{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Arabic and English Translator**"
      ],
      "metadata": {
        "id": "G1KysHBeEJCC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "2eSvM9zX_2d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "72e76f54-afa1-4210-93aa-8e3998f1d53d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting cut_cross_entropy (from unsloth_zoo>=2024.11.8->unsloth)\n",
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            "\u001b[?25hInstalling collected packages: xxhash, triton, shtab, protobuf, hf_transfer, fsspec, dill, multiprocess, xformers, tyro, cut_cross_entropy, bitsandbytes, datasets, trl, unsloth_zoo, unsloth\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 4.25.5\n",
            "    Uninstalling protobuf-4.25.5:\n",
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            "\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",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n",
            "grpcio-status 1.62.3 requires protobuf>=4.21.6, but you have protobuf 3.20.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed bitsandbytes-0.45.0 cut_cross_entropy-24.12.1 datasets-3.1.0 dill-0.3.8 fsspec-2024.9.0 hf_transfer-0.1.8 multiprocess-0.70.16 protobuf-3.20.3 shtab-1.7.1 triton-3.1.0 trl-0.12.2 tyro-0.9.2 unsloth-2024.12.4 unsloth_zoo-2024.12.1 xformers-0.0.28.post3 xxhash-3.5.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              },
              "id": "dbf9df8bf7454dbaab6ef975d2d37581"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: unsloth 2024.12.4\n",
            "Uninstalling unsloth-2024.12.4:\n",
            "  Successfully uninstalled unsloth-2024.12.4\n",
            "Collecting git+https://github.com/unslothai/unsloth.git\n",
            "  Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-req-build-364nvevq\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-364nvevq\n",
            "  Resolved https://github.com/unslothai/unsloth.git to commit df808d074d307d396b8c04640ef51375d68f2ef0\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: unsloth\n",
            "  Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for unsloth: filename=unsloth-2024.12.4-py3-none-any.whl size=173562 sha256=0140891ef1bba47874bd1947f9eeeda74b5c77c0dc7dfd40408bcc0ba08b44bf\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-afmdqqd7/wheels/ed/d4/e9/76fb290ee3df0a5fc21ce5c2c788e29e9607a2353d8342fd0d\n",
            "Successfully built unsloth\n",
            "Installing collected packages: unsloth\n",
            "Successfully installed unsloth-2024.12.4\n"
          ]
        }
      ],
      "source": [
        "!pip install unsloth\n",
        "!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "85Jlo9PnEGjN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from unsloth import FastLanguageModel\n",
        "import torch"
      ],
      "metadata": {
        "id": "wqp8vZ4ImQC8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f661e20-02a9-4d3c-aaae-f2e7816f92b8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "🦥 Unsloth Zoo will now patch everything to make training faster!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==((====))==  Unsloth 2024.12.4: Fast Llama patching. Transformers:4.46.3.\n",
            "   \\\\   /|    GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform: Linux.\n",
            "O^O/ \\_/ \\    Torch: 2.5.1+cu121. CUDA: 8.0. CUDA Toolkit: 12.1. Triton: 3.1.0\n",
            "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.28.post3. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
        },
        {
