
        "id": "qvxH_dKSL1mY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oYV5cM4lbg4V",
        "outputId": "a2130311-62b2-4c85-fa66-2ea77fe62f04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vmThpyDSSLeU",
        "outputId": "e96a3755-0704-4d09-b1af-965f68087e13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read and store content\n",
        "# of an excel file\n",
        "#read_file = pd.read_excel (\"/content/drive/MyDrive/Subjective_scoring.xlsx\")\n",
        "read_file = pd.read_excel (\"/content/drive/MyDrive/case_study.xlsx\")\n",
        "\n",
        "# Write the dataframe object\n",
        "# into csv file\n",
        "read_file.to_csv (\"/content/drive/MyDrive/VQA.csv\",\n",
        "                   index = None,\n",
        "                header=True)\n",
        "\n",
        "# read csv file and convert\n",
        "# into a dataframe object\n",
        "df = pd.DataFrame(pd.read_csv(\"/content/drive/MyDrive/VQA.csv\"))\n",
        "\n",
        "# show the dataframe\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-InAhPvc-R4R",
        "outputId": "92d8561f-8c43-4395-cace-15b677cd5a0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Category                            Questions  \\\n",
              "0          Dark setting  what are the contents of the image?   \n",
              "1                   NaN           what should the driver do?   \n",
              "2         Light Setting  what are the contents of the image?   \n",
              "3                   NaN           what should the driver do?   \n",
              "4               Parking  what are the contents of the image?   \n",
              "5                   NaN            Can the driver park here?   \n",
              "6             Signboard  what are the contents of the image?   \n",
              "7                   NaN           what should the driver do?   \n",
              "8   Pedestrian Crossing  what are the contents of the image?   \n",
              "9                   NaN           what should the driver do?   \n",
              "10             Traffic   what are the contents of the image?   \n",
              "11                  NaN           what should the driver do?   \n",
              "12             Accident  what are the contents of the image?   \n",
              "13                  NaN           what should the driver do?   \n",
              "14            Signboard  what are the contents of the image?   \n",
              "15                  NaN           what should the driver do?   \n",
              "16            Roadworks  what are the contents of the image?   \n",
              "17                  NaN           what should the driver do?   \n",
              "18          Men at work  what are the contents of the image?   \n",
              "19                  NaN           what should the driver do?   \n",
              "20     Fallen Signboard  what are the contents of the image?   \n",
              "21                  NaN           what should the driver do?   \n",
              "\n",
              "                     Answers  ViLBERT  ViLBERT_Score            ViLT  \\\n",
              "0                An Accident   Trucks              0            Cars   \n",
              "1      Slow down and go left      Run              0            Stop   \n",
              "2             Traffic Lights   Clouds              0  Traffic Lights   \n",
              "3                       Stop    Sleep              0            Stop   \n",
              "4                Parking Lot   Clouds              0            Cars   \n",
              "5                        Yes      No               0              No   \n",
              "6                       Road    Paint              0  Traffic Lights   \n",
              "7                       Stop    Sleep              0            Stop   \n",
              "8   People crossing the road   Clouds              0       Buildings   \n",
              "9                       Stop      Run              0            Stop   \n",
              "10                  Traffic    Trucks              0           Buses   \n",
              "11                        Go      Run              0            Stop   \n",
              "12               An Accident   Clouds              0            Cars   \n",
              "13                      Stop     Stop              1            Stop   \n",
              "14                      Road  Windows              0           Trees   \n",
              "15                      Wait    Sleep              0            Stop   \n",
              "16            Traffic Lights   Trucks              0            Cars   \n",
              "17                      Stop     Stop              1            Stop   \n",
              "18               Men at work   Trucks              0          People   \n",
              "19                      Stop    Sleep              0            Stop   \n",
              "20       Stop sign fell down   Trucks              0       Stop sign   \n",
              "21                   Go left      Eat              0            Stop   \n",
              "\n",
              "    ViLT_Score       LXMERT  LXMERT_Score  \n",
              "0         0.00         Cars           0.0  \n",
              "1         0.00           Go           0.5  \n",
              "2         1.00  Power Lines           0.0  \n",
              "3         1.00         Stop           1.0  \n",
              "4         0.00         Cars           0.0  \n",
              "5         0.00          Yes           1.0  \n",
              "6         0.00         Cars           0.0  \n",
              "7         0.00           Go           0.0  \n",
              "8         0.00       People           1.0  \n",
              "9         1.00         Stop           1.0  \n",
              "10        0.00        Buses           0.0  \n",
              "11        0.00           Go           1.0  \n",
              "12        0.00         Cars           0.0  \n",
              "13        1.00         Stop           1.0  \n",
              "14        0.00     Concrete           0.0  \n",
              "15        0.00         Stop           0.0  \n",
              "16        0.00         Cars           0.0  \n",
              "17        1.00           Go           0.0  \n",
              "18        0.75         Cars           0.0  \n",
              "19        1.00         Stop           1.0  \n",
              "20        0.50         Cars           0.0  \n",
              "21        0.00         Stop           0.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e9ee74d3-99f0-4c07-8d59-c481b4934348\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Questions</th>\n",
              "      <th>Answers</th>\n",
              "      <th>ViLBERT</th>\n",
              "      <th>ViLBERT_Score</th>\n",
              "      <th>ViLT</th>\n",
              "      <th>ViLT_Score</th>\n",
              "      <th>LXMERT</th>\n",
              "      <th>LXMERT_Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dark setting</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>An Accident</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Slow down and go left</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Light Setting</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Power Lines</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Parking</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Parking Lot</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Can the driver park here?</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Road</td>\n",
              "      <td>Paint</td>\n",
              "      <td>0</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Pedestrian Crossing</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>People crossing the road</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Buildings</td>\n",
              "      <td>0.00</td>\n",
              "      <td>People</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Traffic</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Buses</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Buses</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Go</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Accident</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>An Accident</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Road</td>\n",
              "      <td>Windows</td>\n",
              "      <td>0</td>\n",
              "      <td>Trees</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Concrete</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Wait</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Roadworks</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Men at work</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Men at work</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>People</td>\n",
              "      <td>0.75</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Fallen Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Stop sign fell down</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop sign</td>\n",
              "      <td>0.50</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Go left</td>\n",
              "      <td>Eat</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##BERT based Models"
      ],
      "metadata": {
        "id": "eFeIDr2EZ9OO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model1 =SentenceTransformer('nli-distilbert-base')"
      ],
      "metadata": {
        "id": "UpxNy8Q11PSI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Function for Embedding Generation and Cosine Similarity"
      ],
      "metadata": {
        "id": "F0Y59ghUkHcx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def pred(model,q_s, ans):\n",
        "    try:\n",
        "      return util.cos_sim(model.encode(q_s), model.encode(ans))[0].cpu().item()\n",
        "    except:\n",
        "      return 0.0"
      ],
      "metadata": {
        "id": "aDrKJNhb1vtX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['predict_nli_ViLT'] = df.apply(lambda row: pred(model1, row['Answers'],row['ViLT']), axis=1)\n",
        "\n",
        "df['predict_nli_LXMERT'] = df.apply(lambda row: pred(model1, row['Answers'],row['LXMERT']), axis=1)\n",
        "\n",
        "df['predict_nli_ViLBERT'] = df.apply(lambda row: pred(model1, row['Answers'],row['ViLBERT']), axis=1)"
      ],
      "metadata": {
        "id": "oN-6ePdO1vtY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9tEeEYki147B",
        "outputId": "1fc6ee05-8e1f-4a0d-ee50-71f8e7e77db2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Category                            Questions  \\\n",
              "0          Dark setting  what are the contents of the image?   \n",
              "1                   NaN           what should the driver do?   \n",
              "2         Light Setting  what are the contents of the image?   \n",
              "3                   NaN           what should the driver do?   \n",
              "4               Parking  what are the contents of the image?   \n",
              "5                   NaN            Can the driver park here?   \n",
              "6             Signboard  what are the contents of the image?   \n",
              "7                   NaN           what should the driver do?   \n",
              "8   Pedestrian Crossing  what are the contents of the image?   \n",
              "9                   NaN           what should the driver do?   \n",
              "10             Traffic   what are the contents of the image?   \n",
              "11                  NaN           what should the driver do?   \n",
              "12             Accident  what are the contents of the image?   \n",
              "13                  NaN           what should the driver do?   \n",
              "14            Signboard  what are the contents of the image?   \n",
              "15                  NaN           what should the driver do?   \n",
              "16            Roadworks  what are the contents of the image?   \n",
              "17                  NaN           what should the driver do?   \n",
              "18          Men at work  what are the contents of the image?   \n",
              "19                  NaN           what should the driver do?   \n",
              "20     Fallen Signboard  what are the contents of the image?   \n",
              "21                  NaN           what should the driver do?   \n",
              "\n",
              "                Ground Truth  ViLBERT  ViLBERT Score            ViLT  \\\n",
              "0                An Accident   Trucks              0            Cars   \n",
              "1      Slow down and go left      Run              0            Stop   \n",
              "2             Traffic Lights   Clouds              0  Traffic Lights   \n",
              "3                       Stop    Sleep              0            Stop   \n",
              "4                Parking Lot   Clouds              0            Cars   \n",
              "5                        Yes      No               0              No   \n",
              "6                       Road    Paint              0  Traffic Lights   \n",
              "7                       Stop    Sleep              0            Stop   \n",
