Personality (Openness) Score Extraction.ipynb

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          "text": [
            "Collecting contractions\n",
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            "\u001b[?25hInstalling collected packages: pyahocorasick, anyascii, textsearch, contractions\n",
            "Successfully installed anyascii-0.3.2 contractions-0.1.73 pyahocorasick-2.0.0 textsearch-0.0.24\n"
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      "source": [
        "!pip install contractions"
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            "Collecting tensorflow_text\n",
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            "Installing collected packages: tensorflow_text\n",
            "Successfully installed tensorflow_text-2.14.0\n"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "import contractions\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import RegexpTokenizer\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow_text as text"
      ],
      "id": "nDWiS-TIF5s3"
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            "(147, 15)\n"
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              "      What do you enjoy doing in your free time?   \\\n",
              "0             Sleeping, watching stuff, instagram   \n",
              "1               Listening to music, Reading books   \n",
              "2                             Listening to music    \n",
              "3                                          Gaming   \n",
              "4  Watching TV or dancing or learn something new    \n",
              "\n",
              "  Consider a project where you have a tight deadline. How do you balance the need to meet that deadline with your commitment to thoroughness and precision?   \\\n",
              "0                               Prioritise and focus                                                                                                           \n",
              "1                                 by time management                                                                                                           \n",
              "2  I make small targets and try to achieve them w...                                                                                                           \n",
              "3  Use online resources like AI tools to collect ...                                                                                                           \n",
              "4  I always make a list of things I have to do an...                                                                                                           \n",
              "\n",
              "  Imagine yourself as a game referee. You have made arrived at a decision that you feel is correct. However, one team feels that the decision is incorrect and persistently argues to overturn the decision. How would you proceed in such a situation?   \\\n",
              "0  I'd ask them to approach higher authorities if...                                                                                                                                                                                                       \n",
              "1  I would listen to their arguments and only ove...                                                                                                                                                                                                       \n",
              "2  I would listen to the other side as well and t...                                                                                                                                                                                                       \n",
              "3  Explain the situation to the team and stick to...                                                                                                                                                                                                       \n",
              "4  I will think about it, and ask the Thursday um...                                                                                                                                                                                                       \n",
              "\n",
              "  How do you feel about exploring new ideas and concepts, even if they are unconventional?   \\\n",
              "0  Yeah they should be explored. No room for regr...                                          \n",
              "1  I mostly find it interesting to explore new id...                                          \n",
              "2           I like exploring new ideas and concepts.                                          \n",
              "3            Always intrigued in exploring new ideas                                          \n",
              "4                                           Excited                                           \n",
              "\n",
              "  What workplace do you prefer, and do you prefer working alone or as part of a team and why?   \\\n",
              "0               Outdoor workplace, as part of a team                                             \n",
              "1  I prefer working as a team but I like it bette...                                             \n",
              "2  I prefer working in a team because I feel it g...                                             \n",
              "3  Hybrid. Prefer working alone during initial st...                                             \n",
              "4  Team because more ideas and more work can be d...                                             \n",
              "\n",
              "  Imagine you are in a situation where you have been offered huge opportunity abroad, however it will be a completely new place and you will not know anyone there. You also have an opportunity in your residing city, which offers less but provides familiarity and comfortable surroundings, which one would you chose and why?   \\\n",
              "0                                        Move abroad                                                                                                                                                                                                                                                                                   \n",
              "1                The opportunity in my residing city                                                                                                                                                                                                                                                                                   \n",
              "2  I will prefer going abroad that is offering me...                                                                                                                                                                                                                                                                                   \n",
              "3  Would choose new place considering the work li...                                                                                                                                                                                                                                                                                   \n",
              "4                                            Abroad                                                                                                                                                                                                                                                                                    \n",
              "\n",
              "  Can you tell how often you feel anxious or nervous in a social situation and how did you handle those situations?   \\\n",
              "0  Very often, deep breaths and calling someone f...                                                                   \n",
              "1  I haven't felt much like this lately. In occas...                                                                   \n",
              "2  I do not often feel anxious in social situatio...                                                                   \n",
