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      "source": [
        "<a href=\"https://colab.research.google.com/github/khaledsoudy-1/FineTuning-BERT-Arabic-Sentiment/blob/main/Fine_Tuning_BERT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "## **Fine-Tuning BERT for Sentiment Analysis** 🚀\n",
        "\n",
        "Created by [Khaled Soudy](https://github.com/khaledsoudy-1) 🥰\n",
        "\n",
        "---\n",
        "Hey there! 👋 Today, we’re unleashing the power of **BERT**—specifically, the **[google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased)** model—to tackle sentiment analysis like a pro! 💡  \n",
        "\n",
        "### What’s the Task?  \n",
        "We’re working with the **[Arabic 100k Reviews](https://www.kaggle.com/datasets/abedkhooli/arabic-100k-reviews)** dataset—a compilation of 100k (well, 99,999) Arabic reviews from hotels, books, movies, products, and airlines. Each review falls into one of three sentiment categories:  \n",
        "1️⃣ **Positive** 🌟  \n",
        "2️⃣ **Negative** 😠  \n",
        "3️⃣ **Mixed** 🤔  \n",
        "\n",
        "#### 📂 **About the Dataset**:  \n",
        "- **Content**: Each review includes a sentiment label (`Positive`, `Negative`, or `Mixed`) and the review text, **separated by tabs** (TSV format).  \n",
        "- **Processing**: Reviews were cleaned of diacritics and non-Arabic characters, ensuring high-quality data.  \n",
        "- **Balanced Classes**: No duplicate reviews, making it a robust dataset for classification tasks.  \n",
        "\n",
        "### Why Fine-Tune BERT?  \n",
        "BERT has a deep contextual understanding of text, but by fine-tuning it with our dataset, we’ll train it to recognize sentiment in Arabic reviews with precision.  \n",
        "\n",
        "### What to Expect?  \n",
        "1️⃣ **Load and explore the dataset** to understand what we’re working with.  \n",
        "2️⃣ **Prepare BERT for sentiment analysis**.  \n",
        "3️⃣ **Train and evaluate** our custom sentiment classifier.  \n",
        "\n",
        "Let’s turn these reviews into actionable insights! 🎉  \n"
      ],
      "metadata": {
        "id": "1wVm7ZxozkRG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## **1- Testing Results Before Fine-Tuning** 🛠️\n",
        "\n",
        "Before fine-tuning the BERT model on our sentiment analysis dataset, we’ll test the pre-trained version as-is. This helps us:\n",
        "\n",
        "Understand the baseline performance of the model.\n",
        "Compare results after we fine-tune the model.\n"
      ],
      "metadata": {
        "id": "u-aG2hKpJMOA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 1: Install Required Libraries\n",
        "To fine-tune BERT, we need the Hugging Face transformers library, which provides pre-trained models, tokenizers, and utilities for NLP tasks. Install it using pip."
      ],
      "metadata": {
        "id": "4S2Stvu9IgGE"
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    },
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      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Um8KNJky1W8S",
        "outputId": "3fcf87d3-49db-4419-cc2b-d1f201861e76"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### Step 2: Load BERT Tokenizer and Model\n"
      ],
      "metadata": {
        "id": "_eyuQgqnJ-gw"
      }
    },
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        "from transformers import BertTokenizer, TFBertForSequenceClassification\n",
        "\n",
        "model_name = \"google-bert/bert-base-uncased\"\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
        "model = TFBertForSequenceClassification.from_pretrained(model_name)"
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        "outputId": "d9323aff-00ec-4aaf-f648-6fba1b1b43f4"
      },
      "execution_count": 7,
      "outputs": [
        {
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We’re using the `google-bert/bert-base-uncased` model, designed for English tasks. Here’s what we’re loading:\n",
        "\n",
        "* `BertTokenizer`: Converts input text into token IDs understandable by the BERT model.\n",
        "* `TFBertForSequenceClassification`: A BERT model tailored for classification tasks (e.g., sentiment analysis).\n",
        "\n",
        "Key Parameters:\n",
        "* `from_pretrained`: Downloads the pre-trained weights of the specified model."
      ],
      "metadata": {
        "id": "EpxQ3lp8K5zw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Step 3: Mount Google Drive\n",
        "We’re working with a dataset stored in Google Drive. Use this code to mount the drive and access the file system."
      ],
      "metadata": {
        "id": "gjc2dpS0KNUK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3LHL_bf1wvp",
        "outputId": "81e7e64d-7f6a-4e8c-df29-5d7548cf401b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Step 4: Load the Dataset\n",
        "Let’s load the **Arabic 100k Reviews dataset**, a TSV file with two columns:\n",
        "\n",
        "* `label`: Sentiment category (*Positive, Negative, or Mixed*).\n",
        "* `text`: Review text in Arabic."
      ],
      "metadata": {
        "id": "agdTGtrwKSLz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = \"/content/drive/MyDrive/Colab Notebooks/05-Fine-Tuning-BERT/ar_reviews_100k.tsv\""
      ],
      "metadata": {
        "id": "liOnDLpo2bX1"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(file_path, sep='\\t')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "jFdbn9B52sdN",
        "outputId": "5ed889b8-d20c-4211-9f38-33c2e20371ef"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          label                                               text\n",
              "0      Positive  ممتاز نوعا ما . النظافة والموقع والتجهيز والشا...\n",
              "1      Positive  أحد أسباب نجاح الإمارات أن كل شخص في هذه الدول...\n",
              "2      Positive  هادفة .. وقوية. تنقلك من صخب شوارع القاهرة الى...\n",
              "3      Positive  خلصنا .. مبدئيا اللي مستني ابهار زي الفيل الاز...\n",
              "4      Positive  ياسات جلوريا جزء لا يتجزأ من دبي . فندق متكامل...\n",
              "...         ...                                                ...\n",
              "99994  Negative  معرفش ليه كنت عاوزة أكملها وهي مش عاجباني من ا...\n",
              "99995  Negative  لا يستحق ان يكون في بوكنق لانه سيئ . لا شي. لا...\n",
              "99996  Negative  كتاب ضعيف جدا ولم استمتع به. فى كل قصه سرد لحا...\n",
              "99997  Negative  مملة جدا. محمد حسن علوان فنان بالكلمات، والوصف...\n",
              "99998  Negative  لن ارجع إليه مرة اخرى . قربه من البحر. المكان ...\n",
              "\n",
              "[99999 rows x 2 columns]"
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            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Key Parameters:**\n",
        "* `sep='\\t'`: Specifies that the file uses tabs as column delimiters."
      ],
      "metadata": {
        "id": "3fixPa3hLZso"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Step 5: Sample Random Rows\n",
        "\n",
        "Let’s take a random sample of 5 rows for testing purposes.\n",
        "\n",
        "* `sample(n=5)`: Randomly selects 5 rows.\n",
        "* `random_state=42`: This ensures that every time you run this code, the same 5 random rows are selected from the dataset (as long as the dataset hasn’t changed).\n",
        "\n",
        "* If you remove `random_state=42`, you may get different random rows each time you run the code.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Mo08L_FtOA-Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random_5_rows = df.sample(n=5, random_state=42)\n",
        "random_5_rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DwDFMniD23uP",
        "outputId": "ad9c20a3-feea-42b8-e3d4-ae66b9863a4b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          label                                               text\n",
              "26002  Positive  أقم على الماشي . بصورة عامة الغرفة جيدة وكل شي...\n",
              "80420  Negative  لم تعجبني كباقي السلسلة و غير متحمس لقراءة الج...\n",
              "19864  Positive  كان المكان ممتاز والأمن والاستقبال اوكي . . ال...\n",
              "81525  Negative  القصة اجمالا مشوقة كونها تعتمد علي شخصيات يجمع...\n",
              "57878     Mixed  اربع نجوم لولا الملل الذي اصابني في النهاية......"