              "8   People crossing the road   Clouds              0       Buildings   \n",
              "9                       Stop      Run              0            Stop   \n",
              "10                  Traffic    Trucks              0           Buses   \n",
              "11                        Go      Run              0            Stop   \n",
              "12               An Accident   Clouds              0            Cars   \n",
              "13                      Stop     Stop              1            Stop   \n",
              "14                      Road  Windows              0           Trees   \n",
              "15                      Wait    Sleep              0            Stop   \n",
              "16            Traffic Lights   Trucks              0            Cars   \n",
              "17                      Stop     Stop              1            Stop   \n",
              "18               Men at work   Trucks              0          People   \n",
              "19                      Stop    Sleep              0            Stop   \n",
              "20       Stop sign fell down   Trucks              0       Stop sign   \n",
              "21                   Go left      Eat              0            Stop   \n",
              "\n",
              "    ViLT Score       LXMERT  LXMERT Score  predict_nli_ViLT  \\\n",
              "0         0.00         Cars           0.0          0.514676   \n",
              "1         0.00           Go           0.5          0.820524   \n",
              "2         1.00  Power Lines           NaN          1.000000   \n",
              "3         1.00         Stop           1.0          1.000000   \n",
              "4         0.00         Cars           0.0          0.775691   \n",
              "5         0.00          Yes           1.0          0.763509   \n",
              "6         0.00         Cars           0.0          0.745302   \n",
              "7         0.00           Go           0.0          1.000000   \n",
              "8         0.00       People           1.0          0.611355   \n",
              "9         1.00         Stop           1.0          1.000000   \n",
              "10        0.00        Buses           0.0          0.696785   \n",
              "11        0.00           Go           1.0          0.832660   \n",
              "12        0.00         Cars           0.0          0.514676   \n",
              "13        1.00         Stop           1.0          1.000000   \n",
              "14        0.00     Concrete           0.0          0.616121   \n",
              "15        0.00         Stop           0.0          0.789975   \n",
              "16        0.00         Cars           0.0          0.723074   \n",
              "17        1.00           Go           0.0          1.000000   \n",
              "18        0.75         Cars           0.0          0.595914   \n",
              "19        1.00         Stop           1.0          1.000000   \n",
              "20        0.50         Cars           0.0          0.876374   \n",
              "21        0.00         Stop           0.0          0.847737   \n",
              "\n",
              "    predict_nli_LXMERT  predict_nli_ViLBERT  \n",
              "0             0.514676             0.484044  \n",
              "1             0.745009             0.622855  \n",
              "2             0.529374             0.634766  \n",
              "3             1.000000             0.675775  \n",
              "4             0.775691             0.513588  \n",
              "5             1.000000             0.763509  \n",
              "6             0.751782             0.539068  \n",
              "7             0.832660             0.675775  \n",
              "8             0.597221             0.576276  \n",
              "9             1.000000             0.616396  \n",
              "10            0.696785             0.651422  \n",
              "11            1.000000             0.856390  \n",
              "12            0.514676             0.582656  \n",
              "13            1.000000             1.000000  \n",
              "14            0.658698             0.673753  \n",
              "15            0.789975             0.675996  \n",
              "16            0.723074             0.550447  \n",
              "17            0.832660             1.000000  \n",
              "18            0.384905             0.444250  \n",
              "19            1.000000             0.675775  \n",
              "20            0.419753             0.483921  \n",
              "21            0.847737             0.581394  "
            ],
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              "\n",
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              "    <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Questions</th>\n",
              "      <th>Ground Truth</th>\n",
              "      <th>ViLBERT</th>\n",
              "      <th>ViLBERT Score</th>\n",
              "      <th>ViLT</th>\n",
              "      <th>ViLT Score</th>\n",
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              "      <th>predict_nli_ViLBERT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dark setting</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>An Accident</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
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              "      <td>0.514676</td>\n",
              "      <td>0.484044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Slow down and go left</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.820524</td>\n",
              "      <td>0.745009</td>\n",
              "      <td>0.622855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Light Setting</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Power Lines</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.529374</td>\n",
              "      <td>0.634766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.675775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Parking</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Parking Lot</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.775691</td>\n",
              "      <td>0.775691</td>\n",
              "      <td>0.513588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Can the driver park here?</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.763509</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.763509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Road</td>\n",
              "      <td>Paint</td>\n",
              "      <td>0</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.745302</td>\n",
              "      <td>0.751782</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>0.832660</td>\n",
              "      <td>0.675775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Pedestrian Crossing</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>People crossing the road</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Buildings</td>\n",
              "      <td>0.00</td>\n",
              "      <td>People</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.611355</td>\n",
              "      <td>0.597221</td>\n",
              "      <td>0.576276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.616396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Traffic</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Buses</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Buses</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.696785</td>\n",
              "      <td>0.696785</td>\n",
              "      <td>0.651422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Go</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.832660</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.856390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Accident</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>An Accident</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.514676</td>\n",
              "      <td>0.514676</td>\n",
              "      <td>0.582656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Road</td>\n",
              "      <td>Windows</td>\n",
              "      <td>0</td>\n",
              "      <td>Trees</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Concrete</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.616121</td>\n",
              "      <td>0.658698</td>\n",
              "      <td>0.673753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Wait</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.789975</td>\n",
              "      <td>0.789975</td>\n",
              "      <td>0.675996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Roadworks</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.723074</td>\n",
              "      <td>0.723074</td>\n",
              "      <td>0.550447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.832660</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Men at work</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Men at work</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>People</td>\n",
              "      <td>0.75</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.595914</td>\n",
              "      <td>0.384905</td>\n",
              "      <td>0.444250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.675775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Fallen Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Stop sign fell down</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop sign</td>\n",
              "      <td>0.50</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.876374</td>\n",
              "      <td>0.419753</td>\n",
              "      <td>0.483921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Go left</td>\n",
              "      <td>Eat</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.847737</td>\n",
              "      <td>0.847737</td>\n",
              "      <td>0.581394</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ca385ca-93cf-48c8-87ab-1577fac0cbbb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7ca385ca-93cf-48c8-87ab-1577fac0cbbb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7ca385ca-93cf-48c8-87ab-1577fac0cbbb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8adabc7e-18b9-472f-b754-803ac79325a6')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\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-8adabc7e-18b9-472f-b754-803ac79325a6 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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def pearson_correlation(x,y):\n",
        "    mean_x = sum(x) / len(x)\n",
        "    mean_y = sum(y) / len(y)\n",
        "    cov = sum((a - mean_x) * (b - mean_y) for (a, b) in zip(x, y)) / len(x)\n",
        "\n",
        "    std_x, std_y = np.std(x), np.std(y)\n",
        "\n",
        "    p = cov / (std_x * std_y)\n",
        "\n",
        "    return float(p)"
      ],
      "metadata": {
        "id": "4NS2VcIo1BNy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Pearson Correlation for ViLT:\", pearson_correlation(df['ViLT_Score'], df['predict_nli_ViLT']))\n",
        "print(\"Pearson Correlation for LXMERT:\", pearson_correlation(df['LXMERT_Score'], df['predict_nli_LXMERT']))\n",
        "print(\"Pearson Correlation for ViLBERT:\", pearson_correlation(df['ViLBERT_Score'], df['predict_nli_ViLBERT']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iLAjpD7P1hI2",
        "outputId": "bfc3cbb9-b17d-4e8a-e72d-047159eb4ca0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pearson Correlation for ViLT: 0.6494520524780797\n",
            "Pearson Correlation for LXMERT: 0.6587834069393875\n",
            "Pearson Correlation for ViLBERT: 0.7686468101272999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2= SentenceTransformer('all-mpnet-base-v2') #This one is identified as one of the best performing model in SBERT documentation"
      ],
      "metadata": {
        "id": "28Nbc9Oi2XMx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['predict_mpnet_ViLT'] = df.apply(lambda row: pred(model2, row['Answers'],row['ViLT']), axis=1)\n",
        "\n",
        "df['predict_mpnet_LXMERT'] = df.apply(lambda row: pred(model2, row['Answers'],row['LXMERT']), axis=1)\n",
        "\n",
        "df['predict_mpnet_ViLBERT'] = df.apply(lambda row: pred(model2, row['Answers'],row['ViLBERT']), axis=1)"
      ],
      "metadata": {
        "id": "dU0oy9eL2nCD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1ePMGdpp2w9J",
        "outputId": "8dc4894f-5520-478f-f7db-f6c46e4663f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Category                            Questions  \\\n",
              "0          Dark setting  what are the contents of the image?   \n",
              "1                   NaN           what should the driver do?   \n",
              "2         Light Setting  what are the contents of the image?   \n",
              "3                   NaN           what should the driver do?   \n",
              "4               Parking  what are the contents of the image?   \n",
              "5                   NaN            Can the driver park here?   \n",
              "6             Signboard  what are the contents of the image?   \n",
              "7                   NaN           what should the driver do?   \n",
              "8   Pedestrian Crossing  what are the contents of the image?   \n",