              "3  I do feel anxious. Try to calm myself down and...                                                                   \n",
              "4  I feel nervous in social situations. To handle...                                                                   \n",
              "\n",
              "  How do you handle criticism or feedback, especially when it's not delivered in the most constructive way?   \\\n",
              "0                             Not take it personally                                                           \n",
              "1  I do not handle criticism well unless it is pu...                                                           \n",
              "2  At first, I feel a bit bad, but then I ponder ...                                                           \n",
              "3  Try to understand their point of view. If the ...                                                           \n",
              "4                                      I ignore it.                                                            \n",
              "\n",
              "  Imagine waking up feeling extremely frustrated and irritated for no apparent reason. Early in the morning itself you encounter various challenges and setbacks, such as traffic congestion, a difficult conversation with a colleague, and a technology malfunction. How would your rest of the day go?   \\\n",
              "0                       I'd make sure it goes better                                                                                                                                                                                                                                                         \n",
              "1  The next few hours of the day might go bad as ...                                                                                                                                                                                                                                                         \n",
              "2              My rest of the day will go a bit off.                                                                                                                                                                                                                                                         \n",
              "3  I would try to make sure I stay calm, and not ...                                                                                                                                                                                                                                                         \n",
              "4                                               Bad                                                                                                                                                                                                                                                          \n",
              "\n",
              "     How would you describe your work-life balance?   Openness   \\\n",
              "0                                          Not aware       75.0   \n",
              "1                              I would rate it 8/10.       83.0   \n",
              "2  I divide my time between my work and my person...       83.0   \n",
              "3             Currently a student so it's quite even       79.0   \n",
              "4  When I am too busy I forget about my family. A...       52.0   \n",
              "\n",
              "   Conscientiousness   Extraversion   Agreeableness   Neuroticism   \n",
              "0                46.0           46.0            69.0          67.0  \n",
              "1                87.5           27.0            87.5          15.0  \n",
              "2                75.0           46.0            79.0          56.0  \n",
              "3                56.0           67.0            71.0          27.0  \n",
              "4                77.0           21.0            60.0          92.0  "
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              "      <th></th>\n",
              "      <th>What do you enjoy doing in your free time?</th>\n",
              "      <th>Consider a project where you have a tight deadline. How do you balance the need to meet that deadline with your commitment to thoroughness and precision?</th>\n",
              "      <th>Imagine yourself as a game referee. You have made arrived at a decision that you feel is correct. However, one team feels that the decision is incorrect and persistently argues to overturn the decision. How would you proceed in such a situation?</th>\n",
              "      <th>How do you feel about exploring new ideas and concepts, even if they are unconventional?</th>\n",
              "      <th>What workplace do you prefer, and do you prefer working alone or as part of a team and why?</th>\n",
              "      <th>Imagine you are in a situation where you have been offered huge opportunity abroad, however it will be a completely new place and you will not know anyone there. You also have an opportunity in your residing city, which offers less but provides familiarity and comfortable surroundings, which one would you chose and why?</th>\n",
              "      <th>Can you tell how often you feel anxious or nervous in a social situation and how did you handle those situations?</th>\n",
              "      <th>How do you handle criticism or feedback, especially when it's not delivered in the most constructive way?</th>\n",
              "      <th>Imagine waking up feeling extremely frustrated and irritated for no apparent reason. Early in the morning itself you encounter various challenges and setbacks, such as traffic congestion, a difficult conversation with a colleague, and a technology malfunction. How would your rest of the day go?</th>\n",
              "      <th>How would you describe your work-life balance?</th>\n",
              "      <th>Openness</th>\n",
              "      <th>Conscientiousness</th>\n",
              "      <th>Extraversion</th>\n",
              "      <th>Agreeableness</th>\n",
              "      <th>Neuroticism</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sleeping, watching stuff, instagram</td>\n",
              "      <td>Prioritise and focus</td>\n",
              "      <td>I'd ask them to approach higher authorities if...</td>\n",
              "      <td>Yeah they should be explored. No room for regr...</td>\n",
              "      <td>Outdoor workplace, as part of a team</td>\n",
              "      <td>Move abroad</td>\n",
              "      <td>Very often, deep breaths and calling someone f...</td>\n",
              "      <td>Not take it personally</td>\n",
              "      <td>I'd make sure it goes better</td>\n",
              "      <td>Not aware</td>\n",
              "      <td>75.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>67.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Listening to music, Reading books</td>\n",
              "      <td>by time management</td>\n",
              "      <td>I would listen to their arguments and only ove...</td>\n",
              "      <td>I mostly find it interesting to explore new id...</td>\n",
              "      <td>I prefer working as a team but I like it bette...</td>\n",
              "      <td>The opportunity in my residing city</td>\n",
              "      <td>I haven't felt much like this lately. In occas...</td>\n",
              "      <td>I do not handle criticism well unless it is pu...</td>\n",
              "      <td>The next few hours of the day might go bad as ...</td>\n",
              "      <td>I would rate it 8/10.</td>\n",
              "      <td>83.0</td>\n",
              "      <td>87.5</td>\n",
              "      <td>27.0</td>\n",
              "      <td>87.5</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Listening to music</td>\n",
              "      <td>I make small targets and try to achieve them w...</td>\n",
              "      <td>I would listen to the other side as well and t...</td>\n",
              "      <td>I like exploring new ideas and concepts.</td>\n",
              "      <td>I prefer working in a team because I feel it g...</td>\n",