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            "text/plain": [
              "model.safetensors:   0%|          | 0.00/2.26G [00:00<?, ?B/s]"
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              "generation_config.json:   0%|          | 0.00/140 [00:00<?, ?B/s]"
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              "tokenizer_config.json:   0%|          | 0.00/3.37k [00:00<?, ?B/s]"
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      ],
      "source": [
        "\n",
        "max_seq_length = 2048\n",
        "dtype = None\n",
        "load_in_4bit = True\n",
        "\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/Phi-3.5-mini-instruct\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wC5LwqRZhEAl"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bZsfBuZDeCL",
        "outputId": "d9f4c1ff-035a-4298-c35b-18c745e3185b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth 2024.12.4 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r=16,\n",
        "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
        "    lora_alpha=16,\n",
        "    lora_dropout=0,\n",
        "    bias=\"none\",\n",
        "    use_gradient_checkpointing=\"unsloth\",\n",
        "    random_state=3407,\n",
        "    use_rslora=False,\n",
        "    loftq_config=None\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset using pandas\n",
        "data_df = pd.read_json('/content/drive/MyDrive/Ara/Arabic.json', encoding='utf-8')\n",
        "data_df.head()"
      ],
      "metadata": {
        "id": "nLxakNjAk7OQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "76de4e93-06ab-4ff8-d7aa-d0f57636f38a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         instruction  \\\n",
              "0   Could you render the given sentence into Arabic?   \n",
              "1  The given sentence is in English, you should t...   \n",
              "2  Your job is to translate the below sentence in...   \n",
              "3  I would like the given sentence to be translat...   \n",
              "4  The given sentence is in English, you should t...   \n",
              "\n",
              "                                               input  \\\n",
              "0               Give three tips for staying healthy.   \n",
              "1  1. Eat a balanced and nutritious diet: Make su...   \n",
              "2                 What are the three primary colors?   \n",
              "3  The three primary colors are red, blue, and ye...   \n",
              "4                 Describe the structure of an atom.   \n",
              "\n",
              "                                              output  \n",
              "0                   أعط ثلاث نصائح للبقاء بصحة جيدة.  \n",
              "1   1. تناول نظامًا غذائيًا متوازنًا ومغذيًا: تأك...  \n",
              "2                    ما هي الألوان الثلاثة الأساسية؟  \n",
              "3  الألوان الثلاثة الأساسية هي الأحمر والأزرق وال...  \n",
              "4                                     صف بنية الذرة.  "
            ],
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              "  <div id=\"df-13e1a4a3-3192-45fd-9196-8c95a36719bb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>instruction</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Could you render the given sentence into Arabic?</td>\n",
              "      <td>Give three tips for staying healthy.</td>\n",
              "      <td>أعط ثلاث نصائح للبقاء بصحة جيدة.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The given sentence is in English, you should t...</td>\n",
              "      <td>1. Eat a balanced and nutritious diet: Make su...</td>\n",