              "9                   NaN           what should the driver do?   \n",
              "10             Traffic   what are the contents of the image?   \n",
              "11                  NaN           what should the driver do?   \n",
              "12             Accident  what are the contents of the image?   \n",
              "13                  NaN           what should the driver do?   \n",
              "14            Signboard  what are the contents of the image?   \n",
              "15                  NaN           what should the driver do?   \n",
              "16            Roadworks  what are the contents of the image?   \n",
              "17                  NaN           what should the driver do?   \n",
              "18          Men at work  what are the contents of the image?   \n",
              "19                  NaN           what should the driver do?   \n",
              "20     Fallen Signboard  what are the contents of the image?   \n",
              "21                  NaN           what should the driver do?   \n",
              "\n",
              "                     Answers  ViLBERT  ViLBERT_Score            ViLT  \\\n",
              "0                An Accident   Trucks              0            Cars   \n",
              "1      Slow down and go left      Run              0            Stop   \n",
              "2             Traffic Lights   Clouds              0  Traffic Lights   \n",
              "3                       Stop    Sleep              0            Stop   \n",
              "4                Parking Lot   Clouds              0            Cars   \n",
              "5                        Yes      No               0              No   \n",
              "6                       Road    Paint              0  Traffic Lights   \n",
              "7                       Stop    Sleep              0            Stop   \n",
              "8   People crossing the road   Clouds              0       Buildings   \n",
              "9                       Stop      Run              0            Stop   \n",
              "10                  Traffic    Trucks              0           Buses   \n",
              "11                        Go      Run              0            Stop   \n",
              "12               An Accident   Clouds              0            Cars   \n",
              "13                      Stop     Stop              1            Stop   \n",
              "14                      Road  Windows              0           Trees   \n",
              "15                      Wait    Sleep              0            Stop   \n",
              "16            Traffic Lights   Trucks              0            Cars   \n",
              "17                      Stop     Stop              1            Stop   \n",
              "18               Men at work   Trucks              0          People   \n",
              "19                      Stop    Sleep              0            Stop   \n",
              "20       Stop sign fell down   Trucks              0       Stop sign   \n",
              "21                   Go left      Eat              0            Stop   \n",
              "\n",
              "    ViLT_Score       LXMERT  LXMERT_Score  predict_nli_ViLT  \\\n",
              "0         0.00         Cars           0.0          0.514676   \n",
              "1         0.00           Go           0.5          0.820524   \n",
              "2         1.00  Power Lines           0.0          1.000000   \n",
              "3         1.00         Stop           1.0          1.000000   \n",
              "4         0.00         Cars           0.0          0.775691   \n",
              "5         0.00          Yes           1.0          0.763509   \n",
              "6         0.00         Cars           0.0          0.745302   \n",
              "7         0.00           Go           0.0          1.000000   \n",
              "8         0.00       People           1.0          0.611355   \n",
              "9         1.00         Stop           1.0          1.000000   \n",
              "10        0.00        Buses           0.0          0.696785   \n",
              "11        0.00           Go           1.0          0.832660   \n",
              "12        0.00         Cars           0.0          0.514676   \n",
              "13        1.00         Stop           1.0          1.000000   \n",
              "14        0.00     Concrete           0.0          0.616121   \n",
              "15        0.00         Stop           0.0          0.789975   \n",
              "16        0.00         Cars           0.0          0.723074   \n",
              "17        1.00           Go           0.0          1.000000   \n",
              "18        0.75         Cars           0.0          0.595914   \n",
              "19        1.00         Stop           1.0          1.000000   \n",
              "20        0.50         Cars           0.0          0.876374   \n",
              "21        0.00         Stop           0.0          0.847737   \n",
              "\n",
              "    predict_nli_LXMERT  predict_nli_ViLBERT  predict_mpnet_ViLT  \\\n",
              "0             0.514676             0.484044            0.336592   \n",
              "1             0.745009             0.622855            0.341135   \n",
              "2             0.529374             0.634766            1.000000   \n",
              "3             1.000000             0.675775            1.000000   \n",
              "4             0.775691             0.513588            0.434045   \n",
              "5             1.000000             0.763509            0.700037   \n",
              "6             0.751782             0.539068            0.385419   \n",
              "7             0.832660             0.675775            1.000000   \n",
              "8             0.597221             0.576276            0.062371   \n",
              "9             1.000000             0.616396            1.000000   \n",
              "10            0.696785             0.651422            0.501099   \n",
              "11            1.000000             0.856390            0.286398   \n",
              "12            0.514676             0.582656            0.336592   \n",
              "13            1.000000             1.000000            1.000000   \n",
              "14            0.658698             0.673753            0.312252   \n",
              "15            0.789975             0.675996            0.341400   \n",
              "16            0.723074             0.550447            0.489651   \n",
              "17            0.832660             1.000000            1.000000   \n",
              "18            0.384905             0.444250            0.234643   \n",
              "19            1.000000             0.675775            1.000000   \n",
              "20            0.419753             0.483921            0.674087   \n",
              "21            0.847737             0.581394            0.284782   \n",
              "\n",
              "    predict_mpnet_LXMERT  predict_mpnet_ViLBERT  \n",
              "0               0.336592               0.287782  \n",
              "1               0.279464               0.319637  \n",
              "2               0.350515               0.330589  \n",
              "3               1.000000               0.300823  \n",
              "4               0.434045               0.292942  \n",
              "5               1.000000               0.700037  \n",
              "6               0.476891               0.225300  \n",
              "7               0.286398               0.300823  \n",
              "8               0.304802               0.032645  \n",
              "9               1.000000               0.327743  \n",
              "10              0.501099               0.525232  \n",
              "11              1.000000               0.422791  \n",
              "12              0.336592               0.201052  \n",
              "13              1.000000               1.000000  \n",
              "14              0.342843               0.167892  \n",
              "15              0.341400               0.381238  \n",
              "16              0.489651               0.469985  \n",
              "17              0.286398               1.000000  \n",
              "18              0.133390               0.205875  \n",
              "19              1.000000               0.300823  \n",
              "20              0.180714               0.209905  \n",
              "21              0.284782               0.187861  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-02527927-37bf-40d9-a8b9-794a4eea1138\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Questions</th>\n",
              "      <th>Answers</th>\n",
              "      <th>ViLBERT</th>\n",
              "      <th>ViLBERT_Score</th>\n",
              "      <th>ViLT</th>\n",
              "      <th>ViLT_Score</th>\n",
              "      <th>LXMERT</th>\n",
              "      <th>LXMERT_Score</th>\n",
              "      <th>predict_nli_ViLT</th>\n",
              "      <th>predict_nli_LXMERT</th>\n",
              "      <th>predict_nli_ViLBERT</th>\n",
              "      <th>predict_mpnet_ViLT</th>\n",
              "      <th>predict_mpnet_LXMERT</th>\n",
              "      <th>predict_mpnet_ViLBERT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dark setting</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>An Accident</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.514676</td>\n",
              "      <td>0.514676</td>\n",
              "      <td>0.484044</td>\n",
              "      <td>0.336592</td>\n",
              "      <td>0.336592</td>\n",
              "      <td>0.287782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Slow down and go left</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.820524</td>\n",
              "      <td>0.745009</td>\n",
              "      <td>0.622855</td>\n",
              "      <td>0.341135</td>\n",
              "      <td>0.279464</td>\n",
              "      <td>0.319637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Light Setting</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Power Lines</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.529374</td>\n",
              "      <td>0.634766</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.350515</td>\n",
              "      <td>0.330589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.675775</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.300823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Parking</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Parking Lot</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.775691</td>\n",
              "      <td>0.775691</td>\n",
              "      <td>0.513588</td>\n",
              "      <td>0.434045</td>\n",
              "      <td>0.434045</td>\n",
              "      <td>0.292942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Can the driver park here?</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.763509</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.763509</td>\n",
              "      <td>0.700037</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.700037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Road</td>\n",
              "      <td>Paint</td>\n",
              "      <td>0</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.745302</td>\n",
              "      <td>0.751782</td>\n",
              "      <td>0.539068</td>\n",
              "      <td>0.385419</td>\n",
              "      <td>0.476891</td>\n",
              "      <td>0.225300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.832660</td>\n",
              "      <td>0.675775</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.286398</td>\n",
              "      <td>0.300823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Pedestrian Crossing</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>People crossing the road</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Buildings</td>\n",
              "      <td>0.00</td>\n",
              "      <td>People</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.611355</td>\n",
              "      <td>0.597221</td>\n",
              "      <td>0.576276</td>\n",
              "      <td>0.062371</td>\n",
              "      <td>0.304802</td>\n",
              "      <td>0.032645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.616396</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.327743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Traffic</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Buses</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Buses</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.696785</td>\n",
              "      <td>0.696785</td>\n",
              "      <td>0.651422</td>\n",
              "      <td>0.501099</td>\n",
              "      <td>0.501099</td>\n",
              "      <td>0.525232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Go</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.832660</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.856390</td>\n",
              "      <td>0.286398</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.422791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Accident</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>An Accident</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.514676</td>\n",