            ],
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              "        document.querySelector('#id_9c826a85-25c9-4157-8abd-a1174282761d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('random_5_rows');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "random_5_rows",
              "summary": "{\n  \"name\": \"random_5_rows\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Positive\",\n          \"Negative\",\n          \"Mixed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"\\u0644\\u0645 \\u062a\\u0639\\u062c\\u0628\\u0646\\u064a \\u0643\\u0628\\u0627\\u0642\\u064a \\u0627\\u0644\\u0633\\u0644\\u0633\\u0644\\u0629 \\u0648 \\u063a\\u064a\\u0631 \\u0645\\u062a\\u062d\\u0645\\u0633 \\u0644\\u0642\\u0631\\u0627\\u0621\\u0629 \\u0627\\u0644\\u062c\\u0632\\u0621 \\u0627\\u0644\\u062b\\u0627\\u0646\\u064a \\u0645\\u0646\\u0647\\u0627\",\n          \"\\u0627\\u0631\\u0628\\u0639 \\u0646\\u062c\\u0648\\u0645 \\u0644\\u0648\\u0644\\u0627 \\u0627\\u0644\\u0645\\u0644\\u0644 \\u0627\\u0644\\u0630\\u064a \\u0627\\u0635\\u0627\\u0628\\u0646\\u064a \\u0641\\u064a \\u0627\\u0644\\u0646\\u0647\\u0627\\u064a\\u0629... \\u0631\\u0648\\u0627\\u064a\\u0629 \\u062c\\u0645\\u064a\\u0644\\u0629 \\u0628\\u0627\\u0644\\u0645\\u062c\\u0645\\u0644 \\u0627\\u062e\\u0630\\u062a \\u0645\\u0646\\u0647\\u0627 \\u0627\\u0644\\u0643\\u062b\\u064a\\u0631 \\u0645\\u0646 \\u0627\\u0644\\u0627\\u0642\\u062a\\u0628\\u0627\\u0633\\u0627\\u062a... \\u0648\\u0627\\u0643\\u062b\\u0631 \\u0645\\u0627 \\u0627\\u0639\\u062c\\u0628\\u0646\\u064a \\u0627\\u062e\\u062a\\u064a\\u0627\\u0631 \\u0631\\u064a\\u0645 \\u0644\\u0644\\u0633\\u0641\\u0631 \\u0648\\u0627\\u0644\\u062e\\u0631\\u0648\\u062c \\u0645\\u0646 \\u0642\\u0639\\u0648\\u0639\\u0629 \\u0632\\u064a\\u0627\\u062f...\",\n          \"\\u0643\\u0627\\u0646 \\u0627\\u0644\\u0645\\u0643\\u0627\\u0646 \\u0645\\u0645\\u062a\\u0627\\u0632 \\u0648\\u0627\\u0644\\u0623\\u0645\\u0646 \\u0648\\u0627\\u0644\\u0627\\u0633\\u062a\\u0642\\u0628\\u0627\\u0644 \\u0627\\u0648\\u0643\\u064a . . \\u0627\\u0644\\u062a\\u0643\\u064a\\u064a\\u0641 \\u0643\\u0627\\u0646 \\u062d\\u0627\\u0631 \\u0648\\u0628\\u0644\\u063a\\u062a \\u0627\\u0643\\u062b\\u0631 \\u0645\\u0646 \\u0645\\u0631\\u0647 \\u064a\\u062a\\u0645 \\u0644\\u0647 \\u0635\\u064a\\u0627\\u0646\\u0647 \\u0648\\u0644\\u0643\\u0646 \\u062f\\u0648\\u0646 \\u062c\\u062f\\u0648\\u064a \\u0628\\u0627\\u0644\\u0627\\u0636\\u0627\\u0641\\u0629 \\u0627\\u0646\\u0647 \\u0627\\u0644\\u0633\\u0631\\u0641\\u0633 \\u0643\\u0627\\u0646 \\u0646\\u0648\\u0639\\u0627 \\u0645\\u0627 \\u0636\\u0639\\u064a\\u0641 \\u062d\\u062a\\u064a \\u0639\\u0645\\u0644 \\u0627\\u0644\\u0644\\u0627\\u0646\\u062f\\u0631\\u064a \\u0645\\u0627 \\u0643\\u0627\\u0646 \\u0645\\u0646\\u0648\\u0641\\u0631 \\u0641\\u064a \\u0646\\u0641\\u0633 \\u0627\\u0644\\u0641\\u0646\\u062f\\u0642 \\u0648\\u0643\\u0627\\u0646 \\u0627\\u0644\\u0644\\u0627\\u0646\\u062f\\u0631\\u064a \\u064a\\u0643\\u0648\\u0646 \\u062e\\u0627\\u0631\\u062c \\u0627\\u0644\\u0641\\u0646\\u062f\\u0642 \\u0648\\u0627\\u0644\\u0633\\u0639\\u0631 \\u0643\\u0627\\u0646 \\u063a\\u0627\\u0644\\u064a \\u0627\\u0644\\u062b\\u0645\\u0646 \\u0648\\u0627\\u0646\\u0627 \\u0627\\u0633\\u062a\\u063a\\u0631\\u0628\\u062a \\u0645\\u0646 \\u0627\\u0644\\u0623\\u0633\\u0639\\u0627\\u0631\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "\n",
        "### Step 6: Create a Copy of the Sample\n",
        "To avoid modifying the original dataset, create a copy of the sampled rows. This ensures the original data remains intact."
      ],
      "metadata": {
        "id": "TnSiAVkYOH7L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's copy these random five rows (To Avoid Modifying the Original DataSet)\n",
        "five_rows_copy = random_5_rows.copy()\n",
        "\n",
        "# Define custom mappings similar to the Dataset\n",
        "custom_mapping = {\n",
        "    0: \"Negative\",\n",
        "    1: \"Mixed\",\n",
        "    2: \"Positive\"\n",
        "}"
      ],
      "metadata": {
        "id": "gWaiJwS83T8W"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### Step 7: Test the BERT Model on the Dataset\n",
        "We’re feeding each row to the BERT model to predict sentiments. Here’s what happens step-by-step:\n",
        "\n",
        "Key Steps:\n",
        "1. Tokenization: Converts the text into tokens using the `tokenizer`.\n",
        "2. Prediction: Passes tokenized inputs through the model to get raw outputs (`logits`).\n",
        "3. Classification: Maps logits to class indices using `argmax` from **pandas Library**.\n",
        "4. Custom Mapping: Converts class indices into human-readable labels."
      ],
      "metadata": {
        "id": "OpqSmDmFOLC1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's test the BERT model on the Dataset\n",
        "import numpy as np\n",
        "\n",
        "# Create an empty list\n",
        "predicted_sentiments_list = []\n",
        "\n",
        "for row in five_rows_copy['label']:\n",
        "  inputs = tokenizer(row)\n",
        "\n",
        "  input_ids = inputs['input_ids']\n",
        "\n",
        "  predictions = model.predict([input_ids])\n",
        "\n",
        "  logits = predictions.logits\n",
        "\n",
        "  predicted_class = np.argmax(logits)\n",
        "\n",
        "  predicted_sentiment = custom_mapping[predicted_class]\n",
        "\n",
        "  # Append to sentiments list\n",
        "  predicted_sentiments_list.append(predicted_sentiment)\n",
        "\n",
        "print(predicted_sentiments_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Svyx8aBc3nJO",
        "outputId": "55b082fd-e064-4391-c1f2-22d18a3ca2aa"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 5s 5s/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 49ms/step\n",
            "1/1 [==============================] - 0s 49ms/step\n",
            "['Mixed', 'Mixed', 'Mixed', 'Mixed', 'Mixed']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Functions and Parameters:\n",
        "* `tokenizer()`: Tokenizes input text.\n",
        "* `logits`: Raw scores from the model for each sentiment class.\n",
        "* `np.argmax(logits)`: Finds the class with the highest score."
      ],
      "metadata": {
        "id": "M1Xi3v8nMU0r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### Step 8: Compare Predicted Sentiments with Original Labels\n",
        "Finally, let’s add a new column to the copied DataFrame (**five_rows_copy**) to compare the original labels with the BERT model’s predictions.\n",
        "\n",
        "* `Bert_Sentiments`: Column containing predicted sentiments."
      ],
      "metadata": {
        "id": "T5rVVEJnMjLT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparing Predicted Sentiments with Original Labels\n",
        "\n",
        "five_rows_copy['Bert_Sentiments'] = predicted_sentiments_list\n",
        "five_rows_copy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UOCPGkvv57kt",
        "outputId": "1cca61d0-cd23-4e41-809a-001936e0d59d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          label                                               text  \\\n",
              "26002  Positive  أقم على الماشي . بصورة عامة الغرفة جيدة وكل شي...   \n",
              "80420  Negative  لم تعجبني كباقي السلسلة و غير متحمس لقراءة الج...   \n",
              "19864  Positive  كان المكان ممتاز والأمن والاستقبال اوكي . . ال...   \n",
              "81525  Negative  القصة اجمالا مشوقة كونها تعتمد علي شخصيات يجمع...   \n",
              "57878     Mixed  اربع نجوم لولا الملل الذي اصابني في النهاية......   \n",
              "\n",
              "      Bert_Sentiments  \n",
              "26002           Mixed  \n",
              "80420           Mixed  \n",
              "19864           Mixed  \n",
              "81525           Mixed  \n",
              "57878           Mixed  "
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              "      } 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",
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              "        document.querySelector('#df-adc9e8d5-5508-4a6d-afc0-94339511b3c0 button');\n",
              "      quickchartButtonEl.style.display =\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "## **Limitations: BERT Model and Arabic Data ?!**🤔\n",
        "The pre-trained BERT model we’re using (`google-bert/bert-base-uncased`) was trained **primarily on English text**, which means it might not perform optimally on Arabic data. 🧐 While BERT’s architecture is powerful and can generalize well, it wasn’t specifically fine-tuned for Arabic language tasks like sentiment analysis. As a result, the model's predictions on Arabic reviews **might not be as reliable or accurate.**\n",
        "\n",
        "\n",
        "**Why Fine-Tune the Model?**\n",
        "\n",
        "By fine-tuning the model using the Arabic 100k Reviews dataset, we can teach the model to better understand the nuances of sentiment in Arabic text. Fine-tuning allows the model to specialize in recognizing the sentiment of Arabic reviews, improving its performance and reliability for this specific task. 🚀"
      ],
      "metadata": {
        "id": "sLiqFmFqRRFq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "## **2- Testing Results After Fine-Tuning 🛠️**\n",
        "Now that we’ve fine-tuned our BERT model on the Arabic 100k Reviews dataset, it’s time to test it on new data! This will help us:\n",
        "\n",
        "* Evaluate how well the fine-tuned model performs on Arabic sentiment analysis.\n",
        "* Compare its performance against the baseline (pre-trained model) and see the improvement.\n",
        "\n",
        "We’ll run the fine-tuned model on a set of reviews and analyze the results to see how much better it handles the Arabic sentiment classification task! 💡"
      ],
      "metadata": {
        "id": "asub7mRtSl0R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 1: Preparing Dataset for Training\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "O7XgnbLZfaTq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **A- Sampling Dataset (if necessary)**"
      ],
      "metadata": {
        "id": "zDumyOdGqJiE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NR3dwhk7fFzU",
        "outputId": "672a8eec-a555-47e8-9555-c52413866e49"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99999, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "* Since our dataset consists of **99,999 rows**, training on the entire dataset could **take a long time** ⏳.\n",
        "\n",
        "* Additionally, there's always a risk of facing errors during lengthy training, which can be frustrating.\n",
        "* To avoid this, **we’ll sample 30,000 rows** to speed up the process and lessen potential issues."