              "      <td>1. تناول نظامًا غذائيًا متوازنًا ومغذيًا: تأك...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Your job is to translate the below sentence in...</td>\n",
              "      <td>What are the three primary colors?</td>\n",
              "      <td>ما هي الألوان الثلاثة الأساسية؟</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I would like the given sentence to be translat...</td>\n",
              "      <td>The three primary colors are red, blue, and ye...</td>\n",
              "      <td>الألوان الثلاثة الأساسية هي الأحمر والأزرق وال...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The given sentence is in English, you should t...</td>\n",
              "      <td>Describe the structure of an atom.</td>\n",
              "      <td>صف بنية الذرة.</td>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13e1a4a3-3192-45fd-9196-8c95a36719bb')\"\n",
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              "  <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",
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              "      height: 32px;\n",
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              "\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-13e1a4a3-3192-45fd-9196-8c95a36719bb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-13e1a4a3-3192-45fd-9196-8c95a36719bb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-babbd338-788c-41ca-8bc1-c3f3cb71cd77\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-babbd338-788c-41ca-8bc1-c3f3cb71cd77')\"\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-babbd338-788c-41ca-8bc1-c3f3cb71cd77 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": "data_df",
              "summary": "{\n  \"name\": \"data_df\",\n  \"rows\": 66358,\n  \"fields\": [\n    {\n      \"column\": \"instruction\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 42016,\n        \"samples\": [\n          \" \\u0623\\u062e\\u0631\\u062c \\u0627\\u0644\\u0631\\u0642\\u0645 \\u0627\\u0644\\u062e\\u0627\\u0645\\u0633 \\u0641\\u064a \\u062a\\u0633\\u0644\\u0633\\u0644 \\u0641\\u064a\\u0628\\u0648\\u0646\\u0627\\u062a\\u0634\\u064a.\",\n          \" \\u0642\\u0645 \\u0628\\u0625\\u0646\\u0634\\u0627\\u0621 \\u0642\\u0635\\u0629 \\u0645\\u0639 \\u0627\\u0644\\u0628\\u0637\\u0644 \\u0627\\u0644\\u0645\\u062d\\u062f\\u062f \\u0648\\u0645\\u0639\\u0636\\u0644\\u0629 \\u063a\\u064a\\u0631 \\u0645\\u062a\\u0648\\u0642\\u0639\\u0629\",\n          \"\\u0642\\u0645 \\u0628\\u062a\\u0633\\u0645\\u064a\\u0629 \\u062b\\u0644\\u0627\\u062b\\u0629 \\u0623\\u0644\\u0648\\u0627\\u0646 \\u0645\\u0646 \\u0634\\u0623\\u0646\\u0647\\u0627 \\u0623\\u0646 \\u062a\\u0634\\u0643\\u0644 \\u0644\\u0648\\u062d\\u0629 \\u0623\\u0644\\u0648\\u0627\\u0646 \\u062c\\u064a\\u062f\\u0629 \\u0644\\u0645\\u0648\\u0642\\u0639 \\u0627\\u0644\\u0648\\u064a\\u0628\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"input\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 39082,\n        \"samples\": [\n          \" \\u0627\\u0646\\u062a\\u0631\\u0646\\u062a \\u0627\\u0644\\u0623\\u0634\\u064a\\u0627\\u0621\",\n          \"Make a resume for the following job.