              "      <td>0.514676</td>\n",
              "      <td>0.582656</td>\n",
              "      <td>0.336592</td>\n",
              "      <td>0.336592</td>\n",
              "      <td>0.201052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Road</td>\n",
              "      <td>Windows</td>\n",
              "      <td>0</td>\n",
              "      <td>Trees</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Concrete</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.616121</td>\n",
              "      <td>0.658698</td>\n",
              "      <td>0.673753</td>\n",
              "      <td>0.312252</td>\n",
              "      <td>0.342843</td>\n",
              "      <td>0.167892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Wait</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.789975</td>\n",
              "      <td>0.789975</td>\n",
              "      <td>0.675996</td>\n",
              "      <td>0.341400</td>\n",
              "      <td>0.341400</td>\n",
              "      <td>0.381238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Roadworks</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.723074</td>\n",
              "      <td>0.723074</td>\n",
              "      <td>0.550447</td>\n",
              "      <td>0.489651</td>\n",
              "      <td>0.489651</td>\n",
              "      <td>0.469985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.832660</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.286398</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Men at work</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Men at work</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>People</td>\n",
              "      <td>0.75</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.595914</td>\n",
              "      <td>0.384905</td>\n",
              "      <td>0.444250</td>\n",
              "      <td>0.234643</td>\n",
              "      <td>0.133390</td>\n",
              "      <td>0.205875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.675775</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.300823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Fallen Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Stop sign fell down</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop sign</td>\n",
              "      <td>0.50</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.876374</td>\n",
              "      <td>0.419753</td>\n",
              "      <td>0.483921</td>\n",
              "      <td>0.674087</td>\n",
              "      <td>0.180714</td>\n",
              "      <td>0.209905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Go left</td>\n",
              "      <td>Eat</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.847737</td>\n",
              "      <td>0.847737</td>\n",
              "      <td>0.581394</td>\n",
              "      <td>0.284782</td>\n",
              "      <td>0.284782</td>\n",
              "      <td>0.187861</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02527927-37bf-40d9-a8b9-794a4eea1138')\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-02527927-37bf-40d9-a8b9-794a4eea1138 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-02527927-37bf-40d9-a8b9-794a4eea1138');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-432dc928-280d-49f9-ab4f-12046dd95bd2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-432dc928-280d-49f9-ab4f-12046dd95bd2')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-432dc928-280d-49f9-ab4f-12046dd95bd2 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": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Pearson Correlation for ViLT:\", pearson_correlation(df['ViLT_Score'], df['predict_mpnet_ViLT']))\n",
        "print(\"Pearson Correlation for LXMERT:\", pearson_correlation(df['LXMERT_Score'], df['predict_mpnet_LXMERT']))\n",
        "print(\"Pearson Correlation for ViLBERT:\", pearson_correlation(df['ViLBERT_Score'], df['predict_mpnet_ViLBERT']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b65527d-f992-4acb-a9a3-ca1bd570bc74",
        "id": "DFNtjs6H2vuk"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pearson Correlation for ViLT: 0.7446737142817109\n",
            "Pearson Correlation for LXMERT: 0.8215087753052542\n",
            "Pearson Correlation for ViLBERT: 0.8286081771849688\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Basic BERT Model"
      ],
      "metadata": {
        "id": "iITYrC7MbbbQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "word_embedding_model = models.Transformer('bert-base-uncased', max_seq_length=256)\n",
        "pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())\n",
        "dense_model = models.Dense(in_features=pooling_model.get_sentence_embedding_dimension(), out_features=256, activation_function=nn.Tanh())\n",
        "\n",
        "model3 = SentenceTransformer(modules=[word_embedding_model, pooling_model, dense_model])"
      ],
      "metadata": {
        "id": "mVZV-AeJ4c7M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
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        "outputId": "77c460c7-5784-4a63-aa92-aafabbafc4b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "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,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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        {
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    {
      "cell_type": "code",
      "source": [
        "df['bert_ViLT'] = df.apply(lambda row: pred(model3, row['Answers'],row['ViLT']), axis=1)\n",
        "\n",
        "df['bert_LXMERT'] = df.apply(lambda row: pred(model3, row['Answers'],row['LXMERT']), axis=1)\n",
        "\n",
        "df['bert_ViLBERT'] = df.apply(lambda row: pred(model3, row['Answers'],row['ViLBERT']), axis=1)"
      ],
      "metadata": {
        "id": "KMlnizGT5afe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jhh-GYzgbmY3",
        "outputId": "59ed9dd9-2cc2-4ea4-c2e1-91c4cd90f98f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Category                            Questions  \\\n",
              "0          Dark setting  what are the contents of the image?   \n",
              "1                   NaN           what should the driver do?   \n",
              "2         Light Setting  what are the contents of the image?   \n",
              "3                   NaN           what should the driver do?   \n",
              "4               Parking  what are the contents of the image?   \n",
              "5                   NaN            Can the driver park here?   \n",
              "6             Signboard  what are the contents of the image?   \n",
              "7                   NaN           what should the driver do?   \n",
              "8   Pedestrian Crossing  what are the contents of the image?   \n",
              "9                   NaN           what should the driver do?   \n",
              "10             Traffic   what are the contents of the image?   \n",
              "11                  NaN           what should the driver do?   \n",
              "12             Accident  what are the contents of the image?   \n",
              "13                  NaN           what should the driver do?   \n",
              "14            Signboard  what are the contents of the image?   \n",
              "15                  NaN           what should the driver do?   \n",
              "16            Roadworks  what are the contents of the image?   \n",
              "17                  NaN           what should the driver do?   \n",
              "18          Men at work  what are the contents of the image?   \n",
              "19                  NaN           what should the driver do?   \n",
              "20     Fallen Signboard  what are the contents of the image?   \n",
              "21                  NaN           what should the driver do?   \n",
              "\n",
              "                     Answers  ViLBERT  ViLBERT_Score            ViLT  \\\n",
              "0                An Accident   Trucks              0            Cars   \n",
              "1      Slow down and go left      Run              0            Stop   \n",
              "2             Traffic Lights   Clouds              0  Traffic Lights   \n",
              "3                       Stop    Sleep              0            Stop   \n",
              "4                Parking Lot   Clouds              0            Cars   \n",
              "5                        Yes      No               0              No   \n",
              "6                       Road    Paint              0  Traffic Lights   \n",
              "7                       Stop    Sleep              0            Stop   \n",
              "8   People crossing the road   Clouds              0       Buildings   \n",
              "9                       Stop      Run              0            Stop   \n",
              "10                  Traffic    Trucks              0           Buses   \n",
              "11                        Go      Run              0            Stop   \n",
              "12               An Accident   Clouds              0            Cars   \n",
              "13                      Stop     Stop              1            Stop   \n",
              "14                      Road  Windows              0           Trees   \n",
              "15                      Wait    Sleep              0            Stop   \n",
              "16            Traffic Lights   Trucks              0            Cars   \n",
              "17                      Stop     Stop              1            Stop   \n",
              "18               Men at work   Trucks              0          People   \n",
              "19                      Stop    Sleep              0            Stop   \n",
              "20       Stop sign fell down   Trucks              0       Stop sign   \n",
              "21                   Go left      Eat              0            Stop   \n",
              "\n",
              "    ViLT_Score       LXMERT  LXMERT_Score  predict_nli_ViLT  \\\n",
              "0         0.00         Cars           0.0          0.514676   \n",
              "1         0.00           Go           0.5          0.820524   \n",
              "2         1.00  Power Lines           0.0          1.000000   \n",
              "3         1.00         Stop           1.0          1.000000   \n",
              "4         0.00         Cars           0.0          0.775691   \n",
              "5         0.00          Yes           1.0          0.763509   \n",
              "6         0.00         Cars           0.0          0.745302   \n",
              "7         0.00           Go           0.0          1.000000   \n",
              "8         0.00       People           1.0          0.611355   \n",
              "9         1.00         Stop           1.0          1.000000   \n",
              "10        0.00        Buses           0.0          0.696785   \n",
              "11        0.00           Go           1.0          0.832660   \n",
              "12        0.00         Cars           0.0          0.514676   \n",
              "13        1.00         Stop           1.0          1.000000   \n",
              "14        0.00     Concrete           0.0          0.616121   \n",
              "15        0.00         Stop           0.0          0.789975   \n",
              "16        0.00         Cars           0.0          0.723074   \n",
              "17        1.00           Go           0.0          1.000000   \n",
              "18        0.75         Cars           0.0          0.595914   \n",
              "19        1.00         Stop           1.0          1.000000   \n",
              "20        0.50         Cars           0.0          0.876374   \n",
              "21        0.00         Stop           0.0          0.847737   \n",
              "\n",
              "    predict_nli_LXMERT  predict_nli_ViLBERT  predict_mpnet_ViLT  \\\n",
              "0             0.514676             0.484044            0.336592   \n",
              "1             0.745009             0.622855            0.341135   \n",
              "2             0.529374             0.634766            1.000000   \n",
              "3             1.000000             0.675775            1.000000   \n",
              "4             0.775691             0.513588            0.434045   \n",
              "5             1.000000             0.763509            0.700037   \n",
              "6             0.751782             0.539068            0.385419   \n",
              "7             0.832660             0.675775            1.000000   \n",
              "8             0.597221             0.576276            0.062371   \n",
              "9             1.000000             0.616396            1.000000   \n",
              "10            0.696785             0.651422            0.501099   \n",
              "11            1.000000             0.856390            0.286398   \n",
              "12            0.514676             0.582656            0.336592   \n",
              "13            1.000000             1.000000            1.000000   \n",
              "14            0.658698             0.673753            0.312252   \n",