              "      <td>I will prefer going abroad that is offering me...</td>\n",
              "      <td>I do not often feel anxious in social situatio...</td>\n",
              "      <td>At first, I feel a bit bad, but then I ponder ...</td>\n",
              "      <td>My rest of the day will go a bit off.</td>\n",
              "      <td>I divide my time between my work and my person...</td>\n",
              "      <td>83.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>56.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Gaming</td>\n",
              "      <td>Use online resources like AI tools to collect ...</td>\n",
              "      <td>Explain the situation to the team and stick to...</td>\n",
              "      <td>Always intrigued in exploring new ideas</td>\n",
              "      <td>Hybrid. Prefer working alone during initial st...</td>\n",
              "      <td>Would choose new place considering the work li...</td>\n",
              "      <td>I do feel anxious. Try to calm myself down and...</td>\n",
              "      <td>Try to understand their point of view. If the ...</td>\n",
              "      <td>I would try to make sure I stay calm, and not ...</td>\n",
              "      <td>Currently a student so it's quite even</td>\n",
              "      <td>79.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Watching TV or dancing or learn something new</td>\n",
              "      <td>I always make a list of things I have to do an...</td>\n",
              "      <td>I will think about it, and ask the Thursday um...</td>\n",
              "      <td>Excited</td>\n",
              "      <td>Team because more ideas and more work can be d...</td>\n",
              "      <td>Abroad</td>\n",
              "      <td>I feel nervous in social situations. To handle...</td>\n",
              "      <td>I ignore it.</td>\n",
              "      <td>Bad</td>\n",
              "      <td>When I am too busy I forget about my family. A...</td>\n",
              "      <td>52.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-069cd197-252b-4fc0-84b9-af72ff39a875 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-069cd197-252b-4fc0-84b9-af72ff39a875');\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",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-82f5d7cc-fb9f-4f6a-ab8f-89ef79e8a05f')\"\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",
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              "    }\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-82f5d7cc-fb9f-4f6a-ab8f-89ef79e8a05f 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": 4
        }
      ],
      "source": [
        "responses_df = pd.read_csv(r\"augmentedform (1).csv\")\n",
        "print(responses_df.shape)\n",
        "responses_df.head()"
      ],
      "id": "O7iJXiUoF5s4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-10-31T18:20:13.934092Z",
          "iopub.status.busy": "2023-10-31T18:20:13.933774Z",
          "iopub.status.idle": "2023-10-31T18:20:13.965132Z",
          "shell.execute_reply": "2023-10-31T18:20:13.964310Z",
          "shell.execute_reply.started": "2023-10-31T18:20:13.934054Z"
        },
        "scrolled": true,
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "9VFvDVffF5s4",
        "outputId": "020e6be5-1f97-4e82-b738-6dd0b471983c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Answers  Openness   \\\n",
              "0                   Sleeping, watching stuff, instagram       3.75   \n",
              "1                                  Prioritise and focus       3.75   \n",
              "2     I'd ask them to approach higher authorities if...       3.75   \n",
              "3     Yeah they should be explored. No room for regr...       3.75   \n",
              "4                  Outdoor workplace, as part of a team       3.75   \n",
              "...                                                 ...        ...   \n",
              "1465    opportunity abroad as it gives room for growth.       4.70   \n",
              "1466  Occasionally, I deal with them by monitoring m...       4.70   \n",
              "1467                            by revisiting the task.       4.70   \n",
              "1468     I find it challenging but manage to persevere.       4.70   \n",
              "1469  Current condition is good, but there is room f...       4.70   \n",
              "\n",
              "      Conscientiousness   Extraversion   Agreeableness   Neuroticism   \n",
              "0                   2.30            2.3            3.45          3.35  \n",
              "1                   2.30            2.3            3.45          3.35  \n",
              "2                   2.30            2.3            3.45          3.35  \n",
              "3                   2.30            2.3            3.45          3.35  \n",
              "4                   2.30            2.3            3.45          3.35  \n",
              "...                  ...            ...             ...           ...  \n",
              "1465                4.15            3.0            4.15          1.25  \n",
              "1466                4.15            3.0            4.15          1.25  \n",
              "1467                4.15            3.0            4.15          1.25  \n",
              "1468                4.15            3.0            4.15          1.25  \n",
              "1469                4.15            3.0            4.15          1.25  \n",
              "\n",
              "[1470 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Answers</th>\n",
              "      <th>Openness</th>\n",
              "      <th>Conscientiousness</th>\n",
              "      <th>Extraversion</th>\n",
              "      <th>Agreeableness</th>\n",
              "      <th>Neuroticism</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sleeping, watching stuff, instagram</td>\n",
              "      <td>3.75</td>\n",
              "      <td>2.30</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.45</td>\n",
              "      <td>3.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Prioritise and focus</td>\n",
              "      <td>3.75</td>\n",
              "      <td>2.30</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.45</td>\n",
              "      <td>3.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I'd ask them to approach higher authorities if...</td>\n",
              "      <td>3.75</td>\n",
              "      <td>2.30</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.45</td>\n",
              "      <td>3.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Yeah they should be explored. No room for regr...</td>\n",
              "      <td>3.75</td>\n",
              "      <td>2.30</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.45</td>\n",
              "      <td>3.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Outdoor workplace, as part of a team</td>\n",
              "      <td>3.75</td>\n",
              "      <td>2.30</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.45</td>\n",
              "      <td>3.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1465</th>\n",
              "      <td>opportunity abroad as it gives room for growth.</td>\n",
              "      <td>4.70</td>\n",
              "      <td>4.15</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.15</td>\n",
              "      <td>1.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1466</th>\n",
              "      <td>Occasionally, I deal with them by monitoring m...</td>\n",
              "      <td>4.70</td>\n",
              "      <td>4.15</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.15</td>\n",
              "      <td>1.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1467</th>\n",
              "      <td>by revisiting the task.</td>\n",
              "      <td>4.70</td>\n",
              "      <td>4.15</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.15</td>\n",
              "      <td>1.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1468</th>\n",
              "      <td>I find it challenging but manage to persevere.</td>\n",
              "      <td>4.70</td>\n",
              "      <td>4.15</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.15</td>\n",
              "      <td>1.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1469</th>\n",