      ],
      "metadata": {
        "id": "8-CaSvw6g9dp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.sample(n=30000, random_state=42)\n",
        "\n",
        "print(f\"Total Number of Rows and Columns After Sampling: {df.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rGFUYoclfXzv",
        "outputId": "8965bb22-9aac-4150-c7fd-aaee92c4d918"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Number of Rows and Columns After Sampling: (30000, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `random_state=42`: It ensures that the sampling process is reproducible, meaning **we'll get the same 30,000 rows every time we run the code**. This is important to ensure consistent results during training and evaluation."
      ],
      "metadata": {
        "id": "fp--Ympohs2d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "#### **B- Visualizing Our Dataset** (Optional)\n",
        "\n",
        "Before we dive into fine-tuning our model, it's essential to understand the distribution of review lengths (**the text column**) in our dataset. Since transformer models like BERT have a limit on the number of tokens (**512 Tokens**) they can process, we need to make sure that our data fits within this limit.\n",
        "\n",
        "--\n",
        "\n",
        "**Why Visualize Review Lengths?**\n",
        "- **Understand the dataset better**: Visualizing the text lengths helps us see whether most reviews (texts) are short or long and decide how to handle them.\n",
        "\n",
        "- **Decide the right `maxlen` for the model**: Since BERT has a token limit (512 tokens), if your input exceeds 512 tokens:\n",
        "It will either throw an error (if truncation isn’t set) or truncate to the first 512 tokens. By Visualising the dataset, we can chose a resonable `maxlen` for most cases."
      ],
      "metadata": {
        "id": "6wxP1ylWs2Ad"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.text.apply(lambda x: len(x.split())).plot(kind='hist')"
      ],
      "metadata": {
        "id": "PAaXC-3sf1v6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "4a083503-ffb4-402d-e21e-72ec3cab16b9"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "**What Does This Output Mean?! 🤔**\n",
        "\n",
        "Looking at the histogram, you can observe that:\n",
        "\n",
        "1.    **Most reviews are short:** A majority of reviews in the dataset fall within a small word count range (likely less than 100 words).\n",
        "\n",
        "\n",
        "2.  **Few reviews are very long (outliers):** There’s a tail at the higher end of the histogram, indicating that a small number of reviews have a large number of words.\n",
        "\n",
        "\n",
        "---\n",
        "**Next Steps ?! 🤨**\n",
        "\n",
        "Depending on your analysis, you may want to:\n",
        "\n",
        "1. **Remove reviews with too few words** that do not contribute much context.\n",
        "\n",
        "2. **Truncate or split long reviews** to ensure they fit within the model's tokenization limits (e.g., BERT has a token limit of 512 tokens per input).\n",
        "\n",
        "3. **setting the `maxlen` parameter**: It specifies the maximum number of words (or tokens) that BERT will process for each review. This is crucial because BERT has a fixed limit on the number of tokens it can handle.\n"
      ],
      "metadata": {
        "id": "_g7xZMwfx1ej"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "**How to Decide the Right `maxlen` ?!** 🔍\n",
        "\n",
        "* **Use shorter `maxlen`:** If your text is generally short (e.g., tweets or short reviews), using a smaller maxlen (like 100 or 150) could work.\n",
        "\n",
        "* **Use longer `maxlen`:** If your text is generally long (e.g., long articles or detailed reviews), you might need to go up to 512 **(the max token limit of BERT)**.\n",
        "\n",
        "* **Test and Experiment:** If you're unsure, you can experiment with different values of `maxlen`. Start with something reasonable like **200** and test the performance of the model. You can adjust it based on results.\n",
        "\n",
        "* **Memory & Speed:** Shorter `maxlen` (e.g., **200**) leads to faster processing and lower memory usage. Longer `maxlen` (e.g., **512**) requires more computational resources.\n",
        "\n",
        "* **Dataset Analysis:** Visualize the token distribution (as we did earlier) to pick a reasonable `maxlen` that covers **most of your data** without truncating too much.\n",
        "\n"
      ],
      "metadata": {
        "id": "8bL83p9R1RH4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "**What If We Choose a Different `maxlen` ?!**🤔\n",
        "\n",
        "* **Too Short (e.g., `maxlen=50`):** If we use a very short limit, like 50, we risk losing important context from longer reviews. This could negatively impact the model's accuracy, especially for reviews that need more words to express sentiment.\n",
        "\n",
        "* **Too Long (e.g., `maxlen=512`):** If we set maxlen too high (such as 512), the model might spend more time processing longer sequences that aren't needed for most of the reviews. This can slow down training and inference without improving performance.\n"
      ],
      "metadata": {
        "id": "8Hs_-3BH3Q2Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "**🔴 Final Decesion on `maxlen` ?!**\n",
        "\n",
        "\n",
        "- Setting `maxlen=200` is a reasonable choice.\n",
        "\n",
        "\n",
        "\n",
        "**Why 200 Tokens?**\n",
        "\n",
        "\n",
        "If we set `maxlen=200`, we’re limiting the input to **200 tokens**, even though BERT can handle up to **512 tokens**. This is useful when you:\n",
        "\n",
        "1. **Want to reduce memory usage or training time.**\n",
        "\n",
        "2. **Know that longer inputs aren't critical for your task.**\n",
        "\n"
      ],
      "metadata": {
        "id": "PIR-V2HdHnOG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "#### **C- Converting the dataset into a format that BERT (or other ML models) can understand.**\n",
        "\n",
        "* **Text Data**: BERT and ML models don’t directly understand raw text. We need to tokenize it into numerical representations (tokens) that the model can process.\n",
        "\n",
        "* **Labels**: Similarly, models require numerical values for the target labels (e.g., sentiment categories like \"Mixed,\" \"Positive,\" and \"Negative\") instead of text.\n",
        "\n",
        "------------------------\n",
        "**How Can We Do that ❓❗**\n",
        "1. **Numerically Encode the Labels:** Use a library like **scikit-learn's `LabelEncoder`** to map text-based labels (e.g., \"Mixed\") to numerical values (e.g., 0, 1, ...).\n",
        "\n",
        "\n",
        "2. **Split the Data:** Divide the dataset into training and validation sets to evaluate the model during training.\n",
        "\n",
        "3. **Tokenize the Text:** Use BERT’s tokenizer to break down sentences tokens and convert them into numerical IDs.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "KUGs13fLUbgA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "for label in np.unique(df['label']):\n",
        "  print(label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6lcxfpRKVFEd",
        "outputId": "a18f8ea4-1e0c-4cda-aae1-e072f4041018"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mixed\n",
            "Negative\n",
            "Positive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `np.unique(df['label'])`: extracts all unique values in the `label` column of the Dataframe.\n"
      ],
      "metadata": {
        "id": "5hqPj9dnfJB2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "LE = LabelEncoder()\n",
        "\n",
        "df['label'] = LE.fit_transform(df['label'])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "joeJILbUWvD2",
        "outputId": "366c4558-aea5-4037-f457-a3cc0ae3de3a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       label                                               text\n",
              "26002      2  أقم على الماشي . بصورة عامة الغرفة جيدة وكل شي...\n",
              "80420      1  لم تعجبني كباقي السلسلة و غير متحمس لقراءة الج...\n",
              "19864      2  كان المكان ممتاز والأمن والاستقبال اوكي . . ال...\n",
              "81525      1  القصة اجمالا مشوقة كونها تعتمد علي شخصيات يجمع...\n",
              "57878      0  اربع نجوم لولا الملل الذي اصابني في النهاية......"