              "15            0.789975             0.675996            0.341400   \n",
              "16            0.723074             0.550447            0.489651   \n",
              "17            0.832660             1.000000            1.000000   \n",
              "18            0.384905             0.444250            0.234643   \n",
              "19            1.000000             0.675775            1.000000   \n",
              "20            0.419753             0.483921            0.674087   \n",
              "21            0.847737             0.581394            0.284782   \n",
              "\n",
              "    predict_mpnet_LXMERT  predict_mpnet_ViLBERT  bert_ViLT  bert_LXMERT  \\\n",
              "0               0.336592               0.287782   0.741210     0.741210   \n",
              "1               0.279464               0.319637   0.452840     0.459277   \n",
              "2               0.350515               0.330589   1.000000     0.760997   \n",
              "3               1.000000               0.300823   1.000000     1.000000   \n",
              "4               0.434045               0.292942   0.642632     0.642632   \n",
              "5               1.000000               0.700037   0.850164     1.000000   \n",
              "6               0.476891               0.225300   0.628178     0.796715   \n",
              "7               0.286398               0.300823   1.000000     0.887554   \n",
              "8               0.304802               0.032645   0.626494     0.575549   \n",
              "9               1.000000               0.327743   1.000000     1.000000   \n",
              "10              0.501099               0.525232   0.814486     0.814486   \n",
              "11              1.000000               0.422791   0.887554     1.000000   \n",
              "12              0.336592               0.201052   0.741210     0.741210   \n",
              "13              1.000000               1.000000   1.000000     1.000000   \n",
              "14              0.342843               0.167892   0.751093     0.778693   \n",
              "15              0.341400               0.381238   0.826688     0.826688   \n",
              "16              0.489651               0.469985   0.680257     0.680257   \n",
              "17              0.286398               1.000000   1.000000     0.887554   \n",
              "18              0.133390               0.205875   0.558173     0.557773   \n",
              "19              1.000000               0.300823   1.000000     1.000000   \n",
              "20              0.180714               0.209905   0.812648     0.570165   \n",
              "21              0.284782               0.187861   0.714927     0.714927   \n",
              "\n",
              "    bert_ViLBERT  \n",
              "0       0.735952  \n",
              "1       0.531457  \n",
              "2       0.655872  \n",
              "3       0.722997  \n",
              "4       0.617960  \n",
              "5       0.850164  \n",
              "6       0.765733  \n",
              "7       0.722997  \n",
              "8       0.590055  \n",
              "9       0.813360  \n",
              "10      0.823054  \n",
              "11      0.835080  \n",
              "12      0.740120  \n",
              "13      1.000000  \n",
              "14      0.784598  \n",
              "15      0.627249  \n",
              "16      0.740977  \n",
              "17      1.000000  \n",
              "18      0.614119  \n",
              "19      0.722997  \n",
              "20      0.625890  \n",
              "21      0.700268  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ad3c3ab7-e33b-4923-adff-2697f126029f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Questions</th>\n",
              "      <th>Answers</th>\n",
              "      <th>ViLBERT</th>\n",
              "      <th>ViLBERT_Score</th>\n",
              "      <th>ViLT</th>\n",
              "      <th>ViLT_Score</th>\n",
              "      <th>LXMERT</th>\n",
              "      <th>LXMERT_Score</th>\n",
              "      <th>predict_nli_ViLT</th>\n",
              "      <th>predict_nli_LXMERT</th>\n",
              "      <th>predict_nli_ViLBERT</th>\n",
              "      <th>predict_mpnet_ViLT</th>\n",
              "      <th>predict_mpnet_LXMERT</th>\n",
              "      <th>predict_mpnet_ViLBERT</th>\n",
              "      <th>bert_ViLT</th>\n",
              "      <th>bert_LXMERT</th>\n",
              "      <th>bert_ViLBERT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dark setting</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>An Accident</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.514676</td>\n",
              "      <td>0.514676</td>\n",
              "      <td>0.484044</td>\n",
              "      <td>0.336592</td>\n",
              "      <td>0.336592</td>\n",
              "      <td>0.287782</td>\n",
              "      <td>0.741210</td>\n",
              "      <td>0.741210</td>\n",
              "      <td>0.735952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Slow down and go left</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.820524</td>\n",
              "      <td>0.745009</td>\n",
              "      <td>0.622855</td>\n",
              "      <td>0.341135</td>\n",
              "      <td>0.279464</td>\n",
              "      <td>0.319637</td>\n",
              "      <td>0.452840</td>\n",
              "      <td>0.459277</td>\n",
              "      <td>0.531457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Light Setting</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Power Lines</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.529374</td>\n",
              "      <td>0.634766</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.350515</td>\n",
              "      <td>0.330589</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.760997</td>\n",
              "      <td>0.655872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.675775</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.300823</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.722997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Parking</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Parking Lot</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.775691</td>\n",
              "      <td>0.775691</td>\n",
              "      <td>0.513588</td>\n",
              "      <td>0.434045</td>\n",
              "      <td>0.434045</td>\n",
              "      <td>0.292942</td>\n",
              "      <td>0.642632</td>\n",
              "      <td>0.642632</td>\n",
              "      <td>0.617960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Can the driver park here?</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.763509</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.763509</td>\n",
              "      <td>0.700037</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.700037</td>\n",
              "      <td>0.850164</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.850164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Road</td>\n",
              "      <td>Paint</td>\n",
              "      <td>0</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.745302</td>\n",
              "      <td>0.751782</td>\n",
              "      <td>0.539068</td>\n",
              "      <td>0.385419</td>\n",
              "      <td>0.476891</td>\n",
              "      <td>0.225300</td>\n",
              "      <td>0.628178</td>\n",
              "      <td>0.796715</td>\n",
              "      <td>0.765733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.832660</td>\n",
              "      <td>0.675775</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.286398</td>\n",
              "      <td>0.300823</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.887554</td>\n",
              "      <td>0.722997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Pedestrian Crossing</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>People crossing the road</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Buildings</td>\n",
              "      <td>0.00</td>\n",
              "      <td>People</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.611355</td>\n",
              "      <td>0.597221</td>\n",
              "      <td>0.576276</td>\n",
              "      <td>0.062371</td>\n",
              "      <td>0.304802</td>\n",
              "      <td>0.032645</td>\n",
              "      <td>0.626494</td>\n",
              "      <td>0.575549</td>\n",
              "      <td>0.590055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.616396</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.327743</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.813360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Traffic</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Buses</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Buses</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.696785</td>\n",
              "      <td>0.696785</td>\n",
              "      <td>0.651422</td>\n",
              "      <td>0.501099</td>\n",
              "      <td>0.501099</td>\n",
              "      <td>0.525232</td>\n",
              "      <td>0.814486</td>\n",
              "      <td>0.814486</td>\n",
              "      <td>0.823054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Go</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.832660</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.856390</td>\n",
              "      <td>0.286398</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.422791</td>\n",
              "      <td>0.887554</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.835080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Accident</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>An Accident</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.514676</td>\n",
              "      <td>0.514676</td>\n",
              "      <td>0.582656</td>\n",
              "      <td>0.336592</td>\n",
              "      <td>0.336592</td>\n",
              "      <td>0.201052</td>\n",
              "      <td>0.741210</td>\n",
              "      <td>0.741210</td>\n",
              "      <td>0.740120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Road</td>\n",
              "      <td>Windows</td>\n",
              "      <td>0</td>\n",
              "      <td>Trees</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Concrete</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.616121</td>\n",
              "      <td>0.658698</td>\n",
              "      <td>0.673753</td>\n",
              "      <td>0.312252</td>\n",
              "      <td>0.342843</td>\n",
              "      <td>0.167892</td>\n",
              "      <td>0.751093</td>\n",
              "      <td>0.778693</td>\n",
              "      <td>0.784598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Wait</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.789975</td>\n",
              "      <td>0.789975</td>\n",
              "      <td>0.675996</td>\n",
              "      <td>0.341400</td>\n",
              "      <td>0.341400</td>\n",
              "      <td>0.381238</td>\n",
              "      <td>0.826688</td>\n",
              "      <td>0.826688</td>\n",
              "      <td>0.627249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Roadworks</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.723074</td>\n",
              "      <td>0.723074</td>\n",
              "      <td>0.550447</td>\n",
              "      <td>0.489651</td>\n",
              "      <td>0.489651</td>\n",
              "      <td>0.469985</td>\n",
              "      <td>0.680257</td>\n",
              "      <td>0.680257</td>\n",
              "      <td>0.740977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.832660</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.286398</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.887554</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Men at work</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Men at work</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>People</td>\n",
              "      <td>0.75</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.595914</td>\n",
              "      <td>0.384905</td>\n",
              "      <td>0.444250</td>\n",
              "      <td>0.234643</td>\n",
              "      <td>0.133390</td>\n",
              "      <td>0.205875</td>\n",
              "      <td>0.558173</td>\n",
              "      <td>0.557773</td>\n",
              "      <td>0.614119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.675775</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.300823</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.722997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Fallen Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Stop sign fell down</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop sign</td>\n",
              "      <td>0.50</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.876374</td>\n",
              "      <td>0.419753</td>\n",
              "      <td>0.483921</td>\n",
              "      <td>0.674087</td>\n",
              "      <td>0.180714</td>\n",
              "      <td>0.209905</td>\n",
              "      <td>0.812648</td>\n",