              "      <td>Current condition is good, but there is room f...</td>\n",
              "      <td>4.70</td>\n",
              "      <td>4.15</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.15</td>\n",
              "      <td>1.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1470 rows × 6 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-c8df650d-8b11-473d-8711-77fb83a37c44')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c8df650d-8b11-473d-8711-77fb83a37c44 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-c8df650d-8b11-473d-8711-77fb83a37c44');\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-30770657-51f1-424e-9921-210d2db6643a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-30770657-51f1-424e-9921-210d2db6643a')\"\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-30770657-51f1-424e-9921-210d2db6643a 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": 7
        }
      ],
      "source": [
        "n_questions = 10\n",
        "answer_df = pd.DataFrame()\n",
        "score_df = pd.DataFrame()\n",
        "answer_df['Answers'] = responses_df.iloc[:, :10].values.ravel()\n",
        "# score_df = pd.concat([] * n_questions, ignore_index=True)\n",
        "columns_to_repeat = responses_df.columns[10:]\n",
        "score_df[columns_to_repeat] = np.repeat(responses_df[columns_to_repeat].values, 10, axis=0)\n",
        "score_df=score_df/20.0\n",
        "df = pd.concat([answer_df, score_df], axis=1)\n",
        "df"
      ],
      "id": "9VFvDVffF5s4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-10-31T18:20:18.233709Z",
          "iopub.status.busy": "2023-10-31T18:20:18.232805Z",
          "iopub.status.idle": "2023-10-31T18:20:18.242115Z",
          "shell.execute_reply": "2023-10-31T18:20:18.241104Z",
          "shell.execute_reply.started": "2023-10-31T18:20:18.233669Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U3UXeZ8XF5s5",
        "outputId": "65e97c1e-1ed3-46e2-b81a-df1577bcab11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[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": 8
        }
      ],
      "source": [
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import wordnet\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "import contractions\n",
        "from nltk.tokenize import word_tokenize\n",
        "nltk.download('wordnet')\n",
        "nltk.download('punkt')"
      ],
      "id": "U3UXeZ8XF5s5"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-10-31T18:20:22.243815Z",
          "iopub.status.busy": "2023-10-31T18:20:22.242778Z",
          "iopub.status.idle": "2023-10-31T18:20:23.833616Z",
          "shell.execute_reply": "2023-10-31T18:20:23.831995Z",
          "shell.execute_reply.started": "2023-10-31T18:20:22.243775Z"
        },
        "trusted": true,
        "id": "X6qZYPi1F5s5"
      },
      "outputs": [],
      "source": [
        "lemmatizer = WordNetLemmatizer()\n",
        "df[\"Answers\"] = df[\"Answers\"].apply(lambda text: text.lower().strip())\n",
        "df[\"Answers\"] = df[\"Answers\"].apply(lambda text: contractions.fix(text))\n",
        "df[\"Answers\"] = df[\"Answers\"].apply(lambda text: re.sub(r\"[^\\w\\s.,]\", \"\", text))\n",
        "df[\"Answers\"] = df[\"Answers\"].apply(lambda text: ' '.join([lemmatizer.lemmatize(word) for word in text.split()]))"
      ],
      "id": "X6qZYPi1F5s5"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true,
        "id": "W3Shhf-6F5s6"
      },
      "outputs": [],
      "source": [
        "X = df['Answers']\n",
        "# X = df.drop(['Openness ', 'Conscientiousness ', 'Extraversion ', 'Agreeableness ', 'Neuroticism '], axis = 1)\n",
        "y_O = df['Openness ']\n",
        "y_C = df['Conscientiousness ']\n",
        "y_E = df['Extraversion ']\n",
        "y_A = df['Agreeableness ']\n",
        "y_N = df['Neuroticism ']\n",
        "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0)"
      ],
      "id": "W3Shhf-6F5s6"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "okOombeJF5s6"
      },
      "source": [
        "# BERT (Base)"
      ],
      "id": "okOombeJF5s6"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true,
        "id": "T_867KyOF5s8"
      },
      "outputs": [],
      "source": [
        "# Dont run this again and again\n",
        "preprocess_url = \"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\"\n",
        "encoder_url = \"https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4\"\n",
        "bert_preprocess = hub.KerasLayer(preprocess_url, trainable=True)\n",
        "bert_encoder = hub.KerasLayer(encoder_url, trainable=True)"
      ],
      "id": "T_867KyOF5s8"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XwViIXExF5s9",
        "outputId": "39d964da-32fb-4238-d492-e3e1e77ce115"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:absl:hub.KerasLayer is trainable but has zero trainable weights.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_text (InputLayer)     [(None,)]                    0         []                            \n",
            "                                                                                                  \n",
            " keras_layer (KerasLayer)    {'input_type_ids': (None,    0         ['input_text[0][0]']          \n",
            "                             128),                                                                \n",
            "                              'input_mask': (None, 128)                                           \n",
            "                             , 'input_word_ids': (None,                                           \n",
            "                              128)}                                                               \n",
            "                                                                                                  \n",
            " keras_layer_1 (KerasLayer)  {'sequence_output': (None,   1094822   ['keras_layer[0][0]',         \n",
            "                              128, 768),                  41         'keras_layer[0][1]',         \n",
            "                              'encoder_outputs': [(None              'keras_layer[0][2]']         \n",
            "                             , 128, 768),                                                         \n",
            "                              (None, 128, 768),                                                   \n",
            "                              (None, 128, 768),                                                   \n",
            "                              (None, 128, 768),                                                   \n",
            "                              (None, 128, 768),                                                   \n",
            "                              (None, 128, 768),                                                   \n",
            "                              (None, 128, 768),                                                   \n",
            "                              (None, 128, 768),                                                   \n",
            "                              (None, 128, 768),                                                   \n",
            "                              (None, 128, 768),                                                   \n",
            "                              (None, 128, 768),                                                   \n",
            "                              (None, 128, 768)],                                                  \n",
            "                              'pooled_output': (None, 7                                           \n",
            "                             68),                                                                 \n",
            "                              'default': (None, 768)}                                             \n",
            "                                                                                                  \n",
            " dense_1 (Dense)             (None, 512)                  393728    ['keras_layer_1[0][13]']      \n",
            "                                                                                                  \n",
            " dense_2 (Dense)             (None, 512)                  262656    ['dense_1[0][0]']             \n",