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              "      <th>26002</th>\n",
              "      <td>2</td>\n",
              "      <td>أقم على الماشي . بصورة عامة الغرفة جيدة وكل شي...</td>\n",
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              "    <tr>\n",
              "      <th>80420</th>\n",
              "      <td>1</td>\n",
              "      <td>لم تعجبني كباقي السلسلة و غير متحمس لقراءة الج...</td>\n",
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              "    <tr>\n",
              "      <th>19864</th>\n",
              "      <td>2</td>\n",
              "      <td>كان المكان ممتاز والأمن والاستقبال اوكي . . ال...</td>\n",
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              "      <th>81525</th>\n",
              "      <td>1</td>\n",
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              "      <th>57878</th>\n",
              "      <td>0</td>\n",
              "      <td>اربع نجوم لولا الملل الذي اصابني في النهاية......</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93de5bb9-1e11-472c-9fbc-fab44732ee49')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </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",
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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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-93de5bb9-1e11-472c-9fbc-fab44732ee49 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-93de5bb9-1e11-472c-9fbc-fab44732ee49');\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\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",
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              "    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",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    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-1f08b620-cb87-477e-8975-3d7bf95c0dbf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 30000,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30000,\n        \"samples\": [\n          \"\\u064a\\u062d\\u062a\\u0627\\u062c \\u0644\\u0644\\u062a\\u0637\\u0648\\u064a\\u0631 . \\u062a\\u0633\\u062c\\u064a\\u0644 \\u0627\\u0644\\u062e\\u0631\\u0648\\u062c \\u0627\\u0644\\u0645\\u062a\\u0623\\u062e\\u0631. \\u062f\\u062e\\u0648\\u0644 \\u062a\\u0646\\u0638\\u064a\\u0641 \\u0627\\u0644\\u063a\\u0631\\u0641 \\u0639\\u0644\\u0649 \\u0627\\u0644\\u063a\\u0631\\u0641\\u0629 \\u0628\\u062f\\u0648\\u0646 \\u0627\\u0633\\u062a\\u0626\\u0630\\u0627\\u0646\",\n          \"\\u0627\\u0644\\u0645\\u062c\\u0647\\u0648\\u062f \\u0627\\u0644\\u0645\\u0628\\u0627\\u0644\\u063a \\u0641\\u064a\\u0647 \\u0641\\u0649 \\u0627\\u062e\\u062a\\u064a\\u0627\\u0631 \\u0648 \\u0631\\u0633\\u0645 \\u0627\\u0644\\u062a\\u0639\\u0627\\u0628\\u064a\\u0631 \\u0648 \\u062a\\u062c\\u0645\\u064a\\u0644 \\u0627\\u0644\\u0644\\u063a\\u0629 \\u0627\\u0636\\u0631 \\u0628\\u0634\\u0643\\u0644 \\u0643\\u0628\\u064a\\u0631 \\u0628\\u0627\\u0646\\u0633\\u064a\\u0627\\u0628\\u064a\\u0629 \\u0627\\u0644\\u0633\\u0631\\u062f \\u0648\\u0627\\u0648\\u062c\\u062f \\u0643\\u0645 \\u0644\\u0627 \\u0628\\u0627\\u0633 \\u0628\\u0647 \\u0645\\u0646 \\u0627\\u0644\\u0645\\u0644\\u0644\",\n          \"\\u0627\\u062e\\u064a\\u0633 \\u0643\\u062a\\u0627\\u0628 \\u0627\\u0644\\u0642\\u0635\\u0647 \\u0645\\u0642\\u062a\\u0628\\u0633\\u0647\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `LabelEncoder()`: This scikit-learn class converts categorical labels into numeric ones.\n",
        "\n",
        "* `LE.fit_transform(df['label'])`: It maps the text labels to numeric values. For example:\n",
        "  * \"Mixed\" → 0\n",
        "  * \"Negative\" → 1\n",
        "  * \"Positive\" → 2\n",
        "\n",
        "* The transformed labels are then stored back in the **`df['label']` column**."
      ],
      "metadata": {
        "id": "Rwc2rRHnYsLb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check after converting labels into numerics\n",
        "for label in np.unique(df['label']):\n",
        "  print(label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXavclEBYpZY",
        "outputId": "ce24f187-29d7-42af-fac8-91bba7262b49"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "1\n",
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Step 2: Splitting the Data for Training and Validation:**\n",
        "To train and evaluate our model, we need to split the dataset into:\n",
        "\n",
        "* **Training Set**: Used to teach the model.\n",
        "* **Validation Set**: Used to evaluate the model's performance.\n",
        "\n"
      ],
      "metadata": {
        "id": "6qD83sL9eCoT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train, val = train_test_split(df, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "tiYUaysvZ35Z"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `train_test_split()`: Splits the data set into training set and validation set.\n",
        "\n",
        "* `test_size=0.2`: Specifies that 20% of the dataset will be used as the validation set.\n",
        "\n"
      ],
      "metadata": {
        "id": "08Y0CcVUgxpR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the new shapes\n",
        "print(train.shape)\n",
        "print(val.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wy1t98fDb1qQ",
        "outputId": "f4a18b28-9a33-484f-824b-f0f9b1b69102"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(24000, 2)\n",
            "(6000, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's check our sets\n",
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "76PyNK_BcyHg",
        "outputId": "b3a60aeb-f5b4-4a5a-b1a3-ffa3e131d3b8"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       label                                               text\n",
              "26667      2       استمتعت . الموقع الخصوصيه. عدم توفر الانترنت\n",
              "66268      0  مكان الفندق رائع قريب من المسجد النبوي . قرب م...\n",
              "88175      1  لن اكرر الاقامة . . رائحة الشراشف سيئة لايوضع ...\n",
              "34168      0                               بسيط وممتع الى حد ما\n",
              "35914      0  مرضي. القرب من الحرم. الاثاث لا يرتقي لفندق خم..."
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              "          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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5b3832b9-6ac8-4df8-be4d-05aba3283a8b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5b3832b9-6ac8-4df8-be4d-05aba3283a8b')\"\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-5b3832b9-6ac8-4df8-be4d-05aba3283a8b 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": "train",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 24000,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24000,\n        \"samples\": [\n          \"\\u0645\\u0631\\u0636\\u064a. \\u0642\\u0631\\u0628\\u0647 \\u0645\\u0646 \\u0628\\u064a\\u062a \\u0627\\u0644\\u062d\\u0628. \\u0648\\u0644\\u0627 \\u0634\\u064a\",\n          \"\\u0643\\u062a\\u0627\\u0628 \\u062c\\u0645\\u064a\\u0644 .. \\u0639\\u062c\\u0628\\u062a\\u0646\\u0649 \\u0627\\u0648\\u0649 \\u0627\\u0646 \\u0627\\u0644\\u0642\\u0635\\u0635 \\u0642\\u0635\\u064a\\u0631\\u0647 \\u0645\\u0634 \\u0628\\u062a\\u0627\\u062e\\u062f \\u0635\\u0641\\u062d\\u0627\\u062a \\u0643\\u062a\\u064a\\u0631 \\u0644\\u062f\\u0631\\u062c\\u0647 \\u0627\\u0646\\u0649 \\u0627\\u062a\\u0648\\u0647 \\u0645\\u0646 \\u0627\\u0644\\u0643\\u0627\\u062a\\u0628. \\u0639\\u062c\\u0628\\u062a\\u0646\\u0649 \\u0627\\u0644\\u0631\\u0633\\u0648\\u0645\\u0627\\u062a \\u0641\\u0649 \\u0643\\u0644 \\u0642\\u0635\\u0647 \\u0648\\u0643\\u0644 \\u0631\\u0633\\u0645\\u0647 \\u0645\\u0627\\u0634\\u064a\\u0647 \\u0645\\u0639 \\u0627\\u0644\\u0642\\u0635\\u0647 \\u0641\\u0639\\u0644\\u0627. \\u0627\\u0643\\u062a\\u0631 \\u062d\\u0627\\u062c\\u0647 \\u0639\\u062c\\u0628\\u062a\\u0646\\u0649 \\u0641\\u0649 \\u0627\\u0644\\u0643\\u062a\\u0627\\u0628 \\u0627\\u0644\\u0628\\u0633\\u0627\\u0637\\u0647 : . \\u0648\\u0643\\u0644 \\u0645\\u062d\\u0637\\u0647 \\u0627\\u062d\\u0644\\u0649 \\u0645\\u0646 \\u0627\\u0644\\u062a\\u0627\\u0646\\u064a\\u0647\",\n          \"\\u0643\\u0648\\u064a\\u0633\\u0647 \\u062c\\u062f\\u0627 \\u0648\\u0646\\u064a\\u0643\\u064a\\u0648\\u0644\\u0627 \\u0631\\u062c\\u0644 \\u062f\\u0627\\u0647\\u064a\\u0647\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Resetting the Indexes 🔄 :**\n",
        "When you split the data, the original row indices from the dataframe are retained. Resetting the index ensures that the new subsets start with sequential indices.\n",
        "\n"
      ],
      "metadata": {
        "id": "tWgXZS1YeoBt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's reset the index values\n",
        "train.reset_index(drop=True, inplace=True)\n",
        "val.reset_index(drop=True, inplace=True)\n",
        "\n",
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6ea3gnNmc-iX",
        "outputId": "7cd31e20-8b86-4156-d660-99f5d024e63c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   label                                               text\n",
              "0      2       استمتعت . الموقع الخصوصيه. عدم توفر الانترنت\n",
              "1      0  مكان الفندق رائع قريب من المسجد النبوي . قرب م...\n",
              "2      1  لن اكرر الاقامة . . رائحة الشراشف سيئة لايوضع ...\n",
              "3      0                               بسيط وممتع الى حد ما\n",
              "4      0  مرضي. القرب من الحرم. الاثاث لا يرتقي لفندق خم..."