              "      <td>0.570165</td>\n",
              "      <td>0.625890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Go left</td>\n",
              "      <td>Eat</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.847737</td>\n",
              "      <td>0.847737</td>\n",
              "      <td>0.581394</td>\n",
              "      <td>0.284782</td>\n",
              "      <td>0.284782</td>\n",
              "      <td>0.187861</td>\n",
              "      <td>0.714927</td>\n",
              "      <td>0.714927</td>\n",
              "      <td>0.700268</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Pearson Correlation for ViLT:\", pearson_correlation(df['ViLT_Score'], df['bert_ViLT']))\n",
        "print(\"Pearson Correlation for LXMERT:\", pearson_correlation(df['LXMERT_Score'], df['bert_LXMERT']))\n",
        "print(\"Pearson Correlation for ViLBERT:\", pearson_correlation(df['ViLBERT_Score'], df['bert_ViLBERT']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "131c19a9-7faf-40e6-cece-952a4986174b",
        "id": "4IJSoFNP3LVJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pearson Correlation for ViLT: 0.6109431917958806\n",
            "Pearson Correlation for LXMERT: 0.5183189020190019\n",
            "Pearson Correlation for ViLBERT: 0.7076686711437513\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##GPT2 Model -- GPT3 is not freely available"
      ],
      "metadata": {
        "id": "Qjl_EhOx-JGN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDPwEDuE-WjW",
        "outputId": "770f4097-1f40-4883-a3a2-01e0800944a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting openai\n",
            "  Downloading openai-0.28.1-py3-none-any.whl (76 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/77.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.0/77.0 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai) (4.66.1)\n",
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            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.3.1)\n",
            "Installing collected packages: openai\n",
            "Successfully installed openai-0.28.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import pandas as pd\n",
        "from transformers import GPT2Tokenizer, GPT2Model\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "# Load the pre-trained GPT-2 model and tokenizer\n",
        "model_name = 'gpt2'\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
        "model = GPT2Model.from_pretrained(model_name)\n",
        "\n",
        "\n",
        "# Generate embeddings for the text pairs in the DataFrame\n",
        "embeddings_list = []\n",
        "\n",
        "for _, row in df.iterrows():\n",
        "    text1 = row['Answers']\n",
        "    text2 = row['ViLT']\n",
        "\n",
        "    # Tokenize and encode the texts using the GPT-2 tokenizer\n",
        "    inputs1 = tokenizer(text1, return_tensors='pt', padding=False, truncation=True)\n",
        "    inputs2 = tokenizer(text2, return_tensors='pt', padding=False, truncation=True)\n",
        "\n",
        "    # Generate embeddings for the texts using GPT-2\n",
        "    with torch.no_grad():\n",
        "        embeddings1 = model(**inputs1).last_hidden_state.mean(dim=1)\n",
        "        embeddings2 = model(**inputs2).last_hidden_state.mean(dim=1)\n",
        "\n",
        "    embeddings_list.append((embeddings1, embeddings2))\n",
        "\n",
        "# Add embeddings to the DataFrame\n",
        "df['embeddings1'], df['embeddings2'] = zip(*embeddings_list)\n",
        "\n",
        "# Calculate cosine similarity between the embeddings and store it in a new column\n",
        "cosine_similarities = []\n",
        "\n",
        "for _, row in df.iterrows():\n",
        "    similarity = cosine_similarity(row['embeddings1'].numpy(), row['embeddings2'].numpy())\n",
        "    cosine_similarities.append(similarity[0][0])\n",
        "\n",
        "df.drop(columns=['embeddings1', 'embeddings2'], inplace=True)\n",
        "\n",
        "df['gpt2_ViLT'] = cosine_similarities\n",
        "\n",
        "# The DataFrame now contains text embeddings and cosine similarity values\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "5Cb81ojp_R8t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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          ]
        },
        "outputId": "330dcbb0-b020-45da-a4d6-8cac8d6c83c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f23c569217448a384cd05c0acab044b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d807461edc94d848a4840e8ac712592"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings_list1 = []\n",
        "\n",
        "for _, row in df.iterrows():\n",
        "    text1 = row['Answers']\n",
        "    text2 = row['LXMERT']\n",
        "\n",
        "    # Tokenize and encode the texts using the GPT-2 tokenizer\n",
        "    inputs1 = tokenizer(text1, return_tensors='pt', padding=False, truncation=True)\n",
        "    inputs2 = tokenizer(text2, return_tensors='pt', padding=False, truncation=True)\n",
        "\n",
        "    # Generate embeddings for the texts using GPT-2\n",
        "    with torch.no_grad():\n",
        "        embeddings1 = model(**inputs1).last_hidden_state.mean(dim=1)\n",
        "        embeddings2 = model(**inputs2).last_hidden_state.mean(dim=1)\n",
        "\n",
        "    embeddings_list1.append((embeddings1, embeddings2))\n",
        "\n",
        "# Add embeddings to the DataFrame\n",
        "df['embeddings1'], df['embeddings2'] = zip(*embeddings_list1)\n",
        "\n",
        "# Calculate cosine similarity between the embeddings and store it in a new column\n",
        "cosine_similarities1 = []\n",
        "\n",
        "for _, row in df.iterrows():\n",
        "    similarity = cosine_similarity(row['embeddings1'].numpy(), row['embeddings2'].numpy())\n",
        "    cosine_similarities1.append(similarity[0][0])\n",
        "\n",
        "df.drop(columns=['embeddings1', 'embeddings2'], inplace=True)\n",
        "\n",
        "df['gpt2_LXMERT'] = cosine_similarities1\n",
        "\n",
        "# The DataFrame now contains text embeddings and cosine similarity values\n"
      ],
      "metadata": {
        "id": "6y0-SesIK-YV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings_list2 = []\n",
        "\n",
        "for _, row in df.iterrows():\n",
        "    text1 = row['Answers']\n",
        "    text2 = row['ViLBERT']\n",
        "\n",
        "    # Tokenize and encode the texts using the GPT-2 tokenizer\n",
        "    inputs1 = tokenizer(text1, return_tensors='pt', padding=False, truncation=True)\n",
        "    inputs2 = tokenizer(text2, return_tensors='pt', padding=False, truncation=True)\n",
        "\n",
        "    # Generate embeddings for the texts using GPT-2\n",
        "    with torch.no_grad():\n",
        "        embeddings1 = model(**inputs1).last_hidden_state.mean(dim=1)\n",
        "        embeddings2 = model(**inputs2).last_hidden_state.mean(dim=1)\n",
        "\n",
        "    embeddings_list2.append((embeddings1, embeddings2))\n",
        "\n",
        "# Add embeddings to the DataFrame\n",
        "df['embeddings1'], df['embeddings2'] = zip(*embeddings_list2)\n",
        "\n",
        "# Calculate cosine similarity between the embeddings and store it in a new column\n",
        "cosine_similarities2 = []\n",
        "\n",
        "for _, row in df.iterrows():\n",
        "    similarity = cosine_similarity(row['embeddings1'].numpy(), row['embeddings2'].numpy())\n",
        "    cosine_similarities2.append(similarity[0][0])\n",
        "\n",
        "df.drop(columns=['embeddings1', 'embeddings2'], inplace=True)\n",
        "\n",
        "df['gpt2_ViLBERT'] = cosine_similarities2\n"
      ],
      "metadata": {
        "id": "73AcoZ5NnICN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "x0ruWWQLIwVx",
        "outputId": "29ba61a1-7f59-4658-e0a4-a3c7ac769e77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Category                            Questions  \\\n",
              "0          Dark setting  what are the contents of the image?   \n",
              "1                   NaN           what should the driver do?   \n",
              "2         Light Setting  what are the contents of the image?   \n",
              "3                   NaN           what should the driver do?   \n",
              "4               Parking  what are the contents of the image?   \n",
              "5                   NaN            Can the driver park here?   \n",
              "6             Signboard  what are the contents of the image?   \n",
              "7                   NaN           what should the driver do?   \n",
              "8   Pedestrian Crossing  what are the contents of the image?   \n",
              "9                   NaN           what should the driver do?   \n",
              "10             Traffic   what are the contents of the image?   \n",
              "11                  NaN           what should the driver do?   \n",
              "12             Accident  what are the contents of the image?   \n",
              "13                  NaN           what should the driver do?   \n",
              "14            Signboard  what are the contents of the image?   \n",
              "15                  NaN           what should the driver do?   \n",
              "16            Roadworks  what are the contents of the image?   \n",
              "17                  NaN           what should the driver do?   \n",
              "18          Men at work  what are the contents of the image?   \n",
              "19                  NaN           what should the driver do?   \n",
              "20     Fallen Signboard  what are the contents of the image?   \n",
              "21                  NaN           what should the driver do?   \n",
              "\n",
              "                     Answers  ViLBERT  ViLBERT_Score            ViLT  \\\n",
              "0                An Accident   Trucks              0            Cars   \n",
              "1      Slow down and go left      Run              0            Stop   \n",
              "2             Traffic Lights   Clouds              0  Traffic Lights   \n",
              "3                       Stop    Sleep              0            Stop   \n",
              "4                Parking Lot   Clouds              0            Cars   \n",
              "5                        Yes      No               0              No   \n",
              "6                       Road    Paint              0  Traffic Lights   \n",
              "7                       Stop    Sleep              0            Stop   \n",
              "8   People crossing the road   Clouds              0       Buildings   \n",
              "9                       Stop      Run              0            Stop   \n",
              "10                  Traffic    Trucks              0           Buses   \n",
              "11                        Go      Run              0            Stop   \n",
              "12               An Accident   Clouds              0            Cars   \n",
              "13                      Stop     Stop              1            Stop   \n",
              "14                      Road  Windows              0           Trees   \n",
              "15                      Wait    Sleep              0            Stop   \n",
              "16            Traffic Lights   Trucks              0            Cars   \n",
              "17                      Stop     Stop              1            Stop   \n",
              "18               Men at work   Trucks              0          People   \n",
              "19                      Stop    Sleep              0            Stop   \n",
              "20       Stop sign fell down   Trucks              0       Stop sign   \n",
              "21                   Go left      Eat              0            Stop   \n",
              "\n",
              "    ViLT_Score       LXMERT  LXMERT_Score  predict_nli_ViLT  ...  \\\n",
              "0         0.00         Cars           0.0          0.514676  ...   \n",
              "1         0.00           Go           0.5          0.820524  ...   \n",
              "2         1.00  Power Lines           0.0          1.000000  ...   \n",
              "3         1.00         Stop           1.0          1.000000  ...   \n",
              "4         0.00         Cars           0.0          0.775691  ...   \n",
              "5         0.00          Yes           1.0          0.763509  ...   \n",
              "6         0.00         Cars           0.0          0.745302  ...   \n",
              "7         0.00           Go           0.0          1.000000  ...   \n",
              "8         0.00       People           1.0          0.611355  ...   \n",
              "9         1.00         Stop           1.0          1.000000  ...   \n",
              "10        0.00        Buses           0.0          0.696785  ...   \n",
              "11        0.00           Go           1.0          0.832660  ...   \n",
              "12        0.00         Cars           0.0          0.514676  ...   \n",
              "13        1.00         Stop           1.0          1.000000  ...   \n",
              "14        0.00     Concrete           0.0          0.616121  ...   \n",
              "15        0.00         Stop           0.0          0.789975  ...   \n",