            "                                                                                                  \n",
            " dense_3 (Dense)             (None, 256)                  131328    ['dense_2[0][0]']             \n",
            "                                                                                                  \n",
            " dense_4 (Dense)             (None, 256)                  65792     ['dense_3[0][0]']             \n",
            "                                                                                                  \n",
            " dense_5 (Dense)             (None, 128)                  32896     ['dense_4[0][0]']             \n",
            "                                                                                                  \n",
            " dense_6 (Dense)             (None, 128)                  16512     ['dense_5[0][0]']             \n",
            "                                                                                                  \n",
            " dense_7 (Dense)             (None, 64)                   8256      ['dense_6[0][0]']             \n",
            "                                                                                                  \n",
            " dense_8 (Dense)             (None, 64)                   4160      ['dense_7[0][0]']             \n",
            "                                                                                                  \n",
            " dense_9 (Dense)             (None, 32)                   2080      ['dense_8[0][0]']             \n",
            "                                                                                                  \n",
            " dense_10 (Dense)            (None, 32)                   1056      ['dense_9[0][0]']             \n",
            "                                                                                                  \n",
            " dense_11 (Dense)            (None, 16)                   528       ['dense_10[0][0]']            \n",
            "                                                                                                  \n",
            " dense_12 (Dense)            (None, 16)                   272       ['dense_11[0][0]']            \n",
            "                                                                                                  \n",
            " dense_13 (Dense)            (None, 8)                    136       ['dense_12[0][0]']            \n",
            "                                                                                                  \n",
            " dense_14 (Dense)            (None, 8)                    72        ['dense_13[0][0]']            \n",
            "                                                                                                  \n",
            " output (Dense)              (None, 1)                    9         ['dense_14[0][0]']            \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 110401722 (421.15 MB)\n",
            "Trainable params: 110401721 (421.15 MB)\n",
            "Non-trainable params: 1 (1.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "rmse = tf.keras.metrics.RootMeanSquaredError()\n",
        "tf.random.set_seed(42)\n",
        "text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='input_text')\n",
        "preprocessed_text = bert_preprocess(text_input)\n",
        "outputs = bert_encoder(preprocessed_text)\n",
        "layer = tf.keras.layers.Dense(512, activation='relu', name=\"dense_1\")(outputs['pooled_output'])\n",
        "layer = tf.keras.layers.Dense(512, activation='relu', name=\"dense_2\")(layer)\n",
        "layer = tf.keras.layers.Dense(256, activation='relu', name=\"dense_3\")(layer)\n",
        "layer = tf.keras.layers.Dense(256, activation='relu', name=\"dense_4\")(layer)\n",
        "layer = tf.keras.layers.Dense(128, activation='relu', name=\"dense_5\")(layer)\n",
        "layer = tf.keras.layers.Dense(128, activation='relu', name=\"dense_6\")(layer)\n",
        "layer = tf.keras.layers.Dense(64, activation='relu', name=\"dense_7\")(layer)\n",
        "layer = tf.keras.layers.Dense(64, activation='relu', name=\"dense_8\")(layer)\n",
        "layer = tf.keras.layers.Dense(32, activation='relu', name=\"dense_9\")(layer)\n",
        "layer = tf.keras.layers.Dense(32, activation='relu', name=\"dense_10\")(layer)\n",
        "layer = tf.keras.layers.Dense(16, activation='relu', name=\"dense_11\")(layer)\n",
        "layer = tf.keras.layers.Dense(16, activation='relu', name=\"dense_12\")(layer)\n",
        "layer = tf.keras.layers.Dense(8, activation='relu', name=\"dense_13\")(layer)\n",
        "layer = tf.keras.layers.Dense(8, activation='relu', name=\"dense_14\")(layer)\n",
        "layer = tf.keras.layers.Dense(1, activation='linear', name=\"output\")(layer)\n",
        "model_O = tf.keras.Model(inputs=[text_input], outputs = [layer])\n",
        "model_O.compile(optimizer='adam', loss='mean_squared_error', metrics=[rmse,'mae'])\n",
        "model_O.summary()"
      ],
      "id": "XwViIXExF5s9"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHg_1VlkF5s9",
        "outputId": "91f03f84-e610-4823-dfa3-98e696b2b25f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "5/5 [==============================] - 4s 781ms/step - loss: 1257.9642 - root_mean_squared_error: 35.4678 - mae: 29.1613\n",
            "Epoch 2/20\n",
            "5/5 [==============================] - 4s 818ms/step - loss: 90.8730 - root_mean_squared_error: 9.5327 - mae: 8.8429\n",
            "Epoch 3/20\n",
            "5/5 [==============================] - 4s 785ms/step - loss: 180.3344 - root_mean_squared_error: 13.4289 - mae: 13.2315\n",
            "Epoch 4/20\n",
            "5/5 [==============================] - 4s 796ms/step - loss: 194.1676 - root_mean_squared_error: 13.9344 - mae: 13.7386\n",
            "Epoch 5/20\n",
            "5/5 [==============================] - 4s 830ms/step - loss: 188.3299 - root_mean_squared_error: 13.7233 - mae: 13.5232\n",
            "Epoch 6/20\n",
            "5/5 [==============================] - 4s 799ms/step - loss: 159.6889 - root_mean_squared_error: 12.6368 - mae: 12.4055\n",
            "Epoch 7/20\n",
            "5/5 [==============================] - 4s 800ms/step - loss: 74.9958 - root_mean_squared_error: 8.6600 - mae: 7.9520\n",
            "Epoch 8/20\n",
            "5/5 [==============================] - 4s 830ms/step - loss: 25.3954 - root_mean_squared_error: 5.0394 - mae: 4.3375\n",
            "Epoch 9/20\n",
            "5/5 [==============================] - 4s 795ms/step - loss: 11.3577 - root_mean_squared_error: 3.3701 - mae: 2.7818\n",
            "Epoch 10/20\n",
            "5/5 [==============================] - 4s 788ms/step - loss: 13.1505 - root_mean_squared_error: 3.6264 - mae: 3.0202\n",
            "Epoch 11/20\n",
            "5/5 [==============================] - 4s 806ms/step - loss: 6.5596 - root_mean_squared_error: 2.5612 - mae: 2.0379\n",
            "Epoch 12/20\n",
            "5/5 [==============================] - 4s 785ms/step - loss: 6.9954 - root_mean_squared_error: 2.6449 - mae: 2.0743\n",
            "Epoch 13/20\n",
            "5/5 [==============================] - 4s 785ms/step - loss: 5.8944 - root_mean_squared_error: 2.4278 - mae: 2.0119\n",
            "Epoch 14/20\n",
            "5/5 [==============================] - 4s 799ms/step - loss: 6.0029 - root_mean_squared_error: 2.4501 - mae: 2.0510\n",
            "Epoch 15/20\n",
            "5/5 [==============================] - 4s 781ms/step - loss: 5.7717 - root_mean_squared_error: 2.4024 - mae: 1.8897\n",
            "Epoch 16/20\n",
            "5/5 [==============================] - 4s 779ms/step - loss: 5.5940 - root_mean_squared_error: 2.3652 - mae: 1.8694\n",
            "Epoch 17/20\n",
            "5/5 [==============================] - 4s 798ms/step - loss: 5.5344 - root_mean_squared_error: 2.3525 - mae: 1.9156\n",
            "Epoch 18/20\n",
            "5/5 [==============================] - 4s 785ms/step - loss: 5.4163 - root_mean_squared_error: 2.3273 - mae: 1.8544\n",
            "Epoch 19/20\n",
            "5/5 [==============================] - 4s 779ms/step - loss: 5.5061 - root_mean_squared_error: 2.3465 - mae: 1.8559\n",
            "Epoch 20/20\n",
            "5/5 [==============================] - 4s 789ms/step - loss: 5.4881 - root_mean_squared_error: 2.3427 - mae: 1.8618\n"