\",\n          \" \\u0645\\u0646 \\u0627\\u0644\\u062c\\u064a\\u062f \\u0639\\u0645\\u0648\\u0645\\u064b\\u0627 \\u0623\\u0646 \\u062a\\u0628\\u062f\\u0623 \\u0627\\u0644\\u0627\\u0633\\u062a\\u062b\\u0645\\u0627\\u0631 \\u0641\\u064a \\u0623\\u0642\\u0631\\u0628 \\u0648\\u0642\\u062a \\u0645\\u0645\\u0643\\u0646 \\u060c \\u062d\\u064a\\u062b \\u064a\\u0645\\u0643\\u0646 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\\u0644\\u0623\\u0648\\u0627\\u0646\\u0647 \\u0623\\u0628\\u062f\\u064b\\u0627 \\u0627\\u0644\\u0628\\u062f\\u0621 \\u0641\\u064a \\u0627\\u0644\\u062a\\u062e\\u0637\\u064a\\u0637 \\u0644\\u0647\\u0630\\u0647 \\u0627\\u0644\\u0623\\u0646\\u0648\\u0627\\u0639 \\u0645\\u0646 \\u0627\\u0644\\u0623\\u0647\\u062f\\u0627\\u0641. \\u0648\\u0645\\u0639 \\u0630\\u0644\\u0643 \\u060c \\u0642\\u0628\\u0644 \\u0623\\u0646 \\u062a\\u0628\\u062f\\u0623 \\u0627\\u0644\\u0627\\u0633\\u062a\\u062b\\u0645\\u0627\\u0631 \\u060c \\u0645\\u0646 \\u0627\\u0644\\u0645\\u0647\\u0645 \\u0623\\u0648\\u0644\\u0627\\u064b \\u0625\\u0646\\u0634\\u0627\\u0621 \\u0623\\u0633\\u0627\\u0633 \\u0645\\u0627\\u0644\\u064a \\u0645\\u062a\\u064a\\u0646. \\u0642\\u062f \\u064a\\u0634\\u0645\\u0644 \\u0630\\u0644\\u0643 \\u0633\\u062f\\u0627\\u062f \\u0627\\u0644\\u062f\\u064a\\u0648\\u0646 \\u0639\\u0627\\u0644\\u064a\\u0629 \\u0627\\u0644\\u0641\\u0627\\u0626\\u062f\\u0629 \\u060c \\u0648\\u0625\\u0646\\u0634\\u0627\\u0621 \\u0635\\u0646\\u062f\\u0648\\u0642 \\u0644\\u0644\\u0637\\u0648\\u0627\\u0631\\u0626 \\u060c \\u0648\\u0648\\u0636\\u0639 \\u0645\\u064a\\u0632\\u0627\\u0646\\u064a\\u0629 \\u0633\\u0644\\u064a\\u0645\\u0629. \\u0628\\u0645\\u062c\\u0631\\u062f \\u0623\\u0646 \\u062a\\u0636\\u0639 \\u0647\\u0630\\u0647 \\u0627\\u0644\\u0623\\u0633\\u0627\\u0633\\u064a\\u0627\\u062a \\u0641\\u064a \\u0645\\u0643\\u0627\\u0646\\u0647\\u0627 \\u0627\\u0644\\u0635\\u062d\\u064a\\u062d \\u060c \\u064a\\u0645\\u0643\\u0646\\u0643 \\u0628\\u0639\\u062f \\u0630\\u0644\\u0643 \\u0627\\u0644\\u0628\\u062f\\u0621 \\u0641\\u064a \\u0627\\u0644\\u062a\\u0631\\u0643\\u064a\\u0632 \\u0639\\u0644\\u0649 \\u0627\\u0644\\u0627\\u0633\\u062a\\u062b\\u0645\\u0627\\u0631 \\u0641\\u064a \\u0627\\u0644\\u0645\\u0633\\u062a\\u0642\\u0628\\u0644. \\u062a\\u0630\\u0643\\u0631 \\u0623\\u0646\\u0647 \\u0645\\u0646 \\u0627\\u0644\\u0645\\u0647\\u0645 \\u0625\\u062c\\u0631\\u0627\\u0621 \\u0627\\u0644\\u0628\\u062d\\u062b 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\\u0627\\u0644\\u0648\\u0635\\u0648\\u0644 \\u0625\\u0644\\u0649 \\u062c\\u0645\\u0647\\u0648\\u0631 \\u0643\\u0628\\u064a\\u0631 \\u0648\\u062a\\u0631\\u0643 \\u062a\\u0623\\u062b\\u064a\\u0631 \\u0628\\u0635\\u0631\\u064a \\u062f\\u0627\\u0626\\u0645. 2. \\u0625\\u0646\\u0647\\u0627 \\u062a\\u0633\\u0627\\u0639\\u062f \\u0641\\u064a \\u0627\\u0644\\u062a\\u0623\\u062b\\u064a\\u0631 \\u0639\\u0644\\u0649 \\u0627\\u0644\\u0646\\u0627\\u062e\\u0628\\u064a\\u0646 \\u0627\\u0644\\u0645\\u062a\\u0631\\u062f\\u062f\\u064a\\u0646 \\u0648\\u062a\\u0639\\u0632\\u0632 \\u0642\\u0631\\u0627\\u0631\\u0627\\u062a \\u0627\\u0644\\u0646\\u0627\\u062e\\u0628\\u064a\\u0646 \\u0645\\u0646 \\u062e\\u0644\\u0627\\u0644 \\u062e\\u0644\\u0642 \\u0627\\u062a\\u0635\\u0627\\u0644 \\u0639\\u0627\\u0637\\u0641\\u064a \\u0645\\u0646 \\u062e\\u0644\\u0627\\u0644 \\u0633\\u0631\\u062f \\u0627\\u0644\\u0642\\u0635\\u0635 \\u0648\\u0627\\u0644\\u0645\\u0648\\u0633\\u064a\\u0642\\u0649 \\u0648\\u0627\\u0644\\u0631\\u0633\\u0627\\u0626\\u0644 \\u0627\\u0644\\u0645\\u0642\\u0646\\u0639\\u0629. 3. \\u0628\\u0627\\u0644\\u0625\\u0636\\u0627\\u0641\\u0629 \\u0625\\u0644\\u0649 \\u0630\\u0644\\u0643 \\u060c \\u062a\\u0633\\u0645\\u062d \\u0627\\u0644\\u0625\\u0639\\u0644\\u0627\\u0646\\u0627\\u062a \\u0627\\u0644\\u062a\\u0644\\u0641\\u0632\\u064a\\u0648\\u0646\\u064a\\u0629 \\u0644\\u0644\\u062d\\u0645\\u0644\\u0627\\u062a \\u0628\\u0625\\u064a\\u0635\\u0627\\u0644 \\u0631\\u0633\\u0627\\u0644\\u062a\\u0647\\u0627 \\u0645\\u0628\\u0627\\u0634\\u0631\\u0629 \\u0625\\u0644\\u0649 \\u0627\\u0644\\u0646\\u0627\\u062e\\u0628\\u064a\\u0646 \\u060c \\u0645\\u0645\\u0627 \\u064a\\u0645\\u0646\\u062d\\u0647\\u0645 \\u0627\\u0644\\u0633\\u064a\\u0637\\u0631\\u0629 \\u0639\\u0644\\u0649 \\u0627\\u0644\\u0633\\u0631\\u062f \\u0648\\u062a\\u0623\\u0637\\u064a\\u0631 \\u062a\\u0631\\u0634\\u064a\\u062d\\u0647\\u0645.