            ],
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              "        vertical-align: middle;\n",
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              "      <th>0</th>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>لن اكرر الاقامة . . رائحة الشراشف سيئة لايوضع ...</td>\n",
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              "      <th>3</th>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    </button>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\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-convert:hover {\n",
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              "      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-2b55123d-f611-437e-8d7e-c1eef12fdc2a 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-2b55123d-f611-437e-8d7e-c1eef12fdc2a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "<div id=\"df-5760b682-2245-4f50-8089-29c7327e361f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5760b682-2245-4f50-8089-29c7327e361f')\"\n",
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              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5760b682-2245-4f50-8089-29c7327e361f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
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              "  </div>\n"
            ],
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            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `drop=True`: Drops the old index column instead of adding it back as a new column.\n",
        "\n",
        "* `inplace=True`: Updates the dataframe directly instead of creating a copy.\n"
      ],
      "metadata": {
        "id": "_L0RcbcKhdE7"
      }
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      "execution_count": 25,
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 24000,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24000,\n        \"samples\": [\n          \"\\u0645\\u0631\\u0636\\u064a. \\u0642\\u0631\\u0628\\u0647 \\u0645\\u0646 \\u0628\\u064a\\u062a \\u0627\\u0644\\u062d\\u0628. \\u0648\\u0644\\u0627 \\u0634\\u064a\",\n          \"\\u0643\\u062a\\u0627\\u0628 \\u062c\\u0645\\u064a\\u0644 .. \\u0639\\u062c\\u0628\\u062a\\u0646\\u0649 \\u0627\\u0648\\u0649 \\u0627\\u0646 \\u0627\\u0644\\u0642\\u0635\\u0635 \\u0642\\u0635\\u064a\\u0631\\u0647 \\u0645\\u0634 \\u0628\\u062a\\u0627\\u062e\\u062f \\u0635\\u0641\\u062d\\u0627\\u062a \\u0643\\u062a\\u064a\\u0631 \\u0644\\u062f\\u0631\\u062c\\u0647 \\u0627\\u0646\\u0649 \\u0627\\u062a\\u0648\\u0647 \\u0645\\u0646 \\u0627\\u0644\\u0643\\u0627\\u062a\\u0628. \\u0639\\u062c\\u0628\\u062a\\u0646\\u0649 \\u0627\\u0644\\u0631\\u0633\\u0648\\u0645\\u0627\\u062a \\u0641\\u0649 \\u0643\\u0644 \\u0642\\u0635\\u0647 \\u0648\\u0643\\u0644 \\u0631\\u0633\\u0645\\u0647 \\u0645\\u0627\\u0634\\u064a\\u0647 \\u0645\\u0639 \\u0627\\u0644\\u0642\\u0635\\u0647 \\u0641\\u0639\\u0644\\u0627. \\u0627\\u0643\\u062a\\u0631 \\u062d\\u0627\\u062c\\u0647 \\u0639\\u062c\\u0628\\u062a\\u0646\\u0649 \\u0641\\u0649 \\u0627\\u0644\\u0643\\u062a\\u0627\\u0628 \\u0627\\u0644\\u0628\\u0633\\u0627\\u0637\\u0647 : . \\u0648\\u0643\\u0644 \\u0645\\u062d\\u0637\\u0647 \\u0627\\u062d\\u0644\\u0649 \\u0645\\u0646 \\u0627\\u0644\\u062a\\u0627\\u0646\\u064a\\u0647\",\n          \"\\u0643\\u0648\\u064a\\u0633\\u0647 \\u062c\\u062f\\u0627 \\u0648\\u0646\\u064a\\u0643\\u064a\\u0648\\u0644\\u0627 \\u0631\\u062c\\u0644 \\u062f\\u0627\\u0647\\u064a\\u0647\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ozuBLxfylULy",
        "outputId": "32cba703-d911-485f-81d6-b51d42171b8d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   label                                               text\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5995ade5-8a7b-43c7-8df8-6aa48245a2f5')\"\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-5995ade5-8a7b-43c7-8df8-6aa48245a2f5 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-5995ade5-8a7b-43c7-8df8-6aa48245a2f5');\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",
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              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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-66be444f-50d8-444a-abe7-160aeaf324b0 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",
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              "summary": "{\n  \"name\": \"val\",\n  \"rows\": 6000,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6000,\n        \"samples\": [\n          \"\\u0645\\u0631\\u0636\\u064a. \\u0627\\u0644\\u0642\\u0631\\u0628 \\u0645\\u0646 \\u062f\\u0628\\u064a \\u0645\\u0648\\u0644 \\u0628\\u0633. \\u063a\\u0627\\u0644\\u064a \\u0648\\u0639\\u062f\\u0645 \\u0648\\u062c\\u0648\\u062f \\u0646\\u0627\\u0636\\u0641\\u0647\",\n          \"\\u0631\\u0648\\u0627\\u064a\\u0629 \\u0630\\u0627\\u062a \\u0648\\u0642\\u0639 \\u0633\\u0631\\u064a\\u0639 .. \\u0625\\u0646\\u0633\\u0627\\u0646\\u064a\\u0629 \\u0628\\u062f\\u0631\\u062c\\u0629 \\u0643\\u0628\\u064a\\u0631\\u0629 .. \\u0645\\u062a\\u0635\\u0648\\u0641\\u0629 \\u0648 \\u0644\\u0643\\u0646 \\u0644\\u0627 \\u062a\\u0639\\u0628\\u0631 \\u0639\\u0646 \\u0627\\u0644\\u0625\\u0633\\u0644\\u0627\\u0645 \\u0628\\u0623\\u064a \\u062d\\u0627\\u0644 .. \\u0628\\u0644 \\u062a\\u062d\\u0627\\u0648\\u0644 \\u0623\\u0646 \\u062a\\u0639\\u0637\\u064a \\u0645\\u0628\\u0631\\u0631\\u0627\\u062a \\u0644\\u0644\\u0623\\u0633\\u0641 \\u0644\\u0644\\u062a\\u062d\\u0631\\u0631 \\u0645\\u0646 \\u0623\\u064a \\u0625\\u0644\\u062a\\u0632\\u0627\\u0645\\u0627\\u062a \\u062f\\u064a\\u0646\\u064a\\u0629 . \\u0623\\u0643\\u062b\\u0631 \\u0645\\u0627 \\u0623\\u0639\\u062c\\u0628\\u0646\\u064a \\u0641\\u064a\\u0647\\u0627 \\u0643\\u0627\\u0646\\u062a \\u0647\\u0630\\u0647 \\u0627\\u0644\\u0639\\u0628\\u0627\\u0631\\u0629:. \\u0645\\u0627\\u0630\\u0627 \\u062a\\u0642\\u0648\\u0644\\u061f \\u0625\\u0646\\u0647 \\u0627\\u0644\\u0645\\u0633\\u062c\\u062f \\u0627\\u0644\\u0623\\u0632\\u0631\\u0642\\u060c \\u0627\\u0644\\u0645\\u0643\\u0627\\u0646 \\u0627\\u0644\\u0630\\u064a \\u064a\\u062d\\u0645\\u0644 \\u0631\\u0627\\u0626\\u062d\\u0629 \\u0627\\u0644\\u062c\\u0633\\u062f \\u0644\\u0627 \\u064a\\u0631\\u0648\\u0642 \\u0644\\u0643\\u061f \\u0647\\u0644 \\u0623\\u0642\\u062f\\u0627\\u0645\\u0643 \\u0644\\u064a\\u0633\\u062a \\u0644\\u0647\\u0627 \\u0631\\u0627\\u0626\\u062d\\u0629 \\u0639\\u0644\\u0649 \\u0627\\u0644\\u0625\\u0637\\u0644\\u0627\\u0642\\u061f \\u0647\\u0644 \\u0645\\u0643\\u0627\\u0646 \\u0627\\u0644\\u0639\\u0628\\u0627\\u062f\\u0629 \\u0627\\u0644\\u0630\\u064a \\u064a\\u062d\\u0645\\u0644 \\u0631\\u0627\\u0626\\u062d\\u0629 \\u0627\\u0644\\u0625\\u0646\\u0633\\u0627\\u0646\\u060c \\u0648 \\u0627\\u0644\\u0630\\u064a \\u0635\\u0646\\u0639 \\u0645\\u0646 \\u0623\\u062c\\u0644 \\u0627\\u0644\\u0625\\u0646\\u0633\\u0627\\u0646\\u060c \\u0648 \\u0627\\u0644\\u0630\\u064a \\u064a\\u0648\\u062c\\u062f \\u0628\\u062f\\u0627\\u062e\\u0644\\u0629 \\u0627\\u0644\\u0625\\u0646\\u0633\\u0627\\u0646\\u060c \\u064a\\u0642\\u0631\\u0641\\u0643\\u061f\\u0625\\u0646 \\u0647\\u0646\\u0627\\u0643 \\u0628\\u0639\\u0636 \\u0627\\u0644\\u0623\\u0641\\u0643\\u0627\\u0631 \\u0627\\u0644\\u0628\\u0627\\u0631\\u064a\\u0633\\u064a\\u0629 \\u0628\\u062f\\u0627\\u062e\\u0644\\u0643\\u060c \\u0623\\u0645\\u0627 \\u0623\\u0646\\u0627 \\u0641\\u0639\\u0637\\u0631 \\u0627\\u0644\\u062c\\u0648\\u0627\\u0631\\u0628 \\u0647\\u0630\\u0627 \\u064a\\u0645\\u0644\\u0624\\u0646\\u064a \\u062b\\u0642\\u0629 \\u0648 \\u062a\\u0648\\u0627\\u0636\\u0639\\u0627. \\u0625\\u0646\\u0647 \\u064a\\u062c\\u0639\\u0644\\u0646\\u064a \\u0623\\u0642\\u0648\\u0644 \\u0644\\u0646\\u0641\\u0633\\u064a \\u0625\\u0646\\u0646\\u064a \\u0644\\u0633\\u062a \\u0623\\u0641\\u0636\\u0644 \\u0645\\u0646 \\u062c\\u0627\\u0631\\u064a\\u060c \\u0625\\u0646\\u064a \\u0623\\u0634\\u0645 \\u0646\\u0641\\u0633\\u064a\\u060c \\u0623\\u0634\\u0645 \\u0623\\u0646\\u0641\\u0633\\u0646\\u0627 \\u062c\\u0645\\u064a\\u0639\\u0627\\u060c \\u0648 \\u064a\\u0630\\u0644\\u0643 \\u0623\\u0643\\u0648\\u0646 \\u0641\\u064a \\u062d\\u0627\\u0644 \\u0623\\u0641\\u0636\\u0644.\",\n          \"\\u0627\\u0644\\u0641\\u0646\\u062f\\u0642 \\u0645\\u0645\\u062a\\u0627\\u0632 \\u0648\\u0627\\u0646\\u0635\\u062d \\u0628\\u0627\\u0644\\u0633\\u0643\\u0646 \\u0641\\u064a\\u0647 . \\u062d\\u0633\\u0646 \\u0627\\u0633\\u062a\\u0642\\u0628\\u0627\\u0644 \\u0645\\u0648\\u0638\\u0641 \\u0627\\u0644\\u0627\\u0633\\u062a\\u0642\\u0628\\u0627\\u0644 \\u0642\\u0631\\u0628 \\u0627\\u0644\\u0641\\u0646\\u062f\\u0642 \\u0645\\u0646 \\u0627\\u0645\\u0627\\u0643\\u0646 \\u0627\\u0644\\u062a\\u0633\\u0648\\u0642.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "___\n",
        "### Step 3: Converting Text and Labels into Arrays\n",
        "\n",
        "When feeding the data into the ML model we need to convert them into **arrays**.\n",
        "This conversion is necessary because ML typically work with arrays rather than DataFrame Objects."
      ],
      "metadata": {
        "id": "jgMf2v7hn8S1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert text and label columns of the traing data into Numpy arrays\n",
        "x_train = train['text'].to_numpy()\n",
        "y_train = train['label'].to_numpy()\n",
        "\n",
        "# Apply for the validation data also\n",
        "x_test = val['text'].to_numpy()\n",
        "y_test = val['label'].to_numpy()"
      ],
      "metadata": {
        "id": "Wtn405gflZ0y"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `.to_numpy()`: Converts the text and label data (stored in pandas DataFrames) into NumPy arrays. This conversion is necessary for feeding the data into ML models, which **typically work with arrays rather than DataFrame objects**.\n"
      ],
      "metadata": {
        "id": "CmjW1InvoncQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Text column of the Train Data:\\n {x_train}\")\n",
        "print(f\"\\nLabel column of the Train Data:\\n {y_train}\")\n",
        "print(type(x_train))\n",
        "print(type(y_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RpJlVz-mmfv2",
        "outputId": "1d09b955-b8f6-4ee6-929a-eb729aedf0ca"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text column of the Train Data:\n",
            " ['استمتعت . الموقع الخصوصيه. عدم توفر الانترنت'\n",
            " 'مكان الفندق رائع قريب من المسجد النبوي . قرب من المسجد النبويبوفيه الغداء كان ممتاز. واجهتنا مشكلة في الدخول حيث أن السعر الموجود لديهم كان يختلف عن السعر الحقيقي مما تطلب الأمر لطباعة الفاوتشر.'\n",
            " 'لن اكرر الاقامة . . رائحة الشراشف سيئة لايوضع في الغرفة مناديل بشكل كافي الاثاث مستهلك'\n",
            " ...\n",
            " 'باختصار شديد :. قصة عادية جدا ، تكررت روايتها على ألسنة أربعة شخصيات ، كل يحكي من زاويته. فيما عدا ذلك ، القصة شديدة السخف ولا متعة فيها. ويبدو أنها كتبت للنقاد وليس لجمهور الأدب'\n",
            " 'رائع. وجود الفندق قرب المطاعم والبحر وأشكر الأخ أحمد في الإستقبال على حسن تعامله وبشاشة وجهه. تسجيل الدخول لم يكون بالسرعة المطلوبه'\n",
            " 'جميل .. ما فهمتش الهيبتا ولا النهاية ولا المغزى من القصص بس جميل .']\n",
            "\n",
            "Label column of the Train Data:\n",
            " [2 0 1 ... 1 2 0]\n",
            "<class 'numpy.ndarray'>\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Text column of the Validation (Test) Data:\\n {x_test}\")\n",
        "print(f\"\\nLabel column of the Validation (Test) Data:\\n {y_test}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJyyyzfFmiFk",
        "outputId": "8115e3a6-336e-4b5b-bcf6-b00e56ac3014"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text column of the Validation (Test) Data:\n",
            " ['يحتاج للتطوير . تسجيل الخروج المتأخر. دخول تنظيف الغرف على الغرفة بدون استئذان'\n",
            " 'المجهود المبالغ فيه فى اختيار و رسم التعابير و تجميل اللغة اضر بشكل كبير بانسيابية السرد واوجد كم لا باس به من الملل'\n",
            " 'اخيس كتاب القصه مقتبسه' ...\n",
            " 'لغة جميلة جدا ...موضوع بات يصيبني منه الغثيان عن الحب الذي ملأ الروايات أتحدث لو كتبت مراجعتي للرواية قبل سنوات لكان أحسن لأنني قرأتها قرابة سنوات من ذي قبل لذا لم يبق لي منها في ذاكرتي إلا الدهشة الاولى ،أنا التي كنت يومذاك أخطو خطواتي القراءاتية الاولى في فن الرواية. لذا هذا الذي كتبته ليس مراجعة بقدر ما هو مرور على كتب كنت قرأتها أحاول استذكارها'\n",
            " 'جيد جدا. اتساع الشقه طاقم العمل ودودين. رائحة الدخان بالغرفه كانت جدا جدا مزعجه'\n",
            " 'رائع. اعجبني طاقم العمل وسرعة خدمتهم..']\n",
            "\n",
            "Label column of the Validation (Test) Data:\n",
            " [0 1 1 ... 0 2 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "___\n",
        "### Step 4: Training the model\n",
        "\n",
        "Now, we're ready to feed the data into the model."
      ],
      "metadata": {
        "id": "HnAATC7EpRHS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **A - Intall Required Libraries**\n",
        "We're goint to use the **`ktrain` library** for training our model.\n",
        "\n",
        "* `ktrain`: This is a high-level wrapper for building and deploying machine learning models. It simplifies working with models like **BERT** for tasks such as classification, text processing, and fine-tuning. **`ktrain` is built on top of TensorFlow** (and other libraries), so you need it for easier interaction with BERT and other models.\n",
        "\n",
        "\n",
        "* `tensorflow`: This is a deep learning framework that provides the foundation for training and running models. **`ktrain` relies on TensorFlow** to actually execute model training, prediction, and evaluation. Without TensorFlow installed, ktrain won't be able to perform its tasks because it's built on top of it."