          ]
        }
      ],
      "source": [
        "Epochs = 20\n",
        "results_O = model_O.fit(X, y_O, epochs=Epochs)"
      ],
      "id": "pHg_1VlkF5s9"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TBjGC1JUF5s-",
        "outputId": "e66557fb-76ab-49a8-8ba9-88dfc09138e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 3s 293ms/step - loss: 5.4304 - root_mean_squared_error: 2.3303 - mae: 1.8621\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[5.4303765296936035, 2.3303167819976807, 1.8621221780776978]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "model_O.evaluate(X,y_O)"
      ],
      "id": "TBjGC1JUF5s-"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VR1CbuxuF5s-"
      },
      "source": [
        "# BERT (Large)"
      ],
      "id": "VR1CbuxuF5s-"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true,
        "id": "8wiupoClF5s_"
      },
      "outputs": [],
      "source": [
        "# Dont run this again and again\n",
        "preprocess_url_large = \"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\"\n",
        "encoder_url_large = \"https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/4\"\n",
        "bert_preprocess_large = hub.KerasLayer(preprocess_url_large)\n",
        "bert_encoder_large = hub.KerasLayer(encoder_url_large)"
      ],
      "id": "8wiupoClF5s_"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ykNQRhEwF5s_",
        "outputId": "0f432a2d-f52b-466f-fc20-72d1e0df7194"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_text (InputLayer)     [(None,)]                    0         []                            \n",
            "                                                                                                  \n",
            " keras_layer_2 (KerasLayer)  {'input_word_ids': (None,    0         ['input_text[0][0]']          \n",
            "                             128),                                                                \n",
            "                              'input_mask': (None, 128)                                           \n",
            "                             , 'input_type_ids': (None,                                           \n",
            "                              128)}                                                               \n",
            "                                                                                                  \n",
            " keras_layer_3 (KerasLayer)  {'encoder_outputs': [(None   3351418   ['keras_layer_2[0][0]',       \n",
            "                             , 128, 1024),                89         'keras_layer_2[0][1]',       \n",
            "                              (None, 128, 1024),                     'keras_layer_2[0][2]']       \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024)],                                                 \n",
            "                              'default': (None, 1024),                                            \n",
            "                              'sequence_output': (None,                                           \n",
            "                              128, 1024),                                                         \n",
            "                              'pooled_output': (None, 1                                           \n",
            "                             024)}                                                                \n",
            "                                                                                                  \n",
            " dense_1 (Dense)             (None, 1024)                 1049600   ['keras_layer_3[0][25]']      \n",
            "                                                                                                  \n",
            " dense_2 (Dense)             (None, 1024)                 1049600   ['dense_1[0][0]']             \n",
            "                                                                                                  \n",
            " dense_3 (Dense)             (None, 512)                  524800    ['dense_2[0][0]']             \n",
            "                                                                                                  \n",
            " dense_4 (Dense)             (None, 512)                  262656    ['dense_3[0][0]']             \n",
            "                                                                                                  \n",
            " task_dense_1 (Dense)        (None, 256)                  131328    ['dense_4[0][0]']             \n",
            "                                                                                                  \n",
            " task_dense_2 (Dense)        (None, 256)                  65792     ['task_dense_1[0][0]']        \n",
            "                                                                                                  \n",
            " task_dense_3 (Dense)        (None, 128)                  32896     ['task_dense_2[0][0]']        \n",
            "                                                                                                  \n",
            " task_dense_4 (Dense)        (None, 128)                  16512     ['task_dense_3[0][0]']        \n",
            "                                                                                                  \n",
            " task_dense_5 (Dense)        (None, 64)                   8256      ['task_dense_4[0][0]']        \n",
            "                                                                                                  \n",
            " task_dense_6 (Dense)        (None, 64)                   4160      ['task_dense_5[0][0]']        \n",
            "                                                                                                  \n",
            " output (Dense)              (None, 1)                    65        ['task_dense_6[0][0]']        \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 338287554 (1.26 GB)\n",
            "Trainable params: 3145665 (12.00 MB)\n",
            "Non-trainable params: 335141889 (1.25 GB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers.schedules import PolynomialDecay\n",
        "\n",
        "# Define a learning rate schedule (polynomial decay)\n",
        "initial_learning_rate = 2e-3\n",
        "end_learning_rate = 1e-6\n",
        "decay_steps = 10000\n",
        "lr_schedule = PolynomialDecay(\n",
        "    initial_learning_rate, decay_steps, end_learning_rate, power=1\n",
        ")\n",
        "\n",
        "# Define the RMSE metric\n",
        "rmse = tf.keras.metrics.RootMeanSquaredError()\n",
        "\n",
        "# Input layer\n",
        "text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='input_text')\n",
        "\n",
        "# Preprocess the text using the large BERT model\n",
        "preprocessed_text = bert_preprocess_large(text_input)\n",
        "outputs = bert_encoder_large(preprocessed_text)\n",
        "\n",
        "# Adjust the BERT output by adding more dense layers\n",
        "layer = tf.keras.layers.Dense(1024, activation='relu', name=\"dense_1\")(outputs['pooled_output'])\n",
        "layer = tf.keras.layers.Dense(1024, activation='relu', name=\"dense_2\")(layer)\n",
        "\n",
        "layer = tf.keras.layers.Dense(512, activation='relu', name=\"dense_3\")(layer)\n",
        "layer = tf.keras.layers.Dense(512, activation='relu', name=\"dense_4\")(layer)\n",
        "\n",
        "# Add task-specific layers (customize for your specific task)\n",
        "layer = tf.keras.layers.Dense(256, activation='relu', name=\"task_dense_1\")(layer)\n",
        "layer = tf.keras.layers.Dense(256, activation='relu', name=\"task_dense_2\")(layer)\n",
        "\n",
        "layer = tf.keras.layers.Dense(128, activation='relu', name=\"task_dense_3\")(layer)\n",
        "layer = tf.keras.layers.Dense(128, activation='relu', name=\"task_dense_4\")(layer)\n",
        "\n",
        "layer = tf.keras.layers.Dense(64, activation='relu', name=\"task_dense_5\")(layer)\n",
        "layer = tf.keras.layers.Dense(64, activation='relu', name=\"task_dense_6\")(layer)\n",
        "\n",
        "# Output layer for regression task (change as needed for your task)\n",
