Sentiment_Analysis_with_Transformers.ipynb

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        "<a href=\"https://colab.research.google.com/github/YopaNelly/AI-ML-Basics/blob/main/Sentiment_Analysis_with_Transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Install the required library\n",
        "!pip install transformers\n",
        "\n",
        "# Import the libraries\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from transformers import BertTokenizer, TFBertForSequenceClassification\n",
        "from transformers import InputExample, InputFeatures\n",
        "import tensorflow as tf\n"
      ],
      "metadata": {
        "colab": {
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        "id": "NW3Yxv-5XwEP",
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            "Requirement already satisfied: numpy<2.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.4)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.6.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.7.4)\n"
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        "df = pd.read_csv(\"DisneylandReviews.csv\", encoding='latin-1')\n",
        "df.head()"
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        "outputId": "d626e8c8-3748-46f5-b97f-3feb112b59c9"
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      "execution_count": 3,
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              "   Review_ID  Rating Year_Month     Reviewer_Location  \\\n",
              "0  670772142       4     2019-4             Australia   \n",
              "1  670682799       4     2019-5           Philippines   \n",
              "2  670623270       4     2019-4  United Arab Emirates   \n",
              "3  670607911       4     2019-4             Australia   \n",
              "4  670607296       4     2019-4        United Kingdom   \n",
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              "                                         Review_Text               Branch  \n",
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              "1  Its been a while since d last time we visit HK...  Disneyland_HongKong  \n",
              "2  Thanks God it wasn   t too hot or too humid wh...  Disneyland_HongKong  \n",
              "3  HK Disneyland is a great compact park. Unfortu...  Disneyland_HongKong  \n",
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              "\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-1086abcd-2a5f-4256-8a60-87339a1ab5c2 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-1086abcd-2a5f-4256-8a60-87339a1ab5c2');\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-ee8450f8-3b02-4d70-81ac-e3a778b76a93\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee8450f8-3b02-4d70-81ac-e3a778b76a93')\"\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-ee8450f8-3b02-4d70-81ac-e3a778b76a93 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 42656,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 165709224,\n        \"min\": 1398724,\n        \"max\": 670801367,\n        \"num_unique_values\": 42636,\n        \"samples\": [\n          443940017,\n          289625345,\n          143729284\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 112,\n        \"samples\": [\n          \"2016-2\",\n          \"2014-1\",\n          \"2019-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 162,\n        \"samples\": [\n          \"El Salvador\",\n          \"South Sudan\",\n          \"Honduras\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 42632,\n        \"samples\": [\n          \"take a child with you to see their excitement. I had my 4 yr old grandson to see how they enjoyed. there are any number of locations to spend the whole day. time passes to the night until the fireworks ending the show.\",\n          \"If you can get discount tickets do your best to do that. The weekdays are the best to go as they're not as crowded. Get fast passes for the rides that offer it. The workers are really nice. One of the workers on Nemo overheard it was my daughter's 3rd birthday. She asked why she didn't have a birthday button and I said I didn't know about them. When we got off the ride she had one waiting for us and put my daughter's name on it. It was really special.\",\n          \"As always the great joy for the kids makes up for the ridiculously high costs of food and drink! Still a winner for the kids   our 3 year old grandson and our 25 year old daughter and her husband. Fast pass is a must if you don't want to queue for hours. But rides deliver and staff were I have no idea how! The food and drinks are atrociously expensive and the food inside is dreadful. Dinner at Pirates of the Caribbean was 2.5 hours of misery, warm food and dreadful service. How do they get it so wrong. Eat in the village just outside the park so much better! \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Disneyland_HongKong\",\n          \"Disneyland_California\",\n          \"Disneyland_Paris\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drop the other columns and work only with Rating\tand Review_Text\n",
        "\n",
        "df = df[['Rating',  'Review_Text']]\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FhEVU-s7Y_PD",
        "outputId": "dd710725-3eeb-45bf-813c-cbb983a54145"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rating                                        Review_Text\n",
              "0       4  If you've ever been to Disneyland anywhere you...\n",
              "1       4  Its been a while since d last time we visit HK...\n",
              "2       4  Thanks God it wasn   t too hot or too humid wh...\n",
              "3       4  HK Disneyland is a great compact park. Unfortu...\n",
              "4       4  the location is not in the city, took around 1..."