              "16        0.00         Cars           0.0          0.723074  ...   \n",
              "17        1.00           Go           0.0          1.000000  ...   \n",
              "18        0.75         Cars           0.0          0.595914  ...   \n",
              "19        1.00         Stop           1.0          1.000000  ...   \n",
              "20        0.50         Cars           0.0          0.876374  ...   \n",
              "21        0.00         Stop           0.0          0.847737  ...   \n",
              "\n",
              "    predict_nli_ViLBERT  predict_mpnet_ViLT  predict_mpnet_LXMERT  \\\n",
              "0              0.484044            0.336592              0.336592   \n",
              "1              0.622855            0.341135              0.279464   \n",
              "2              0.634766            1.000000              0.350515   \n",
              "3              0.675775            1.000000              1.000000   \n",
              "4              0.513588            0.434045              0.434045   \n",
              "5              0.763509            0.700037              1.000000   \n",
              "6              0.539068            0.385419              0.476891   \n",
              "7              0.675775            1.000000              0.286398   \n",
              "8              0.576276            0.062371              0.304802   \n",
              "9              0.616396            1.000000              1.000000   \n",
              "10             0.651422            0.501099              0.501099   \n",
              "11             0.856390            0.286398              1.000000   \n",
              "12             0.582656            0.336592              0.336592   \n",
              "13             1.000000            1.000000              1.000000   \n",
              "14             0.673753            0.312252              0.342843   \n",
              "15             0.675996            0.341400              0.341400   \n",
              "16             0.550447            0.489651              0.489651   \n",
              "17             1.000000            1.000000              0.286398   \n",
              "18             0.444250            0.234643              0.133390   \n",
              "19             0.675775            1.000000              1.000000   \n",
              "20             0.483921            0.674087              0.180714   \n",
              "21             0.581394            0.284782              0.284782   \n",
              "\n",
              "    predict_mpnet_ViLBERT  bert_ViLT  bert_LXMERT  bert_ViLBERT  gpt2_ViLT  \\\n",
              "0                0.287782   0.741210     0.741210      0.735952   0.996490   \n",
              "1                0.319637   0.452840     0.459277      0.531457   0.968816   \n",
              "2                0.330589   1.000000     0.760997      0.655872   1.000000   \n",
              "3                0.300823   1.000000     1.000000      0.722997   1.000000   \n",
              "4                0.292942   0.642632     0.642632      0.617960   0.990138   \n",
              "5                0.700037   0.850164     1.000000      0.850164   0.992837   \n",
              "6                0.225300   0.628178     0.796715      0.765733   0.985569   \n",
              "7                0.300823   1.000000     0.887554      0.722997   1.000000   \n",
              "8                0.032645   0.626494     0.575549      0.590055   0.981600   \n",
              "9                0.327743   1.000000     1.000000      0.813360   1.000000   \n",
              "10               0.525232   0.814486     0.814486      0.823054   0.996634   \n",
              "11               0.422791   0.887554     1.000000      0.835080   0.993942   \n",
              "12               0.201052   0.741210     0.741210      0.740120   0.996490   \n",
              "13               1.000000   1.000000     1.000000      1.000000   1.000000   \n",
              "14               0.167892   0.751093     0.778693      0.784598   0.990597   \n",
              "15               0.381238   0.826688     0.826688      0.627249   0.996701   \n",
              "16               0.469985   0.680257     0.680257      0.740977   0.994302   \n",
              "17               1.000000   1.000000     0.887554      1.000000   1.000000   \n",
              "18               0.205875   0.558173     0.557773      0.614119   0.975645   \n",
              "19               0.300823   1.000000     1.000000      0.722997   1.000000   \n",
              "20               0.209905   0.812648     0.570165      0.625890   0.995909   \n",
              "21               0.187861   0.714927     0.714927      0.700268   0.983989   \n",
              "\n",
              "    gpt2_LXMERT  gpt2_ViLBERT  \n",
              "0      0.996490      0.994659  \n",
              "1      0.960553      0.965148  \n",
              "2      0.998134      0.996017  \n",
              "3      1.000000      0.994700  \n",
              "4      0.990138      0.994135  \n",
              "5      1.000000      0.951846  \n",
              "6      0.990855      0.985829  \n",
              "7      0.993942      0.994700  \n",
              "8      0.979405      0.979031  \n",
              "9      1.000000      0.996244  \n",
              "10     0.996634      0.995525  \n",
              "11     1.000000      0.998844  \n",
              "12     0.996490      0.996639  \n",
              "13     1.000000      1.000000  \n",
              "14     0.981507      0.995274  \n",
              "15     0.996701      0.998095  \n",
              "16     0.994302      0.997197  \n",
              "17     0.993942      1.000000  \n",
              "18     0.980002      0.988780  \n",
              "19     1.000000      0.994700  \n",
              "20     0.993313      0.995085  \n",
              "21     0.983989      0.985305  \n",
              "\n",
              "[22 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c6c67ca-c9a3-4fd5-aa9e-010664ccaecb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Questions</th>\n",
              "      <th>Answers</th>\n",
              "      <th>ViLBERT</th>\n",
              "      <th>ViLBERT_Score</th>\n",
              "      <th>ViLT</th>\n",
              "      <th>ViLT_Score</th>\n",
              "      <th>LXMERT</th>\n",
              "      <th>LXMERT_Score</th>\n",
              "      <th>predict_nli_ViLT</th>\n",
              "      <th>...</th>\n",
              "      <th>predict_nli_ViLBERT</th>\n",
              "      <th>predict_mpnet_ViLT</th>\n",
              "      <th>predict_mpnet_LXMERT</th>\n",
              "      <th>predict_mpnet_ViLBERT</th>\n",
              "      <th>bert_ViLT</th>\n",
              "      <th>bert_LXMERT</th>\n",
              "      <th>bert_ViLBERT</th>\n",
              "      <th>gpt2_ViLT</th>\n",
              "      <th>gpt2_LXMERT</th>\n",
              "      <th>gpt2_ViLBERT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dark setting</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>An Accident</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.514676</td>\n",
              "      <td>...</td>\n",
              "      <td>0.484044</td>\n",
              "      <td>0.336592</td>\n",
              "      <td>0.336592</td>\n",
              "      <td>0.287782</td>\n",
              "      <td>0.741210</td>\n",
              "      <td>0.741210</td>\n",
              "      <td>0.735952</td>\n",
              "      <td>0.996490</td>\n",
              "      <td>0.996490</td>\n",
              "      <td>0.994659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Slow down and go left</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.820524</td>\n",
              "      <td>...</td>\n",
              "      <td>0.622855</td>\n",
              "      <td>0.341135</td>\n",
              "      <td>0.279464</td>\n",
              "      <td>0.319637</td>\n",
              "      <td>0.452840</td>\n",
              "      <td>0.459277</td>\n",
              "      <td>0.531457</td>\n",
              "      <td>0.968816</td>\n",
              "      <td>0.960553</td>\n",
              "      <td>0.965148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Light Setting</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Power Lines</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.634766</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.350515</td>\n",
              "      <td>0.330589</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.760997</td>\n",
              "      <td>0.655872</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.998134</td>\n",
              "      <td>0.996017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.675775</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.300823</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.722997</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.994700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Parking</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Parking Lot</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.775691</td>\n",
              "      <td>...</td>\n",
              "      <td>0.513588</td>\n",
              "      <td>0.434045</td>\n",
              "      <td>0.434045</td>\n",
              "      <td>0.292942</td>\n",
              "      <td>0.642632</td>\n",
              "      <td>0.642632</td>\n",
              "      <td>0.617960</td>\n",
              "      <td>0.990138</td>\n",
              "      <td>0.990138</td>\n",
              "      <td>0.994135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Can the driver park here?</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.763509</td>\n",
              "      <td>...</td>\n",
              "      <td>0.763509</td>\n",
              "      <td>0.700037</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.700037</td>\n",
              "      <td>0.850164</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.850164</td>\n",
              "      <td>0.992837</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.951846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Road</td>\n",
              "      <td>Paint</td>\n",
              "      <td>0</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.745302</td>\n",
              "      <td>...</td>\n",
              "      <td>0.539068</td>\n",
              "      <td>0.385419</td>\n",
              "      <td>0.476891</td>\n",
              "      <td>0.225300</td>\n",
              "      <td>0.628178</td>\n",
              "      <td>0.796715</td>\n",
              "      <td>0.765733</td>\n",
              "      <td>0.985569</td>\n",
              "      <td>0.990855</td>\n",
              "      <td>0.985829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.675775</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.286398</td>\n",
              "      <td>0.300823</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.887554</td>\n",
              "      <td>0.722997</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.993942</td>\n",
              "      <td>0.994700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Pedestrian Crossing</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>People crossing the road</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Buildings</td>\n",
              "      <td>0.00</td>\n",
              "      <td>People</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.611355</td>\n",
              "      <td>...</td>\n",
              "      <td>0.576276</td>\n",
              "      <td>0.062371</td>\n",
              "      <td>0.304802</td>\n",
              "      <td>0.032645</td>\n",
              "      <td>0.626494</td>\n",
              "      <td>0.575549</td>\n",
              "      <td>0.590055</td>\n",
              "      <td>0.981600</td>\n",
              "      <td>0.979405</td>\n",
              "      <td>0.979031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.616396</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.327743</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.813360</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.996244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Traffic</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Buses</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Buses</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.696785</td>\n",
              "      <td>...</td>\n",
              "      <td>0.651422</td>\n",
              "      <td>0.501099</td>\n",
              "      <td>0.501099</td>\n",
              "      <td>0.525232</td>\n",
              "      <td>0.814486</td>\n",
              "      <td>0.814486</td>\n",
              "      <td>0.823054</td>\n",
              "      <td>0.996634</td>\n",
              "      <td>0.996634</td>\n",
              "      <td>0.995525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Go</td>\n",
              "      <td>Run</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.832660</td>\n",
              "      <td>...</td>\n",
              "      <td>0.856390</td>\n",
              "      <td>0.286398</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.422791</td>\n",
              "      <td>0.887554</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.835080</td>\n",