      ],
      "metadata": {
        "id": "bjPo-S5TpxXA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ktrain==0.32.3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zv_Hff7-moMK",
        "outputId": "28693f0e-4e8d-41ee-b7e9-ca062823cbe2"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hBuilding wheels for collected packages: ktrain, keras_bert, keras-transformer, keras-embed-sim, keras-layer-normalization, keras-multi-head, keras-pos-embd, keras-position-wise-feed-forward, keras-self-attention, cchardet, langdetect\n",
            "  Building wheel for ktrain (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/ad/98/97/a5737b88195079c12f551b6bb0fdcb52294171b012dc14e6e7\n",
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            "  Stored in directory: /root/.cache/pip/wheels/89/0c/04/646b6fdf6375911b42c8d540a8a3fda8d5d77634e5dcbe7b26\n",
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            "  Stored in directory: /root/.cache/pip/wheels/cb/23/4b/06d7ae21714f70fcc25b48f972cc8e5e7f4b6b764a038b509d\n",
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            "  Created wheel for keras-pos-embd: filename=keras_pos_embd-0.13.0-py3-none-any.whl size=6945 sha256=62bcc13050c2d169bf2c78d007b4f6a0a995d4751b48b96865864e80465d73a3\n",
            "  Stored in directory: /root/.cache/pip/wheels/78/07/1b/b1ca47b6ac338554b75c8f52c54e6a2bfbe1b07d79579979a4\n",
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            "  Created wheel for keras-position-wise-feed-forward: filename=keras_position_wise_feed_forward-0.8.0-py3-none-any.whl size=4970 sha256=fe74c98b8d48e56034afd206aeb45b0c4d361cfeeda0bda0c656521b61ef68ba\n",
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            "  Created wheel for keras-self-attention: filename=keras_self_attention-0.51.0-py3-none-any.whl size=18895 sha256=04d2a31bcb081e373a89be01eb846dd1c60ae5f1db9844f9057b6d95f7efd931\n",
            "  Stored in directory: /root/.cache/pip/wheels/b8/f7/24/607b483144fb9c47b4ba2c5fba6b68e54aeee2d5bf6c05302e\n",
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            "  Created wheel for cchardet: filename=cchardet-2.1.7-cp310-cp310-linux_x86_64.whl size=289398 sha256=f455fe2c5f2a2c784e7ccb2519a5b3fc4e4fa949ba9cbcb76f51eb970ef7da0f\n",
            "  Stored in directory: /root/.cache/pip/wheels/ee/e0/ab/e01326f15c59438d080b1496dbab8091e952ec72f35e3c437e\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993222 sha256=629b40b10d9d93e5c9a196d41a63cfa362a8f0e03fa96448b819405479036d6b\n",
            "  Stored in directory: /root/.cache/pip/wheels/95/03/7d/59ea870c70ce4e5a370638b5462a7711ab78fba2f655d05106\n",
            "Successfully built ktrain keras_bert keras-transformer keras-embed-sim keras-layer-normalization keras-multi-head keras-pos-embd keras-position-wise-feed-forward keras-self-attention cchardet langdetect\n",
            "Installing collected packages: whoosh, cchardet, syntok, sacremoses, langdetect, keras-self-attention, keras-position-wise-feed-forward, keras-pos-embd, keras-layer-normalization, keras-embed-sim, keras-multi-head, keras-transformer, transformers, keras_bert, ktrain\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.46.3\n",
            "    Uninstalling transformers-4.46.3:\n",
            "      Successfully uninstalled transformers-4.46.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "sentence-transformers 3.2.1 requires transformers<5.0.0,>=4.41.0, but you have transformers 4.17.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed cchardet-2.1.7 keras-embed-sim-0.10.0 keras-layer-normalization-0.16.0 keras-multi-head-0.29.0 keras-pos-embd-0.13.0 keras-position-wise-feed-forward-0.8.0 keras-self-attention-0.51.0 keras-transformer-0.40.0 keras_bert-0.89.0 ktrain-0.32.3 langdetect-1.0.9 sacremoses-0.1.1 syntok-1.4.4 transformers-4.17.0 whoosh-2.7.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow==2.15.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5GLO-pxexKrH",
        "outputId": "90680d72-2e30-4e25-8724-c336b22863e2"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow==2.15.0\n",
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            "\u001b[?25hInstalling collected packages: wrapt, tensorflow-estimator, ml-dtypes, keras, tensorboard, tensorflow\n",
            "  Attempting uninstall: wrapt\n",
            "    Found existing installation: wrapt 1.17.0\n",
            "    Uninstalling wrapt-1.17.0:\n",
            "      Successfully uninstalled wrapt-1.17.0\n",
            "  Attempting uninstall: ml-dtypes\n",
            "    Found existing installation: ml-dtypes 0.4.1\n",
            "    Uninstalling ml-dtypes-0.4.1:\n",
            "      Successfully uninstalled ml-dtypes-0.4.1\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 3.5.0\n",
            "    Uninstalling keras-3.5.0:\n",
            "      Successfully uninstalled keras-3.5.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.17.1\n",
            "    Uninstalling tensorboard-2.17.1:\n",
            "      Successfully uninstalled tensorboard-2.17.1\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.17.1\n",
            "    Uninstalling tensorflow-2.17.1:\n",
            "      Successfully uninstalled tensorflow-2.17.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorstore 0.1.69 requires ml-dtypes>=0.3.1, but you have ml-dtypes 0.2.0 which is incompatible.\n",
            "tf-keras 2.17.0 requires tensorflow<2.18,>=2.17, but you have tensorflow 2.15.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed keras-2.15.0 ml-dtypes-0.2.0 tensorboard-2.15.2 tensorflow-2.15.0 tensorflow-estimator-2.15.0 wrapt-1.14.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **B - Creating a Transformer Object**\n",
        "\n",
        "The `ktrain.text.Transformer` class allows us to define and preprocess the data for a specific model.\n"
      ],
      "metadata": {
        "id": "7mTn0JtiE85f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from ktrain import text\n",
        "\n",
        "# Specify the model name, categories and maxlen for the text transformer object.\n",
        "model_name = 'bert-base-uncased'\n",
        "class_names = ['Mixed', 'Negative', 'Positive']\n",
        "\n",
        "text_transformer = text.Transformer(model_name=model_name, class_names=class_names, maxlen=200)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "90121ac062744eff8a32fcb297f1e5e1",
            "063e39028bd14ab887be42dd3f3d14b7",
            "06c5b09846a14f1a9c23a899b109dfa6",
            "36a1295607ff4eac90fc14af3b371c03",
            "59ca5f3e82414c39b1039520281102d0",
            "73b3782728314a99a0e609893a7773ec",
            "648e4f73085c432e98a15b03da7361c7",
            "9b872606b5d24d1f8e6acff20ca16f24",
            "e8cc1c91f7a84d4391a77b95ce400516",
            "c77411f9491a45d997bf793b90bb2873",
            "aaddfcb04fb3425c915aed07841c548f"
          ]
        },
        "id": "Ob-_6FdpqB2o",
        "outputId": "e3445dd9-4891-4647-cf87-8a108fe4cd25"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90121ac062744eff8a32fcb297f1e5e1"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `text.Transformer`: **Transformer** (`model_name`, `maxlen: int = 128`, `class_names: Any = []`, `classes: Any = []`, `batch_size: Any` | None = None, use_with_learner: bool = True)\n",
        "\n",
        "* `model_name='bert-base-uncased'`: Refers to the pre-trained BERT model that will be fine-tuned.\n",
        "\n",
        "* `class_names=['Mixed', 'Negative', 'Positive']`: Defines the sentiment classes for our task.\n",
        "\n",
        "* `maxlen=200`: Sets the maximum sequence length for the text data. Reviews longer than this will be truncated, and shorter reviews will be padded.\n"
      ],
      "metadata": {
        "id": "3aCDorJaFEq9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **C - Building and Preporecessing the Data**\n",
        "We need to preprocess the data before feeding it into the BERT model.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Depp3YK0Gmet"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import ktrain\n",
        "\n",
        "model = text_transformer.get_classifier()\n",
        "train_data = text_transformer.preprocess_train(x_train, y_train)\n",
        "val_data = text_transformer.preprocess_test(x_test, y_test)\n",
        "\n",
        "learner = ktrain.get_learner(model=model, train_data=train_data, val_data=val_data, batch_size=16)"
      ],
      "metadata": {
        "id": "wwjLPoYFuwbX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `ktrain.get_learner()`: Returns a Learner instance that can be used to tune and train Keras models.\n",
        "\n",
        "* **`get_learner`**: (`model: Any`, `train_data: Any` | None = None, `val_data: Any` | None = None, `batch_size: int` = U.DEFAULT_BS, eval_batch_size: int = U.DEFAULT_BS, workers: int = 1, use_multiprocessing: bool = False) -> Any\n",
        "\n",
        "\n",
        "\n",
        "* `get_classifier()`: Returns the BERT model ready for fine-tuning.\n",
        "\n",
        "* `text_transformer.preprocess_train(x_train, y_train)`: This method processes the training data (x_train and y_train), tokenizes the text (converts it into tokens that the model can understand), and encodes the labels.\n",
        "\n",
        "* `text_transformer.preprocess_test(x_test, y_test)`: Similarly, this method processes the validation data (x_test and y_test), preparing it in the same format as the training data for evaluation."