\",\n          \" // \\u064a\\u0639\\u0644\\u0646 \\u0639\\u0646 \\u0645\\u062a\\u063a\\u064a\\u0631 \\u0639\\u062f\\u062f \\u0635\\u062d\\u064a\\u062d \\\"i\\\" \\u0648\\u064a\\u062e\\u0635\\u0635 \\u0642\\u064a\\u0645\\u0629 1 \\u0644\\u0647.\",\n          \"1. \\u0627\\u0644\\u0645\\u062a\\u062d\\u0641 \\u0627\\u0644\\u0628\\u0631\\u064a\\u0637\\u0627\\u0646\\u064a - \\u0645\\u062a\\u062d\\u0641 \\u0645\\u0634\\u0647\\u0648\\u0631 \\u0639\\u0627\\u0644\\u0645\\u064a\\u064b\\u0627 \\u064a\\u0639\\u0631\\u0636 \\u0645\\u062c\\u0645\\u0648\\u0639\\u0629 \\u0643\\u0628\\u064a\\u0631\\u0629 \\u0645\\u0646 \\u0627\\u0644\\u0642\\u0637\\u0639 \\u0627\\u0644\\u0623\\u062b\\u0631\\u064a\\u0629 \\u0627\\u0644\\u062a\\u0627\\u0631\\u064a\\u062e\\u064a\\u0629 \\u0645\\u0646 \\u062c\\u0645\\u064a\\u0639 \\u0623\\u0646\\u062d\\u0627\\u0621 \\u0627\\u0644\\u0639\\u0627\\u0644\\u0645. 2. \\u0627\\u0644\\u0645\\u0639\\u0631\\u0636 \\u0627\\u0644\\u0648\\u0637\\u0646\\u064a - \\u064a\\u0636\\u0645 \\u0647\\u0630\\u0627 \\u0627\\u0644\\u0645\\u062a\\u062d\\u0641 \\u0648\\u0627\\u062d\\u062f\\u0629 \\u0645\\u0646 \\u0623\\u0631\\u0642\\u0649 \\u0645\\u062c\\u0645\\u0648\\u0639\\u0627\\u062a \\u0627\\u0644\\u0644\\u0648\\u062d\\u0627\\u062a \\u0641\\u064a \\u0627\\u0644\\u0639\\u0627\\u0644\\u0645 \\u060c \\u0645\\u0639 \\u0623\\u0639\\u0645\\u0627\\u0644 \\u0644\\u0623\\u0633\\u0627\\u062a\\u0630\\u0629 \\u0645\\u062b\\u0644 \\u0641\\u0627\\u0646 \\u062c\\u0648\\u062e \\u0648\\u0631\\u0627\\u0645\\u0628\\u0631\\u0627\\u0646\\u062a \\u0648\\u0644\\u064a\\u0648\\u0646\\u0627\\u0631\\u062f\\u0648 \\u062f\\u0627\\u0641\\u0646\\u0634\\u064a. 3. Tate Modern - \\u0645\\u062a\\u062d\\u0641 \\u0644\\u0644\\u0641\\u0646 \\u0627\\u0644\\u0645\\u0639\\u0627\\u0635\\u0631 \\u064a\\u0639\\u0631\\u0636 \\u0623\\u0639\\u0645\\u0627\\u0644 \\u0641\\u0646\\u0627\\u0646\\u064a\\u0646 \\u0645\\u0639\\u0627\\u0635\\u0631\\u064a\\u0646 \\u0645\\u062b\\u0644 \\u0628\\u064a\\u0643\\u0627\\u0633\\u0648 \\u0648\\u0648\\u0627\\u0631\\u0647\\u0648\\u0644. 4. \\u0645\\u062a\\u062d\\u0641 \\u0641\\u064a\\u0643\\u062a\\u0648\\u0631\\u064a\\u0627 \\u0648\\u0623\\u0644\\u0628\\u0631\\u062a - \\u0623\\u062d\\u062f \\u0623\\u0643\\u0628\\u0631 \\u0627\\u0644\\u0645\\u062a\\u0627\\u062d\\u0641 \\u0641\\u064a \\u0627\\u0644\\u0639\\u0627\\u0644\\u0645 \\u0644\\u0644\\u0641\\u0646\\u0648\\u0646 \\u0627\\u0644\\u0632\\u062e\\u0631\\u0641\\u064a\\u0629 \\u0648\\u0627\\u0644\\u062a\\u0635\\u0645\\u064a\\u0645 \\u060c \\u0645\\u0639 \\u0645\\u062c\\u0645\\u0648\\u0639\\u0629 \\u062a\\u0645\\u062a\\u062f \\u0644\\u0623\\u0643\\u062b\\u0631 \\u0645\\u0646 5000 \\u0633\\u0646\\u0629 \\u0645\\u0646 \\u0627\\u0644\\u0641\\u0646 \\u0645\\u0646 \\u0627\\u0644\\u062b\\u0642\\u0627\\u0641\\u0627\\u062a \\u062d\\u0648\\u0644 \\u0627\\u0644\\u0639\\u0627\\u0644\\u0645.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from transformers import AutoTokenizer\n",
        "from datasets import Dataset\n",
        "\n",
        "Arabic_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction:\n",
        "{}\n",
        "\n",
        "### Input:\n",
        "{}\n",
        "\n",
        "### Response:\n",
        "{}\"\"\"\n",
        "\n",
        "EOS_TOKEN = tokenizer.eos_token\n",
        "\n",
        "data_df = pd.read_json('/content/drive/MyDrive/Ara/Arabic.json', encoding='utf-8')\n",
        "data_df = data_df.head(400)\n",