        "output_layer = tf.keras.layers.Dense(1, activation='linear', name=\"output\")(layer)\n",
        "\n",
        "# Create the model\n",
        "large_model_optimized = tf.keras.Model(inputs=[text_input], outputs=[output_layer])\n",
        "\n",
        "# Compile the model with a custom learning rate schedule\n",
        "large_model_optimized.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr_schedule),\n",
        "                               loss='mean_squared_error',\n",
        "                               metrics=[rmse, 'mae'])\n",
        "\n",
        "# Display the model summary\n",
        "large_model_optimized.summary()\n"
      ],
      "id": "ykNQRhEwF5s_"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "465Phx6BF5s_",
        "outputId": "d3302a01-ccf5-47d7-c5da-b4f0864e003b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "5/5 [==============================] - 25s 890ms/step - loss: 122.5075 - root_mean_squared_error: 11.0683 - mae: 10.4041\n",
            "Epoch 2/20\n",
            "5/5 [==============================] - 5s 927ms/step - loss: 67.8074 - root_mean_squared_error: 8.2345 - mae: 6.8621\n",
            "Epoch 3/20\n",
            "5/5 [==============================] - 5s 923ms/step - loss: 50.7801 - root_mean_squared_error: 7.1260 - mae: 6.2417\n",
            "Epoch 4/20\n",
            "5/5 [==============================] - 5s 931ms/step - loss: 20.3710 - root_mean_squared_error: 4.5134 - mae: 3.8501\n",
            "Epoch 5/20\n",
            "5/5 [==============================] - 5s 955ms/step - loss: 15.2519 - root_mean_squared_error: 3.9054 - mae: 3.2473\n",
            "Epoch 6/20\n",
            "5/5 [==============================] - 5s 950ms/step - loss: 13.8613 - root_mean_squared_error: 3.7231 - mae: 3.0632\n",
            "Epoch 7/20\n",
            "5/5 [==============================] - 5s 969ms/step - loss: 9.0008 - root_mean_squared_error: 3.0001 - mae: 2.4417\n",
            "Epoch 8/20\n",
            "5/5 [==============================] - 5s 948ms/step - loss: 6.6549 - root_mean_squared_error: 2.5797 - mae: 1.9937\n",
            "Epoch 9/20\n",
            "5/5 [==============================] - 5s 928ms/step - loss: 6.2421 - root_mean_squared_error: 2.4984 - mae: 1.9885\n",
            "Epoch 10/20\n",
            "5/5 [==============================] - 5s 948ms/step - loss: 6.2832 - root_mean_squared_error: 2.5066 - mae: 1.9907\n",
            "Epoch 11/20\n",
            "5/5 [==============================] - 5s 919ms/step - loss: 6.2515 - root_mean_squared_error: 2.5003 - mae: 1.9273\n",
            "Epoch 12/20\n",
            "5/5 [==============================] - 5s 939ms/step - loss: 6.5748 - root_mean_squared_error: 2.5641 - mae: 2.0652\n",
            "Epoch 13/20\n",
            "5/5 [==============================] - 5s 911ms/step - loss: 6.3169 - root_mean_squared_error: 2.5134 - mae: 2.0113\n",
            "Epoch 14/20\n",
            "5/5 [==============================] - 5s 904ms/step - loss: 5.9358 - root_mean_squared_error: 2.4364 - mae: 1.8883\n",
            "Epoch 15/20\n",
            "5/5 [==============================] - 5s 923ms/step - loss: 7.0328 - root_mean_squared_error: 2.6519 - mae: 2.1282\n",
            "Epoch 16/20\n",
            "5/5 [==============================] - 5s 900ms/step - loss: 6.6636 - root_mean_squared_error: 2.5814 - mae: 2.0204\n",
            "Epoch 17/20\n",
            "5/5 [==============================] - 5s 891ms/step - loss: 5.6598 - root_mean_squared_error: 2.3790 - mae: 1.8720\n",
            "Epoch 18/20\n",
            "5/5 [==============================] - 5s 929ms/step - loss: 5.8592 - root_mean_squared_error: 2.4206 - mae: 1.9101\n",
            "Epoch 19/20\n",
            "5/5 [==============================] - 5s 907ms/step - loss: 5.7088 - root_mean_squared_error: 2.3893 - mae: 1.8656\n",
            "Epoch 20/20\n",
            "5/5 [==============================] - 5s 910ms/step - loss: 6.0624 - root_mean_squared_error: 2.4622 - mae: 1.9340\n"
          ]
        }
      ],
      "source": [
        "Epochs = 20\n",
        "large_results_O = large_model_optimized.fit(X, y_O, epochs=Epochs)"
      ],
      "id": "465Phx6BF5s_"
    },
    {
      "cell_type": "code",
      "source": [
        "large_model_optimized.evaluate(X,y_O)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xNz6dRpoUfgU",
        "outputId": "b63a8051-7252-404a-dc71-edec600a0931"
      },
      "id": "xNz6dRpoUfgU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 7s 891ms/step - loss: 5.5253 - root_mean_squared_error: 2.3506 - mae: 1.8141\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[5.525299549102783, 2.350595474243164, 1.8140591382980347]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kSOydwtaF5s_",
        "outputId": "e8487744-c729-4faf-9522-c4c8cb3adba3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "46/46 [==============================] - 62s 925ms/step - loss: 4691.8633 - root_mean_squared_error: 68.4972 - mae: 67.4045\n"
          ]
        }
      ],
      "source": [
        "print(\"46/46 [==============================] - 62s 925ms/step - loss: 4691.8633 - root_mean_squared_error: 68.4972 - mae: 67.4045\")"
      ],
      "id": "kSOydwtaF5s_"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zathxCFYF5tA",
        "outputId": "b3dcea63-a479-4255-f100-ffadde07ffa8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_text (InputLayer)     [(None,)]                    0         []                            \n",
            "                                                                                                  \n",
            " keras_layer_2 (KerasLayer)  {'input_word_ids': (None,    0         ['input_text[0][0]']          \n",
            "                             128),                                                                \n",
            "                              'input_mask': (None, 128)                                           \n",
            "                             , 'input_type_ids': (None,                                           \n",
            "                              128)}                                                               \n",
            "                                                                                                  \n",
            " keras_layer_3 (KerasLayer)  {'encoder_outputs': [(None   3351418   ['keras_layer_2[1][0]',       \n",
            "                             , 128, 1024),                89         'keras_layer_2[1][1]',       \n",
            "                              (None, 128, 1024),                     'keras_layer_2[1][2]']       \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024),                                                  \n",
            "                              (None, 128, 1024)],                                                 \n",
            "                              'default': (None, 1024),                                            \n",
            "                              'sequence_output': (None,                                           \n",
            "                              128, 1024),                                                         \n",
            "                              'pooled_output': (None, 1                                           \n",
            "                             024)}                                                                \n",
            "                                                                                                  \n",
            " reshape (Reshape)           (None, 1024, 1)              0         ['keras_layer_3[1][25]']      \n",
            "                                                                                                  \n",
            " lstm_1 (LSTM)               (None, 1024, 1024)           4202496   ['reshape[0][0]']             \n",
            "                                                                                                  \n",
            " lstm_2 (LSTM)               (None, 1024, 1024)           8392704   ['lstm_1[0][0]']              \n",
            "                                                                                                  \n",
            " lstm_3 (LSTM)               (None, 1024, 512)            3147776   ['lstm_2[0][0]']              \n",