      ],
      "metadata": {
        "id": "jaw_rndUG98n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **D - Training The Model**\n",
        "\n",
        "Let’s fine-tune BERT using **one-cycle learning** to optimize the learning rate and achieve faster convergence.\n"
      ],
      "metadata": {
        "id": "uqpwmaq_HZlJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "start = time.time()\n",
        "learner.fit_onecycle(lr=2e-5, epochs=3)\n",
        "end = time.time()\n",
        "\n",
        "print(f\"Total Training Time: {round(end - start)} seconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kjD6XXV_yPzx",
        "outputId": "17602e66-f16f-4851-a1f1-40632cff9c5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "begin training using onecycle policy with max lr of 2e-05...\n",
            "Epoch 1/3\n",
            "1500/1500 [==============================] - 1206s 789ms/step - loss: 0.9444 - accuracy: 0.5000 - val_loss: 0.8000 - val_accuracy: 0.5950\n",
            "Epoch 2/3\n",
            "1500/1500 [==============================] - 1185s 790ms/step - loss: 0.7990 - accuracy: 0.6040 - val_loss: 0.7501 - val_accuracy: 0.6385\n",
            "Epoch 3/3\n",
            "1500/1500 [==============================] - 1179s 786ms/step - loss: 0.7255 - accuracy: 0.6551 - val_loss: 0.7370 - val_accuracy: 0.6445\n",
            "Total Training Time: 3577 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `fit_onecycle(lr=2e-5, epochs=3)`: Trains the model for 3 epochs using a learning rate of 2e-5 (0.00002).\n",
        "\n",
        "* `2e-5`: The learning rate used for training. This value is small, as fine-tuning pre-trained models generally requires smaller learning rates to avoid damaging the model's pre-trained knowledge.\n",
        "\n",
        "* `epochs=3`: The number of epochs, i.e., how many times the entire training dataset will be processed by the model.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "PjtkQ85QJY_H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Step 5: Validating the Model"
      ],
      "metadata": {
        "id": "U7Ixr_ktOL3m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "learner.validate(class_names=class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4xABy_lCzpTw",
        "outputId": "a1f93e9e-6ec9-4e9a-a036-812887ae85f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "188/188 [==============================] - 94s 470ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Mixed       0.58      0.53      0.55      1991\n",
            "    Negative       0.65      0.73      0.69      2067\n",
            "    Positive       0.70      0.67      0.68      1942\n",
            "\n",
            "    accuracy                           0.64      6000\n",
            "   macro avg       0.64      0.64      0.64      6000\n",
            "weighted avg       0.64      0.64      0.64      6000\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1057,  545,  389],\n",
              "       [ 390, 1514,  163],\n",
              "       [ 384,  262, 1296]])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `learner.validate(class_names=class_names)`: evaluates the model’s performance on the validation dataset and prints out metrics such as accuracy, precision, recall, and F1-score for each of the sentiment categories *(Mixed, Negative, Positive)*.\n",
        "\n"
      ],
      "metadata": {
        "id": "nMhu97cfOTa7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Step 6: Saving the Fine-Tuned Model\n"
      ],
      "metadata": {
        "id": "Kllzd1cVOh0a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictor = ktrain.get_predictor(model=learner.model, preproc=text_transformer)\n",
        "\n",
        "# Saving the fine-tuned model\n",
        "predictor.save(\"/content/drive/MyDrive/Colab Notebooks/05-Fine-Tuning-BERT/Finetuned_Arabic_Sentiment_BERT\")"
      ],
      "metadata": {
        "id": "5np6XwLpM4nw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `ktrain.get_predictor(model=learner.model, preproc=text_transformer)`: Creates a predictor object using the trained model (`learner.model`) and the pre-processing pipeline (`preproc=text_transformer`). This allows you to easily use the model for predictions.\n",
        "\n",
        "* `.save()`: Saves the predictor object, including the model and pre-processing steps, to the specified directory. This makes it easy to reload the model later.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "JJtxVfW0Oqnd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Step 7: Loading the Saved Model.\n"
      ],
      "metadata": {
        "id": "aoycF7g1Ifka"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from ktrain import load_predictor\n",
        "\n",
        "# Load the saved predictor\n",
        "predictor = load_predictor('/content/drive/MyDrive/Colab Notebooks/05-Fine-Tuning-BERT/Finetuned_Arabic_Sentiment_BERT')"
      ],
      "metadata": {
        "id": "QHCCrdHzOFyf"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 8: Testing the Model with examples"
      ],
      "metadata": {
        "id": "oco88s3fLWeJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example = [\"كله رائع بجد ربنا يكرمك\", \"اتقوا الله فينا بكفي رفع اسعار الرواتب بالحضيض\"]\n",
        "\n",
        "predictions = predictor.predict(example)\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CTMKuBAjLIZM",
        "outputId": "995b43da-473a-4600-fd00-2c73a003a2fd"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 7s 7s/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Positive', 'Mixed']"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Key Takeaway:**\n",
        "* These examples show that the model effectively handles the complexities of sentiment analysis in Arabic, distinguishing positive remarks from more nuanced or mixed emotions. This demonstrates the power of fine-tuning BERT for specific tasks and datasets. 🎉"
      ],
      "metadata": {
        "id": "z5VXgtq0MATP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Step 9: Testing the Model on out Dataset\n",
        "\n",
        "Now, let's test the fine-tuned model on our dataset to evaluate its preformance."
      ],
      "metadata": {
        "id": "vRPwmfFKMVeo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random_5_rows"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9ufDIW8QLjFp",
        "outputId": "a8157658-7736-4209-f050-0a3175e31a2b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          label                                               text\n",
              "26002  Positive  أقم على الماشي . بصورة عامة الغرفة جيدة وكل شي...\n",
              "80420  Negative  لم تعجبني كباقي السلسلة و غير متحمس لقراءة الج...\n",
              "19864  Positive  كان المكان ممتاز والأمن والاستقبال اوكي . . ال...\n",
              "81525  Negative  القصة اجمالا مشوقة كونها تعتمد علي شخصيات يجمع...\n",
              "57878     Mixed  اربع نجوم لولا الملل الذي اصابني في النهاية......"
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            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's copy these random five rows (To Avoid Modifying the Original DataSet)"
      ],
      "metadata": {
        "id": "m3_zQ0UwNfwb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random_5_rows_copy = random_5_rows.copy()"
      ],
      "metadata": {
        "id": "q2_UCWjONHlM"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an empty list for the predicted sentiments\n",
        "predicted_sentiments = []\n",
        "\n",
        "for text in random_5_rows_copy['text']:\n",
        "  predictions = predictor.predict(text)\n",
        "\n",
        "  predicted_sentiments.append(predictions)\n",
        "\n",
        "print(predicted_sentiments)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8EsYg11aNi2-",
        "outputId": "8846fb47-1d52-4369-ab9d-ff9244165e99"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 176ms/step\n",
            "1/1 [==============================] - 0s 189ms/step\n",
            "1/1 [==============================] - 0s 172ms/step\n",
            "1/1 [==============================] - 0s 168ms/step\n",
            "1/1 [==============================] - 0s 159ms/step\n",
            "['Mixed', 'Negative', 'Mixed', 'Mixed', 'Mixed']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 9: Compare the Predicted Sentiments with the Original\n"
      ],
      "metadata": {
        "id": "vGPWIoGeOdlU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random_5_rows_copy['Predicted_Sentiments'] = predicted_sentiments\n",
        "random_5_rows_copy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "JxmjQup-N6O2",
        "outputId": "e1b119a7-72a0-4057-c8b0-1b7069a2d22e"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          label                                               text  \\\n",
              "26002  Positive  أقم على الماشي . بصورة عامة الغرفة جيدة وكل شي...   \n",
              "80420  Negative  لم تعجبني كباقي السلسلة و غير متحمس لقراءة الج...   \n",
              "19864  Positive  كان المكان ممتاز والأمن والاستقبال اوكي . . ال...   \n",
              "81525  Negative  القصة اجمالا مشوقة كونها تعتمد علي شخصيات يجمع...   \n",
              "57878     Mixed  اربع نجوم لولا الملل الذي اصابني في النهاية......   \n",
              "\n",
              "      Predicted_Sentiments  \n",
              "26002                Mixed  \n",
              "80420             Negative  \n",
              "19864                Mixed  \n",
              "81525                Mixed  \n",
              "57878                Mixed  "
            ],
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              "\n",
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          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Final Observations** 🌟  \n",
        "\n",
        "Upon review, the model's predictions often seem **more accurate** than the dataset labels.  \n",
        "\n",
        "For example:  \n",
        "- **Example 26002**: Labeled **Positive**, but the model correctly identified it as **Mixed** due to both positive and negative remarks.  \n",
        "\n",
        "- **Example 19864**: Labeled **Positive**, yet the model’s prediction of **Mixed** better reflects the review's tone, which included significant complaints.\n",
        "\n",
        "---\n",
        "\n",
        "### **Conclusion**:  \n",
        "The model captures nuances better than the dataset labels, showing its strength in understanding complex sentiments. This highlights the power of fine-tuned BERT for sentiment analysis! 🚀"
      ],
      "metadata": {
        "id": "E2FgyUl1P18G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "---\n",
        "\n",
        "### **About the Author** 👨‍💻  \n",
        "\n",
        "This fine-tuning project was created by **Khaled Soudy**. Check out more of my work on [GitHub](https://github.com/khaledsoudy-1)! 😊  \n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "8b4uqZS2QWAo"
      }
    }
  ]
}