        "\n",
        "def formatting_prompts_func(row):\n",
        "    instruction = row['instruction']\n",
        "    input_text = row['input']\n",
        "    output_text = row['output']\n",
        "    return f\"{Arabic_prompt.format(instruction, input_text, output_text)}{EOS_TOKEN}\"\n",
        "\n",
        "data_df['formatted_text'] = data_df.apply(formatting_prompts_func, axis=1)\n",
        "\n",
        "dataset = Dataset.from_dict({\"text\": data_df['formatted_text'].tolist()})\n",
        "split_data = dataset.train_test_split(test_size=0.2, seed=42)\n",
        "train_dataset = split_data[\"train\"]\n",
        "validation_dataset = split_data[\"test\"]\n",
        "\n",
        "print(f\"Total dataset size: {len(dataset)}\")\n",
        "print(f\"Training dataset size: {len(train_dataset)}\")\n",
        "print(f\"Validation dataset size: {len(validation_dataset)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KdEl4qfAk7Xp",
        "outputId": "ced94792-f1c3-4a58-eb76-e6acf752b994"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total dataset size: 400\n",
            "Training dataset size: 320\n",
            "Validation dataset size: 80\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idAEIeSQ3xdS"
      },
      "source": [
        "<a name=\"Train\"></a>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188,
          "referenced_widgets": [
            "b8aeb76dfc6949f4b79af25c7c597383",
            "e8ac8ca4b4244e31b00bb6daeb0d5769",
            "619d21c97b864413ab5eeb80c46cee6c",
            "e0a2959b0ce14f1cba29c39e88a0ed89",
            "acc42289ec6642ea98fc813805cfa1c4",
            "f46ccdd94ab746f19c2fe2e9798a1840",
            "8fc24b9978774d44a95fffad5b50d0e9",
            "e2e96ab460a04d52a87cf68056fa0412",
            "ae5c7d36abb040628b07fe7c1b0647cc",
            "4147fe1eb1b84d52856c4196e4fcd7a0",
            "8e3e0b1f50b4408880ad0d610b4f88ad",
            "7febf02e32044696a0cac579e448f002",
            "65557073a7d4482bb574ef612e1275e4",
            "9c39f2e282974061943435b3943d0b79",
            "1005311767be404c922319cb08baae81",
            "41f34b5a302948fca891b04f4c6cbaa4",
            "6ba5a422ee4b45e4853ed1a3a0ee6b89",
            "67997f7702bc4a8289453e6a5ea42fc8",
            "e98a87f8958d4cd389a2d1b4ec5a7a62",
            "c31f6091d6fa440799cf39d964e2c9fe",
            "4244c549ec264c08b2c8bfe5889da6cd",
            "a0270e8d0611407c97f1f565abef3da2"
          ]
        },
        "id": "95_Nn-89DhsL",
        "outputId": "eae86b15-af52-4855-b0ec-4b1a9c75dfa8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/320 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b8aeb76dfc6949f4b79af25c7c597383"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/80 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7febf02e32044696a0cac579e448f002"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "from unsloth import is_bfloat16_supported\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=validation_dataset,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=max_seq_length,\n",
        "    dataset_num_proc=2,\n",
        "    packing=False,\n",
        "    args=TrainingArguments(\n",
        "        per_device_train_batch_size=2,\n",
        "        gradient_accumulation_steps=4,\n",
        "        warmup_steps=5,\n",
        "        max_steps=30,\n",
        "        learning_rate=2e-4,\n",
        "        fp16=not is_bfloat16_supported(),\n",
        "        bf16=is_bfloat16_supported(),\n",
        "        logging_steps=1,\n",
        "        evaluation_strategy=\"steps\",\n",
        "        eval_steps=5,\n",