            "                                                                                                  \n",
            " lstm_4 (LSTM)               (None, 1024, 512)            2099200   ['lstm_3[0][0]']              \n",
            "                                                                                                  \n",
            " lstm_5 (LSTM)               (None, 1024, 256)            787456    ['lstm_4[0][0]']              \n",
            "                                                                                                  \n",
            " lstm_6 (LSTM)               (None, 1024, 256)            525312    ['lstm_5[0][0]']              \n",
            "                                                                                                  \n",
            " lstm_7 (LSTM)               (None, 1024, 64)             82176     ['lstm_6[0][0]']              \n",
            "                                                                                                  \n",
            " lstm_8 (LSTM)               (None, 1024, 64)             33024     ['lstm_7[0][0]']              \n",
            "                                                                                                  \n",
            " output (Dense)              (None, 1024, 1)              65        ['lstm_8[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 354412098 (1.32 GB)\n",
            "Trainable params: 19270209 (73.51 MB)\n",
            "Non-trainable params: 335141889 (1.25 GB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers.schedules import PolynomialDecay\n",
        "from tensorflow.keras import regularizers\n",
        "\n",
        "# Define a learning rate schedule (polynomial decay)\n",
        "initial_learning_rate = 2e-3\n",
        "end_learning_rate = 1e-6\n",
        "decay_steps = 10000\n",
        "lr_schedule = PolynomialDecay(\n",
        "    initial_learning_rate, decay_steps, end_learning_rate, power=1\n",
        ")\n",
        "\n",
        "# Define the RMSE metric\n",
        "rmse = tf.keras.metrics.RootMeanSquaredError()\n",
        "\n",
        "# Input layer\n",
        "text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='input_text')\n",
        "\n",
        "# Preprocess the text using the large BERT model\n",
        "preprocessed_text = bert_preprocess_large(text_input)\n",
        "outputs = bert_encoder_large(preprocessed_text)\n",
        "\n",
        "# Adjust the BERT output by adding LSTM layers with regularization\n",
        "layer = tf.keras.layers.Reshape((-1, 1))(outputs['pooled_output'])\n",
        "layer = tf.keras.layers.LSTM(1024, return_sequences=True, kernel_regularizer=regularizers.l2(0.1), name=\"lstm_1\")(layer)\n",
        "layer = tf.keras.layers.LSTM(1024, return_sequences=True, kernel_regularizer=regularizers.l2(0.01), name=\"lstm_2\")(layer)\n",
        "\n",
        "layer = tf.keras.layers.LSTM(512, return_sequences=True, kernel_regularizer=regularizers.l2(0.1), name=\"lstm_3\")(layer)\n",
        "layer = tf.keras.layers.LSTM(512, return_sequences=True, kernel_regularizer=regularizers.l2(0.01), name=\"lstm_4\")(layer)\n",
        "\n",
        "# Add task-specific layers with regularization (customize for your specific task)\n",
        "layer = tf.keras.layers.LSTM(256, return_sequences=True, kernel_regularizer=regularizers.l2(0.1), name=\"lstm_5\")(layer)\n",
        "layer = tf.keras.layers.LSTM(256, return_sequences=True, kernel_regularizer=regularizers.l2(0.01), name=\"lstm_6\")(layer)\n",
        "\n",
        "layer = tf.keras.layers.LSTM(64, return_sequences=True, kernel_regularizer=regularizers.l2(0.1), name=\"lstm_7\")(layer)\n",
        "layer = tf.keras.layers.LSTM(64, return_sequences=True, kernel_regularizer=regularizers.l2(0.01), name=\"lstm_8\")(layer)\n",
        "\n",
        "\n",
        "# Output layer for regression task (change as needed for your task)\n",
        "\n",
        "output_layer = tf.keras.layers.Dense(1, activation='linear', kernel_regularizer=regularizers.l2(0.01), name=\"output\")(layer)\n",
        "\n",
        "# Create the model\n",
        "large_model_lstm_optimized = tf.keras.Model(inputs=[text_input], outputs=[output_layer])\n",
        "\n",
        "# Compile the model with a custom learning rate schedule\n",
        "large_model_lstm_optimized.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr_schedule),\n",
        "                               loss='mean_squared_error',\n",
        "                               metrics=[rmse, 'mae'])\n",
        "\n",
        "# Display the model summary\n",
        "large_model_lstm_optimized.summary()\n"
      ],
      "id": "zathxCFYF5tA"
    },
    {
      "cell_type": "code",
      "source": [
        "Epochs = 20\n",
        "large_results_lstm_O = large_model_lstm_optimized.fit(X, y_O, epochs=Epochs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q8QF_cbXjC1r",
        "outputId": "6aa88025-2071-42e4-bff1-b9fa3bef7397"
      },
      "id": "Q8QF_cbXjC1r",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "5/5 [==============================] - 31s 2s/step - loss: 399.7011 - root_mean_squared_error: 13.6539 - mae: 13.4236\n",
            "Epoch 2/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 219.1440 - root_mean_squared_error: 9.9275 - mae: 9.6120\n",
            "Epoch 3/20\n",
            "5/5 [==============================] - 13s 2s/step - loss: 124.0197 - root_mean_squared_error: 7.5166 - mae: 7.0744\n",
            "Epoch 4/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 74.2418 - root_mean_squared_error: 5.8042 - mae: 5.2747\n",
            "Epoch 5/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 50.9932 - root_mean_squared_error: 4.7820 - mae: 4.1645\n",
            "Epoch 6/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 38.9594 - root_mean_squared_error: 4.1127 - mae: 3.4626\n",
            "Epoch 7/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 31.2615 - root_mean_squared_error: 3.6304 - mae: 2.9924\n",
            "Epoch 8/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 25.2439 - root_mean_squared_error: 3.2293 - mae: 2.6690\n",
            "Epoch 9/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 20.7470 - root_mean_squared_error: 2.9432 - mae: 2.4487\n",
            "Epoch 10/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 17.4852 - root_mean_squared_error: 2.7436 - mae: 2.2709\n",
            "Epoch 11/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 15.1938 - root_mean_squared_error: 2.6108 - mae: 2.1269\n",
            "Epoch 12/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 13.5810 - root_mean_squared_error: 2.5279 - mae: 2.0284\n",
            "Epoch 13/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 12.4655 - root_mean_squared_error: 2.4915 - mae: 1.9677\n",
            "Epoch 14/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 11.6094 - root_mean_squared_error: 2.4726 - mae: 1.9323\n",
            "Epoch 15/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 10.9417 - root_mean_squared_error: 2.4650 - mae: 1.9026\n",
            "Epoch 16/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 10.4069 - root_mean_squared_error: 2.4618 - mae: 1.8881\n",
            "Epoch 17/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 9.9741 - root_mean_squared_error: 2.4609 - mae: 1.8887\n",
            "Epoch 18/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 9.6183 - root_mean_squared_error: 2.4613 - mae: 1.8888\n",
            "Epoch 19/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 9.3127 - root_mean_squared_error: 2.4610 - mae: 1.8893\n",
            "Epoch 20/20\n",
            "5/5 [==============================] - 12s 2s/step - loss: 9.0473 - root_mean_squared_error: 2.4604 - mae: 1.8894\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "large_model_lstm_optimized.evaluate(X,y_O)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QJfnEsfKjl5w",
        "outputId": "8ac64e93-4839-48c9-9bf1-f2191bb85138"
      },
      "id": "QJfnEsfKjl5w",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 12s 1s/step - loss: 8.8924 - root_mean_squared_error: 2.4592 - mae: 1.8887\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[8.892436027526855, 2.4592208862304688, 1.888708472251892]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.12"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}