              "      <td>0.993942</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.998844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Accident</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>An Accident</td>\n",
              "      <td>Clouds</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.514676</td>\n",
              "      <td>...</td>\n",
              "      <td>0.582656</td>\n",
              "      <td>0.336592</td>\n",
              "      <td>0.336592</td>\n",
              "      <td>0.201052</td>\n",
              "      <td>0.741210</td>\n",
              "      <td>0.741210</td>\n",
              "      <td>0.740120</td>\n",
              "      <td>0.996490</td>\n",
              "      <td>0.996490</td>\n",
              "      <td>0.996639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Road</td>\n",
              "      <td>Windows</td>\n",
              "      <td>0</td>\n",
              "      <td>Trees</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Concrete</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.616121</td>\n",
              "      <td>...</td>\n",
              "      <td>0.673753</td>\n",
              "      <td>0.312252</td>\n",
              "      <td>0.342843</td>\n",
              "      <td>0.167892</td>\n",
              "      <td>0.751093</td>\n",
              "      <td>0.778693</td>\n",
              "      <td>0.784598</td>\n",
              "      <td>0.990597</td>\n",
              "      <td>0.981507</td>\n",
              "      <td>0.995274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Wait</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.789975</td>\n",
              "      <td>...</td>\n",
              "      <td>0.675996</td>\n",
              "      <td>0.341400</td>\n",
              "      <td>0.341400</td>\n",
              "      <td>0.381238</td>\n",
              "      <td>0.826688</td>\n",
              "      <td>0.826688</td>\n",
              "      <td>0.627249</td>\n",
              "      <td>0.996701</td>\n",
              "      <td>0.996701</td>\n",
              "      <td>0.998095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Roadworks</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Traffic Lights</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.723074</td>\n",
              "      <td>...</td>\n",
              "      <td>0.550447</td>\n",
              "      <td>0.489651</td>\n",
              "      <td>0.489651</td>\n",
              "      <td>0.469985</td>\n",
              "      <td>0.680257</td>\n",
              "      <td>0.680257</td>\n",
              "      <td>0.740977</td>\n",
              "      <td>0.994302</td>\n",
              "      <td>0.994302</td>\n",
              "      <td>0.997197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Go</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.286398</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.887554</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.993942</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Men at work</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Men at work</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>People</td>\n",
              "      <td>0.75</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.595914</td>\n",
              "      <td>...</td>\n",
              "      <td>0.444250</td>\n",
              "      <td>0.234643</td>\n",
              "      <td>0.133390</td>\n",
              "      <td>0.205875</td>\n",
              "      <td>0.558173</td>\n",
              "      <td>0.557773</td>\n",
              "      <td>0.614119</td>\n",
              "      <td>0.975645</td>\n",
              "      <td>0.980002</td>\n",
              "      <td>0.988780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Stop</td>\n",
              "      <td>Sleep</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.675775</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.300823</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.722997</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.994700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Fallen Signboard</td>\n",
              "      <td>what are the contents of the image?</td>\n",
              "      <td>Stop sign fell down</td>\n",
              "      <td>Trucks</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop sign</td>\n",
              "      <td>0.50</td>\n",
              "      <td>Cars</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.876374</td>\n",
              "      <td>...</td>\n",
              "      <td>0.483921</td>\n",
              "      <td>0.674087</td>\n",
              "      <td>0.180714</td>\n",
              "      <td>0.209905</td>\n",
              "      <td>0.812648</td>\n",
              "      <td>0.570165</td>\n",
              "      <td>0.625890</td>\n",
              "      <td>0.995909</td>\n",
              "      <td>0.993313</td>\n",
              "      <td>0.995085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>what should the driver do?</td>\n",
              "      <td>Go left</td>\n",
              "      <td>Eat</td>\n",
              "      <td>0</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Stop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.847737</td>\n",
              "      <td>...</td>\n",
              "      <td>0.581394</td>\n",
              "      <td>0.284782</td>\n",
              "      <td>0.284782</td>\n",
              "      <td>0.187861</td>\n",
              "      <td>0.714927</td>\n",
              "      <td>0.714927</td>\n",
              "      <td>0.700268</td>\n",
              "      <td>0.983989</td>\n",
              "      <td>0.983989</td>\n",
              "      <td>0.985305</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c6c67ca-c9a3-4fd5-aa9e-010664ccaecb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8c6c67ca-c9a3-4fd5-aa9e-010664ccaecb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8c6c67ca-c9a3-4fd5-aa9e-010664ccaecb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-11f2a15f-768d-4403-a23e-3ab6d0f8d746\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-11f2a15f-768d-4403-a23e-3ab6d0f8d746')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-11f2a15f-768d-4403-a23e-3ab6d0f8d746 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": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Pearson Correlation for ViLT:\", pearson_correlation(df['ViLT_Score'], df['gpt2_ViLT']))\n",
        "print(\"Pearson Correlation for LXMERT:\", pearson_correlation(df['LXMERT_Score'], df['gpt2_LXMERT']))\n",
        "print(\"Pearson Correlation for ViLBERT:\", pearson_correlation(df['ViLBERT_Score'], df['gpt2_ViLBERT']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02533acf-dad5-40c0-c9a4-ee29f4ce6f29",
        "id": "zfChzws13xVh"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pearson Correlation for ViLT: 0.39034178436647293\n",
            "Pearson Correlation for LXMERT: 0.19518511712539008\n",
            "Pearson Correlation for ViLBERT: 0.2521560136791964\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mean Absolute Error"
      ],
      "metadata": {
        "id": "OCezieqw4JjF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error"
      ],
      "metadata": {
        "id": "mJtmdxPs4OJv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\" For NLI Model \")\n",
        "print(\"MAE for ViLT:\", mean_absolute_error(df['ViLT_Score'],df['predict_nli_ViLT']))\n",
        "print(\"MAE for LXMERT:\", mean_absolute_error(df['ViLT_Score'],df['predict_nli_LXMERT']))\n",
        "print(\"MAE for ViLBERT:\", mean_absolute_error(df['ViLBERT_Score'],df['predict_nli_ViLBERT']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vYTjoFgx4Z6w",
        "outputId": "81d69825-0e0e-4fa3-c1da-e710fcc4be54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " For NLI Model \n",
            "MAE for ViLT: 0.49011569944295014\n",
            "MAE for LXMERT: 0.524149635976011\n",
            "MAE for ViLBERT: 0.558093400164084\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\" For mpnet Model\")\n",
        "print(\"MAE for ViLT:\", mean_absolute_error(df['ViLT_Score'],df['predict_mpnet_ViLT']))\n",
        "print(\"MAE for LXMERT:\", mean_absolute_error(df['ViLT_Score'],df['predict_mpnet_LXMERT']))\n",
        "print(\"MAE for ViLBERT:\", mean_absolute_error(df['ViLBERT_Score'],df['predict_mpnet_ViLBERT']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7iGMWvSF6kvR",
        "outputId": "40e71da5-f9b1-46f7-c587-217b5a1d7373"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " For mpnet Model\n",
            "MAE for ViLT: 0.2955099100416357\n",
            "MAE for LXMERT: 0.3960701450705528\n",
            "MAE for ViLBERT: 0.28140803951431403\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\" For BERT Model\")\n",
        "print(\"MAE for ViLT:\", mean_absolute_error(df['ViLT_Score'],df['bert_ViLT']))\n",
        "print(\"MAE for LXMERT:\", mean_absolute_error(df['ViLT_Score'],df['bert_LXMERT']))\n",
        "print(\"MAE for ViLBERT:\", mean_absolute_error(df['ViLBERT_Score'],df['bert_ViLBERT']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "snmLeFJF6uLs",
        "outputId": "a4484e63-93fb-40dd-c72b-9cef0150098c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " For BERT Model\n",
            "MAE for ViLT: 0.4937367520549081\n",
            "MAE for LXMERT: 0.5124108791351318\n",
            "MAE for ViLBERT: 0.6464045101946051\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\" For GPT-2 Model\")\n",
        "print(\"MAE for ViLT:\", mean_absolute_error(df['ViLT_Score'],df['gpt2_ViLT']))\n",
        "print(\"MAE for LXMERT:\", mean_absolute_error(df['ViLT_Score'],df['gpt2_LXMERT']))\n",
        "print(\"MAE for ViLBERT:\", mean_absolute_error(df['ViLBERT_Score'],df['gpt2_ViLBERT']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sPrOBIbP7Yaj",
        "outputId": "a17809a2-e691-4a82-f7d1-53ab3710c088"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " For GPT-2 Model\n",
            "MAE for ViLT: 0.6631663550030101\n",
            "MAE for LXMERT: 0.663283879106695\n",
            "MAE for ViLBERT: 0.8998978435993195\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Root Mean Squared Error for All Models"
      ],
      "metadata": {
        "id": "_G3yVTC5fzQo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "import math\n",
        "#nli RMSE   \"Sqaured= True (MSE) , squared= False (RMSE)\"\n",
        "print(\"RMSE ViLT\",math.sqrt(mean_squared_error(df['ViLT_Score'],df['predict_nli_ViLT'], squared =False)))\n",
        "print(\"RMSE LXMERT\",math.sqrt(mean_squared_error(df['LXMERT_Score'],df['predict_nli_LXMERT'], squared =False)))\n",
        "print(\"RMSE ViLBERT\",math.sqrt(mean_squared_error(df['ViLBERT_Score'],df['predict_nli_ViLBERT'], squared =False)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8VDIVAMnBHi",
        "outputId": "8acd8180-7a9a-42cc-839c-048d5605332e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE ViLT 0.7742616155469544\n",
            "RMSE LXMERT 0.7427342639487056\n",
            "RMSE ViLBERT 0.7697902009848183\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mpnet RMSE\n",
        "print(\"RMSE ViLT\",math.sqrt(mean_squared_error(df['ViLT_Score'],df['predict_mpnet_ViLT'], squared =False)))\n",
        "print(\"RMSE LXMERT\",math.sqrt(mean_squared_error(df['LXMERT_Score'],df['predict_mpnet_LXMERT'], squared =False)))\n",
        "print(\"RMSE ViLBERT\",math.sqrt(mean_squared_error(df['ViLBERT_Score'],df['predict_mpnet_ViLBERT'], squared =False)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AyNh8suFqebI",
        "outputId": "938bed25-bc5d-4cac-9ba5-c3f1246bb4c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE ViLT 0.6241067985199525\n",
            "RMSE LXMERT 0.5697198774188846\n",
            "RMSE ViLBERT 0.5693696059081225\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##BERT RMSE\n",
        "print(\"RMSE ViLT\",math.sqrt(mean_squared_error(df['ViLT_Score'],df['bert_ViLT'], squared =False)))\n",
        "print(\"RMSE LXMERT\",math.sqrt(mean_squared_error(df['LXMERT_Score'],df['bert_LXMERT'], squared =False)))\n",
        "print(\"RMSE ViLBERT\",math.sqrt(mean_squared_error(df['ViLBERT_Score'],df['bert_ViLBERT'], squared =False)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-N_C7eEy89pm",
        "outputId": "9f005de9-ace8-4212-df24-a270c2941e9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE ViLT 0.7773646759158049\n",
            "RMSE LXMERT 0.7776637145862153\n",
            "RMSE ViLBERT 0.8264435296828151\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#GPT-2 RMSE\n",
        "print(\"RMSE ViLT\",math.sqrt(mean_squared_error(df['ViLT_Score'],df['gpt2_ViLT'], squared =False)))\n",
        "print(\"RMSE LXMERT\",math.sqrt(mean_squared_error(df['LXMERT_Score'],df['gpt2_LXMERT'], squared =False)))\n",
        "print(\"RMSE ViLBERT\",math.sqrt(mean_squared_error(df['ViLBERT_Score'],df['gpt2_ViLBERT'], squared =False)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UzJME1d893c0",
        "outputId": "bf05481c-2a2b-4619-c6cb-68a92d01742f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE ViLT 0.8937145362891836\n",
            "RMSE LXMERT 0.8929432233152603\n",
            "RMSE ViLBERT 0.9715383351247422\n"
          ]
        }
      ]
    }
  ]
}