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\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",
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              "        document.querySelector('#df-33dcd665-07da-4fb5-bf8c-9fb214a046c8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
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              "        const element = document.querySelector('#df-33dcd665-07da-4fb5-bf8c-9fb214a046c8');\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --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",
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              "      --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",
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              "    display: none;\n",
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              "    height: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
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              "\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-f53277bb-2a07-4f0a-a025-455cbe957f20 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 42656,\n  \"fields\": [\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 42632,\n        \"samples\": [\n          \"take a child with you to see their excitement. I had my 4 yr old grandson to see how they enjoyed. there are any number of locations to spend the whole day. time passes to the night until the fireworks ending the show.\",\n          \"If you can get discount tickets do your best to do that. The weekdays are the best to go as they're not as crowded. Get fast passes for the rides that offer it. The workers are really nice. One of the workers on Nemo overheard it was my daughter's 3rd birthday. She asked why she didn't have a birthday button and I said I didn't know about them. When we got off the ride she had one waiting for us and put my daughter's name on it. It was really special.\",\n          \"As always the great joy for the kids makes up for the ridiculously high costs of food and drink! Still a winner for the kids   our 3 year old grandson and our 25 year old daughter and her husband. Fast pass is a must if you don't want to queue for hours. But rides deliver and staff were I have no idea how! The food and drinks are atrociously expensive and the food inside is dreadful. Dinner at Pirates of the Caribbean was 2.5 hours of misery, warm food and dreadful service. How do they get it so wrong. Eat in the village just outside the park so much better! \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "source": [
        "# @title Rating\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "df['Rating'].plot(kind='hist', bins=20, title='Rating')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "MVpKz8BwZ14D",
        "outputId": "51c21ad3-48ad-4f8f-fe73-c927f3179140"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Rating\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEmxW8hZZmf4",
        "outputId": "ef9c36dd-b708-4c0f-d7ea-e88e668deba4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 3, 5, 2, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "vn6MkKzKaExb",
        "outputId": "b2dfad1c-7c50-4f31-b005-9bf47b283aeb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Rating          int64\n",
              "Review_Text    object\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Rating</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Review_Text</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# group the rating in to 2 groups ie: 3, 4 and 5 to 1 and 1 and 2 becomes 0\n",
        "\n",
        "def transform_rating(rating):\n",
        "  if rating >=3:\n",
        "    return 1\n",
        "  else:\n",
        "    return 0\n",
        "df['Rating'] = df['Rating'].apply(transform_rating)\n",
        "df['Rating'].plot(kind='hist', bins=20, title='Rating')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "ue_wK8rQaIc4",
        "outputId": "578bc584-850e-492d-d5da-ea346d6632b1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tkV2bNqUakfK",
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      },
      "execution_count": 9,
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            "text/plain": [
              "   Rating                                        Review_Text\n",
              "0       1  If you've ever been to Disneyland anywhere you...\n",
              "1       1  Its been a while since d last time we visit HK...\n",
              "2       1  Thanks God it wasn   t too hot or too humid wh...\n",
              "3       1  HK Disneyland is a great compact park. Unfortu...\n",
              "4       1  the location is not in the city, took around 1..."
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 42656,\n  \"fields\": [\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 42632,\n        \"samples\": [\n          \"take a child with you to see their excitement. I had my 4 yr old grandson to see how they enjoyed. there are any number of locations to spend the whole day. time passes to the night until the fireworks ending the show.\",\n          \"If you can get discount tickets do your best to do that. The weekdays are the best to go as they're not as crowded. Get fast passes for the rides that offer it. The workers are really nice. One of the workers on Nemo overheard it was my daughter's 3rd birthday. She asked why she didn't have a birthday button and I said I didn't know about them. When we got off the ride she had one waiting for us and put my daughter's name on it. It was really special.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
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    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
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        "colab": {
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          "height": 147
        },
        "id": "1civ8P1abqTX",
        "outputId": "6ba4ddab-f7a3-46cf-b0d9-8cb03577d681"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Rating         0\n",
              "Review_Text    0\n",
              "dtype: int64"
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              "<div>\n",
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          "metadata": {},
          "execution_count": 10
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        "# Preprocess the text\n",
        "X = df['Review_Text'].values\n",
        "y = df['Rating'].values\n",
        "\n",
        "# Split the data\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "fSDMFHuQaqTQ"
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      "execution_count": 11,
      "outputs": []
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        "# Load the pre-trained model and tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Tokenize the data\n",
        "train_encodings = tokenizer(X_train.tolist(), truncation=True, padding=True, max_length=128)\n",
        "val_encodings = tokenizer(X_val.tolist(), truncation=True, padding=True, max_length=128)"
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        {
          "output_type": "stream",
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "40b35fb7693b4ec2808bbc16f86c1fee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some weights or buffers of the TF 2.0 model TFBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create TensorFlow datasets\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices((dict(train_encodings), y_train))\n",
        "val_dataset = tf.data.Dataset.from_tensor_slices((dict(val_encodings), y_val))\n",
        "\n",
        "# Fine-tune the model\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=3e-5),\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "ZpM2nfDabW1O"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataset.shuffle(1000).batch(16),\n",
        "          epochs=1,\n",
        "          batch_size=16,\n",
        "          validation_data=val_dataset.batch(16))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0Zm_7qiiAGi",
        "outputId": "c217b097-1b50-4fd9-d059-543a05bf7c6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   6/2133 [..............................] - ETA: 14:33:05 - loss: 0.4686 - accuracy: 0.8854"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Result and Evaluation**\n",
        "\n",
        "The Transformer model is being trained with a batch size of 16 for 1 epoch.\n",
        "The current progress indicates that out of 2133 steps, 205 have been completed.\n",
        "The model has a loss of 0.1772, meaning it is making relatively small errors, and an accuracy of 93.14%, indicating that the model is correctly predicting most of the time during this training phase. However, the training is expected to take a significant amount of time, with an estimated remaining time of over 13 hours.\n"
      ],
      "metadata": {
        "id": "HfcuLymTtmnJ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bDg-n9M3cfSZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "loss, accuracy = model.evaluate(val_dataset.batch(16))\n",
        "print(f'Validation Accuracy: {accuracy:.2f}')\n"
      ],
      "metadata": {
        "id": "1Q1ihuKxcKEM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}