        "        save_strategy=\"steps\",\n",
        "        save_steps=5,\n",
        "        save_total_limit=2,\n",
        "        optim=\"adamw_8bit\",\n",
        "        weight_decay=0.01,\n",
        "        lr_scheduler_type=\"linear\",\n",
        "        seed=3407,\n",
        "        output_dir=\"outputs\",\n",
        "        report_to=\"none\",\n",
        "    ),\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "yqxqAZ7KJ4oL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "outputId": "a0f44458-34b5-4ec4-b082-c18a917b381b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 320 | Num Epochs = 1\n",
            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
            "\\        /    Total batch size = 8 | Total steps = 30\n",
            " \"-____-\"     Number of trainable parameters = 29,884,416\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [30/30 01:41, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.338700</td>\n",
              "      <td>1.055234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.743600</td>\n",
              "      <td>0.725511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.504200</td>\n",
              "      <td>0.647183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.535600</td>\n",
              "      <td>0.589806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.492000</td>\n",
              "      <td>0.555610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.548500</td>\n",
              "      <td>0.542290</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fine-tuned model training completed.\n",
            "Training metrics saved as 'training_curve_Phi_3_5_Arabic.csv'\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "trainer_stats = trainer.train()\n",
        "\n",
        "metrics = trainer.state.log_history\n",
        "df = pd.DataFrame(metrics)\n",
        "df.to_csv(\"training_curve_Phi_3_5_Arabic.csv\", index=False)\n",
        "\n",
        "print(\"Fine-tuned model training completed.\")\n",
        "print(\"Training metrics saved as 'training_curve_Phi_3_5_Arabic.csv'\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model(\"Phi_3_5_Fine_tuned_model_Arabic\")\n",
        "tokenizer.save_pretrained(\"Phi_3_5_Fine_tuned_model_Arabic\")\n",
        "\n",
        "print(\"Fine-tuned model saved as 'Phi_3_5_Fine_tuned_model_Arabic'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0B5t2AVDeVh",
        "outputId": "8f9d1b12-0972-48e9-f577-e0e08a613f0c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fine-tuned model saved as 'Phi_3_5_Fine_tuned_model_Arabic'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if \"loss\" in df.columns:\n",
        "    plt.plot(df[\"loss\"], label=\"Training Loss\", marker='o')\n",
        "\n",
        "if \"eval_loss\" in df.columns:\n",
        "    plt.plot(df[\"eval_loss\"], label=\"Validation Loss\", marker='x')\n",
        "\n",
        "plt.title(\"Training and Validation Loss Curve\")\n",
        "plt.xlabel(\"Steps\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "\n",
        "plt.savefig(\"loss_curve_Phi_3_5_Arabic.png\")\n",
        "plt.show()\n",
        "\n",
        "print(\"Model and tokenizer saved.\")\n",
        "print(\"Training losses stored in 'training_losses_Phi3_5_Fine_tuned_model.csv'\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "ZMkcCqKRMb15",
        "outputId": "78d5fcef-ef32-40b3-bbfb-6fe62f6a0e5f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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            "Model and tokenizer saved.\n",
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